Will the Globe Encounter the Warmest Winter after the Hottest Summer in 2023?

In the boreal summer and autumn of 2023,the globe experienced an extremely hot period across both oceans and continents.The consecutive record-breaking mean surface temperature has caused many to speculate upon how the global temperature will evolve in the coming 2023/24 boreal winter.In this report,as shown in the multi-model ensemble mean(MME)prediction released by the Institute of Atmospheric Physics at the Chinese Academy of Sciences,a medium-to-strong eastern Pacific El Niño event will reach its mature phase in the following 2−3 months,which tends to excite an anomalous anticyclone over the western North Pacific and the Pacific-North American teleconnection,thus serving to modulate the winter climate in East Asia and North America.Despite some uncertainty due to unpredictable internal atmospheric variability,the global mean surface temperature(GMST)in the 2023/24 winter will likely be the warmest in recorded history as a consequence of both the El Niño event and the long-term global warming trend.Specifically,the middle and low latitudes of Eurasia are expected to experience an anomalously warm winter,and the surface air temperature anomaly in China will likely exceed 2.4 standard deviations above climatology and subsequently be recorded as the warmest winter since 1991.Moreover,the necessary early warnings are still reliable in the timely updated mediumterm numerical weather forecasts and sub-seasonal-to-seasonal prediction.

Analysis of surface temperature bias over the Tibetan plateau in the CAS FGOALS-f3-L model

The Chinese Academy of Sciences Flexible Global Ocean-Atmosphere-Land System atmospheric component model(FGOALS-f3-L)participated in Phase 6 of the Coupled Model Intercomparison Project,but its reproducibility of surface temperature(T_(s))over the Tibetan Plateau(TP)as a key climatically sensitive region remains unclear.This study evaluates the capability of FGOALS-f3-L in reproducing the climatological T_(s)over the TP relative to the Climate Forecast System Reanalysis.The results show that FGOALS-f3-L can reasonably capture the spatial pattern of T_(s)but underestimates the annual mean T_(s)for the whole TP.The simulated T_(s)for the whole TP shows a cold bias in winter and spring and a warm bias in summer and autumn.Further quantitative analysis based on the surface energy budget equation shows that the surface albedo feedback(SAF)term strongly contributes to the annual,winter,and spring mean cold bias in the western TP and to the warm bias in the eastern TP.Compared with the SAF term,the surface sensible and latent heat flux terms make nearly opposite contributions to the T_(s)bias and considerably offset the bias due to the SAF term.The cloud radiative forcing term strongly contributes to the annual and seasonal mean weak cold bias in the eastern TP.The longwave radiation term associated with the overestimated water vapor content accounts for a large portion of the warm bias over the whole TP in summer and autumn.Improving land surface and cloud processes in FGOALS-f3-L is critical to reduce the T_(s)bias over the TP.

CAS FGOALS-f3-L Model Datasets for CMIP6 Historical Atmospheric Model Intercomparison Project Simulation

The outputs of the Chinese Academy of Sciences(CAS) Flexible Global Ocean–Atmosphere–Land System(FGOALS-f3-L) model for the baseline experiment of the Atmospheric Model Intercomparison Project simulation in the Diagnostic,Evaluation and Characterization of Klima common experiments of phase 6 of the Coupled Model Intercomparison Project(CMIP6) are described in this paper. The CAS FGOALS-f3-L model, experiment settings, and outputs are all given. In total,there are three ensemble experiments over the period 1979–2014, which are performed with different initial states. The model outputs contain a total of 37 variables and include the required three-hourly mean, six-hourly transient, daily and monthly mean datasets. The baseline performances of the model are validated at different time scales. The preliminary evaluation suggests that the CAS FGOALS-f3-L model can capture the basic patterns of atmospheric circulation and precipitation well, including the propagation of the Madden–Julian Oscillation, activities of tropical cyclones, and the characterization of extreme precipitation. These datasets contribute to the benchmark of current model behaviors for the desired continuity of CMIP.

