Dynamical downscaling of regional climate: A review of methods and limitations

Xu, Zhongfeng; Han, Ying; Yang, Zong‐Liang

doi:10.1007/s11430-018-9261-5

Cited by 133 publications

(72 citation statements)

References 78 publications

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“…This fact could be related to the interaction of the microphysical scheme with the association of PBL (MYJ) scheme, which is in line with other studies with higher precipitation totals and more convective precipitation [89,90]. Additionally, for precipitation over areas of complex topography, wet bias is particularly found to be common to several RCMs [91,92], and is probably caused by an overestimation of orographic precipitation enhancement [93], and/or to an inaccurate PBL simulation [7,11,94]. The Taylor diagrams shown in Figure 3 provide the comparative assessment of the four different model experiments to the choice of the physical parameterizations, to simulate the seasonal spatial pattern of daily precipitation during the examined period.…”

Section: Precipitationsupporting

confidence: 84%

Section: Precipitationsupporting

confidence: 83%

“…According to El-Samra et al [1] however, a number of physical mechanisms (e.g., convection, clouds and precipitation, heterogeneity of surface fluxes and planetary boundary layer (PBL) turbulence) is not accurately represented in the GCMs for the simulation of physically consistent regional and local circulations, particularly for the regions characterized by complex topographical features, due to the rather coarse spatial resolution of approximately 80 to 300 km. To overcome such obstacles, the dynamical downscaling technique has been developed using Regional Climate Models (RCMs), and applied successfully in a number of climate modeling studies so far, such as those of [2][3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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A Sensitivity Study of High-Resolution Climate Simulations for Greece

Politi

Sfetsos

Vlachogiannis

et al. 2020

Climate

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In the present study, the ability of the Advanced Weather Research and Forecasting numerical model (WRF-ARW) to perform climate regionalization studies in the topographically complex region of Greece, was examined in order to explore the possibility of a more reliable selection of physical schemes for the simulation of historical and future high resolution (5 km) climate model experiments to investigate the impact of climate change. This work is directly linked to a previous study investigating the performance of seven different model setups for one year, from which the need was derived for further examination of four different simulations to investigate the model sensitivity on the representation of surface variables statistics during a 5-year period. The results have been compared with observational data for maximum and minimum air temperature and daily precipitation through statistical analysis. Clear similarities were found in precipitation patterns among simulations and observations, yielding smoothly its inter-annual variability, especially during the wettest months and summer periods, with the lowest positive percentage BIAS calculated at about 19% for the selected combination of physics parameterizations (PP3). Regarding the maximum and minimum temperature, statistical analysis showed a high correlation above 0.9, and negative bias around 1−1.5 °C, and positive bias near 2 °C, respectively.

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Section: Precipitationsupporting

confidence: 84%

Section: Precipitationsupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

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A Sensitivity Study of High-Resolution Climate Simulations for Greece

Politi

Sfetsos

Vlachogiannis

et al. 2020

Climate

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“…In addition, vector wind is one of the most important lateral boundary forcing variables in dynamical downscaling simulations. The mean states and interannual variation of wind in GCM can significantly affect the downscaled temperature, precipitation, and extreme events (e.g., Yang 2012, 2015;Bruyère et al 2013;Xu et al 2018). Therefore, our evaluation can also guide model selections for the purpose of dynamical downscaling simulation.…”

Section: Discussionmentioning

confidence: 99%

Evaluating vector winds in the Asian-Australian monsoon region simulated by 37 CMIP5 models

Huang

Guo

2018

Clim Dyn

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Vector wind plays a crucial role in shaping regional climate through transferring energy and moisture. In this study, we evaluate 37 Coupled Model Intercomparison Project Phase 5 (CMIP5) models and multi-model ensembles (MME) in terms of the climatological mean state, annual cycle, and interannual variability of vector winds in the Asian-Australian monsoon (A-AM) region. Unlike most previous studies those assessed meridional and zonal wind separately, we treat vector wind as a whole by employing a recently developed vector field evaluation method. The results are summarized as follows: (1) MME exhibits the best performance in reproducing the climatological mean of vector winds, followed by CESM1-CAM5 and three MPI-ESM models. However, models still show significant biases characterized by overestimated lower level vector winds and its spatial variation. The biases are mainly rooted in the anomaly components of vector winds and are observed in the regions with complex topography. (2) CMIP5 models can well simulate the annual cycle of upper-tropospheric vector winds, especially in the extratropical regions, but show large biases and dispersion over complex terrains in the lower troposphere.(3) MME still outperforms individual model for the simulation of interannual variance of vector winds, although most CMIP5 models overestimate the strength of vector wind variability in the lower troposphere. (4) Model skills in simulating climatological means, annual cycle, and interannual variability are positively correlated with each other to a certain degree over the A-AM region, suggesting an improvement in climatological mean may lead to a better simulation in the annual cycle or interannual variability of vector winds.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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“…Another concern associated with the MVBC method is the change in climate trends between the present and future climates; the changes in climate trends are included in the perturbation component (Xu et al, ). If the changes in climate trends are considerably large, applying bias correction to the future perturbation component means the correction of not only the synoptic‐scale fluctuations but also climate trends.…”

Section: Modified Boundary Dds Methodsmentioning

confidence: 99%

Methodology of the Constraint Condition in Dynamical Downscaling for Regional Climate Evaluation: A Review

Adachi

Tomita

2020

JGR Atmospheres

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The dynamical downscaling method with a regional climate model (RCM) is widely used to assess the spatially detailed information about regional climate. However, the RCM result is considerably influenced by the systematic errors inherent to a general circulation model (GCM), which provides the initial and boundary conditions to the RCM. Such systematic errors sometimes lead to meaningless downscaled results. Many modified boundary dynamical downscaling (MBDDS) methods have been proposed to reduce the influences of the systematic errors of a GCM and extract meaningful signals for regional climate change. This study comprehensively reviews the MBDDS methods. The MBDDS methods partially modify the climate information projected by a GCM and use them as the boundary conditions of an RCM. The objectives of the methods are organized into two main objectives, that is, to obtain more reliable projections by correcting the biases in boundary conditions and to better understand the regional climate change mechanisms. To ensure comprehensive understanding of the MBDDS methods, this study attempts to interpret the errors included in the downscaled results using mathematical expressions, separating the GCM‐originated bias and RCM's own bias. Using this analysis, the MBDDS methods are classified based on the following questions: What effect is expected from the bias correction? Which of the climate change components projected by a GCM is considered when assessing the future climate change? The direction and issues that need to be addressed in the future for better understanding the regional climate change are also discussed.

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Dynamical downscaling of regional climate: A review of methods and limitations

Cited by 133 publications

References 78 publications

A Sensitivity Study of High-Resolution Climate Simulations for Greece

A Sensitivity Study of High-Resolution Climate Simulations for Greece

Evaluating vector winds in the Asian-Australian monsoon region simulated by 37 CMIP5 models

Methodology of the Constraint Condition in Dynamical Downscaling for Regional Climate Evaluation: A Review

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