Simulation and Projection of Climate Extremes in China by a Set of Statistical Downscaled Data
Linxiao WeiLüliu LiuJing ChengYao WuXiaoge XinBaogang YangHongyu TangYonghua LiYong WangTianyu ZhangFen Zhang
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This study assesses present-day extreme climate changes over China by using a set of phase 6 of the Coupled Model Intercomparison Project (CMIP6) statistical downscaled data and raw models outputs. The downscaled data is produced by the adapted spatial disaggregation and equal distance cumulative distribution function (EDCDF) method at the resolution of 0.25° × 0.25° for the present day (1961–2014) and the future period (2015–2100) under the Shared Socioeconomic Path-way (SSP) 2-4.5 than SSP5-8.5 emission scenario. The results show that the downscaling method improves the spatial distributions of extreme climate events in China with higher spatial pattern correlations, Taylor Skill Scores and closer magnitudes no matter single model or multi model ensemble (MME). In the future projections, large inter-model variability between the downscaled models still exists, particular for maximum consecutive 5-day precipitation (RX5). The downscaled MME projects that total precipitation (PTOT) and RX5, will increase with time, especially for the northwest China. The projected heavy precipitation days (R20) also increase in the future. The region of significant increase in R20 locates in the south of river Yangtze. Maxi-mum annual temperature (TXX) and percentage of warm days (TX90p) are projected to increase across the whole country with larger magnitude over the west China. Projected changes of minimum annual temperature (TNN) over the northeastern China is the most significant area. The higher of the emission scenario, the more significant of extreme climates. This reveals that the spatial distribution of extreme climate events will become more uneven in the future.Abstract. Many climate extremes, including heatwaves and heavy precipitation events, are projected to worsen under climate change, with important impacts for society. Future projections required for adaptation are often based on climate model simulations. Given finite resources, trade-offs must be made concerning model resolution, ensemble size, and level of model complexity. Here we focus on the resolution component. A given resolution can be achieved over a region using either global climate models (GCMs) or at lower cost using regional climate models (RCMs) that dynamically downscale coarser GCMs. Both approaches to increasing resolution may better capture small-scale processes and features (downscaling effect), but increased GCM resolution may also improve the representation of the large-scale atmospheric circulation (upscaling effect). The size of this upscaling effect is therefore important for deciding modelling strategies. Here we evaluate the benefits of increased model resolution for both global and regional climate models for simulating temperature, precipitation, and wind extremes over Europe at resolutions that could currently be realistically used for coordinated sets of climate projections at the pan-European scale. First we examine the benefits of regional downscaling by comparing EURO-CORDEX simulations at 12.5 and 50 km resolution to their coarser CMIP5 driving simulations. Secondly, we compare global-scale HadGEM3-A simulations at three resolutions (130, 60, and 25 km). Finally, we separate out resolution-dependent differences for HadGEM3-A into downscaling and upscaling components using a circulation analogue technique. Results suggest limited benefits of increased resolution for heatwaves, except in reducing hot biases over mountainous regions. Precipitation extremes are sensitive to resolution, particularly over complex orography, with larger totals and heavier tails of the distribution at higher resolution, particularly in the CORDEX vs. CMIP5 analysis. CMIP5 models underestimate precipitation extremes, whilst CORDEX simulations overestimate compared to E-OBS, particularly at 12.5 km, but results are sensitive to the observational dataset used, with the MESAN reanalysis giving higher totals and heavier tails than E-OBS. Wind extremes are somewhat stronger and heavier tailed at higher resolution, except in coastal regions where large coastal grid boxes spread strong ocean winds further over land. The circulation analogue analysis suggests that differences with resolution for the HadGEM3-A GCM are primarily due to downscaling effects.
