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REVISED PROOF
1
2
3Climate change projections using the IPSL-CM5 Earth System
4Model: from CMIP3 to CMIP5
5J.-L. Dufresne •M.-A. Foujols •S. Denvil •A. Caubel •O. Marti •O. Aumont •Y. Balkanski •S. Bekki •
6H. Bellenger •R. Benshila •S. Bony •L. Bopp •P. Braconnot •P. Brockmann •P. Cadule •F. Cheruy •
7F. Codron •A. Cozic •D. Cugnet •N. de Noblet •J.-P. Duvel •C. Ethe
´•L. Fairhead •T. Fichefet •
8S. Flavoni •P. Friedlingstein •J.-Y. Grandpeix •L. Guez •E. Guilyardi •D. Hauglustaine •F. Hourdin •
9A. Idelkadi •J. Ghattas •S. Joussaume •M. Kageyama •G. Krinner •S. Labetoulle •A. Lahellec •
10 M.-P. Lefebvre •F. Lefevre •C. Levy •Z. X. Li •J. Lloyd •F. Lott •G. Madec •M. Mancip •M. Marchand •
11 S. Masson •Y. Meurdesoif •J. Mignot •I. Musat •S. Parouty •J. Polcher •C. Rio •M. Schulz •D. Swingedouw •
12 S. Szopa •C. Talandier •P. Terray •N. Viovy •N. Vuichard
13 Received: 14 November 2011 / Accepted: 15 December 2012
14 ÓThe Author(s) 2013. This article is published with open access at Springerlink.com
15 Abstract We present the global general circulation
16 model IPSL-CM5 developed to study the long-term
17 response of the climate system to natural and anthropo-
18 genic forcings as part of the 5th Phase of the Coupled
19 Model Intercomparison Project (CMIP5). This model
20 includes an interactive carbon cycle, a representation of
21 tropospheric and stratospheric chemistry, and a compre-
22 hensive representation of aerosols. As it represents the
23 principal dynamical, physical, and bio-geochemical pro-
24 cesses relevant to the climate system, it may be referred to
25 as an Earth System Model. However, the IPSL-CM5 model
26
may be used in a multitude of configurations associated
27
with different boundary conditions and with a range of
28
complexities in terms of processes and interactions. This
29
paper presents an overview of the different model com-
30
ponents and explains how they were coupled and used to
31
simulate historical climate changes over the past 150 years
32
and different scenarios of future climate change. A single
33
version of the IPSL-CM5 model (IPSL-CM5A-LR) was
34
used to provide climate projections associated with dif-
35
ferent socio-economic scenarios, including the different
36
Representative Concentration Pathways considered by
37
CMIP5 and several scenarios from the Special Report on
38
Emission Scenarios considered by CMIP3. Results suggest
39
that the magnitude of global warming projections primarily
40
depends on the socio-economic scenario considered, that
This paper is a contribution to the special issue on the IPSL and
CNRM global climate and Earth System Models, both developed in
France and contributing to the 5th coupled model intercomparison
project.
J.-L. Dufresne (&)S. Bony F. Cheruy F. Codron
J.-P. Duvel L. Fairhead J.-Y. Grandpeix L. Guez
F. Hourdin A. Idelkadi A. Lahellec M.-P. Lefebvre
Z. X. Li F. Lott I. Musat J. Polcher C. Rio
Laboratoire de Me
´te
´orologie Dynamique (LMD/IPSL), Centre
National de la Recherche Scientifique (CNRS), Ecole Normale
Supe
´rieure (ENS), Ecole Polytechnique (EP), Universite
´Pierre
et Marie Curie (UPMC), Paris, France
e-mail: Jean-Louis.Dufresne@lmd.jussieu.fr
M.-A. Foujols S. Denvil P. Cadule C. Ethe
´J. Ghattas
M. Mancip
Institut Pierre Simon Laplace (IPSL), Centre National
de la Recherche Scientifique (CNRS), Universite
´de
Versailles Saint-Quentin (UVSQ), Universite
´Pierre et Marie
Curie (UPMC), Commissariat a
`l’Energie Atomique (CEA),
Institut de Recherche pour le De
´veloppement (IRD), Ecole
Normale Supe
´rieure (ENS), Ecole Polytechnique (EP),
Universite
´Denis Diderot, Universite
´Paris-Est Cre
´teil,
Paris, France
A. Caubel O. Marti Y. Balkanski L. Bopp P. Braconnot
P. Brockmann A. Cozic N. de Noblet P. Friedlingstein
D. Hauglustaine S. Joussaume M. Kageyama
Y. Meurdesoif M. Schulz D. Swingedouw S. Szopa
N. Viovy N. Vuichard
Laboratoire des Sciences du Climat et de l’Environnement
(LSCE/IPSL), Centre National de la Recherche Scientifique
(CNRS), Commissariat a
`l’Energie Atomique (CEA), Universite
´
de Versailles Saint-Quentin (UVSQ), Gif-sur-Yvette, France
O. Aumont C. Talandier
Laboratoire de Physique des Oce
´ans (LPO), Centre National de
la Recherche Scientifique (CNRS), Institut Franc¸ais de
Recherche pour l’Exploitation de la Mer (Ifremer), Institut de
Recherche pour le De
´veloppement (IRD), Universite
´de
Bretagne Occidentale (UBO), Brest, France
S. Bekki D. Cugnet F. Lefevre M. Marchand
Laboratoire Atmosphe
`res, Milieux, Observations Spatiales
(LATMOS/IPSL), Centre National de la Recherche Scientifique
123
Clim Dyn
DOI 10.1007/s00382-012-1636-1
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44
REVISED PROOF
41 there is potential for an aggressive mitigation policy to
42 limit global warming to about two degrees, and that the
43 behavior of some components of the climate system such
44 as the Arctic sea ice and the Atlantic Meridional Over-
45 turning Circulation may change drastically by the end of
46 the twenty-first century in the case of a no climate policy
47 scenario. Although the magnitude of regional temperature
48 and precipitation changes depends fairly linearly on the
49 magnitude of the projected global warming (and thus on
50 the scenario considered), the geographical pattern of these
51 changes is strikingly similar for the different scenarios. The
52 representation of atmospheric physical processes in the
53 model is shown to strongly influence the simulated climate
54 variability and both the magnitude and pattern of the pro-
55 jected climate changes.
56
57 Keywords Climate Climate change Climate
58 projections Earth System Model CMIP5 CMIP3
59 Greenhouse gases Aerosols Carbon cycle Allowable
60 emissions RCP scenarios Land use changes
61 1 Introduction
62 As climate change projections rely on climate model
63 results, the scientific community organizes regular inter-
64 national projects to intercompare these models. Over the
65 years, the various phases of the Coupled Model Inter-
66 comparison Project (CMIP) have grown steadily both in
67 terms of participants’ number and scientific impacts. The
68
model outputs made available by the third phase of CMIP
69
(CMIP3, Meehl et al. 2005;2007a) have led to hundreds of
70
publications and provided important inputs to the IPCC
71
fourth assessment report (IPCC, 2007). The fifth phase,
72
CMIP5 (Taylor et al. 2012), is also expected to serve the
73
scientific community for many years and to provide major
74
inputs to the forthcoming IPCC fifth assessment report.
75
The IPSL-CM4 model (Marti et al. 2010) developed at
76
Institut Pierre Simon Laplace (IPSL) contributed to
77
CMIP3. It is a classical climate model that couples an
78
atmosphere–land surface model to a ocean–sea ice model.
79
It has been used to simulate and to analyze tropical climate
80
variability (Braconnot et al. 2007), climate change pro-
81
jections (Dufresne et al. 2005), paleo climates (Alkama
82
et al. 2008; Marzin and Braconnot 2009), and the impact of
83
Greenland ice sheet melting on the Atlantic meridional
84
overturning circulation (Swingedouw et al. 2007b,2009)
85
among other studies. Using the same physical package,
86
separate developments have been carried out to simulate
87
tropospheric chemistry (Hauglustaine et al. 2004), tropo-
88
spheric aerosols (Balkanski et al. 2010), stratospheric
89
chemistry (Jourdain et al. 2008), and the carbon cycle
90
(Friedlingstein et al. 2006; Cadule et al. 2009). The model
91
with the carbon cycle (IPSL-CM4-LOOP) has been used to
92
study feedbacks between climate and biogeochemical
93
processes. For instance, Lenton et al. (2009) have shown
94
that a change in stratospheric ozone may modify the carbon
95
cycle through a modification of the atmospheric and oce-
96
anic circulations. Lengaigne et al. (2009) have suggested
97
positive feedbacks between sea-ice extent and chlorophyll
98
distribution in the Arctic region on a seasonal time scale.
99
The IPSL-CM5 model, which is presented here and
100
contributes to CMIP5, is an Earth System Model (ESM)
101
that includes all the previous developments. It is a platform
102
that allows for a consistent suite of models with various
103
degrees of complexity, various numbers of components and
104
processes, and different resolutions. Similar approaches
105
have been adopted in other climate modeling centers (e.g.
106
Martin et al. 2011). This flexibility is difficult to implement
107
and to keep up to date but it is useful for many studies. For
108
instance, when studying the various feedbacks of the cli-
109
mate system, it is common to replace some components or
110
processes by prescribed conditions.
111
When evaluating the performance of the aerosol and
112
chemistry components in the atmosphere, one may want to
113
nudge the global atmospheric circulation to the observed
114
one. For more theoretical studies or to investigate the
115
robustness of some climate features, one may wish to
116
drastically simplify the system by simulating for instance
117
an idealized aqua-planet.
118
It is very useful to have different versions of a model
119
with different ’’physical packages’’, i.e. different sets of
120
consistent parameterizations. First, it allows for the
(CNRS), Universite
´de Versailles Saint-Quentin (UVSQ),
Universite
´Pierre et Marie Curie (UPMC), Paris, France
H. Bellenger R. Benshila S. Flavoni E. Guilyardi
S. Labetoulle C. Levy J. Lloyd G. Madec S. Masson
J. Mignot C. Talandier P. Terray
Laboratoire d’Oce
´anographie et du Climat: Expe
´rimentation et
Approches Nume
´riques (LOCEAN/IPSL), Centre National de la
Recherche Scientifique (CNRS), Universite
´Pierre et Marie
Curie (UPMC), Institut de Recherche pour le De
´veloppement
(IRD), Museum National d’Histoire Naturelle (MNHM), Paris,
France
T. Fichefet
Georges Lemaı
ˆtre Centre for Earth and Climate Research, Earth
and Life Institute, Universite
´Catholique de Louvain,
1348 Louvain-la-Neuve, Belgium
P. Friedlingstein
College of Engineering, Mathematics and Physical Sciences,
University of Exeter, Exeter EX4 4QF, UK
G. Krinner S. Parouty
Laboratoire de Glaciologie et Ge
´ophysique de l’Environnement
(LGGE), Centre National de la Recherche Scientifique (CNRS),
Universite
´Joseph Fourier (UJF), Grenoble, France
J.-L. Dufresne et al.
123
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121 analysis of the role of some physical processes on the
122 climate system such as deep convection (e.g. Braconnot
123 et al. 2007). Second, it facilitates the developments of the
124 ESM, which is an ongoing process. Indeed developing and
125 adjusting the physical package requires time. As these
126 developments strongly impact the characteristics of the
127 biogeochemistry variables (e.g. aerosol concentration,
128 chemistry composition,...), it is important that a frozen
129 version of the physical package is used while the models
130 including the other processes are being developed. In the
131 previous IPSL-CM4 model, most of the chemistry and
132 aerosol studies where first made using the LMDZ atmo-
133 spheric model with the Tiedtke convective scheme (Tied-
134 tke 1989) while the Emanuel convective scheme (Emanuel
135 1991) was included and developed to improve the char-
136 acteristics of the simulated climate. However these two
137 versions were not included in a single framework and have
138 diverged over the years. Conversely, the new IPSL model
139 includes two physical packages within the same frame-
140 work. IPSL-CM5A is an improved extension of IPSL-CM4
141 and is now used as an ESM. IPSL-CM5B includes a brand
142 new set of physical parameterizations in the atmospheric
143 model (Hourdin et al. 2013b).
144 The following main priorities were given to IPSL-
145 CM5A in order to fulfill our scientific priorities. The first
146 was to include all necessary processes to study climate-
147 chemistry and climate-biogeochemistry interactions. This
148 was achieved by including and adapting the new compo-
149 nents and improvements developed at the IPSL during the
150 last 10 years, and by increasing the vertical resolution of
151 the stratosphere to make the coupling with stratospheric
152 chemistry possible. The second priority was to reduce the
153 mid-latitude cold bias (Swingedouw et al. 2007a; Marti
154 et al. 2010), and dedicated work on the impact of the
155 atmospheric grid on this cold bias has been undertaken
156 (Hourdin et al. 2013a). Finally, a rather coarse resolution
157 for both the atmosphere and the ocean was favored to allow
158 for long term simulations and ensembles simulations in a
159 reasonable amount of computing time. For the IPSL-CM5B
160 model, the objectives of developments were very different.
161 The main objective was to test some major developments
162 of the parameterizations of boundary layer, deep convec-
163 tion and clouds processes. Although this new version is
164 expected to have some possibly important biases due to
165 incomplete developments and lack of tuning, its should be
166 considered as a prototype of the next model generation.
167 The outline of the paper is the following. The IPSL-
168 CM5 model and its components are briefly presented in
169 Sect. 2. The different model configurations and the dif-
170 ferent forcings used to perform the CMIP5 long-term
171 experiments are presented in Sect. 3. Among these exper-
172 iments, climate change simulations of the twentieth century
173 and projections for the twenty-first century are analyzed in
174
Sects. 4and 5. Then the climate variability and response to
175
the same forcing are analyzed for different versions of the
176
IPSL model (Sect. 6). Summary and conclusions are given
177
in Sect. 7.
178
2 The IPSL-CM5 model and its components
179
2.1 The platform
180
The IPSL-CM5 ESM platform allows for a large range of
181
model configurations, which aim at addressing different
182
scientific questions. These configurations may differ in
183
various ways: physical parameterizations, horizontal reso-
184
lution, vertical resolution, number of components (atmo-
185
sphere and land surface only, ocean and sea ice only,
186
coupled atmosphere–land surface–ocean–sea ice) and
187
number of processes (physical, chemistry, aerosols, carbon
188
cycle) (Fig. 1).
189
The IPSL-CM5 model is built around a physical core
190
that includes the atmosphere, land-surface, ocean and sea-
191
ice components. It also includes biogeochemical processes
192
through different models: stratospheric and tropospheric
193
chemistry, aerosols, terrestrial and oceanic carbon cycle
194
(Fig. 1a). To test specific hypotheses or feedback mecha-
195
nisms, components of the model may be suppressed and
196
replaced by prescribed boundary conditions or values
197
(Sect. 3). A general overview of the various models
198
included in the IPSL-CM5 model is given in the next sub-
199
sections.
200
2.2 Atmosphere
201
2.2.1 Atmospheric GCM: LMDZ5A and LMDZ5B
202
LMDZ is an atmospheric general circulation model
203
developed at the Laboratoire de Me
´te
´orologie Dynamique.
204
The dynamical part of the code is based on a finite-dif-
205
ference formulation of the primitive equations of meteo-
206
rology (Sadourny and Laval 1984) on a staggered and
207
stretchable longitude-latitude grid (the Z in LMDZ stands
208
for zoom). Water vapor, liquid water and atmospheric trace
209
species are advected with a monotonic second order finite
210
volume scheme (Van Leer 1977; Hourdin and Armengaud
211
1999). The model uses a classical so-called hybrid r-p
212
coordinate in the vertical. The number of layers has been
213
increased from 19 to 39 compared to the previous LMDZ4
214
version, with 15 levels above 20 km. The maximum alti-
215
tude for the L39 discretization is about the same as for the
216
stratospheric LMDZ4-L50 version (Lott et al. 2005). It is
217
fine enough to resolve the mid-latitude waves propagation
218
in the stratosphere and to produce sudden-stratospheric
219
warmings. Two versions of LMDZ5, which differ by the
IPSL-CM5 Earth System Model
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220 parameterization of turbulence, convection, and clouds can
221 be used within IPSL-CM5.
222 In the LMDZ5A version, (Hourdin et al. 2013a) the
223 physical parameterizations are very similar to that in the
224 previous LMDZ4 version used for CMIP3 (Hourdin et al.
225 2006). The radiation scheme is inherited from the European
226 Center for Medium-Range Weather Forecasts (Fouquart
227 and Bonnel 1980; Morcrette et al. 1986). The dynamical
228 effects of the subgrid-scale orography are parameterized
229 according to Lott (1999). Turbulent transport in the plan-
230 etary boundary layer is treated as a vertical eddy diffusion
231 (Laval et al. 1981) with counter-gradient correction and
232 dry convective adjustment. The surface boundary layer is
233 treated according to Louis (1979). Cloud cover and cloud
234
water content are computed using a statistical scheme
235
(Bony and Emanuel 2001). For deep convection, the
236
LMDZ5A version uses the ’’episodic mixing and buoyancy
237
sorting’’ scheme originally developed by Emanuel (1991).
238
LMDZ5A is used within the IPSL-CM5A model.
239
In the ’’New Physics’’ LMDZ5B version, (Hourdin et al.
240
2013b) the boundary layer is represented by a combined
241
eddy-diffusion plus ’’thermal plume model’’ to represent
242
the coherent structures of the convective boundary layer
243
(Hourdin et al. 2002; Rio and Hourdin 2008; Rio et al.
244
2010). The cloud scheme is coupled to both the convection
245
scheme (Bony and Emanuel 2001) and the boundary layer
246
scheme (Jam et al. 2013) assuming that the subgrid scale
247
distribution of total water can be represented by a
(a)
(c)
(b)
(d)
Fig. 1 Schematic of the IPSL-CM5 ESM platform. The individual
models constituting the platform are in magenta boxes, the computed
variables are in green boxes and the prescribed variables are in red
boxes. The physical and biogeochemistry models exchange aerosol,
ozone and CO
2
concentrations, as detailed on the figure. They also
exchange concentration of other constituents as well as many physical
or dynamical variables, gathered in the ’’other var’’ label. In a, the
’’plain configuration’’ is shown with all the models being active. In b,
the ’’atmospheric chemistry configuration’’ is shown where the ocean
and the carbon cycle models have been replaced by prescribed
boundary conditions: ocean surface temperature, sea-ice fraction and
CO
2
concentration. In c, the ’’climate-carbon configuration’’ is shown
where the chemistry and aerosol models have been replaced by
prescribed conditions (ozone and aerosols 3D fields). The CO
2
concentration is prescribed and the ’’implied CO
2
emissions’’ are
computed. In d, the same configuration as in cis shown except that
CO
2
emissions are prescribed and CO
2
concentration is computed
J.-L. Dufresne et al.
