地球系统数值模拟研究进展与科学前沿

doi:10.13745/j.esf.sf.2025.3.9

摘要/Abstract

摘要：

地球系统模式是理解和预测全球变化的核心工具,近年来取得了显著进展。其性能提升体现在圈层耦合过程的精细化发展,以及圈层内复杂物理和化学过程的逐步引入。不确定性的降低则得益于新方法和新技术的发展和应用。然而,地球系统模式仍面临诸多挑战,包括对复杂交互过程的表征能力不足、社会-生态系统过程模拟的局限性,以及区域极端事件模拟能力的提升需求。未来的发展需深化跨学科协作,借助新技术强化数据获取与模型预测能力,同时聚焦社会-生态系统过程及其影响机制的研究,以增强对区域极端事件的模拟与预测能力,构建完善的陆-海-气-人相互耦合的新一代地球系统模式,为人类社会的可持续发展及全球变化的应对和预测提供更科学的支撑。

关键词: 地球系统模式, 发展历程, 研究进展, 不足与挑战, 陆-海-气-人耦合

Abstract:

Earth system models are essential tools for understanding and predicting global change and have made significant strides in recent years. These advancements are evident in the more detailed representation of coupled processes across Earth’s spheres and the integration of increasingly sophisticated physical and chemical processes within them. The reduction in model uncertainties has been driven by the adoption of innovative methods and cutting-edge technologies. Nonetheless, challenges persist, including limitations in capturing complex interactive processes, difficulties in simulating socio-ecological systems, and the need to enhance the modeling of regional extreme events. Moving forward, efforts should prioritize fostering interdisciplinary collaboration, harnessing emerging technologies to improve data acquisition and predictive accuracy, and deepening research into socio-ecological processes and their impacts. Such initiatives will bolster the ability to model and predict regional extreme events while paving the way for next-generation Earth system models that seamlessly integrate land, ocean, atmosphere, and human interactions. These advancements are vital for supporting sustainable development and providing a robust scientific foundation to address and anticipate global changes.

Key words: Earth system model, development history, research progress, limitations and challenges, land-sea-atmosphere-human interaction

中图分类号:

N945

朱佳雷, 董建志, 张永根, 孙少波, 姜哲, 周浩然, 赵曦, 李攀, 陈伟, 王礼春, 李新, 刘丛强. 地球系统数值模拟研究进展与科学前沿[J]. 地学前缘, 2025, 32(3): 118-136.

ZHU Jialei, DONG Jianzhi, ZHANG Yonggen, SUN Shaobo, JIANG Zhe, ZHOU Haoran, ZHAO Xi, LI Pan, CHEN Wei, WANG Lichun, LI Xin, Liu Cong-Qiang. Progress and scientific frontiers in numerical simulation of the Earth system[J]. Earth Science Frontiers, 2025, 32(3): 118-136.

