Future changes of climate extremes in the 21st century over Xiongan New Area and Jing-Jin-Ji district were investigated based on high resolution (6.25 km) combined statistical and dynamical downscaling datasets, which were produced using the observation of CLDAS, five sets of regional climate change simulations by RegCM4.4, and statistical downscaling with quantile mapping. Of that, the RegCM4.4 simulations were conducted over East Asia under RCP4.5 scenario driven by five different CMIP5 global climate models of CSIRO-Mk3-6-0, EC-EARTH, HadGEM2-ES, MPI-ESM-MR and NorESM1-M. Validations of the present climate show that the multi-model ensemble mean can well reproduce the spatial distribution of most climate extremes, and better performance can be found in the temperature-related climate extremes. However, some biases can also be observed, especially for the consecutive drought days (CDD). In the context of global warming, increased extreme warm events, decreased extreme cold events and consecutive drought days and increased extreme heavy precipitation events are projected in Xiongan New Area and the whole Jing-Jin-Ji district. In specific, increased TXx (Maximum value of daily maximum temperature) and TNn (Minimum value of daily minimum temperature) can be found, with the value exceeding 2.4℃ and 3.2℃, respectively. More pronounced increase of SU (Number of summer days) over the mountainous areas compared with the plain is observed, while greater increase of TR (Number of tropical nights) is found over the plain. The increase of SU and TR are in the range of 20 ? 40 d and5 ? 40 d, respectively. Both the FD (Number of frost days) and ID (Number of icing days) will decrease, with the decline above 10 d and 5 d, respectively. Precipitation-related climate extremes including CDD, R1mm (Annual count of days when daily precipitation≥1mm) and R10mm (Annual count of days when daily precipitation≥10 mm) are mainly on decrease with small values of ?10% ? 0 while increase of RX5day (Maximum consecutive 5-day precipitation), SDII (Simple precipitation intensity index) and R20mm (Annual count of days when daily precipitation≥20 mm) are found in most areas with the values in the range of 0 ? 25%. Regional mean changes show that the linear trends are more significantly in temperature-related climate extremes compared with those in precipitation-related climate extremes. Comparing the two regions, greater uncertainty of the simulations in Xiongan New Area can be found, which indicates the deficiency of the model in local scale areas.
石英,韩振宇,徐影,周波涛,吴佳. 6.25 km高分辨率降尺度数据对雄安新区及整个京津冀地区未来极端气候事件的预估[J]. 气候变化研究进展, 2019, 15(2): 140-149.
Ying SHI,Zhen-Yu HAN,Ying XU,Bo-Tao ZHOU,Jia WU. Future changes of climate extremes in Xiongan New Area and Jing-Jin-Ji district based on high resolution (6.25 km) combined statistical and dynamical downscaling datasets. Climate Change Research, 2019, 15(2): 140-149.
Gao X J, Zhao Z C, Ding Y H , et al. Climate change due to greenhouse effects in China as simulated by a regional climate model[J]. Advances in Atmospheric Sciences, 2001,18(6):1224-1230
Zhou T J, Li Z X . Simulation of the East Asian summer monsoon by using a variable resolution atmospheric GCM[J]. Climate Dynamics, 2002,19:167-180
Xu Y, Gao X J, Giorgi F . Upgrades to the REA method for producing probabilistic climate change predictions[J]. Climate Research, 2010,41:61-81
Xu Y, Wu J, Shi Y , et al. Change in extreme climate events over China based on CMIP[J]. Atmospheric and Oceanic Science Letters, 2015,8:185-192
Zhou T J, Yu R C . Twentieth century surface air temperature over China and the globe simulated by coupled climate models[J]. Journal of Climate, 2006,19:5843-5858
Gao X J, Shi Y, Song R Y , et al. Reduction of future monsoon precipitation over China: comparison between a high resolution RCM simulation and the driving GCM[J]. Meteorology and Atmospheric Physics, 2008,100(1):73-86
Gao X J, Shi Y, Zhang D F , et al. Uncertainties in monsoon precipitation projections over China: results from two high-resolution RCM simulations[J]. Climate Research, 2012,52(1):213-226
Ji Z M, Kang S C . Evaluation of extreme climate events using a regional climate model for China[J]. International Journal of Climatology, 2015,35:888-902
Mei C, Liu J H, Chen M T , et al. Multi-decadal spatial and temporal changes of extreme precipitation patterns in northern China (Jing-Jin-Ji district, 1960-2013)[J]. Quaternary International, 2018,476:1-13
Jiang R G, Yu X, Xie J C , et al. Recent changes in daily climate extremes in a serious water shortage metropolitan region, a case study in Jing-Jin-Ji of China[J]. Theoretical Applied Climatology, 2018,134:565-584
Han Z Y, Shi Y, Wu J , et al. High resolution combined statistical and dynamical downscaling for multi-variables in the Beijing-Tianjin-Hebei region of China[J]. Submitted to International Journal of Climatology, 2018
Giorgi F, Coppola E, Solmon F , et al. RegCM4: model description and illustrative basic performance over selected CORDEX domains[J]. Climate Research, 2012,52:7-29
Giorgi F, Jones C, Asrar G . Addressing climate information needs at the regional level: the CORDEX framework[J]. WMO Bulletin, 2009,58:175-183
Han Z Y, Zhou B T, Xu Y , et al. Projected changes in haze pollution potential in China: an ensemble of regional climate model simulations[J]. Atmospheric Chemistry and Physics, 2017,17(16):10109-10123
Gao X J, Wu J, Shi Y , et al. Future changes in thermal comfort conditions over China based on multi-RegCM4 simulations[J]. Atmospheric and Oceanic Science Letters, 2018. DOI: 10.1080/16742834.2018.147158
Shi Y, Wang G L, Gao X J . Role of resolution in regional climate change projections over China[J]. Climate Dynamics, 2018,51:2375-2396
Cannon A J, Sobie S R, Murdock T Q . Bias correction of GCM precipitation by quantile mapping: how well do methods preserve changes in quantiles and extremes?[J]. Journal of Climate, 2015,28(17):6938-6959
Shi C X, Xie Z H, Qian H , et al. China land soil moisture EnKF data assimilation based on satellite remote sensing data[J]. Science China: Earth Sciences, 2011,54:1430-1440
Shi C X, Jiang L P, Zhang T , , et al. Status. Status and plans of CMA Land Data Assimilation System (CLDAS) project [C]. EGU General Assembly Conference Abstracts, 2014, 16, EGU2014-5671
Zhang X B, Alexander L, Hegerl G C , et al. Indices for monitoring changes in extremes based on daily temperature and precipitation data[J]. WIREs Climate Change, 2011,2:851-870