Precipitation projections of the first multi-model ensemble of regional climate simulations at convection permitting scale

2020 ◽  
Author(s):  
Emanuela Pichelli ◽  
Erika Coppola ◽  
Nikolina Ban ◽  
Filippo Giorgi ◽  
Paolo Stocchi ◽  
...  
Keyword(s):  
Climate Model ◽  
Regional Climate ◽  
Heavy Precipitation ◽  
Precipitation Intensity ◽  
Historical Period ◽  
Model Ensemble ◽  
Climate Simulations ◽  
Local Impacts ◽  
Precipitation Changes ◽  
Impacts Of Climate Change

<p>We present a multi-model ensemble of regional climate model scenario simulations run at scales allowing for explicit treatment of convective processes (2-3km) over historical and end of century time slices, providing an overview of future precipitation changes over the Alpine domain within the convection-permitting CORDEX-FPS initiative. The 12 simulations of the ensemble have been performed by different research groups around Europe. The simulations are compared with high resolution observations to assess the performance over the historical period and the ensemble of 12 to 25 km resolution driving models is used as a benchmark.</p><p>An improvement of the representation of fine scale details of the analyzed fields on a seasonal scale is found, as well as of the onset and peak of the summer diurnal convection. An enhancement of the projected patterns of change and modifications of its sign for the daily precipitation intensity and heavy precipitation over some regions are found with respect to coarse resolution ensemble. A change of the amplitude of the diurnal cycle for precipitation intensity and frequency is also shown, as well also a larger positive change for high to extreme events for daily and hourly precipitation distributions. The results  are challenging and promising for further assessment of the local impacts of climate change.</p>

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 A pragmatic approach for the downscaling and bias correction of regional climate simulations – evaluation in hydrological modeling

2011 ◽  
Vol 4 (1) ◽  
pp. 45-63 ◽  
Author(s):  
T. Marke ◽  
W. Mauser ◽  
A. Pfeiffer ◽  
G. Zängl
Keyword(s):  
Hydrological Modeling ◽  
Hydrological Model ◽  
Climate Model ◽  
Regional Climate ◽  
Model Performance ◽  
Reanalysis Data ◽  
Historical Period ◽  
Climate Simulations ◽  
Meteorological Observations ◽  
Climate Model Simulations

Abstract. The present study investigates a statistical approach for the downscaling of climate simulations focusing on those meteorological parameters most commonly required as input for climate change impact models (temperature, precipitation, air humidity and wind speed), including the option to correct biases in the climate model simulations. The approach is evaluated by the utilization of a hydrometeorological model chain consisting of (i) the regional climate model MM5 (driven by reanalysis data at the boundaries of the model domain), (ii) the downscaling and model interface SCALMET, and (iii) the hydrological model PROMET. The results of four hydrological model runs are compared to discharge recordings at the gauge of the Upper Danube Watershed (Central Europe) for the historical period of 1972–2000 on a daily time basis. The comparison reveals that the presented approaches allow for a more accurate simulation of discharge for the catchment of the Upper Danube Watershed and the considered gauge at the outlet in Achleiten. The correction for subgrid-scale variability is shown to reduce biases in simulated discharge compared to the utilization of bilinear interpolation. Further enhancements in model performance could be achieved by a correction of biases in the RCM data within the downscaling process. Although the presented downscaling approach strongly improves the performance of the hydrological model, deviations from the observed discharge conditions persist that are not found when driving the hydrological model with spatially distributed meteorological observations.

scholarly journals Impacts of Climate Change on the Hydro-Climate of Peninsular Malaysia

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1798 ◽  
Author(s):  
Ir. Mohd Zaki bin Mat Amin ◽  
Ali Ercan ◽  
Kei Ishida ◽  
M. Levent Kavvas ◽  
Z.Q. Chen ◽  
...  
Keyword(s):  
Climate Change ◽  
21St Century ◽  
Climate Model ◽  
Regional Climate ◽  
Water Storage ◽  
Peninsular Malaysia ◽  
Soil Water Storage ◽  
Historical Period ◽  
Coastal Regions ◽  
Impacts Of Climate Change