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248 generalized log-normal distribution in the first case, and by
249 a bi-Gaussian distribution in the second case. In both cases,
250 the statistical moments of the total water distribution are
251 diagnosed as a function of both large-scale environmental
252 variables and subgrid scale variables predicted by the
253 convection or turbulence parameterizations. The triggering
254 and the closure of the Emanuel (1991) convective scheme
255 have been modified and are now based on the notions of
256 Available Lifting Energy for the triggering and Available
257 Lifting Power for the closure. A parameterization of the
258 cold pools generated by the re-evaporation of convective
259 rainfall has been introduced (Grandpeix and Lafore 2010;
260 Grandpeix et al. 2010). The LMDZ5B version is charac-
261 terized by a much better representation of the boundary
262 layer and associated clouds, by a delay of several hours of
263 the diurnal cycle of continental convection, and by a
264 stronger and more realistic tropical variability. LMDZ5B is
265 used within the IPSL-CM5B model.
266 2.2.2 Stratospheric chemistry: REPROBUS
267 The REPROBUS (Reactive Processes Ruling the Ozone
268 Budget in the Stratosphere) module (Lefevre et al. 1994;
269 1998) coupled to a tracer transport scheme is used to
270 interactively compute the global distribution of trace gases,
271 aerosols, and clouds within the stratosphere in the LMDZ
272 atmospheric model. The module is extensively described in
273 Jourdain et al. (2008). It includes 55 chemical species, the
274 associated stratospheric gas-phase, and heterogeneous
275 chemical reactions. Absorption cross-sections and kinetics
276 data are based on the latest Jet Propulsion Laboratory
277 recommendations (Sander et al. 2006). The photolysis rates
278 are calculated offline using a look-up table generated with
279 the Tropospheric and Ultraviolet visible radiative model
280 (Madronich and Flocke 1998). The heterogeneous chem-
281 istry component takes into account the reactions on sulfuric
282 acid aerosols, and liquid (ternary solution) and solid (Nitric
283 Acid Trihydrate particles, ice) Polar Stratospheric Clouds
284 (PSCs). The gravitational sedimentation of PSCs is also
285 simulated.
286 2.2.3 Tropospheric chemistry and aerosol: INCA
287 The INteraction with Chemistry and Aerosol (INCA)
288 model simulates the distribution of aerosols and gaseous
289 reactive species in the troposphere. The model accounts for
290 surface and in-situ emissions (lightning, aircraft), scav-
291 enging processes and chemical transformations. LMDZ-
292 INCA simulations are performed with a horizontal grid of
293 3.75°in longitude and 1.9°in latitude (96 995 grid
294 points). The vertical grid is based on the former LMDZ4 19
295 levels. Fundamentals for the gas phase chemistry are pre-
296 sented in Hauglustaine et al. (2004) and Folberth et al.
297
(2006). The tropospheric photochemistry is described
298
through a total of 117 tracers including 22 tracers to rep-
299
resent aerosols and 82 reactive chemical tracers to repre-
300
sent tropospheric chemistry. The model includes 223
301
homogeneous chemical reactions, 43 photolytic reactions
302
and 6 heterogeneous reactions including non-methane
303
hydrocarbon oxidation pathways and aerosol formation.
304
Biogenic surface emissions of organic compounds and soil
305
emissions are provided from offline simulations with the
306
ORCHIDEE land surface model as described by Lathie
`re
307
et al. (2005). In this tropospheric model, ozone concen-
308
trations are relaxed toward present-day observations at the
309
uppermost model levels (altitudes higher than the 380 K
310
potential temperature level). The changes in stratospheric
311
ozone from pre-ozone hole conditions to the future are
312
therefore not accounted for in the simulations.
313
The INCA module simulates the distribution of anthro-
314
pogenic aerosols such as sulfates, black carbon (BC),
315
particulate organic matter, as well as natural aerosols such
316
as sea-salt and dust. The aerosol code keeps track of both
317
the number concentration and the mass of aerosols using a
318
modal approach to treat the size distribution, which is
319
described by a superposition of log-normal modes (Schulz
320
et al. 1998). Three size modes are considered: a sub-
321
micronic (diameters less than 1 lm), a micronic (diameters
322
between 1 and 10 lm) and a super-micronic (diameters
323
[10 lm). To account for the diversity in chemical com-
324
position, hygroscopicity, and mixing state, we distinguish
325
between soluble and insoluble modes. Sea-salt, SO
4
, and
326
methane sulfonic acid are treated as soluble components of
327
the aerosol, dust is treated as insoluble species, whereas BC
328
and particulate organic matter appear both in the soluble or
329
insoluble fractions. The aging of primary insoluble carbo-
330
naceous particles transfers insoluble aerosol number and
331
mass to soluble with a half-life time of 1.1 days. Details on
332
the aerosol component of INCA can be found in Schulz
333
(2007), Balkanski (2011).
334
The INCA model setup used to generate the aerosols and
335
tropospheric ozone fields used in the CMIP5 simulations
336
performed with IPSL-CM5 as well as the associated radi-
337
ative forcings are described in detail by Szopa et al. (2013)
338
(see also Sects. 3.5 and 3.7).
339
2.2.4 Coupling between chemistry, aerosol, radiation
340
and atmospheric circulation
341
The radiative impact of dust, sea salt, BC and organic
342
carbon aerosols was introduced in LMDZ as described in
343
De
´andreis (2008) and Balkanski (2011). The growth in
344
aerosol size with increased relative humidity is computed
345
using the method described by Schulz (2007). The effect of
346
aerosol on cloud droplet radius without affecting cloud
347
liquid water content (the so-called first indirect effect) is
IPSL-CM5 Earth System Model
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REVISED PROOF
348 also accounted for. To parameterize this effect, the cloud
349 droplet number concentration is computed from the total
350 mass of soluble aerosol through the prognostic equation
351 from Boucher and Lohmann (1995). The coefficient were
352 taken from aerosol-cloud relationships derived from the
353 Polder satellite measurements (Quaas and Boucher 2005).
354 Both direct and first indirect aerosol radiative forcings are
355 estimated through multiple calls to the radiative code.
356 The tropospheric chemistry and aerosols may be either
357 computed or prescribed. When computed, the INCA and
358 LMDZ models are coupled at each time step to account for
359 interactions between chemistry, aerosol and climate.
360 Otherwise, the aerosol concentration is usually prescribed
361 from monthly mean values linearly interpolated for each
362 day. De
´andreis et al. (2012) have analyzed in detail the
363 difference in results obtained with the online and offline
364 setups for sulfate aerosols. They showed that the local
365 effect of the aerosols on the surface temperature is larger
366 for the online than for the offline simulations, although the
367 global effect is very similar.
368 Similarly, the stratospheric chemistry and, in particular,
369 ozone may be either computed or prescribed. When com-
370 puted, the REPROBUS and LMDZ models are coupled at
371 each time step to account for chemistry-climate interac-
372 tions. When prescribed, LMDZ is forced by day-time and
373 night-time ozone concentrations above the mid-strato-
374 sphere whereas it is forced by daily mean ozone fields
375 below. Indeed, ozone concentration exhibits a strong
376 diurnal cycle in the upper stratosphere and mesosphere.
377 Neglecting these diurnal variations leads to an overesti-
378 mation of the infra-red radiative cooling and therefore to a
379 cold bias in the atmosphere.
380 2.3 Land surface model: ORCHIDEE
381 ORCHIDEE (ORganizing Carbon and Hydrology In
382 Dynamic EcosystEms) is a land-surface model that simu-
383 lates the energy and water cycles of soil and vegetation, the
384 terrestrial carbon cycle, and the vegetation composition
385 and distribution (Krinner et al. 2005). The land surface is
386 described as a mosaic of twelve plant functional types
387 (PFTs) and bare soil. The definition of PFT is based on
388 ecological parameters such as plant physiognomy (tree or
389 grass), leaves (needleleaf or broadleaf), phenology (ever-
390 green, summergreen or raingreen) and photosynthesis
391 pathways for crops and grasses (C3 or C4). Relevant bio-
392 physical and biogeochemical parameters are prescribed for
393 each PFT.
394 Exchanges of energy (latent, sensible, and kinetic
395 energy) and water, between the atmosphere and the bio-
396 sphere are based on the work of Ducoudre
´et al. (1993) and
397 de Rosnay and Polcher (1998) and they are computed with
398
a 30-min time step together with the exchange of carbon
399
during photosynthesis. The soil water budget in the stan-
400
dard version of ORCHIDEE is done with a two-layer
401
bucket model (de Rosnay and Polcher 1998). The water
402
that is not infiltrated or drained at the bottom of the soil is
403
transported through rivers and aquifers (d’Orgeval et al.
404
2008). This routing scheme allows the re-evaporation of
405
the water on its way to the ocean through floodplains or
406
irrigation (de Rosnay et al. 2003).
407
The exchanges of water and energy at the land surface
408
are interlinked with the exchange of carbon. The vegetation
409
state (i.e. foliage density, interception capacity, soil-water
410
stresses) is computed dynamically within ORCHIDEE
411
(Krinner et al. 2005) and accounts for carbon assimilation,
412
carbon allocation and senescence processes. Carbon
413
exchange at the leaf level during photosynthesis is based on
414
Farquhar et al. (1980) and Collatz et al. (1992) for C3 and
415
C4 photosynthetic pathways, respectively. Concomitant
416
water exchange through transpiration is linked to photo-
417
synthesis via the stomatal conductance, following the for-
418
mulation of Ball et al (1987). Photosynthesis is computed
419
with a 30-min time step while carbon allocation in the
420
different soil-plant reservoirs is performed with a daily
421
time step.
422
The PFT distribution is fully prescribed in the simula-
423
tions presented in this article. The relative distribution of
424
natural PFTs within each grid cell is prescribed by using
425
PFT distribution maps where only the fractions of crop-
426
lands and total natural lands per grid cell vary at a yearly
427
time step. The elaboration of these maps is detailed in the
428
Sect. 3.7 below.
429
When coupled, both LMDZ and ORCHIDEE models
430
have the same spatial resolution and time step. The cou-
431
pling procedure for heat and water fluxes uses an implicit
432
approach as described in Marti et al. (2010).
433
2.4 Ocean and sea-ice
434
The ocean and sea-ice component is based on NEMOv3.2
435
(Nucleus for European Modelling of the Ocean, Madec
436
2008), which includes OPA for the dynamics of the
437
ocean, PISCES for ocean biochemistry, and LIM for sea-
438
ice dynamics and thermodynamics. The configuration is
439
ORCA2 (Madec and Imbard 1996), which uses a tri-polar
440
global grid and its associated physics. South of 40°N, the
441
grid is an isotropic Mercator grid with a nominal resolution
442
of 2°. A latitudinal grid refinement of 0.5°is used in the
443
tropics. North of 40°N the grid is quasi-isotropic, the North
444
Pole singularity being mapped onto a line between points
445
in Canada and Siberia. In the vertical 31 depth levels are
446
used (with thicknesses from 10 m near the surface to
447
500 m at 5,000 m).
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448 2.4.1 Oceanic GCM: NEMO-OPA
449 NEMOv3.2 takes advantage of several improvements over
450 OPA8.2, which was used in IPSL-CM4. It uses a partial
451 step formulation (Barnier et al. 2006), which ensures a
452 better representation of bottom bathymetry and thus stream
453 flow and friction at the bottom of the ocean. Advection of
454 temperature and salinity is computed using a total variance
455 dissipation scheme (Le
´vy et al. 2001; Cravatte et al. 2007).
456 An energy and enstrophy conserving scheme is used in the
457 momentum equation (Arakawa and Lamb 1981; Le Som-
458 mer et al. 2009). The mixed layer dynamics is parameter-
459 ized using the Turbulent Kinetic Energy (TKE) closure
460 scheme of Blanke and Delecluse (1993) improved by
461 Madec (2008). Improvements include a double diffusion
462 process (Merryfield et al. 1999), Langmuir cells (Axell
463 2002) and the contribution of surface wave breaking
464 (Mellor and Blumberg 2004; Burchard and Rennau 2008).
465 A parameterization of bottom intensified tidal-driven
466 mixing similar to Simmons et al. (2004) is used in com-
467 bination with a specific tidal mixing parameterization in
468 the Indonesian region (Koch-Larrouy et al. 2007;2010).
469 NEMOv3.2 also includes representation of the interaction
470 between incoming shortwave radiation into the ocean and
471 the phytoplankton (Lengaigne et al. 2009).
472 The horizontal eddy viscosity coefficient (ahm) value is
473 4.10
4
m
2
.s
-1
and the lateral eddy diffusivity coefficient
474 (aht) value is 10
3
m
2
.s
-1
. The coefficient ahm reduces to
475 aht in the tropics, except along western boundaries. The
476 tracer diffusion is along isoneutral surfaces. A Gent and
477 Mcwilliams (1990) term is applied in the advective for-
478 mulation. Its coefficient is computed from the local growth
479 rate of baroclinic instability. It decreases in the 20S–20N
480 band and vanishes at the equator. At the ocean floor, there
481 is a linear bottom friction with a coefficient of 4.10
-4
, and
482 a background bottom turbulent kinetic energy of 2.5 10
-3
483 m
2
.s
-2
. The model has a Beckmann and Do
¨scher (1997)
484 diffusive bottom boundary layer scheme with a value of
485 10
4
m
2
.s
-1
. A spatially varying geothermal flux is applied
486 at the bottom of the ocean (Emile-Geay and Madec 2009)
487 with a global mean value of 86.4 mW.m
-2
.
488 2.4.2 Sea ice: NEMO-LIM2
489 LIM2 (Louvain-la-Neuve Sea Ice Model, Version 2) is a
490 two-level thermodynamic-dynamic sea ice model (Fichefet
491 and Morales Maqueda 1997,1999). Sensible heat storage
492 and vertical heat conduction within snow and ice are
493 determined by a three-layer model. The storage of latent
494 heat inside the ice, which results from the trapping of
495 shortwave radiation by brine pockets, is taken into account.
496 The surface albedo is parameterized as a function of sur-
497 face temperature and snow and ice thicknesses. Vertical
498
and lateral growth/decay rates of ice are obtained from
499
prognostic energy budgets at both the bottom and surface
500
boundaries of the snow-ice cover and in leads. For the
501
momentum balance, sea ice is considered as a two-
502
dimensional continuum in dynamical interaction with the
503
atmosphere and ocean. The viscous-plastic constitutive law
504
proposed by Hibler (1979) is used for computing the
505
internal ice force. The ice strength is a function of ice
506
thickness and compactness. The advected physical fields
507
are the ice concentration, the snow and ice volume,
508
enthalpy, and the brine reservoir. The sea ice and ocean
509
models have the same horizontal grid.
510
2.4.3 Ocean carbon cycle: NEMO-PISCES
511
PISCES (Pelagic Interaction Scheme for Carbon and
512
Ecosystem Studies) (Aumont and Bopp 2006) simulates
513
the cycling of carbon, oxygen, and the major nutrients
514
determining phytoplankton growth (phosphate, nitrate,
515
ammonium, iron and silicic acid). The carbon chemistry
516
of the model is based on the Ocean Carbon Model
517
Intercomparison Project (OCMIP2) protocol (Najjar et al.
518
2007) and the parameterization proposed by Wanninkhof
519
(1992) is used to compute air-sea gas exchange of CO
2
520
and O
2
.
521
PISCES includes a simple representation of the marine
522
ecosystem with two phytoplankton size classes represent-
523
ing nanophytoplankton and diatoms, as well as two zoo-
524
plankton size classes representing microzooplankton and
525
mesozooplankton. Phytoplankton growth is limited by the
526
availability of nutrients, temperature, and light. There are
527
three non-living components of organic carbon in the
528
model: semi-labile dissolved organic carbon with a lifetime
529
of several weeks to a few years, as well as large and small
530
detrital particles, which are fuelled by mortality, aggrega-
531
tion, fecal pellet production and grazing. Biogenic silica
532
and calcite particles are also included.
533
Nutrients and/or carbon are supplied to the ocean from
534
three different sources: atmospheric deposition, rivers, and
535
sediment mobilization. These sources are explicitly inclu-
536
ded but do not vary in time apart from a climatological
537
seasonal cycle for the atmospheric input. Atmospheric
538
deposition (Fe, N, P and Si) has been estimated from the
539
INCA model (Aumont et al. 2008). River discharge of
540
carbon and nutrients is taken from Ludwig et al. (1996).
541
Iron input from sediment mobilization has been parame-
542
terized as in Aumont and Bopp (2006).
543
PISCES is used here to compute air-sea fluxes of carbon
544
and also the effect of a biophysical coupling: the chloro-
545
phyll concentration produced by the biological component
546
retroacts on the ocean heat budget by modulating the
547
absorption of light as well as the oceanic heating rate (see
548
Lengaigne et al. (2007) for a detailed description).
IPSL-CM5 Earth System Model
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549 2.4.4 Atmosphere–Ocean–Sea ice coupling
550 The Atmosphere/Ocean/Sea ice coupling in IPSL-CM5 is
551 very similar yet improved compared to the coupling used in
552 IPSL-CM4 (Marti et al. 2010). The atmospheric model has
553 a fractional land-sea mask, each grid box being divided
554 into four sub-surfaces corresponding to land surface, free
555 ocean, sea ice and glaciers. The OASIS coupler (Valcke
556 2006) is used to interpolate and exchange the variables and
557 to synchronize the models. Since a comprehensive model
558 of glacier and land-ice is not yet included, the local snow
559 mass is limited to 3,000 kg.m
2
to avoid infinite accumu-
560 lation, and the snow mass above this limit is sent as
561 ‘‘calving’’ to the ocean. The coupling and the interpolation
562 procedures ensure local conservation of energy and water,
563 avoiding the need of any transformation to conserve these
564 global quantities. One improvement compared to Marti
565 et al. (2010) consists in the daily mean velocity of the
566 ocean surface being now sent to the atmosphere and used
567 as boundary conditions for the atmospheric boundary layer
568 scheme.
569 2.5 Model tuning
570 GCMs include many parameterizations, which are approxi-
571 mate descriptions of sub-grid processes. These parameter-
572 izations are formulated via a series of parameters that are
573 usually not directly observable and must be tuned so that
574 the parameterizations fit as well as possible the statistical
575 behavior of the physical processes. Therefore the tuning
576 process is a fundamental aspect of climate model devel-
577 opment. It is usually performed at different stages: for
578 individual parameterizations, for individual model com-
579 ponents (atmosphere, ocean, land surface,...) and for the
580 full coupled climate model. This tuning process is non-
581 linear. It includes iterations among these three stages and it
582 inherits from successive tunings performed separately
583 on the individual components or on coupled model along
584 years of model development.
