The Tibetan Plateau bridge:Influence of remote teleconnections from extratropical and tropical forcings on climate anomalies

本文回顾了青藏高原桥梁作用方面的最新研究进展,涉及北大西洋气候异常对春,夏亚洲季风和厄尔尼诺-南方涛动(ENSO)事件的遥相关影响,热带海洋异常和中国东部极端气候异常之间的联系以及华南春雨的季节内变化等.介绍了年际时间尺度上,冬-春季北大西洋海表温度强迫如何影响南亚季风的季节性转变以及随后ENSO事件的触发.5月份青藏高原上空显著的负感热斜压结构,为北大西洋影响亚洲季风和ENSO提供了桥梁效应,夏季北大西洋涛动与华东夏季降水变化显著相关,高原潜热在这一关系中起着桥梁作用.另一方面,这种高原桥梁效应也存在于从热带海洋异常到东亚夏季极端降水事件的连接中,以及从中纬度波列到华南春雨准双周振荡的联系中.

Superiority of a Convolutional Neural Network Model over Dynamical Models in Predicting Central Pacific ENSO

The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key target.Previous studies have shown that deep learning methods possess a certain level of superiority in predicting ENSO indices.The present study develops a deep learning model for predicting the spatial pattern of sea surface temperature anomalies(SSTAs)in the equatorial Pacific by training a convolutional neural network(CNN)model with historical simulations from CMIP6 models.Compared with dynamical models,the CNN model has higher skill in predicting the SSTAs in the equatorial western-central Pacific,but not in the eastern Pacific.The CNN model can successfully capture the small-scale precursors in the initial SSTAs for the development of central Pacific ENSO to distinguish the spatial mode up to a lead time of seven months.A fusion model combining the predictions of the CNN model and the dynamical models achieves higher skill than each of them for both central and eastern Pacific ENSO.

Future changes in precipitation and water availability over the Tibetan Plateau projected by CMIP6 models constrained by climate sensitivity

Precipitation projections over the Tibetan Plateau(TP)show diversity among existing studies,partly due to model uncertainty.How to develop a reliable projection remains inconclusive.Here,based on the IPCC AR6–assessed likely range of equilibrium climate sensitivity(ECS)and the climatological precipitation performance,the authors constrain the CMIP6(phase 6 of the Coupled Model Intercomparison Project)model projection of summer precipitation and water availability over the TP.The best estimates of precipitation changes are 0.24,0.25,and 0.45 mm d^(−1)(5.9%,6.1%,and 11.2%)under the Shared Socioeconomic Pathway(SSP)scenarios of SSP1–2.6,SSP2–4.5,and SSP5–8.5 from 2050–2099 relative to 1965–2014,respectively.The corresponding constrained projections of water availability measured by precipitation minus evaporation(P–E)are 0.10,0.09,and 0.22 mm d^(−1)(5.7%,4.9%,and 13.2%),respectively.The increase of precipitation and P–E projected by the high-ECS models,whose ECS values are higher than the upper limit of the likely range,are about 1.7 times larger than those estimated by constrained projections.Spatially,there is a larger increase in precipitation and P–E over the eastern TP,while the western part shows a relatively weak difference in precipitation and a drier trend in P–E.The wetter TP projected by the high-ECS models resulted from both an approximately 1.2–1.4 times stronger hydrological sensitivity and additional warming of 0.6℃–1.2℃ under all three scenarios during 2050–2099.This study emphasizes that selecting climate models with climate sensitivity within the likely range is crucial to reducing the uncertainty in the projection of TP precipitation and water availability changes.

Toward a Learnable Climate Model in the Artificial Intelligence Era

Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of physics in climate science has occasionally been overlooked.Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics,rather than an“either/or”scenario.Scrutinizing controversies around current physical inconsistencies in large AI models,we stress the critical need for detailed dynamic diagnostics and physical constraints.Furthermore,we provide illustrative examples to guide future assessments and constraints for AI models.Regarding AI integration with numerical models,we argue that offline AI parameterization schemes may fall short of achieving global optimality,emphasizing the importance of constructing online schemes.Additionally,we highlight the significance of fostering a community culture and propose the OCR(Open,Comparable,Reproducible)principles.Through a better community culture and a deep integration of physics and AI,we contend that developing a learnable climate model,balancing AI and physics,is an achievable goal.

Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks:Climatology,Interannual Variability,and Extremes

Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.

