Two-Way Integration of WRF and CCSM for Regional Climate Simulations

2013 ◽  
Author(s):  
Wuyin Lin ◽  
Minghua Zhang ◽  
Juanxiong He ◽  
Xiangmin Jiao ◽  
Ying Chen ◽  
...  
Keyword(s):  
Regional Climate ◽  
Climate Simulations

Climatological characterization of tropical cyclones detected in the regional climate simulations over the CORDEX‐SEA domain

2021 ◽  
Author(s):  
Jennifer Tibay ◽  
Faye Cruz ◽  
Fredolin Tangang ◽  
Liew Juneng ◽  
Thanh Ngo‐Duc ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
Regional Climate ◽  
Climate Simulations

scholarly journals Influence of changed vegetations fields on regional climate simulations in the Barents Sea Region

2007 ◽  
Vol 87 (1-2) ◽  
pp. 35-50 ◽  
Author(s):  
Holger Göttel ◽  
Jörn Alexander ◽  
Elke Keup-Thiel ◽  
Diana Rechid ◽  
Stefan Hagemann ◽  
...  
Keyword(s):  
Barents Sea ◽  
Regional Climate ◽  
The Barents Sea ◽  
Climate Simulations

The first multi-model ensemble of regional climate simulations at kilometer-scale resolution part 2: historical and future simulations of precipitation

2021 ◽  
Author(s):  
Emanuela Pichelli ◽  
Erika Coppola ◽  
Stefan Sobolowski ◽  
Nikolina Ban ◽  
Filippo Giorgi ◽  
...  
Keyword(s):  
Regional Climate ◽  
Model Ensemble ◽  
Climate Simulations

scholarly journals Compound extremes in a changing climate – a Markov chain approach

2016 ◽  
Vol 23 (6) ◽  
pp. 375-390 ◽  
Author(s):  
Katrin Sedlmeier ◽  
Sebastian Mieruch ◽  
Gerd Schädler ◽  
Christoph Kottmeier
Keyword(s):  
Extreme Events ◽  
Climate Models ◽  
Heat Waves ◽  
Dynamic Change ◽  
Regional Climate ◽  
Heavy Precipitation ◽  
Northern Germany ◽  
Russian Region ◽  
Climate Simulations ◽  
Potential Impact

Abstract. Studies using climate models and observed trends indicate that extreme weather has changed and may continue to change in the future. The potential impact of extreme events such as heat waves or droughts depends not only on their number of occurrences but also on "how these extremes occur", i.e., the interplay and succession of the events. These quantities are quite unexplored, for past changes as well as for future changes and call for sophisticated methods of analysis. To address this issue, we use Markov chains for the analysis of the dynamics and succession of multivariate or compound extreme events. We apply the method to observational data (1951–2010) and an ensemble of regional climate simulations for central Europe (1971–2000, 2021–2050) for two types of compound extremes, heavy precipitation and cold in winter and hot and dry days in summer. We identify three regions in Europe, which turned out to be likely susceptible to a future change in the succession of heavy precipitation and cold in winter, including a region in southwestern France, northern Germany and in Russia around Moscow. A change in the succession of hot and dry days in summer can be expected for regions in Spain and Bulgaria. The susceptibility to a dynamic change of hot and dry extremes in the Russian region will probably decrease.

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 Regional Climate Simulations With the Community Earth System Model

2018 ◽  
Vol 10 (6) ◽  
pp. 1245-1265 ◽  
Author(s):  
A. Gettelman ◽  
P. Callaghan ◽  
V. E. Larson ◽  
C. M. Zarzycki ◽  
J. T. Bacmeister ◽  
...  
Keyword(s):  
Regional Climate ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Climate Simulations ◽  
Community Earth System Model
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