Studies have revealed that predictability of the atmospheric general circulation is generally high in the tropics throughout the year and that there is some predictability in the Northern extra-tropical winter atmosph...Studies have revealed that predictability of the atmospheric general circulation is generally high in the tropics throughout the year and that there is some predictability in the Northern extra-tropical winter atmospheric circulation through some patterns of tele connection. Predictability of the general circulation at the polar regions has still remained as a ‘ cold’ topic and little has been known about this question. Based on a preliminary study on the predictability by using the Institute of Atmospheric Physics (IAP) general circulation model, it is found that the SST-related predictability of the Southern winter lower atmospheric circulation in Antarctica is reasonably high and that there is some predictability in the 500 hPa and 200 hPa geopotential height fields over Europe and the Okhotsk Sea region during the Northern winter. It is suggested that more researches on this issue based on data analysis and model simulations are needed to obtain better understanding.展开更多
On the basis of two ensemble experiments conducted by a general atmospheric circulation model(Institute of Atmospheric Physics nine-level atmospheric general circulation model coupled with land surface model,hereinaft...On the basis of two ensemble experiments conducted by a general atmospheric circulation model(Institute of Atmospheric Physics nine-level atmospheric general circulation model coupled with land surface model,hereinafter referred to as IAP9L_CoLM),the impacts of realistic Eurasian snow conditions on summer climate predictability were investigated.The predictive skill of sea level pressures(SLP)and middle and upper tropospheric geopotential heights at mid-high latitudes of Eurasia was enhanced when improved Eurasian snow conditions were introduced into the model.Furthermore,the model skill in reproducing the interannual variation and spatial distribution of the surface air temperature(SAT)anomalies over China was improved by applying realistic(prescribed)Eurasian snow conditions.The predictive skill of the summer precipitation in China was low;however,when realistic snow conditions were employed,the predictability increased,illustrating the effectiveness of the application of realistic Eurasian snow conditions.Overall,the results of the present study suggested that Eurasian snow conditions have a significant effect on dynamical seasonal prediction in China.When Eurasian snow conditions in the global climate model(GCM)can be more realistically represented,the predictability of summer climate over China increases.展开更多
Through the analysis of ensembles of coupled model simulations and projections collected from CMIP3 and CMIP5, we demonstrate that a fundamental spatial scale limit might exist below which useful additional refinement...Through the analysis of ensembles of coupled model simulations and projections collected from CMIP3 and CMIP5, we demonstrate that a fundamental spatial scale limit might exist below which useful additional refinement of climate model predictions and projections may not be possible. That limit varies among climate variables and from region to region. We show that the uncertainty（noise） in surface temperature predictions（represented by the spread among an ensemble of global climate model simulations） generally exceeds the ensemble mean（signal） at horizontal scales below 1000 km throughout North America, implying poor predictability at those scales. More limited skill is shown for the predictability of regional precipitation. The ensemble spread in this case tends to exceed or equal the ensemble mean for scales below 2000 km. These findings highlight the challenges in predicting regionally specific future climate anomalies, especially for hydroclimatic impacts such as drought and wetness.展开更多
Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives ...Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives of billions who depend on or are affected by monsoons, as it is essential for the water cycle, food security, ecology, disaster prevention, and the economy of monsoon regions. Given the extensive literature on Asian monsoon climate prediction, we limit our focus to reviewing the seasonal prediction and predictability of the Asian Summer Monsoon (ASM). However, much of this review is also relevant to monsoon predictions in other seasons and regions. Over the past two decades, considerable progress has been made in the seasonal forecasting of the ASM, driven by an enhanced understanding of the sources of predictability and the dynamics of seasonal variability, along with advanced development in sophisticated models and technologies. This review centers on advances in understanding the physical foundation for monsoon climate prediction (section 2), significant findings and insights into the primary and regional sources of predictability arising from feedback processes among various climate components (sections 3 and 4), the effects of global warming and external forcings on predictability (section 5), developments in seasonal prediction models and techniques (section 6), the challenges and limitations of monsoon climate prediction (section 7), and emerging research trends with suggestions for future directions (section 8). We hope this review will stimulate creative activities to enhance monsoon climate prediction.展开更多