Variation in the permafrost active layer over the Tibetan Plateau during 1980–2020

The active layer,acting as an intermediary of water and heat exchange between permafrost and atmosphere,greatly influences biogeochemical cycles in permafrost areas and is notably sensitive to climate fluctuations.Utilizing the Chinese Meteorological Forcing Dataset to drive the Community Land Model,version 5.0,this study simulates the spatial and temporal characteristics of active layer thickness(ALT)on the Tibetan Plateau(TP)from 1980 to 2020.Results show that the ALT,primarily observed in the central and western parts of the TP where there are insufficient station observations,exhibits significant interdecadal changes after 2000.The average thickness on the TP decreases from 2.54 m during 1980–1999 to 2.28 m during 2000–2020.This change is mainly observed in the western permafrost region,displaying a sharp regional inconsistency compared to the eastern region.A persistent increasing trend of ALT is found in the eastern permafrost region,rather than an interdecadal change.The aforementioned changes in ALT are closely tied to the variations in the surrounding atmospheric environment,particularly air temperature.Additionally,the area of the active layer on the TP displays a profound interdecadal change around 2000,arising from the permafrost thawing and forming.It consistently decreases before 2000 but barely changes after 2000.The regional variation in the permafrost active layer over the TP revealed in this study indicates a complex response of the contemporary climate under global warming.

Isolated deep convections over the Tibetan Plateau in the rainy season during 2001–2020

The Tibetan Plateau(TP)is a prevalent region for convection systems due to its unique thermodynamic forcing.This study investigated isolated deep convections(IDCs),which have a smaller spatial and temporal size than mesoscale convective systems(MCSs),over the TP in the rainy season(June-September)during 2001–2020.The authors used satellite precipitation and brightness temperature observations from the Global Precipitation Measurement mission.Results show that IDCs mainly concentrate over the southern TP.The IDC number per rainy season decreases from around 140 over the southern TP to around 10 over the northern TP,with an average 54.2.The initiation time of IDCs exhibits an obvious diurnal cycle,with the peak at 1400–1500 LST and the valley at 0900–1000 LST.Most IDCs last less than five hours and more than half appear for only one hour.IDCs generally have a cold cloud area of 7422.9 km^(2),containing a precipitation area of approximately 65%.The larger the IDC,the larger the fraction of intense precipitation it contains.IDCs contribute approximately 20%–30%to total precipitation and approximately 30%–40%to extreme precipitation over the TP,with a larger percentage in July and August than in June and September.In terms of spatial distribution,IDCs contribute more to both total precipitation and extreme precipitation over the TP compared to the surrounding plain regions.IDCs over the TP account for a larger fraction than MCSs,indicating the important role of IDCs over the region.

A teleconnection pattern of upper-tropospheric circulation anomalies over the Eurasian continent associated with the interannual variability of atmospheric convection over the tropical western North Pacific in July

热带西北太平洋大气对流的年际变化对东亚夏季气候有着显著影响,而且受到前冬ENSO的影响.然而,还有相当多的西北太平洋对流年际变化不能由前冬ENSO解释.本文表明,7月西北太平洋对流受到欧亚大陆对流层上层遥相关型的显著影响,且这一影响与前冬ENSO的影响程度相当.该遥相关型表现为位于欧洲的气旋式环流异常,中亚的反气旋式环流异常和东亚带状延伸的气旋式环流异常,并表现为7月对流层上层环流的第一主模态.基于本文的结果并结合前人的研究,作者认为这一遥相关型能通过亚洲急流出口区附近的罗斯贝波破碎引起西北太平洋对流异常.

The Unprecedented Extreme Anticyclonic Anomaly over Northeast Asia in July 2021 and Its Climatic Impacts

This study investigates the evolution of an extreme anomalous anticyclone(AA)event over Northeast Asia,which was one of the dominant circulation systems responsible for the catastrophic extreme precipitation event in July 2021 in Henan,and further explores the significant impact of this AA on surface temperatures beneath it.The results indicate that this AA event over Northeast Asia was unprecedented in terms of intensity and duration.The AA was very persistent and extremely strong for 10 consecutive days from 13 to 22 July 2021.This long-lived and unprecedented AA led to the persistence of warmer surface temperatures beyond the temporal span of the pronounced 500-hPa anticyclonic signature as the surface air temperatures over land in Northeast Asia remained extremely warm through 29 July 2021.Moreover,the sea surface temperatures in the Sea of Japan/East Sea were extremely high for 30 consecutive days from 13 July to 11 August 2021,persisting well after the weakening or departure of this AA.These results emphasize the extreme nature of this AA over Northeast Asia in July 2021 and its role in multiple extreme climate events,even over remote regions.Furthermore,possible reasons for this long-lasting AA are explored,and it is suggested to be a byproduct of a teleconnection pattern over extratropical Eurasia during the first half of its life cycle,and of the Pacific-Japan teleconnection pattern during the latter half.