Overview of the CMIP6 Historical Experiment Datasets with the Climate System Model CAS FGOALS-f3-L

The three-member historical simulations by the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System model,version f3-L(CAS FGOALS-f3-L),which is contributing to phase 6 of the Coupled Model Intercomparison Project(CMIP6),are described in this study.The details of the CAS FGOALS-f3-L model,experiment settings and output datasets are briefly introduced.The datasets include monthly and daily outputs from the atmospheric,oceanic,land and sea-ice component models of CAS FGOALS-f3-L,and all these data have been published online in the Earth System Grid Federation(ESGF,https://esgf-node.llnl.gov/projects/cmip6/).The three ensembles are initialized from the 600th,650th and 700th model year of the preindustrial experiment(piControl)and forced by the same historical forcing provided by CMIP6 from 1850 to 2014.The performance of the coupled model is validated in comparison with some recent observed atmospheric and oceanic datasets.It is shown that CAS FGOALS-f3-L is able to reproduce the main features of the modern climate,including the climatology of air surface temperature and precipitation,the long-term changes in global mean surface air temperature,ocean heat content and sea surface steric height,and the horizontal and vertical distribution of temperature in the ocean and atmosphere.Meanwhile,like other state-of-the-art coupled GCMs,there are still some obvious biases in the historical simulations,which are also illustrated.This paper can help users to better understand the advantages and biases of the model and the datasets。

CAS FGOALS-f3-L Model Datasets for CMIP6 GMMIP Tier-1 and Tier-3 Experiments

The Chinese Academy of Sciences(CAS)Flexible Global Ocean Atmosphere Land System(FGOALS-f3-L)model datasets prepared for the sixth phase of the Coupled Model Intercomparison Project(CMIP6)Global Monsoons Model Intercomparison Project(GMMIP)Tier-1 and Tier-3 experiments are introduced in this paper,and the model descriptions,experimental design and model outputs are demonstrated.There are three simulations in Tier-1,with different initial states,and five simulations in Tier-3,with different topographies or surface thermal status.Specifically,Tier-3 contains four orographic perturbation experiments that remove the Tibetan Iranian Plateau,East African and Arabian Peninsula highlands,Sierra Madre,and Andes,and one thermal perturbation experiment that removes the surface sensible heating over the Tibetan Iranian Plateau and surrounding regions at altitudes above 500 m.These datasets will contribute to CMIP6’s value as a benchmark to evaluate the importance of long-term and short-term trends of the sea surface temperature in monsoon circulations and precipitation,and to a better understanding of the orographic impact on the global monsoon system over highlands.

Simulation and Improvements of Oceanic Circulation and Sea Ice by the Coupled Climate System Model FGOALS-f3-L

This study documents simulated oceanic circulations and sea ice by the coupled climate system model FGOALS-f3-L developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics,Chinese Academy of Sciences,under historical forcing from phase 6 of the Coupled Model Intercomparison Project(CMIP6).FGOALS-f3-L reproduces the fundamental features of global oceanic circulations,such as sea surface temperature(SST),sea surface salinity(SSS),mixed layer depth(MLD),vertical temperature and salinity,and meridional overturning circulations.There are notable improvements compared with the previous version,FGOALS-s2,such as a reduction in warm SST biases near the western and eastern boundaries of oceans and salty SSS biases in the tropical western Atlantic and eastern boundaries,and a mitigation of deep MLD biases at high latitudes.However,several obvious biases remain.The most significant biases include cold SST biases in the northwestern Pacific(over 4°C),freshwater SSS biases and deep MLD biases in the subtropics,and temperature and salinity biases in deep ocean at high latitudes.The simulated sea ice shows a reasonable distribution but stronger seasonal cycle than observed.The spatial patterns of sea ice are more realistic in FGOALS-f3-L than its previous version because the latitude–longitude grid is replaced with a tripolar grid in the ocean and sea ice model.The most significant biases are the overestimated sea ice and underestimated SSS in the Labrador Sea and Barents Sea,which are related to the shallower MLD and weaker vertical mixing.

The FGOALS climate system model as a modeling tool for supporting climate sciences:An overview

