New Record Ocean Temperatures and Related Climate Indicators in 2023

The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human activities.In 2023,the sea surface temperature(SST)and upper 2000 m ocean heat content(OHC)reached record highs.The 0–2000 m OHC in 2023 exceeded that of 2022 by 15±10 ZJ(1 Zetta Joules=1021 Joules)(updated IAP/CAS data);9±5 ZJ(NCEI/NOAA data).The Tropical Atlantic Ocean,the Mediterranean Sea,and southern oceans recorded their highest OHC observed since the 1950s.Associated with the onset of a strong El Niño,the global SST reached its record high in 2023 with an annual mean of~0.23℃ higher than 2022 and an astounding>0.3℃ above 2022 values for the second half of 2023.The density stratification and spatial temperature inhomogeneity indexes reached their highest values in 2023.

Prediction of three-dimensional ocean temperature in the South China Sea based on time series gridded data and a dynamic spatiotemporal graph neural network

Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related fields.Currently,one of the commonly used methods for ocean temperature prediction is based on data-driven,but research on this method is mostly limited to the sea surface,with few studies on the prediction of internal ocean temperature.Existing graph neural network-based methods usually use predefined graphs or learned static graphs,which cannot capture the dynamic associations among data.In this study,we propose a novel dynamic spatiotemporal graph neural network(DSTGN)to predict threedimensional ocean temperature(3D-OT),which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior knowledge.Temporal and spatial dependencies in the time series were then captured using temporal and graph convolutions.We also integrated dynamic graph learning,static graph learning,graph convolution,and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid data.In this study,we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis,with data covering the vertical variation of temperature from the sea surface to 1000 m below the sea surface.We compared five mainstream models that are commonly used for ocean temperature prediction,and the results showed that the method achieved the best prediction results at all prediction scales.

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.

Predictability of the upper ocean heat content in a Community Earth System Model ensemble prediction system

Upper ocean heat content(OHC)has been widely recognized as a crucial precursor to high-impact climate variability,especially for that being indispensable to the long-term memory of the ocean.Assessing the predictability of OHC using state-of-the-art climate models is invaluable for improving and advancing climate forecasts.Recently developed retrospective forecast experiments,based on a Community Earth System Model ensemble prediction system,offer a great opportunity to comprehensively explore OHC predictability.Our results indicate that the skill of actual OHC predictions varies across different oceans and diminishes as the lead time of prediction extends.The spatial distribution of the actual prediction skill closely resembles the corresponding persistence skill,indicating that the persistence of OHC serves as the primary predictive signal for its predictability.The decline in actual prediction skill is more pronounced in the Indian and Atlantic oceans than in the Pacific Ocean,particularly within tropical regions.Additionally,notable seasonal variations in the actual prediction skills across different oceans align well with the phase-locking features of OHC variability.The potential predictability of OHC generally surpasses the actual prediction skill at all lead times,highlighting significant room for improvement in current OHC predictions,especially for the North Indian Ocean and the Atlantic Ocean.Achieving such improvements necessitates a collaborative effort to enhance the quality of ocean observations,develop effective data assimilation methods,and reduce model bias.

Advancing Malaria Prediction in Uganda through AI and Geospatial Analysis Models

The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R2 of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.

Thornthwaite moisture index and depth of suction change under current and future climate‒An Australian study

Climate change is one of the major global challenges and it can have a significant influence on the behaviour and resilience of geotechnical structures.The changes in moisture content in soil lead to effective stress changes and can be accompanied by significant volume changes in reactive/expansive soils.The volume change leads to ground movement and can exert additional stresses on structures founded on or within a shallow depth of such soils.Climate change is likely to amplify the ground movement potential and the associated problems are likely to worsen.The effect of atmospheric boundary interaction on soil behaviour has often been correlated to Thornthwaite moisture index(TMI).In this study,the long-term weather data and anticipated future projections for various emission scenarios were used to generate a series of TMI maps for Australia.The changes in TMI were then correlated to the depth of suction change(H s),an important input in ground movement calculation.Under all climate scenarios considered,reductions in TMI and increases in H s values were observed.A hypothetical design scenario of a footing on expansive soil under current and future climate is discussed.It is observed that a design that might be considered adequate under the current climate scenario,may fail under future scenarios and accommodations should be made in the design for such events.

