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中国科学:地球科学（英文版）
中国科学:地球科学（英文版）

周光召

月刊

1674-7313

sales@scichina.org

010-64019820

100717

北京东黄城根北街16号

中国科学:地球科学（英文版）/Journal Science China(Earth Sciences)CSCDEISCI
查看更多>>《中国科学》是中国科学院主办、中国科学杂志社出版的自然科学专业性学术刊物。《中国科学》任务是反映中国自然科学各学科中的最新科研成果，以促进国内外的学术交流。《中国科学》以论文形式报道中国基础研究和应用研究方面具有创造性的、高水平的和有重要意义的科研成果。在国际学术界，《中国科学》作为代表中国最高水平的学术刊物也受到高度重视。国际上最具有权威的检索刊物SCI，多年来一直收录《中国科学》的论文。1999年《中国科学》夺得国家期刊奖的第一名。
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    Improving global weather and ocean wave forecast with large artificial intelligence models

    Fenghua LINGLin OUYANGBoufeniza Redouane LARBIJing-Jia LUO...
    3641-3654页
    查看更多>>摘要：The rapid advancement of artificial intelligence technologies,particularly in recent years,has led to the emergence of several large parameter artificial intelligence weather forecast models.These models represent a significant breakthrough,overcoming the limitations of traditional numerical weather prediction models and indicating the emergence of profound potential tools for atmosphere-ocean forecasts.This study explores the evolution of these advanced artificial intelligence forecast models,and based on the identified commonalities,proposes the"Three Large Rules"for large weather forecast models:a large number of parameters,a large number of predictands,and large potential applications.We discuss the capacity of artificial intelligence to revolutionize numerical weather prediction,briefly outlining the underlying reasons for the significant im-provement in weather forecasting.While acknowledging the high accuracy,computational efficiency,and ease of deployment of large artificial intelligence forecast models,we also emphasize the irreplaceable values of traditional numerical forecasts and explore the challenges in the future development of large-scale artificial intelligence atmosphere-ocean forecast models.We believe that the optimal future of atmosphere-ocean weather forecast lies in achieving a seamless integration of artificial intelligence and traditional numerical models.Such a synthesis is anticipated to offer a more advanced and reliable approach for improved atmosphere-ocean forecasts.Finally,we illustrate how forecasters can leverage the large weather forecast models through an example by building an artificial intelligence model for global ocean wave forecast.
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    A hybrid deep learning and data assimilation method for model error estimation

    Ziyi PENGLili LEIZhe-Min TAN
    3655-3670页
    查看更多>>摘要：Forecast errors of numerical weather prediction consist of model errors and the growth of initial condition errors,while the initial condition is often optimized based on short-term forecasts.Thus it is difficult to untangle the initial condition error and model error,but it is essential to infer model errors not just for prediction but also for data assimilation(DA).A hybrid deep learning(DL)and DA method is proposed here,aiming to correct model errors.It uses a convolutional neural network(CNN)to extract characteristics of initial conditions and forecast errors,and then provides estimations for model errors.The CNN-based model error estimation method can consider the model error resulted from inaccurate model parameters,or si-multaneously consider the model error and initial condition error.Based on the Lorenz05 model,offline and online experiments demonstrate that the CNN-based model error estimation method can effectively correct model errors resulted from inaccurate model parameters,including the forcing F,coupling coefficient c,and relative scale b.For both online and offline model error estimations,simultaneously considering model errors and initial condition errors are beneficial to infer the model errors,compared to considering model errors only.Moreover,using the observations to verify the forecasts has advantages over using the analyses,to estimate the model errors.Using observations can also achieve a faster convergence of model error estimation with online DA than using analyses.
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    A deep learning-based global tropical cyclogenesis prediction model and its interpretability analysis

    Bin MUXin WANGShijin YUANYuxuan CHEN...
    3671-3695页
    查看更多>>摘要：Tropical cloud clusters(TCCs)can potentially develop into tropical cyclones(TCs),leading to significant casualties and economic losses.Accurate prediction of tropical cyclogenesis(TCG)is crucial for early warnings.Most traditional deep learning methods applied to TCG prediction rely on predictors from a single time point,neglect the ocean-atmosphere inter-actions,and exhibit low model interpretability.This study proposes the Tropical Cyclogenesis Prediction-Net(TCGP-Net)based on the Swin Transformer,which leverages convolutional operations and attention mechanisms to encode spatiotemporal features and capture the temporal evolution of predictors.This model incorporates the coupled ocean-atmosphere interactions,including multiple variables such as sea surface temperature.Additionally,causal inference and integrated gradients are employed to validate the effectiveness of the predictors and provide an interpretability analysis of the model's decision-making process.The model is trained using GridSat satellite data and ERA5 reanalysis datasets.Experimental results demonstrate that TCGP-Net achieves high accuracy and stability,with a detection rate of 97.9％and a false alarm rate of 2.2％for predicting TCG 24 hours in advance,significantly outperforming existing models.This indicates that TCGP-Net is a reliable tool for tropical cyclogenesis prediction.
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    FuXi-Extreme:Improving extreme rainfall and wind forecasts with diffusion model

