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Increased dry-season length over southern Amazonia
in recent decades and its implication for future
climate projection
Rong Fu
a,1
, Lei Yin
a
, Wenhong Li
b
, Paola A. Arias
c
, Robert E. Dickinson
a
, Lei Huang
a
, Sudip Chakraborty
a
,
Katia Fernandes
d
, Brant Liebmann
e
, Rosie Fisher
f
, and Ranga B. Myneni
g
a
Jackson School of Geosciences, University of Texas at Austin, Austin, TX 78712;
b
Earth and Ocean Sciences, Nicholas School of the Environment, Duke
University, Durham, NC 27708-0227;
c
Grupo de Ingeniería y Gestión Ambiental, Universidad de Antioquia, Medellín, Colombia;
d
International Research
Institute for Climate and Society, Lamont–Doherty Earth Observatory, Columbia University, Palisades, NY 10964;
e
Physical Science Division, Earth System
Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO 80305;
f
Earth System Laboratory, Climate and Global Dynamics Division,
National Center for Atmospheric Research, Boulder, CO 80307; and
g
Department of Earth and Environment, Boston University, Boston, MA 02215
Edited by Peter M. Cox, University of Exeter, Exeter, United Kingdom, and accepted by the Editorial Board September 24, 2013 (received for review
February 8, 2013)
We have observed that the dry-season length (DSL) has increased
over southern Amazonia since 1979, primarily owing to a delay of
its ending dates (dry-season end, DSE), and is accompanied by
a prolonged fire season. A poleward shift of the subtropical jet
over South America and an increase of local convective inhibition
energy in austral winter (June–August) seem to cause the delay of
the DSE in austral spring (September–November). These changes
cannot be simply linked to the variability of the tropical Pacific and
Atlantic Oceans. Although they show some resemblance to the
effects of anthropogenic forcings reported in the literature, we
cannot attribute them to this cause because of inadequate repre-
sentation of these processes in the global climate models that
were presented in the Intergovernmental Panel on Climate
Change’s Fifth Assessment Report. These models significantly
underestimate the variability of the DSE and DSL and their con-
trolling processes. Such biases imply that the future change of the
DSE and DSL may be underestimated by the climate projections
provided by the Intergovernmental Panel on Climate Change’s
Fifth Assessment Report models. Although it is not clear whether
the observed increase of the DSL will continue in the future, were
it to continue at half the rate of that observed, the long DSL and
fire season that contributed to the 2005 drought would become
the new norm by the late 21st century. The large uncertainty
shown in this study highlights the need for a focused effort to better
understand and simulate these changes over southern Amazonia.
climate variability
|
rainforests
|
climate model projection
Fifteen percent of global photosynthesis occurs in the Amazon
rainforest (1), where 25% of plant species are found (2). This
rainforest ecosystem normally removes C from the atmosphere
but released more than 1 Pg of C to the atmosphere in the 2005
drought (3). Consequently, even a partial loss of these forests
would substantially increase global atmospheric CO
2
(4, 5) and
reduce biodiversity. The dry-season length (DSL) is among the
most important climate limitations for sustaining rainforests (6–
9), especially in southern Amazonia, where rainforests are ex-
posed to relatively long dry seasons and vulnerable to increasing
conversion of native forests to cultivated crops (10–12). The
extreme droughts in 2005 and 2010 had strong impacts on the
rainforest and its C cycle (3, 13, 14). These unusual events, along
with possible increase of drought severity and DSL during the
past few decades (e.g., refs. 15 and 16) heighten the urgency of
understanding what causes these dry anomalies and whether they
will continue into the future. Contrary to the observed drying,
some global climate models that previously projected strong
drying over Amazonia now project much weaker drying by
the end of the 21st century as these models evolve (17). Do
these observed events represent the extremes of natural climate
variability, or do climate projections underestimate potential
future changes? This study explores one aspect of these ques-
tions by focusing on the change of DSL.