Based on a normalized difference vegetation index(NDVI)dataset for 1982-2021,this work investigates the principal modes of interannual variability in summer NDVI over eastern Siberia using the year-to-year increment m...Based on a normalized difference vegetation index(NDVI)dataset for 1982-2021,this work investigates the principal modes of interannual variability in summer NDVI over eastern Siberia using the year-to-year increment method and empirical orthogonal function(EOF)analysis.The first three principal modes(EOF1-3)of the year-to-year increment of summer NDVI(NDVI_DY)exhibit a regionally consistent mode,a western-eastern dipole mode,and a northern-southern dipole mode,respectively.Further analysis shows that sea surface temperature(SST)in the tropical Indian Ocean in February-March and western Siberian soil moisture in April could influence EOF1.EOF2 is modulated by April Northwest Pacific SST and western Siberian soil moisture in May.May North Atlantic SST and sea ice in the Kara Sea in the preceding October significantly affect EOF3.Using the year-to-year increment method and multiple linear regression analysis,prediction schemes for EOF1-3 are developed based on these predictors.To assess the predictive skill of these schemes,one-year-out cross-validation and independent hindcast methods are employed.The temporal correlation coefficients between observed EOF1-3 and the cross-validation results are 0.62,0.46,and 0.37,respectively,exceeding the 95%confidence level.In addition,reconstructed schemes for summer NDVI are developed using predicted NDVI_DY and the observed principal modes of NDVI_DY.Independent hindcasts of NDVI anomalies during 2019-2021 also present consistent distributions with the observed results.展开更多
An experiment using the Community Climate System Model(CCSM4), a participant of the Coupled Model Intercomparison Project phase-5(CMIP5), is analyzed to assess the skills of this model in simulating and predicting the...An experiment using the Community Climate System Model(CCSM4), a participant of the Coupled Model Intercomparison Project phase-5(CMIP5), is analyzed to assess the skills of this model in simulating and predicting the climate variabilities associated with the oceanic channel dynamics across the Indo-Pacific Oceans. The results of these analyses suggest that the model is able to reproduce the observed lag correlation between the oceanic anomalies in the southeastern tropical Indian Ocean and those in the cold tongue in the eastern equatorial Pacific Ocean at a time lag of 1 year. This success may be largely attributed to the successful simulation of the interannual variations of the Indonesian Throughflow, which carries the anomalies of the Indian Ocean Dipole(IOD) into the western equatorial Pacific Ocean to produce subsurface temperature anomalies, which in turn propagate to the eastern equatorial Pacific to generate ENSO. This connection is termed the "oceanic channel dynamics" and is shown to be consistent with the observational analyses. However, the model simulates a weaker connection between the IOD and the interannual variability of the Indonesian Throughflow transport than found in the observations. In addition, the model overestimates the westerly wind anomalies in the western-central equatorial Pacific in the year following the IOD, which forces unrealistic upwelling Rossby waves in the western equatorial Pacific and downwelling Kelvin waves in the east. This assessment suggests that the CCSM4 coupled climate system has underestimated the oceanic channel dynamics and overestimated the atmospheric bridge processes.展开更多
The predictability of dangerous atmospheric phenomena such as tornado outbreaks has generally been limited to a week or less. However, recent work has demonstrated the importance of the Rossby wavetrain phasing over t...The predictability of dangerous atmospheric phenomena such as tornado outbreaks has generally been limited to a week or less. However, recent work has demonstrated the importance of the Rossby wavetrain phasing over the United States in establishing outbreak-favorable environments. The predictability of Rossby wavetrain phasing is strongly related to numerous climate-scale interannual variability indices, which are predictable many months in advance. To formalize the relationship between interannual variability indices and seasonal tornado outbreak frequency, indices derived from monthly mean Northern Hemisphere 500-hPa and 1000-hPa geopotential height fields and Ni？