scholarly journals Improving snowfall representation in climate simulations via statistical models informed by air temperature and total precipitation

2020 ◽  
Author(s):  
Flavio Maria Emanuele Pons ◽  
Davide Faranda
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Accurate Estimation ◽  
Near Surface ◽  
Temperature And Precipitation ◽  
Climate Simulations ◽  
Threshold Temperatures ◽  
Out Of Sample

Abstract. The description and analysis of compound extremes affecting mid and high latitudes in the winter requires an accurate estimation of snowfall. Such variable is often missing for in-situ observations, and biased in climate model outputs, both in magnitude and number of events. While climate models can be adjusted using bias correction (BC), snowfall presents additional challenges compared to other variables, preventing from applying traditional univariate BC methods. We extend the existing literature on the estimation of the snowfall fraction from near-surface temperature, which usually involves binary thresholds or fitting parametric nonlinear functions. We show that, combining breakpoint search algorithms to define threshold temperatures and segmented regression models, it is possible to obtain accurate out-of-sample estimates of snowfall over Europe in ERA5 reanalysis, and to perform effective BC on the IPSL-WRF high resolution EURO-CORDEX climate model only relying on bias adjusted temperature and precipitation. This method offers a feasible way to reconstruct or adjust snowfall observations without requiring multivariate or conditional bias correction and stochastic generation of unobserved events.

scholarly journals Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R<sup>2</sup>D<sup>2</sup>) bias correction

2018 ◽  
Vol 22 (6) ◽  
pp. 3175-3196 ◽  
Author(s):  
Mathieu Vrac
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Grid Cell ◽  
Dependence Structure ◽  
Historical Period ◽  
Temporal Properties ◽  
Temperature And Precipitation ◽  
Climate Simulations ◽  
Site Dependence

Abstract. Climate simulations often suffer from statistical biases with respect to observations or reanalyses. It is therefore common to correct (or adjust) those simulations before using them as inputs into impact models. However, most bias correction (BC) methods are univariate and so do not account for the statistical dependences linking the different locations and/or physical variables of interest. In addition, they are often deterministic, and stochasticity is frequently needed to investigate climate uncertainty and to add constrained randomness to climate simulations that do not possess a realistic variability. This study presents a multivariate method of rank resampling for distributions and dependences (R2D2) bias correction allowing one to adjust not only the univariate distributions but also their inter-variable and inter-site dependence structures. Moreover, the proposed R2D2 method provides some stochasticity since it can generate as many multivariate corrected outputs as the number of statistical dimensions (i.e., number of grid cell  ×  number of climate variables) of the simulations to be corrected. It is based on an assumption of stability in time of the dependence structure – making it possible to deal with a high number of statistical dimensions – that lets the climate model drive the temporal properties and their changes in time. R2D2 is applied on temperature and precipitation reanalysis time series with respect to high-resolution reference data over the southeast of France (1506 grid cell). Bivariate, 1506-dimensional and 3012-dimensional versions of R2D2 are tested over a historical period and compared to a univariate BC. How the different BC methods behave in a climate change context is also illustrated with an application to regional climate simulations over the 2071–2100 period. The results indicate that the 1d-BC basically reproduces the climate model multivariate properties, 2d-R2D2 is only satisfying in the inter-variable context, 1506d-R2D2 strongly improves inter-site properties and 3012d-R2D2 is able to account for both. Applications of the proposed R2D2 method to various climate datasets are relevant for many impact studies. The perspectives of improvements are numerous, such as introducing stochasticity in the dependence itself, questioning its stability assumption, and accounting for temporal properties adjustment while including more physics in the adjustment procedures.

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 Adjusting spatial dependence of climate model outputs with cycle-consistent adversarial networks

2021 ◽  
Author(s):  
Bastien François ◽  
Soulivanh Thao ◽  
Mathieu Vrac
Keyword(s):  
Bias Correction ◽  
Spatial Dependence ◽  
Climate Model ◽  
Correction Method ◽  
Mapping Method ◽  
Dependence Structure ◽  
Spatial Correlations ◽  
Temperature And Precipitation ◽  
Climate Simulations ◽  
Spatial Correlation Structure