Evidence from Observations
Rain-gauge data from Amazonia are sparse and generally in-
adequate for assessing a trend in rainfall amounts. However, the
dry-season end (DSE) over southern Amazonia is marked by
a relatively rapid increase of rainfall on the order of 4–5mm/d
over areas of thousands of square kilometers during austral spring,
and vice versa for the dry-season arrival (DSA) during austral fall
(March–May) (18, 19). Hence, the timing of the DSA and DSE
should be more clearly detectable by the rain-gauge network than
the change in rainfall amount.
The DSL and DSE are derived from the National Oceanic and
Atmospheric Administration (NOAA) Climate Prediction Center’s
improved 1° gridded historical daily precipitation analysis over
the Brazilian and Bolivian Amazon for the period of January
1978 to December 2007 (referred to as the Silva data; ref. 20)
and the NOAA Climate Diagnostics Center’s 1° gridded daily
precipitation data over the Brazilian and other northern Ama-
zonian countries for the period of January 1940 to December
2011 (referred to as the recently updated SA24 data; ref. 21).
Significance
Whether the dry-season length will increase is a central question
in determining the fate of the rainforests over Amazonia and th e
future global atmospheric CO
2
concentration. We show ob-
servationally that the dry-season length over southern Ama-
zonia has increased significantly since 1979. We do not know
what has caused this change, although it resembles the effects
of anthropogenic climate change. The global climate models
that were presented in the Intergovernmental Panel on Climate
Change’sfifth assessment report seem to substantially un-
derestimate the variability of the dry-season length. Such a bi-
as implies that the future change of the dry-season length, and
hence the risk of rainforest die-back, may be underestimated
by the projections of these models.
Author contributions: R. Fu designed research; R. Fu, L.Y., W.L., and K.F. performed re-
search; B.L., R. Fisher,and R.B.M. contributed new reagents/analytic tools; L.Y., W.L., P.A.A.,
L.H., and S.C. analyzed data; and R. Fu, R.E.D., and R. Fisher wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission. P.M.C. is a guest editor invited by the
Editorial Board.
Freely available online through the PNAS open access option.
1
To whom correspondence should be addressed. E-mail: rongfu@jsg.utexas.edu.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1302584110/-/DCSupplemental.
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These two regional daily rainfall datasets are based on ∼300–450
rain gauges that have been present throughout Amazonia (20,
21) for most of the time since 1979, more than those included
in the global daily rainfall data (22). Both datasets show patterns
of temporal variability, including their trends, similar to that
obtained from the Global Precipitation Climatology Project
(GPCP) monthly rainfall data and the Tropical Rainfall Mea-
suring Mission (TRMM) satellite for the periods they overlap,
although these regional rain gauge-based datasets show lower
rainfall amounts compared with the satellite-based GPCP and
TRMM (Fig. S1). The Silva dataset (20) has fewer rain gauges
over the northeastern part of our southern Amazonian domain
(5°–15°S, 50°–70°W), whereas the SA24 dataset (21) does not
include rain gauges over the Bolivian Amazon, in the south-
western part of this domain. To mitigate such differences in the
areas covered by rain gauges, we average these two rainfall
datasets over each map cell for the period of 1979–2007 when
they overlap and use SA24 for the period of 2008–2011 to form
a merged daily rainfall dataset, referred to as the P
M
data. For
the period of January 1979–December 2011, daily rain rates of
the P
M
data are first spatially averaged over the southern Ama-
zonian domain and then temporally averaged over a 5-d period
(pentad) to reduce synoptic noise in estimating the DSA and
DSE dates. The observed DSE is determined by the first date
when the pentad mean rain rate changes from below to above
the climatological annual mean rain rate of the same rainfall
dataset during six out of eight pentads, and vice versa for the
DSA (19). This definition captures the rapid transition from
a lower to higher rainfall regime associated with the DSE, and
vice versa for the DSA. The DSE and DSA are not influenced by
any bias of rainfall amount, as long as the temporal patterns of
the rainfall variation are not affected. Similar definitions have
been widely used in the literature (18, 19, 23). For analysis of
models, we modify our criterion to five out of eight pentads to
best match the modeled DSE and DSA with observations.