图/表 1

参考文献 197

[1]	ORLOWSKY B, SENEVIRATNE S I. Global changes in extreme events: regional and seasonal dimension[J]. Climatic Change, 2012, 110(3): 669-696.
[2]	HOCK R, HUSS M. Chapter 9 Chapter 9 - Glaciers and climate change[M]//HOCK R, HUSS M. Climate Change: Observed Impacts on Planet Earth. 3rd ed. Amsterdam: Elsevier. 2021: 157-176.
[3]	STANEV E V, PENEVA E L. Regional sea level response to global climatic change: black sea examples[J]. Global and Planetary Change, 2001, 32(1): 33-47.
[4]	VAN VLIET M T H, THORSLUND J, STROKAL M, et al. Global river water quality under climate change and hydroclimatic extremes[J]. Nature Reviews Earth & Environment, 2023, 4(10): 687-702.
[5]	WOOLWAY R I, KRAEMER B M, LENTERS J D, et al. Global lake responses to climate change[J]. Nature Reviews Earth & Environment, 2020, 1(8): 388-403.
[6]	陈亚宁, 李稚, 范煜婷, 等. 西北干旱区气候变化对水文水资源影响研究进展[J]. 地理学报, 2014, 69: 1295-1304. DOI
[7]	DESER C, LEHNER F, RODGERS K B, et al. Insights from Earth system model initial-condition large ensembles and future prospects[J]. Nature Climate Change, 2020, 10(4): 277-286.
[8]	张焓, 戴永久, 张树磊. 区域地球系统模式研究进展[J]. 大气科学学报, 2024, 47: 216-223.
[9]	周天军, 陈梓明, 邹立维, 等. 中国地球气候系统模式的发展及其模拟和预估[J]. 气象学报, 2020, 78: 332-350.
[10]	STEFFEN W, RICHARDSON K, ROCKSTRÖM J, et al. The emergence and evolution of Earth system science[J]. Nature Reviews Earth & Environment, 2020, 1(1): 54-63.
[11]	王斌, 周天军, 俞永强. 地球系统模式发展展望[J]. 气象学报, 2008, 66: 857-869.
[12]	BONAN G B, DONEY S C. Climate, ecosystems, and planetary futures: the challenge to predict life in Earth system models[J]. Science, 2018, 359(6375): eaam8328.
[13]	NAZEMI A, WHEATER H S. On inclusion of water resource management in Earth system models; Part 2: representation of water supply and allocation and opportunities for improved modeling[J]. Hydrology and Earth System Sciences, 2015, 19(1): 63-90.
[14]	FAN Y, YANG Z, LO M H, et al. Applying double cropping and interactive irrigation in the North China Plain using WRF4.5[J]. Geoscientific Model Development, 2024, 17(18): 6929-6947.
[15]	JÖCKEL P, TOST H, POZZER A, et al. Earth system chemistry integrated modelling (ESCiMo) with the modular Earth submodel system (MESSy) version 2.51[J]. Geoscientific Model Development, 2016, 9(3): 1153-1200.
[16]	LAMARQUE J F, EMMONS L K, HESS P G, et al. CAM-chem: description and evaluation of interactive atmospheric chemistry in the community Earth system model[J]. Geoscientific Model Development, 2012, 5(2): 369-411.
[17]	PRINN R G. Development and application of Earth system models[J]. Proceedings of the National Academy of Sciences, 2013, 110(1): 3673-3680.
[18]	HURRELL J W, HOLLAND M M, GENT P R, et al. The community Earth system model: a framework for collaborative research[J]. Bulletin of the American Meteorological Society, 2013, 94(9): 1339-1360.
[19]	PAN X, CHEN D, PAN B, et al. Evolution and prospects of Earth system models: challenges and opportunities[J]. Earth-Science Reviews, 2025, 260: 104986.
[20]	FLATO G. Earth system models: an overview[J]. Wiley Interdisciplinary Reviews: Climate Change, 2011, 2(6): 783-800.
[21]	TREUT H L, SOMERVILLE R, CUBASCH U, et al. Historical overview of climate change science[R]. Climate change 2007:the physical science basis. contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge, UK and New York, USA: Cambridge University Press, 2007.
[22]	周天军, 邹立维, 吴波, 等. 中国地球气候系统模式研究进展: CMIP计划实施近20年回顾[J]. 气象学报, 2014, 72: 892-907.
[23]	KAWAMIYA M, HAJIMA T, TACHIIRI K, et al. Two decades of Earth system modeling with an emphasis on model for interdisciplinary research on climate (MIROC)[J]. Progress in Earth and Planetary Science, 2020, 7(1): 64.
[24]	SELLAR A A, JONES C G, MULCAHY J P, et al. UKESM1: description and evaluation of the U. K. Earth system model[J]. Journal of Advances in Modeling Earth Systems, 2019, 11(12): 4513-4558.
[25]	CUBASCH U, WUEBBLES D J, CHEN D, et al. Introduction[R]. Climate change 2013: the physical science basis. contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press, 2013.
[26]	COX P M, BETTS R A, JONES C D, et al. Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model[J]. Nature, 2000, 408(6809): 184-187.
[27]	FRIEDLINGSTEIN P, BOPP L, CIAIS P, et al. Positive feedback between future climate change and the carbon cycle[J]. Geophysical Research Letters, 2001, 28(8): 1543-1546.
[28]	FLATO G, MAROTZKE J, ABIODUN B, et al. Evaluation of climate models[R]. Climate change 2013:the physical science basis. contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge, UK and New York, USA: Cambridge University Press, 2013.
[29]	RANDALL D A, WOOD R A, BONY S, et al. Climate models and their evaluation[R]. Climate change 2007:the physical science basis. contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge, UK and New York, USA: Cambridge University Press, 2007.
