Content uploaded by Harilaos Loukos
Author content
All content in this area was uploaded by Harilaos Loukos on Feb 02, 2018
Content may be subject to copyright.
T E C H N I C A L REPORT
BI A S A D J U S T I N G C L I M A T E
M OD E L P R O J E C T I O N S
| FEBRUARY 2018
Technical Report
Bias Adjusting Climate Model Projections
Florian Cochard - Harilaos Loukos - Thomas Noël
Version 1.0
February 2018
Technical Report: Bias Adjusting Climate Model Projections | 1
Cover photo: Mountains and Clouds by rachel_thecat / CC BY-SA 2.0
Please cite this report as:
the climate data factory ”2018), Technical report: bias adjusting climate model
projections.
Technical Report: Bias Adjusting Climate Model Projections | 2
Table of contents
Table of contents 2
Intent of this Document 4
Data Field Descriptions 5
Data Origin and Methods 7
Introduction 7
Methods 8
Datasets 8
CMIP5 8
CORDEX 8
Preprocessing 9
Remapping 9
Bias Adjustment 10
Delta methods 10
Quantile matching methods 10
Standardization 11
Quality Control 12
QA-DKRZ 12
In-House 12
Spatial Extraction 13
Country-level 13
City-level 14
Considerations and Recommended Use 15
Recommended Use 15
Assumptions and Limitations 15
References 16
Dataset and Document Revision History 20
Annex 21
Technical Report: Bias Adjusting Climate Model Projections | 3
Technical Report: Bias Adjusting Climate Model Projections | 4
1. Intent of this Document
This document is an overview of the different data sets provided by the climate data
factory on its site theclimatedatafactory.com. It is intended for users who wish to
apply these data in climate change impact studies, from local to global scale. This
document describes essential information about data origin, processing methods,
metadata information and assumption and limitations. References and
supplementary information are provided at the end of this document.
The data sets on the climate data factory are meant to support users involved in
climate change adaptation topics, such as impact researchers, adaptation
practitioners, urban planners or energy professionals, conducting local, regional or
global studies. Each data set is remapped on a reference grid, bias-adjusted for direct
use in impacts studies and quality controlled to comply with climate communitys
standards, data consistency and metadata. Dont hesitate to contact us to share
insights and comments at: support@theclimatedatafactory.com.
Website URL:
https://theclimatedatafactory.com
Technical point of contact:
Thomas Noel thomas[at]theclimatedatafactory.com
Technical Report: Bias Adjusting Climate Model Projections | 5
2. Data Field Descriptions
Variable name, units
tas
Daily Near-Surface Air Temperature
Degrees Kelvin
Spatial resolution
0.50 degrees x 0.50 degrees ”CMIP5)
0.11 degrees x 0.11 degrees ”EURO-CORDEX)
Temporal resolution
Daily from 1951-01-01 to 2100-12-31
Units are in days since a reference date ”e.g., 1850-01-01). The
reference date varies by model and experiment, and is based on
the reference date used in the corresponding CMIP5 GCM or
CORDEX RCM experiment.
Coverage
Country to city level
Variable name, units
tasmin
Daily Minimum Near-Surface Air Temperature
Degrees Kelvin
Spatial resolution
0.50 degrees x 0.50 degrees ”CMIP5)
0.11 degrees x 0.11 degrees ”EURO-CORDEX)
Temporal resolution
Daily from 1951-01-01 to 2100-12-31
Units are in days since a reference date ”e.g., 1850-01-01). The
reference date varies by model and experiment, and is based on
the reference date used in the corresponding CMIP5 GCM or
EURO-CORDEX RCM experiment.
Coverage
Country to city level
Variable name, units
tasmax
Daily Maximum Near-Surface Air Temperature
Degrees Kelvin
Spatial resolution
0.50 degrees x 0.50 degrees ”CMIP5)
0.11 degrees x 0.11 degrees ”EURO-CORDEX)
Temporal resolution
Daily from 1951-01-01 to 2100-12-31
Units are in days since a reference date ”e.g., 1850-01-01). The
reference date varies by model and experiment, and is based on
the reference date used in the corresponding CMIP5 GCM or
CORDEX RCM experiment.