The trends are computed using a least square fit. The confi-
dence intervals and significance are determined based on the
effective sample size and a ttest, following Santer et al. (24). The
trend significance is further tested by the right-tailed (positive)
Vogelsang trend test, a more conservative test for strongly auto-
correlated and nonstationary time series (25).
Fig. 1 shows the temporal variations of the DSL, DSE, and the
mean rainfall during the dry-to-wet transition in austral spring
season derived from the P
M
dataset. The strong delay of the DSE
in 2004 and 2005 is consistent with previous reports on the 2005
Amazonian drought (26, 27). The 2010 drought was mainly
caused by strong rainfall reduction in early and middle 2010,
followed by a rapid increase of rainfall at the end of October (16)
(Fig. S2). Hence, the DSE in 2010 was not delayed. As shown in
Table 1, the DSL has increased at a rate of 1.3 ±0.5 pentad or
about 6.5 ±2.5 d per decade for uncertainties of P<5% (24).
This increase is mainly caused by a delay of the DSE at a rate of
0.9 ±0.4 pentads or 4.5 ±2.0 d per decade (P<5%), as also
evident in a decrease of rainfall by 0.19 ±0.04 mm/d per decade
(P<5%) during austral spring. The more stringent Vogelsang
test (25) still shows that these trends are significantly positive,
but with uncertainty P<10%. This delay of the DSE in recent
decades is consistent with that inferred from a monthly rainfall
dataset (16), the significant trends of decreasing rainfall at two
long-term rain gauge stations located within our southern Ama-
zonia domain (28), and also a decrease of convective cloudiness
during austral spring detected by satellites (29). No significant
changes of the DSA and rain rate are detected (Fig. S3).
The main fire season over southern Amazonia spans the pe-
riod of August–October, during the transition from the dry to the
wet season. A delayed DSE would prolong the fire season,
leading to an increase of fire counts during October and No-
vember. Thus, the latter measured by satellite can provide an
independent verification of the former. Fig. 2 shows that a delay
of the DSE is correlated with fire counts in the prolonged fire
season (the correlation coefficient for the de-trended data is R=
0.83, P<0.01, based on the method of ref. 30). Similarly, the
correlation coefficient for the de-trended DSL and fire counts is
0.88, P<0.01. This relationship is further supported by an in-
crease of the McArthur Forest Fire Danger Index (FFDI, ref.
31), as determined from two independent atmospheric reanalysis
products, the European Center for Medium Range Forecast
Reanalysis (ERA)-Interim (32) and the National Center for
Environment Prediction (NCEP) reanalyses (33). A high FFDI
value represents a favorable meteorological condition for fire.
These consistent changes between three physically related but
independently obtained variables lend additional support to the
observed delay of the DSE.
What could cause this delay of the DSE over southern Ama-
zonia? Previous studies have established that stronger convective
inhibition energy (CIN) and/or a poleward displacement of the
subtropical jet over South America (SJ
SA
) in austral winter are
important contributors to an anomalously late DSE in austral
spring (19, 34, 35). The former increases the work required to lift
air near the surface to the level of free convection, above which
the rising air becomes buoyant. The latter blocks cold-front
incursions from the extratropics that would trigger rainfall over
Southern Amazonia DSE and DSL
1980 1985 1990 1995 2000 2005 2010
55
60
65
70
DSE
(Pentad of year)
30
35
40
45
50
DSL
[Number of pentads]
Southern Amazonia SON rainrate
1980 1985 1990 1995 2000 2005 2010
3.0
3.5
4.0
4.5
5.0
5.5
6.0
[mm/day]
SA24-Silva Average
GPCP
A
B
Fig. 1. (A) Annual time series of the DSL (red line) and DSE (blue line) dates
derived from the P
M
daily rainfall data over the southern Amazonian do-
main show a decrease of DSL due to a delay of DSE. The unit is pentad (5 d).