[30]	BLYTH E M, ARORA V K, CLARK D B, et al. Advances in land surface modelling[J]. Current Climate Change Reports, 2021, 7(2): 45-71.
[31]	ZAEHLE S. Terrestrial nitrogen-carbon cycle interactions at the global scale[J]. Philosophical Transactions of the Royal Society B: Biological Sciences, 2013, 368(1621): 20130125.
[32]	FISHER R A, KOVEN C D. Perspectives on the future of land durface models and the challenges of representing complex terrestrial systems[J]. Journal of Advances in Modeling Earth Systems, 2020, 12(4): e2018MS001453.
[33]	FENNEL K, MATTERN J P, DONEY S C, et al. Ocean biogeochemical modelling[J]. Nature Reviews Methods Primers, 2022, 2(1): 76.
[34]	TERHAAR J, GORIS N, MÜLLER J D, et al. Assessment of global ocean biogeochemistry models for ocean carbon sink estimates in RECCAP2 and recommendations for future studies[J]. Journal of Advances in Modeling Earth Systems, 2024, 16(3): e2023MS003840.
[35]	FRIEDLINGSTEIN P, O’SULLIVAN M, JONES M W, et al. Global carbon budget 2022[J]. Earth System Science Data, 2022, 14(11): 4811-4900.
[36]	CHEN D, ROJAS M, SAMSET B H, et al. Framing, Context, and Methods[R]. Climate change 2021:the physical science basis. contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change. Cambridge, UK and New York, USA: Cambridge University Press, 2021.
[37]	董文杰, 延晓冬, 丑洁明. 人-地系统动力学耦合模型研究的评述与展望[J]. 科学通报, 2024, 69: 3454-3465.
[38]	VAN VUUREN D P, LOWE J, STEHFEST E, et al. How well do integrated assessment models simulate climate change?[J]. Climatic Change, 2011, 104(2): 255-285.
[39]	PRINN R, JACOBY H, SOKOLOV A, et al. Integrated global system model for climate policy assessment: feedbacks and sensitivity studies[J]. Climatic Change, 1999, 41(3): 469-546.
[40]	YANG S, DONG W, CHOU J, et al. A brief introduction to BNU-HESM1. 0 and its Earth surface temperature simulations[J]. Advances in Atmospheric Sciences, 2015, 32(12): 1683-1688.
[41]	DANABASOGLU G, LAMARQUE J F, BACMEISTER J, et al. The community Earth system model version 2 (CESM2)[J]. Journal of Advances in Modeling Earth Systems, 2020, 12(2): e2019MS001916.
[42]	LEUNG L R, BADER D C, TAYLOR M A, et al. An introduction to the E3SM special collection: goals, science drivers, development, and analysis[J]. Journal of Advances in Modeling Earth Systems, 2020, 12(11): e2019MS001821.
[43]	MAURITSEN T, BADER J, BECKER T, et al. Developments in the MPI-M Earth system model version 1.2 (MPI-ESM1.2) and its response to increasing CO₂[J]. Journal of Advances in Modeling Earth Systems, 2019, 11(4): 998-1038. DOI PMID
[44]	ANDREWS M B, RIDLEY J K, WOOD R A, et al. Historical simulations with HadGEM3-GC3.1 for CMIP6[J]. Journal of Advances in Modeling Earth Systems, 2020, 12(6): e2019MS001995.
[45]	LIN Y, HUANG X, LIANG Y, et al. Community integrated Earth system model (CIESM): description and evaluation[J]. Journal of Advances in Modeling Earth Systems, 2020, 12(8): 1-29.
[46]	LI G, CHENG L, WANG X. Evaluation of the CAS-ESM2-0 performance in simulating the global ocean salinity change[J]. Atmosphere, 2023, 14(1): 107.
[47]	BAUER P, THORPE A, BRUNET G. The quiet revolution of numerical weather prediction[J]. Nature, 2015, 525(7567): 47-55.
[48]	MANABE S, BRYAN K. Climate calculations with a combined ocean-atmosphere model[J]. Journal of Atmospheric Sciences, 1969, 26(4): 786-789.
[49]	WASHINGTON W, PARKINSON C. An introduction to three-dimensional climate modeling[J]. Oceanography, 2005, 18(3): 82-83.
[50]	KIEHL J T, BRIEGLEB B P. The relative roles of sulfate aerosols and greenhouse gases in climate forcing[J]. Science, 1993, 260(5106): 311-314. PMID
[51]	MITCHELL J F B, JOHNS T C, GREGORY J M, et al. Climate response to increasing levels of greenhouse gases and sulphate aerosols[J]. Nature, 1995, 376(6540): 501-504.
[52]	MORRISON H, CURRY J A, KHVOROSTYANOV V I. A new double-moment microphysics parameterization for application in cloud and climate models. Part I: description[J]. Journal of the Atmospheric Sciences, 2005, 62(6): 1665-1677.
[53]	STIER P, FEICHTER J, KINNE S, et al. The aerosol-climate model ECHAM5-HAM[J]. Atmospheric Chemistry and Physics, 2005, 5(4): 1125-1156.
[54]	MORGENSTERN O, BRAESICKE P, O’CONNOR F M, et al. Evaluation of the new UKCA climate-composition model-Part 1: the stratosphere[J]. Geoscientific Model Development, 2009, 2(1): 43-57.
[55]	EYRING V, LAMARQUE J F, HESS P, et al. Overview of IGAC/SPARC chemistry-climate model initiative (CCMI) community simulations in support of upcoming ozone and climate assessments[J]. SPARC newsletter, 2013, 40: 48-66.
[56]	EYRING V, BONY S, MEEHL G A, et al. Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization[J]. Geoscientific Model Development, 2016, 9(5): 1937-1958.
[57]	MEEHL G A, SENIOR C A, EYRING V, et al. Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models[J]. Science Advances, 2020, 6(26): eaba1981.