585 In coupled models with no flux adjustment, one
586 important variable is the net heat budget of the Earth sys-
587 tem, which has to be close to zero (i.e. within a few tenths
588 of Wm
-2
) in order to avoid a major temperature drift. The
589 observed present-day top of the atmosphere (TOA) energy
590 budget shows a small imbalance of about 0.9 ±0.3Wm
-2
591 (Hansen et al. 2011; Lyman et al. 2010; Stevens and
592 Schwartz 2012; Trenberth and Fasullo 2012). This imbal-
593 ance, which is due to recent changes in atmospheric
594 composition and to the ocean thermal inertia, leads to the
595 current global warming. A perfect climate model run with
596 the current atmospheric composition and initialized with
597 present-day conditions should produce a comparable
598 imbalance and should drift naturally toward a warmer
599
climate. Therefore there is no obvious choice on how to
600
simulate an equilibrium global temperature close to current
601
observations. Performing control runs with present-day
602
conditions requires making some ad hoc adaptations. We
603
have chosen to compensate the oceanic heat uptake by
604
uniformly increasing the albedo of the oceanic surface by
605
0.01 during (and only during) this tuning phase. Most runs
606
performed in this phase covered a few decades and only a
607
few of them were extended to a few centuries. No historical
608
runs were performed and no adjustment was made to
609
specifically reproduce the temperature increase which has
610
been observed for a few decades.
611
The following adjustments were made for the IPSL-
612
CM5A-LR model. For the atmospheric model, the final
613
tuning of the global energy balance was achieved by con-
614
sidering a sub-set of three parameters of the cloud
615
parameterizations (Hourdin et al. 2013a): two upper clouds
616
parameters (maximum precipitation efficiency of the deep
617
convection scheme and fall velocity of the ice cloud par-
618
ticles) and one parameter related to the conversion of cloud
619
water to rainfall in the large-scale cloud scheme. In addi-
620
tion to the global energy balance, particular attention was
621
given to the partitioning between SW and LW radiative
622
fluxes and between clear sky and all sky radiative fluxes.
623
The mean values, zonal distribution, and partition between
624
convective and subsiding regimes in the tropics were
625
considered.
626
In addition to the global energy balance, some other
627
aspects were also considered during the final tuning. For
628
the land-surface model, the soil depth was increased from
629
2- to 4-m to reduce the strong underestimation of the leaf
630
area index (LAI) and of the carbon pools in the north-
631
eastern Amazon and in other tropical regions. The soil
632
depth increase allows for greater seasonal soil water
633
retention and reduces these biases. For the ocean, the new
634
TKE parameterization has been tuned to reduce the error of
635
the modeled mixed layer depth pattern and to obtain the
636
best match with observations for the sea surface tempera-
637
ture (SST) pattern.
638
As shown later in Sect. 4.2, the IPSL-CM5A-LR his-
639
torical runs show a cold bias of about 1 K compared to
640
present-day observations. This bias is due to the fact that
641
during the tuning phase the oceanic model was far from
642
equilibrium and the aerosols, volcanoes, and ozone forc-
643
ings did not reach their final values. When this problem
644
was identified it was too late to rerun the whole set of
645
simulations within the CMIP5 schedule. A better method-
646
ology than the one used here would probably have been to
647
perform the final tunings in order to reach a net heat budget
648
equilibrium with the global mean pre-industrial tempera-
649
ture even though this temperature is not precisely known.
650
With the same parameters as in the IPSL-CM5A-LR
651
version, the medium-resolution IPSL-CM5A-MR version
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REVISED PROOF
652 was producing a mean temperature warmer by only a few
653 tenths of a degree. It was thus decided to reduce the mean
654 temperature bias in this configuration with a uniform 0.01
655 increase of the solar absorption coefficient in the ocean.
656 For the IPSL-CM5B-LR model, all components and
657 parameter values are the same as in the IPSL-CM5A-LR
658 model except for the atmospheric component, which is now
659 LMDZ5B (Hourdin et al. 2013b). The radiative flux at the
660 TOA has been adjusted using the same methodology and
661 tuning parameters as for IPSL-CM5A. However the net
662 radiative flux at the TOA is not zero even at equilibrium
663 because the energy is not fully conserved in the atmospheric
664 model LMDZ5B: the difference between the net flux at the
665 TOA and at the surface is about -0.71Wm
-2
in IPSL-
666 CM5B-LR and about 0.01 Wm
-2
in IPSL-CM5A-LR.
667 3 Experiments, model configurations and forcings
668 for CMIP5
669 3.1 The CMIP5 experimental protocol
670 The CMIP5 project (Taylor et al. 2012) has been designed
671 to address a much wider range of scientific questions than
672 CMIP3 (Meehl et al. 2005), requiring a wider spectrum of
673 models, configurations, and experiments. Here we only
674 report on the long-term experiments. They include a few-
675 centuries long pre-industrial control simulation, the his-
676 torical simulations (1850–2005), and the future projections
677 simulations (2006–2100, 2006–2300). The future projec-
678 tions are performed under the new scenarios proposed by
679 CMIP5, the RCP (Representative Concentration Pathway)
680 scenarios (Moss et al. 2010; van Vuuren et al. 2011), each
681 labeled according to the approximate value of the radiative
682 forcing (in Wm
-2
) at the end of the twenty-first century:
683 RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5. The RCPs are
684 supplemented with extensions (Extended Concentration
685 Pathways, ECPs) until year 2300 without reference to
686 specific underlying societal, technological or population
687 scenarios (Meinshausen et al. 2011). As in Taylor et al.
688 (2012) we refer to both RCPs and ECPs as RCPs in the
689 remainder of this paper. CMIP5 also included simulations
690 with idealized forcings (1 % year CO
2
increase, 4 times
691 CO
2
abrupt increase), forcings corresponding to prescribed
692 or idealized sea-surface conditions (e.g. observed SST,
693 aqua-planet), forcings representative of specific paleo-cli-
694 mate periods, and others. The total length of all these
695 simulations is a few thousands of years. This of course calls
696 for optimizations and compromises between the available
697 computing time and the simulations’ degrees of complex-
698 ity. Our general strategy has been to run the atmospheric
699 component of the ESM at a rather low resolution and to
700 treat some of the atmospheric chemistry and transport
701
processes controlling the greenhouse gases and the aerosols
702
outside the ESM in a semi-offline way.
703
3.2 Model horizontal resolution
704
In the standard version of the IPSL-CM4 model used for
705
CMIP3, the atmospheric model has 72 points in longitude
706
and 96 points in latitude, corresponding to a resolution of
707
3.75°92.5°. For CMIP5 a rather coarse resolution was
708
used, which allows for the coverage of most of the long
709
term simulations in a reasonable amount of time. A com-
710
putationally affordable model is also helpful to obtain an
711
initial state of the climate system close to equilibrium,
712
which requires multi-century runs particularly when the
713
carbon cycle is included.
714
A systematic exploration of the impact of the atmo-
715
spheric grid configuration on the simulated climate was
716
conducted with IPSL-CM4 by (Hourdin et al. 2013a). They
717
found that the grid refinement has a strong impact on the jet
718
locations and on the pronounced mid latitude cold bias,
719
which was one of the major deficiencies of the IPSL-CM4
720
model. The impact of grid refinement on the jets location
721
was also studied by Guemas and Codron (2011)inan
722
idealized dynamical-core setting. They found that an
723
increase of the resolution in latitude produced a poleward
724
shift of the jet because an enhanced baroclinic wave
725
activity brought more momentum from the Tropics. An
726
increased resolution in longitude produced no such shift
727
because a tendency towards more cyclonic wave breaking
728
canceled the increase of wave activity in that case. The
729
errors associated with the equatorward jet position could
730
thus be reduced at moderate computational cost by
731
increasing the resolution in latitude more than in longitude.
732
Based on these results two grids were used for CMIP5.
733
They have almost the same number of points in longitude
734
and latitude so that the meshes are isotropic (dx=dy)at
735
latitude 60°and dx=2dyat the equator. At Low Reso-
736
lution (LR), the model has 96 995 points corresponding
737
to a resolution of 3.75°91.875°in longitude and latitude
738
respectively and at Medium Resolution (MR) the model
739
has 144 9143 points, corresponding to a resolution of
740
2.5°91.25°.
741
3.3 Ozone concentrations
742
Interannual ozone variations are considered in the IPSL-
743
CM5 simulations for CMIP5. This was not the case in the
744
IPSL-CM4 simulations for CMIP3 for which the model
745
was only forced with a constant seasonally-varying ozone
746
field. Nevertheless this interannually varying ozone cannot
747
be routinely computed online using the very comprehen-
748
sive aerosols and chemistry coupled models (Sects. 2.2.2
749
and 2.2.3) in the IPSL ESM because they require a lot of
IPSL-CM5 Earth System Model
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750 computing time: LMDZ-INCA and LMDZ-REPROBUS
751 both need 50–100 tracers, and running these models
752 increases the CPU time by more than a factor of 10 com-
753 pared to the atmospheric model LMDZ alone.
754 To circumvent this difficulty, variations in ozone con-
755 centration shorter than a month even initially caused by
756 short-term climate variability were assumed to play a rel-
757 atively small, possibly negligible, role in the long-term
758 evolution of climate. This assumption has been shown to be
759 valid for stratospheric ozone (e.g. Son et al. 2010). On long
760 time scales stratospheric ozone is mostly influenced by
761 climate change via stratospheric cooling due to CO
2
762 increase and tropospheric ozone is influenced by changes
763 in global mean temperature via the water vapor concen-
764 tration. These climate effects on ozone are accounted for in
765 chemistry climate models run with prescribed SST
766 (Fig. 1b). In turn the climate evolution depends on the
767 long-term changes in ozone concentration. The treatment
768 of the two-way interactions between ozone and climate can
769 thus be simplified by decoupling them using a semi-offline
770 approach instead of the fully coupled online approach.
771 This approach is fully described in Szopa et al. (2013)
772 and consists in specifying the ozone fields predicted by
773 dedicated atmospheric chemistry coupled model simula-
774 tions in the ESM. In order to do so, both the INCA and the
775 REPROBUS atmospheric chemistry models were used.
776 Since the RCP climate model simulations were not yet
777 available, the SST and sea ice concentrations prescribed in
778 the chemistry simulations were taken from existing his-
779 torical and scenario runs performed with the IPSL-CM4
780 model. We use the SST of the SRES-A2 scenario for the
781 RCP 8.5 simulation, the SST of the SRES-A1B scenario for
782 the RCP 6.0 simulation, the SST of the SRES-B1 scenario
783 for the RCP 4.5 simulation and the SST of the scenario E1
784 (Johns et al. 2011) for the RCP 2.6 simulation. The dif-
785 ferences between the prescribed SST and those obtained
786 with the RCP scenarios are not expected to strongly impact
787 the atmospheric chemistry. First, the LMDZ-INCA model
788 (Sect. 2.2.3) with 19 vertical levels has been used to gen-
789 erate time-varying 3D fields of ozone in the troposphere.
790 The simulations include decadal emissions of methane,
791 carbon monoxide, nitrogen oxides and non methane
792 hydrocarbons for anthropogenic and biomass burning
793 emissions. They are taken from Lamarque et al. (2010) for
794 the historical period and from Lamarque et al. (2011) for
795 the RCP scenarios. Also, the monthly biogenic emissions
796 are from Lathie
`re et al. (2005) and are kept constant over
797 the period. Second, the LMDZ-REPROBUS model (Sect.
798 2.2.2) with 50 vertical levels is used to generate time-
799 varying 3D fields of ozone in the stratosphere. Instead of
800 running all the scenarios, time-varying ozone fields for
801 some of the RCP scenarios are reconstructed by interpo-
802 lating or extrapolating linearly from the CCMVal REF-B2
803
and SCN-B2c scenarios (Morgenstern et al. 2010) using a
804
time-varying weighing coefficient proportional to the CO
2
805
level. This approach is based on the somewhat linear
806
dependence of stratospheric ozone changes on CO
2
chan-
807
ges, which has been found in coupled chemistry models run
808
under the RCP scenarios (Eyring et al. 2010a,b). The
809
INCA (tropospheric) and REPROBUS (stratospheric)
810
ozone fields are then merged with a transition region cen-
811
tered on the tropopause region and averaged over longi-
812
tudes to produce time-varying zonally-averaged monthly-
813
mean ozone fields.
814
Figure 2shows the total column ozone as a function of
815
latitude and time, from 1960 to 2100, for RCP 2.6 and RCP
816
6.0 scenarios, as well as for the ACC/SPARC ozone
817
dataset, which is the commonly used ozone climatology in
818
CMIP5 (Cionni et al. 2011; Eyring et al. 2012). The time
819
evolutions of the globally-averaged total column ozone in
820
the RCP 2.6, 4.5, 6.0 and 8.5 scenarios and in the ACC/
821
SPARC climatology are shown on Fig. 3. The evolutions
822
of column ozone as a function of latitude and time are
823
similar in our CMIP5 climatologies and in ACC/SPARC
824
climatology. From 1960 onwards, column ozone decreases
825
at all latitudes with smaller trends over the tropics and
826
largest trends over Antarctica. This evolution is mostly due
827
to the increase in ODSs (Ozone Depleting Substances) until
828
the end of the twentieth century. The pre-2000 ozone
829
decrease is followed by an increase with a rate that depends
830
on the RCP scenario and on the region.
831
There are three main differences between our CMIP5
832
ozone forcings and the ACC/SPARC dataset. First, the
833
Antarctic ozone hole is more pronounced in our dataset than
834
in the ACC/SPARC dataset. Second, although the decrease
835
in column ozone is stronger over Antarctica in our dataset,
836
the decline in global ozone during the end of the last century
837
is weaker (Fig. 3) indicating that the past tropical column
838
ozone declines less quickly in our climatology. Third, the
839
values of column ozone are generally higher in our dataset.
840
Globally-averaged total column ozone is about 10–18
841
DU higher in our RCP 6.0 climatology than in the ACC/
842
SPARC climatology (Fig. 3). The faster the growth in
843
GHG emissions (increasing from RCP 2.6 to RCP 8.5), the
844
stronger the rate of ozone increase is during the twenty-first
845
century in our forcings. By 2030 or 2040, depending on the
846
RCP scenario, the 1960 levels in global column ozone are
847
reached in all forcings (Fig. 3). However from 2040
848
onward, the global ozone levels off in RCP 2.6, continues
849
to increase slightly in RCP 4.5 and RCP 6.0 and increase
850
quite sharply in RCP 8.5. The ozone super-recovery (i.e.
851
ozone levels exceeding the 1960s levels in the late twenty-
852
first century) is most visible at mid-latitudes and at north-
853
ern high latitudes. The time evolution of the ACC/SPARC
854
global ozone resembles the evolution of our RCP 2.6 global
855
ozone. It is worth pointing out that much larger differences
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REVISED PROOF
856
in column ozone have been found when comparing all the
857
climatologies used to force the CMIP5 simulations (Eyring
858
et al. 2012).
859
3.4 Aerosol concentrations
860
For CMIP5 the radiative impact of dust, sea salt, BC and
861
organic carbon aerosols are modeled in LMDZ following
862
De
´andreis (2008) and Balkanski (2011). Again this is a
863
substantial improvement compared to the IPSL-CM4
864
model used for CMIP3 in which only the sulfate aerosols
865
were considered (Dufresne et al. 2005).
866
As for ozone, aerosol microphysics strongly depends on
867
weather and climate. However, there is no strong evidence
868
that short-term variations in aerosol concentration play a
869
significant role in the long-term evolution of climate. The
870
treatment of the coupling between aerosols and climate can
871
again be simplified by using a semi-offline approach. For
872
the aerosols this approach is supported by De
´andreis et al.
873
(2012) who made a careful comparison between online and
874
offline runs in the case of sulfate aerosols. They found little
875
differences in the model results between the two approa-
876
ches. Nevertheless, the short term variations of dust aero-
877
sols probably impact individual meteorological events.
878
This effect should be tested in a fully coupled environment.
879
The past and future evolutions of aerosol distribution are
880
computed using the LMDZ-INCA model (Sect. 2.2.3).
881
Anthropogenic and biomass burning emissions are pro-
882
vided by Lamarque et al. (2010) for the historical period,
883
and by Lamarque et al. (2011) for the RCP scenarios. Since
884
the IPSL-CM5 model has biases in surface winds, the
885
natural emissions of dust and sea salt are computed using
886
the 10 m wind components provided by ECMWF for 2006
887
and, consequently, have seasonal cycles but no inter-annual
888
variations. The computed monthly mean aerosol fields are
889
then smoothed with an 11-year running mean. The meth-
890
odology to build the aerosol field as well as its evolution
891
and realism is described in more detail in Szopa et al.
892
(2013). In the first release of these climatologies (used for
893
the IPSL-CM5A-LR simulations) the particulate organic
894
matter computation was underestimated by almost 20 %.
895
This induces a slight underestimation of the aerosol cooling
896
effect but additional simulations show that it has very little
897
impact on climate. There is no coupling between dust and
898
sea-salt emissions and climate via the surface winds.
899
Nonetheless, the couplings via the transport, the wet and
900
dry deposition and the forcing via land-use changes are
901
described in the model.
902
3.5 CO
2
concentrations and emissions
903
In CMIP5, the models are driven by CO
2
concentrations in
904
most of the runs and by CO
2
emissions in some of them
Total ozone IPSL-CM5 and ACC-SPARC
1960 1980 2000 2020 2040 2060 2080 2100
years
280
290
300
310
320
330
O3 column (DU)
RCP8.5
RCP6.0
RCP4.5
RCP2.6
ACC-SPARC
Fig. 3 Time series of globally-averaged total column ozone (in
Dobson unit) from 1960 to 2100 for the IPSL-CM5 and ACC-SPARC
climatologies. IPSL RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5 ozone
climatologies are shown with green,blue,red and brown solid lines
respectively. Only the RCP 6.0 ACC-SPARC climatology is shown
(purple solid line). All the data have been annually averaged and
smoothed with an 11-year running mean filter
Fig. 2 Zonal mean of the total column ozone (in Dobson unit) as a
function of latitude and time, from 1960 to 2100 for the IPSL-CM5
(top) and ACC-SPARC (bottom) climatologies. The RCP 6.0 scenario
is used for the future period (2006–2100). All the data have been
annually averaged and smoothed with an 11-year running mean filter
IPSL-CM5 Earth System Model
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905 (Taylor et al. 2012). These two classes of simulations can
906 both be performed with the full carbon-cycle configuration
907 of the IPSL-CM5A-LR model (Fig. 1c, d). Unlike the
908 chemistry and aerosols models, the interactive carbon cycle
909 configuration of the model is affordable to run. The main
910 difficulty lies in the estimation of the initial state of carbon
911 stocks, which requires very long runs to reach a steady-
912 state. Despite using some dedicated approaches to speed up
913 the spin-up, a few hundred years of model integration are
914 required in order for the various carbon pools to be close to
915 equilibrium and hence suitable for use as initial states.
916 For the non-interactive (i.e. offline) concentration-dri-
917 ven simulations from 1850 to 2300, CO
2
being well mixed
918 in the atmosphere, the prescribed global CO
2
concentration
919 is directly used by LMDZ to compute the radiative budget
920 and by the PISCES and ORCHIDEE models to compute
921 air-sea CO
2
exchange and land photosynthesis respec-
922 tively. The prescribed evolution of CO
2
concentrations is
923 taken from the CMIP5 recommended dataset and is
924 described in Meinshausen et al. (2011). For the historical
925 period 1850–2005, the CO
2
concentration has been derived
926 from the Law Dome ice core record, the SIO Mauna Loa
927 record and the NOAA global-mean record. From 2006 and
928 onwards, CO
2
emissions have been projected by four dif-
929 ferent Integrated Assessment Models (IAMs) (van Vuuren
930 et al. 2011), and corresponding CO
2
concentrations have
931 been generated with the same reduced-complexity carbon
932 cycle-climate model MAGICC6 (Meinshausen et al. 2011).