In this study, a regional climate model was used to dynamically downscale 15 future climate projections from three GCMs covering four emission scenarios (SRES B1, A1FI, A1B, A2) based on Coupled Model Intercomparison Project phase 3 (CMIP3) datasets to 6-km horizontal resolution over the whole Peninsular Malaysia. Impacts of climate change in the 21st century on the precipitation, air temperature, and soil water storage were assessed covering ten watersheds and twelve coastal regions. Then, by coupling a physical hydrology model with the regional climate model, the impacts of the climate change on river flows were assessed at the outlets of ten watersheds in Peninsular Malaysia. It was found that the increase in the 30-year mean annual precipitation from 1970–2000 to 2070–2100 will vary from 17.1 to 36.3 percent among the ten watersheds, and from 22.9 to 45.4 percent among twelve coastal regions. The ensemble average of the basin-average annual mean air temperature will increase about 2.52 °C to 2.95 °C from 2010 to 2100. In comparison to the historical period, the change in the 30-year mean basin-average annual mean soil water storage over the ten watersheds will vary from 0.7 to 10.9 percent at the end of 21st century, and that over the twelve coastal regions will vary from −1.7 to 15.8 percent. Ensemble averages of the annual mean flows of the 15 projections show increasing trends for the 10 watersheds, especially in the second half of the 21st century. In comparison to the historical period, the change in the 30-year average annual mean flows will vary from −2.1 to 14.3 percent in the early 21st century, 4.4 to 23.8 percent in the middle 21st century, and 19.1 to 45.8 percent in the end of 21st century.

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 The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, part I: evaluation of precipitation

2021 ◽  
Author(s):  
Nikolina Ban ◽  
Cécile Caillaud ◽  
Erika Coppola ◽  
Emanuela Pichelli ◽  
Stefan Sobolowski ◽  
...  
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Heavy Precipitation ◽  
Model Ensemble ◽  
Grid Spacing ◽  
Model Simulations ◽  
Coarse Resolution ◽  
Climate Simulations ◽  
Scale Models ◽  
Horizontal Grid

AbstractHere we present the first multi-model ensemble of regional climate simulations at kilometer-scale horizontal grid spacing over a decade long period. A total of 23 simulations run with a horizontal grid spacing of $$\sim $$ ∼ 3 km, driven by ERA-Interim reanalysis, and performed by 22 European research groups are analysed. Six different regional climate models (RCMs) are represented in the ensemble. The simulations are compared against available high-resolution precipitation observations and coarse resolution ($$\sim $$ ∼ 12 km) RCMs with parameterized convection. The model simulations and observations are compared with respect to mean precipitation, precipitation intensity and frequency, and heavy precipitation on daily and hourly timescales in different seasons. The results show that kilometer-scale models produce a more realistic representation of precipitation than the coarse resolution RCMs. The most significant improvements are found for heavy precipitation and precipitation frequency on both daily and hourly time scales in the summer season. In general, kilometer-scale models tend to produce more intense precipitation and reduced wet-hour frequency compared to coarse resolution models. On average, the multi-model mean shows a reduction of bias from $$\sim \,$$ ∼  −40% at 12 km to $$\sim \,$$ ∼  −3% at 3 km for heavy hourly precipitation in summer. Furthermore, the uncertainty ranges i.e. the variability between the models for wet hour frequency is reduced by half with the use of kilometer-scale models. Although differences between the model simulations at the kilometer-scale and observations still exist, it is evident that these simulations are superior to the coarse-resolution RCM simulations in the representing precipitation in the present-day climate, and thus offer a promising way forward for investigations of climate and climate change at local to regional scales.

scholarly journals A pragmatic approach for the downscaling and bias correction of regional climate simulations: evaluation in hydrological modeling

2011 ◽  
Vol 4 (3) ◽  
pp. 759-770 ◽  
Author(s):  
T. Marke ◽  
W. Mauser ◽  
A. Pfeiffer ◽  
G. Zängl
Keyword(s):  
Cross Validation ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Climate Model ◽  
Regional Climate ◽  
Historical Period ◽  
Climate Simulations ◽  
Validation Experiment ◽  
Spatially Distributed ◽  
Model Chain