Coverage
Country to city level
Technical Report: Bias Adjusting Climate Model Projections | 6
Variable name, units
pr
Precipitation ”mean of the daily precipitation rate)
kg m-2 s-1
Spatial resolution
0.50 degrees x 0.50 degrees ”CMIP5)
0.11 degrees x 0.11 degrees ”EURO-CORDEX)
Temporal resolution
Daily from 1951-01-01 to 2100-12-31
Units are in days since a reference date ”e.g., 1850-01-01). The
reference date varies by model and experiment, and is based on
the reference date used in the corresponding CMIP5 GCM or
CORDEX RCM experiment.
Coverage
Country to city level
Variable name, units
rsds
Daily Surface Downwelling Shortwave Radiation
W m-2
Spatial resolution
0.50 degrees x 0.50 degrees ”CMIP5)
0.44degrees x 0.44 degrees ”EURO-CORDEX)
Temporal resolution
Daily from 1951-01-01 to 2100-12-31
Units are in days since a reference date ”e.g., 1850-01-01). The
reference date varies by model and experiment, and is based on
the reference date used in the corresponding CMIP5 GCM or
CORDEX RCM experiment.
Coverage
Country to city level
Variable name, units
sfcWind
Daily Near-Surface Wind Speed
m s-1
Spatial resolution
0.50 degrees x 0.50 degrees ”CMIP5)
0.11 degrees x 0.11 degrees ”EURO-CORDEX)
Temporal resolution
Daily from 1951-01-01 to 2100-12-31
Units are in days since a reference date ”e.g., 1850-01-01). The
reference date varies by model and experiment, and is based on
the reference date used in the corresponding CMIP5 GCM or
CORDEX RCM experiment.
Coverage
Country to city level
Technical Report: Bias Adjusting Climate Model Projections | 7
3. Data Origin and Methods
3.1. Introduction
The data sets on the climate data factory include remapped, bias-adjusted and quality
controlled climate scenarios for 66 countries and more than 4,300 cities worldwide.
They are derived from the General Circulation Model ”GCM) and Regional Climate
Model ”RCM) runs conducted under 2 model intercomparison projects: the Coupled
Model Intercomparison Project Phase 5 ”CMIP5) ”Taylor et al. 2012) and the Coordinated
Regional Downscaling Intercomparison project ”CORDEX) ”Giorgi et al. 2009), and forced
under 2 greenhouse gas emissions scenarios known as Representative Concentration
Pathways ”RCPs) ”Moss et al. 2010). The CMIP5 climate projections were used for the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change ”IPCC AR5).
Raw CMIP5 and CORDEX archives are extracted from the Earth System Grid
Federation ”ESGF) data portal with Synda, a software developed by the Institut Pierre
Simon Laplace.
A climate model is an approximate representation of the real world climate drivers.
This simplification is due to incomplete understanding of climate physics and is
required for computational purpose. This inevitably introduces random models errors
in models simulations when their statistical properties ”e.g., mean, variance) are
compared to climatological observations, thus limiting the use of raw models data in
impact studies.
The Cumulative Distribution Function transform ”CDF-t) method ”Michelangeli et al.,
2009, Vrac et al., 2016, Famien et al., 2017) used in generating data sets on the climate
data factory is a bias-adjustment method we co-developed with academics to address
climate models limitations. CDF-t is a variant of the quantile mapping ”QM) method
which consists in comparing the cumulative distribution function ”CDF) of a climate
variable ”e.g., temperature) at large scale ”e.g., from GCM) to the CDF of the same
variable at a local scale. CDF-t method has been extensively used in the literature and
validated for many variables ”e.g., Kallache et al., 2011; Vrac et al., 2012; Lavaysse et
al., 2012; Vautard et al., 2013; Vrac and Friederichs, 2015).