933 In the RCP 2.6 scenario, CO
2
concentration peaks at
934 440 ppmv in 2050 and then declines. In the RCP 6.0 and
935 RCP 4.5 scenarios, CO
2
concentration stabilizes at 752 and
936 543 ppmv in 2150 respectively. In the RCP 8.5 scenario,
937 CO
2
concentration reaches 935 ppmv in 2100 and contin-
938 ues to increase up to 1961 ppmv in 2250.
939 3.6 Other greenhouse gas concentrations
940 Other greenhouse gases (apart from ozone) are assumed to
941 be well mixed in the atmosphere and are prescribed as time
942 series of annual global mean mixing ratio. The concentra-
943 tions of CH
4
,N
2
O, CFC-11 and CFC-12 are directly pre-
944 scribed in the radiative code of LMDZ. The concentrations
945 are taken from the recommended CMIP5 dataset
1
and are
946 described in Meinshausen et al. (2011). As the radiative
947 schemes of GCMs do not generally represent separately all
948 the fluorinated gases emitted by human activities, the
949 radiative effects of all fluorinated gases controlled under the
950 Montreal and Kyoto protocols are represented in terms of
951 concentrations of ’’equivalent CFC-12’’ and ’’equivalent
952 HFC-134a’’respectively. The ’’equivalent CFC-12’’ con-
953 centration is directly used in LMDZ whereas the
954
’’equivalent HFC-134a’’ is converted in ’’equivalent CFC-
955
11’’ prior to being used. For this conversion, the radiative
956
efficiency of the two gases are used: 0.15 W.m
-2
.ppb
-1
for
957
HFC-134a and 0.25 W.m
-2
.ppb
-1
for CFC-11 (Ramasw-
958
amy et al. 2001, Table 6.7).
959
3.7 Land use changes
960
We use the transient historical and future crop and pasture
961
datasets developed by Hurtt et al. (2011) (hereafter referred
962
to as the UNH dataset) for both the historical period and the
963
4 RCPs scenarios for the future period. All the information
964
is provided on 0.5°90.5°horizontal grid.
965
Those datasets provide information on human activities
966
(crop land and grazed pastureland) in each grid-cell but do not
967
provide specific information on the characteristics of the
968
natural vegetation. Moreover, the information provided can-
969
not be directly used by land surface models embedded within
970
GCMs like ORCHIDEE. The land-cover map used for both
971
the historical and future period has been obtained starting from
972
an observed present-day land-cover map (Loveland et al.
973
2000), which already includes both natural and anthropogenic
974
vegetation types with the following methodology.
975
Firstly, the area covered by crops per year and per grid-
976
cell is set to the value provided by the UNH dataset. The
977
expansion of this crop area occurs at the expense of all
978
natural vegetation types proportionally. This means that the
979
percent by which natural grasses and tree areas are reduced
980
is the same for all biomes/PFTs. Conversely, a reduction of
981
anthropogenic area implies a proportional increase in all
982
natural vegetation types which exist in any given grid-cell.
983
If no information is available on the natural distribution of
984
vegetation at a specific location (i.e. 100 % anthropogenic
985
on the original land-cover map used), the nearest point
986
which has natural vegetation is searched and this vegeta-
987
tion is introduced. Finally, the extent covered by desert in
988
each grid-cell is unchanged from pre-industrial times until
989
the end of the twenty-first century. We only encroach on
990
desert if the anthropogenic area is larger than the natural
991
vegetation part of the grid-cell.
992
After this first step where the change in crop area has
993
been handled, the remaining area is a combination of nat-
994
ural vegetation and grazing activities. Grazing activities
995
were included as follows: if the grazed area is smaller than
996
the area covered with grasses and shrubs, no further change
997
to the land-cover map has been made. If the grazed area is
998
larger than the area covered with grasses and shrubs, part of
999
the forested area is removed.
1000
3.8 Solar irradiance and volcanic aerosols
1001
The IPSL model is directly forced by the annual mean of
1002
solar irradiance using the data recommended by CMIP5
1FL01
1
see http://cmip-pcmdi.llnl.gov/cmip5/forcing.html.
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REVISED PROOF
1003 (Lean 2009; Lean et al. 2005). For the past, the estimate of
1004 the total solar irradiance (TSI) variations is the sum of two
1005 terms, the first is related to an estimate of the past solar
1006 cycles (Fro
¨hlich and Lean 2004) and the second to an
1007 estimate of long term variations (Wang et al. 2005). For the
1008 future, it is assumed that there is no long term variation but
1009 repeated solar cycles identical to the last cycle (cycle 23),
1010 i.e. with solar irradiance values from 1996 to 2008 (Fig. 4,
1011 continuous line). For other than historical and scenario
1012 simulations, the TSI is held constant and equal to the mean
1013 TSI estimate between the years 1845 and 1855, i.e. 1365.7
1014 Wm
-2
(Fig. 4, dashed line).
1015 The volcanic radiative forcing is accounted for by an
1016 additional change to the solar constant. For the historical
1017 period, the aerosol optical depth of volcanic aerosol is an
1018 updated version of Sato et al. (1993, 516 http://data.giss.
1019 nasa.gov/modelforce/strataer/). The aerosol optical depth s
1020 is converted to radiative forcing F
v
(Wm
-2
) according to
1021 the relationship F
v
=-23 ssuggested by Hansen et al.
1022 (2005). The average value
Fvof this forcing over the period
1023 1860-2000 is -0.25 Wm
-2
, and the solar forcing Fpre-
1024 scribed to the model is:
F¼TSI þ4ðFv
FvÞ
1að1Þ
10261026 where a=0.31 is the planetary albedo. For the future
1027 scenarios, the volcanic forcing is assumed to be constant,
1028 i.e. a constant volcanic eruption produces a constant radi-
1029 ative forcing Fv¼
Fv. This explains the jump of Fbetween
1030 2005 and 2006 (Fig. 4, continuous line); in 2005 there is
1031 almost no volcanic aerosols, as observed, whereas in 2006
1032 a constant volcanic eruption takes place that produces a
1033 constant radiative forcing.
1034
4 Recent warming and current climate
1035
The initial state and the simulation of some key climatic
1036
variables in the control and in the historical runs are
1037
described in this Section. Three versions of the IPSL-CM5
1038
model are currently used for CMIP5: IPSL-CM5A-LR,
1039
which has been extensively used to perform large ensem-
1040
bles of runs, IPSL-CM5A-MR, which has a higher hori-
1041
zontal resolution of the atmosphere (1.25°92.5°, see
1042
Sect. 3.2) and IPSL-CM5B-LR for which the atmospheric
1043
parameterizations have been modified (see Sect. 2.2.1). A
1044
comparison with results from the IPSL-CM4 model, which
1045
has been used for CMIP3 (Dufresne et al. 2005) and whose
1046
key climatic characteristics have been presented in Bra-
1047
connot et al. (2007) and Marti et al. (2010) is also pre-
1048
sented in this Section.
1049
For the IPSL-CM5A-LR model, many other aspects of
1050
the simulated climate are presented in companion papers
1051
such as the global climatology (Hourdin et al. 2013a),
1052
cloud properties (Konsta et al. 2013), land-atmosphere
1053
interactions (Cheruy et al. 2013), tropical variability (Maury
1054
et al. 2013; Duvel et al. 2013), mid-latitude variability
1055
(Gastineau et al. 2013; Vial et al. 2013; Cattiaux et al.
1056
2013), climate over Europe (Menut et al. 2013), the
1057
AMOC bi-decadal variability in (Escudier et al. 2013),
1058
predictability in perfect model framework (Persechino
1059
et al. 2013) and over the last 60 years (Swingedouw et al.
1060
2013).
1061
4.1 Initial state and control run
1062
The initial state of the IPSL-CM5A-LR model was
1063
obtained in four steps. First, a 2,500-year long simulation
1064
of the oceanic model without carbon cycle where the
1065
atmospheric conditions are imposed and correspond to the
1066
version 2 of the Coordinated Ocean-ice Reference Exper-
1067
iments data sets (Large and Yeager 2009) was achieved.
1068
Second, the full carbon-cycle configuration of the IPSL-
1069
CM5A-LR model was integrated for a period of 600 years
1070
with the solar constant and the concentrations of GHGs and
1071
aerosols corresponding to their pre-industrial values. Third,
1072
because this last simulation is too short for the ocean and
1073
biosphere carbon pools to reach equilibrium, offline sim-
1074
ulations a few thousand year-long with the ocean and land
1075
carbon cycle models (ORCHIDEE and PISCES) were
1076
conducted separately. These offline simulations were
1077
forced by the atmospheric and oceanic variables from the
1078
preceding 600-year simulation and by a constant pre-
1079
industrial value for the atmospheric CO
2
. Fourth, and once
1080
the carbon pools are equilibrated, their values are included
1081
back into the complete IPSL-CM5A-LR model, which is
1082
again integrated for another 400 years. At this time, carbon
1083
pools are close to equilibrium in the coupled model as well.
1850 1900 1950 2000 2050 2100
y
ear
1350
1355
1360
1365
1370
Irradiance (W/m²)
solar + volcanoes
solar
reference value
Fig. 4 Time evolution of the total solar irradiance with (solid line)
and without (dashed line) volcanic eruptions. Also reported is the
reference value used for all the runs except the historical and the
scenario runs (dotted line)
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REVISED PROOF
1084 This long integration is used as initial state for the control
1085 pre-industrial simulations.
1086 To illustrate the stability of the IPSL-CM5A-LR control
1087 run, Fig. 5shows the global average values of a few
1088 variables during the first 1,000 years of this run. The sur-
1089 face temperature has almost no drift and the heat budget is
1090 close to zero. There is no discernible difference between
1091 the flux at the TOA and at the surface, which means that
1092 the internal heat budget of the atmosphere is conserved.
1093 The small imbalance in the heat budget at the TOA (about
1094 0.25 Wm
-2
) is due to a small non conservation of energy in
1095 the sea-ice model, the ocean model and at their interface.
1096 The surface salinity has almost no drift, nor has the sea
1097 surface height (about 2 cm/century, not shown), confirming
1098 that the water cycle is closed. Also, there is no drift of the
1099 carbon flux over land and there is a small drift of the
1100
carbon flux over oceans, which begins at 0.4 PgC/year and
1101
decreases to less than 0.1 PgC/year at the end of the 1,000-
1102
year period.
1103
The initial state of IPSL-CM5A-MR was obtained
1104
starting from the initial state of the IPSL-CM5A-LR con-
1105
trol run. After a 300-year long run with the full carbon-
1106
cycle configuration of IPSL-CM5A-MR, only the carbon
1107
cycle over land was not in equilibrium. A few thousand
1108
year long offline simulation with the land carbon cycle
1109
model was performed to bring the biosphere carbon pools
1110
to equilibrium. Finally the complete IPSL-CM5A-MR
1111
model was integrated again for another 200 years to obtain
1112
the initial state of the control simulation.
1113
The initial state of IPSL-CM5B-LR was obtained start-
1114
ing from the initial state of IPSL-CM5A-LR control run
1115
and by performing a 280-year long simulation. Although
(a)
(b)
(c)
(d)
(e)
Fig. 5 Time evolution of athe
global mean heat budget at
surface and at the TOA, bthe
global mean surface air
temperature, cthe sea-ice
volume in the northern (black)
and southern (red) hemispheres,
dthe global mean surface
salinity and ethe carbon flux
(PgC/year) over ocean (black)
and over land (red), for the first
1,000 years of the control run in
the IPSL-CM5A-LR model. The
data are smoothed using a
11-year Hanning filter
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REVISED PROOF
1116 the full carbon-cycle configuration is used in IPSL-CM5B-
1117 LR, this spin-up period is not long enough for the carbon
1118 pools to reach an equilibrium. The carbon variables are
1119 therefore not relevant for this model version. They have not
1120 been made available on the CMIP5 data base and will not
1121 be discussed in this paper.
1122 4.2 Twentieth century temperature
1123 Figure 6a displays the time evolution of the global mean
1124 air surface temperature from observations (Hadcrut3v
1125 dataset, Jones et al. 1999; Hadcrut3v dataset, Brohan et al.
1126 2006) and simulated by the IPSL-CM4 which participated
1127 in CMIP3, the IPSL-CM5A-LR, the IPSL-CM5A-MR, and
1128 the IPSL-CM5B-LR models. On this figure, the IPSL-
1129 CM5A and IPSL-CM5B simulations include all the
1130 anthropogenic and natural forcings as described in Sect. 3
1131 whereas the IPSL-CM4 simulation only includes the GHGs
1132 and sulfate aerosol forcings with no natural forcing (Duf-
1133 resne et al. 2005). As expected all the historical simula-
1134 tions indicate a substantial global warming induced by
1135 increased greenhouse gas concentrations in the atmosphere.
1136 For all models the global trend and multi-annual variability
1137 agree rather well with observations but the warming trend
1138 simulated during recent decades (e.g. from 1960 onwards)
1139 by most of the model configurations seems exaggerated.
1140 To extract the temperature trends more accurately, the
1141 monthly temperature time series from the simulations and
1142 from the observations were subjected to the STL (Sea-
1143 sonal-Trend decomposition procedure based on Loess)
1144 additive scheme, which is a powerful statistical technique
1145 for describing a time series (Cleveland et al. 1990). The
1146 STL is a filtering procedure where the analyzed X(t)
1147 monthly time series is decomposed into three terms:
XðtÞ¼TðtÞþAðtÞþRðtÞð2Þ
11491149 The T(t) term quantifies the trend and low-frequency
1150 variations in the time series. The A(t) term describes the
1151 annual cycle and its modulation through time. Finally the
1152 R(t) term contains the interannual signal and the noise
1153 present in the data. As demonstrated by Morissey (1990)or
1154 Terray (2011), this procedure is particularly useful to
1155 extract the interannual and trend signals from non-
1156 stationary and noisy climate datasets. Here the grid-box
1157 temperature time series are first expressed as monthly
1158 anomalies with respect to the 1961–1990 climatology
1159 before computing the global area-averaged time series and
1160 running the STL statistical procedure.
1161 The trends estimated using the STL decomposition
1162 appear very clearly on Fig. 6-b. The simulations performed
1163 with IPSL-CM5 (A-LR, A-MR and B-LR) are closer to
1164 observations than the simulations performed with IPSL-
1165 CM4. This was expected because the IPSL-CM5 models
1166
include more realistic forcings than the IPSL-CM4 model.
1167
For example, the IPSL-CM4 simulation does not reproduce
1168
the two cold periods observed around 1910 and 1960. The
1169
IPSL-CM5 models simulate the cooling around 1960 but
1170
the 1910s cooling is simulated too early. These improve-
1171
ments in the new model version essentially come from the
1172
inclusion of the volcanic forcing. However IPSL-CM5A
1173
simulates a larger temperature increase than IPSL-CM4
1174
after 1970 compared to observations although both models
1175
have a similar climate sensitivity (Sect. 6.1). During this
1176
period the difference is probably due to the changes in
(a)
(b)
Fig. 6 a Time evolution of the global mean air surface temperature
anomaly as observed (Hadcrut3v dataset, black) and simulated by the
IPSL-CM5A-LR (light blue), the IPSL-CM5A-MR (blue), the IPSL-
CM5B-LR (magenta) and the IPSL-CM4 (green) models. The
temperatures are smoothed using a 5-year Hanning filter bTrends
of the same variable estimated from the global area-averaged
temperature anomalies monthly time series as defined by the STL
procedure (see text). The unit is Kand the temperature anomalies are
computed with respect to the 1961-1990 period. Note that 5 members
are available for IPSL-CM5A-LR, 2 members are available for IPSL-
CM5A-MR, and only 1 member is available for IPSL-CM5B-LR and
IPSL-CM4. On panel athe averaged value of these members is shown
for clarity whereas on panel, bthe trends have been estimated
separately in each simulation member and each of these trends is
shown
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REVISED PROOF
1177 ozone and absorbing aerosol concentrations, both of them
1178 increasing significantly after 1950.
1179 For the IPSL-CM5A model, there is almost no differ-
1180 ence between the low- and mid-resolution configurations
1181 (LR and MR). The differences between those simulations
1182 are within the range of internal variability. IPSL-CM5B-
1183 LR exhibits a much smaller temperature increase after
1184 1970 than IPSL-CM5A and this difference further increa-
1185 ses in the future period (Sect. 5.1). The IPSL-CM5B-LR
1186 model has a much smaller climate sensitivity than the other
1187 model versions as will be shown in Sect. 6.1 and this is
1188 probably the main reason for this smaller temperature
1189 increase.
1190 Compared to the observed temperature (Hadcrut3v
1191 dataset, Jones et al. 1999; Hadcrut3v dataset, Brohan et al.
1192 2006) over the period 1961–1990, the models have the
1193 following biases on average: -0.7 K for IPSL-CM4,
1194 -1.4 K for IPSL-CM5A-LR, -0.4 K for IPSL-CM5A-MR
1195 and -0.6 K for IPSL-CM5B-LR. The geographical struc-
1196 ture of the temperature bias shows common patterns for
1197 IPSL-CM4, IPSL-CM5A-LR and IPSL-CM5A-MR. The
1198 amplitude of these biases is weakest in IPSL-CM5A-MR
1199 (Fig. 7), it is slightly stronger in IPSL-CM5A-LR and it is
1200 significantly stronger in IPSL-CM4. In the Pacific and
1201 Atlantic tropical oceans there is a systematic bias with the
1202 eastern part of the ocean basins being too warm compared
1203 to the western part, which is a common weakness of cou-
1204 pled models. Over the Pacific, another common bias is a
1205 cold tongue along the equator. In the mid latitudes there is
1206 a systematic cold bias whose amplitude is weaker in IPSL-
1207 CM5A-LR and MR than in IPSL-CM4. At high latitudes,
1208 there is a warm bias over eastern Siberia, Alaska and
1209 western Canada in the northern hemisphere and poleward
1210 of 60°S in the southern hemisphere. The geographical
1211 pattern of the temperature bias does not change signifi-
1212 cantly on a seasonal scale.
1213 The IPSL-CM5B-LR model displays a significantly
1214 different bias pattern compared to other models. There is
1215 a strong asymmetry between the two hemispheres with a
1216 large cold bias over most of the northern hemisphere and a
1217 large warm bias in the southern hemisphere, particularly
1218 poleward of 60°S. In the tropics, this model exhibits an
1219 east-west bias in the ocean basins but there is no cold
1220 tongue over the equator. The temperatures in the tropics are
1221 reasonable, which is not the case in the mid and high lat-
1222 itude regions, probably due to an equatorward shift of the
1223 mid-latitude jets. This shift, which is larger in IPSL-
1224 CM5B-LR than in IPSL-CM5A-LR despite the same res-
1225 olution (Hourdin et al. 2013b) is not yet understood. In the
1226 Arctic region, IPSL-CM5B-LR is about 4°C colder than
1227 IPSL-CM5A-LR in the AMIP simulations where the sea
1228 surface temperature and the sea-ice fraction are prescribed.