AbstractClimate model outputs are commonly corrected using statistical univariate bias correction methods. Most of the time, those 1d-corrections do not modify the ranks of the time series to be corrected. This implies that biases in the spatial or inter-variable dependences of the simulated variables are not adjusted. Hence, over the last few years, some multivariate bias correction (MBC) methods have been developed to account for inter-variable structures, inter-site ones, or both. As proof-of-concept, we propose to adapt a computer vision technique used for Image-to-Image translation tasks (CycleGAN) for the adjustment of spatial dependence structures of climate model projections. The proposed algorithm, named MBC-CycleGAN, aims to transfer simulated maps (seen as images) with inappropriate spatial dependence structure from climate model outputs to more realistic images with spatial properties similar to the observed ones. For evaluation purposes, the method is applied to adjust maps of temperature and precipitation from climate simulations through two cross-validation approaches. The first one is designed to assess two different post-processing schemes (Perfect Prognosis and Model Output Statistics). The second one assesses the influence of nonstationary properties of climate simulations on the performance of MBC-CycleGAN to adjust spatial dependences. Results are compared against a popular univariate bias correction method, a “quantile-mapping” method, which ignores inter-site dependencies in the correction procedure, and two state-of-the-art multivariate bias correction algorithms aiming to adjust spatial correlation structure. In comparison with these alternatives, the MBC-CycleGAN algorithm reasonably corrects spatial correlations of climate simulations for both temperature and precipitation, encouraging further research on the improvement of this approach for multivariate bias correction of climate model projections.

scholarly journals Multivariate bias adjustment of high-dimensional climate simulations: The Rank Resampling for Distributions and Dependences (R<sup>2</sup>D<sup>2</sup>) Bias Correction

2018 ◽  
Author(s):  
Mathieu Vrac
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Dependence Structure ◽  
Historical Period ◽  
Grid Cells ◽  
Temporal Properties ◽  
Temperature And Precipitation ◽  
Climate Simulations ◽  
Site Dependence

Abstract. Climate simulations often suffer from statistical biases with respect to observations or reanalyses. It is therefore common to correct (or adjust) those simulations before using them as inputs into impact models. However, most bias correction (BC) methods are univariate and so do not account for the statistical dependences linking the different locations and/or physical variables of interest. In addition, they are often deterministic, while stochasticity is frequently needed to investigate climate uncertainty and to add constrained randomness to climate simulations that do not possess a realistic variability. This study presents a multivariate method of rank resampling for distributions and dependences (R2D2) bias correction allowing to adjust not only the univariate distributions, but also their inter-variable and inter-site dependence structures. Moreover, the proposed R2D2 method provides some stochasticity since it can generate as many multivariate corrected outputs as the number of statistical dimensions (i.e., number of grid-cells × number of climate variables) of the simulations to be corrected. It is based on an assumption of stability in time of the dependence structure – allowing to deal with a high number of statistical dimensions –, that lets the climate model drive the temporal properties and their changes in time. R2D2 is applied on temperature and precipitation reanalyses time series with respect to high-resolution reference data over South-East of France (1506 grid-cells). Bivariate, 1506-dimensional and 3012-dimensional versions of R2D2 are tested over a historical period and compared to a univariate BC. How the different BC methods behave in a climate change context is also illustrated with an application to regional climate simulations over the 2071–2100 period. The results indicate that the 1d-BC basically reproduces the climate model multivariate properties, 2d-R2D2 is only satisfying in the inter-variable context, 1506d-R2D2 strongly improves inter-site properties and 3012d-R2D2 is able to account for both. Applications of the proposed R2D2 method to various climate datasets are relevant for many impact studies. The perspectives of improvements are numerous, such as introducing stochasticity in the dependence itself, questioning its stability assumption, and accounting for temporal properties adjustment while including more physics in the adjustment procedures.

scholarly journals Adjusting spatial dependence of climate model outputs with Cycle-Consistent Adversarial Networks

2021 ◽  
Author(s):  
Bastien François ◽  
Soulivanh Thao ◽  
Mathieu Vrac
Keyword(s):  
Bias Correction ◽  
Spatial Dependence ◽  
Climate Model ◽  
Correction Method ◽  
Mapping Method ◽  
Dependence Structure ◽  
Spatial Correlations ◽  
Temperature And Precipitation ◽  
Climate Simulations ◽  
Spatial Correlation Structure