Climate system models are useful tools for understanding the interactions among the components of the climate system and predicting/projecting future climate change. The development of climate models has been a central focus of the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences(LASG/IAP) since the establishment of the laboratory in 1985. In China, many pioneering component models and fully coupled models of the climate system have been developed by LASG/IAP. The fully coupled climate system developed in the recent decade is named FGOALS(Flexible Global Ocean-Atmosphere-Land System Model). In this paper, an application-oriented review of the LASG/IAP FGOALS model is presented. The improved model performances are demonstrated in the context of cloud-radiation processes, Asian monsoon, ENSO phenomena, Atlantic Meridional Overturning Circulation(AMOC) and sea ice. The FGOALS model has contributed to both CMIP5(Coupled Model Intercomparison Project-phase 5) and IPCC(Intergovernmental Panel on Climate Change) AR5(the Fifth Assessment Report). The release of FGOALS data has supported the publication of nearly 500 papers around the world. The results of FGOALS are cited ~106 times in the IPCC WG1(Working Group 1) AR5. In addition to the traditional long-term simulations and projections, near-term decadal climate prediction is a new set of CMIP experiment, progress of LAGS/IAP in the development of nearterm decadal prediction system is reviewed. The FGOALS model has supported many Chinese national-level research projects and contributed to the national climate change assessment report. The crucial role of FGOALS as a modeling tool for supporting climate sciences is highlighted by demonstrating the model's performances in the simulation of the evolution of Earth's climate from the past to the future.

CAS FGOALS-f3-H Dataset for the High-Resolution Model Intercomparison Project(HighResMIP)Tier 2

Following the High-Resolution Model Intercomparison Project(HighResMIP)Tier 2 protocol under the Coupled Model Intercomparison Project Phase 6(CMIP6),three numerical experiments are conducted with the Chinese Academy of Sciences Flexible Global Ocean-Atmosphere-Land System Model,version f3-H(CAS FGOALS-f3-H),and a 101-year(1950–2050)global high-resolution simulation dataset is presented in this study.The basic configuration of the FGOALSf3-H model and numerical experiments design are briefly described,and then the historical simulation is validated.Forced by observed radiative agents from 1950 to 2014,the coupled model essentially reproduces the observed long-term trends of temperature,precipitation,and sea ice extent,as well as the large-scale pattern of temperature and precipitation.With an approximate 0.25°horizontal resolution in the atmosphere and 0.1°in the ocean,the coupled models also simulate energetic western boundary currents and the Antarctic Circulation Current(ACC),reasonable characteristics of extreme precipitation,and realistic frontal scale air-sea interaction.The dataset and supporting detailed information have been published in the Earth System Grid Federation.

Can Eurasia Experience a Cold Winter under a Third-Year La Nina in 2022/23?

The Northern Hemisphere(NH)often experiences frequent cold air outbreaks and heavy snowfalls during La Nina winters.In 2022,a third-year La Nina event has exceeded both the oceanic and atmospheric thresholds since spring and is predicted to reach its mature phase in December 2022.Under such a significant global climate signal,whether the Eurasian Continent will experience a tough cold winter should not be assumed,despite the direct influence of mid-to high-latitude,large-scale atmospheric circulations upon frequent Eurasian cold extremes,whose teleconnection physically operates by favoring Arctic air invasions into Eurasia as a consequence of the reduction of the meridional background temperature gradient in the NH.In the 2022/23 winter,as indicated by the seasonal predictions from various climate models and statistical approaches developed at the Institute of Atmospheric Physics,abnormal warming will very likely cover most parts of Europe under the control of the North Atlantic Oscillation and the anomalous anticyclone near the Ural Mountains,despite the cooling effects of La Nina.At the same time,the possibility of frequent cold conditions in mid-latitude Asia is also recognized for this upcoming winter,in accordance with the tendency for cold air invasions to be triggered by the synergistic effect of a warm Arctic and a cold tropical Pacific on the hemispheric scale.However,how the future climate will evolve in the 2022/23 winter is still subject to some uncertainty,mostly in terms of unpredictable internal atmospheric variability.Consequently,the status of the mid-to high-latitude atmospheric circulation should be timely updated by medium-term numerical weather forecasts and sub-seasonal-to-seasonal prediction for the necessary date information and early warnings.

Convective/Large-scale Rainfall Partitions of Tropical Heavy Precipitation in CMIP6 Atmospheric Models

Convective/large-scale(C/L)precipitation partitions are crucial for achieving realistic rainfall modeling and are classified in 16 phase 6 of the Coupled Model Intercomparison Project(CMIP6)atmospheric models.Only 4 models capture the feature that convective rainfall significantly exceeds the large-scale rainfall component in the tropics while the other 12 models show 50%–100%large-scale rainfall component in heavy rainfall.Increased horizontal resolution generally increases the convective rainfall percentage,but not in all models.The former 4 models can realistically reproduce two peaks of moisture vertical distribution,respectively located in the upper and the lower troposphere.In contrast,the latter 12 models correspond to three types of moisture vertical profile biases:(1)whole mid-to-lower tropospheric wet biases(60%–80%large-scale rainfall);(2)mid-tropospheric wet peak(50%convective/large-scale rainfall);and(3)lower-tropospheric wet peak(90%–100%large-scale rainfall).And the associated vertical distribution of unique clouds potentially causes different climate feedback,suggesting accurate C/L rainfall components are necessary to reliable climate projection.