o 3.4 indices for ENSO phase were compared to annual tornado outbreak seasonal frequencies. Statistical models predicting seasonal outbreak frequency were established using linear（stepwise multivariate linear regressione SMLR） and nonlinear（support vector regressione SVR） statistical modeling techniques.The stepwise methodology revealed predictors that are important in establishing outbreak-favorable environments at long lead times. Additionally, the results of the statistical modeling revealed that the nonlinear SVR technique reduced root mean square errors produced by the control SMLR technique by 28% and provided more consistent forecasts. A preliminary physical analysis revealed that years with high outbreak frequencies were associated with the presence of 500-mb troughs over the central and western US during the peak of outbreak season, while lower frequencies were consistent with ridging over the US or northwest flow over the Plains. These patterns support the results of the statistical modeling, which demonstrate the utility of geopotential height variability as a predictability measure of outbreak frequency.展开更多
Based on near-term climate simulations for IPCC-AR5 （The Fifth Assessment Report）, probabilistic multimodel ensemble prediction （PMME） of decadal variability of surface air temperature in East Asia （20°-50&#...Based on near-term climate simulations for IPCC-AR5 （The Fifth Assessment Report）, probabilistic multimodel ensemble prediction （PMME） of decadal variability of surface air temperature in East Asia （20°-50°N, 100°- 145°E） was conducted using the multivariate Gaussian ensemble kernel dressing （GED） methodology. The ensemble system exhibited high performance in hindcasting the deeadal （1981-2010） mean and trend of temperature anomalies with respect to 1961-90, with a RPS of 0.94 and 0.88 respectively. The interpretation of PMME for future decades （2006-35） over East Asia was made on the basis of the bivariate probability density of the mean and trend. The results showed that, under the RCP4.5 （Representative Concentration Pathway 4.5 W m-2） scenario, the annual mean temperature increases on average by about 1.1-1.2 K and the temperature trend reaches 0.6-0.7 K （30 yr）-1. The pattern for both quantities was found to be that the temperature increase will be less intense in the south. While the temperature increase in terms of the 30-yr mean was found to be virtually certain, the results for the 30-yr trend showed an almost 25% chance of a negative value. This indicated that, using a multimodel ensemble system, even if a longer-term warming exists for 2006-35 over East Asia, the trend for temperature may produce a negative value. Temperature was found to be more affected by seasonal variability, with the increase in temperature over East Asia more intense in autumn （mainly）, faster in summer to the west of 115°E, and faster still in autumn to the east of 115°E.展开更多
This paper proposes a new approach which we refer to as ＂segregated prediction＂ to predict climate time series which are nonstationary. This approach is based on the empirical mode decomposition method （EMD）, whic...This paper proposes a new approach which we refer to as ＂segregated prediction＂ to predict climate time series which are nonstationary. This approach is based on the empirical mode decomposition method （EMD）, which can decompose a time signal into a finite and usually small number of basic oscillatory components. To test the capabilities of this approach, some prediction experiments are carried out for several climate time series. The experimental results show that this approach can decompose the nonstationarity of the climate time series and segregate nonlinear interactions between the different mode components, which thereby is able to improve prediction accuracy of these original climate time series.展开更多
Based on an analysis of the relationship between the tropical cyclone genesis frequency and large-scale circulation anomaly in NCEP reanalysis, large-scale atmosphere circulation information forecast by the JAMSTEC SI...Based on an analysis of the relationship between the tropical cyclone genesis frequency and large-scale circulation anomaly in NCEP reanalysis, large-scale atmosphere circulation information forecast by the JAMSTEC SINTEX-F coupled model is used to build a statistical model to predict the cyclogenesis frequency over the South China Sea and the western North Pacific. The SINTEX-F coupled model has relatively good prediction skill for some circulation features associated with the cyclogenesis frequency including sea level pressure, wind vertical shear, Intertropical Convergence Zone and cross-equatorial air flows. Predictors derived from these large-scale circulations have good relationships with the cyclogenesis frequency over the South China Sea and the western North Pacific. A multivariate linear regression(MLR) model is further designed using these predictors. This model shows good prediction skill with the anomaly correlation coefficient reaching, based on the cross validation, 0.71 between the observed and predicted cyclogenesis frequency. However, it also shows relatively large prediction errors in extreme tropical cyclone years(1994 and 1998, for example).展开更多
In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the ...In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the self-adaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998-2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN) model, does not occur, indicating a better practical application potential of the MFNN model.展开更多