Abstract Climate model outputs are commonly corrected using statistical univariate bias correction methods. Most of the time, those 1d-corrections do not modify the ranks of the time series to be corrected. This implies that biases in the spatial or inter-variable dependences of the simulated variables are not adjusted. Hence, over the last few years, some multivariate bias correction (MBC) methods have been developed to account for inter-variable structures, inter-site ones, or both. As proof-of-concept, we propose to adapt a computer vision technique used for Image-to-Image translation tasks (CycleGAN) for the adjustment of spatial dependence structures of climate model projections. The proposed algorithm, named MBC-CycleGAN, aims to transfer simulated maps (seen as images) with inappropriate spatial dependence structure from climate model outputs to more realistic images with spatial properties similar to the observed ones. For evaluation purposes, the method is applied to adjust maps of temperature and precipitation from climate simulations through two cross-validation approaches. The first one is designed to assess two different post-processing schemes (Perfect Prognosis and Model Output Statistics). The second one assesses the influence of nonstationary properties of climate simulations on the performance of MBC-CycleGAN to adjust spatial dependences. Results are compared against a popular univariate bias correction method, a ``quantile-mapping'' method, which ignores inter-site dependencies in the correction procedure, and two state-of-the-art multivariate bias correction algorithms aiming to adjust spatial correlation structure. In comparison with these alternatives, the MBC-CycleGAN algorithm reasonably corrects spatial correlations of climate simulations for both temperature and precipitation, encouraging further research on the improvement of this approach for multivariate bias correction of climate model projections.

scholarly journals Large sensitivity of a Greenland ice sheet model to atmospheric forcing fields

2012 ◽  
Vol 6 (2) ◽  
pp. 1037-1083 ◽  
Author(s):  
A. Quiquet ◽  
H. J. Punge ◽  
C. Ritz ◽  
X. Fettweis ◽  
M. Kageyama ◽  
...  
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Global Climate Models ◽  
Near Surface ◽  
Temperature And Precipitation ◽  
The Impact

Abstract. The prediction of future climate and ice sheet evolution requires coupling of ice sheet and climate models. Before proceeding to a coupled setup, we propose to analyze the impact of model simulated climate on an ice sheet. Here, we undertake this exercise for a set of regional and global climate models. Modelled near surface air temperature and precipitation are provided as upper boundary condition to the GRISLI (GRenoble Ice Shelf and Land Ice model) hybrid ice sheet model (ISM) in its Greenland configuration. After 20 kyr of simulation, the resulting ice sheets highlight the differences between the climate models. While modelled ice sheet sizes are generally comparable to the observed ones, there are considerable deviations among the ice sheets on regional scales. These can be explained by difficulties in modelling local temperature and precipitation near the coast. This is especially true in the case of global models. But the deviations of each climate model are also due to the differences in the atmospheric general circulation. In the context of coupling ice sheet and climate models, we conclude that appropriate downscaling methods will be needed and systematic corrections of the climatic variables at the interface may be required in some cases to obtain realistic results for the Greenland ice sheet (GIS).

scholarly journals Multivariate bias corrections of climate simulations: Which benefits for which losses?

2020 ◽  
Author(s):  
Bastien François ◽  
Mathieu Vrac ◽  
Alex J. Cannon ◽  
Yoann Robin ◽  
Denis Allard
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Temporal Structure ◽  
Climate Projections ◽  
Dimensional Changes ◽  
Climate Simulations ◽  
Temporal Structures ◽  
Corrected Data ◽  
Temporal Dependencies

Abstract. Climate models are the major tools to estimate climate variables evolutions in the future. However, climate simulations often present statistical biases and have to be corrected against observations before being used in impact assessments. Several bias correction (BC) methods have therefore been developed in the literature over the last two decades, in order to adjust simulations according to historical records and obtain climate projections with appropriate statistical attributes. Most of the existing and popular BC methods are univariate, i.e., correcting one physical variable and one location at a time, and thus can fail to reconstruct inter-variable, spatial or temporal dependencies of the observations. These remaining biases in the correction can then affect the subsequent analyses. This has led to further research on multivariate aspects for statistical post-processing BC methods. Recently, some multivariate bias correction (MBC) methods have been proposed, with different approaches to restore multidimensional dependencies. However, these methods are not well apprehended yet by researchers and practitioners due to differences in their applicability and assumptions, therefore leading potentially to different results. This study is intended to intercompare four existing MBCs to provide end-users with aid in choosing such methods for their applications. For evaluation and illustration purposes, these methods are applied to correct simulation outputs from one climate model through a cross-validation methodology, which allows for the assessment of inter-variable, spatial and temporal criteria. Then, a second methodology is performed for assessing the ability of the MBC methods to account for the multi-dimensional evolutions of the climate model. Additionally, two reference datasets are used to assess the influence of their spatial resolution on (M)BC results. Most of the methods reasonably correct inter-variable and inter-site correlations. However, none of them adjust correctly the temporal structure as they generate bias corrected data with usually weak temporal dependencies compared to observations. Major differences are found concerning the applicability and stability of the methods in high-dimensional contexts, and in their capability to reproduce the multi-dimensional changes of the model. Based on these conclusions, perspectives for MBC developments are suggested, such as methods to adjust not only multivariate correlations but also temporal structures and allowing to account for multi-dimensional evolutions of the model in the correction.