On the left axis, the 55th pentad corresponds to September 2–7 of the cal-
endar date and the 70th pentad corresponds to December 10–15. (B) Time
series of austral spring seasonal rainfall over southern Amazonia derived
from the P
M
and GPCP datasets show decrease of rainfall consistent with the
delay of DSE shown in (A). The linear trend is determined by a least-square
fitting. Trends are significant at P<5% based on Santer et al. (24).
Table 1. The linear trends, confident interval and significance of
the DSL and DSE for the periods 1979–2011 and 1979–2005
Data DSL DSE
P
M
(1979–2011) 1.3 ±0.5 pen/dec 0.9 ±0.4 pen/dec
8.0 ±2.5 d/dec 4.5 ±2.0 d/dec
P
M
(1979–2005) 2.8 ±0.6 pen/dec 2.3 ±0.4 pen/dec
14.0 ±3.0 d/dec 11.5 ±2.0 d/dec
Uncertainty less than 5% (P<5%) as determined by the two-tailed ttest
with consideration of effective degree of freedom (24). The DSL and DSE are
derived from the P
M
merged daily rainfall data and the unit is pentads per
decade (pen/dec) and days per decade (d/dec).
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a large area and result in DSE (35). The influence of these
preconditions on the DSE can be altered by random variations of
the atmospheric circulation and oceanic circulations in austral
spring. Such influences should be reduced by averaging over
time, leading to more clear relationships between the SJ
SA
, CIN,
and DSE on a decadal scale than on an interannual scale. Fig. 3
shows that the delay of the DSE tends to occur when the SJ
SA
is
displaced poleward and CIN is relatively large. Likewise, an
earlier DSE tends to occur when the SJ
SA
is displaced equator-
ward and CIN is relatively low. Strong CIN combined with
equatorward displacement of SJ
SA
or low CIN with poleward
SJ
SA
do not seem to cause delay of the DSE, presumably because
they compensate each other’s effects on the DSE. On a decadal
scale, CIN increased in the 1990s relative to the 1980s (P<5%,
ref. 36). In the 2000s, CIN increased further from its values in the
1980s and 1990s, and SJ
SA
becomes significantly more poleward.
On an interannual scale, these connections between the SJ
SA
,
CIN, and DSE variations are less obvious, presumably owing to
the influence on the DSE of random interannual variations of
the tropical sea surface temperatures (SSTAs) and the Southern
Annular Mode of the atmosphere in austral spring.
What has caused the increase of CIN and poleward displace-
ment of the SJ
SA
? Most previous studies have linked change of
rainfall over southern Amazonia to the Pacific Decadal Oscil-
lation (PDO), changes of meridional SST gradient in the tropical
Atlantic ocean associated with the Atlantic Multidecadal Oscil-
lation (AMO, 26, 27, 37) and that of SJ
SA
to the El Niño
Southern Oscillation (ENSO) (35, 37). Because the periods of
available rainfall data are too short to obtain significant corre-
lations of these variables on a decadal scale, we evaluate the
relationship between CIN, SJ
SA
, and the SSTAs indices using
their unfiltered de-trended time series for austral winter. Be-
cause the results are dominated by the interannual variations, the
correlation coefficients between AMO and CIN may be weak-
ened by strong interference from ENSO compared with those
that might be obtained on a decadal scale from a longer record.
The result indicates that CIN is marginally correlated with the
PDO index (P=8%, 30), whereas SJ
SA
is not correlated with any
of the ENSO, PDO, or AMO indices (Table S1). The lack of any
robust correlation is consistent with the facts that (i) the SSTAs
associated with ENSO, PDO, and AMO are generally weaker in
austral winter and so are their influences on atmospheric circu-
lation compared with austral summer or fall (38–40) and (ii ) CIN
is influenced by soil moisture anomalies, vegetation root depth,
and lower troposphere temperature. Hence, any relationship
with SSTAs is likely to be complex and nonlinear.