Seasonal constraint of dynamic water temperature on riverine dissolved inorganic nitrogen transport in land surface modeling

水体温度变化对河流可溶性无机氮(DIN)输送有着强烈控制作用.然而,在全球尺度上河流DIN输送量对水温度变化的响应尚不清楚.因此,本文基于陆面过程模式,耦合河流水温估算和DIN传输方案,设定有,无动态水温情景,对比研究陆面模拟中水温变化对河流DIN通量变化的影响.结果表明:在考虑水温动态变化后,在30°N和30°S之间, DIN通量年振幅减小5%–25%.在中国东部地区,水温动态变化使河流DIN通量在夏季减少1%–3%,在冬季增加1%–5%,对DIN通量具有明显的季节性约束作用,表明动态水温的表达在河流DIN输送模拟中的重要性.

Ascending phase of solar cycle 25 tilts the current El Nino-Southern oscillation transition

自2020年初夏,赤道太平洋地区出现拉尼娜现象并持续两年半多(以下简称2020拉尼娜),对其未来演变的预测引起了很多关注,考虑到11年太阳周期活动对热带太平洋SST异常可能存在锁相影响,本研究分析了当前太阳活动周(即第25太阳周(SC25))对目前热带太平洋ENSO现象未来演变的调节作用,基于历史太阳周的统计特征,作者对第25太阳周达到其最大值的时间提出三种可能的情景,并讨论了不同情景下的太阳活动对未来两年ENSO演变的可能影响,第25太阳周的持续上升阶段在一定程度上抑制了当前2023厄尔尼诺现象发展为超级事件.

Impacts of monsoon break events in the western North Pacific on the cross-equatorial flows over the Maritime Continent

本文利用ERA5逐日再分析资料,探讨了1979-2020年间西北太平洋季风中断事件对海洋性大陆越赤道气流的影响.合成结果表明,西北太平洋季风中断事件会造成高,低空越赤道气流减弱,即高层南风异常,低层北风异常,与此相关的环流异常表现为西北太平洋高层气旋,低层反气旋的斜压结构。特别的是,西北太平洋季风中断对高空越赤道气流的影响更为显著,92%的季风中断事件都导致高空越赤道气流减弱,而只有70%的事件造成低空越赤道气流减弱,这是由于低空越赤道气流同时还受到赤道中东太平洋海温异常的调控.

CAS-ESM2.0 Dataset for the Carbon Dioxide Removal Model Intercomparison Project(CDRMIP)

Understanding the response of the Earth system to varying concentrations of carbon dioxide(CO_(2))is critical for projecting possible future climate change and for providing insight into mitigation and adaptation strategies in the near future.In this study,we generate a dataset by conducting an experiment involving carbon dioxide removal(CDR)—a potential way to suppress global warming—using the Chinese Academy of Sciences Earth System Model version 2.0(CASESM2.0).A preliminary evaluation is provided.The model is integrated from 200–340 years as a 1%yr^(−1) CO_(2) concentration increase experiment,and then to~478 years as a carbon dioxide removal experiment until CO_(2) returns to its original value.Finally,another 80 years is integrated in which CO_(2) is kept constant.Changes in the 2-m temperature,precipitation,sea surface temperature,ocean temperature,Atlantic meridional overturning circulation(AMOC),and sea surface height are all analyzed.In the ramp-up period,the global mean 2-m temperature and precipitation both increase while the AMOC weakens.Values of all the above variables change in the opposite direction in the ramp-down period,with a delayed peak relative to the CO_(2) peak.After CO_(2) returns to its original value,the global mean 2-m temperature is still~1 K higher than in the original state,and precipitation is~0.07 mm d^(–1) higher.At the end of the simulation,there is a~0.5°C increase in ocean temperature and a 1 Sv weakening of the AMOC.Our model simulation produces similar results to those of comparable experiments previously reported in the literature.

