基于CMIP6多模式的长江流域蒸散发预估及影响因素

蒸散发是水文循环和能量传输的中间环节,同时也是联结土壤、植被、大气过程的纽带。基于第六次国际耦合模式比较计划（CMIP6）12个全球气候模式数据,研究了SSP1-2.6、SSP2-4.5和SSP5-8.5三种情景下,长江流域2020-2099年实际蒸散发E_T（Evapotranspiration,简称ET）的时空变化及其影响因素。研究结果表明,在3种气候变化情景下长江流域ET相较基准期（1995-2014年）均存在显著增加趋势,且长江中下游地区增加趋势最为显著;SSP1-2.6情景ET较基准期先快速增加,21世纪60年代之后减缓并趋于平稳,SSP2-4.5和SSP5-8.5情景下均呈持续增加趋势。研究了降水（Precipitation,简称Pr）、气温（Air Temperature,简称T）和叶面积指数L_AI（Leaf Area Index,简称LAI）对长江流域ET的影响;SSP1-2.6和SSP2-4.5情景下,长江流域ET受T影响最为显著,而SSP5-8.5情景下,LAI是影响ET的主导因素。在3种气候情景下,辐射强迫越大,植被增加趋势越显著,对ET的影响越强（SSP5-8.5、SSP2-4.5、SSP1-2.6情景下影响逐渐减弱）,而ET对LAI的敏感性则逐渐降低（SSP1-2.6、SSP2-4.5、SSP5-8.5情景下敏感性逐渐降低）。     

Simulated Relationship between Wintertime ENSO and East Asian Summer Rainfall: From CMIP3 to CMIP6

El Ni?o-Southern Oscillation(ENSO)events have a strong influence on East Asian summer rainfall(EASR).This paper investigates the simulated ENSO-EASR relationship in CMIP6 models and compares the results with those in CMIP3 and CMIP5 models.In general,the CMIP6 models show almost no appreciable progress in representing the ENSO-EASR relationship compared with the CMIP5 models.The correlation coefficients in the CMIP6 models are relatively smaller and exhibit a slightly greater intermodel diversity than those in the CMIP5 models.Three physical processes related to the delayed effect of ENSO on EASR are further analyzed.Results show that,firstly,the relationships between ENSO and the tropical Indian Ocean(TIO)sea surface temperature(SST)in the CMIP6 models are more realistic,stronger,and have less intermodel diversity than those in the CMIP3 and CMIP5 models.Secondly,the teleconnections between the TIO SST and Philippine Sea convection(PSC)in the CMIP6 models are almost the same as those in the CMIP5 models,and stronger than those in the CMIP3 models.Finally,the CMIP3,CMIP5,and CMIP6 models exhibit essentially identical capabilities in representing the PSC-EASR relationship.Almost all the three generations of models underestimate the ENSO-EASR,TIO SST-PSC,and PSC-EASR relationships.Moreover,almost all the CMIP6 models that successfully capture the significant TIO SST-PSC relationship realistically simulate the ENSO-EASR relationship and vice versa,which is,however,not the case in the CMIP5 models.     

全球气候模式中赤道东太平洋海表温度的季节循环：从CMIP5到CMIP6

The sea surface temperature(SST) seasonal cycle in the eastern equatorial Pacific(EEP) plays an important role in the El Ni?o–Southern Oscillation(ENSO) phenomenon. However, the reasonable simulation of SST seasonal cycle in the EEP is still a challenge for climate models. In this paper, we evaluated the performance of 17 CMIP6 climate models in simulating the seasonal cycle in the EEP and compared them with 43 CMIP5 climate models. In general, only CESM2 and SAM0-UNICON are able to successfully capture the annual mean SST characteristics,and the results showed that CMIP6 models have no fundamental improvement in the model annual mean bias.For the seasonal cycle, 14 out of 17 climate models are able to represent the major characteristics of the observed SST annual evolution. In spring, 12 models capture the 1–2 months leading the eastern equatorial Pacific region 1(EP1; 5°S–5°N, 110°–85°W) against the eastern equatorial Pacific region 2(EP2; 5°S–5°N, 140°–110°W). In autumn,only two models, GISS-E2-G and SAM0-UNICON, correctly show that the EP1 and EP2 SSTs vary in phase. For the CMIP6 MME SST simulation in EP1, both the cold bias along the equator in the warm phase and the warm bias in the cold phase lead to a weaker annual SST cycle in the CGCMs, which is similar to the CMIP5 results. However,both the seasonal cold bias and warm bias are considerably decreased for CMIP6, which leads the annual SST cycle to more closely reflect the observation. For the CMIP6 MME SST simulation in EP2, the amplitude is similar to the observed value due to the quasi-constant cold bias throughout the year, although the cold bias is clearly improved after August compared with CMIP5 models. Overall, although SAM0-UNICON successfully captured the seasonal cycle characteristics in the EEP and the improvement from CMIP5 to CMIP6 in simulating EEP SST is clear, the fundamental climate models simulated biases still exist.     