Seamless Prediction in China: A Review

Seamless prediction is a weather-climate integrated prediction covering multiple time scales that include days,weeks,months,seasons,years,and decades.Seamless prediction can provide different industries with information such as weather conditions and climate variations from the next few days to years,which have important impacts on economic and social development and important reference value for short-,medium-and long-term decision-making and planning of the country.Therefore,seamless prediction has received widespread attention from the international scientific community recently.As Chinese scientists have also carried out relevant research,this paper reviews the research in China on developments and applications of seamless prediction methods and prediction systems in recent years.Among them,the main progress of seamless prediction methods studies is reviewed from four aspects:short-and medium-range weather forecasting,subseasonal-to-seasonal,seasonal-to-interannual,and decadal climate prediction.In terms of development and application of seamless prediction systems,the main achievements made by meteorological operational departments,scientific institutes,and universities in China in recent years are reviewed.Finally,some of the issues in seamless prediction that need further study are discussed.

CAS FGOALS-f3-L Large-ensemble Simulations for the CMIP6 Polar Amplification Model Intercomparison Project

Large-ensemble simulations of the atmosphere-only time-slice experiments for the Polar Amplification Model Intercomparison Project(PAMIP)were carried out by the model group of the Chinese Academy of Sciences(CAS)Flexible Global Ocean-Atmosphere-Land System(FGOALS-f3-L).Eight groups of experiments forced by different combinations of the sea surface temperature(SST)and sea ice concentration(SIC)for pre-industrial,present-day,and future conditions were performed and published.The time-lag method was used to generate the 100 ensemble members,with each member integrating from 1 April 2000 to 30 June 2001 and the first two months as the spin-up period.The basic model responses of the surface air temperature(SAT)and precipitation were documented.The results indicate that Arctic amplification is mainly caused by Arctic SIC forcing changes.The SAT responses to the Arctic SIC decrease alone show an obvious increase over high latitudes,which is similar to the results from the combined forcing of SST and SIC.However,the change in global precipitation is dominated by the changes in the global SST rather than SIC,partly because tropical precipitation is mainly driven by local SST changes.The uncertainty of the model responses was also investigated through the analysis of the large-ensemble members.The relative roles of SST and SIC,together with their combined influence on Arctic amplification,are also discussed.All of these model datasets will contribute to PAMIP multi-model analysis and improve the understanding of polar amplification.

Aerosol characteristics over the Tibetan Plateau simulated with a coupled aerosol-climate model(FGOALS-f3-L)

This study presents the simulated aerosol spatiotemporal characteristics over the Tibetan Plateau(TP)with a newly developed coupled aerosol-climate model(FGOALS-f3-L).The aerosol properties are simulated over the TP for the period 2002-11.The results indicate that soil dust,sulfate,and carbonaceous aerosols(black carbon(BC),organic carbon(OC)and BC/OC)account for 53.6%,32.2%,and 14.2%of the total aerosol mass over the TP,respectively.The simulated aerosol surface mass concentrations and aerosol optical depths(AODs)are evaluated with ground-based and satellite observations,respectively.Underestimations of the aerosol surface mass concentration are found at the Lhasa site,especially for BC and OC.The spatial distribution and interannual variation of AOD are consistent with MODIS observations,with the RMSE of 0.081 and bias of 0.036.Due to the uncertainty of the parameterization of dust emissions,the model’s performance in summer and autumn is much better than that in spring.

