Formation and Variation of the Atmospheric Heat Source over the Tibetan Plateau and Its Climate Effects

To cherish the memory of the late Professor Duzheng YE on what would have been his 100 th birthday, and to celebrate his great accomplishment in opening a new era of Tibetan Plateau（TP） meteorology, this review paper provides an assessment of the atmospheric heat source（AHS） over the TP from different data resources, including observations from local meteorological stations, satellite remote sensing data, and various reanalysis datasets. The uncertainty and applicability of these heat source data are evaluated. Analysis regarding the formation of the AHS over the TP demonstrates that it is not only the cause of the atmospheric circulation, but is also a result of that circulation. Based on numerical experiments, the review further demonstrates that land–sea thermal contrast is only one part of the monsoon story. The thermal forcing of the Tibetan–Iranian Plateau plays a significant role in generating the Asian summer monsoon（ASM）, i.e., in addition to pumping water vapor from sea to land and from the lower to the upper troposphere, it also generates a subtropical monsoon–type meridional circulation subject to the angular momentum conservation, providing an ascending-air large-scale background for the development of the ASM.

Climate Change of 4℃ Global Warming above Pre-industrial Levels

Using a set of numerical experiments from 39 CMIP5 climate models, we project the emergence time for 4？C global warming with respect to pre-industrial levels and associated climate changes under the RCP8.5 greenhouse gas concentration scenario. Results show that, according to the 39 models, the median year in which 4？C global warming will occur is 2084.Based on the median results of models that project a 4？C global warming by 2100, land areas will generally exhibit stronger warming than the oceans annually and seasonally, and the strongest enhancement occurs in the Arctic, with the exception of the summer season. Change signals for temperature go outside its natural internal variabilities globally, and the signal-tonoise ratio averages 9.6 for the annual mean and ranges from 6.3 to 7.2 for the seasonal mean over the globe, with the greatest values appearing at low latitudes because of low noise. Decreased precipitation generally occurs in the subtropics, whilst increased precipitation mainly appears at high latitudes. The precipitation changes in most of the high latitudes are greater than the background variability, and the global mean signal-to-noise ratio is 0.5 and ranges from 0.2 to 0.4 for the annual and seasonal means, respectively. Attention should be paid to limiting global warming to 1.5？C, in which case temperature and precipitation will experience a far more moderate change than the natural internal variability. Large inter-model disagreement appears at high latitudes for temperature changes and at mid and low latitudes for precipitation changes. Overall, the intermodel consistency is better for temperature than for precipitation.

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.

Climate policy uncertainty and corporate investment:evidence from the Chinese energy industry

In recent years,with the increasing attention paid to climate risks,the changes in climate policies are also more full of uncertainties,which have brought tremendous impact to economic entities,including companies.Using the dynamic threshold model,this study investigates the nonlinear and the asymmetric effect of climate policy uncertainty on Chinese firm investment decisions with panel data of 128 Chinese energy-related companies from 2007 to 2019.The empirical findings indicate that the influence of climate policy uncertainty on firm investment is significantly nonlinear.Overall,climate policy uncertainty is not apparently related to corporate investments in the high-level range,while it negatively affects the investments in the low-level range.In addition,to be more specific,the negative impact of climate policy uncertainty on the mining industry is tremendous,while the influence on the production and supply of electricity,heat,gas,and water sector is remarkably positive.The results of this study could help the company managers and policymakers to arrange appropriate related strategies under different climate policy conditions.

Spatial distribution of δ2H and δ18O values in the hydrologic cycle of the Nile Basin

Existing δ2H and δ18O values for precipitation and surface water in the Nile Basin were used to analyze precipitation inputs and the influence of evaporation on the isotopic signal of the Nile River and its tributaries. The goal of the data analysis was to better understand basin processes that influence seasonal streamflow for the source waters of the Nile River, because climate and hydrologic models have continued to produce high uncertainty in the prediction of precipitation and streamflow in the Nile Basin. An evaluation of differences in precipitation δ2H and δ18O values through linear regression and distribution analysis indicate variation by region and season in the isotopic signal of precipitation across the Nile Basin. The White Nile Basin receives precipitation with a more depleted isotopic signal compared to the Blue Nile Basin. The hot temperatures of the Sahelian spring produce a greater evaporation signal in the precipitation isotope distribution compared to precipitation in the Sahara/Mediterranean region, which can be influenced by storms moving in from the Mediterranean Sea. The larger evaporative effect is reversed for the two regions in summer because of the cooling of the Sahel from inflow of Indian Ocean monsoon moisture that predominantly influences the climate of the Blue Nile Basin. The regional precipitation isotopic signals convey to each region＇s streamflow, which is further modified by additional evaporation according to the local climate. Isotope ratios for White Nile streamflow are significantly altered by evaporation in the Sudd, but this isotopic signal is minimized for streamflow in the Nile River during the winter, spring and summer seasons because of the flow dominance of the Blue Nile. During fall, the contribution from the White Nile may exceed that of the Blue Nile, and the heavier isotopic signal of the White Nile becomes apparent. The variation in climatic conditions of the Nile River Basin provides a means of identifying mechanistic processes through changes in isotope ratios of hydrogen and oxygen, which have utility for separating precipitation origin and the effect of evaporation during seasonal periods. The existing isotope record for precipitation and streamflow in the Nile Basin can be used to evaluate predicted streamflow in the Nile River from a changing climate that is expected to induce further changes in precipitation patterns across the Nile Basin.

