Decadal prediction skill for Eurasian surface air temperature in CMIP6 models

评估CMIP6年代际预测试验对季节平均SAT的预测技巧的结果表明:模式不能有效预测冬季和秋季SAT的年代际变率.IPSL-CM6A-LR和多模式集合平均对于春季SAT展现了预测技巧,其中对于变率的预测技巧好于振幅的结果.基于蒙古和我国华北地区的显著预测技巧,模式对于夏季SAT表现出最佳的预测水平.与外部强迫相比,模式对于SAT的预测技巧可能来自初始化.模式中的一个明显系统性误差值得注意,即模式中冬季SAT的变率可以持续到其他季节,而在观测中其他季节的SAT变化与冬季SAT相对独立.

Verification and Improvement of the Ability of CFSv2 to Predict the Antarctic Oscillation in Boreal Spring

The boreal spring Antarctic Oscillation(AAO)has a significant impact on the spring and summer climate in China.This study evaluates the capability of the NCEP's Climate Forecast System,version 2(CFSv2),in predicting the boreal spring AAO for the period 1983-2015.The results indicate that CFSv2 has poor skill in predicting the spring AAO,failing to predict the zonally symmetric spatial pattern of the AAO,with an insignificant correlation of 0.02 between the predicted and observed AAO Index(AAOI).Considering the interannual increment approach can amplify the prediction signals,we firstly establish a dynamical-statistical model to improve the interannual increment of the AAOI(DY AAOI),with two predictors of CFSv2-forecasted concurrent spring sea surface temperatures and observed preceding autumn sea ice.This dynamical-statistical model demonstrates good capability in predicting DY AAOI,with a significant correlation coeffcient of 0.58 between the observation and prediction during 1983-2015 in the two-year-out cross-validation.Then,we obtain an improved AAOI by adding the improved DY AAOI to the preceding observed AAOI.The improved AAOI shows a significant correlation coeffcient of 0.45 with the observed AAOI during 1983-2015.Moreover,the unrealistic atmospheric response to March-April-May sea ice in CFSv2 may be the possible cause for the failure of CFSv2 to predict the AAO.This study gives new clues regarding AAO prediction and short-term climate prediction.

Impact of Vegetation Feedback on the Mid-Pliocene Warm Climate

Studying the vegetation feedback during warm periods of the past can lead to better understanding of those in the future.In this study,we conducted several simulations to analyze vegetation feedback during the mid-Pliocene warm period.The results indicate that the main features of vegetation change in the mid-Pliocene were a northward shift of needleleaf tree,an expansion of broadleaf tree and shrub,and a northward expansion of grass,as compared to the pre-industrial period.The global annual mean warming ratio caused by vegetation feedback was 12.1％,and this warming ratio was much larger in northern middle and high latitudes.The warming caused by vegetation change was directly related to the surface albedo change and was further amplified by snow/sea ice-albedo feedback.

Impacts of the SSTs over Equatorial Central–Eastern Pacific and Southeastern Indian Ocean on the Cold and Rainy/Snowy/Icy Weather in Southern China

Low temperature together with snow/freezing rain is disastrous in winter over southern China.Previous studies suggest that this is related to the sea surface temperature(SST)anomalies,especially La Nina conditions,over the equatorial central–eastern Pacific Ocean(EP).In reality,however,La Nina episodes are not always accompanied by rainy/snowy/icy(CRSI)days in southern China,such as the case in winter 2020/2021.Is there any other factor that works jointly with the EP SST to affect the winter CRSI weather in southern China?To address this question,CRSI days are defined and calculated based on station observation data,and the related SST anomalies and atmospheric circulations are examined based on the Hadley Centre SST data and the NCEP/NCAR reanalysis data for winters of1978/1979–2017/2018.The results indicate that the CRSI weather with more CRSI days is featured with both decreased temperature and increased winter precipitation over southern China.The SSTs over both the EP and the southeastern Indian Ocean(SIO)are closely related to the CRSI days in southern China with correlation coefficients of-0.29 and 0.39,significant at the 90%and 95%confidence levels,respectively.The SST over EP affects significantly air temperature,as revealed by previous studies,with cooler EP closely related to the deepened East Asian trough,which benefits stronger East Asian winter monsoon(EAWM)and lower air temperature in southern China.Nevertheless,this paper discovers that the SST over SIO affects precipitation of southern China,with a correlation coefficient of 0.42,significant at the 99%confidence level,with warmer SIO correlated with deepened southern branch trough(SBT)and strengthened western North Pacific anomalous anticyclone(WNPAC),favoring more water vapor convergence and enhanced precipitation in southern China.Given presence of La Ni?a in both winters,compared to the winter of 2020/2021,the winter of 2021/2022 witnessed more CRSI days,perhaps due to the warmer SIO.