We have applied the CDF-t method to create a data archive of bias-adjusted CMIP5
and EURO-CORDEX climate projections. The purpose of these data sets lies in bridging
the gap between raw models data and climate change impact studies. CFD-t method
was applied on 20 CMIP5 GCMs ”Table 1 in Annex) and 18 EURO-CORDEX RCMs ”Table
2 in Annex) for the periods from 2006 to 2100 ”Climate Projections) under 2 RCP
scenarios ”RCP4.5 and RCP8.5) and from 1951 through 2005 ”Historical) for the
historical experiment. The observation-based reference dataset for CMIP5 is the
WATCH Forcing Data methodology applied to ERA Interim reanalysis data ”WFDEI;
Technical Report: Bias Adjusting Climate Model Projections | 8
Weedon et al., 2014) from 1979 to 2012 on a 0.5° x 0.5° grid. The observation-based
reference dataset for EURO-CORDEX is the Mesoscale Analysis System ”MESAN;
Landelius et al. 2016) from 1989 to 2012 on a 0.11° x 0.11° grid. The final result is a
data archive size of more than 5 TB.
This document provides a description of the data origin and the CDF-t method as
applied in the bias-adjustment of the CMIP5 GCMs and EURO-CORDEX RCMs data. The
code for CDF-t method is freely available as an R package ”link:
https://www.rdocumentation.org/packages/CDF-t/versions/1.0.1/topics/CDF-t).
Additional technical details may also be found in Michelangeli et al., 2009 and Vrac et
al. 2016.
3.2. Methods
3.2.1. Datasets
CMIP5
Climate Model Data: we compiled 39 climate simulations from 22 CMIP5 GCMs and 2
RCP scenarios ”RCP4.5 and RCP8.5; see Table 1 in Annex) . Each of the climate
1
simulations include daily near-surface temperature, daily maximum near-surface
temperature, daily minimum near-surface temperature, daily precipitation, daily
near-surface wind speed and daily surface downwelling shortwave radiation for the
periods from 1951 through 2005 ”Historical) and from 2006 to 2100 ”Climate
Projections). Unless specified, all 39 climate simulations are bias-adjusted through the
same procedures.
Observational Climate Data: we used the WATCH Forcing Data methodology applied
to ERA Interim reanalysis data ”WFDEI; Weedon et al., 2014) as the observation-based
reference dataset. This dataset is based on the European Centre for Medium-range
Weather Forecasts ”ECMWF) ERA-Interim reanalysis. It includes eight meteorological
variables at 3-hourly time steps, and as daily averages from 1979 to 2012, for the
global land surface at 0.5° x 0.5° resolution, including Antarctica ”Weedon et al., 2014).
We used the 0.5° x 0.5° resolution and historical data from 1979 to 2012 for daily
near-surface temperature, daily maximum near-surface temperature, daily minimum
near-surface temperature, daily precipitation, daily near-surface wind speed and daily
surface downwelling shortwave radiation.
CORDEX
Climate Model Data: we compiled 18 climate simulations from 4 CORDEX RCMs forced
by 5 GCMs and 2 RCP scenarios ”RCP4.5 and RCP8.5; see Table 2 in Annex). Each of the
climate simulations include daily temperature, maximum temperature, minimum
1 17 GCMs for RCP4.5 projection and 22 GCMs for RCP8.5 projection.
Technical Report: Bias Adjusting Climate Model Projections | 9
temperature, precipitation, wind speed and solar radiation for the periods from 1951
through 2005 ”Historical) and from 2006 to 2100 ”Climate Projections). Unless
specified, all 18 climate simulations are bias-adjusted and quality controlled with the
same procedures.
Observational Climate Data: we used the Mesoscale Analysis System ”MESAN;
Häggmark et al., 2000) as the observation-based reference dataset. MESAN is a system
for operational mesoscale univariate analysis of selected meteorological parameters
”see Landelius et al. 2016 for more information). We used the 0.11° x 0.11° resolution
and historical data from 1989 to 2012 for daily near-surface temperature, daily
maximum near-surface temperature, daily minimum near-surface temperature, daily
precipitation and daily near-surface wind speed. Daily surface downwelling shortwave
radiation was missing from MESAN, so we used a 0.44° x 0.44° resolution grid based
on WFDEI data.