IMPROVEMENT OF OCEAN DATA ASSIMILATION SYSTEM AND CLIMATE PREDICTION BY ASSIMILATING ARGO DATA

The Argo(Array for Real-time Geostrophic Oceanography) data from 1998 to 2003 were used in the Beijing Climate Center-Global Ocean Data Assimilation System(BCC-GODAS). The results show that the utilization of Argo global ocean data in BCC-GODAS brings about remarkable improvements in assimilation effects. The assimilated sea surface temperature(SST) of BCC-GODAS can well represent the climatological states of observational data. Comparison experiments based on a global coupled atmosphere-ocean general circulation model(AOCGM) were conducted for exploring the roles of ocean data assimilation system with or without Argo data in improving the climate predictability of rainfall in boreal summer. Firstly, the global ocean data assimilation system BCC-GODAS was used to obtain ocean assimilation data under the conditions with or without Argo data. Then, the global coupled atmosphere-ocean general circulation model(AOCGM) was utilized to do hindcast experiments with the two sets of the assimilation data as initial oceanic fields. The simulated results demonstrate that the seasonal predictability of rainfall in boreal summer, particularly in China, increases greatly when initial oceanic conditions with Argo data are utilized. The distribution of summer rainfall in China hindcast by the AOGCM under the condition when Argo data are used is more in accordance with observation than that when no Agro data are used. The area of positive correlation between hindcast and observation enlarges and the hindcast skill of rainfall over China in summer improves significantly when Argo data are used.

Progress in the Development and Application of Climate Ocean Models and Ocean-Atmosphere Coupled Models in China

A review is presented about the development and application of climate ocean models and oceanatmosphere coupled models developed in China as well as a review of climate variability and climate change studies performed with these models. While the history of model development is briefly reviewed, emphasis has been put on the achievements made in the last five years. Advances in model development are described along with a summary on scientific issues addressed by using these models. The focus of the review is the climate ocean models and the associated coupled models, including both global and regional models, developed at the Institute of Atmospheric Physics, Chinese Academy of Sciences. The progress of either coupled model development made by other institutions or climate modeling using internationally developed models also is reviewed.

Multi-Year Simulations and Experimental Seasonal Predictions for Rainy Seasons in China by Using a Nested Regional Climate Model (RegCM_NCC) Part Ⅱ:The Experimental Seasonal Prediction

A nested regional climate model has been experimentally used in the seasonal prediction at the China National Climate Center （NCC） since 2001. The NCC/IAP （Institute of Atmospheric Physics） T63 coupled GCM （CGCM） provides the boundary and initial conditions for driving the regional climate model （RegCM_NCC）. The latter has a 60-km horizontal resolution and improved physical parameterization schemes including the mass flux cumulus parameterization scheme, the turbulent kinetic energy closure scheme （TKE） and an improved land process model （LPM）. The large-scale terrain features such as the Tibetan Plateau are included in the larger domain to produce the topographic forcing on the rain-producing systems. A sensitivity study of the East Asian climate with regard to the above physical processes has been presented in the first part of the present paper. This is the second part, as a continuation of Part Ⅰ. In order to verify the performance of the nested regional climate model, a ten-year simulation driven by NCEP reanalysis datasets has been made to explore the performance of the East Asian climate simulation and to identify the model＇s systematic errors. At the same time, comparative simulation experiments for 5 years between the RegCM2 and RegCM_NCC have been done to further understand their differences in simulation performance. Also, a ten-year hindcast （1991-2000） for summer （June-August）, the rainy season in China, has been undertaken. The preliminary results have shown that the RegCM_NCC is capable of predicting the major seasonal rain belts. The best predicted regions with high anomaly correlation coefficient （ACC） are located in the eastern part of West China, in Northeast China and in North China, where the CGCM has maximum prediction skill as well. This fact may reflect the importance of the largescale forcing. One significant improvement of the prediction derived from RegCM_NCC is the increase of ACC in the Yangtze River valley where the CGCM has a very low, even a negative, ACC. The reason behind this improvement is likely to be related to the more realistic representation of the large-scale terrain features of the Tibetan Plateau. Presumably, many rain-producing systems may be generated over or near the Tibetan Plateau and may then move eastward along the Yangtze River basin steered by upper-level westerly airflow, thus leading to enhancement of rainfalls in the mid and lower basins of the Yangtze River. The real-time experimental predictions for summer in 2001, 2002, 2003 and 2004 by using this nested RegCM-NCC were made. The results are basically reasonable compared with the observations.