Causes of Mid-Pliocene Strengthened Summer and Weakened Winter Monsoons over East Asia

The mid-Pliocene warm period was the most recent geological period in Earth＇s history that featured long-term warming. Both geological evidence and model results indicate that East Asian summer winds （EASWs） strengthened in monsoonal China, and that East Asian winter winds （EAWWs） weakened in northern monsoonal China during this period, as compared to the pre-industrial period. However, the corresponding mechanisms are still unclear. In this paper, the results of a set of numerical simulations are reported to analyze the effects of changed boundary conditions on the mid-Pliocene East Asian monsoon climate, based on PRISM3 （Pliocene Research Interpretation and Synoptic Mapping） palaeoenvironmental recon- struction. The model results showed that the combined changes of sea surface temperatures, atmospheric CO2 concentration, and ice sheet extent were necessary to generate an overall warm climate on a large scale, and that these factors exerted the greatest effects on the strengthening of EASWs in monsoonal China. The orographic change produced significant local warming and had the greatest effect on the weakening of EAWWs in northern monsoonal China in the mid-Pliocene. Thus, these two factors both had important but different effects on the monsoon change. In comparison, the effects of vegetational change on the strengthened EASWs and weakened EAWWs were relatively weak. The changed monsoon winds can be ex- plained by a reorganization of the meridional temperature gradient and zonal thermal contrast. Moreover, the effect of orbital parameters cannot be ignored. Results showed that changes in orbital parameters could have marked!y affected the EASWs and EAWWs, and caused significant short-term oscillations in the mid-Pliocene monsoon climate in East Asia.

Mid-Pliocene Westerlies from Plio MIP Simulations

The midlatitude westerlies are one of the major components of the global atmospheric circulation. They play an important role in midlatitude weather and climate, and are particularly significant in interpreting aeolian sediments. In this study, we analyzed the behavior and the possible mechanism involved in the change of the westerlies, mainly in terms of the jet stream position, in the mid-Pliocene warm period （3.3 to 3.0 million years ago） using simulations of 15 climate models from the Pliocene Model Intercomparison Project （PlioMIP）. Compared to the reference period, the mid-Pliocene midlatitude westerlies generally shifted poleward （approximately 3.6° of latitude in the Northern Hemisphere and 1.9~ of latitude in the Southern Hemisphere at 850 hPa level） with a dipole pattern. The dipole pattern of the tropospheric zonal wind anomalies was closely related to the change of the tropospheric meridional temperature gradient as a result of thermal structure adjustment. The poleward shift of the midlatitude westerly jet corresponded to the poleward shift of the mean meridional circulation. The sea surface temperatures and sea ice may have affected the simulated temperature structure and zonal winds, causing the spread of the westerly anomalies in the mid-Pliocene between the atmosphere-only and coupled atmosphere-ocean general circulation model simulations.

Strengthened African Summer Monsoon in the Mid-Piacenzian

Using model results from the first phase of the Pliocene Model Intercomparison Project （PlioMIP） and four experiments with CAM4, the intensified African summer monsoon （ASM） in the mid-Piacenzian and corresponding mechanisms are analyzed. The results from PlioMIP show that the ASM intensified and summer precipitation increased in North Africa during the mid-Piacenzian, which can be explained by the increased net energy in the atmospheric column above North Africa. Further experiments with CAM4 indicated that the combined changes in the mid-Piacenzian of atmospheric CO2 concentration and SST, as well as the vegetation change, could have substantially increased the net energy in the atmospheric column over North Africa and further intensified the ASM. The experiments also demonstrated that topography change had a weak effect. Overall, the combined changes of atmospheric CO2 concentration and SST were the most important factor that brought about the intensified ASM in the mid-Piacenzian.

