Statistical downscaling and dynamical downscaling of regional climate in China: Present climate evaluations and future climate projections

Tang, Jianping; Niu, Xiaorui; Hong-xia, Gao; Wang, Xueyuan; Wu, Jian

doi:10.1002/2015jd023977

Cited by 150 publications

(82 citation statements)

References 61 publications

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“…By using RCMs, which is also called the dynamic downscaling technique, many studies of the regional climate over China have been performed (e.g. Zou and Zhou, ; Yu et al ., ; Bao et al ., ; Qin and Xie, ; Tang et al ., ; Yang et al ., ). In addition, increasing attention has been paid to climate extremes in recent years because of the potentially severe consequences (Fowler and Wilby, ; IPCC, ; Liu et al ., ; Moore et al ., ).…”

Section: Introductionmentioning

confidence: 99%

Climate change projections over China using regional climate models forced by two CMIP5 global models. Part I: evaluation of historical simulations

Hui

Tang

Wang

et al. 2017

Intl Journal of Climatology

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Forced by two Coupled Model Intercomparison Project phase 5 (CMIP5) global models, EC‐EARTH and IPSL‐CM5A, both regional climate models (RCMs) of Regional Climate Model system version 4 (RegCM4) and Weather Research and Forecasting (WRF) are used to perform regional climate simulations over China for 1980–2000. In general, the simulations in Institute Pierre Simon Laplace‐Climate Model version 5A (IPSL‐CM5A) are improved by the downscaling of many aspects of both precipitation and surface air temperature, with improvements including the significant enhancement of the resolution and presentation of regional processes in the RCMs. However, the improvement over EC‐EARTH is limited. In addition, the added values also depend on the seasons, geophysical locations over the country and the variables presented. For the mean climates, both RCMs are able to exhibit better annual and winter precipitation measurements when downscaling IPSL‐CM5A over most areas. The superiority of the RCMs over IPSL‐CM5A for the surface air temperature is only obtained in summer. Seasonal cycles of both precipitation and surface air temperature are well simulated by all the models. The RCMs succeed in improving the seasonal cycles of precipitation over both GCMs in most sub‐regions, but the seasonal cycles of temperature are only improved over IPSL‐CM5A. In addition, the downscaling demonstrates more consistent probability distribution functions (PDFs) of precipitation with those of the observations in most sub‐regions. For extreme precipitation, the RCMs provide outstanding convective wet days (CWD) with reduced biases and enhanced presentations of spatial patterns. For the other three extreme precipitation indices, the improvement of the RCMs is relatively weak, with superior over the IPSL‐CM5A in several areas. The extreme temperature indices are better modelled by the RCMs, except for the minimum values of the daily minimum temperatures (TNn), with greater added values over IPSL‐CM5A than those over EC‐EARTH. Comparing two RCMs, WRF shows advantages in the downscaling precipitation, while RegCM4 works better for the surface air temperature.

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Section: Introductionmentioning

confidence: 99%

Climate change projections over China using regional climate models forced by two CMIP5 global models. Part I: evaluation of historical simulations

Hui

Tang

Wang

et al. 2017

Intl Journal of Climatology

Self Cite

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“…Dynamic downscaling based on regional climate models (RCM) that have high resolution are often adopted to study local climate and extreme events forced by GCMs [ Bao et al ., ; Burger et al ., ; Gao et al ., ; Knutson et al ., ; Pielke and Wilby , ; Shi et al ., ; Singh et al ., ; Tang et al ., ; Zhai et al ., ; Zou and Zhou , ]. Wang et al .…”

Section: Introductionmentioning

confidence: 99%

Detecting changes in future precipitation extremes over eight river basins in China using RegCM4 downscaling

Qin

Xie

2016

JGR Atmospheres

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To detect the frequency and intensity of precipitation extremes in China for the middle 21st century, simulations were conducted with the regional climate model RegCM4 forced by the global climate model GFDL_ESM2M under the middle emission scenario (RCP4.5). Compared with observed precipitation extremes for the reference period from 1982 to 2001, RegCM4 generally performed better in most river basins of China relative to GFDL. In the future period 2032–2051, more wet extremes will occur relative to the present period in most study areas, especially in southeast China while significantly less dry extremes will occur in arid and semiarid areas in northwest China. In contrast, areas in northwest China showed an increase in the trend of dry extremes (CDD) and a decrease in the trend of wet extremes (R95p and Rx5day), which might result in more drought in the future. Finally, we discuss in detail the possible reason of these processes, such as zonal wind, vertical wind, and water vapor. In the Huaihe river basin (HU), reduced south winds in summer (June–August) and a decrease of the upward vertical p velocity cause less future precipitation and might lead to changes of extreme events. We also completed correlation analysis between the precipitation extreme indices and the climate factors and found that the precipitation extremes were more sensitive to the annual and seasonal mean precipitation, total water vapor, and upward vertical wind relative to the geopotential height and 2 m temperature over most river basins in China. Perhaps the changes of some wet extremes could be verified partly through changes of annual precipitation due to their high consistence.

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“…SDSM is a widely-used downscaling model, and is relatively simple to apply. Extensive literature is available on the application of SDSM for climate-related studies and downscaling [22][23][24][25]. SDSM performs seven functions of quality control and transformations, screening, model calibration, weather generator, data analysis, graphical analysis and scenario generation [21].…”

Section: Downscaling Overviewmentioning

confidence: 99%

Future Climate of Colombo Downscaled with SDSM-Neural Network

Dorji

Herath

Mishra

2017

Climate

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Abstract:The Global Climate Model (GCM) run at a coarse spatial resolution cannot be directly used for climate impact studies. Downscaling is required to extract the sub-grid and local scale information. This paper investigates if the artificial neural network (ANN) is better than the widely-used regression-based statistical downscaling model (SDSM) for downscaling climate for a site in Colombo, Sri Lanka. Based on seasonal and annual model biases and the root mean squared error (RMSE), the ANN performed better than the SDSM for precipitation. This paper proposes a novel methodology for improving climate predictions by combining SDSM with neural networks. This method will allow a user to apply SDSM with a neural network model for higher skills in downscaling. The study uses the Canadian Earth System Model (CanESM2) of the IPCC Fifth Assessment Report, reanalysis from the National Center for Environmental Prediction (NCEP), and the Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) project data as the observation. SDSM and the focused time-delayed neural network (TDNN) models are used for the downscaling. The projected annual increase for Representative Concentration Pathway (RCP) is 8.5; the average temperature is 2.83 • C (SDSM) and 3.03 • C (TDNN), and rainfall is 33% (SDSM) and 63% (TDNN) for 2080's.

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Statistical downscaling and dynamical downscaling of regional climate in China: Present climate evaluations and future climate projections

Cited by 150 publications

References 61 publications

Climate change projections over China using regional climate models forced by two CMIP5 global models. Part I: evaluation of historical simulations

Climate change projections over China using regional climate models forced by two CMIP5 global models. Part I: evaluation of historical simulations

Detecting changes in future precipitation extremes over eight river basins in China using RegCM4 downscaling

Future Climate of Colombo Downscaled with SDSM-Neural Network

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