Exploring the performance of bias correction applied outside the calibration period’s climate regime

2020 ◽  
Author(s):  
Katharina Klehmet ◽  
Peter Berg ◽  
Pascual Herrera ◽  
David Leidinger ◽  
Anthony Lemoine ◽  
...  
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Joint Probability ◽  
Correction Method ◽  
Calibration Data ◽  
Climate Regime ◽  
Split Sample ◽  
Set Up ◽  
The Impact

<p>It is common practice to apply some form of bias correction to climate models before use in impact modelling, such as hydrology. The standard method is to evaluate the correction method based on a cross validation procedure with two or more sub-periods. This allows the method to be assessed on data not previously seen in the calibration step. However, with standard split-sample setups, the data is most likely in a similar climate regime as the calibration data. In effect, the method is evaluated in the same climate regime as it is calibrated, and informs little about the performance outside the current climate regime.</p><p>To address this issue, a discrete split sample test (DSST) was set up so that as diverse climate regimes as possible were sampled. The simplest climate analogue would be to perform the DSST on the coldest years and evaluate on the warmest, to mimic a changing temperature. Here, the tests are extended to more exotic indicators, such as snow pack, the joint probability of wet and cold seasons, the number of hot days in a year, the convective activity during summer; all related to a specific case study issue. Six different bias correction methods of both standard quantile mapping and other approaches to scale the reference time series are included. The methods are applied in a pseudo-reality framework to six climate model projections from Euro-CORDEX 12.5 km simulations. The analysis is focused on comparing the DSST performance with the impact on the climate change signals, and to the reliability of each method when applied to different climate regimes.</p>

scholarly journals Effects of climate model radiation, humidity and wind estimates on hydrological simulations

2012 ◽  
Vol 16 (2) ◽  
pp. 305-318 ◽  
Author(s):  
I. Haddeland ◽  
J. Heinke ◽  
F. Voß ◽  
S. Eisner ◽  
C. Chen ◽  
...  
Keyword(s):  
Bias Correction ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Meteorological Data ◽  
Correction Method ◽  
Hydrological Models ◽  
Temperature And Precipitation ◽  
Effects Of Radiation ◽  
Precipitation And Temperature

Abstract. Due to biases in the output of climate models, a bias correction is often needed to make the output suitable for use in hydrological simulations. In most cases only the temperature and precipitation values are bias corrected. However, often there are also biases in other variables such as radiation, humidity and wind speed. In this study we tested to what extent it is also needed to bias correct these variables. Responses to radiation, humidity and wind estimates from two climate models for four large-scale hydrological models are analysed. For the period 1971–2000 these hydrological simulations are compared to simulations using meteorological data based on observations and reanalysis; i.e. the baseline simulation. In both forcing datasets originating from climate models precipitation and temperature are bias corrected to the baseline forcing dataset. Hence, it is only effects of radiation, humidity and wind estimates that are tested here. The direct use of climate model outputs result in substantial different evapotranspiration and runoff estimates, when compared to the baseline simulations. A simple bias correction method is implemented and tested by rerunning the hydrological models using bias corrected radiation, humidity and wind values. The results indicate that bias correction can successfully be used to match the baseline simulations. Finally, historical (1971–2000) and future (2071–2100) model simulations resulting from using bias corrected forcings are compared to the results using non-bias corrected forcings. The relative changes in simulated evapotranspiration and runoff are relatively similar for the bias corrected and non bias corrected hydrological projections, although the absolute evapotranspiration and runoff numbers are often very different. The simulated relative and absolute differences when using bias corrected and non bias corrected climate model radiation, humidity and wind values are, however, smaller than literature reported differences resulting from using bias corrected and non bias corrected climate model precipitation and temperature values.

scholarly journals Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2266 ◽  
Author(s):  
Enrique Soriano ◽  
Luis Mediero ◽  
Carlos Garijo
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Control Period ◽  
Flood Frequency ◽  
Climate Projections ◽  
The Future ◽  
Flood Quantiles ◽  
The Impact

Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations.

scholarly journals High-end climate change impact on European runoff and low flows – exploring the effects of forcing biases