Abstract. The present study investigates a statistical approach for the downscaling of climate simulations focusing on those meteorological parameters most commonly required as input for climate change impact models (temperature, precipitation, air humidity and wind speed), including the option to correct biases in the climate model simulations. The approach is evaluated by the utilization of a hydrometeorological model chain consisting of (i) the regional climate model MM5 (driven by reanalysis data at the boundaries of the model domain), (ii) the downscaling and model interface SCALMET, and (iii) the physically based hydrological model PROMET. The results of different hydrological model runs set up for the historical period 1971–2000 are compared to discharge recordings at the gauge of the Upper Danube Watershed (Central Europe) on a daily time basis. To avoid "in-sample" evaluation, a cross-validation approach is followed splitting the period in two halves of 15 yr. While one half is utilized to derive the downscaling functions based on spatially distributed observations (e.g. 1971–1985), the other is used for the application of the downscaling functions within the hydrometeorological model chain (e.g. 1986–2000). By alternately using both parts for the generation and the application of the downscaling functions, discharge simulations are generated for the whole period 1971–2000. The comparison of discharge simulations and observations reveals that the presented approaches allow for a more accurate simulation of discharge in the catchment of the Upper Danube Watershed and the considered gauge at the outlet in Achleiten. The correction for subgrid-scale variability is shown to reduce biases in simulated discharge compared to the utilization of bilinear interpolation. Further enhancements in model performance could be achieved by a correction of biases in the RCM data within the downscaling process. These findings apply to the cross-validation experiment as well as to an "in-sample" application, where the whole period 1971–2000 is used for the generation and the application of the downscaling functions. Although the presented downscaling approach strongly improves the performance of the hydrological model, deviations from the observed discharge conditions persist that are not found when driving the hydrological model with spatially distributed meteorological observations.

scholarly journals Investigating future precipitation changes over China through a high-resolution regional climate model ensemble

2017 ◽  
Vol 5 (3) ◽  
pp. 285-303 ◽  
Author(s):  
Junhong Guo ◽  
Guohe Huang ◽  
Xiuquan Wang ◽  
Yongping Li ◽  
Qianguo Lin
Keyword(s):  
High Resolution ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Model Ensemble ◽  
Precipitation Changes

scholarly journals The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, Part I: Evaluation of precipitation

2020 ◽  
Author(s):  
Nikolina Ban ◽  
Erwan Brisson ◽  
Cécile Caillaud ◽  
Erika Coppola ◽  
Emanuela Pichelli ◽  
...  
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Heavy Precipitation ◽  
Horizontal Resolution ◽  
Model Ensemble ◽  
Precipitation Frequency ◽  
Model Simulations ◽  
Coarse Resolution ◽  
Climate Simulations ◽  
Scale Models

&lt;p&gt;Here we present the first multi-model ensemble of climate simulations at kilometer-scale horizontal resolution over a decade long period. A total of 22 simulations, performed by 21 European research groups are analyzed. Six different regional climate models (RCMs) are represented in the ensemble. The simulations are compared against available high-resolution precipitation observations and coarse resolution (12 km) RCMs with parameterized convection. The model simulations and observations are compared with respect to mean precipitation, precipitation intensity and frequency, and heavy precipitation on daily and hourly timescales in different seasons.&lt;/p&gt;&lt;p&gt;The results show that kilometer-scale models produce more realistic representation of precipitation than the coarse resolution RCMs. The most significant improvements are found for heavy precipitation and precipitation frequency on both daily and hourly time scales in the summer season. In general, kilometer-scale models tend to produce more intense precipitation and reduced wet-hour frequency compared to coarse resolution models. Although differences between the model simulations at the kilometer-scale and observations exist, it is evident that they are superior to the coarse-resolution RCMs in the simulation of precipitation in the present-day climate, and thus offer a promising way forward for investigations of climate and climate change at local to regional scales.&lt;/p&gt;

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