Fig. 7 Geographical distribution of the bias in the annual mean air
surface temperature climatology (with respect to the period
1961–1990) simulated by, from top to bottom, IPSL-CM4, IPSL-
CM5A-LR, IPSL-CM5A-MR and IPSL-CM5B-LR models, com-
pared to estimate from observations (Jones et al. 1999). The global
mean difference with observations is removed in order to focus on the
bias structure. This global mean difference is -0.7K for IPSL-CM4,
-1.4K for IPSL-CM5A-LR, -0.4K for IPSL-CM5A-MR and -0.6K
for IPSL-CM5B-LR. For all models, the climatology is computed
using the first member of the historical run. The unit is K
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1229 This difference is amplified by about 50 % in the coupled
1230 simulations. Over the Antarctic, there is also a cold bias of
1231 about 4°C in the AMIP simulations and this cold bias
1232 almost vanishes in the coupled simulations due to the
1233 strong warming of the southern ocean (Fig. 7).
1234 4.3 Tropical precipitation and tropical variability
1235 The tropics are of primary importance for climate vari-
1236 ability and climate sensitivity, and the improvement of the
1237 simulation of the tropical climate has been a main goal of
1238 IPSL for many years. A new convective scheme (Emanuel
1239 1991) and cloud scheme (Bony and Emanuel 2001) were
1240 introduced in the LMDZ4 atmospheric model (Hourdin
1241 et al. 2006), leading to an improved simulated tropical
1242 climate in the IPSL-CM4 model (Braconnot et al. 2007).
1243 No major changes of the atmospheric parameterizations
1244 were made in IPSL-CM5A compared to IPSL-CM4
1245 whereas parameterizations were strongly modified in the
1246 atmospheric component of IPSL-CM5B in order to
1247 improve the representation of some processes that are
1248 known to be important for the tropical climate such as:
1249 boundary layer, convection and clouds processes (see Sect.
1250 2.2.1). The impact of these developments on the mean
1251 climate are documented in Hourdin et al. (2013b), in par-
1252 ticular on the atmosphere-only configuration. The mean
1253 precipitation in the tropics and two major modes of tropical
1254 variability, the El Nin
˜o Southern Oscillation (ENSO) and
1255 the Madden Julian Oscillation (MJO), simulated in the
1256 different versions of the IPSL coupled model are described
1257 here. These modes have a large impact on the tropical and
1258 global circulation (e.g. Cassou 2008; e.g. Alexander et al.
1259 2002; e.g. Maury et al. 2013) and their representation in
1260 current climate models varies greatly (e.g. Guilyardi et al.
1261 2009; e.g. Xavier et al. 2010).
1262 4.3.1 Tropical mean precipitation
1263 Figure 8presents the 10-year (1990–1999) annual mean
1264 rainfall from GPCP (Global Precipitation Climatology
1265 Dataset) observations (Huffman et al. 2001) and for his-
1266 torical simulations with the four versions of the IPSL model
1267 (IPSL-CM4, IPSL-CM5A-LR, IPSL-CM5A-MR and IPSL-
1268 CM5B-LR). The precipitation pattern is similar for all
1269 model versions, which are able to qualitatively reproduce
1270 the main observed structures. The same major biases are
1271 present in all model configurations. In the tropics the
1272 models show the so-called double Intertropical Conver-
1273 gence Zone (ITCZ) structure with a first realistic precipi-
1274 tation maximum around 5°N and a secondary maximum
1275 around 5°S, which is not observed. The monsoon rainfall
1276 over West Africa and the Indian sub-continent does not
1277 extend sufficiently to the north. In the southern subtropics
1278
the models fail to simulate the large regions without rain
1279
observed over the ocean. Over Africa and the Arabian
1280
Peninsula on the contrary, the area with no rainfall is wider
1281
than observed. Precipitation is systematically overestimated
1282
in the Andes mountains and underestimated over the
1283
Amazon region. The simulated rainfall is too strong on the
1284
East tropical Indian Ocean compared to observations.
1285
When focusing on the differences between model con-
1286
figurations, the impact of horizontal grid refinement from
1287
CM5A-LR to CM5A-MR is particularly weak. It slightly
1288
improves the representation of the Indian and West African
1289
monsoons, which extend farther to the north, but it tends to
1290
reinforce the double ITCZ structure.
1291
Changing the cloud and convective physics from IPSL-
1292
CM5A-LR to IPSL-CM5B-LR has a somewhat larger and
1293
often opposite impact. The monsoons are more confined in
1294
CM5B-LR and the rainfall excess over the East tropical
1295
ocean is even larger. The double ITCZ is less marked both
1296
over the Pacific and Atlantic Oceans. Also the South
1297
Pacific and Atlantic Convergence Zones (SPCZ and
1298
SACZ), which are not well captured in the CM5A-LR and -
1299
MR configurations, are much better simulated with the new
1300
physical parameterizations.
1301
4.3.2 Madden-Julian oscillation
1302
When forced by prescribed SST, the LMDZ5B atmo-
1303
spheric model simulates a much larger tropical rainfall
1304
variability than LMDZ5A, which is in better agreement
1305
with observations in particular in the location and spectral
1306
range associated with the MJO (Hourdin et al. 2013b). A
1307
more detailed analysis of the MJO in the IPSL-CM5A and
1308
CM5B coupled models, which use these two atmospheric
1309
models, is presented here. The differences between the
1310
IPSL-CM5A-LR and CM5A-MR results are small and only
1311
the former will be presented. We restrict our analysis to the
1312
January-March period (JFM) because differences on the
1313
simulated MJO between IPSL-CM5A and CM5B are
1314
stronger during this season.
1315
The large-scale convective perturbations associated with
1316
the MJO are extracted using the Local Mode Analysis
1317
(LMA, Goulet and Duvel 2000). The LMA is based on a
1318
series of complex EOF (CEOF) computed on relatively
1319
small time sections (every 5 days on a 120-day time win-
1320
dow) of the outgoing longwave radiation (OLR) time ser-
1321
ies. The first complex eigenvector best characterizes (in
1322
phase and amplitude) the intraseasonal fluctuation for the
1323
120-day time section. The corresponding percentage of
1324
variance represents the degree of spatial organization of
1325
this event. The LMA retains only maxima in the time series
1326
of the percentage of variance. For JFM, the LMA extracts
1327
41 events for 30 years of observations (NOAA OLR,
1328
Liebmann and Smith 1996), 52 events for 30 years of the
IPSL-CM5 Earth System Model
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1329 IPSL-CM5A-LR run and 34 events for 25 years of the
1330 IPSL-CM5B-LR run. The average time-scale for these
1331 events is roughly 40 days for all three datasets.
1332
An average pattern is computed from the JFM events
1333
having a percentage of variance above the annual aver-
1334
age. This average pattern gives the amplitude and phase
Fig. 8 10-year (1990–1999)
annual mean rainfall (mm/day)
over the tropics in the GPCP
observations and simulated by
the IPSL-CM4, IPSL-CM5A-
LR, IPSL-CM5A-MR and
IPSL-CM5B-LR models (from
top to bottom)
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1335 distributions that best represent the considered events. This
1336 average pattern is shown on Fig. 9for observations, IPSL-
1337 CM5A-LR and IPSL-CM5B-LR. In the observations, the
1338 intraseasonal variability is confined between the equator
1339 and 20°S. From the phases of the average pattern (Fig. 9a)
1340 we may deduce that on average, intraseasonal perturbations
1341 propagate eastward with a nearly constant speed of about
1342 5–6 ms
-1
(considering the phase opposition between
1343 roughly 90°E and 180°E and an average period of 40 days).
1344 The IPSL-CM5A-LR model produces MJO events that are
1345 confined in the Indian Ocean and propagate eastward at
1346 around 2 ms
-1
only (Fig. 9b) over the eastern Indian
1347 Ocean. The IPSL-CM5B-LR model produces perturbations
1348 that are more centered on the Maritime Continent and
1349
propagating at a speed of about 2.5 ms
-1
(Fig. 9c) over the
1350
eastern Indian Ocean and faster (around 4 ms
-1
) across
1351
northern Australia. The longitudinal position of the main
1352
MJO signal and the latitudinal position in the Indian ocean
1353
are thus improved in IPSL-CM5B-LR. However the slow
1354
propagation over the eastern Indian Ocean and the too
1355
strong variability north of the equator in the Pacific remain.
1356
The ability of a model to represent organized convective
1357
perturbations on a large scale is critical for a correct sim-
1358
ulation of the intraseasonal variability (Bellenger et al.
1359
2009; Xavier et al. 2010). The percentage of variance
1360
measures the degree of large-scale organization of the in-
1361
traseasonal variability. A large percentage of variance
1362
means that the intraseasonal variability of the region is
(a)
(b)
(c)
Fig. 9 Average intraseasonal OLR perturbation pattern for JFM,
aNOAA OLR, bIPSL-CM5A-LR and cIPSL-CM5B-LR: (colors
and stick length) Amplitude; (sticks angle) Relative phase with a
clockwise rotation with time and a full rotation for one period of
about 40 days; (contours) percentage of intraseasonal variance due to
large-scale organized perturbations (40, 50 and 60 % in bold)
IPSL-CM5 Earth System Model
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1363 mostly due to large-scale organized perturbations and not
1364 to local red noise (see Duvel et al. 2013). This percentage
1365 of variance is larger in IPSL-CM5B than in IPSL-CM5A
1366 but it is still smaller than in observations (contours on
1367 Fig. 9).
1368 4.3.3 El Nin
˜o Southern Oscillation
1369 The ENSO spatial structure for the 3 models as measured
1370 by the SST standard deviation is compared to observations
1371 in Fig. 10. For the simulations we used 200 years of
1372 monthly outputs. The IPSL-CM5A and CM5B versions
1373 produce a weaker ENSO SST variability (by about 0.3 K)
1374 than the IPSL-CM4 model with a pattern which is in good
1375 qualitative agreement with observations. The spurious
1376 westward extension of the SST pattern is reduced in
1377 CM5B-LR when compared to CM4 and CM5A-LR. The
1378 three model versions underestimate the SST variability
1379 along the South American coast, which is related to a
1380 common warm bias in this region.
1381 ENSO spectral characteristics are difficult to estimate
1382 from 200 years or shorter time series (Wittenberg 2009).
1383 However spectra of the SST monthly anomalies over the
1384 Nin
˜o3 region (90°W–150°W and 5°S–5°N) are indicative
1385 of an ENSO with longer periods in the later versions of
1386
IPSL-CM. Spectral peaks around 3–3.5 years are visible
1387
for IPSL-CM5A-LR and CM5B-LR whereas CM4 shows a
1388
peak around 2.7 years (Fig. 11a). IPSL-CM5A-LR is in
1389
good qualitative agreement with observations showing a
1390
second spectral peak beyond 4 years. In addition ENSO is
1391
characterized by a strong seasonal phase locking with a
1392
peak in November–January and a minimum in April. This
1393
seasonality is well reproduced by IPSL-CM4 but the new
1394
versions fail at reproducing this feature. IPSL-CM5A-LR
1395
shows a marked seasonality with a peak in May–June and a
1396
minimum in October–November, whereas IPSL-CM5B-LR
1397
hardly shows any seasonal variation (not shown).
1398
A number of studies point to a dominant role of the
1399
atmospheric GCMs in the simulation of ENSO (Guilyardi
1400
et al. 2009; Kim and Jin 2011; Clement et al. 2011). The
1401
main atmospheric feedbacks are evaluated following Lloyd
1402
et al. (2011,2012). The feedback between the east-west
1403
SST gradient and wind speed (Bjerknes feedback) is
1404
evaluated by the linear regression coefficient between the
1405
zonal wind stress anomaly in the Nin
˜o4 region (160°E-
1406
150°W and 5°S- 5°N) and the Nin
˜o3 SST anomaly. The
1407
heat flux feedback is evaluated by the regression coefficient
1408
between Nin
˜o3 heat flux and SST anomalies. This feedback
1409
is dominated by the shortwave and the latent heat fluxes
1410
and the former has a key role in explaining the spread of
Fig. 10 Standard deviations (K) of monthly SST anomalies with respect to the mean seasonal cycle for HadISST1 (1870–2008) (Rayner et al.
2003) and for 200 years of IPSL-CM4, IPSL-CM5A-LR and IPSL-CM5B-LR
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REVISED PROOF
1411 ENSO characteristics among models (Lloyd et al. 2012).
1412 Figure 11b shows the process-based metrics associated to
1413 these atmospheric feedbacks. For all the four process-based
1414 metrics IPSL-CM5B-LR shows a better agreement with the
1415 reanalysis than IPSL-CM4 and IPSL-CM5A-LR. Both the
1416 Bjerknes and heat flux feedbacks are stronger in IPSL-
1417 CM5B-LR and closer to observations. In particular, the
1418 stronger heat flux feedback is due to a better simulated
1419 latent feedback and to an improvement in the shortwave
1420 feedback, which has the right sign compared to IPSL-CM4
1421 and CM5A-LR but is much too weak compared to obser-
1422 vations. This change in the shortwave feedback sign in the
1423 Nin
˜o3 region is due to an increased occurrence of con-
1424 vective clouds that are responsible for a negative shortwave
1425 feedback. This improvement in CM5B-LR is mostly
1426 associated to the improved mean state in which the cold
1427 tongue spurious westward extension bias is reduced (Sect.
1428 4.2). In contrast IPSL-CM4 has permanent upwelling
1429 conditions, which favor the subsidence regime and positive
1430 values for the shortwave feedback (Guilyardi et al. 2009;
1431 Lloyd et al. 2012). In summary, IPSL-CM5 (A and B)
1432 simulate a weaker ENSO than IPSL-CM4 closer to the
1433 observed amplitude and associated with a better represen-
1434 tation of atmosphere feedbacks in IPSL-CM5B-LR.
1435
5 Future climate changes
1436
Projections of future climate changes are based on sce-
1437
narios. The RCP scenarios used in CMIP5 are too different
1438
from the SRES scenarios used in CMIP3 (Sect. 3.1)to
1439
allow a direct comparison of CMIP3 and CMIP5 results for
1440
the scenario experiments. In this section the results
1441
obtained with the IPSL-CM5 models following the RCP
1442
scenarios are discussed. The comparison between results
1443
from one model, IPSL-CM5A-LR, following the SRES
1444
scenarios and the very same model following the RCP
1445
scenarios is also discussed.
1446
5.1 Future warming projections using RCP scenarios
1447
The global mean surface air temperature increase during
1448
the first three decades (2005–2035) is similar in the three
1449
IPSL-CM5 models (Fig. 12a) and for all the RCP scenar-
1450
ios. The temperature increase in the medium- and low-
1451
resolution versions of the IPSL-CM5A model remains very
1452
similar throughout the twenty-first century. Starting around
1453
2040 the IPSL-CM5B model simulates a smaller temper-
1454
ature increase than the other model versions. The global
1455
mean air surface temperature increase levels off in the
0.0
0.2
0.4
0.6
0.8
1.0
(a)
(b)
Fig. 11 a Normalized power
spectra of SST over the Nin
˜o3
region for HadISST1 (black),
IPSL-CM4 (green), IPSL-
CM5A-LR (red) and IPSL-
CM5B-LR (blue). bEvaluation
of the Bjerknes and heat flux
feedbacks. The two main
components of the latter, the
shortwave and latent heat flux
feedbacks, are also shown. For
the feedback coefficients, the
reference is ERA40
(1958–2001) and OAFlux
(1984–2004)
IPSL-CM5 Earth System Model
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REVISED PROOF
1456 middle of the century for the RCP 2.6 scenario and at the
1457 end of the twenty-first century for the RCP 4.5 scenario,
1458 but it continues to increase for the RCP 6.0 and RCP 8.5
1459 scenarios.
1460 The prescribed aerosol concentration and the parameter-
1461 izations of the aerosol direct and first indirect effects are the
1462 same in IPSL-CM5A and CM5B but their radiative effects
1463 differ (Fig. 12b). The aerosol first indirect effect is larger
1464 in absolute value in IPSL-CM5B-LR compared to IPSL-
1465 CM5A-LR probably because of the larger fraction of
1466 low-level clouds in IPSL-CM5B-LR compared to IPSL-CM5A-
1467 LR. The aerosol direct effect is smaller in IPSL-CM5B-LR
1468 compared to IPSL-CM5A-LR probably because a higher
1469 cloud fraction reduces the direct effect of aerosols. Overall,
1470 the total radiative effects of aerosols is slightly larger
1471 (&0.1 Wm
-2
) in IPSL-CM5B-LR than in IPSL-CM5A-LR.
1472 This partly contributes to the smaller global mean surface air
1473 temperature increase in the IPSL-CM5B-LR model. How-
1474 ever IPSL-CM5B-LR has a much smaller climate sensitivity
1475 than the other model versions as discussed in Sect. 6.1 and
1476 this is probably the main reason for the smaller temperature
1477 increase in the late twentieth century.
1478 As one may expect, the difference among scenarios
1479 appears earlier for the net heat flux at the TOA than for the
1480 surface temperature. This is illustrated on Fig. 13 for the
1481 IPSL-CM5A-LR model. The net heat flux at the TOA
1482 differs among scenarios starting in the early twenty-first
1483 century. These differences gradually become more pro-
1484 nounced and start to affect the temperature evolution. At
1485 the end of the twenty-third century, the difference in global
1486 mean annual temperature is 11°C between the scenario
1487 with the highest radiative forcing (RCP 8.5) and the sce-
1488 nario with the lowest radiative forcing (RCP 2.6). For the
1489 low RCP 2.6 scenario, the radiative forcing decreases and
1490 the temperature is almost constant from 2050 onward. It
1491 slightly decreases despite a positive net flux at the TOA
1492 due to the heat uptake by the ocean (not shown).
1493 Many factors affect the local air surface temperature
1494 changes. One factor is the geographical distribution of the
1495 forcings such as aerosols concentration and land use. A
1496 second factor is the geographical distribution of the climate
1497 response to these forcings and in particular the relative
1498 strength of local and global feedbacks. In order to distin-
1499 guish the geographical distribution pattern from the global
1500 mean value, the local temperature amplification factor is
1501 defined as the ratio between the local temperature change
1502 and the global mean temperature change. The zonal mean
1503 average of this temperature amplification has been shown
1504 to be only weakly dependent on the scenario for the CMIP3
1505 simulations (Meehl et al. 2007b). The pattern of this local
1506 temperature amplification factor has been used as ‘‘pattern
1507 scaling’’ technique to estimate temperature changes under
1508 different scenarios (Mitchell et al. 1999; Moss et al. 2010).