[58]	BRYAN K, COX M D. A numerical investigation of the oceanic general circulation[J]. Tellus, 1967, 19(1): 54-80.
[59]	FOX-KEMPER B, ADCROFT A, BÖNING C W, et al. Challenges and prospects in ocean circulation models[J]. Frontiers in Marine Science, 2019, 6: 65.
[60]	KALRA T S, GANJU N K, TESTA J M. Development of a submerged aquatic vegetation growth model in the coupled ocean-atmosphere-wave-sediment transport (COAWST v3.4) model[J]. Geoscientific Model Development, 2020, 13(11): 5211-5228.
[61]	ZAMBON J B, HE R, WARNER J C, et al. Impact of SST and surface waves on hurricane florence (2018): a coupled modeling investigation[J]. Weather and Forecasting, 2021, 36(5): 1713-1734.
[62]	YUAN Y, YU F, CHEN Z, et al. Towards a real-time modeling of global ocean waves by the fully GPU-accelerated spectral wave model WAM6-GPU v1.0[J]. Geoscientific Model Development, 2024, 17(16): 6123-6136.
[63]	ZHANG Y J, FERNANDEZ-MONTBLANC T, PRINGLE W, et al. Global seamless tidal simulation using a 3D unstructured-grid model (SCHISM v5.10.0)[J]. Geoscientific Model Development, 2023, 16(9): 2565-2581.
[64]	HART-DAVIS M G, PICCIONI G, DETTMERING D, et al. EOT20: a global ocean tide model from multi-mission satellite altimetry[J]. Earth System Science Data, 2021, 13(8): 3869-3884.
[65]	CAMPOS-CABA R, ALESSANDRI J, CAMUS P, et al. Assessing storm surge model performance: what error indicators can measure the model’s skill?[J]. Ocean Science, 2024, 20(6): 1513-1526.
[66]	戴永久. 陆面过程模式研发中的问题[J]. 大气科学学报, 2020, 43: 33-38.
[67]	郑辉, 刘树华, CLEMENT P, 等. 北京大学陆面过程模式PKULM(peking university land model)介绍及检验[J]. 地球物理学报, 2016, 59: 79-92. DOI
[68]	ARORA V K, SEILER C, WANG L, et al. Towards an ensemble-based evaluation of land surface models in light of uncertain forcings and observations[J]. Biogeosciences, 2023, 20(7): 1313-1355.
[69]	陈海山, 孙照渤. 陆气相互作用及陆面模式的研究进展[J]. 南京气象学院学报, 2002: 277-288.
[70]	DAI Y, ZENG X, DICKINSON R E, et al. The common land model[J]. Bulletin of the American Meteorological Society, 2003, 84(8): 1013-1024.
[71]	NIU G Y, YANG Z L, MITCHELL K E, et al. The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements[J]. Journal of Geophysical Research: Atmospheres, 2011, 116(D12110): 1-16.
[72]	OLESON K W, DAI Y, BONAN B, et al. Technical description of the community land model (CLM)(NCAR technical note NCAR/TN-461+STR)[R]. Boulder, Colorado, USA: National Center for Atmospheric Research (NCAR), 2004.
[73]	HE C, VALAYAMKUNNATH P, BARLAGE M, et al. Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability[J]. Geoscientific Model Development, 2023, 16(17): 5131-5151.
[74]	NITTA T, ARAKAWA T, HATONO M, et al. Development of integrated land simulator[J]. Progress in Earth and Planetary Science, 2020, 7(1): 68.
[75]	OLESON K W, FEDDEMA J. Parameterization and surface data improvements and new capabilities for the community land model urban (CLMU)[J]. Journal of Advances in Modeling Earth Systems, 2020, 12(2): e2018MS001586.
[76]	LAWRENCE D M, FISHER R A, KOVEN C D, et al. The community land model version 5: description of new features, benchmarking, and impact of forcing uncertainty[J]. Journal of Advances in Modeling Earth Systems, 2019, 11(12): 4245-4287.
[77]	戴永久, 魏楠, 黄安宁, 等. 通用陆面模式(CoLM)湖泊过程方案与性能评估[J]. 科学通报, 2018, 63: 3002-3021.
[78]	EYRING V, RIGHI M, LAUER A, et al. ESMValTool (v1.0): a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP[J]. Geoscientific Model Development, 2016, 9(5): 1747-1802.
[79]	BRIENT F, BONY S. Interpretation of the positive low-cloud feedback predicted by a climate model under global warming[J]. Climate Dynamics, 2013, 40(9): 2415-2431.
[80]	TSUSHIMA Y, RINGER M A, KOSHIRO T, et al. Robustness, uncertainties, and emergent constraints in the radiative responses of stratocumulus cloud regimes to future warming[J]. Climate Dynamics, 2016, 46(9): 3025-3039.
[81]	DEANGELIS A M, QU X, ZELINKA M D, et al. An observational radiative constraint on hydrologic cycle intensification[J]. Nature, 2015, 528(7581): 249-253.
[82]	LI G, XIE S-P, HE C, et al. Western pacific emergent constraint lowers projected increase in Indian summer monsoon rainfall[J]. Nature Climate Change, 2017, 7(10): 708-712.
[83]	WENZEL S, COX P M, EYRING V, et al. Emergent constraints on climate-carbon cycle feedbacks in the CMIP5 Earth system models[J]. Journal of Geophysical Research: Biogeosciences, 2014, 119(5): 794-807.
[84]	WENZEL S, COX P M, EYRING V, et al. Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO₂[J]. Nature, 2016, 538(7626): 499-501.
[85]	KWIATKOWSKI L, BOPP L, AUMONT O, et al. Emergent constraints on projections of declining primary production in the tropical oceans[J]. Nature Climate Change, 2017, 7(5): 355-358.
[86]	CHADBURN S E, BURKE E J, COX P M, et al. An observation-based constraint on permafrost loss as a function of global warming[J]. Nature Climate Change, 2017, 7(5): 340-344.