Could the decadal phase change of PDO and AMO qualita-
tively explain the change of CIN and SJ
SA
? The PDO has been
decreasing since the 1990s and became overall negative during
2000s. Such a change would not explain a poleward shift of SJ
SA
(38) and an increase of CIN over southern Amazonia. AMO has
shown a positive trend since 1979. However, the correlation
between AMO and CIN is insignificant. Thus, natural interannual
and decadal oceanic variability cannot be used to explain the
changes of CIN and SJ
SA
during the last few decades.
Could greenhouse effect-forced changes explain the increase
of CIN and poleward shift of SJ
SA
? Previous studies have
established that the observed decreasing atmospheric tempera-
ture lapse rate and increasing surface temperature over the
tropics during the past several decades are consistent with the
“fingerprint”of the greenhouse effect (41). Over Amazonia,
surface relative humidity has been decreasing owing to an in-
crease of surface temperature over the past few decades (42).
These changes could increase CIN, especially during winter (the
dry season), when the surface air humidity cannot increase pro-
portionally with temperature.
The poleward shift of SJ
SA
can be contributed by both a
globally poleward shift of the southern hemisphere subtropical
jets (SJ
SH
) and by the atmospheric planetary wave response to
warm SSTAs over the central Pacific that is distinctively different
from those induced by the ENSO and PDO (39, 43). In austral
winter, the former is attributed to having been forced by the
increase of greenhouse gases (43, 44), whereas the trend of the
latter is attributed to having been forced by the warming of
the central Pacific and Indian oceans in turn forced by green-
house gases (39). The depletion of Antarctic ozone contributes
Dry season ending
FFDI (ON)
0
3
6
9
12
15
Fire counts (ON)
50 55 60 65 70
0
3
6
9
12
15
Fig. 2. The DSE (unit is pentad) versus FFDI (green squares, units are non-
dimensional) and fire count (red circles, unit is number of pixels) in October
and November for the period of 2000–2011 suggest prolonged fire season
with delayed DSE. The 50th pentad corresponds to September 10–15 and the
70th pentad corresponds to December 10–15. The fire counts are derived
from the moderate resolution imaging spectroradiometer fire-count data.
FFDI is first derived from the ERA-Interim and NCEP reanalysis, respectively,
and then averaged to obtain its values shown in the figure.
0.04 0.05 0.06 0.07
−35
−30
−25
−20
−15
CIN
(
kJ k
g
−1
)
SJSA (°)
ERAI+NCEP pentad
56
57
58
59
60
61
62
63
64
65
79−89
90−00
01−11
Fig. 3. DSE date as a function of the latitudinal location of the SJ
SA
and CIN
in austral winter over southern Amazonia for the period of 1979–2011
suggests a preference of poleward SJ
SA
and strong CIN by the delayed DSE.
CIN and SJ
SA
are first calculated using the inputs from ERA-Interim (ERAI)
and NCEP reanalysis, respectively, then averaged to obtain the values shown
in this figure. The triangle, square, and diamond symbols denote decadal
means for the 1980s, 1990s, and 2000s. The error bars on these symbols
represent the SEs with uncertainties of P<5% (36).
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importantly to the poleward shift of SJ
SH
in austral summer and
fall, but not in the winter season. It peaks in the middle strato-
sphere during austral spring (45) and its influence on atmospheric
circulation propagates downward to the upper troposphere to in-
fluence the SJ
SH
during austral summer. However, after the ozone
hole recovers in austral fall, its effect becomes negligible for the
austral winter (45). Hence, the observed changes of CIN and SJ
SA
seem to be broadly consistent with those expected from forcing by
greenhouse warming, and not a consequence of the ozone hole.
Land use can reduce land surface latent fluxes, and biomass
burning aerosols can stabilize the atmospheric temperature
stratification and weaken the dry-to-wet-season transition (46–
49). Both could contribute to the delay of the DSE. However,
long-term rain gauges and satellite observations show more clear
decrease of rainfall and high clouds over the southwestern and
northeastern parts of southern Amazonia where land use and fire
are less prevalent than they are over the “Fire Arch”in south-
eastern Amazonia (28, 29). Furthermore, the DSE did not
change before or after the 1991 Mount Pinatubo eruption (Fig.