3.2.2. Preprocessing
Raw CMIP5 and CORDEX archives are extracted from the Earth System Grid
Federation ”ESGF) data portal with Synda, a software developed by the Institut Pierre
Simon Laplace. Models data are first checked to make sure there are neither technical
nor numerical bugs, and to validate metadata integrity.
3.2.3. Remapping
Models intercomparison, bias adjustment or comparison of output model simulations
with observations require spatial interpolation of models data on a reference grid. The
remapping step consists in spatially interpolating raw daily models data to a finer
reference-grid resolution. We use the Climate Data Operators ”CDO) ”CDO, 2016)
software from the Max Planck Institute which gathers various algorithms for
interpolation used by the scientific community.
Only CMIP5 GCMs are remapped on the 0.5° x 0.5° grid of WFDEI. Indeed, all raw
CORDEX variables are already available on the 0.11° x 0.11° grid from MESAN, except
rsds variable which is missing from MESAN observations. Hence, the rsds variable from
CORDEX 0.11° x 0.11° is remapped on the CORDEX grid at 0.44° x 0.44° resolution ”see
Figure 1).
From one variable to another, different interpolation methods are used, depending on
the trend ”linear, non-linear) or distribution ”gaussian, non gaussian, etc.) of the CMIP5
variable:
●tas, tasmin, tasman and sfcWind are interpolated with a bicubic method
●pr and rsds are interpolated with a conservative method ”first and second
order)
Technical Report: Bias Adjusting Climate Model Projections | 10
3.2.4. Bias Adjustment
Models have skills in simulating future climate but show systematic biases when
statistically compared to climatological observations. Bias-adjustment methods are
used to calibrate model simulations to ensure their statistical properties are similar
to those of the corresponding observed values ”climate4impacts.com glossary). There
are in the litterature two types of approaches to adjust climate model outputs:
●Delta methods are the simplest and consist in adjusting the average simulation
outputs
●Quantile matching methods are most advanced and commonly found in the
litterature. Rather than focusing solely on the average outputs, they aim at
correcting the complete statistical distribution of model variables.
Delta methods
Delta methods ”Hay et al., 2000; Lenderink et al., 2007; Gudmundsson et al., 2012)
consist in disturbing time-series of simulated variables through constant addition or
multiplication of an adjustment coefficient. These methods can whether adjust the
mean and/or the standard deviation of a simulated variable. However, this basic
approach can only be used with time-series of observations, and arent appropriate to
adjust the simulated climate variability.
Quantile matching methods
Quantile mapping ”QM) ”Wood et al., 2004; Maurer et al., 2007; Déqué et al., 2007;
Christensen et al., 2008; Lopez et al., 2009) are more advanced adjustment methods
and consist in comparing the cumulative distribution function ”CDF) of a variable ”e.g.,
temperature) at a global scale ”e.g., from GCM) to the CDF of the variable at a
local-scale ”e.g., station). Different variations of the QM approach are referenced in
the literature of which Quantile Delta Mapping ”Cannon et al., 2015), Scaled
Distribution Mapping ”Switanek et al., 2016) and Linear Regression Quantile Mapping
”Passow et al., 2017). For a review of methods see Galmarini et al 2018).
We use a variant of QM method called Cumulative Distribution Function transform
”CDF-t). While standard QM approach only projects the CDF of a simulated large-scale
variable ”i.e., from GCM) onto the CDF of the historical to compute and match
quantiles, CDF-t accounts for the evolution of the large-scale CDF from historical to
future time period ”Michelangeli et al., 2009, Vrac et al., 2012). As a result, the adjusted
climate projections have the same CDF as the observations data, and potential biases
in the statistical structure of the raw climate model simulations are removed.
CDF-t was first used to adjust wind variable ”Michelangeli et al., 2009) and is now
referenced in more than 100 peer-reviewed publications to adjust different sets of
variables such as temperature, precipitation or solar radiation ”e.g., Oettli et al., 2011 ;
Technical Report: Bias Adjusting Climate Model Projections | 11
Vrac et al., 2012 ; Lavaysse et al., 2012 ; Colette et al., 2012 ; Tisseuil et al., 2012 ; Vrac
et al., 2016).
