Assessment of CMIP3-CMIP5 Climate Models Precipitation Projection and Implication of Flood Vulnerability of Bangkok

Reliable estimates of precipitation are essential for both research and practical applications. CMIP3 and CMIP5 climate simulations provide both historical simulations and future projections of extreme climate. The 2011 monsoon season was one of case studies with exceptionally heavy and led to extensive and long-lasting flooding in the Chao Phraya river basin, Thailand. Flooding was exacerbated by the rapid expansion of urban areas into flood plains and was the costliest natural disaster in the country’s history, with direct damages estimated at US$45 billion. The present paper focuses on the precipitation downscaling of CMIP3 and CMIP5 models. The majority of CMIP3 and CMIP5 models overestimate the dry spell (in June and July) and underestimate the peak precipitation (in May and September). The interquartile model range for precipitation, which is spanned by the 25th and 75th quantiles, is closer to the observed data for CMIP5 than CMIP3 models. However, overall results suggest that the performance of CMIP5 models cannot be readily distinguished from of CMIP3 models, although there are clear signals of improvements over Bangkok. The correlation coefficient is found between 0.6 - 0.8, implying that most of the models simulate the mean rainfall reasonably well. Both model generations have approximately the same standard deviation as observed, but more spatial variability and more RMS error are found for the future projections. Use of the Multi Model mean shows continuously increased rainfall from the near future to the far future while the Multi Model Median shows increased rainfall only for the far future. These findings in changing precipitation are discussed through the flood behavior in 2011. Results from flood simulation with several adaptation measures reveal that flood cannot be completely avoided. One of the best practices for highflood risk communities is to raise the house with open space in the first floor.

Future Changes and Uncertainties in Temperature and Precipitation over China Based on CMIP5 Models

Climate changes in future 21 st century China and their uncertainties are evaluated based on 22 climate models from the Coupled Model Intercomparison Project Phase 5（CMIP5）. By 2081–2100, the annual mean surface air temperature（SAT） is predicted to increase by 1.3℃± 0.7℃, 2.6℃± 0.8℃ and 5.2℃± 1.2℃ under the Representative Concentration Pathway（RCP） scenarios RCP2.6, RCP4.5 and RCP8.5, relative to 1986–2005, respectively. The future change in SAT averaged over China increases the most in autumn/winter and the least in spring, while the uncertainty shows little seasonal variation.Spatially, the annual and seasonal mean SAT both show a homogeneous warming pattern across China, with a warming rate increasing from southeastern China to the Tibetan Plateau and northern China, invariant with time and emissions scenario.The associated uncertainty in SAT decreases from northern to southern China. Meanwhile, by 2081–2100, the annual mean precipitation increases by 5% ± 5%, 8% ± 6% and 12% ± 8% under RCP2.6, RCP4.5 and RCP8.5, respectively. The national average precipitation anomaly percentage, largest in spring and smallest in winter, and its uncertainty, largest in winter and smallest in autumn, show visible seasonal variations. Although at a low confidence level, a homogeneous wetting pattern is projected across China on the annual mean scale, with a larger increasing percentage in northern China and a weak drying in southern China in the early 21 st century. The associated uncertainty is also generally larger in northern China and smaller in southwestern China. In addition, both SAT and precipitation usually show larger seasonal variability on the sub-regional scale compared with the national average.

Uncertainty in Projection of Climate Extremes:A Comparison of CMIP5 and CMIP6

Climate projections by global climate models(GCMs)are subject to considerable and multi-source uncertainties.This study aims to compare the uncertainty in projection of precipitation and temperature extremes between Coupled Model Intercomparison Project(CMIP)phase 5(CMIP5)and phase 6(CMIP6),using 24 GCMs forced by 3 emission scenarios in each phase of CMIP.In this study,the total uncertainty(T)of climate projections is decomposed into the greenhouse gas emission scenario uncertainty(S,mean inter-scenario variance of the signals over all the models),GCM uncertainty(M,mean inter-model variance of signals over all emission scenarios),and internal climate variability uncertainty(V,variance in noises over all models,emission scenarios,and projection lead times);namely,T=S+M+V.The results of analysis demonstrate that the magnitudes of S,M,and T present similarly increasing trends over the 21 st century.The magnitudes of S,M,V,and T in CMIP6 are 0.94-0.96,1.38-2.07,1.04-1.69,and 1.20-1.93 times as high as those in CMIP5.Both CMIP5 and CMIP6 exhibit similar spatial variation patterns of uncertainties and similar ranks of contributions from different sources of uncertainties.The uncertainty for precipitation is lower in midlatitudes and parts of the equatorial region,but higher in low latitudes and the polar region.The uncertainty for temperature is higher over land areas than oceans,and higher in the Northern Hemisphere than the Southern Hemisphere.For precipitation,T is mainly determined by M and V in the early 21 st century,by M and S at the end of the 21 st century;and the turning point will appear in the 2070 s.For temperature,T is dominated by M in the early 21 st century,and by S at the end of the 21 st century,with the turning point occuring in the 2060 s.The relative contributions of S to T in CMIP6(12.5%-14.3%for precipitation and 31.6%-36.2%for temperature)are lower than those in CMIP5(15.1%-17.5%for precipitation and 38.6%-43.8%for temperature).By contrast,the relative contributions of M in CMIP6(50.6%-59.8%for precipitation and 59.4%-60.3%for temperature)are higher than those in CMIP5(47.5%-57.9%for precipitation and 51.7%-53.6%for temperature).The higher magnitude and relative contributions of M in CMIP6 indicate larger difference among projections of various GCMs.Therefore,more GCMs are needed to ensure the robustness of climate projections.

Model Projections of Precipitation Minus Evaporation in China

Changes in precipitation minus evaporation （P-E） are analyzed to investigate the possible impacts of climate change on water resource conditions in China. Simulations of SRES A1B and 20C3M scenarios from the WCRP CMIP3 GCMs are employed in the study. Time slice analysis shows that there would be more annual mean P-E across China in 2040-2055 and 2080-2099, compared to 1980-1999, with the largest percentage change over Northwest China and the Bohai Rim area. Precipitation and evaporation would also increase over entire China during these two periods. Annual mean P-E, precipitation, and evaporation averaged over the whole China and its eight sub-areas all yield generally upward trends during the 21st century. This indicates that on annual mean scale, the global warming related precipitation dominates the hydroclimate conditions in China. On seasonal mean scale, although precipitation is projected to increase over China, P-E exhibits both decreasing and increasing trends over certain regions of China. This suggests that the variation of global warming related evaporation dominates hydroclimate conditions over some parts of China, especially in northern China. Therefore, in hydroclimate condition projections, considering both evaporation and precipitation changes should be more reasonable than considering only precipitation.