[87]	HALL A, QU X. Using the current seasonal cycle to constrain snow albedo feedback in future climate change[J]. Geophysical Research Letters, 2006, 33(L03502): 1-4.
[88]	HOFFMAN F M, RANDERSON J T, ARORA V K, et al. Causes and implications of persistent atmospheric carbon dioxide biases in Earth system models[J]. Journal of Geophysical Research: Biogeosciences, 2014, 119(2): 141-162.
[89]	KNUTTI R, MASSON D, GETTELMAN A. Climate model genealogy: generation CMIP5 and how we got there[J]. Geophysical Research Letters, 2013, 40: 1194-1199.
[90]	COX P M, HUNTINGFORD C, WILLIAMSON M S. Emergent constraint on equilibrium climate sensitivity from global temperature variability[J]. Nature, 2018, 553(7688): 319-322.
[91]	COX P M, PEARSON D, BOOTH B B, et al. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability[J]. Nature, 2013, 494(7437): 341-344.
[92]	SHERWOOD S C, BONY S, DUFRESNE J-L. Spread in model climate sensitivity traced to atmospheric convective mixing[J]. Nature, 2014, 505(7481): 37-42.
[93]	BRIENT F, SCHNEIDER T. Constraints on climate sensitivity from space-based measurements of low-cloud reflection[J]. Journal of Climate, 2016, 29(16): 5821-5835.
[94]	DESSLER A E, FORSTER P M. An estimate of equilibrium climate sensitivity from interannual variability[J]. Journal of Geophysical Research-Atmospheres, 2018, 123(16): 8634-8645.
[95]	KLEIN S A, HALL A. Emergent constraints for cloud feedbacks[J]. Current Climate Change Reports, 2015, 1(4): 276-287.
[96]	LIPAT B R, TSELIOUDIS G, GRISE K M, et al. CMIP5 models’ shortwave cloud radiative response and climate sensitivity linked to the climatological Hadley cell extent[J]. Geophysical Research Letters, 2017, 44(11): 5739-5748.
[97]	EPSTEIN E S. Stochastic dynamic prediction[J]. Tellus, 1969, 21(6): 739-759.
[98]	LEITH C E. Theoretical skill of monte-carlo forecasts[J]. Monthly Weather Review, 1974, 102(6): 409-418.
[99]	PALMER T. The ECMWF ensemble prediction system: looking back (more than) 25 years and projecting forward 25 years[J]. Quarterly Journal of the Royal Meteorological Society, 2019, 145(S1): 12-24.
[100]	KALNAY E. Atmospheric modeling, data assimilation and predictability[M]. Cambridge: Cambridge University Press, 2002.
[101]	HOUTEKAMER P L, ZHANG F. Review of the ensemble kalman filter for atmospheric data assimilation[J]. Monthly Weather Review, 2016, 144(12): 4489-4532.
[102]	YANG K, ZHU L, CHEN Y, et al. Land surface model calibration through microwave data assimilation for improving soil moisture simulations[J]. Journal of Hydrology, 2016, 533: 266-276.
[103]	FIELDING M D, JANISKOVá M. Direct 4D-Var assimilation of space-borne cloud radar reflectivity and lidar backscatter. Part I: observation operator and implementation[J]. Quarterly Journal of the Royal Meteorological Society, 2020, 146(733): 3877-3899.
[104]	GEER A J. Physical characteristics of frozen hydrometeors inferred with parameter estimation[J]. Atmospheric Measurement Techniques, 2021, 14(8): 5369-5395.
[105]	JANISKOVá M, FIELDING M D. Direct 4D-Var assimilation of space-borne cloud radar and lidar observations. Part II: impact on analysis and subsequent forecast[J]. Quarterly Journal of the Royal Meteorological Society, 2020, 146(733): 3900-3916.
[106]	KOTSUKI S, SATO Y, MIYOSHI T. Data assimilation for climate research: model parameter estimation of large-scale condensation scheme[J]. Journal of Geophysical Research: Atmospheres, 2020, 125(1).
[107]	LI X, LIU F, MA C, et al. Land data assimilation: harmonizing theory and data in land surface process studies[J]. Reviews of Geophysics, 2024, 62(1): 1-45.
[108]	CHEN D. Coupled data assimilation for enso prediction[M]//CHEN D. Advances in geosciences. Singapore; World Scientific Publishing Company, 2010: 45-62.
[109]	WANG J, XU Y, YANG L, et al. Data assimilation of high-resolution satellite rainfall product improves rainfall simulation associated with landfalling tropical cyclones in the Yangtze river delta[J]. Remote Sensing, 2020, 12(2): 276.
[110]	HAN W, HE T-L, JIANG Z, et al. The capability of deep learning model to predict ozone across continents in China, the United States and Europe[J]. Geophysical Research Letters, 2023, 50(24): e2023GL104928.
[111]	WANG M, CHEN X, JIANG Z, et al. Meteorological and anthropogenic drivers of surface ozone change in the North China Plain in 2015-2021[J]. Science of The Total Environment, 2024, 906: 167763.
[112]	BOCQUET M, BRAJARD J, CARRASSI A, et al. Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models[J]. Nonlinear Processes in Geophysics, 2019, 26(3): 143-162.
[113]	GEER A J. Learning Earth system models from observations: machine learning or data assimilation?[J]. Philosophical Transactions of the Royal Society a-Mathematical Physical and Engineering Sciences, 2021, 379(2194).