The Predictability Problems in Numerical Weather and Climate Prediction

The uncertainties caused by the errors of the initial states and the parameters in the numerical model are investigated. Three problems of predictability in numerical weather and climate prediction are proposed, which are related to the maximum predictable time, the maximum prediction error, and the maximum admissible errors of the initial values and the parameters in the model respectively. The three problems are then formulated into nonlinear optimization problems. Effective approaches to deal with these nonlinear optimization problems are provided. The Lorenz’ model is employed to demonstrate how to use these ideas in dealing with these three problems.

The Influence of the Indian Ocean Dipole on Atmospheric Circulation and Climate

The SST variation in the equatorial Indian Ocean is studied with special interest in analyzing its dipole oscillation feature. The dipole oscillation appears to be stronger in September-November and weaker in January-April with higher SST in the west region and lower SST in the east region as the positive phase and higher SST in the east region and lower SST in the west region as the negative phase. Generally, the amplitude of the positive phase is larger than the negative phase. The interannual variation (4-5 year period) and the interdecadal variation (25-30 year period) also exist in the dipole. The analyses also showed the significant impact of the Indian Ocean dipole on the Asian monsoon activity, because the lower tropospheric wind fields over the Southern Asia, the Tibetan high in the upper troposphere and the subtropical high over the northwestern Pacific are all related to the Indian Ocean dipole. On the other, the Indian Ocean dipole still has significant impact on atmospheric circulation and climate in North America and the southern Indian Ocean region (including Australia and South Africa).

Decadal/Interdecadal Variations of the Ocean Temperature and its Impacts on Climate

Decadal/interdecadal climate variability is an important research focus of the CLIVAR Program and has been paid more attention. Over recent years, a lot of studies in relation to interdecadal climate variations have been also completed by Chinese scientists. This paper presents an overview of some advances in the study of decadal/interdecadal variations of the ocean temperature and its climate impacts, which includes interdccadal climate variability in China, the interdecadal modes of sea surface temperature （SST） anomalies in the North Pacific, and in particular, the impacts of interdecadal SST variations on the Asian monsoon rainfall. As summarized in this paper, some results have been achieved by using climate diagnostic studies of historical climatic datasets. Two fundamental interdecadal SST variability modes （7- 10-years mode and 25 35-years mode） have been identified over the North Pacific associated with different anomalous patterns of atmospheric circulation. The southern Indian Ocean dipole （SIOD） shows a major feature of interdecadal variation, with a positive （negative） phase favoring a weakened （enhanced） Asian summer monsoon in the following summer. It is also found that the China monsoon rainfall exhibits interdecadal variations with more wet （dry） monsoon years in the Yangtze River （South China and North China） before 1976, but vice versa after 1976. The weakened relationship between the Indian summer rainfall and ENSO is a feature of interdecadal variations, suggesting an important role of the interdecadal variation of the SIOD in the climate over the south Asia and southeast Asia. In addition, evidence indicates that the climate shift in the 1960s may be related to the anomalies of the North Atlantic Oscillation （NAO） and North Pacific Oscillation （NPO）. Overall, the present research has improved our understanding of the decadal/interdecadal variations of SST and their impacts on the Asian monsoon rainfall. However, the research also highlights a number of problems for future research, in particular the mechanisms responsible for the monsoon long4erm predictability, which is a great challenge in climate research.

Review on observational studies of western tropical Pacific Ocean circulation and climate

The Western Tropical Pacific(WTP) Ocean holds the largest area of warm water(>28℃) in the world ocean referred to as the Western Pacific Warm Pool(WPWP),which modulates the regional and global climate through strong atmospheric convection and its variability.The WTP is unique in terms of its complex 3-D ocean circulation system and intensive multiscale variability,making it crucial in the water and energy cycle of the global ocean.Great advances have been made in understanding the complexity of the WTP ocean circulation and associated climate impact by the international scientific community since the 1960 s through field experiments.In this study,we review the evolving insight to the 3-D structure and multi-scale variability of the ocean circulation in the WTP and their climatic impacts based on in-situ ocean observations in the past decades,with emphasis on the achievements since 2000.The challenges and open que stions remaining are reviewed as well as future plan for international study of the WTP ocean circulation and climate.