We applied CDF-t to 5 variables ”i.e., tas, tasmin, tasmax, rsds, sfcWind) over the
period 1951-2100 ”historical, RCP4.5, RCP8.5). The precipitation variable ”i.e., pr) was
adjusted with an updated version of CDF-t referred to as Singularity Stochastic
Removal ”SSR) which considers rainfall occurrence and intensity issues ”see Vrac et
al., 2016 for more details).
Figure 1. Processing chain from raw to adjusted climate models data
3.2.5. Standardization
Standardization consists in rewriting output data files and related metadata to comply
with the climate communitys standards ”e.g., the Climate and Forecast metadata
convention and the Data Reference Syntax). We use the Climate Model Output
Rewriter 2 ”CMOR 2) library.
Technical Report: Bias Adjusting Climate Model Projections | 12
3.2.6. Quality Control
For each bias-adjusted variable, we check data compliance with climate community“s
standards, data consistency and metadata. Doing quality control is crucial in the data
publication process and data re-use. We use the QA-DKRZ methodology combined
with an additional in-house quality control that checks values of adjusted and
standardized variables data.
QA-DKRZ
During the Quality Assurance process of the DKRZ, the following criteria are checked:
1. Number of data sets is correct and > 0
2. Size of every data set is > 0
3. The data sets and corresponding metadata are accessible
4. The data sizes are controlled and correct
5. The spatial-temporal coverage description ”metadata) is consistent to the data,
time steps are correct and the time coordinate is continuous
6. The format is correct
7. Variable description and data are consistent
In-House
In addition, an In-house quality control is built upon CDO and NCO tools and consists
twofold in:
●Analyzing the difference between adjusted model and observation values on
the reference period
●Analyzing the time evolution difference between adjusted and non-adjusted
model.
Difference between adjusted model and observations
First, we estimate two quantities:
●average for the months of the year on the reference period for observations
●average for the months of the year on the reference period for adjusted model
Then, we estimate the difference between these two quantities and get 12 files in
output ”one per month). For each month ”i.e., for each file), we take the 10th and 90th
quantile which gives 12 values for each quantile.
Finally, we control that these 12 values are comprised in the following ranges:
●temperature between [ -1 ; 1 ] in K
●precipitation between [-0.5 ; 0.5] in mm.day-1
●solar radiation between [-5 ; 5] in W.m-2
●surface wind between [-0.5 ; 0.5] in m.s-1
Technical Report: Bias Adjusting Climate Model Projections | 13
If values are outside the range, the script raises an error with a 1 status.
Difference between adjusted model and non-adjusted model
First, we estimate four quantities:
●Average for the seasons on the reference period for the non-adjusted model
●Average for the seasons on the reference period for the adjusted model
●Average for the seasons on the future period ”2071-2100) for the non-adjusted
model
●Average for the seasons on the future period ”2071-2100) for the adjusted
model
Then, we compute the evolution between future and reference periods for the
non-adjusted and adjusted model. We estimate the difference between them and get
4 files in output ”one per season). For each season ”i.e., for each file), we take the 10th
and 90th quantile of the difference which gives 4 values for each quantile.
Finally, we control these 4 values are comprised in the following range:
●temperature between [-2 ; 2] in K
●precipitation between [-1 ; 1]in mm.day-1
●solar radiation between [-10 ; 10] in W.m-2
●surface wind between [-1 ; 1] in m.s-1
If values are outside the range, the quality control raises an error with a 1 status.
3.2.7. Spatial Extraction
Raw ESGF files are available as global ”CMIP5) or continental ”CORDEX) domains ”Asia,
Europe, etc.), so we extract country-level and city-level information to help users
focusing on their area of interest.
Country-level
Country-level extraction method consists in identifying the border grid points for a
country and drawing a rectangle around them. The drawback is that neighboring
country points can be included in this rectangle. In the next version, well create a
mask per country to only consider country points.