[114]	HUNEEUS N, CHEVALLIER F, BOUCHER O. Estimating aerosol emissions by assimilating observed aerosol optical depth in a global aerosol model[J]. Atmospheric Chemistry and Physics, 2012, 12(10): 4585-4606.
[115]	JIANG Z, WORDEN J R, WORDEN H, et al. A 15-year record of CO emissions constrained by MOPITT CO observations[J]. Atmospheric Chemistry and Physics, 2017, 17(7): 4565-4583.
[116]	WILLIAMSON D L. The evolution of dynamical cores for global atmospheric models[J]. Journal of the Meteorological Society of Japan, 2007, 85B(1): 241-269.
[117]	XIE S C, BOYLE J, KLEIN S A, et al. Simulations of arctic mixed-phase clouds in forecasts with CAM3 and AM2 for M-PACE[J]. Journal of Geophysical Research: Atmospheres, 2008, 113(D04211): 1-6.
[118]	BONAN G. Climate change and terrestrial ecosystem modeling[M]. Cambridge: Cambridge University Press, 2019.
[119]	CAPOTONDI A, DESER C, PHILLIPS A S, et al. ENSO and Pacific decadal variability in the community Earth system model version 2[J]. Journal of Advances in Modeling Earth Systems, 2020, 12(12): e2019MS002022.
[120]	ORTH R, SENEVIRATNE S I. Variability of soil moisture and sea surface temperatures similarly important for warm-season land climate in the community Earth system model[J]. Journal of Climate, 2017, 30(6): 2141-2162.
[121]	HEWITT H T, ROBERTS M, MATHIOT P, et al. Resolving and parameterising the ocean mesoscale in Earth system models[J]. Current Climate Change Reports, 2020, 6(4): 137-152.
[122]	XUE P, MALANOTTE-RIZZOLI P, WEI J, et al. Coupled ocean-atmosphere modeling over the maritime continent: a review[J]. Journal of Geophysical Research: Oceans, 2020, 125(6): e2019JC014978.
[123]	FRÖLICHER T L, RODGERS K B, STOCK C A, et al. Sources of uncertainties in 21st century projections of potential ocean ecosystem stressors[J]. Global Biogeochemical Cycles, 2016, 30(8): 1224-1243.
[124]	STOCK C A, DUNNE J P, FAN S, et al. Ocean biogeochemistry in GFDL’s Earth system model 4.1 and its response to increasing atmospheric CO₂[J]. Journal of Advances in Modeling Earth Systems, 2020, 12(10): e2019MS002043.
[125]	PARK J Y, STOCK C A, DUNNE J P, et al. Seasonal to multiannual marine ecosystem prediction with a global Earth system model[J]. Science, 2019, 365(6450): 284-288.
[126]	ANI C J, ROBSON B. Responses of marine ecosystems to climate change impacts and their treatment in biogeochemical ecosystem models[J]. Marine Pollution Bulletin, 2021, 166: 112223.
[127]	BEAMESDERFER E R, BUECHNER C, FAIOLA C, et al. Advancing cross-disciplinary understanding of land-atmosphere interactions[J]. Journal of Geophysical Research: Biogeosciences, 2022, 127(2): e2021JG006707.
[128]	SUN S, CHEN B, YAN J, et al. Potential impacts of land use and land cover change (LUCC) and climate change on evapotranspiration and gross primary productivity in the Haihe River Basin, China[J]. Journal of Cleaner Production, 2024, 476: 143729.
[129]	HUANG Y, WANG Y P, ZIEHN T. Nonlinear interactions of land carbon cycle feedbacks in Earth system models[J]. Global Change Biology, 2022, 28(1): 296-306.
[130]	MIRALLES D G, GENTINE P, SENEVIRATNE S I, et al. Land-atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges[J]. Annals of the New York Academy of Sciences, 2019, 1436(1): 19-35. DOI PMID
[131]	MORRISON H, VAN LIER-WALQUI M, FRIDLIND A M, et al. Confronting the challenge of modeling cloud and precipitation microphysics[J]. Journal of Advances in Modeling Earth Systems, 2020, 12(8): e2019MS001689.
[132]	SPAFFORD L, MACDOUGALL A H. Validation of terrestrial biogeochemistry in CMIP6 Earth system models: a review[J]. Geoscientific Model Development, 2021, 14(9): 5863-5889.
[133]	NOTZ D, BITZ C M. Sea ice in Earth system models//NOTZ D, BITZ C M. Sea Ice. Cham; Springer, 2017: 304-325.
[134]	CALLAGHAN T V, JOHANSSON M, KEY J, et al. Feedbacks and interactions: from the Arctic cryosphere to the climate system[J]. AMBIO, 2011, 40(1): 75-86.
[135]	BOY M, THOMSON E S, ACOSTA NAVARRO J C, et al. Interactions between the atmosphere, cryosphere, and ecosystems at northern high latitudes[J]. Atmospheric Chemistry and Physics, 2019, 19(3): 2015-2061.
[136]	GOOSSE H, KAY J E, ARMOUR K C, et al. Quantifying climate feedbacks in polar regions[J]. Nature Communications, 2018, 9(1): 1919. DOI PMID
[137]	SINGH H K A, LANDRUM L, HOLLAND M M, et al. An overview of Antarctic sea ice in the community Earth system model version 2, Part I: analysis of the seasonal cycle in the context of sea ice thermodynamics and coupled atmosphere-ocean-ice processes[J]. Journal of Advances in Modeling Earth Systems, 2021, 13(3): e2020MS002143.
[138]	VIZCAINO M. Ice sheets as interactive components of Earth system models: progress and challenges[J]. WIREs Climate Change, 2014, 5(4): 557-568.
[139]	HOLLAND M M, HUNKE E C J O. A review of Arctic sea ice climate predictability in large-scale Earth system models[J]. Oceanography, 2022, 35(3-4): 20-27.