1). Thus, what influence biomass burning aerosols and land use
have on the observed delay of the DSE remains unclear beyond
the observation that they do not seem to be the dominant cause.
Comparison with Climate Models
Attributing the changes of DSE, CIN, and SJ
SA
to anthropogenic
climate change and projecting their future changes require
creditable climate models. Hence, we evaluate the 50 simulations
provided by eight global climate models presented in the In-
tergovernmental Panel on Climate Change’s Fifth Assessment
Report (IPCC AR5) based on the availability of their daily
outputs of rainfall and other needed climate variables. These
models are identified along with relevant information in Datasets
and Methods and Table S2. The changes from the historical
simulations of the global climate models that were presented in
the IPCC AR5 are expected to be a result of random natural
climate variability and appropriate anthropogenic and external
forcing. If these models were perfect and the numbers of simu-
lations were sufficient to generate a full spectrum of climate
variability, one or more of these simulations would have captured
the observed changes. Thus, the discrepancies between the models
and observations should be caused either by undersampling of the
possible changes owing to insufficient numbers of simulations or
model errors, or both. Because the IPCC AR5 historical simu-
lations end in 2005, we compare the observed changes for the
period of 1979–2005 to the modeled 27-y changes in Fig. 4.
The trend distribution of the simulations of natural vari-
ability is generated by 158 samples that represent nonoverlapped
27-y changes from a total of 4,266 y of simulation by multiple
climate models. These simulations represent climate variability
of the DSE changes for up to a few thousand years return period,
and thus should adequately represent the range of the DSE
natural variability for the time scale relevant to this study. The
historical simulations by the eight climate models provide 40
samples of nonoverlapped 27-y changes for the period from the
mid-19th century to 2005 and the climate projections under the
Representative Concentration Pathway 8.5 scenario (RCP8.5)
(50) provide 38 samples of the nonoverlapped 27-y changes for
the period from 2006 up to 2299. Their ranges of the probability
distributions are comparable to those represented by 158 sam-
ples of the natural variability simulations, despite their smaller
number of samples. Thus, the differences between the statistical
distribution of modeled changes and those observed should be
mainly due to the models’uncertainty, instead of due to under
sampling of the climate variability.
Fig. 4 shows that all of the changes of the DSE during 27-y
periods modeled by the natural climate variability, the historical
simulations, and the RCP8.5 future scenario are significantly
smaller than those observed, although the occurrences of delayed
DSE trends increase with anthropogenic climate change. For ex-
ample, the historical simulations, with realistic anthropogenic
forcing, show an increased frequency of delayed trends of the
DSE compared with those of the natural variability scenario, and
more so for the RCP8.5 future scenario. However, these simu-
lations do not produce any trends that are comparable to the
large observed trend. The RCP8.5 scenario assumes that by 2100
the global anthropogenic radiative forcing would reach 8.5 Wm
−2
and that the atmospheric CO
2
concentration would become
∼1,360 ppm. However, the projected DSE changes are still sig-
nificantly smaller than the observed DSE delay during the past
27-y period (with the uncertainty of P<5%, Fig. 4). Because the
change of the DSE dominates that of the DSL, the simulated and
projected DSL changes are also significantly smaller than those
observed (Fig. S4).