LASG Global AGCM with a Two-moment Cloud Microphysics Scheme:Energy Balance and Cloud Radiative Forcing Characteristics

Cloud dominates influence factors of atmospheric radiation, while aerosol–cloud interactions are of vital importance in its spatiotemporal distribution. In this study, a two-moment(mass and number) cloud microphysics scheme, which significantly improved the treatment of the coupled processes of aerosols and clouds, was incorporated into version 1.1 of the IAP/LASG global Finite-volume Atmospheric Model(FAMIL1.1). For illustrative purposes, the characteristics of the energy balance and cloud radiative forcing(CRF) in an AMIP-type simulation with prescribed aerosols were compared with those in observational/reanalysis data. Even within the constraints of the prescribed aerosol mass, the model simulated global mean energy balance at the top of the atmosphere(TOA) and at the Earth’s surface, as well as their seasonal variation, are in good agreement with the observational data. The maximum deviation terms lie in the surface downwelling longwave radiation and surface latent heat flux, which are 3.5 W m-2(1%) and 3 W m-2(3.5%), individually. The spatial correlations of the annual TOA net radiation flux and the net CRF between simulation and observation were around 0.97 and 0.90, respectively. A major weakness is that FAMIL1.1 predicts more liquid water content and less ice water content over most oceans. Detailed comparisons are presented for a number of regions, with a focus on the Asian monsoon region(AMR). The results indicate that FAMIL1.1 well reproduces the summer–winter contrast for both the geographical distribution of the longwave CRF and shortwave CRF over the AMR. Finally, the model bias and possible solutions, as well as further works to develop FAMIL1.1 are discussed.

CAS FGOALS-f3-L Model Datasets for CMIP6 DCPP Experiment

The outputs of the Chinese Academy of Sciences(CAS)Flexible Global Ocean-Atmosphere-Land System(FGOALSf3-L)model for the decadal climate prediction project(DCPP)of the Coupled Model Intercomparison Project Phase 6(CMIP6)are described in this paper.The FGOALS-f3-L was initialized through the upgraded,weakly coupled data assimilation scheme,referred to as EnOI-IAU,which assimilates observational anomalies of sea surface temperature(SST)and upper-level(0–1000-m)ocean temperature and salinity profiles into the coupled model.Then,nine ensemble members of 10-year hindcast/forecast experiments were conducted for each initial year over the period of 1960–2021,based on initial conditions produced by three initialization experiments.The hindcast and forecast experiments follow the experiment designs of the Component-A and Component-B of the DCPP,respectively.The decadal prediction output datasets contain a total of 44 monthly mean atmospheric and oceanic variables.The preliminary evaluation indicates that the hindcast experiments show significant predictive skill for the interannual variations of SST in the north Pacific and multi-year variations of SST in the subtropical Pacific and the southern Indian Ocean.

Datasets for the CMIP6 Scenario Model Intercomparison Project (ScenarioMIP) Simulations with the Coupled Model CAS FGOALS-f3-L

The datasets for the tier-1 Scenario Model Intercomparison Project(ScenarioMIP)experiments from the Chinese Academy of Sciences(CAS)Flexible Global Ocean-Atmosphere-Land System model,finite-volume version 3(CAS FGOALS-f3-L)are described in this study.ScenarioMIP is one of the core MIP experiments in phase 6 of the Coupled Model Intercomparison Project(CMIP6).Considering future CO2,CH4,N2O and other gases’concentrations,as well as land use,the design of ScenarioMIP involves eight pathways,including two tiers(tier-1 and tier-2)of priority.Tier-1 includes four combined Shared Socioeconomic Pathways(SSPs)with radiative forcing,i.e.,SSP1-2.6,SSP2-4.5,SSP3-7.0 and SSP5-8.5,in which the globally averaged radiative forcing at the top of the atmosphere around the year 2100 is approximately 2.6,4.5,7.0 and 8.5 W m−2,respectively.This study provides an introduction to the ScenarioMIP datasets of this model,such as their storage location,sizes,variables,etc.Preliminary analysis indicates that surface air temperatures will increase by about 1.89℃,3.07℃,4.06℃ and 5.17℃ by around 2100 under these four scenarios,respectively.Meanwhile,some other key climate variables,such as sea-ice extension,precipitation,heat content,and sea level rise,also show significant long-term trends associated with the radiative forcing increases.These datasets will help us understand how the climate will change under different anthropogenic and radiative forcings.