    Xiaohui ZHONGLei CHENJun LIUChensen LIN...
    3696-3708页
    查看更多>>摘要：Significant advancements in the development of machine learning(ML)models for weather forecasting have produced remarkable results.State-of-the-art ML-based weather forecast models,such as FuXi,have demonstrated superior statistical forecast performance in comparison to the high-resolution forecasts(HRES)of the European Centre for Medium-Range Weather Forecasts(ECMWF).However,a common limitation of these ML models is their tendency to generate in-creasingly smooth predictions as forecast lead times increase,which often results in the underestimation of intensities of extreme weather events.To address this challenge,we developed the FuXi-Extreme model,which employs a denoising diffusion probabilistic model(DDPM)to enhance finer-scale details in the surface forecast data generated by the FuXi model in 5-day forecasts.An evaluation of extreme total precipitation(TP),10-meter wind speed(WS10),and 2-meter temperature(T2M)illustrates the superior performance of FuXi-Extreme over both FuXi and HRES.Moreover,when evaluating tropical cyclone(TC)forecasts based on International Best Track Archive for Climate Stewardship(IBTrACS)dataset,both FuXi and FuXi-Extreme shows superior performance in TC track forecasts compared to HRES,but they show inferior performance in TC intensity forecasts in comparison to HRES.
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    Real-time predictions of the 2023-2024 climate conditions in the tropical Pacific using a purely data-driven Transformer model

    Rong-Hua ZHANGLu ZHOUChuan GAOLingjiang TAO...
    3709-3726页
    查看更多>>摘要：Following triple La Niña events during 2020-2022,the future evolution of climate conditions over the tropical Pacific has been a focused interest in ENSO-related communities.Observations and modeling studies indicate that an El Niño event is occurring in 2023;however,large uncertainties remain in terms of its detailed evolution,and the factors affecting its resultant amplitude remain to be understood.Here,a novel deep learning-based Transformer model is adopted to make real-time predictions for the 2023-2024 climate conditions in the tropical Pacific.Several key fields vital to the El Niño and Southern Oscillation(ENSO)in the tropical Pacific are collectively and simultaneously utilized in model training and in making pre-dictions;therefore,this purely data-driven model is configured in both training and predicting procedures such that the coupled ocean-atmosphere interactions are adequately represented.Also similar to dynamic models,the prediction procedure is executed in a rolling manner to allow ocean-atmosphere anomaly exchanges month by month;the related key fields during multi-month time intervals(TIs)prior to prediction target months are taken as input predictors,serving as initial conditions to precondition the future evolution more effectively.Real-time predictions indicate that the climate conditions in the tropical Pacific are surely to develop into an El Nino state in late 2023.Furthermore,sensitivity experiments are conducted to examine how prediction skills are affected by the input predictor specifications,including TIs during which information on initial conditions is retained for making predictions.A comparison with other dynamic coupled models is also made to demonstrate the prediction performance for the 2023-2024 El Nino event.
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    Short-term solar eruptive activity prediction models based on machine learning approaches:A review

    Xin HUANGZhongrui ZHAOYufeng ZHONGLong XU...
    3727-3764页
    查看更多>>摘要：Solar eruptive activities,mainly including solar flares,coronal mass ejections(CME),and solar proton events(SPE),have an important impact on space weather and our technosphere.The short-term solar eruptive activity prediction is an active field of research in the space weather prediction.Numerical,statistical,and machine learning methods are proposed to build prediction models of the solar eruptive activities.With the development of space-based and ground-based facilities,a large amount of observational data of the Sun is accumulated,and data-driven prediction models of solar eruptive activities have made a significant progress.In this review,we briefly introduce the machine learning algorithms applied in solar eruptive activity prediction,summarize the prediction modeling process,overview the progress made in the field of solar eruptive activity prediction model,and look forward to the possible directions in the future.
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    From fundamental theory to realistic modeling of the birth of solar eruptions