Analysis and prediction of global vegetation dynamics:past variations and future perspectives

Spatiotemporal dynamic vegetation changes affect global climate change,energy balances and the hydrological cycle.Predicting these dynamics over a long time series is important for the study and analysis of global environmental change.Based on leaf area index(LAI),climate,and radiation flux data of past and future scenarios,this study looked at historical dynamic changes in global vegetation LAI,and proposed a coupled multiple linear regression and improved gray model(CMLRIGM)to predict future global LAI.The results show that CMLRIGM predictions are more accurate than results predicted by the multiple linear regression(MLR)model or the improved gray model(IGM)alone.This coupled model can effectively resolve the problem posed by the underestimation of annual average of global vegetation LAI predicted by MLR and the overestimate predicted by IGM.From 1981 to 2018,the annual average of LAI in most areas covered by global vegetation(71.4%)showed an increase with a growth rate of 0.0028 a-1;of this area,significant increases occurred in 34.42%of the total area.From 2016 to 2060,the CMLRIGM model has predicted that the annual average global vegetation LAI will increase,accounting for approximately 68.5%of the global vegetation coverage,with a growth rate of 0.004 a-1.The growth rate will increase in the future scenario,and it may be related to the driving factors of the high emission scenario used in this study.This research may provide a basis for simulating spatiotemporal dynamic changes in global vegetation conditions over a long time series.

The ocean response to climate change guides both adaptation and mitigation efforts

The ocean’s thermal inertia is a major contributor to irreversible ocean changes exceeding time scales that matter to human society.This fact is a challenge to societies as they prepare for the consequences of climate change,especially with respect to the ocean.Here the authors review the requirements for human actions from the ocean’s perspective.In the near term(∼2030),goals such as the United Nations Sustainable Development Goals(SDGs)will be critical.Over longer times(∼2050–2060 and beyond),global carbon neutrality targets may be met as countries continue to work toward reducing emissions.Both adaptation and mitigation plans need to be fully implemented in the interim,and the Global Ocean Observation System should be sustained so that changes can be continuously monitored.In the longer-term(after∼2060),slow emerging changes such as deep ocean warming and sea level rise are committed to continue even in the scenario where net zero emissions are reached.Thus,climate actions have to extend to time scales of hundreds of years.At these time scales,preparation for“high impact,low probability”risks—such as an abrupt showdown of Atlantic Meridional Overturning Circulation,ecosystem change,or irreversible ice sheet loss—should be fully integrated into long-term planning.

STCANet:Spatiotemporal Coupled Attention Network for Ocean Surface Current Prediction

Currently,numerical models based on idealized assumptions,complex algorithms and high computational costs are unsatisfactory for ocean surface current prediction.Moreover,the complex temporal and spatial variability of ocean currents also makes the prediction methods based on time series data challenging.The deep network model can automatically learn and extract complex features hidden in large amount of complex data,so it is a promising method for high quality prediction of ocean currents.In this paper,we propose a spatiotemporal coupled attention deep network model STCANet that can extract abundant temporal and spatial coupling information on the behavior characteristics of ocean currents for improving the prediction accuracy.Firstly,Spatial Module is designed and implemented to extract the spatiotemporal coupling characteristics of ocean currents,and meanwhile the spatial correlations and dependencies among adjacent sea areas are obtained through Spatial Channel Attention Module(SCAM).Secondly,we use the GatedRecurrent-Unit(GRU)to extract temporal relationships of ocean currents,and design and implement the nearest neighbor time attention module to extract the interdependences of ocean currents between adjacent times,which can further improve the accuracy of ocean current prediction.Finally,a series of comparative experiments on the MediSea_Dataset and EastSea_Dataset showed that the prediction quality of our model greatly outperforms those of other benchmark models such as History Average(HA),Autoregressive Integrated Moving Average Model(ARIMA),Long Short-term Memory(LSTM),Gate Recurrent Unit(GRU)and CNN_GRU.

Preliminary Studies on Predicting the Tropical Indian Ocean Sea Surface Temperature through Combined Statistical Methods and Dynamic ENSO Prediction

The sea surface temperature （SST） in the In- dian Ocean affects the regional climate over the Asian continent mostly through a modulation of the monsoon system. It is still difficult to provide an a priori indication of the seasonal variability over the Indian Ocean. It is widely recognized that the warm and cold events of SST over the tropical Indian Ocean are strongly linked to those of the equatorial eastern Pacific. In this study, a statistical prediction model has been developed to predict the monthly SST over the tropical Indian Ocean. This model is a linear regression model based on the lag relationship between the SST over the tropical Indian Ocean and the Nino3.4 （5°S-5°N, 170°W-120°W） SST Index. The pre- dictor （i.e., Nino3.4 SST Index） has been operationally predicted by a large size ensemble E1 Nifio and the Southern Oscillation （ENSO） forecast system with cou- pled data assimilation （Leefs_CDA）, which achieves a high predictive skill of up to a 24-month lead time for the equatorial eastern Pacific SST. As a result, the prediction skill of the present statistical model over the tropical In- dian Ocean is better than that of persistence prediction for January 1982 through December 2009.

