The representation of location by a regional climate model in complex terrain

Abstract. To assess potential impacts of climate change for a specific location, one typically employs climate model simulations at the grid box corresponding to the same geographical location. For most of Europe, this choice is well justified. But, based on regional climate simulations, we show that simulated climate might be systematically displaced compared to observations. In particular in the rain shadow of mountain ranges, a local grid box is therefore often not representative of observed climate: the simulated windward weather does not flow far enough across the mountains; local grid boxes experience the wrong air masses and atmospheric circulation. In some cases, also the local climate change signal is deteriorated. Classical bias correction methods fail to correct these location errors. Often, however, a distant simulated time series is representative of the considered observed precipitation, such that a non-local bias correction is possible. These findings also clarify limitations of bias correcting global model errors, and of bias correction against station data.

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The representation of location by regional climate models in complex terrain

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-3011-2015 ◽

2015 ◽

Vol 12 (3) ◽

pp. 3011-3028 ◽

Cited By ~ 4

Author(s):

D. Maraun ◽

M. Widmann

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Climate Change Signal ◽

Model Errors ◽

Model Simulations ◽

Local Grid ◽

Climate Model Simulations

Abstract. To assess potential impacts of climate change for a specific location, one typically employs climate model simulations at the grid box corresponding to the same geographical location. But based on regional climate model simulations, we show that simulated climate might be systematically displaced compared to observations. In particular in the rain shadow of moutain ranges, a local grid box is therefore often not representative of observed climate: the simulated windward weather does not flow far enough across the mountains; local grid boxes experience the wrong airmasses and atmospheric circulation. In some cases, also the local climate change signal is deteriorated. Classical bias correction methods fail to correct these location errors. Often, however, a distant simulated time series is representative of the considered observed precipitation, such that a non-local bias correction is possible. These findings also clarify limitations of bias correcting global model errors, and of bias correction against station data.

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Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions?

Hydrology and Earth System Sciences ◽

10.5194/hess-17-5061-2013 ◽

2013 ◽

Vol 17 (12) ◽

pp. 5061-5077 ◽

Cited By ~ 195

Author(s):

C. Teutschbein ◽

J. Seibert

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Change Impact ◽

Climate Model ◽

Regional Climate ◽

Model Errors ◽

Climate Conditions ◽

Split Sample ◽

Sample Test ◽

Change Impact

Abstract. In hydrological climate-change impact studies, regional climate models (RCMs) are commonly used to transfer large-scale global climate model (GCM) data to smaller scales and to provide more detailed regional information. Due to systematic and random model errors, however, RCM simulations often show considerable deviations from observations. This has led to the development of a number of correction approaches that rely on the assumption that RCM errors do not change over time. It is in principle not possible to test whether this underlying assumption of error stationarity is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well correction methods perform for conditions different from those used for calibration with the relatively simple differential split-sample test. For five Swedish catchments, precipitation and temperature simulations from 15 different RCMs driven by ERA40 (the 40 yr reanalysis product of the European Centre for Medium-Range Weather Forecasts (ECMWF)) were corrected with different commonly used bias correction methods. We then performed differential split-sample tests by dividing the data series into cold and warm respective dry and wet years. This enabled us to cross-evaluate the performance of different correction procedures under systematically varying climate conditions. The differential split-sample test identified major differences in the ability of the applied correction methods to reduce model errors and to cope with non-stationary biases. More advanced correction methods performed better, whereas large deviations remained for climate model simulations corrected with simpler approaches. Therefore, we question the use of simple correction methods such as the widely used delta-change approach and linear transformation for RCM-based climate-change impact studies. Instead, we recommend using higher-skill correction methods such as distribution mapping.