Relationship between South China Sea precipitation variability and tropical Indo-Pacific SST anomalies in IPCC CMIP5 models during spring-to-summer transition

The present study evaluates the precipitation variability over the South China Sea (SCS) and its relationship to tropical Indo-Pacific SST anomalies during spring-to-summer transition (April-May-June, AMJ) simulated by 23 Intergovernmental Panel on Climate Change Coupled Model Intercomparison Project Phase 5 coupled models. Most of the models have the capacity to capture the AMJ precipitation variability in the SCS. The precipitation and SST anomaly (SSTA) distribution in the SCS, tropical Pacific Ocean (TPO), and tropical Indian Ocean (TIO) domains is evaluated based on the pattern correlation coefficients between model simulations and observations. The analysis leads to several points of note. First, the performance of the SCS precipitation anomaly pattern in AMJ is model dependent. Second, the SSTA pattern in the TPO and TIO is important for capturing the AMJ SCS precipitation variability. Third, a realistic simulation of the western equatorial Pacific (WEP) and local SST impacts is necessary for reproducing the AMJ SCS precipitation variability in some models. Fourth, the overly strong WEP SST impacts may disrupt the relationship between the SCS precipitation and the TPO-TIO SST. Further work remains to be conducted to unravel the specific reasons for the discrepancies between models and observations in various aspects.     

Evaluation of CMIP5 Climate Models in Simulating 1979–2005 Oceanic Latent Heat Flux over the Pacific

The climatological mean state,seasonal variation and long-term upward trend of 1979–2005 latent heat flux(LHF) in historical runs of 14 coupled general circulation models from CMIP5(Coupled Model Intercomparison Project Phase 5) are evaluated against OAFlux(Objectively Analyzed air–sea Fluxes) data. Inter-model diversity of these models in simulating the annual mean climatological LHF is discussed. Results show that the models can capture the climatological LHF fairly well,but the amplitudes are generally overestimated. Model-simulated seasonal variations of LHF match well with observations with overestimated amplitudes. The possible origins of these biases are wind speed biases in the CMIP5 models. Inter-model diversity analysis shows that the overall stronger or weaker LHF over the tropical and subtropical Pacific region,and the meridional variability of LHF,are the two most notable diversities of the CMIP5 models. Regression analysis indicates that the inter-model diversity may come from the diversity of simulated SST and near-surface atmospheric specific humidity.Comparing the observed long-term upward trend,the trends of LHF and wind speed are largely underestimated,while trends of SST and air specific humidity are grossly overestimated,which may be the origins of the model biases in reproducing the trend of LHF.     

Analyses of extreme climate events over china based on CMIP5 historical and future simulations

Based on observations and 12 simulations from Coupled Model Intercomparison Project Phase 5 （CMIP5） models, cli- matic extremes and their changes over China in the past and under the future scenarios of three Representative Concentration Pathways （RCPs） are analyzed. In observations, frost days （FD） and low-temperature threshold days （TN10P） show a de- creasing trend, and summer days （SU）, high-temperature threshold days （TX90P）, heavy precipitation days （R20）, and the contribution of heavy precipitation days （P95T） show an increasing trend. Most models are able to simulate the main char- acteristics of most extreme indices. In particular, the mean FD and TX90P are reproduced the best, and the basic trends of FD, TN10P, SU and TX90P are represented. For the FD and SU indexes, most models show good ability in capturing the spatial differences between the mean state of the periods 1986--2005 and 1961-80; however, for other indices, the simulation abilities for spatial disparity are less satisfactory and need to be improved. Under the high emissions scenario of RCP8.5, the century-scale linear changes of the multi-model ensemble （MME） for FD, SU, TN10P, TX90P, R20 and P95T are -46.9, 46.0, -27.1, 175.4, and 2.9 days, and 9.9%, respectively; and the spatial change scope for each index is consistent with the emissions intensity. Due to the complexities of physical process pararneterizations and the limitation of forcing data, great uncertainty still exists with respect to the simulation of climatic extremes.     

Temperature Extremes from Canadian Regional Climate Model (CRCM) Climate Change Projections

Future climate projections of extreme events can help forewarn society of high-impact events and allow the development of better adaptation strategies. In this study a non-stationary model for Generalized Extreme Value (GEV) distributions is used to analyze the trend in extreme temperatures in the context of a changing climate and compare it with the trend in average temperatures.

The analysis is performed using the climate projections of the Canadian Regional Climate Model (CRCM), under an IPCC SRES A2 greenhouse gas emissions scenario, over North America. Annual extremes in daily minimum and maximum temperatures are analyzed. Significant positive trends for the location parameter of the GEV distribution are found, indicating an expected increase in extreme temperature values. The scale parameter of the GEV distribution, on the other hand, reveals a decrease in the variability of temperature extremes in some continental regions. Trends in the annual minimum and maximum temperatures are compared with trends in average winter and summer temperatures, respectively. In some regions, extreme temperatures exhibit a significantly larger increase than the seasonal average temperatures.