DECADAL VARIABILITY OF SUBSURFACE OCEAN TEMPERATURE ANOMALY IN TROPICAL PACIFIC AND ITS IMPACT ON CLIMATE OF CHINA

In this study,we investigate the decadal variability of subsurface ocean temperature anomaly(SOTA)in the tropical Pacific and associated anomalous atmospheric circulation over Asia-North Pacific-North America by analyzing 50 years of atmosphere-ocean data from the National Center for Environmental Prediction(NCEP)reanalysis project and Simple Ocean Data Assimilation(SODA).Relationship between the ENSO-Like variability and climate of China is also revealed.The results show that the decadal variability of tropical Pacific SOTA has two dominant ENSO-like modes:the primary mode is an ENSO-Like mature phase pattern,and the second mode is associated with the ENSO-like transition(developing or decaying)phase.These two modes consist of a cycle of ENSO-Like variability,which exhibits a quasi-40a fluctuation,superimposed with an oscillation of a 13a period.The ENSO-Like variability in the tropical Pacific influences the atmosphere system at the mid-and higher-latitudes and subtropical regions,resulting in decadal variability of south wind over North China,the East Asian monsoon and climate of China.During the mature phase of El Ni o-Like variability,the anomalous north wind prevails over the north part of China and the East Asian monsoon weakens,with little rain in North China but much rain in the middle-and lower-reaches of the Yangtze River.With El Ni o-Like decaying(La Ni a-Like developing),anomalous northerly wind also prevails over North China,then the East Asian monsoon weakens with drought occurring in North China.The situation during the La Ni a-Like variability is the opposite.The pattern of anomalous climate of China is primarily dominated by the first ENSO-like variability,while the second mode can modulate the contribution of the first one,depending on whether its phase agrees with that of the first mode.The climate shift in China around 1978 and successive occurrence of drought for more than 20 years in North China are primarily induced by the first two ENSO-like variabilities.The latest La Ni a-Like phase starts from 1998 and will presumably end around 2018.It is expected that more rainfall would be in North China and less rainfall would appear in the middle-and lower-reaches of the Yangtze River valley during this period.

ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system:comparison of full-field and anomaly initialization

Model initialization is a key process of climate predictions using dynamical models. In this study, the authors evaluated the performances of two distinct initialization approaches--anomaly and full-field initializations--in ENSO predictions conducted using the IAP-DecPreS near-term climate prediction system developed by the Institute of Atmospheric Physics （lAP）. IAP-DecPreS is composed of the FGOALS-s2 coupled general circulation model and a newly developed ocean data assimilation scheme called＇ensemble optimal interpolation-incremental analysis update＇ （EnOI-IAU）. It was found that, for IAP-DecPreS, the hindcast runs using the anomaly initialization have higher predictive skills for both conventional ENSO and El Nino Modoki, as compared to using the full-field initialization. The anomaly hindcasts can predict super El Nino/La Nina 10 months in advance and have good skill for most moderate and weak ENSO events about 4-7 months in advance.The predictive skill of the anomaly hindcasts for El Nino Modoki is close to that for conventional ENSO. On the other hand, the anomaly hindcasts at 1- and 4-month lead time can reproduce the major features of large-scale patterns of sea surface temperature, precipitation and atmospheric circulation anomalies during conventional ENSO and El Nino Modoki winter.

Progress and Challenge of the Short-Term Climate Prediction

The experience of developing a short-term climate prediction system at the Institute of Atmospheric Science of the Chinese Academy of Sciences is summarized,and some problems to be solved in future are discussed in this paper.It is suggested that a good system for short-term climate prediction should at least consist of (1) well-tested model(s),(2) sufficient data and good methods for the initialization and assimilation,(3) a good system for quantitative corrections,(4) a good ensemble prediction method,and (5) appropriate prediction products,such as mathematical expectation,standard deviation,probability,among others.