Evaluation of the interannual variability in the East Asian summer monsoon in AMIP and historical experiments of CAS FGOALS-f3-L

对东亚夏季季风(EASM)模拟的评估可以提高我们对亚洲季风动力和气候模拟的理解.在这项研究中,通过使用中国科学院(CAS)全球海洋-大气-陆地系统(FGOALS-f3-L)模式参加的第六次耦合模式相互比较计划(CMIP6)中的大气模式相互比较计划(AMIP)和历史(historical)试验,明确了EASM的年际变率的模拟能力.通过多变量经验正交函数(MV-EOF)分析发现,观测的EASM的主导模态为西太平洋上的太平洋-日本模态,并伴有局部反气旋异常.主导模态的方差贡献率为24.6%.历史(historical)试验可以基本再现这种空间模态,其方差贡献率较AMIP试验更接近于观测.与AMIP试验相比,历史(historical)试验还能更好地模拟EASM变率的时间频率.然而,由于历史(historical)模拟没有在积分开始时应用初始化过程,而AMIP试验受到海表面温度(SST)的约束,因此主成分(PC1)的位相在历史(historical)试验中没有得到较好地再现.进一步分析发现,印度洋和西太平洋热带地区的海气相互作用对EASM的模拟非常重要,而EASM气候变率的模拟可能与厄尔尼诺-南方涛动(ENSO)的模拟能力有关,这值得进一步分析.

Monthly prediction of tropical cyclone activity over the South China Sea using the FGOALS-f2 ensemble prediction system

The monthly prediction skill for tropical cyclone(TC)activity in the South China Sea(SCS)during the typhoon season(July to November)was evaluated using the FGOALS-f2 ensemble prediction system.Specifically,the prediction skill of the system at a 10-day lead time for monthly TC activity is given based on 35-year(1981–2015)hindcasts with 24 ensemble members.The results show that FGOALS-f2 can capture the climatology of TC track densities in each month,but there is a delay in the monthly southward movement in the area of high track densities of TCs.The temporal correlation coefficient of TC frequency fluctuates across the different months,among which the highest appears in October(0.59)and the lowest in August(0.30).The rank correlation coefficients of TC track densities are relatively higher(R>0.6)in July,September,and November,while those in August and October are relatively lower(R within 0.2 to 0.6).For real-time prediction of TCs in 2020(July to November),FGOALS-f2 demonstrates a skillful probabilistic prediction of TC genesis and movement.Besides,the system successfully forecasts the correct sign of monthly anomalies of TC frequency and accumulated cyclone energy for 2020(July to November)in the SCS.

Simulating Aerosol Optical Depth and Direct Radiative Effects over the Tibetan Plateau with a High-Resolution CAS FGOALS-f3 Model

Current global climate models cannot resolve the complex topography over the Tibetan Plateau(TP)due to their coarse resolution.This study investigates the impacts of horizontal resolution on simulating aerosol and its direct radiative effect(DRE)over the TP by applying two horizontal resolutions of about 100 km and 25 km to the Chinese Academy of Sciences Flexible Global Ocean-Atmosphere Land System(CAS FGOALS-f3)over a 10-year period.Compared to the AErosol RObotic NETwork observations,a high-resolution model(HRM)can better reproduce the spatial distribution and seasonal cycles of aerosol optical depth(AOD)compared to a low-resolution model(LRM).The HRM bias and RMSE of AOD decreased by 0.08 and 0.12,and the correlation coefficient increased by 0.22 compared to the LRM.An LRM is not sufficient to reproduce the aerosol variations associated with fine-scale topographic forcing,such as in the eastern marginal region of the TP.The difference between hydrophilic aerosols in an HRM and LRM is caused by the divergence of the simulated relative humidity(RH).More reasonable distributions and variations of RH are conducive to simulating hydrophilic aerosols.An increase of the 10-m wind speed in winter by an HRM leads to increased dust emissions.The simulated aerosol DREs at the top of the atmosphere(TOA)and at the surface by the HRM are–0.76 W m^(–2)and–8.72 W m^(–2)over the TP,respectively.Both resolution models can capture the key feature that dust TOA DRE transitions from positive in spring to negative in the other seasons.

Subseasonal to seasonal Arctic sea-ice prediction:A grand challenge of climate science

On 15 September 2020,the Arctic sea-ice extent(SIE)reached its annual minimum,which,based on data from the National Snow and Ice Data Center(NSIDC,2020a),was about 3.74 million km^(2)(1.44 million square miles).This value was about 40%less than the climate average(~6.27 million km^(2))during 1980–2010.It was second only to the record low(3.34 million km^(2))set on 16 September 2012,but significantly smaller than the previous second-lowest(4.145 million km^(2),set on 7 September 2016)and third-lowest(4.147 million km^(2),set on 14 September 2007)values,making 2020 the second-lowest SIE year of the satellite era(42 years of data).