1509
Figure 14 shows the pattern of the local temperature
1510
amplification factor for the two extreme RCP scenarios
1511
(RCP 2.6 on the left, RCP 8.5 on the right) simulated by the
1512
IPSL-CM5A-LR, the CM5A-MR and the CM5B-LR
1513
models at the end of the twenty-first century (three upper
1514
rows). This geographical pattern is very similar in RCP 2.6
1515
and RCP 8.5 scenarios (as well as in RCP 4.5 and RCP 6.0,
1516
not shown) even though the forcings are quite different, in
1517
particular the land use and BC forcings, which have strong
1518
local signatures. However the normalized warming is
1519
generally larger over the continent and smaller in the
1520
Arctic region for the RCP 8.5 scenario. The general pattern
(a)
(b)
Fig. 12 a Time evolution of the global mean surface air temperature
anomaly (in K) computed by the IPSL-CM5A-LR (thick line), the
IPSL-CM5A-MR (thin line with crosses) and the IPSL-CM5B-LR
(thick dash line) models, with historical conditions for the period
1950–2005 (black) and with RCPs conditions for the period
2006–2100: RCP 2.6 (blue), RCP 4.5 (green), RCP 6.0 (light blue),
and RCP 8.5 (red). The temperature anomaly is computed with
respect to the 1985–2015 period. bTime evolution of the total (thick
line) and the first indirect (thin line) aerosol radiative effects for the
same runs as on panel (a). For clarity, results are only shown for the
RCP 4.5 (green) and the RCP 8.5 (red) scenarios and for the IPSL-
CM5A-LR (line) and the IPSL-CM5B-LR (dash line) models. The
unit is W.m
-2
. For aand b, only one ensemble member is considered
and the results are smoothed using a 7-year Hanning filter
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1521 of temperature change is consistent with the one previously
1522 obtained (Meehl et al. 2007b). More specifically, there is a
1523 larger temperature increase over the continents than over
1524 the oceans, a strong amplification in the Arctic regions, and
1525 the smallest warming is found over the Southern Ocean.
1526 The IPSL-CM5B-LR model shows a very large and prob-
1527 ably unrealistic temperature increase poleward of 60°N,
1528 which may be related to the very cold bias in these regions
1529 (Fig. 7), to the equatorward shift of the atmospheric zonal
1530 wind stress and to the very weak Atlantic meridional
1531 overturning circulation of this model (Sect. 5.5).
1532 The RCP simulations have been extended until the end
1533 of the twenty-third century for the IPSL-CM5A-LR model.
1534 The differences among geographical patterns of tempera-
1535 ture amplification in the two extreme scenarios are larger at
1536 the end of the twenty-third century than at the end of the
1537
twenty-first century even though they remain surprisingly
1538
small compared to the very large differences between the
1539
two global mean temperature changes: 1.9 K for RCP 2.6
1540
and 12.7 K for RCP 8.5. Continental warming is larger in
1541
the RCP 8.5 scenario. The relatively small polar warming
1542
in RCP 8.5 reflects a very different polar amplification,
1543
which will be analyzed below (Sect. 5.6). For the RCP 2.6
1544
scenario, there are minor differences between the end of
1545
the twenty-first and twenty-third centuries. The warming
1546
over the southern ocean at the end of the twenty-thired
1547
century remains small compared to the global warming.
1548
For the RCP 4.5 scenario, the pattern of the local temper-
1549
ature amplification in 2300 is very similar to the one for
1550
scenario RCP 2.6 (not shown).
1551
5.2 Future warming projections using SRES scenarios
1552
In this section the global mean surface air temperature
1553
increase and the radiative forcings obtained for the SRES
1554
scenarios used in CMIP3 are compared with those obtained
1555
for the RCP scenarios used in CMIP5. With the same IPSL-
1556
CM5A-LR model, simulations with both SRES and RCP
1557
forcings were performed. The concentration of long-lived
1558
greenhouse gases are fully specified in both SRES and
1559
RCP, which is not the case for ozone. Here we assumed
1560
that the ozone concentration of the SRES-A2, SRES-A1B
1561
and SRES-B1 scenarios were the same as the ozone con-
1562
centration of the RCP 8.5, RCP 6.0 and RCP 4.5 scenarios,
1563
respectively. Little information regarding aerosols was
1564
given for the SRES scenarios whereas the information is
1565
available for the RCP scenarios. Therefore, six types of
1566
aerosols were considered in RCP simulations (see Sect.
1567
2.2.3) but only the sulfate aerosol was considered in the
1568
SRES runs. For the SRES scenarios the sulfate aerosol
1569
concentrations computed by Pham et al. (2005) were used.
1570
To avoid a discontinuity of forcings at the beginning of
1571
these scenarios, a historical simulation was performed
1572
using the consistent distribution of sulfate aerosols (Bou-
1573
cher and Pham 2002). Land use changes were also con-
1574
sidered in the RCP runs but not in the SRES runs for which
1575
the land use of year 2000 was used for the whole twenty-
1576
first century. These choices are consistent with the fact that
1577
in CMIP3 most models considered ozone and sulfate aer-
1578
osol forcings but no forcing due to other aerosols species
1579
nor forcing due to land use changes, whereas for CMIP5
1580
most models are expected to consider a larger variety of
1581
aerosols as well as land use changes.
1582
The range of future global mean warming for the RCP
1583
scenarios is much larger (Fig. 15) than for the SRES sce-
1584
narios. The RCP 8.5 scenario leads to a higher warming
1585
than the SRES-A2 scenario, and the RCP 2.6 scenario leads
1586
to a stabilization of the global mean surface temperature, a
1587
feature that no SRES scenario simulates. Also, the global
(b)
(a)
Fig. 13 For the IPSL-CM5A-LR model, time evolution of the global
mean surface air temperature (a) and the net TOA radiative flux
(b) for the control run (magenta), the historical runs (black), and for
the RCP 2.6 (blue), the RCP 4.5 (green), the RCP 6.0 (light blue), and
the RCP 8.5 (red) scenarios. In athe thin lines correspond to the
annual value of individual run members, the thick lines correspond to
the 11-year running mean of one particular member. In bthe lines
correspond to the 11-year running mean of one particular member.
For all scenarios members extend to year 2300 except for the RCP 6.0
scenario for which the only member stops in 2100
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(a) (b)
(c) (d)
(e) (f)
(g) (h)
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1588 mean surface temperature for RCP and SRES projections
1589 differs significantly except for RCP 4.5 and SRES-B1. For
1590 these two scenarios the long-lived greenhouse gases forc-
1591 ing and the temperature increase are very similar although
1592 the simulated temperature increase is somewhat smaller
1593 around 2040 for SRES-B1 compared to RCP 4.5 due to the
1594 radiative effect of aerosol, which is larger for SRES-B1.
1595 The aerosol radiative forcings are very different between
1596 the two families of scenarios. These differences do not
1597 originate from the diagnostics because the aerosol forcings
1598 are calculated online with the same method in the different
1599 simulations. One difference is that in the RCP family
1600 aerosol concentrations reach a maximum around 2020 and
1601 then decrease whereas in the SRES family the aerosol
1602 concentrations increase until 2030–2050. The second dif-
1603 ference is that only the sulfate aerosol was considered in
1604 the SRES experiments whereas absorbing aerosols were
1605 also considered in the RCP experiments, which strongly
1606 reduce the total aerosol radiative forcing. However for all
1607 scenarios the relative contribution of anthropogenic aero-
1608 sols forcing compared to the total anthropogenic forcing is
1609 smaller in 2100 than in 2000.
1610 A common feature observed in the model results using
1611 both scenario families is the delay between the time when
1612 the radiative forcing in two scenarios differ and the time
1613 when the temperature increase in response to these forcing
1614 differ. The different trend in radiative forcing between
1615 SRES-A2 and A1B scenarios on one hand, and between
1616 RCP 6.0 and RCP 4.5 on the other hand, starts around
1617 2060. The divergence in temperature increase occurs
1618 20 years later but is still small at the end of the century.
1619 5.3 Computing the CO
2
flux and the ‘‘compatible
1620 emissions’’ of CO
2
1621 For the historical period and for each of the RCP scenarios,
1622 the land (ORCHIDEE) and ocean (PISCES) carbon cycle
1623 models generate spatially-explicit carbon fluxes in
1624 response to the atmospheric CO
2
concentrations and sim-
1625 ulated climate. The simulated net land carbon flux includes
1626 a land-use component but the decomposition of this net
1627 flux into its land-use and natural parts has not yet been
1628
analyzed. Piao et al. (2009) however did show that a
1629
similar version of ORCHIDEE was able to reproduce the
1630
estimated land use change related to carbon emissions
1631
when forced over the historical period by the Climate
1632
Research Unit temperatures and precipitations datasets
1633
(Jones et al. 1999; Brohan et al. 2006; Doherty et al.
1634
1999). Only the results of IPSL-CM5A-LR and CM5A-MR
1635
runs are presented here because the carbon pools have not
1636
reached an equilibrium state for IPSL-CM5B-LR (Sect.
1637
4.1).
1638
In the historical simulations with IPSL-CM5A-LR the
1639
net ocean and land fluxes increase in the 1990–1999 decade
1640
to reach 2.2 (±0.05) and 1.28 (±0.1) Pg/year, respec-
1641
tively (Fig. 16). These values are in the range of recent
1642
estimations (Le Que
´re
´et al. 2009) for the 1990–1999
(a)
(b)
Fig. 15 Time evolution of athe global mean air surface temperature
anomalies (K) and of bthe long-lived greenhouse gases
(CO
2
,CH
4
,N
2
O, CFC... but no ozone) (positive values) and aerosol
(negative values) radiative forcing (Wm
-2
) (direct ?first indirect)
simulated with IPSL-CM5A-LR for the historical and for the future
periods using the forcing of the RCP (line) and SRES (dash)
scenarios. The historical runs are in black. The four RCP scenarios
used in CMIP5 are RCP 2.6 (blue), RCP 4.5 (green), RCP 6 (light
blue), and RCP 8.5 (red). The three SRES scenarios used in CMIP3
are SRES-B1 (green), SRES-A1B (light blue), and SRES-A2 (red)
Fig. 14 Geographical distribution of the normalized temperature
change for the RCP 2.6 (left column) and the RCP 8.5 (right column)
scenarios at the end of the twenty-first century (2070–2100 period,
three upper rows) for IPSL-CM5A-LR (a,b,first row), IPSL-CM5A-
MR (c,d,second row) and IPSL-CM5B-LR (e,f,third row).
Normalized temperature change at the end of the twenty-third century
(2270–2300 period) are shown on the bottom row (g,h) for the IPSL-
CM5A-LR model. The temperature changes are computed relative to
the pre-industrial run (100-year average) and the normalized temper-
ature change is defined as the local temperature change divided by the
global average temperature change
b
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REVISED PROOF
1643 decade: 2.2 ±0.4 PgC/year for the ocean and 1.1 ±0.9
1644 PgC/year for the land.
1645 Over the 2005–2300 period, the ocean uptake increases
1646 up to 6 PgC/year in 2100 for the RCP 8.5 scenario. The
1647 ocean uptake peaks at 5 PgC/year in 2080 for the RCP 6.0
1648 scenario, and at 3.7 PgC/year in 2030 for the RCP 4.5
1649 scenario before decreasing throughout the remainder of the
1650 simulations. For the RCP 2.6 scenario, the ocean uptake
1651 does not exceed 3.2 PgC/year over the 2005–2300 period
1652 and is close to zero in 2300. The differences in net land flux
1653 between the different scenarios over the 2005–2300 period
1654 is less pronounced. The net land flux (including land-use
1655 emissions) peaks at 5 PgC/year in the RCP 8.5, RCP 6.0
1656 and RCP 4.5 scenarios during the twenty-first century. For
1657 the RCP 2.6 scenario, the net land flux does not exceed
1658 3 PgC/year. After 2150 the net land flux is close to zero or
1659 negative for all RCP scenarios (i.e. the land becomes a
1660 source of carbon for the atmosphere).
1661 We diagnosed the anthropogenic emissions compatible
1662 with the simulated land (F
l
) and ocean (F
o
) carbon fluxes
1663 and prescribed CO
2
concentrations using the following
1664 equation for the emission rates
Fe¼dMC
dt þðFoþFlÞð3Þ
16661666 where M
C
is the mass of carbon in the atmosphere. The
1667 ORCHIDEE model explicitly simulates the natural and
1668 land-use components of land-atmosphere carbon fluxes so
1669 ’’compatible emissions’’ refer here to fossil fuel ?cement
1670 production only emissions. The computed compatible
1671 emissions for the historical and RCPs simulations are
1672 shown in Fig. 17.
1673 For the 1990–1999 decade, the compatible emissions
1674 amount to 6.6 (±0.2) PgC/year, which compares well with
1675 data-based estimates of 6.4 (±0.4) PgC/year (Forster et al.
1676 2007). In 2100 the cumulative compatible emissions differ
1677 markedly between the scenarios and amount to 2,288 (±3,
1678 4 simulations), 1,644 (1 simulation), 1,349 (±10, 4 sim-
1679 ulations), 793 (±1, 4 simulations) PgC, for the RCP 8.5,
1680 the RCP 6.0, the RCP 4.5 and the RCP 2.6 scenarios,
1681 respectively. The uncertainties given here are the standard
1682 deviation of the estimates when multi-member simulations
1683 are available.
1684 When using the mid-resolution model (IPSL-CM5A-
1685 MR) forced by the same RCP scenarios, the cumulative
1686 compatible emissions amount to 2,244, 1,303 and 772 PgC
1687 in 2100 for RCP 8.5, RCP 4.5 and RCP 2.6, respectively
1688 (Fig. 17c). These values are similar to the ones obtained
1689 with IPSL-CM5A-LR but they are lower by 2–3 % for
1690 each of the scenarios. These differences are explained by a
1691 weaker uptake of carbon by both the ocean and the land
1692 biosphere. The reasons for this difference may be related to
1693 the reduction of the southern westerlies biases in IPSL-
1694
CM5A-MR compared to IPSL-CM5A-LR (see Hourdin
1695
et al. 2013a) and its impact on oceanic carbon uptake as
1696
demonstrated in Swart and Fyfe (2012). For the land, the
1697
reduction of the global cool bias discussed above induces a
1698
reduction of the positive effect of global warming on the
1699
functioning of high- and mid-latitude vegetation, which
Fig. 16 Time evolution of the prescribed CO
2
concentration (top),
computed ocean carbon uptake (middle) and land carbon uptake
(bottom) for the historical period (black) and for the RCP 2.6 (blue),
the RCP 4.5 (green), the RCP 6.0 (light blue), and the RCP 8.5 (red)
scenarios. The model used is IPSL-CM5A-LR, the concentration is in
ppmv and the carbon flux is in PgC/year. Note that the simulated net
land carbon flux includes a land-use component (see text)
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1700 leads to a slight reduction in the ability of the vegetation to
1701 absorb CO
2
.
1702 The cumulative emissions also differ from the initial
1703 IAMs (Integrated Assessment Models) emissions. For the
1704 RCP 8.5 scenario, the IAM emissions amount to 2,521 PgC
1705 in 2100. This is 230 PgC (280 PgC for IPSL-CM5A-MR)
1706
less than with the initial IAMs. These differences are
1707
caused by weaker sinks than the ones used in IAMs, which
1708
could be due to a weaker response to atmospheric CO
2
or
1709
to a stronger climate-carbon feedback in our simulations.
1710
More analysis is needed to confirm this hypothesis. For the
1711
RCP 2.6 scenario however, the IAM emissions and our
1712
estimates agree (790 vs 772 PgC, respectively).
1713
In 2300, cumulative compatible emissions for IPSL-
1714
CM5A-LR are 4,946, 1,797 and 627 PgC for the RCP 8.5,
1715
the RCP 4.5 and the RCP 2.6 scenarios, respectively.
1716
Interestingly, the RCP 2.6 compatible emissions reach
1717
negative values from 2100 onwards.
1718
5.4 Future precipitation changes
1719
In contrast to surface-air temperature changes, which are
1720
positive over most of the globe, precipitation changes
1721
exhibit a complex regional pattern. To facilitate the com-
1722
parison of precipitation projections associated with differ-
1723
ent scenarios, we use the ‘‘normalized relative precipitation
1724
change’’, i.e. the relative change in precipitation (dP/P
1725
computed at each grid point) normalized by the global-
1726
mean surface-air temperature change. Units are thus %
1727
K
-1
. The geographical distribution of the normalized rel-
1728
ative precipitation changes for the different model versions
1729
and for the different scenarios features well-known patterns
1730
such as precipitation decrease in most of the subtropics and
1731
an increase in the equatorial regions and in the mid and
1732
high latitudes (Fig. 18).
1733
Despite the differences among the forcings in each
1734
scenario, the pattern of the change in precipitation in 2100
1735
for a given model version is strikingly similar for the dif-
1736
ferent RCPs scenarios (Fig. 18a-f). The regions where
1737
precipitation decreases are almost the same for all sce-
1738
narios, both over ocean and land, and the amplitudes of the
1739
normalized precipitation changes are very similar. Over
1740
north Asia and north America, the regions where precipi-
1741
tation increases are very similar but the normalized
1742
amplitude is a somewhat larger for the scenario with the
1743
lowest radiative forcing (RCP 2.6) than for the scenario
1744
with the highest radiative forcing (RCP 8.5). This is con-
1745
sistent with the results published by Johns et al. (2011).
1746
The relative precipitation change has very similar pat-
1747
terns for the IPSL-CM5A-LR and the CM5A-MR models,
1748
which only differ in the horizontal resolution of the
1749
atmospheric model (Fig. 18a–d). Increased resolution
1750
provides more details in the geographical distribution, for
1751
instance in the Himalayan region, but does not lead to
1752
significant large scale pattern differences.
1753
In contrast, the relative precipitation change displays
1754
dramatic differences for the IPSL-CM5A-LR and the
1755
CM5B-LR models, which only differ in the physical
1756
package of the atmospheric model (Fig. 18a, b, e, f). In the
(a)
(b)
(c)
Fig. 17 Time evolution of the compatible CO
2
emissions (a, in PgC/
year) and of the cumulative emissions (b, in PgC) for the historical
period (black) and for the RCP 2.6 (blue), the RCP 4.5 (green), the
RCP 6.0 (light blue), and the RCP 8.5 (red) scenarios, simulated by
the IPSL-CM5A-LR model. The time period is restricted to
1850–2100 in (c) where the results are shown for both the IPSL-
CM5A-LR and IPSL-CM5A-MR models. The compatible emissions
refer here to fossil-fuel ?cement production only and do not include
land-use emissions
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(a) (b)
(c) (d)
(e) (f)
(g) (h)
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REVISED PROOF
1757 Pacific ocean the precipitation changes along the equator
1758 are located in the center and in the east of the basin in
1759 CM5B, whereas it is located more westward in CM5A with
1760 a double ITCZ signature. There is no signature of the SPCZ
1761 in the precipitation response simulated by CM5B. Over the
1762 tropical continents the differences in precipitation changes
1763 are also large between CM5A and CM5B, especially over
1764 India, East Africa, South America and Australia. The
1765 amplitude and the sign of the precipitation changes differ.
1766 These large differences among models in the precipitation
1767 changes contrast with the relatively small differences in the
1768 climatology of precipitation among models (Fig. 8).