2016 ◽  
Vol 20 (5) ◽  
pp. 1785-1808 ◽  
Author(s):  
Lamprini V. Papadimitriou ◽  
Aristeidis G. Koutroulis ◽  
Manolis G. Grillakis ◽  
Ioannis K. Tsanis
Keyword(s):  
Global Warming ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Data Set ◽  
Low Flows ◽  
The Impact ◽  
Selection Of

Abstract. Climate models project a much more substantial warming than the 2 °C target under the more probable emission scenarios, making higher-end scenarios increasingly plausible. Freshwater availability under such conditions is a key issue of concern. In this study, an ensemble of Euro-CORDEX projections under RCP8.5 is used to assess the mean and low hydrological states under +4 °C of global warming for the European region. Five major European catchments were analysed in terms of future drought climatology and the impact of +2 °C versus +4 °C global warming was investigated. The effect of bias correction of the climate model outputs and the observations used for this adjustment was also quantified. Projections indicate an intensification of the water cycle at higher levels of warming. Even for areas where the average state may not considerably be affected, low flows are expected to reduce, leading to changes in the number of dry days and thus drought climatology. The identified increasing or decreasing runoff trends are substantially intensified when moving from the +2 to the +4° of global warming. Bias correction resulted in an improved representation of the historical hydrology. It is also found that the selection of the observational data set for the application of the bias correction has an impact on the projected signal that could be of the same order of magnitude to the selection of the Global Climate Model (GCM).

scholarly journals Multivariate stochastic bias corrections with optimal transport

2019 ◽  
Vol 23 (2) ◽  
pp. 773-786 ◽  
Author(s):  
Yoann Robin ◽  
Mathieu Vrac ◽  
Philippe Naveau ◽  
Pascal Yiou
Keyword(s):  
Bias Correction ◽  
Transport Theory ◽  
Climate Model ◽  
Optimal Transport ◽  
Transfer Functions ◽  
Joint Probability ◽  
Random Variable ◽  
Correction Method ◽  
Joint Probability Distribution ◽  
Definition Of

Abstract. Bias correction methods are used to calibrate climate model outputs with respect to observational records. The goal is to ensure that statistical features (such as means and variances) of climate simulations are coherent with observations. In this article, a multivariate stochastic bias correction method is developed based on optimal transport. Bias correction methods are usually defined as transfer functions between random variables. We show that such transfer functions induce a joint probability distribution between the biased random variable and its correction. The optimal transport theory allows us to construct a joint distribution that minimizes an energy spent in bias correction. This extends the classical univariate quantile mapping techniques in the multivariate case. We also propose a definition of non-stationary bias correction as a transfer of the model to the observational world, and we extend our method in this context. Those methodologies are first tested on an idealized chaotic system with three variables. In those controlled experiments, the correlations between variables appear almost perfectly corrected by our method, as opposed to a univariate correction. Our methodology is also tested on daily precipitation and temperatures over 12 locations in southern France. The correction of the inter-variable and inter-site structures of temperatures and precipitation appears in agreement with the multi-dimensional evolution of the model, hence satisfying our suggested definition of non-stationarity.

scholarly journals Climate model bias correction and the role of timescales

2010 ◽  
Vol 7 (5) ◽  
pp. 7863-7898 ◽  
Author(s):  
J. O. Haerter ◽  
S. Hagemann ◽  
C. Moseley ◽  
C. Piani
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Correction Method ◽  
Statistical Properties ◽  
Statistical Bias ◽  
Negative Effects ◽  
Climate Model Bias ◽  
Corrected Data ◽  
Statistical Bias Correction

Abstract. It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing biases. In principle, statistical bias correction methodologies act on model output so the statistical properties of the corrected data match those of the observations. However the improvements to the statistical properties of the data are limited to the specific time scale of the fluctuations that are considered. For example, a statistical bias correction methodology for mean daily values might be detrimental to monthly statistics. Also, in applying bias corrections derived from present day to scenario simulations, an assumption is made of persistence of the bias over the largest timescales. We examine the effects of mixing fluctuations on different time scales and suggest an improved statistical methodology, referred to here as a cascade bias correction method, that eliminates, or greatly reduces, the negative effects.

scholarly journals Sensitivity Study of Different RegCM4.4 Model Set-Ups – Recent Results from the TVRegCM Experiment