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Climate change impact assessment on pluvial flooding using a distribution-based bias correction of regional climate model simulations

Journal of Hydrology ◽

10.1016/j.jhydrol.2021.126239 ◽

2021 ◽

pp. 126239

Author(s):

Parisa Hosseinzadehtalaei ◽

Nabilla Khairunnisa Ishadi ◽

Hossein Tabari ◽

Patrick Willems

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Change Impact ◽

Climate Model ◽

Regional Climate ◽

Climate Change Impact Assessment ◽

Model Simulations ◽

Pluvial Flooding ◽

Change Impact ◽

Climate Model Simulations

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On the added value of the regional climate model REMO in the assessment of climate change signal over Central Africa

Climate Dynamics ◽

10.1007/s00382-017-3547-7 ◽

2017 ◽

Vol 49 (11-12) ◽

pp. 3813-3838 ◽

Cited By ~ 24

Author(s):

Thierry C. Fotso-Nguemo ◽

Derbetini A. Vondou ◽

Wilfried M. Pokam ◽

Zéphirin Yepdo Djomou ◽

Ismaïla Diallo ◽

...

Keyword(s):

Climate Change ◽

Regional Climate Model ◽

Climate Model ◽

Regional Climate ◽

Central Africa ◽

Added Value ◽

Climate Change Signal

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How Does a Regional Climate Model Modify the Projected Climate Change Signal of the Driving GCM: A Study over Different CORDEX Regions Using REMO

Atmosphere ◽

10.3390/atmos4020214 ◽

2013 ◽

Vol 4 (2) ◽

pp. 214-236 ◽

Cited By ~ 65

Author(s):

Claas Teichmann ◽

Bastian Eggert ◽

Alberto Elizalde ◽

Andreas Haensler ◽

Daniela Jacob ◽

...

Keyword(s):

Climate Change ◽

Regional Climate Model ◽

Climate Model ◽

Regional Climate ◽

Climate Change Signal

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Climate Model Biases and Modification of the Climate Change Signal by Intensity-Dependent Bias Correction

Journal of Climate ◽

10.1175/jcli-d-17-0765.1 ◽

2018 ◽

Vol 31 (16) ◽

pp. 6591-6610 ◽

Cited By ~ 13

Author(s):

Martin Aleksandrov Ivanov ◽

Jürg Luterbacher ◽

Sven Kotlarski

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Daily Precipitation ◽

Analytical Description ◽

Analytical Theory ◽

Climate Change Signal ◽

Future Research ◽

Model Biases

Climate change impact research and risk assessment require accurate estimates of the climate change signal (CCS). Raw climate model data include systematic biases that affect the CCS of high-impact variables such as daily precipitation and wind speed. This paper presents a novel, general, and extensible analytical theory of the effect of these biases on the CCS of the distribution mean and quantiles. The theory reveals that misrepresented model intensities and probability of nonzero (positive) events have the potential to distort raw model CCS estimates. We test the analytical description in a challenging application of bias correction and downscaling to daily precipitation over alpine terrain, where the output of 15 regional climate models (RCMs) is reduced to local weather stations. The theoretically predicted CCS modification well approximates the modification by the bias correction method, even for the station–RCM combinations with the largest absolute modifications. These results demonstrate that the CCS modification by bias correction is a direct consequence of removing model biases. Therefore, provided that application of intensity-dependent bias correction is scientifically appropriate, the CCS modification should be a desirable effect. The analytical theory can be used as a tool to 1) detect model biases with high potential to distort the CCS and 2) efficiently generate novel, improved CCS datasets. The latter are highly relevant for the development of appropriate climate change adaptation, mitigation, and resilience strategies. Future research needs to focus on developing process-based bias corrections that depend on simulated intensities rather than preserving the raw model CCS.