Evaluation of Routed-Runoff from Land Surface Models and Reanalyses Using Observed Streamflow in Chinese River Basins

Previous studies have demonstrated that offline land surface models(LSMs)and global hydrological models(GHMs)can reasonably reproduce streamflow in large river basins.Global reanalyses supply fine spatiotemporal runoff estimates,but they are not fully intercompared and evaluated in China.This study assesses the routed-runoff from five offline LSM/GHM runs(VIC-CN05.1,CLM-CFSR,CLM-ERAI,CLM-MERRA,and CLM-NCEP)and three reanalysis datasets(ERAI/Land,JRA55,and MERRA-2)against the gauged streamflow(26 stations)in major Chinese river basins during 1980–2008.The Catchment-based Macro-scale Floodplain model(CaMa-Flood)is employed to route those runoff datasets to the hydrological stations.Four statistical quantities,including the correlation coefficient(R),standard deviation(STD),Nash–Sutcliffe efficiency coefficient(NSE),and relative error(RE),along with a ranking method,are used to quantify the quality of those products.The results show that the spatial patterns of both modeled and observed streamflow in summer are similar,but their magnitudes are different.Except for MERRA-2,the other products can reproduce well the interannual variability of streamflow in both the Yangtze and Yellow River basins.All products generally underestimate the magnitude and variance of monthly streamflow,while VIC-CN05.1 and JRA55 are closer to observations compared to other products.The correlation coefficients for all products are overall larger than 0.61,with the highest value(0.85)from VIC-CN05.1.In addition to CLM-MERRA,MERRA-2,and CLM-NCEP with relatively small precipitation,other products can simulate peak flow well with positive NSEs up to 0.41(ERAI/Land).Considerable uncertainties exist among the eight products at the Yellow River outlet,which might be because the LSMs ignore frequent human activities.Based on the above statistics,performances of the eight runoff products are ranked in descending order as follows:VIC-CN05.1,ERAI/Land,JRA55,CLM-CFSR,CLM-ERAI,MERRA-2,CLM-MERRA,and CLM-NCEP,which provides a reference for flood/hydrological drought warning and hydroclimatic research in the future.

Seasonal Climate Prediction Models for the Number of Landfalling Tropical Cyclones in China

Two prediction models are developed to predict the number of landfalling tropical cyclones(LTCs) in China during June–August(JJA). One is a statistical model using preceding predictors from the observation, and the other is a hybrid model using both the aforementioned preceding predictors and concurrent summer large-scale environmental conditions from the NCEP Climate Forecast System version 2(CFSv2).(1) For the statistical model, the year-to-year increment method is adopted to analyze the predictors and their physical processes, and the JJA number of LTCs in China is then predicted by using the previous boreal summer sea surface temperature(SST) in Southwest Indonesia,preceding October South Australia sea level pressure, and winter SST in the Sea of Japan. The temporal correlation coefficient between the observed and predicted number of LTCs during 1983–2017 is 0.63.(2) For the hybrid prediction model, the prediction skill of CFSv2 initiated each month from February to May in capturing the relationships between summer environmental conditions(denoted by seven potential factors: three steering factors and four genesis factors) and the JJA number of LTCs is firstly evaluated. For the 2-and 1-month leads, CFSv2 has successfully reproduced these relationships. For the 4-, 3-, and 2-month leads, the predictor of geopotential height at 500 h Pa over the western North Pacific(WNP) shows the worst forecasting skill among these factors. In general, the summer relative vorticity at 850 h Pa over the WNP is a modest predictor, with stable and good forecasting skills at all lead times.

A Possible Approach for Decadal Prediction of the PDO

The Pacific Decadal Oscillation(PDO)is a leading mode of decadal sea surface temperature variability in the North Pacific.Skillful PDO prediction can be beneficial in many aspects because of its global and regional impacts.However,current climate models cannot provide satisfied decadal prediction of the PDO and related decadal variability of sea surface temperature.In this study,we propose a new approach,i.e.,the increment method,to predicting the PDO.A series of validations demonstrate that the increment method is effective in improving decadal prediction of PDO and it can well capture the phase change of PDO with high accuracy.The prediction processes include three steps.First,a five-year smoothing is performed;second,effective preceding predictors for PDO are selected,with all predictors and predictands in the form of a three-year decadal increment(DI);third,the prediction model is set up for PDO three-year decadal increment(DI_PDO),and PDO prediction can be obtained by adding the predicted DI_PDO to the observed PDO three years ago.This new method can also be applied for decadal climate prediction of other modes(e.g.,Atlantic multidecadal oscillation)and predictands(e.g.,sea surface temperature).