    Chaowei JIANG
    3765-3788页
    查看更多>>摘要：Solar eruptions,primarily manifested as solar flares,filament eruptions and coronal mass ejections,represent explosive releases of magnetic energy stored in the solar corona,with the potential to drive severe space weather.The initiation of solar eruptions remains an open question,leading to various theoretical models that are inferred from observations.However,these models are subjects of debate due to the absence of direct measurements of the three-dimensional(3D)magnetic fields in the corona.Numerical simulations,based on solving magnetohydrodynamics(MHD)equations that govern the macroscopic dynamics of solar corona,serve as a touchstone for testing these theoretical models.One early proposed model suggested that eruptions could be triggered by reconnection within a single sheared magnetic arcade,which is known as the tether-cutting reconnection model,but it was never confirmed through 3D MHD simulations until very recently.Consequently,two models have gained more popularity:one involving the eruption of a twisted magnetic flux rope(MFR)due to ideal instability(or loss of equilibrium),and the other known as the breakout eruption,which requires a quadrupolar configuration with a delicately located magnetic null point.Other mixed mechanisms,involving both ideal instability and reconnection,are also proposed in association with localized magnetic flux emergence.Now with the validation of the tether-cutting model,the fundamental mechanisms are boiled down to two types of models,one primarily based on the ideal instability of a pre-existing MFR,and the other based on the reconnection of sheared field lines with or without an MFR.Recently,the modelling of the birth of solar eruption using observed data-based MHD simulations has advanced rapidly,becoming a crucial research tool in the study of the initiation mechanisms.These realistic modellings reveal a higher level of complexity compared to all currently available theories and idealized models.
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    Stable isotopes in atmospheric water vapour:Patterns,mechanisms and perspectives

    Baijun SHANGJing GAOGebanruo CHENYuqing WU...
    3789-3813页
    查看更多>>摘要：Stable isotopes in atmospheric water vapour are important tracers for investigating water vapour transport,mixing and phase transition.In recent years,with the rapid development of analytical techniques,research on water vapour stable isotopes has been widely conducted worldwide.In this review,we summarize basic theory and examine various methodologies employed to study stable isotopes in atmospheric water vapour,ranging from traditional collection methods to more recent advancements in laser absorption spectroscopy,satellite remote sensing,and isotope general circulation models(iGCMs).We pointed out the critical role played by isotopes in tracing moisture sources,understanding precipitation patterns,and re-constructing past climates.We identify gaps in knowledge,particularly in the representation of isotopic processes in climate models.Furthermore,we highlighted future research should focus on enhancing isotopic measurement technologies,integrating isotopic data with climatic indicators to improve model accuracy,and expanding isotopic studies to underserved water cycle dynamics.This review aims to provide suggestions for future investigations to deepen our understanding of atmospheric water vapour stable isotopes and their significance in climatology and hydrology.
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    Advances in understanding the mechanisms of Arctic amplification

    Jiefeng LIChuanfeng ZHAOAnnan CHENHaotian ZHANG...
    3814-3829页
    查看更多>>摘要：The near-surface temperatures in the Arctic are increasing at more than twice the global average rate,a phenomenon known as Arctic amplification(AA).In recent years,numerous studies using ground-based and satellite observations,along with model simulations,have explored the potential mechanisms behind AA,offering a variety of observational evidence and theoretical explanations.Although the understanding of AA drivers has improved,significant uncertainties remain in quantifying the contributions of different influential factors.On the basis of the latest research,this article thoroughly examines the factors driving rapid warming in the Arctic,including local feedbacks,atmospheric circulation,ocean currents,and aerosols,and compares quantitative results across studies.The analysis highlights the complex interplay of multiple factors contributing to AA,with no clear consensus on the relative contributions of each driver.Finally,the article underscores key challenges in current research,emphasizing the need for more reliable observational data,a deeper understanding of AA mechanisms,improved model parameterizations,and the disentanglement of interactions among driving factors,all of which are essential for future investigations.
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    Records of Fukushima accident-derived cesium-137 in the Chukchi Sea sediment:Implication for a new time marker?

    Xu RENJinlong WANGGi Hoon HONGLinwei LI...
    3830-3839页
    查看更多>>摘要：The Fukushima accident released a large amount of 137Cs into the environment.In this study,we used a sediment core collected from the Chukchi Sea during the 2018 Arctic Xuelong Scientific Expedition to investigate the Fukushima accident-derived 137Cs.Using 210Pb(210Pbex)and 137Cs chronology,we find that the 137Cs peak at 2 cm corresponds to the year 2011±1.This implies that the Fukushima accident-derived137Cs arrived in the Chukchi Sea much earlier than that by oceanic current transport.Our three calculation results(sediment core deposition flux:(4.0±0.4)× 10-6 Bq cm-2 d-1;atmospheric deposition flux:(1.4-2.5)× 10-5 Bq cm-2 d-1;biological deposition flux:(4.0±0.9)×10-6 Bq cm-2 d-1)suggest that atmospheric deposition and biological transport could cause such peak records.The results indicate that the 2011 peak of 137Cs can serve as a new temporal marker for estimating the sedimentation rate in the region affected by the Fukushima accident.We state that assessing the impact of coastal accidents and subsequent wastewater discharge in marine environments needs more consideration of biological carryover in addition to physical oceanography transport.
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