Representation of the Stratospheric Circulation in CRA-40 Reanalysis:The Arctic Polar Vortex and the Quasi-Biennial Oscillation

The representation of the Arctic stratospheric circulation and the quasi-biennial oscillation(QBO)during the period 1981–2019 in a 40-yr Chinese global reanalysis dataset(CRA-40)is evaluated by comparing two widely used reanalysis datasets,ERA-5 and MERRA-2.CRA-40 demonstrates a comparable performance with ERA-5 and MERRA-2 in characterizing the winter and spring circulation in the lower and middle Arctic stratosphere.Specifically,differences in the climatological polar-mean temperature and polar night jet among the three reanalyses are within±0.5 K and±0.5 m s^(–1),respectively.The onset dates of the stratospheric sudden warming and stratospheric final warming events at 10 hPa in CRA-40,together with the dynamics and circulation anomalies during the onset process of warming events,are nearly identical to the other two reanalyses with slight differences.By contrast,the CRA-40 dataset demonstrates a deteriorated performance in describing the QBO below 10 hPa compared to the other two reanalysis products,manifested by the larger easterly biases of the QBO index,the remarkably weaker amplitude of the QBO,and the weaker wavelet power of the QBO period.Such pronounced biases are mainly concentrated in the period 1981–98 and largely reduced by at least 39%in 1999–2019.Thus,particular caution is needed in studying the QBO based on CRA-40.All three reanalyses exhibit greater disagreement in the upper stratosphere compared to the lower and middle stratosphere for both the polar region and the tropics.

Understanding Simulated Causes of Damaging Surface Winds in a Derecho-Producing Mesoscale Convective System near the East China Coast Based on Convection-Permitting Simulations

A mesoscale convective system(MCS) occurred over the East China coastal provinces and the East China Sea on 30April 2021, producing damaging surface winds near the coastal city Nantong with observed speeds reaching 45 m s^(–1). A simulation using the Weather Research and Forecasting model with a 1.5-km grid spacing generally reproduces the development and subsequent organization of this convective system into an MCS, with an eastward protruding bow segment over the sea. In the simulation, an east-west-oriented high wind swath is generated behind the gust front of the MCS. Descending dry rear-to-front inflows behind the bow and trailing gust front are found to feed the downdrafts in the main precipitation regions. The inflows help to establish spreading cold outflows and enhance the downdrafts through evaporative cooling. Meanwhile, front-to-rear inflows from the south are present, associated with severely rearward-tilted updrafts initially forming over the gust front. Such inflows descend behind(north of) the gust front, significantly enhancing downdrafts and near-surface winds within the cold pool. Consistently, calculated trajectories show that these parcels that contribute to the derecho originate primarily from the region ahead(south) of the east-west-oriented gust front, and dry southwesterly flows in the low-to-middle levels contribute to strong downdrafts within the MCS. Moreover, momentum budget analyses reveal that a large westward-directed horizontal pressure gradient force within the simulated cold pool produced rapid flow acceleration towards Nantong. The analyses enrich the understanding of damaging wind characteristics over coastal East China and will prove helpful to operational forecasters.

Assessment of Crop Yield in China Simulated by Thirteen Global Gridded Crop Models

Global gridded crop models(GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far only a few studies have assessed the performance of GGCMs in China, and these studies mainly focused on the average and interannual variability of national and regional yields. Here, a systematic national-and provincial-scale evaluation of the simulations by13 GGCMs [12 from the GGCM Intercomparison(GGCMI) project, phase 1, and CLM5-crop] of the yields of four crops(wheat, maize, rice, and soybean) in China during 1980–2009 was carried out through comparison with crop yield statistics collected from the National Bureau of Statistics of China. Results showed that GGCMI models generally underestimate the national yield of rice but overestimate it for the other three crops, while CLM5-crop can reproduce the national yields of wheat, maize, and rice well. Most GGCMs struggle to simulate the spatial patterns of crop yields. In terms of temporal variability, GGCMI models generally fail to capture the observed significant increases, but some can skillfully simulate the interannual variability. Conversely, CLM5-crop can represent the increases in wheat, maize, and rice, but works less well in simulating the interannual variability. At least one model can skillfully reproduce the temporal variability of yields in the top-10 producing provinces in China, albeit with a few exceptions. This study, for the first time, provides a complete picture of GGCM performance in China, which is important for GGCM development and understanding the reliability and uncertainty of national-and provincial-scale crop yield prediction in China.