scholarly journals Adjusting spatial dependence of climate model outputs with Cycle-Consistent Adversarial Networks 

2021 ◽  
Author(s):  
Bastien François ◽  
Soulivanh Thao ◽  
Mathieu Vrac
Keyword(s):  
Bias Correction ◽  
Spatial Dependence ◽  
Climate Model ◽  
Correction Method ◽  
Dependence Structure ◽  
Spatial Correlations ◽  
Temperature And Precipitation ◽  
Climate Simulations ◽  
Spatial Correlation Structure ◽  
Model Output Statistics

&lt;p&gt;Climate model outputs are commonly corrected using statistical univariate bias correction methods. Most of the time, those 1d-corrections do not modify the ranks of the time series to be corrected. This implies that biases in the spatial or inter-variable dependences of the simulated variables are not adjusted. Hence, over the last few years, some multivariate bias correction (MBC) methods have been developed to account for inter-variable structures, inter-site ones, or both. As proof-of-concept, we propose to adapt&amp;#160; a computer vision technique used for Image-to-Image translation tasks (CycleGAN) for the adjustment of spatial dependence structures of climate model projections. The proposed algorithm, named MBC-CycleGAN, aims to transfer simulated maps (seen as images) with inappropriate spatial dependence structure from climate model outputs to more realistic images with spatial properties similar to the observed ones. For evaluation purposes, the method is applied to adjust maps of temperature and precipitation from climate simulations through two cross-validation approaches. The first one is designed to assess two different post-processing schemes (Perfect Prognosis and Model Output Statistics). The second one assesses the influence of non-stationary properties of climate simulations on the performance of MBC-CycleGAN to adjust spatial dependences. Results are compared against a popular univariate bias correction method, a &quot;quantile-mapping&quot; method, which ignores inter-site dependencies in the correction procedure, and two state-of-the-art multivariate bias correction algorithms aiming to adjust spatial correlation structure. In comparison with these alternatives, the MBC-CycleGAN algorithm reasonably corrects spatial correlations of climate simulations for both temperature and precipitation, encouraging further research on the improvement of this approach for multivariate bias correction of climate model projections.&lt;/p&gt;

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.

Multivariate Bias Correction of Climate Simulations: an Intercomparison Study

2020 ◽  
Author(s):  
Bastien François ◽  
Mathieu Vrac ◽  
Alex Cannon ◽  
Yoann Robin ◽  
Denis Allard
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Temporal Structure ◽  
Climate Projections ◽  
Dimensional Changes ◽  
Climate Simulations ◽  
Temporal Structures ◽  
Intercomparison Study ◽  
Temporal Dependencies

&lt;p&gt;Climate models are the major tools to estimate climate variables evolutions in the future. However, climate simulations often present statistical biases and have to be corrected against observations before being used in impact assessments. Several bias correction (BC) methods have therefore been developed in the literature over the last two decades, in order to adjust simulations according to historical records and obtain climate projections with appropriate statistical attributes. Most of the existing and popular BC methods are univariate, i.e., correcting one physical variable and one location at a time, and thus can fail to reconstruct inter-variable, spatial or temporal dependencies of the observations. These remaining biases in the correction can then affect the subsequent analyses. This has led to further research on multivariate aspects for statistical postprocessing BC methods. Recently, some multivariate bias correction (MBC) methods have been proposed, with different approaches to restore multidimensional dependencies. However, these methods are not well apprehended yet by researchers and practitioners due to differences in their applicability and assumptions, therefore leading potentially to different results. This study is intended to intercompare four existing MBCs to provide end-users with aid in choosing such methods for their applications. For evaluation and illustration purposes, these methods are applied to correct simulation outputs from one climate model through a cross-validation methodology, which allows for the assessment of inter-variable, spatial and temporal criteria. Then, a second methodology is performed for assessing the ability of the MBC methods to account for the multi-dimensional evolutions of the climate model. Additionally, two reference datasets are used to assess the influence of their spatial resolution on (M)BC results. Most of the methods reasonably correct inter-variable and inter-site correlations. However, none of them adjust correctly the temporal structure as they generate bias corrected data with usually weak temporal dependencies compared to observations. Major differences are found concerning the applicability and stability of the methods in high-dimensional contexts, and in their capability to reproduce the multi-dimensional changes of the model. Based on those conclusions, perspectives for MBC developments are suggested, such as methods to adjust not only multivariate correlations but also temporal structures and allowing to account for multi-dimensional evolutions of the model in the correction.&lt;/p&gt;

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