Intercomparison of multi-model ensemble-processing strategies within a consistent framework for climate projection in China

Climate change adaptation and relevant policy-making need reliable projections of future climate.Methods based on multi-model ensemble are generally considered as the most efficient way to achieve the goal.However,their efficiency varies and inter-comparison is a challenging task,as they use a variety of target variables,geographic regions,time periods,or model pools.Here,we construct and use a consistent framework to evaluate the performance of five ensemble-processing methods,i.e.,multimodel ensemble mean(MME),rank-based weighting(RANK),reliability ensemble averaging(REA),climate model weighting by independence and performance(ClimWIP),and Bayesian model averaging(BMA).We investigate the annual mean temperature(Tav)and total precipitation(Prcptot)changes(relative to 1995–2014)over China and its seven subregions at 1.5 and 2℃warming levels(relative to pre-industrial).All ensemble-processing methods perform better than MME,and achieve generally consistent results in terms of median values.But they show different results in terms of inter-model spread,served as a measure of uncertainty,and signal-to-noise ratio(SNR).ClimWIP is the most optimal method with its good performance in simulating current climate and in providing credible future projections.The uncertainty,measured by the range of 10th–90th percentiles,is reduced by about 30%for Tav,and 15%for Prcptot in China,with a certain variation among subregions.Based on ClimWIP,and averaged over whole China under 1.5/2℃global warming levels,Tav increases by about 1.1/1.8℃(relative to 1995–2014),while Prcptot increases by about 5.4%/11.2%,respectively.Reliability of projections is found dependent on investigated regions and indices.The projection for Tav is credible across all regions,as its SNR is generally larger than 2,while the SNR is lower than 1 for Prcptot over most regions under 1.5℃warming.The largest warming is found in northeastern China,with increase of 1.3(0.6–1.7)/2.0(1.4–2.6)℃(ensemble’s median and range of the 10th–90th percentiles)under 1.5/2℃warming,followed by northern and northwestern China.The smallest but the most robust warming is in southwestern China,with values exceeding 0.9(0.6–1.1)/1.5(1.1–1.7)℃.The most robust projection and largest increase is achieved in northwestern China for Prcptot,with increase of 9.1%(–1.6–24.7%)/17.9%(0.5–36.4%)under 1.5/2℃warming.Followed by northern China,where the increase is 6.0%(–2.6–17.8%)/11.8%(2.4–25.1%),respectively.The precipitation projection is of large uncertainty in southwestern China,even with uncertain sign of variation.For the additional half-degree warming,Tav increases more than 0.5℃throughout China.Almost all regions witness an increase of Prcptot,with the largest increase in northwestern China.

Effects of the Physical Process Ensemble Technique on Simulation of Summer Precipitation over China

The effects of the physical process ensemble technique on simulation of summer precipitation over China have been studied by using a p-σregional climate model with 9 vertical levels（pσ-RCM9）.The results show that there are obvious differences among simulations of summer precipitation over China from different individual ensemble members.The simulated precipitation over China is sensitive to different cumulus convection,radiative transfer,and land surface process parameterizations.These differences lead to large uncertainties in the simulation results.The standard deviation of the simulated summer precipitation departure percentage over West China is larger than that over East China,signifying that the simulated precipitation over East China has higher reliability and consistency than that over West China.The Talagr and diagram shows that the ensemble system has reasonable dispersion in the simulated summer mean precipitation over East China.The summer ensemble mean precipitation over East China evaluated by various indices is better than most single simulations.The physical process ensemble technique reduces the uncertainties of the model physics in precipitation and improves the simulation results as a whole.Further, adopting the optimized ensemble mean method can obviously improve the performance of the pσ-RCM9 model in simulation of summer precipitation over East China.