1769 At the end of the twenty-third century the differences
1770 among geographical patterns of the relative precipitation
1771 change simulated by IPSL-CM5A-LR for the two extremes
1772 scenarios are very large (Fig. 18g, h). They are much larger
1773 than the differences in the relative temperature changes
1774 (Fig. 14g, h). For instance, the relative precipitation
1775 changes along the equator in the Pacific ocean are much
1776 larger and located more westward in RCP 8.5 than in RCP
1777 2.6. Also, the extent of the drier regions in the subtropics is
1778 increased and the relative precipitation increase at high
1779 latitudes is larger in RCP 8.5 than in RCP 2.6.
1780 A useful framework to interpret the projected precipi-
1781 tation changes consists in decomposing those changes into
1782 precipitation changes related to atmospheric circulation
1783 changes and precipitation changes related to water vapor
1784 changes, referred to as dynamical and thermodynamical
1785 components, respectively. At mid and high latitudes, the
1786 precipitation increase is mainly explained by the thermo-
1787 dynamical component (Emori and Brown 2005).
1788 Over the tropical oceans and in the absence of atmo-
1789 spheric circulation change, an increase of water vapor in
1790 the boundary layer leads to an increase of moisture con-
1791 vergence, and therefore to an increase of precipitation in
1792 the convective regions and an increase of moisture diver-
1793 gence in the subsidence regions (Chou and Neelin 2004;
1794 Held and Soden 2006). This latter effect may be partly
1795 compensated by an increase of evaporation but the net
1796 effect is an increase of the precipitation contrast between
1797 wet and dry regions (Chou et al. 2009). However the
1798
atmospheric circulation significantly changes in response
1799
to the temperature increase and this circulation change is
1800
closely coupled to precipitation changes. We use the
1801
monthly-mean vertical velocity at 500 hPa (x
500
)asa
1802
proxy for large-scale atmospheric vertical motions.
1803
Figure 19 shows the change in x
500
(compared to pre-
1804
industrial climate) predicted by the IPSL-CM5A-LR and
1805
IPSL-CM5B-LR models at the end of the twenty-first
1806
century in the RCP 8.5 scenario.
1807
In the middle of the Pacific, along the equator, the large
1808
precipitation increase simulated by IPSL-CM5B-LR
1809
(Fig. 18f) is associated with a large increase in the large-
1810
scale rising motion (or weakening of the large-scale sub-
1811
sidence) in the same region (negative values of x
500
,
1812
Fig. 19b). In contrast, the change in precipitation simulated
1813
by IPSL-CM5A-LR is very small in this region (Fig. 18b)
1814
and so is the change in vertical velocity (Fig. 19a). Along
1815
the ITCZ, the strength of large-scale rising motions
1816
decreases in both model versions (Fig. 19) but more
1817
strongly in IPSL-CM5B-LR over the warm-pool (about
1818
20 hPa day
-1
). This circulation change partly counteracts
1819
the precipitation increase induced by the larger water vapor
1820
amount in the atmosphere and explains why the two model
1821
versions predict very different changes in precipitation in
1822
this region (Fig. 18b). Further analysis and understanding
1823
of the reasons why the precipitation changes projected by
1824
these two models are so different will be the subject of a
1825
forthcoming paper.
1826
5.5 Atlantic meridional overturning circulation
1827
The Atlantic Meridional Overturning Circulation (AMOC)
1828
maximum is represented in Fig. 20 for different simula-
1829
tions from the IPSL-CM5A-LR and the IPSL-CM5A-MR
1830
models. This index represents the strength of the meridi-
1831
onal circulation over the North Atlantic (30°S-80°N,
1832
500 m-5,000 m) and the amount of ocean water sinking at
1833
depth in the North Atlantic. This overturning circulation is
1834
very weak in the IPSL-CM5B-LR pre-industrial run
1835
(AMOC index about 4 Sv) probably due to a strong bias in
1836
the zonal wind and it will not be discussed in this section.
1837
In the control simulations the mean AMOC maximum is
1838
10.3 Sv in the IPSL-CM5A-LR model and 13.5 Sv in the
1839
IPSL-CM5A-MR model. Both values are too weak com-
1840
pared to observational estimates (Kanzow et al. 2010)
1841
because of a lack of convection in the Labrador Sea. This
1842
bias was also featured in previous versions of the IPSL
1843
model (Swingedouw et al. 2007a). The improvement in the
1844
IPSL-CM5A-MR is mainly related to a smaller equator-
1845
ward shift in the atmospheric zonal wind stress, which is
1846
very strong in IPSL-CM5A-LR (Marti et al. 2010). As a
1847
consequence, the North Atlantic Ocean is saltier in IPSL-
1848
CM5A-MR and convection occurs east of the Labrador
Fig. 18 Geographical distribution of the normalized relative precip-
itation changes for the RCP 2.6 (left column) and the RCP 8.5 (right
column) scenarios at the end of the twenty-first century (2070–2100
period, three upper rows) for IPSL-CM5A-LR (a,b,first row), IPSL-
CM5A-MR (c,d,second row) and IPSL-CM5B-LR (e,f,third row).
Normalized relative precipitation change at the end of the twenty-
third century (2270–2300 period) are shown on the bottom row
(g,h) for the IPSL-CM5A-LR model. The local precipitation changes
are computed relative to their local preindustrial values on a yearly
mean basis and are then normalized with the global average
temperature change. Regions where the annual mean precipitation
is less than 0.01 mm/day (i.e. the Sahara region except for IPSL-
CM5B-LR which has higher precipitation there) are in white
b
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REVISED PROOF
1849 Sea. Over the historical era, the AMOC maximum remains
1850 very close to its value in the control simulation. In all
1851 projections the AMOC weakens from 2020 onward and by
1852 2050 its intensity is weaker than in the control run. On
1853 longer time scales the projections that have been extended
1854 using IPSL-CM5A-LR (RCP 2.6, RCP 4.5 and RCP 8.5)
1855 show very different behaviours. A recovery of the AMOC
1856 maximum by 2100 was simulated using the RCP 2.6 sce-
1857 nario, reaching the control value around 2200 and contin-
1858 uing to increase slowly until 2300, while RCP 8.5 exhibits
1859 a continuous decrease of the AMOC maximum to less than
1860 4 Sv in 2300. Such a state can be considered as an AMOC
1861 collapse.
1862 To further explain the AMOC response, the evolution of
1863 deep convection in the northern North Atlantic was ana-
1864 lyzed for IPSL-CM5A-LR. These areas of deep convection
1865 have been identified for this model by Escudier et al.
1866 (2013) and are shown to drive the AMOC variability. In
1867 particular, Fig. 21-a shows that the low frequency changes
1868 of mixed layer depth (MLD) averaged over these areas lead
1869 to variations in the AMOC maximum in about a decade: a
1870 slight MLD increase in the 1960’s in the historical simu-
1871 lations leads to an AMOC increase and deep convection
1872 weakening in the projections starting around 2010 followed
1873 by different behaviors in the longer term depending on the
1874 scenario (recovery in RCP 2.6 and RCP 4.5 and collapse in
1875 RCP 8.5). The MLD is well correlated (in phase) with the
1876 surface density in the convection sites (Escudier et al.
1877 2013), which is indeed the trigger for deep convection.
1878 After linearization the surface density can be decomposed
1879 into a haline and a thermal component to better understand
1880 if the changes in MLD are due to a change in salinity or in
1881 temperature. Figure 21.c and d show that the thermal
1882
component is decreasing in all the simulations as early as
1883
the 1960s. The haline component has a more complex
1884
behavior. It increases in the 1960s and remains higher than
1885
in the control simulations in all the projections until 2060.
1886
Later on, it decreases significantly in the RCP 8.5 long
1887
projections while it remains at the level of the control
1888
simulation in RCP 4.5 and even above it in RCP 2.6.
1889
The increase in local SST is part of the increase of the
1890
global surface temperature in response to the GHG
1891
increase. The increase in sea surface salinity from the
1892
1960s is the result of the balance between two opposite
1893
effects which are the transport of saltier waters from the
1894
tropics where the evaporation increases and precipitation
1895
decreases compared to pre-industrial values (not shown),
1896
and the increase in precipitation and runoff at high lati-
1897
tudes. In this model the balance seems to favor a salinifi-
1898
cation of the North Atlantic, which stabilizes the AMOC as
1899
was also the case in the former version of this model
1900
(Swingedouw et al. 2007b). The total evaporation inte-
1901
grated over the whole Atlantic (from 30°Sto80°N and
1902
including the Arctic basin) increases from 0.49 Sv in the
1903
control simulations (the Atlantic is an evaporative basin as
1904
in the real system) up to 0.62, 0.65 and 1.23 Sv for the last
1905
30 years of RCP 2.6, RCP 4.5 and RCP 8.5, respectively.
1906
This is associated with a large increase in fresh water
1907
export by the atmosphere from the Atlantic to the Pacific as
1908
in IPSL-CM4 (Fig. 11 from Swingedouw et al. (2007b)).
1909
Nevertheless, because of the thermal component that tends
1910
to weaken deep convection in the northern North Atlantic,
1911
the AMOC gradually weakens. For a sufficient weakening
1912
(as in RCP 8.5) of this large-scale northward transport of
1913
heat and salt, an oceanic feedback becomes dominant: the
1914
northward oceanic salinity transport associated with the
(a) (b)
Fig. 19 In color, geographical distribution of the mean vertical
velocity change at 500 hPa x
500
(hPa day
-1
) simulated by IPSL-
CM5A-LR (a,left) and IPSL-CM5B-LR (b,right) at the end of the
twenty-first century (2070-2100 period) for the RCP 8.5 scenario
relative to its value in the pre-industrial control run. The mean vertical
velocity at 500 hPa for the control run is contoured (contour values:
-40, -20 and 20 hPa day
-1
with dash lines for negative values).
Negative values of x
500
correspond to large-scale rising motion,
positive value to subsidence
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REVISED PROOF
1915 AMOC decreases, leading to a decrease in sea surface
1916 salinity in the convection sites and a collapse of the
1917 AMOC. This mechanism is the so-called Stommel positive
1918 feedback (Stommel 1961). It explains the negative contri-
1919 bution of the haline component of the density in RCP 8.5
1920 around 2060 (Fig. 21c).
1921 The Greenland ice sheet melting is not taken into
1922 account in the IPSL-CM5A models although it can have a
1923 large impact on the AMOC (Swingedouw et al. 2007b).
1924 The analysis of such an effect will be achieved through the
1925 coupling of IPSL-CM5A-LR with a Greenland ice sheet
1926 model and will be presented in a future study.
1927 5.6 Polar amplification and sea-ice extent
1928 Due to the large extent of snow and ice covered surfaces
1929 over polar areas and their significant decrease with global
1930 warming, specific feedback mechanisms take place at high
1931 latitudes (Manabe and Stouffer 1980). Snow and ice are
1932 strongly sensitive to air temperature but they also strongly
1933 affect the surface energy budget by increasing the surface
1934 albedo and thermally isolating the oceanic surface from the
1935 air. As a result, the temperature increase due to global
1936 warming in the Arctic as simulated by most models is
1937 amplified (Meehl et al. 2007b). It is also the case for the
1938 IPSL models (Fig. 14). We focus here on the IPSL-CM5A-
1939 LR model results.
1940 To quantify the polar amplification effect, we defined
1941 the ratio between the mean increase of surface air tem-
1942 perature poleward of the Arctic and Antarctic circles
1943
respectively, and the globally averaged temperature
1944
increase. To better understand the relationship between
1945
polar amplification and sea ice extent, the total sea ice area
1946
in September for each scenario is computed, September
1947
being the month during which this area is minimum and
1948
thus the month during which the Arctic Ocean is predicted
1949
to first become seasonally free of ice (Fig. 22). In the
1950
Southern Ocean, summer sea ice area is limited by
1951
the Antarctic continent located over the pole. Therefore,
1952
the absolute value of the Antarctic sea-ice area is more
1953
sensitive to climate change in winter than in summer.
1954
Figure 23 shows the polar amplification for the Arctic
1955
(top) and Antarctic (bottom) until 2300. The amplitude of
1956
the internal variability is large for all scenarios, in particular
1957
during the initial 25 years (dashed lines). By the end of the
1958
twenty-first century (for which simulations for all scenarios
1959
are available) the warming in the Arctic as projected by
1960
IPSL-CM5A-LR reaches about twice the global value
1961
independent of the scenario. In the RCP 8.5 scenario the
1962
Arctic ocean becomes free of ice at the end of summer by
1963
2070 (Fig. 22). About 30 years later and after weak oscil-
1964
lations, the Arctic amplification slowly and continuously
1965
decreases. In the RCP 4.5 scenario, the Arctic is never
1966
projected to become free of sea ice but the minimum sea ice
1967
area decreases to about a fifth of its present-day value. The
1968
Arctic amplification in RCP 2.6 displays the highest vari-
1969
ability in agreement with pronounced minimum sea ice area
1970
variability and no significant trend. The strong variability in
1971
RCP 2.6 might arise from a seasonal effect. Summer Arctic
1972
amplification strongly depends on sea ice cover and snow
Fig. 20 Time evolution of the Atlantic Meridional Overturning
Circulation (AMOC) maximum taken between 500 m and the ocean
floor and from 30°Sto80°N for the preindustrial control run
(magenta), the historical period (black) and the RCP 2.6 (blue), RCP
4.5 (green), RCP 6.0 (light blue) and RCP 8.5 (red) scenarios.
Simulations using IPSL-CM5A-LR are in continuous line and the
ones using IPSL-CM5A-MR are in dashed line. For IPSL-CM5A-LR
simulations for which multi-member ensembles are available, the
lines show the ensemble means and the shading in gray,light red and
light green display the two standard deviation error bar for the
historical, RCP 8.5 and RCP 4.5 experiments respectively
IPSL-CM5 Earth System Model
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REVISED PROOF
1973 covered areas are the main source of winter Arctic ampli-
1974 fication variability (Hall 2004). Given that snow extent is
1975 larger and potentially more variable, the impact of land
1976 covered with snow in the scenario with the lowest radiative
1977 forcing (RCP 2.6) might be one reason for the high Arctic
1978 amplification variability in RCP 2.6. Another reason is that
1979 the global and regional mean climate change signal in RCP
1980 2.6 is of course weaker than in the other scenarios. Therefore
1981 the computed polar amplification is necessarily more
1982 strongly affected by internal variability on all relevant
1983 spatial and temporal scales for this scenario.
1984 In the southern hemisphere, the computed polar ampli-
1985 fication is very close to one. Austral amplification mostly
1986 takes place over sea ice and decreases poleward (Hall
1987 2004). It is therefore not included in the area where the
1988 polar amplification was computed (Fig. 14). Variability is
1989 highest in the scenario with the lowest radiative forcing
1990 (RCP 2.6) and strongly correlated with sea ice area. Unlike
1991 in the northern hemisphere, seasonal snow cover in the
1992
southern hemisphere is small. Therefore sea ice is the most
1993
obvious polar surface amplifier of mean climate change
1994
and internal variability via the snow-albedo feedback
1995
mainly in summer and its effect on ocean-atmosphere heat
1996
fluxes mainly in winter. The two sets of curves (Figs. 22
1997
bottom, 23 bottom) are indeed highly correlated. The
1998
warming over the Antarctic continent only reaches the
1999
global value in the RCP 8.5 scenario around 2300. Large
2000
effective heat capacity of the Southern Ocean delays the
2001
Antarctic warming.
2002
6 Temperature and precipitation changes using
2003
idealized scenarios
2004
6.1 Climate sensitivity and feedbacks
2005
Two types of experiments are particularly useful in CMIP5
2006
to estimate the temperature response to an increase in CO
2
(a)
(b)
(c)
(d)
Fig. 21 Same as Fig. 20 but for
athe mixed layer depth (MLD)
in meters for winter season
(DJFM) averaged over the
convection sites as defined in
Escudier et al. (2013), bsurface
density averaged over the same
region (in kg/m3),
cdecomposition in haline
components (related to salinity)
of the linearized surface density
(in kg/m3), dthermal
components (related to
temperature) of the same
linearization. The convection
sites are located in the Nordic
Seas, south of Greenland just
outside the Labrador Sea, and in
an extended area south of
Iceland including the Irminger
Sea (Escudier et al. 2013)
J.-L. Dufresne et al.
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2007 concentration: the 1 % per year experiment in which,
2008 starting from the control run, the CO
2
concentration
2009 increases by 1 % per year until a quadrupling of its initial
2010 value (i.e. after 140 years), and the abrupt 4CO
2
experi-
2011 ment in which the CO
2
concentration is instantaneously
2012 increased to 4 times its initial value and is then held
2013
constant. This latter experiment was not run for the IPSL-
2014
CM4 model because it does not belong to the CMIP3
2015
experimental design.
2016
The feedback analysis framework detailed by Dufresne
2017
and Bony (2008) was used to analyse the temperature
2018
response to the CO2 forcing. In response to a radiative
2019
forcing at the TOA DQt, the changes in surface temperature
2020
DTsand radiative flux at the TOA DFtare related by the
2021
following equation:
DTs¼DFtDQt
k:ð4Þ
20232023where kis the ‘‘climate feedback parameter’’ (fluxes are
2024
positive downward). Within this framework, when the model
2025
reaches a new equilibrium after a constant forcing has been
2026
applied, the net flux at the TOA DFtapproaches zero,
2027
yielding an equilibrium temperature change DTe
s¼DQt=k.
2028
The definition of the forcing DQtis not unequivocal. A
2029
classical method to compute this forcing is to assume an
2030
adjustment of the stratospheric temperature (e.g. Forster
2031
et al. 2007). Using a radiative offline calculation with
2032
stratospheric adjustment, we obtained DQtð2CO2Þ 2033
3:5W:m2(3.7 Wm
-2
in clear sky conditions) for a dou-
2034
bling of the CO
2
concentration, and twice these values
2035
(DQtð4CO2Þ7:0Wm2, (7.4 Wm
-2
clear sky)) for a
2036
quadrupling of the CO
2
concentration. The same values
2037
were obtained for the IPSL-CM4, IPSL-CM5A and IPSL-
2038
CM5B models, which have the same radiative code. For
2039
intermediate values xof the ratio between the CO
2
con-
2040
centration and its pre-industrial value, the radiative forcing
2041
is estimated using the usual relationship: DQtðxÞ¼ 2042
DQtð2CO2Þ:logðxÞ=logð2Þ. Using this forcing and the
2043
results of the 1 %-per-year experiment, the time series of
2044
the climate feedback parameter kwere computed for the
2045
different versions of the IPSL-CM model. The values
2046
reported in Table 1are the 30-year average values of k
2047
around the time of CO
2
doubling (i.e. between years 56 and
2048
85). The feedback parameter kin IPSL-CM5A-LR is very
2049
similar to that in the previous version, IPSL-CM4, and it is
2050
also very similar to that in IPSL-CM5A-MR. On the other
2051
hand, the value of the feedback parameter in IPSL-CM5B-
2052
LR differs by about 70 % from that in the other model
2053
versions. The same results hold for the equilibrium tem-
2054
perature change DTe
sð2CO2Þfor a doubling of the CO
2
2055
concentration (often called ‘‘climate sensitivity’’).