The CRCM projections are compared with those of its driving model and framed in the context of the Coupled Model Intercomparison Project, phase 3 (CMIP3) Global Climate Model projections. This enables us to establish the CRCM position within the CMIP3 climate projection uncertainty range. The CRCM is validated against the HadEX2 dataset in order to assess the CRCM representation of temperature extremes in the present climate. The validation is also framed in the context of CMIP3 validation results. The CRCM cold extremes validate better and are closer to the driving model and CMIP3 projections than the hot extremes.     

CMIP5模式对ENSO现象的模拟能力评估

针对参与耦合模式比较计划(CMIP5)的17个海-气耦合模式对20世纪气候的模拟结果,从热带太平洋海表温度和大气海平面气压变化的综合分析角度较详细评估了模式对厄尔尼诺-南方涛动(ENSO)现象的模拟能力。结果表明,这些模式基本上能模拟出ENSO现象的一些主要特征,包括热带太平洋海温的空间分布及其时空演变特征、与海平面气压变化的关联、ENSO周期变化及锁相特征等,但不同模式的模拟结果仍然差异较大。(1)从模拟的热带太平洋年平均海温的偏差来看,多模式集合平均值与观测的均方根误差小于1.0℃,但单个模式的误差相对要大一些。误差较小的为1.2—1.3℃,多数模式在1.6℃以下,但也有个别模式的误差超过2.0℃。(2)从经验正交函数分解结果来看,热带太平洋实测月平均海表温度距平和海平面气压距平的年际尺度变化第1模态主要表现为ENSO变化特征,第2模态反映的是海温的长期变化趋势。只有少数几个CMIP5模式能够再现这种特征,多数模式所模拟的海温距平/海平面气压距平时空变化的第1、第2特征向量分布顺序与观测分析正好相反,ENSO变成了第2模态,趋势成了最主要的模态。尽管如此,所有模式都能模拟出南方涛动变化与热带太平洋海温距平时空变化的密切关联,无论是作为第1还是第2特征模态,所有模式模拟的南方涛动与热带太平洋海温距平时空变化都有密切相关。(3)谱分析结果表明,ENSO现象具有2—7年的周期,其中,4年的周期最明显。大多数模式模拟的ENSO周期在此范围内,但有些模式的主要周期偏短,为2年左右。个别模式的ENSO主要周期为11年,已超出2—7年的范围。(4)多数模式模拟的厄尔尼诺及拉尼娜的峰值出现在冬季(11—2月),与观测基本吻合。另有少数模式模拟的峰值出现在9—10月,比观测略提前。只有个别模式模拟的峰值出现在夏季,与观测相差太大。     

The hiatus and accelerated warming decades in CMIP5 simulations

Observed hiatus or accelerated warming phenomena are compared with numerical simulations from the Coupled Model Intercomparison Project Phase 5 （CMIP5） archives,and the associated physical mechanisms are explored based on the CMIP5 models.Decadal trends in total ocean heat content （OHC） are strongly constrained by net top-of-atmosphere （TOA） radiation.During hiatus decades,most CMIP5 models exhibit a significant decrease in the SST and upper OHC and a significant increase of heat penetrating into the subsurface or deep ocean,opposite to the accelerated warming decades.The shallow meridional overturning of the Pacific subtropical cell experiences a significant strengthening （slowdown） for the hiatus （accelerated warming） decades associated with the strengthened （weakened） trade winds over the tropical Pacific.Both surface heating and ocean dynamics contribute to the decadal changes in SST over the Indian Ocean,and the Indonesian Throughflow has a close relationship with the changes of subsurface temperature in the Indian Ocean.The Atlantic Meridional Overturing Circulation （Antarctic Bottom Water） tends to weaken （strengthen） during hiatus decades,opposite to the accelerated warming decades.In short,the results highlight the important roles of air-sea interactions and ocean circulations for modulation of surface and subsurface temperature.     

Statistical Characteristics of ENSO Events in CMIP5 Models

By applying the historical-run outputs from 24 Coupled Model Intercomparison Project Phase 5（CMIP5） models and the NOAA Extended Reconstructed SST V3 b dataset（ERSST）, the characteristics of different types of ENSO in the selected CMIP5 models, including cold-season-matured Eastern Pacific（C-EP） ENSO, warmseason-matured EP（W-EP） ENSO, cold-season-matured Central Pacific（C-CP） ENSO, and warm-season-matured CP（W-CP） ENSO, were examined in comparison with those in the ERSST dataset. The results showed that, in general, consistent with observations, EP ENSO events in most of the model runs were relatively much stronger than CP ENSO events, and cold-season-matured ENSO events were relatively much more frequent than warm-season-matured ENSO events for both EP and CP ENSO events. The composite amplitudes of ENSO events in most of the models were generally weaker than in observations, particularly for EP El Ni？o and CP La Ni？a. Moreover, most of the models successfully reproduced the amplitude asymmetries between El Ni？o and La Ni？a for cold-season-matured EP and CP ENSO events, exhibiting an average stronger/weaker EP El Ni？o/La Ni？a regime and a weaker/stronger CP El Ni？o/La Ni？a regime. Most of the models, however, failed to reproduce the observed regimes of stronger/weaker W-EP El Ni？o/ La Ni？a and weaker/stronger W-CP El Ni？o/La Ni？a.