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Abstract The Weather Research and Forecasting (WRF) model has been configured as a regional climate model for the Hawaii region (HRCM) to assess the uncertainties associated with the pseudo–global warming (PGW) downscaling method using different warming increments from phase 5 of the Coupled Model Intercomparison Project (CMIP5) model experiments. Results from 15-km downscaling experiments using warming increments from 10 individual CMIP5 models for the two warming scenarios representative concentration pathway 4.5 (RCP4.5) and 8.5 (RCP8.5) are compared with experiments using multimodel mean warming increments. The results show that changes in 2-m temperatures, 10-m wind speed, rainfall, water vapor path, and trade wind inversion vary significantly among the individual model experiments. This translates into large uncertainties when picking one particular CMIP5 model to provide the warming increments for dynamical downscaling in the Hawaii region. The simulations also show that, despite the large interexperiment spread, a single downscaling experiment using multimodel mean warming increments gives very similar results to the ensemble mean of downscaling experiments using warming increments obtained from 10 individual CMIP5 models. Robust changes of the projected climate by the end of the twenty-first century in the Hawaii region shown by most downscaling experiments include increasing 2-m temperatures with stronger warming at higher elevations, a large increase in precipitable water, and an increase in the number of days with a trade wind inversion (TWI). Furthermore, most experiments agree on a reduction in TWI height and an increase in the TWI strength. Uncertainties in the projected changes in rainfall and 10-m wind speed are large and there is little consensus among the individual downscaling experiments.
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Based on the 1971—2000 monthly precipitation data observed at 34 meteorological stations in the Chongqing region and the 100 m×100 m DEM(Digital Elevation Model) of Chongqing,the spatial distribution of precipitation of Chongqing was studied.In light of the principles of mountain climatology and using GIS(Geographical Information Systems) technology,the factors affecting the spatial distribution of precipitation were analyzed,the model to simulate the spatial distribution of averaged monthly precipitation was established,and the spatial distribution of averaged monthly precipitation was computed.The results show that the precipitation increased with the elevation,and the maximum value of monthly precipitation appeared in the northeast mountainous region;and the seasonal change of precipitation in Chongqing was distinctive.
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Precipitation types
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Abstract An approach for downscaling daily precipitation extremes using historical analogs is applied to simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The method employs a multistep procedure in which the occurrence of extreme precipitation on a given target day is determined on the basis of the probability of extreme precipitation on that day’s closest historical analogs. If extreme precipitation is expected, daily precipitation observations associated with the historical analogs are used to approximate precipitation amounts on the target day. By applying the analog method to historical simulations, the ability of the CMIP5 models to simulate synoptic weather patterns associated with extreme precipitation is assessed. Differences between downscaled and observed precipitation extremes are investigated by comparing the precipitation frequency distributions for a subset of rarely selected extreme analog days with those for all observed days with extreme precipitation. A supplemental composite analysis of the synoptic weather patterns on these rarely selected analog days is utilized to elucidate the meteorological factors that contribute to such discrepancies. Overall, the analog method as applied to CMIP5 simulations yields realistic estimates of historical precipitation extremes, with return-period precipitation biases that are comparable in magnitude to those obtained from dynamically downscaled simulations. The analysis of rarely selected analog days reveals that precipitation amounts on these days are generally larger than precipitation amounts on all days with extreme precipitation, leading to an underestimation of return-period precipitation amounts at many stations. Furthermore, the synoptic composite analysis reveals that tropical cyclones are a common feature on these rarely selected analog days.
Quantitative precipitation forecast
Quantitative precipitation estimation
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This study examines potential future changes of precipitation in China based on Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model projections for the medium (RCP4.5) and high (RCP8.5) emission scenarios. We first evaluate the biases of climate model output and correct the biases through quantile mapping. After bias correction, we examine the changes in mean precipitation as well as shifts in its frequency distribution. We also evaluate the changes in extreme precipitation based on frequency analysis techniques. Our results show that by the end of the century, mean precipitation is going to increase by 8% (12%) under RCP4.5 (RCP8.5) scenarios, resulted from a combination of an increase in precipitation intensity and a slight decrease in precipitation frequency. Spatially, precipitation is projected to increase more in northern China than southern China, and the increase is the least in the southeast. Seasonally, precipitation is projected to increase more in fall and winter, and less in spring and summer. The precipitation intensity distribution is likely to shift towards more heavy events, with a decrease in the contribution from light events and a significant increase in contribution from heavy events. Extreme precipitation is going to increase at much higher rates than mean precipitation, and the increase is more spatially uniform. Changes in annual and seasonal precipitation are closely linked with temperature change. Total precipitation increases at 2.6% (1.9%) per degree warming under RCP4.5 (RCP8.5), but extreme precipitation has much higher sensitivities ranging 4.5–6.5% per degree warming for events of various return intervals. The percentage increase per degree is generally smaller for RCP8.5 than RCP4.5 scenarios, suggesting a reduced sensitivity at higher temperature. In addition, the precipitation increase seems to be linked with changes in the atmospheric circulations that transport moisture in different regions in China. These changes have significant implications for the management of water resources and water‐related hazards.
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