In recent decades,the damage and economic losses caused by climate change and extreme climate events have been increasing rapidly.Although scientists all over the world have made great efforts to understand and predic...In recent decades,the damage and economic losses caused by climate change and extreme climate events have been increasing rapidly.Although scientists all over the world have made great efforts to understand and predict climatic variations,there are still several major problems for improving climate prediction.In 2020,the Center for Climate System Prediction Research(CCSP) was established with support from the National Natural Science Foundation of China.CCSP aims to tackle three scientific problems related to climate prediction—namely,El Ni?o-Southern Oscillation(ENSO) prediction,extended-range weather forecasting,and interannual-to-decadal climate prediction—and hence provide a solid scientific basis for more reliable climate predictions and disaster prevention.In this paper,the major objectives and scientific challenges of CCSP are reported,along with related achievements of its research groups in monsoon dynamics,land-atmosphere interaction and model development,ENSO variability,intraseasonal oscillation,and climate prediction.CCSP will endeavor to tackle key scientific problems in these areas.展开更多
The operational climate forecast system （CFS） of the US National Centers for Environmental Prediction provides climate predictions over the world, and CFS products are becoming an important source of information for...The operational climate forecast system （CFS） of the US National Centers for Environmental Prediction provides climate predictions over the world, and CFS products are becoming an important source of information for regional climate predictions in many Asian countries where monsoon climate dominates. Recent studies have shown that, on monthly-to-seasonal time-scales, the CFS is highly skillful in simulating and predicting the variability of the Asian monsoon. The higher-frequency variability of the Asian summer monsoon in the CFS is analyzed, using output from a version with a spectral triangular truncation of 126 waves in horizontal and 64 sigma layers in vertical, focusing on synoptic, quasi-biweekly, and intraseasonal time-scales. The onset processes of different regional monsoon components were investigated within Asia. Although the CFS generally overestimates variability of monsoon on these time-scales, it successfully captures many major features of the variance patterns, especially for the synoptic timescale. The CFS also captures the timing of summer monsoon onsets over India and the Indo-China Peninsula. However, it encounters difficulties in simulating the onset of the South China Sea monsoon. The success and failure of the CFS in simulating the onset of monsoon precipitation can also be seen from the associated features of simulated atmospheric circulation processes. Overall, the CFS is capable of simulating the synoptic-to-intraseasonal variability of the Asian summer monsoon with skills. As for seasonal-tointerannual time-scales shown previously, the model is expected to possess a potential for skillful predictions of the high-frequency variability of the Asian monsoon.展开更多
A new nudging scheme is proposed for the operational prediction system of the National Marine Environmental Forecasting Center(NMEFC)of China,mainly aimed at improving El Niño–Southern Oscillation(ENSO)and India...A new nudging scheme is proposed for the operational prediction system of the National Marine Environmental Forecasting Center(NMEFC)of China,mainly aimed at improving El Niño–Southern Oscillation(ENSO)and Indian Ocean Dipole(IOD)predictions.Compared with the origin nudging scheme of NMEFC,the new scheme adds a nudge assimilation for wind components,and increases the nudging weight at the subsurface.Increasing the nudging weight at the subsurface directly improved the simulation performance of the ocean component,while assimilating low-level wind components not only affected the atmospheric component but also benefited the oceanic simulation.Hindcast experiments showed that the new scheme remarkably improved both ENSO and IOD prediction skills.The skillful prediction lead time of ENSO was up to 11 months,1 month longer than a hindcast using the original nudging scheme.Skillful prediction of IOD could be made 4–5 months ahead by the new scheme,with a 0.2 higher correlation at a 3-month lead time.These prediction skills approach the level of some of the best state-of-the-art coupled general circulation models.Improved ENSO and IOD predictions occurred across all seasons,but mainly for target months in the boreal spring for the ENSO and the boreal spring and summer for the IOD.展开更多