A Neural-network-based Alternative Scheme to Include Nonhydrostatic Processes in an Atmospheric Dynamical Core

Here,a nonhydrostatic alternative scheme(NAS)is proposed for the grey zone where the nonhydrostatic impact on the atmosphere is evident but not large enough to justify the necessity to include an implicit nonhydrostatic solver in an atmospheric dynamical core.The NAS is designed to replace this solver,which can be incorporated into any hydrostatic models so that existing well-developed hydrostatic models can effectively serve for a longer time.Recent advances in machine learning(ML)provide a potential tool for capturing the main complicated nonlinear-nonhydrostatic relationship.In this study,an ML approach called a neural network(NN)was adopted to select leading input features and develop the NAS.The NNs were trained and evaluated with 12-day simulation results of dry baroclinic-wave tests by the Weather Research and Forecasting(WRF)model.The forward time difference of the nonhydrostatic tendency was used as the target variable,and the five selected features were the nonhydrostatic tendency at the last time step,and four hydrostatic variables at the current step including geopotential height,pressure in two different forms,and potential temperature,respectively.Finally,a practical NAS was developed with these features and trained layer by layer at a 20-km horizontal resolution,which can accurately reproduce the temporal variation and vertical distribution of the nonhydrostatic tendency.Corrected by the NN-based NAS,the improved hydrostatic solver at different horizontal resolutions can run stably for at least one month and effectively reduce most of the nonhydrostatic errors in terms of system bias,anomaly root-mean-square error,and the error of the wave spatial pattern,which proves the feasibility and superiority of this scheme.

2023: Weather and Climate Extremes Hitting the Globe with Emerging Features

Globally,2023 was the warmest observed year on record since at least 1850 and,according to proxy evidence,possibly of the past 100000 years.As in recent years,the record warmth has again been accompanied with yet more extreme weather and climate events throughout the world.Here,we provide an overview of those of 2023,with details and key background causes to help build upon our understanding of the roles of internal climate variability and anthropogenic climate change.We also highlight emerging features associated with some of these extreme events.Hot extremes are occurring earlier in the year,and increasingly simultaneously in differing parts of the world(e.g.,the concurrent hot extremes in the Northern Hemisphere in July 2023).Intense cyclones are exacerbating precipitation extremes(e.g.,the North China flooding in July and the Libya flooding in September).Droughts in some regions(e.g.,California and the Horn of Africa)have transitioned into flood conditions.Climate extremes also show increasing interactions with ecosystems via wildfires(e.g.,those in Hawaii in August and in Canada from spring to autumn 2023)and sandstorms(e.g.,those in Mongolia in April 2023).Finally,we also consider the challenges to research that these emerging characteristics present for the strategy and practice of adaptation.

Insights from the Second Tibetan Plateau Scientific Expedition: Unveiling the westerly–monsoon synergy and hydroclimate changes

The Tibetan Plateau(TP),often referred to as the“Asian Water Tower”,holds vast reserves of glaciers,snow,and permafrost,serving as the crucial source for major rivers that support billions of people across Asia.The TP’s unique geographical positioning fosters significant interplay between the westerly and monsoon systems,the hydroclimate changes on the TP and its interactions with these two major atmospheric circulation systems through both the thermodynamic and dynamic processes,as well as the atmospheric water cycle of the TP.These interactions have far-reaching impacts on the weather and climate of China,Asia,and even the global atmospheric circulation.

An Assessment of Indo-Pacific Oceanic Channel Dynamics in the FGOALS-g2 Coupled Climate System Model

Lag correlations of sea surface temperature anomalies （SSTAs）, sea surface height anomalies （SSHAs）, subsurface temperature anomalies, and surface zonal wind anomalies （SZWAs） produced by the Flexible Global Ocean-Atmosphere-Land System modeh Grid-point Version 2 （FGOALS-g2） are analyzed and com- pared with observations. The insignificant, albeit positive, lag correlations between the SSTAs in the south- eastern tropical Indian Ocean （STIO） in fall and the SSTAs in the central-eastern Pacific cold tongue in the following summer through fall are found to be not in agreement with the observational analysis. The model, however, does reproduce the significant lag correlations between tile SSHAs in the STIO in fall and those in the cold tongue at the one-year time lag in the observations. These, along with the significant lag correlations between the SSTAs in the STIO in fall and the subsurface temperature anomalies in the equatorial Pacific vertical section in the following year, suggest that the Indonesian Throughflow plays an important role in propagating the Indian Ocean anomalies into the equatorial Pacific Ocean. Analyses of the interannual anomalies of the Indonesian Throughflow transport suggest that the FGOALS-g2 climate system simulates, but underestimates, the oceanic channel dynamics between the Indian and Pacific Oceans. FGOALS-g2 is shown to produce lag correlations between the SZWAs over the western equatorial Pacific in fall and the cold tongue SSTAs at the one-year time lag that are too strong to be realistic in comparison with observations. The analyses suggest that the atmospheric bridge over the Indo-Pacific Ocean is overestimated in the FGOALS-g2 coupled climate model.