Springtime Convective Quasi-Biweekly Oscillation and Interannual Variation of Its Intensity over the South China Sea and Western North Pacific

This study investigates characteristics of the convective quasi-biweekly oscillation(QBWO) over the South China Sea(SCS) and western North Pacific(WNP) in spring, and the interannual variation of its intensity. Convective QBWO over the WNP and SCS shows both similarities and differences. Convective QBWO over the WNP originates mainly from southeast of the Philippine Sea and propagates northwestward. In contrast, convective QBWO over the SCS can be traced mainly to east of the Philippines and features a westward propagation. Such a westward or northwestward propagation is probably related to n = 1 equatorial Rossby waves. During the evolution of convective QBWO over the WNP and SCS, the vertical motion and specific humidity exhibit a barotropic structure and the vertical relative vorticity shows a baroclinic structure in the troposphere. The dominant mode of interannual variation of convective QBWO intensity over the SCS–WNP region in spring is homogeneous. Its positive phase indicates enhanced convective QBWO intensity accompanied by local enhanced QBWO intensity of vertical motion throughout the troposphere as well as local enhanced(weakened) QBWO intensity of kinetic energy, vertical relative vorticity,and wind in the lower(upper) troposphere. The positive phase usually results from local increases of the background moisture and anomalous vertical shear of easterlies. The latter contributes to the relationship between the dominant mode and QBWO intensities of kinetic energy, vertical relative vorticity, and wind. Finally, a connection between the dominant mode and the sea surface temperature anomalies in the tropical Pacific Ocean is demonstrated.

Projection of Landslides in China during the 21st Century under the RCP8.5 Scenario

More and more rainstorms and other extreme weather events occur in the context of global warming, which may increase the risks of landslides. In this paper, changes of landslides in the 21 st century of China under the high emission scenario RCP8.5(Representative Concentration Pathway) are projected by using a statistical landslide forecasting model and the regional climate model RegCM4.0. The statistical landslide model is based on an improved landslide susceptibility map of China and a rainfall intensity–duration threshold. First, it is driven by observed rainfall and RegCM4.0 rainfall in 1980–99, and it can reproduce the spatial distribution of landslides in China pretty well.Then, it is used to forecast the landslide changes over China in the future under the RCP8.5 scenario. The results consistently reveal that landslides will increase significantly in most areas of China, especially in the southeastern, northeastern, and western parts of Northwest China. The change pattern at the end of the 21 st century is generally consistent with that in the middle of the 21 st century, but with larger increment and magnitude. In terms of the probability,the proportion of grid points that are very likely and extremely likely to experience landslides will also increase.

Has the Prediction of the South China Sea Summer Monsoon Improved Since the Late 1970s?

Based on the evaluation of state-of-the-art coupled ocean-atmosphere general circulation models （CGCMs） from the ENSEMBLES （Ensemble-based Predictions of Climate Changes and Their Impacts） and DEME- TER （Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction） projects, it is found that the prediction of the South China Sea summer monsoon （SCSSM） has improved since the late 1970s. These CGCMs show better skills in prediction of the atmospheric circulation and precipitation within the SCSSM domain during 1979-2005 than that during 1960-1978. Possible reasons for this improvement are investigated. First, the relationship between the SSTs over the tropical Pacific, North Pacific and tropical Indian Ocean, and SCSSM has intensified since the late 1970s. Meanwhile, the SCSSM-related SSTs, with their larger amplitude of interannual variability, have been better predicted. Moreover, the larger amplitude of the interannual variability of the SCSSM and improved initializations for CGCMs after the late 1970s contribute to the better prediction of the SCSSM. In addition, considering that the CGCMs have certain limitations in SCSSM rainfall prediction, we applied the year-to-year increment approach to these CGCMs from the DEMETER and ENSEMBLES projects to improve the prediction of SCSSM rainfall before and after the late 1970s.

A New Prediction Model for Grain Yield in Northeast China Based on Spring North Atlantic Oscillation and Late-Winter Bering Sea Ice Cover

Accurate estimations of grain output in the agriculturally important region of Northeast China are of great strategic significance for guaranteeing food security.New prediction models for maize and rice yields are built in this paper based on the spring North Atlantic Oscillation index and the Bering Sea ice cover index.The year-to-year increment is first forecasted and then the original yield value is obtained by adding the historical yield of the previous year.The multivariate linear prediction model of maize shows good predictive ability,with a low normalized root-mean-square error（NRMSE）of 13.9%,and the simulated yield accounts for 81%of the total variance of the observation.To improve the performance of the multivariate linear model,a combined forecasting model of rice is built by considering the weight of the predictors.The NRMSE of the model is 12.9%and the predicted rice yield explains 71%of the total variance.The corresponding cross-validation test and independent samples test further demonstrate the efficiency of the models.It is inferred that the statistical models established here by applying year-to-year increment approach could make rational prediction for the maize and rice yield in Northeast China before harvest.The present study may shed new light on yield prediction in advance by use of antecedent large-scale climate signals adequately.