Based on the theory of reconstructing state space, a technique for spatiotemporal series prediction is presented. By means of this technique and NCEP/NCAR data of the monthly mean geopotential height anomaly of the 50...Based on the theory of reconstructing state space, a technique for spatiotemporal series prediction is presented. By means of this technique and NCEP/NCAR data of the monthly mean geopotential height anomaly of the 500-hPa isobaric surface in the Northern Hemisphere, a regional prediction experiment is also carried out. If using the correlation coefficient R between the observed field and the prediction field to measure the prediction accuracy, the averaged R given by 48 prediction samples reaches 21%, which corresponds to the current prediction level for the short range climate process.展开更多
The characters of experiments of prediction on monthly mean atmospheric circulation, seasonal predic-tion and seasonal forecast of summer rainfall over China are summarized in the present paper. The results demonstrat...The characters of experiments of prediction on monthly mean atmospheric circulation, seasonal predic-tion and seasonal forecast of summer rainfall over China are summarized in the present paper. The results demonstrate that climate prediction can be made only if the time average is taken. However, the improvement of the skill score of seasonal forecasts depends on the studies on physical parameters and mechanisms that are responsible for seasonal anomaly. Finally, the predictability of seasonal forecast of temperature and precipitation is discussed, including effectiveness and accuracy. Key words Seasonal climate prediction - Summer rainfall over China - Predictability Supported by “ National Key Programme for Developing Basic Sciences”—Research on the Forma tion Mechanism and Prediction Theory of Severe Climate Disasters in China (G199804900) and “ National Key Project”—Studies on Short Term Climate Prediction System in China展开更多
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 th...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.展开更多
In this work a neural network model for climate forecasting is presented. The model is built by training a neural network with available reanalysis data. In order to assess the model, the development methodology consi...In this work a neural network model for climate forecasting is presented. The model is built by training a neural network with available reanalysis data. In order to assess the model, the development methodology considers the use of data reduction strategies that eliminate data redundancy thus reducing the complexity of the models. The results presented in this paper considered the use of Rough Sets Theory principles in extracting relevant information from the available data to achieve the reduction of redundancy among the variables used for forecasting purposes. The paper presents results of climate prediction made with the use of the neural network based model. The results obtained in the conducted experiments show the effectiveness of the methodology, presenting estimates similar to observations.展开更多
Studies on the seasonal to extraseasonal climate prediction at the Institute of Atmospheric Physics (IAP) in recent years were reviewed. The first short-term climate prediction experiment was carried out based on the ...Studies on the seasonal to extraseasonal climate prediction at the Institute of Atmospheric Physics (IAP) in recent years were reviewed. The first short-term climate prediction experiment was carried out based on the atmospheric general circulation model (AGCM) coupled to a tropical Pacific oceanic general circulation model (OGCM) In 1997, an ENSO prediction system including an oceanic initialization scheme was set up. At the same time, researches on the SST-induced climate predictability over East Asia were made. Based on the blennial signal in the interannual climate variability, an effective method was proposed for correcting the model predicted results recently In order to consider the impacts of the initial soil mois- ture anomalies, an empirical scheme was designed to compute the soil moisture by use of the atmospheric quantities like temperature, precipitation, and so on. Sets of prediction experiments were carried out to study the impacts of SST and the initial atmospheric conditinns on the flood occurring over China in 1998.展开更多
文摘Studies have revealed that predictability of the atmospheric general circulation is generally high in the tropics throughout the year and that there is some predictability in the Northern extra-tropical winter atmospheric circulation through some patterns of tele connection. Predictability of the general circulation at the polar regions has still remained as a ‘ cold’ topic and little has been known about this question. Based on a preliminary study on the predictability by using the Institute of Atmospheric Physics (IAP) general circulation model, it is found that the SST-related predictability of the Southern winter lower atmospheric circulation in Antarctica is reasonably high and that there is some predictability in the 500 hPa and 200 hPa geopotential height fields over Europe and the Okhotsk Sea region during the Northern winter. It is suggested that more researches on this issue based on data analysis and model simulations are needed to obtain better understanding.