[140]	HAWKESWORTH C J, CAWOOD P A, DHUIME B, et al. Earth’s continental lithosphere through time[J]. Annual review of earth and planetary sciences, 2017, 45(1): 169-198.
[141]	STENCHIKOV G. Chapter 29: The role of volcanic activity in climate and global changes[M]//STENCHIKOV G. Climate Change. Amsterdam; Elsevier, 2021: 607-643.
[142]	BIGGS R, PREISER R, DE VOS A, et al. The routledge handbook of research methods for social-ecological systems[M]. New York: Routledge, 2021.
[143]	FOLKE C, BIGGS R, NORSTRÖM A V, et al. Social-ecological resilience and biosphere-based sustainability science[J]. Ecology and Society, 2016, 21(3): 431-446.
[144]	LI Y, SANG S, MOTE S, et al. Challenges and opportunities for modeling coupled human and natural systems[J]. National Science Review, 2023, 10(7): 10-13.
[145]	LIU J, DIETZ T, CARPENTER S R, et al. Complexity of coupled human and natural systems[J]. Science, 2007, 317(5844): 1513-1516. DOI PMID
[146]	FAO. Agricultural trade, climate change and food security[M]. Rome: FAO, 2018.
[147]	MIRZABAEV A, BEZNER KERR R, HASEGAWA T, et al. Severe climate change risks to food security and nutrition[J]. Climate Risk Management, 2023, 39: 100473.
[148]	ISLAM S N, WINKEL J. Climate change and social inequality[R]. Department of Economic & Social Affairs, 2017, 152: 1-30.
[149]	CAMPBELL-LENDRUM D, NEVILLE T, SCHWEIZER C, et al. Climate change and health: three grand challenges[J]. Nature Medicine, 2023, 29(7): 1631-1638.
[150]	HE C, LIU Z, WU J, et al. Future global urban water scarcity and potential solutions[J]. Nature Communications, 2021, 12(1): 4667. DOI PMID
[151]	FOWLER D, BRIMBLECOMBE P, BURROWS J, et al. A chronology of global air quality[J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2020, 378(2183): 20190314.
[152]	RUAS R D B, COSTA L M S, BERED F. Urbanization driving changes in plant species and communities: a global view[J]. Global Ecology and Conservation, 2022, 38: e02243.
[153]	OVERPECK J T, MEEHL G A, BONY S, et al. Climate data challenges in the 21st century[J]. Science, 2011, 331(6018): 700-702. DOI PMID
[154]	OECD. Fostering cross-border data flows with trust[R]. Oecd Digital Economy Papers, 2022, 343: 1-37.
[155]	LANIAK G F, OLCHIN G, GOODALL J, et al. Integrated environmental modeling: a vision and roadmap for the future[J]. Environmental Modelling & Software, 2013, 39: 3-23.
[156]	MOALLEMI E A, GAO L, EKER S, et al. Diversifying models for analysing global change scenarios and sustainability pathways[J]. Global Sustainability, 2022, 5(e7): 1-17.
[157]	HAUSFATHER Z, DRAKE H F, ABBOTT T, et al. Evaluating the performance of past climate model projections[J]. Geophysical Research Letters, 2020, 47(1): e2019GL085378.
[158]	ALCAMO J, HENRICHS T. Chapter two towards guidelines for environmental scenario analysis[M] //ALCAMOJ, HENRICHST. Developmentsin Integrated Environmental Assessment.Amsterdam; Elsevier, 2008: 13-35.
[159]	PETTORELLI N, LAURANCE W F, O’BRIEN T G, et al. Satellite remote sensing for applied ecologists: opportunities and challenges[J]. Journal of Applied Ecology, 2014, 51(4): 839-848.
[160]	OECD. Responding to societal challenges with data: access, sharing, stewardship and control[J]. Oecd Digital Economy Papers, 2022, 342: 1-40.
[161]	SHARMA S, JAIN G. High performance computing (Hpc) in the cloud: a proactive fault tolerance (Pft) strategy[J]. International Journal of Intelligent Systems and Applications in Engineering, 2023, 11: 71-78.
[162]	ROLNICK D, DONTI P L, KAACK L H, et al. Tackling climate change with machine learning[J]. ACM computing surveys, 2022, 55(2): 1-96.
[163]	ZHANG X, HEGERL G, SENEVIRATNE S, et al. WCRP grand challenge: science underpinning the prediction and attribution of extreme events[R]. Geneva: World Climate Research Programme (WCRP), 2014.
[164]	ALEXANDER L V, ZHANG X, HEGERL G, et al. Implementation plan for WCRP grand challenge on understanding and predicting weather and climate extremes[R]. Geneva: World Climate Research Programme (WCRP), 2014.
[165]	SILLMANN J, THORARINSDOTTIR T, KEENLYSIDE N, et al. Understanding, modeling and predicting weather and climate extremes: challenges and opportunities[J]. Weather and Climate Extremes, 2017, 18: 65-74.
[166]	DONG T, DONG W. Evaluation of extreme precipitation over Asia in CMIP6 models[J]. Climate Dynamics, 2021, 57(7): 1751-1769.
[167]	RASCH P J, XIE S, MA P L, et al. An overview of the atmospheric component of the energy exascale Earth system model[J]. Journal of Advances in Modeling Earth Systems, 2019, 11(8): 2377-2411.
[168]	LEHMANN J, COUMOU D. The influence of mid-latitude storm tracks on hot, cold, dry and wet extremes[J]. Scientific Reports, 2015, 5(1): 17491.
[169]	COUMOU D, LEHMANN J, BECKMANN J. The weakening summer circulation in the Northern Hemisphere mid-latitudes[J]. Science, 2015, 348(6232): 324-327.