2017 ◽  
Vol 17 (5) ◽  
pp. 17-26 ◽  
Author(s):  
Hristo Chervenkov ◽  
Vladimir Ivanov ◽  
Georgi Gadzhev ◽  
Kostadin Ganev
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Scale ◽  
Spatial And Temporal Patterns ◽  
Monthly Basis ◽  
Global Circulation Models ◽  
Model Set ◽  
Set Up ◽  
The Impact

Abstract The oncoming climate changes will exert influence on the ecosystems, on all branches of the international economy, and on the quality of life. Global Circulation Models (GCMs) are the most widespread and successful tools employed for both numerical weather forecast and climate research since the 1980s. However, growing demands on accurate and reliable information on regional and sub-regional scale are not directly met by relatively coarse resolution global models, mainly due to the excessive costs affiliated with the use of the model in very high resolution. Regional Climate Models (RCMs) are important instruments used for downscaling climate simulations from GCMs. Main aim of the numerical experiment Tuning an Validation of Regional Climate Model (RegCM-TVRegCM) is to quantify the impact of some tunable factors in the RegCM set-up on the model outputs. Thus, on the first stage of the study, the skill of 20 different model configurations in representing the basic spatial and temporal patterns of the Southeast European (SE) climate for the period 1999-2009, is evaluated. Based on these outcomes, the present work is dedicated on more detailed inspection of the model set-ups with recognizable better performance. The Pearson’s correlation coefficient between the time series of the temperature and precipitation of the 6 most promising model set-ups and the E-OBS on monthly basis are calculated. The main conclusion is that this test does not reveal single one model set-up that definitely over performs the other considered ones.

scholarly journals Effects of climate model radiation, humidity and wind estimates on hydrological simulations

2011 ◽  
Vol 8 (4) ◽  
pp. 7919-7945 ◽  
Author(s):  
I. Haddeland ◽  
J. Heinke ◽  
F. Voß ◽  
S. Eisner ◽  
C. Chen ◽  
...  
Keyword(s):  
Bias Correction ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Meteorological Data ◽  
Correction Method ◽  
Hydrological Models ◽  
Temperature And Precipitation ◽  
Effects Of Radiation ◽  
Precipitation And Temperature

Abstract. Due to biases in the output of climate models, a bias correction is often needed to make the output suitable for use in hydrological simulations. In most cases only the temperature and precipitation values are bias corrected. However, often there are also biases in other variables such as radiation, humidity and wind speed. In this study we tested to what extent it is also needed to bias correct these variables. Responses to radiation, humidity and wind estimates from two climate models for four large-scale hydrological models are analysed. For the period 1971–2000 these hydrological simulations are compared to simulations using meteorological data based on observations and reanalysis; i.e. the baseline simulation. In both forcing datasets originating from climate models precipitation and temperature are bias corrected to the baseline forcing dataset. Hence, it is only effects of radiation, humidity and wind estimates that are tested here. The direct use of climate model outputs result in substantial different evapotranspiration and runoff estimates, when compared to the baseline simulations. A simple bias correction method is implemented and tested by rerunning the hydrological models using bias corrected radiation, humidity and wind values. The results indicate that bias correction can successfully be used to match the baseline simulations. Finally, historical (1971–2000) and future (2071–2100) model simulations resulting from using bias corrected forcings are compared to the results using non-bias corrected forcings. The relative changes in simulated evapotranspiration and runoff are relatively similar for the bias corrected and non bias corrected hydrological projections, although the absolute evapotranspiration and runoff numbers are often very different. The simulated relative and absolute differences when using bias corrected and non bias corrected climate model radiation, humidity and wind values are, however, smaller than literature reported differences resulting from using bias corrected and non bias corrected climate model precipitation and temperature values.

scholarly journals High-end climate change impact on European water availability and stress: exploring the presence of biases

2015 ◽  
Vol 12 (7) ◽  
pp. 7267-7325 ◽  
Author(s):  
L. V. Papadimitriou ◽  
A. G. Koutroulis ◽  
M. G. Grillakis ◽  
I. K. Tsanis
Keyword(s):  
Global Warming ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Water Cycle ◽  
Order Of Magnitude ◽  
Change Impact ◽  
The Mean ◽  
The Impact ◽  
Selection Of