Why is there such a large discrepancy between modeled and
observed changes of the DSE? Either the observed change of
DSE during 1979–2005 represents an extremely large swing from
natural variability with a return period greater than 4,000 y or the
climate models underestimate the natural and forced climatic
variability of the DSE. To explore the possibility of the latter, we
show in Fig. 5 that no model could reproduce the observed re-
lationship between the changes of DSE, SJ
SA
, and CIN in any of
their historical simulations. The majority of the simulations show
much weaker changes of SJ
SA
and CIN. These biases are con-
sistent with the underestimation of SJ
SH
variability reported in
the literature (44) and the overestimation of the influences of the
Pacific and Atlantic Intertropical Convergence Zones on Ama-
zonia dry-season rainfall (51). The latter would undermine land-
surface feedback and reduce the sensitivity of the CIN to land-
surface warming and drying. Thus, the comparisons between
historical simulations and observations suggest that the climate
models evaluated in this study probably underestimate the sen-
sitivity of the DSE, SJ
SA
, and CIN to climate variability and
anthropogenic change and that they could in turn underestimate
potential future changes of the DSE and DSL over southern
Amazonia (Fig. 4 and Figs. S4 and S5).
Implications
This study suggests that the IPCC AR5 models may underes-
timate the variability of the DSE (Fig. 4) and DSL (Fig. S4) over
Trend of DSE (pen/dec)
Frequency (%)
−2 −1 0 1 2 3 4
0
5
10
15
20
25
30
35
NV
Historical
RCP8.5
Fig. 4. Distributions of the nonoverlapped 27-y trends of the DSE gener-
ated by the natural variability simulations (blue), historical simulations (red),
and projections of future changes under the RCP8.5 scenario (green), re-
spectively, suggest that the modeled DSE changes, including the projected
future change, are significantly weaker than that which are observed during
1979–2005. The top 5% of the modeled trend samples are marked by blue,
red, and green vertical dashed lines for the natural variability, historical
simulations, and RCP8.5 scenario, respectively. The observed 27-y trend and
confidence interval with uncertainties of P<5% are marked by the black
circle and horizontal bar in the upper right corner and are derived from the
P
M
daily rainfall data following method of Santer et al. (24).
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southern Amazonia and their sensitivity to the natural variability
and anthropogenic forcing of the climate system. These biases
could lead to an underestimate of the potential future climatic
drying over southern Amazonia. However, one cannot simply
extrapolate the observed changes to the future. Hence, we do not
know how the DSL and DSE will change in the future without
knowing what has caused their past changes. However, a risk of
a future larger increase of DSL and delay of DSE does seem to
be nonzero, as implied by its connection to the apparent in-
fluence of greenhouse-forced climate change on the CIN and
SJ
SA
. Although its risk is highly uncertain, such a future increase
of the DSL would have strong impacts on southern Amazonia
were it to occur. For example, if we assume that the DSL were
to increase at half of the rates we observed during 1979–2011,
the DSL would be about 1 ±1/3 mo longer by 2090 than that in
the 2000s. Consequently, the long DSL and fire season during
the 2004–2005 drought would become the new norm (Fig. 2).
Given the observed slow recovery of the rainforests after the
2005 drought (14), these changes could greatly increase the
danger of a transition from a rainforest to a savanna regime
over southern Amazonia (10, 12, 52), especially with the longer
DSL coupled to the higher surface temperatures and more
fragmented forests expected in the future (11, 12, 53), and even
accounting for the increase of dry season resilience of the
rainforest in an elevated CO
2
environment (17). Given such
potential impacts on the global and regional C cycle and bio-
diversity, the large uncertainty in determining future changes of
the DSL and DSE shown by this study highlights the importance
and urgency of better monitoring and understanding the changes
of the dry season over southern Amazonia. In addition to the
impact of global climate forcing, the roles that regional biomass
burning and land use play in the DSL over southern Amazonia
also need to be clarified.