[170]	FRANK D, REICHSTEIN M, BAHN M, et al. Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts[J]. Global Change Biology, 2015, 21(8): 2861-2880. DOI PMID
[171]	PAN S, YANG J, TIAN H, et al. Climate extreme versus carbon extreme: responses of terrestrial carbon fluxes to temperature and precipitation[J]. Journal of Geophysical Research: Biogeosciences, 2020, 125(4): e2019JG005252.
[172]	KIM Y H, MIN S K, ZHANG X, et al. Evaluation of the CMIP6 multi-model ensemble for climate extreme indices[J]. Weather and Climate Extremes, 2020, 29: 100269.
[173]	ABDELMOATY H M, PAPALEXIOU S M, RAJULAPATI C R, et al. Biases beyond the mean in CMIP6 extreme precipitation: a global investigation[J]. Earth’s Future, 2021, 9(10): e2021EF002196.
[174]	SOBEL A H, LEE C-Y, BOWEN S G, et al. Near-term tropical cyclone risk and coupled Earth system model biases[J]. Proceedings of the National Academy of Sciences of the United States of America, 2023, 120(33): e2209631120.
[175]	FENNEL K, MATTERN J P, DONEY S C, et al. Ocean biogeochemical modelling[J]. Nature Reviews Methods Primers, 2022, 2: 76.
[176]	ALMAZROUI M, SAEED F, SAEED S, et al. Projected changes in climate extremes using CMIP6 simulations over SREX regions[J]. Earth Systems and Environment, 2021, 5: 481-497.
[177]	PETROVAIRINA Y, MIRALLES D G, BRIENT F, et al. Observation-constrained projections reveal longer-than-expected dry spells[J]. Nature, 2024, 633(8030): 594-600.
[178]	VOGEL M M, ZSCHEISCHLER J, SENEVIRATNE S I. Varying soil moisture-atmosphere feedbacks explain divergent temperature extremes and precipitation projections in Central Europe[J]. Earth System Dynamics, 2018, 9(3): 1107-1125.
[179]	HIROTA N, MICHIBATA T, SHIOGAMA H, et al. Impacts of precipitation modeling on cloud feedback in MIROC6[J]. Geophysical Research Letters, 2022, 49(5): e2021GL096523.
[180]	VICENTE-SERRANO S M, PEÑA-ANGULO D, BEGUERÍA S, et al. Global drought trends and future projections[J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2022, 380(2238): 20210285.
[181]	DOMEISEN D I V, ELTAHIR E A B, FISCHER E M, et al. Prediction and projection of heatwaves[J]. Nature Reviews Earth & Environment, 2023, 4(1): 36-50.
[182]	GORI A, LIN N, XI D, et al. Tropical cyclone climatology change greatly exacerbates US extreme rainfall-surge hazard[J]. Nature Climate Change, 2022, 12(2): 171-178.
[183]	JUDT F, KLOCKE D, RIOS-BERRIOS R, et al. Tropical cyclones in global storm-resolving models[J]. Journal of the Meteorological Society of Japan. Ser. II, 2021, 99(3): 579-602.
[184]	SHEPHERD T G. Atmospheric circulation as a source of uncertainty in climate change projections[J]. Nature Geoscience, 2014, 7(10): 703-708.
[185]	BADOR M, BOé J, TERRAY L, et al. Impact of higher spatial atmospheric resolution on precipitation extremes over land in global climate models[J]. Journal of Geophysical Research: Atmospheres, 2020, 125(13): e2019JD032184.
[186]	MEEHL G A, RICHTER J H, TENG H, et al. Initialized Earth system prediction from subseasonal to decadal timescales[J]. Nature Reviews Earth & Environment, 2021, 2(5): 340-357.
[187]	GETTELMAN A, GEER A J, FORBES R M, et al. The future of Earth system prediction: advances in model-data fusion[J]. Science Advances, 2022, 8(14): eabn3488.
[188]	ZHANG F, WENG Y, SIPPEL J A, et al. Cloud-resolving hurricane initialization and prediction through assimilation of doppler radar observations with an ensemble kalman filter[J]. Monthly Weather Review, 2009, 137(7): 2105-2125.
[189]	WANG G-G, CHENG H, ZHANG Y, et al. ENSO analysis and prediction using deep learning: a review[J]. Neurocomputing, 2023, 520: 216-229.
[190]	LING F, LU Z, LUO J-J, et al. Diffusion model-based probabilistic downscaling for 180-year East Asian climate reconstruction[J]. npj Climate and Atmospheric Science, 2024, 7(1): 131.
[191]	COLEMAN A, ANCELL B, SCHWARTZ C. Can we predict the predictability of high-impact weather events?[J]. Monthly Weather Review, 2024, 152(11): 2483-2504.
[192]	IRRGANG C, BOERS N, SONNEWALD M, et al. Towards neural Earth system modelling by integrating artificial intelligence in Earth system science[J]. Nature Machine Intelligence, 2021, 3(8): 667-674.
[193]	HOLLAND J H. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence[M]. Cambridge, Massachusetts: The MIT Press, 1992.
[194]	PALMER T N. Stochastic weather and climate models[J]. Nature Reviews Physics, 2019, 1(7): 463-471.
[195]	GOTTS N M, VAN VOORN G A K, POLHILL J G, et al. Agent-based modelling of socio-ecological systems: models, projects and ontologies[J]. Ecological Complexity, 2019, 40: 100728.
[196]	DI LUCA A, DE ELÍA R, LAPRISE R. Challenges in the quest for added value of regional climate dynamical downscaling[J]. Current Climate Change Reports, 2015, 1(1): 10-21.
[197]	GE Y, JIN Y, STEIN A, et al. Principles and methods of scaling geospatial Earth science data[J]. Earth-Science Reviews, 2019, 197: 102897.