Technical Report: Bias Adjusting Climate Model Projections | 14
City-level
Model grid points are spaced approximately every 100 km to 50 km for CMIP5 models
and 15 to 10 km for CORDEX models. To extract city-level information, we consider the
nearest grid point for a city ”we only consider continental points). City level data
correspond to a single grid point. They give a trend but do not account for local
phenomena like the urban heat island effect that modulates small scale changes and
requires higher resolution ”typically 100m) and specific modeling to be resolved.
Technical Report: Bias Adjusting Climate Model Projections | 15
4. Considerations and Recommended Use
4.1. Recommended Use
The data sets on the climate data factory are meant to support users involved in
climate change adaptation topics, of which impact researchers, adaptation
practitioners, urban planners or energy sector. Data are intended for use in scientific
research and impact studies from local to global scales. Extensive metadata
information are provided in netCDF files.
4.2. Assumptions and Limitations
Bias-adjustment consists in calibrating model simulations to ensure their statistical
properties are similar to those of the corresponding observed values ”as from
climat4impact.eu). Some authors claim that bias-adjustment techniques introduce
another level of uncertainty making evaluation of projections uncertainty even harder
”e.g., Ehret et al. 2012, Maraun et al., 2016). There are still differences of opinion
regarding whether direct or bias-adjusted climate model simulations should be used
in impact modeling and assessment. On the one hand, the use of direct climate model
simulations ensures spatial and temporal consistency across variables, on the other
hand the substantial biases of raw variables renders direct climate model simulations
unrealistic and ultimately unsuitable for climate change impact modeling. While the
climate modeling community continues to improve climate models, statistical bias
adjustment is currently necessary to make climate projections fit for purpose in
impact modeling and assessment ”Ficklin et al 2016, climate4impact.eu).
As described in Section 3.2.4, The CDF-t bias-adjustment method preserves long-term
trend in climate models data. CDF-t performances are not sensitive to the climate
model performance but to the variability and trend of its driving large-scale fields
”reanalysis or GCMs/RCMs control runs) which can perform better or worse depending
on the variable but also on the season ”Vrac et al., 2012). However, to represent a
correct CDF under historical and/or present climate conditions does not guarantee to
correctly represent the evolution of the CDF in a climate change context. In addition,
CDF-t method is a univariate adjustment method which is applied location by location,
and is not designed to reproduce multi-dimensional properties ”e.g., variable
covariance and spatial correlations). Development of a multivariate and spatial
version of the CDF-t method is underway ”Vrac 2018). The aforementioned limitations
are not specific to the CDF-t method but common to any univariate Quantile-Quantile
method.
Technical Report: Bias Adjusting Climate Model Projections | 16
5. References
Climate Data Operators, Max Planck Institute, 2016.
https://code.zmaw.de/projects/cdo
Christensen, J., F. Boberg, O. Christensen, and P. Lucas-Picher ”2008), On the need for
bias correction of regional climate change projections of temperature and
precipitation, Geophys. Res. Lett., 35, L20709, doi:10.1029/2008GL035694.
Cannon, Alex J., Stephen R. Sobie, and Trevor Q. Murdock. "Bias correction of GCM
precipitation by quantile mapping: How well do methods preserve changes in
quantiles and extremes?." Journal of Climate 28.17 ”2015): 6938-6959.
Colette, A., Vautard, R., & Vrac, M. ”2012). Regional climate downscaling with prior
statistical correction of the global climate forcing. Geophysical Research Letters,
39”13).
Déqué, M. ”2007), Frequency of precipitation and temperature extremes over France
in an anthropogenic scenario: Model results and statistical correction according to
observed values, Global Planet. Change, 57, 16–26.
Ehret U. et al. ”2012) Should we apply bias correction to global and regional climate
model data?, Hydrol. Earth Syst. Sci., 16, 3391–3404.
Famien, A. M., Janicot, S., Ochou, A. D., Vrac, M., Defrance, D., Sultan, B., and Noël, T.: A
bias-corrected CMIP5 dataset for Africa using CDF-t method. A contribution to
agricultural impact studies, Earth Syst. Dynam. Discuss.,
https://doi.org/10.5194/esd-2017-111, in review, 2017.
