To avoid dangerous climate change impact, the Paris Agreement sets out two ambitious goals: to limit the global warming to be well below 2 ℃ and to pursue effort for the global warming to be below 1.5 ℃ above the pre-industrial level. As climate change risks may be region-dependent, changes in magnitude and probability of extreme precipitation over China are investigated under those two global warming levels based on simulations from the Coupled Model Inter-Comparison Projects Phase 5. The focus is on the added changes due to the additional half a degree warming from 1.5 ℃ to 2 ℃ . Results show that regional average changes in the magnitude do not depend on the return periods with a relative increase around 7% and 11% at the 1.5 ℃ and 2 ℃ global warming levels, respectively. The additional half a degree global warming adds an additional increase in the magnitude by nearly 4%. The regional average changes in term of occurrence probabilities show dependence on the return periods, with rarer events(longer return periods) having larger increase of risk. For the 100-year historical event, the probability is projected to increase by a factor of 1.6 and 2.4 at the 1.5 ℃ and 2 ℃ global warming levels, respectively.The projected changes in extreme precipitation are independent of the RCP scenarios.
Regional extreme cold events have changed notably with recent global warming.Understanding how these cold extremes change in China is an urgent issue.This study examines the responses of the dominant mode of China coldwave intensity （CWI） to global warming by comparing observations with simulations from the Intergovernmental Panel on Climate Change （IPCC） Fourth Assessment Report （AR4）.The leading modes of the CWI derived from empirical orthogonal function （EOF） analysis have different features in different epochs.During the cold period （1957-1979）,the leading mode is characterized by centers of extreme values of CWI in northern China; while during the warm period （1980-2009）,the leading mode features two maximum loading centers over northern and southern China.The southward extension of the extreme value center is associated with an increase in the intensity of coldwave variations in southern China relative to previous decades.A multi-model ensemble of seven state-of-the-art climate models shows an extension of the maximum loading of the CWI leading mode into southern China by the end of the 21st century （2080-2099） under the A1B global warming scenario （atmospheric CO2 concentration of 720 ppm）.These results indicate that the primary response of the leading mode of CWI to global warming might be a southward extension of the extreme value center.This response may be associated with the southward shift of the storm track observed during recent decades.A significant change in the baroclinic growth rates around 40°N is accompanied by a consistent change in synoptic eddies in the troposphere,which may indicate a shift in the preferred latitude for the growth of eddies.As a result,the storm track tends to move southward,suggesting that southern China may experience increased storminess due to increased baroclinic instability in the troposphere.
By using observational daily precipitation data over the Yangtze-Huaihe River basin, ERA-40 data, and the data from eight CMIP5 climate models, statistical downscaling models are constructed based on BP-CCA （combination of empirical orthogonal function and canonical correlation analysis） to project future changes of precipitation. The results show that the absolute values of domain-averaged precipitation relative errors of most models are reduced from 8%-46% to 1% 7% after statistical downscaling. The spatial correlations are all improved from less than 0.40 to more than 0.60. As a result of the statistical downscaling multi- model ensemble （SDMME）, the relative error is improved from -15.8% to -1.3%, and the spatial correlation increases significantly from 0.46 to 0.88. These results demonstrate that the simulation skill of SDMME is relatively better than that of the multimodel ensemble （MME） and the downscaling of most individual models. The projections of SDMME reveal that under the RCP （Representative Concentration Pathway） 4.5 scenario, the projected domain-averaged precipitation changes for the early （2016-2035）, middle （2046 2065）, and late （2081-2100） 21st century are 1.8%, 6.1%, and 9.9%, respectively. For the early period, the increasing trends of precipitation in the western region are relatively weak, while the precipitation in the east shows a decreasing trend. Furthermore, the reliability of the projected changes over the area east of l15°E is higher than that in the west. The stations with significant increasing trends are primarily located over the western region in both the middle and late periods~ with larger magnitude for the latter. Stations with high reliability mainly appear in the region north of 28.5°N for both periods.
High-resolution surface air temperature data are critical to regional climate modeling in terms of energy balance,urban climate change,and so on.This study demonstrates the feasibility of using Moderate Resolution Imaging Spectroradiometer（MODIS）land surface temperature（LST）to estimate air temperature at a high resolution over the Yangtze River Delta region,China.It is found that daytime LST is highly correlated with maximum air temperature,and the linear regression coefficients vary with the type of land surface.The air temperature at a resolution of 1 km is estimated from the MODIS LST with linear regression models.The estimated air temperature shows a clear spatial structure of urban heat islands.Spatial patterns of LST and air temperature differences are detected,indicating maximum differences over urban and forest regions during summer.Validations are performed with independent data samples,demonstrating that the mean absolute error of the estimated air temperature is approximately 2.5°C,and the uncertainty is about 3.1°C,if using all valid LST data.The error is reduced by 0.4°C（15%）if using best-quality LST with errors of less than 1 K.The estimated high-resolution air temperature data have great potential to be used in validating high-resolution climate models and other regional applications.