2056
Another classical metric to characterize the response to
2057
an increase in CO
2
concentration is the ‘‘transient climate
2058
response’’ (TCR), i.e. the surface air temperature increase
2059
in a 1 %-per-year experiment when the CO
2
concentration
2060
has doubled, i.e. 70 years after it started to increase (here
2061
we computed the 30-year average, i.e. the average between
2062
years 56 and 85). This transient temperature change is
2063
found to be very similar for IPSL-CM5A-LR and IPSL-
Fig. 22 Time evolution of the sea ice area (km
2
) in September, for
the four RCP scenarios and for the north (top) and the south (bottom)
hemispheres. A 10-year running average is applied
(a)
(b)
Fig. 23 Time evolution of polar amplification for both hemisphere,
poleward of the Arctic (top) and Antarctic (bottom) circles, for the
four RCP scenarios. The polar amplification is computed every month
and plotted with a 10-year running average. The simulation ends in
2100 for the RCP 6.0 scenario. The temperature increase is computed
relative to the preindustrial run
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REVISED PROOF
2064 CM5A-MR (Table 1). This result is consistent with those
2065 obtained by Hourdin et al. (2013a) with a broader range of
2066 horizontal resolutions of the atmospheric model. This
2067 transient temperature change is also similar for IPSL-CM4
2068 and IPSL-CM5A-LR. Again, IPSL-CM5B-LR is different
2069 from the other models, with a much lower value (&-
2070 25 %) of the TCR.
2071 As stated earlier, the definition of the forcing DQtis not
2072 unequivocal and recent work shows that the decomposition
2073 of the forcing into a fast and a slow part allows for a better
2074 analysis and understanding of the temperature and precip-
2075 itation responses to a CO
2
forcing (Andrews and Forster
2076 2008; Gregory and Webb 2008). The forcing including the
2077 fast response can be obtained using the abrupt 4xCO2
2078 experiment (Gregory et al. 2004). In response to a constant
2079 forcing, Eq. 4implies that the slope of the regression of the
2080 net flux at the TOA as a function of the global mean sur-
2081 face temperature provides an estimate of climate feedback.
2082 The intercept of the regression line and the Y axis
2083 (DTs¼0) is an estimate of the radiative forcing including
2084 the fast response of the atmosphere (Fig. 24). The intercept
2085 of the regression line and the X axis (DFt¼0) is an esti-
2086 mate of temperature change at equilibrium DTe
s. Here we
2087 suppose that the radiative forcing and the temperature
2088 change at equilibrium for a doubling of CO
2
are half of the
2089 values for a quadrupling of CO
2
.
2090 For the IPSL-CM5A-LR and CM5A-MR models, the
2091 radiative forcing obtained with this method is only slightly
2092 smaller than the classical one: 3.1 and 3.3 instead of 3.5
2093 Wm
-2
(Table 1). However this small difference masks the
2094 large variation in shortwave and longwave forcings, which
2095 compensate each other. For IPSL-CM5B-LR, the differ-
2096 ence is larger: 2.7 instead of 3.5 Wm
-2
(i.e. &-20 %).
2097 With the regression method, the feedback parameter is
2098 significantly smaller (in absolute value) and the tempera-
2099 ture change at equilibrium is significantly larger than the
2100 one obtained with the 1 %-per-year experiment. This dif-
2101 ference between the two methods holds for all the model
2102 versions. The difference in temperature change at equilib-
2103 rium should be zero if the two methods and the feedback
2104
framework were perfect, which is not the case. It is
2105
therefore important to compare values that have been
2106
estimated using the same method.
2107
In addition to the net flux for all sky conditions, the net
2108
flux for clear sky conditions and the net flux change due to
2109
the presence of clouds can also be used when performing
2110
the linear regression with the global mean surface air
2111
temperature (Fig. 24b, c). Under clear sky conditions, the
2112
radiative forcing estimates using the regression method are
2113
similar for all the model versions. The values of the
2114
feedback parameter are also similar although the absolute
2115
value for IPSL-CM5B-LR is lower. When focusing on the
2116
effect of clouds, the differences between IPSL-CM5A-LR
2117
and CM5A-MR are small whereas the differences between
2118
IPSL-CM5A-LR and CM5B-LR are large (Fig. 24c). The
2119
differences between IPSL-CM5A-LR and CM5B-LR are
2120
mainly due to change of the cloud radiative effect in the
2121
short wave domain (not shown).
2122
An important result for IPSL-CM5 is the very strong
2123
difference between the climate sensitivities obtained with
2124
IPSL-CM5A-LR and IPSL-CM5B-LR. While the climate
2125
sensitivity of IPSL-CM5A-LR (DTe
sð2CO2Þ4:1K) lies in
2126
the upper part of the sensitivity range of the CMIP3
2127
models, the sensitivity of IPSL-CM5B-LR (DTe
sð2CO2Þ 2128
2:6K) falls in the lower part (Meehl et al. 2007b). The
2129
analysis of the reasons for these differences requires further
2130
work.
2131
6.2 Patterns of changes in surface air temperature
2132
and in precipitation
2133
As illustrated in previous sections, the normalized patterns
2134
of temperature and precipitation changes are weakly
2135
dependent on the scenario (Figs. 14 and 18). However, the
2136
IPSL-CM4 model used for CMIP3 was not included in
2137
these figures as no simulation with this model was per-
2138
formed with the forcings of the RCP scenarios. In this
2139
section, we use the results of the 1 %-per-year experiment
2140
to compare IPSL-CM4 with IPSL-CM5. The temperature
2141
and precipitation changes are computed over a 30-year
Table 1 Radiative forcing for a doubling of CO2DQtð2CO2Þ, feedback parameter k, transient TCR(CO
2
) and equilibrium DTe
sð2CO2Þsurface
air temperature increase in response to a CO
2
doubling for the different IPSL-CM model versions
Model 1%/Year CO
2
increase Abrupt 4xCO
2
DQtð2CO2ÞðWm2Þk(Wm
-2
K
-1
) TCR(2CO
2
) (K) DTe
sð2CO2Þ(K) DQtð2CO2ÞðWm2Þk(Wm
-2
K
-1
)DTe
sð2CO2Þ(K)
IPSL-CM4 3.5 -0.92 2.13 3.79
IPSL-CM5A-LR 3.5 -0.98 2.09 3.59 3.12 -0.76 4.10
IPSL-CM5A-MR 3.5 -1.01 2.05 3.47 3.29 -0.80 4.12
IPSL-CM5B-LR 3.5 -1.68 1.52 2.09 2.66 -1.03 2.59
These values (except the transient temperature response) are estimated using either the 1 %/year CO
2
increase experiment or the abrupt 4CO
2
experiment
J.-L. Dufresne et al.
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REVISED PROOF
2142 average period centered around the time of CO
2
doubling,
2143 i.e. between years 56 and 85 after the beginning of the
2144 experiment.
2145 The changes simulated by the IPSL-CM4 model and the
2146 IPSL-CM5A-LR model are quite different, especially over
2147 the continents (Fig. 25). The normalized temperature
2148 increase over north America is larger in IPSL-CM4 than in
2149 IPSL-CM5A-LR and precipitation changes are signifi-
2150 cantly different over south America, India and over the
2151 center of the Pacific ocean. Although dedicated simulations
2152 to attribute the origins of these differences have not been
2153 performed, they are consistent with some known modifi-
2154 cations. For example, the LAI was prescribed in CM4
2155 whereas it is computed by the phenology part of the veg-
2156 etation model (Sect. 2.3) in CM5. Numerical instabilities of
2157 the surface temperature, which were present in IPSL-CM4,
2158 have been now suppressed. The soil depth has been
2159 increased allowing greater seasonal soil water retention,
2160 especially in the tropics. Similar differences of temperature
2161 and precipitation changes over the continents between the
2162 IPSL-CM4 model and the IPSL-CM5A-LR model are also
2163 highlighted in paleoclimate experiments (Kageyama et al.
2164 2013a). Finally, the change of the horizontal and vertical
2165 resolutions of the atmospheric model and the tuning pro-
2166 cess that followed have reduced the biases in the location
2167 of the mid-latitude jets and have slightly modified the
2168 precipitation over the Pacific ocean (Hourdin et al. 2013a).
2169 For the IPSL-CM5A-LR model, the patterns of tem-
2170 perature and precipitation changes obtained with the 1 %
2171 per year experiment (Fig. 25) are similar to those obtained
2172 with the RCP scenarios (Fig. 18), confirming that these
2173 patterns are not very sensitive to the scenarios. The same
2174 similarity of patterns between 1 % per year experiment and
2175
RCP scenarios holds for IPSL-CM5A-MR and IPSL-
2176
CM5B-LR (not shown).
2177
7 Summary and conclusion
2178
The IPSL-CM5 ESM presented in this paper represents a
2179
major evolution in the development of coupled dynamical-
2180
physical-biogeochemical global general circulation mod-
2181
els. This model aims at studying the Earth’s system and
2182
anticipating its evolution under natural and anthropogenic
2183
influences. The interactive carbon cycle, the tropospheric
2184
and stratospheric chemistry, and a comprehensive
2185
description of aerosols represented in the model allow
2186
science questions that could not be addressed with the
2187
IPSL-CM4 coupled ocean-atmosphere climate model used
2188
in CMIP3. These questions include the study of carbon-
2189
climate feedbacks and the estimate of CO
2
emissions
2190
compatible with specific atmospheric concentrations of
2191
CO
2
and land-use, the assessment of chemistry-climate
2192
interactions, the estimate of the role played by different
2193
forcings such as stratospheric ozone, tropospheric ozone,
2194
and aerosols other than sulfate. An important feature of this
2195
model is that it may be used in a large variety of config-
2196
urations associated with a range of boundary conditions
2197
and it includes the possibility of switching on and off
2198
specific feedbacks (e.g. carbon-climate feedbacks, chem-
2199
istry-climate feedbacks, ocean-atmosphere interactions).
2200
During the development phase of the model, this possibility
2201
has always been considered as a key feature to facilitate the
2202
interpretation of the model results. In some configurations
2203
the model may also be used with two different versions of
2204
atmospheric parameterizations (referred to as CM5A and
02468
−20246810
(a) all sky
CM5A−LR
CM5A−MR
CM5B−LR
02468
−20246810
(b) clear sky
CM5A−LR
CM5A−MR
CM5B−LR
02468
−20246810
(c) cloud effect
CM5A−LR
CM5A−MR
CM5B−LR
Fig. 24 Scatter plot of the net flux change (DFtin Wm
-2
) at the
TOA as a function of the global mean surface air temperature change
(DTsin K) simulated in response to an abrupt quadrupling of CO
2
concentration. The net flux at the TOA is computed for aall sky
conditions and bclear sky conditions. The difference between these
two terms is the change in the cloud radiative effect c. Annual mean
values are shown in black for IPSL-CM5A-LR, in blue for IPSL-
CM5A-MR, and in red for IPSL-CM5B-LR. The straight lines
corresponds to linear regressions of the data. Intersection with the
horizontal axis (DFt=0Wm
-2
) gives the expected temperature
change at equilibrium, intersection with the vertical axis (DTs¼0)
gives an estimate of the radiative forcing. The flux and temperature
changes are computed relative to the values of the pre-industrial
control experiment
IPSL-CM5 Earth System Model
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REVISED PROOF
2205 CM5B) and at different horizontal resolutions (referred to
2206 as CM5A-LR and CM5A-MR).
2207 The IPSL-CM5A-LR version of the model has been
2208 used to perform most of the numerical experiments defined
2209 in CMIP5 (Taylor et al. 2012) such as simulations of the
2210 present climate, paleoclimate (Kageyama et al. 2013a,b),
2211 climate projections associated with different RCPs sce-
2212 narios, and multiple idealized experiments aiming at a
2213 better interpretation of ESM results and inter-model dif-
2214 ferences. In particular, the ozone and aerosols radiative
2215 forcings used to simulate the evolution of climate both for
2216 the historical and future periods have been derived from
2217 components of the IPSL-CM5 platform rather than from
2218 external models. As part of CMIP5 this model has also
2219 been used to perform decadal hindcasts and forecasts ini-
2220 tialized by a realistic ocean state and to explore the
2221
predictability of the climate system at decadal timescales
2222
(Swingedouw et al. 2013).
2223
The evaluation of IPSL-CM5A-LR simulations shows
2224
that the model exhibits many biases considered as long-
2225
standing systematic biases of many coupled ocean-atmo-
2226
sphere models such as a warm bias of the ocean surface
2227
over equatorial upwelling regions, the presence of a double
2228
ITCZ in the equatorial eastern Pacific, the overestimation
2229
of precipitation in regimes of atmospheric subsidence, the
2230
underestimation of tropical intra-seasonal variability, and
2231
an underestimation of the AMOC. In addition, the model
2232
exhibits a substantial and pervasive cold bias especially at
2233
mid-latitudes. The pre-industrial control simulation does
2234
not exhibit any climate drift and the model predicts real-
2235
istic amplitude and spectral characteristics of the ENSO
2236
variability. Over the historical period, the net ocean and
(a) (b)
(c) (d)
Fig. 25 Geographical distribution of the normalized surface air
temperature change (K, upper row) and the normalized relative
precipitation changes (%.K
-1
,lower row) simulated by the IPSL-
CM4 (left column) and IPSL-CM5A-LR (right column) models in
response to a doubling of the concentration of CO
2
. The temperature
and precipitation changes are computed relative to the pre-industrial
control run. The local temperature change is normalized with the
global average temperature change. The local precipitation changes
are computed relative to their local pre-industrial values on a yearly
mean basis and are then normalized with the global average
temperature change. The regions where the annual mean precipitation
in the pre-industrial run is less than 0.01 mm/day (i.e. the Sahara
region) are left blank
J.-L. Dufresne et al.
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REVISED PROOF
2237 land CO
2
fluxes are fully consistent with recent estima-
2238 tions. Compared to its IPSL-CM4 parent (the IPSL OA-
2239 GCM used in CMIP3), many aspects of the simulations
2240 have been improved partly due to the increase of horizontal
2241 and vertical model resolutions, to the improvement of the
2242 land surface model and its coupling with the atmosphere,
2243 and to several improvements of the ocean model. A further
2244 increase in horizontal resolution of the atmospheric model
2245 does not result in significant further improvements except
2246 for the location of the extratropical jets. Coupled ocean-
2247 atmosphere simulations performed with an improved
2248 atmospheric GCM (IPSL-CM5B) exhibit improvements in
2249 terms of tropical climatology (e.g. reduced double ITCZ,
2250 improved cloudiness) and tropical variability (e.g. MJO,
2251 ENSO) of the current climate, although the representation
2252 of the mid-latitude atmospheric circulation and the oceanic
2253 circulation needs to be improved.
2254 The IPSL-CM5A-LR ESM has been used to perform
2255 climate projections associated with different sets of socio-
2256 economic scenarios including CMIP5 RCPs and CMIP3
2257 SRES. Consistently with other model results, the magni-
2258 tude of global warming projections strongly depends on the
2259 socio-economic scenario considered. Simulations associ-
2260 ated with different RCPs suggest that an aggressive miti-
2261 gation policy (RCP 2.6) to limit global warming to about
2262 two degrees is possible. However it would require a sub-
2263 stantial and fast reduction of CO
2
emissions with no
2264 emission at the end of the twenty-first century and even
2265 negative emissions after that. The emissions refer here to
2266 fossil-fuel plus cement production emissions and they do
2267 not include land-use emissions. We also found that the
2268 behavior of some climate system components may change
2269 drastically by the end of the twenty-first century in the case
2270 of a no climate policy scenario (RCP 8.5): the Arctic ocean
2271 would become free of sea ice by about 2070, and the
2272 Atlantic Meridional Overturning Circulation would col-
2273 lapse mainly due to an oceanic feedback: the northward
2274 oceanic salinity transport associated with the AMOC
2275 decreases, leading to a decrease in sea surface salinity in
2276 the convection sites and a further decrease of the AMOC.
2277 The magnitude of regional temperature and precipitation
2278 changes is found to depend almost linearly on the magni-
2279 tude of the projected global warming and thus on the
2280 scenario considered. However the geographical patterns of
2281 temperature and precipitation changes were strikingly
2282 similar for the different scenarios. This suggests that a key
2283 and critical step towards a better anticipation and assess-
2284 ment of the regional climate response to different climate
2285 policy scenarios will consist in physically understanding
2286 what controls these robust regional patterns using the wide
2287 range of CMIP5 idealized experiments for each model.
2288 The climate sensitivity and regional climate changes
2289 associated with a given scenario are significantly different
2290
when using different representations of physical processes.
2291
The pattern of precipitation changes over continents and
2292
the transient climate response are significantly different
2293
between the IPSL-CM4 and IPSL-CM5A models. The
2294
equilibrium climate sensitivity of IPSL-CM5A and IPSL-
2295
CM5B are drastically different: 3.9 and 2.4 K, respec-
2296
tively. The reasons for these differences are currently under
2297
investigation and will be reported in a future paper.
2298
The comparison between multi-model CMIP3 and
2299
CMIP5 climate projections needs to account for significant
2300
differences between the forcings of the RCP and SRES
2301
scenarios. Nevertheless we found similarities between cli-
2302
mate projections associated with RCP 4.5 and SRES B1
2303
scenarios. This is consistent with the similar value of the
2304
radiative forcing due to greenhouse gases for these two
2305
scenarios and it is also consistent with the results obtained
2306
with a statistical approach using a model of reduced
2307
complexity (Rogelj et al. 2012). The comparison of SRES
2308
B1 and RCP 4.5 projections might be a useful benchmark
2309
to assess how the spread of model projections has evolved
2310
between CMIP3 and CMIP5.
2311
Acknowledgments The development of the IPSL coupled model 2312
and of its various components has largely benefited from the work of 2313
numerous colleagues, post-doctoral scientists, or Ph.D. students. We 2314
gratefully acknowledgement their contribution to this community 2315
effort, and among them Gillali Abdelaziz, Gae
¨lle Drouot, Alexandre 2316
Durand. The research leading to these results was supported by 2317
CNRS, CEA, the INSU-LEFE French Program under the project 2318
MissTerre, the European Commission’s 7th Framework Programme, 2319
under the projects COMBINE (Grant n°226520) and IS-ENES (grant 2320
n°228203). This work was made possible thanks to the HPC 2321
resources of CCRT and IDRIS made available by GENCI (Grand 2322
Equipement National de Calcul Intensif), CEA (Commissariat a
`
2323
l’Energie Atomique et aux Energies Alternatives) and CNRS (Centre 2324
National de la Recherche Scientifique) (project 016178). The authors 2325
wish to thank Audine Laurian for the careful copy-editing of the 2326
manuscript. The authors are grateful to the two anonymous reviewers 2327
of this paper for their numerous and useful comments on the original 2328
manuscript.
2329
Open Access This article is distributed under the terms of the 2330
Creative Commons Attribution License which permits any use, dis- 2331
tribution, and reproduction in any medium, provided the original 2332
author(s) and the source are credited.
2333
2334
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