Ficklin D. et al ”2016) The Influence of Climate Model Biases on Projections of Aridity
and Drought, Journal of Climate, 1269.
Galmarini, S. et al ”2018) Adjusting Climate Model Bias for Agricultural Impact
Assessment:
how to cut the mustard?, BAMS, submitted.
Giorgi, F., Jones, C., & Asrar, G. R. ”2009). Addressing climate information needs at the
regional level: the CORDEX framework. World Meteorological Organization ”WMO)
Bulletin, 58”3), 175.
Technical Report: Bias Adjusting Climate Model Projections | 17
Gudmundsson, Lukas, et al. "Technical Note: Downscaling RCM precipitation to the
station scale using statistical transformations–a comparison of methods." Hydrology
and Earth System Sciences 16.9 ”2012): 3383-3390.
Hay, L., R. Wilby, and G. Leavesley ”2000), A comparison of delta change and
downscaled GCM scenarios for three mountainous basins in the United States, J. Am.
Water Resour., 36, 387–397.
Häggmark L, Ivarsson KI, Gollvik S, Olofsson PO. 2000. MESAN, an operational
mesoscale analysis system. Tellus52A: 2–20, doi:10.1034/j.1600-0870.2000.520102.x.
Landelius, T., Dahlgren, P., Gollvik, S., Jansson, A., & Olsson, E. ”2016). A high-resolution
regional reanalysis for Europe. Part 2: 2D analysis of surface temperature,
precipitation and wind. Quarterly Journal of the Royal Meteorological Society,
142”698), 2132-2142.
Lavaysse, C., M. Vrac, P. Drobinski, M. Lengaigne, and T. Vischel ”2012), Statistical
downscaling of the French Mediterranean climate: Assessment for present and
projection in an anthropogenic scenario, Nat. Hazards Earth Syst. Sci., 12, 651–670,
doi:10.5194/nhess-12-651-2012.
Lenderink, G., A. Buishand, and W. van Deursen ”2007), Estimates of future discharges
of the River Rhine using two scenario methodologies: Direct versus delta approach,
Hydrol. Earth Syst. Sci., 11, 1145–1159, doi:10.5194/hess-11-1145-2007.
Lopez, Ana, et al. "From climate model ensembles to climate change impacts and
adaptation: A case study of water resource management in the southwest of
England." Water Resources Research 45.8 ”2009).
Maraun, D., 2016. Bias Correcting Climate Change Simulations - a Critical Review.
Current Climate Change Reports 2, 211–220.
Maurer, Edwin P. "Uncertainty in hydrologic impacts of climate change in the Sierra
Nevada, California, under two emissions scenarios." Climatic Change 82.3-4 ”2007):
309-325.
Michelangeli, P. A., Vrac, M., & Loukos, H. ”2009). Probabilistic downscaling
approaches: Application to wind cumulative distribution functions. Geophysical
Research Letters, 36”11).
Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., Van Vuuren, D.
P., ... & Meehl, G. A. ”2010). The next generation of scenarios for climate change
research and assessment. Nature, 463”7282), 747-756.
Technical Report: Bias Adjusting Climate Model Projections | 18
Oettli, P., B. Sultan, C. Baron, and M. Vrac ”2011), Are regional climate models relevant
for crop yield prediction in West Africa?, Environ. Res. Lett, 6, 14008,
doi:10.1088/1748-9326/6/1/014008.
Passow, Christian, and Reik Donner. "Linear Regression Quantile Mapping ”RQM)-A
new approach to bias correction with consistent quantile trends." ”2017).
Switanek, M. B., Troch, P. A., Castro, C. L., Leuprecht, A., Chang, H.-I., Mukherjee, R.,
and Demaria, E. M. C.: Scaled distribution mapping: a bias correction method that
preserves raw climate model projected changes, Hydrol. Earth Syst. Sci. Discuss.,
doi:10.5194/hess-2016-435, in review, 2016.