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High-resolution Med-CORDEX regional climate model simulations for hydrological impact studies: a first evaluation in Morocco

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-5687-2013 ◽

2013 ◽

Vol 10 (5) ◽

pp. 5687-5737 ◽

Cited By ~ 3

Author(s):

Y. Tramblay ◽

D. Ruelland ◽

S. Somot ◽

R. Bouaicha ◽

E. Servat

Keyword(s):

Climate Change ◽

High Resolution ◽

Regional Climate Model ◽

Bias Correction ◽

Hydrological Model ◽

Climate Model ◽

Regional Climate ◽

Impact Studies ◽

Hydrological Impact ◽

Split Sample

Abstract. In the framework of the international CORDEX program, new regional climate model (RCM) simulations at high spatial resolutions are becoming available for the Mediterranean region (Med-CORDEX initiative). This study provides the first evaluation for hydrological impact studies of these high-resolution simulations. Different approaches are compared to analyze the climate change impacts on the hydrology of a catchment located in North Morocco, using a high-resolution RCM (ALADIN-Climate) from the Med-CORDEX initiative at two different spatial resolutions (50 km and 12 km) and for two different Radiative Concentration Pathway scenarios (RCP4.5 and RCP8.5). The main issues addressed in the present study are: (i) what is the impact of increased RCM resolution on present-climate hydrological simulations and on future projections? (ii) Are the bias-correction of the RCM model and the parameters of the hydrological model stationary and transferable to different climatic conditions? (iii) What is the climate and hydrological change signal based on the new Radiative Concentration Pathways scenarios (RCP4.5 and RCP8.5)? Results indicate that high resolution simulations at 12 km better reproduce the seasonal patterns, the seasonal distributions and the extreme events of precipitation. The parameters of the hydrological model, calibrated to reproduce runoff at the monthly time step over the 1984–2010 period, do not show a strong variability between dry and wet calibration periods in a differential split-sample test. However the bias correction of precipitation by quantile-matching does not give satisfactory results in validation using the same differential split-sample testing method. Therefore a quantile-perturbation method that does not rely on any stationarity assumption and produces ensembles of perturbed series of precipitation was introduced. The climate change signal under scenarios 4.5 and 8.5 indicates a decrease of respectively −30% to −57% in surface runoff for the mid-term (2041–2062), when for the same period the projections for precipitation are ranging between −15% and −19% and for temperature between +1.28°C and +1.87°C.

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Is bias correction of Regional Climate Model (RCM) simulations possible for non-stationary conditions?

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-12765-2012 ◽

2012 ◽

Vol 9 (11) ◽

pp. 12765-12795 ◽

Cited By ~ 18

Author(s):

C. Teutschbein ◽

J. Seibert

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Change Impact ◽

Climate Model ◽

Regional Climate ◽

Climate Conditions ◽

Impact Studies ◽

Split Sample ◽

Sample Test ◽

Change Impact

Abstract. In hydrological climate-change impact studies, Regional Climate Models (RCMs) are commonly used to transfer large-scale Global Climate Model (GCM) data to smaller scales and to provide more detailed regional information. However, there are often considerable biases in RCM simulations, which have led to the development of a number of bias correction approaches to provide more realistic climate simulations for impact studies. Bias correction procedures rely on the assumption that RCM biases do not change over time, because correction algorithms and their parameterizations are derived for current climate conditions and assumed to apply also for future climate conditions. This underlying assumption of bias stationarity is the main concern when using bias correction procedures. It is in principle not possible to test whether this assumption is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well bias correction methods perform for conditions different from those used for calibration. For five Swedish catchments, several time series of RCM simulated precipitation and temperature were obtained from the ENSEMBLES data base and different commonly-used bias correction methods were applied. We then performed a differential split-sample test by dividing the data series into cold and warm respective dry and wet years. This enabled us to evaluate the performance of different bias correction procedures under systematically varying climate conditions. The differential split-sample test resulted in a large spread and a clear bias for some of the correction methods during validation years. More advanced correction methods such as distribution mapping performed relatively well even in the validation period, whereas simpler approaches resulted in the largest deviations and least reliable corrections for changed conditions. Therefore, we question the use of simple bias correction methods such as the widely used delta-change approach and linear scaling for RCM-based climate-change impact studies and recommend using higher-skill bias correction methods.

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