Datasets and Methods
The CIN, SJ
SA
, and FFDI are all derived from the ERA-Interim
and the NCEP reanalyses. CIN in Fig. 3 is computed from 6-h
temperature, humidity, and geopotential height profiles. In Fig. 5,
a CIN index (54) is used because the models do not provide the
instantaneous temperature and humidity profiles needed for
computing CIN. The latitude of the SJ
SA
is determined by the
equatorward latitudinal location of the 28 m·s
−1
monthly zonal
wind contour at 200 hPa between 30°and 90°W. This index most
consistently captures the latitudinal variation of the SJ
SA
asso-
ciated with tropical meridional circulation changes in the two
reanalysis products, although the use of other similar zonal wind
indices does not change the variations of the SJ
SA
. The variation
of SJ
SA
based on this index is consistent with those determined
by the zero stream function in latitudinal-height space and the
250 W·m
−2
Outgoing Longwave Radiation Contour (55) used in
the literature to describe changes of global subtropical jets. The
FFDI (31) is computed as
FFDI =1:275D0:987eðT
29:5858−H
28:9855+W
42:735Þ
D=0:191ðI+104ÞðN+1Þ1:5
3:52ðN+1Þ1:5+R−1;
where Tis the daily maximum temperature (°C), His the daily
minimum relative humidity (%), Wis the daily mean wind speed
at 10 m (km/h), Nis the number of days since the last rain, Ris
the total rainfall (mm) in the most recent 24 h with rain, and I
is the total rainfall (mm) needed to restore the soil moisture con-
tent to 200 mm. These inputs are provided by the 6-h outputs from
the ERA-Interim and NCEP reanalysis. Fire count is obtained
from the moderate resolution imaging spectroradiometer on board
the National Aeronautics and Space Administration (NASA) aqua
satellite (56) (ftp://fire:burnt@fuoco.geog.umd.edu/).
The AMO index is obtained from www.esrl.noaa.gov/psd/data/
timeseries/AMO/. The PDO index is obtained from http://jisao.
washington.edu/pdo/PDO.latest. The Niño3 and Niño4 indices
are obtained from www.esrl.noaa.gov/psd/data/climateindices/list/.
Eight of the climate models that were part of the IPCC AR5
are used in this study based on availability of the daily outputs of
precipitation and other needed climate variables. These models
are the National Center for Atmospheric Research Community
Climate System Model Version 4 (CCSM4), the NOAA Geo-
physical Fluid Dynamics Laboratory Climate Model Version 3
(GFDL-CM3), the Earth System Model (GFDL-ESM2M), the
NASA Goddard Institute for Space Studies (GISS)-E2-H and
GISS-E2-R models, the United Kingdom Met Office Hadley
Center (HadGEM2-CC and HadGEM2-ES) models, and the
Max Planck Institute for Meteorology (MPI-ESM-LR) model.
All of the model output and observational datasets are remap-
ped to the 2.5° latitude and longitude grids when they are
compared with each other in Figs. 4 and 5. A brief summary of
model resolutions, available ensemble simulations and sources of
the models are provided in Table S2.
ACKNOWLEDGMENTS. We thank Inez Fung for discussion that initiated and
inspired this work, James Hurrell, and the two anonymous reviewers and the
editor for insightful comments. This work is supported by National Science
Foundation Grant AGS 0937400 and National Oceanic and Atmospheric
Administration Climate Program Office Modeling, Analysis, Prediction, and
Projection Program Grant NA10OAAR4310157. We acknowledge the World
Climate Research Programme’s Working Group on Coupled Modeling for
organizing the Coupled Modeling Intercomparison Project. We thank the
climate modeling groups for producing and making their model outputs
available. The US Department of Energy’s Program for Climate Modeling
Diagnosis and Intercomparison provides coordinating support and led de-
velopment of software infrastructure in partnership with the Global Orga-
nization for Earth System Science Portals.
A
B
C
D
E
G
H
I
Trend of CIN Index (K/dec)
Trend of SJSA (deg/dec)
0 0.2 0.4 0.6 0.8
−2
−1
0
1
2
pen/dec
−2
−1
0
1
2
Fig. 5. Distribution of the DSE trends (color shades) as a function of the
trends of the SJ
SA
(yaxis) and CIN (x axis) in austral winter for the period of
1979–2005 derived from the historical simulations of the eight IPCC AR5
models (circles) is different from that which is observed (red square). The
character in the center of each circle indicates the model’s name shown in
Table S2. The ensemble mean of the eight models is indicated by the di-
amond symbol.
18114
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