Taylor, K. E., Stouffer, R. J., & Meehl, G. A. ”2012). An overview of CMIP5 and the
experiment design. Bulletin of the American Meteorological Society, 93”4), 485-498.
Tisseuil, C., Vrac, M., Grenouillet, G., Wade, A. J., Gevrey, M., Oberdorff, T., ... & Lek, S.
”2012). Strengthening the link between climate, hydrological and species distribution
modeling to assess the impacts of climate change on freshwater biodiversity. Science
of the total environment, 424, 193-201.
Vrac, M., P. Drobinski, A. Merlo, M. Herrmann, C. Lavaysse, L. Li, and S. Somot, 2012:
Dynamical and statistical downscaling of the French Mediterranean climate:
Uncertainty assessment. Nat. Hazards Earth Syst. Sci., 12, 2769–2784,
doi:https://doi.org/10.5194/nhess-12-2769-2012. Crossref
Vrac, M., T. Noël, and R. Vautard ”2016), Bias correction of precipitation through
Singularity Stochastic Removal: Because occurrences matter. J. Geophys. Res. Atmos.,
121, 5237–5258, doi:10.1002/2015JD024511.
Vrac, M.: Multivariate bias adjustment of high-dimensional climate simulations: The
Rank Resampling for Distributions and Dependences ”R2D2) Bias Correction, Hydrol.
Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-747, in review, 2018.
Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., & Viterbo, P. ”2014). The
WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to
ERA-Interim reanalysis data. Water Resources Research, 50”9), 7505-7514.
Wood, Andrew W., et al. "Hydrologic implications of dynamical and statistical
approaches to downscaling climate model outputs." Climatic change 62.1 ”2004):
189-216.
Technical Report: Bias Adjusting Climate Model Projections | 19
Technical Report: Bias Adjusting Climate Model Projections | 20
6. Dataset and Document Revision History
Rev 0 - 1 February 2018 - Document created.
Technical Report: Bias Adjusting Climate Model Projections | 21
7. Annex
GCM
RCP4.5
RCP8.5
BCC-CSM1-1-M
x
BNU-ESM
x
x
CanESM2
x
CCSM4
x
x
CESM1-BGC
x
x
CESM1-CAM5
x
x
CMCC-CMS
x
CNRM-CM5
x
x
EC-EARTH
x
x
GFDL-CM3
x
x
GFDL-ESM2G
x
x
GFDL-ESM2M
x
x
IPSL-CM5A-MR
x
x
IPSL-CM5B-LR
x
x
MIROC5
x
x
MIROC-ESM-CHEM
x
x
MIROC-ESM
x
x
MPI-ESM-LR
x
x
MPI-ESM-MR
x
MRI-CGCM3
x
x
MRI-ESM1
x
NorESM1-M
x
Table 1. CMIP5 experiment Global Circulation Models (GCM) for surface temperature
variable per RCP scenario (RCP4.5, RCP8.5) available on the climate data factory
Technical Report: Bias Adjusting Climate Model Projections | 22
GCM
RCM
RCP4.5
RCP8.5
CNRM-CM5
CLMcom-CCLM4-8-17
x
x
CNRM-CM5
SMHI-RCA4
x
EC-EARTH
CLMcom-CCLM4-8-17
x
x
EC-EARTH
DMI-HIRHAM5
x
x
EC-EARTH
KNMI-RACMO22E
x
x
EC-EARTH
SMHI-RCA4
x
x
HadGEM2-ES
CLMcom-CCLM4-8-17
x
HadGEM2-ES
KNMI-RACMO22E
x
HadGEM2-ES
SMHI-RCA4
x
IPSL-CM5A-MR
SMHI-RCA4
x
MPI-ESM-LR
CLMcom-CCLM4-8-17
x
x
MPI-ESM-LR
SMHI-RCA4
x
Table 2. CORDEX experiment Regional Climate Models (RCM) and their forcing GCM for
surface temperature variable, per RCP scenario (RCP4.5, RCP8.5) available on the climate
data factory
Technical Report: Bias Adjusting Climate Model Projections | 23
Cover photo:
Mountains and Clouds
by rachel_thecat / CC BY-SA 2.0