Abstract. Climate models project a much more substantial warming than the 2 °C target making higher end scenarios increasingly plausible. Freshwater availability under such conditions is a key issue of concern. In this study, an ensemble of Euro-CORDEX projections under RCP8.5 is used to assess the mean and low hydrological states under +4 °C of global warming for the European region. Five major European catchments were analyzed in terms of future drought climatology and the impact of +2 vs. +4 °C global warming was investigated. The effect of bias correction of the climate model outputs and the observations used for this adjustment was also quantified. Projections indicate an intensification of the water cycle at higher levels of warming. Even for areas where the average state may not considerably be affected, low flows are expected to reduce leading to changes in the number of dry days and thus drought climatology. The identified increasing or decreasing runoff trends are substantially intensified when moving from the +2 to the +4 °C of global warming. Bias correction resulted in an improved representation of the historical hydrology. It is also found that the selection of the observational dataset for the application of the bias correction has an impact on the projected signal that could be of the same order of magnitude to the selection of the RCM.

scholarly journals Multivariate stochastic bias corrections with optimal transport

2018 ◽  
Author(s):  
Yoann Robin ◽  
Mathieu Vrac ◽  
Philippe Naveau ◽  
Pascal Yiou
Keyword(s):  
Bias Correction ◽  
Transport Theory ◽  
Climate Model ◽  
Optimal Transport ◽  
Transfer Functions ◽  
Joint Probability ◽  
Random Variable ◽  
Correction Method ◽  
Joint Probability Distribution ◽  
Definition Of

Abstract. Bias correction methods are used to calibrate climate model outputs with respect to observational records. The goal is to ensure that statistical features (such as means and variances) of climate simulations are coherent with observations. In this article, a multivariate stochastic bias correction method is developed based on optimal transport. Bias correction methods are usually defined as transfer functions between random variables. We show that such transfer functions induce a joint probability distribution between the biased random variable and its correction. The optimal transport theory allows us constructing a joint distribution that minimizes an energy spent in bias correction. This extends the classical univariate quantile mapping techniques in the multivariate case. We also propose a definition of non-stationary bias correction as a transfer of the model to the observational world, and we extend our method in this context. Those methodologies are first tested on an idealized chaotic system with three variables. In those controlled experiments, the correlations between variables appear almost perfectly corrected by our method, as opposed to a univariate correction. Our methodology is also tested on daily precipitation and temperatures over 12 locations in southern France. The correction of the inter-variable and inter-site structures of temperatures and precipitation appears in agreement with the multi-dimensional evolution of the model, hence satisfying our suggested definition of non-stationarity.

scholarly journals A trend-preserving bias correction – the ISI-MIP approach

2013 ◽  
Vol 4 (1) ◽  
pp. 49-92 ◽  
Author(s):  
S. Hempel ◽  
K. Frieler ◽  
L. Warszawski ◽  
J. Schewe ◽  
F. Piontek
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Historical Data ◽  
Correction Method ◽  
Model Intercomparison ◽  
Model Data ◽  
Climate Signal ◽  
Bias Correction Method ◽  
Intercomparison Project ◽  
The Impact

Abstract. Statistical bias correction is commonly applied within climate impact modeling to correct climate model data for systematic deviations of the simulated historical data from observations. Methods are based on transfer functions generated to map the distribution of the simulated historical data to that of the observations. Those are subsequently applied to correct the future projections. Thereby the climate signal is modified in a way not necessarily preserving the trend of the original climate model data. Here, we present the bias correction method that was developed within ISI-MIP, the first Inter-Sectoral Impact Model Intercomparison Project. ISI-MIP is designed to synthesise impact projections in the agriculture, water, biome, health, and infrastructure sectors at different levels of global warming. However, bias-corrected climate data that are used as input for the impact simulations could be only provided over land areas. To ensure consistency with the global (land + ocean) temperature information the bias correction method has to preserve the warming signal. Here we present the applied bias correction method that preserves the absolute changes in monthly temperature, and relative changes in monthly values of precipitation and the other variables needed for ISI-MIP. The proposed methodology represents a modification of the transfer function approach applied in the Water Model Intercomparison Project (Water-MIP). Correction of the monthly mean is followed by correction of the daily variability about the monthly mean.

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