基金supported by the Special Public Sector Research of Meteorology (Grant No. GYHY200906018)the National Basic Research Program of China (Grant No. 2009CB421407)the National Key Technologies R&D Program of China (Grant No. 2007BAC29B03)
文摘On the basis of two ensemble experiments conducted by a general atmospheric circulation model(Institute of Atmospheric Physics nine-level atmospheric general circulation model coupled with land surface model,hereinafter referred to as IAP9L_CoLM),the impacts of realistic Eurasian snow conditions on summer climate predictability were investigated.The predictive skill of sea level pressures(SLP)and middle and upper tropospheric geopotential heights at mid-high latitudes of Eurasia was enhanced when improved Eurasian snow conditions were introduced into the model.Furthermore,the model skill in reproducing the interannual variation and spatial distribution of the surface air temperature(SAT)anomalies over China was improved by applying realistic(prescribed)Eurasian snow conditions.The predictive skill of the summer precipitation in China was low;however,when realistic snow conditions were employed,the predictability increased,illustrating the effectiveness of the application of realistic Eurasian snow conditions.Overall,the results of the present study suggested that Eurasian snow conditions have a significant effect on dynamical seasonal prediction in China.When Eurasian snow conditions in the global climate model(GCM)can be more realistically represented,the predictability of summer climate over China increases.
基金partially supported by the NSF(Grant No.AGS-1305798)the ONR(Grant No.N000140910526)
文摘Through the analysis of ensembles of coupled model simulations and projections collected from CMIP3 and CMIP5, we demonstrate that a fundamental spatial scale limit might exist below which useful additional refinement of climate model predictions and projections may not be possible. That limit varies among climate variables and from region to region. We show that the uncertainty（noise） in surface temperature predictions（represented by the spread among an ensemble of global climate model simulations） generally exceeds the ensemble mean（signal） at horizontal scales below 1000 km throughout North America, implying poor predictability at those scales. More limited skill is shown for the predictability of regional precipitation. The ensemble spread in this case tends to exceed or equal the ensemble mean for scales below 2000 km. These findings highlight the challenges in predicting regionally specific future climate anomalies, especially for hydroclimatic impacts such as drought and wetness.
基金supported by the National Natural Science Foundation of China(Grant No.U2342208)support from NSF/Climate Dynamics Award#2025057。
文摘Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives of billions who depend on or are affected by monsoons, as it is essential for the water cycle, food security, ecology, disaster prevention, and the economy of monsoon regions. Given the extensive literature on Asian monsoon climate prediction, we limit our focus to reviewing the seasonal prediction and predictability of the Asian Summer Monsoon (ASM). However, much of this review is also relevant to monsoon predictions in other seasons and regions. Over the past two decades, considerable progress has been made in the seasonal forecasting of the ASM, driven by an enhanced understanding of the sources of predictability and the dynamics of seasonal variability, along with advanced development in sophisticated models and technologies. This review centers on advances in understanding the physical foundation for monsoon climate prediction (section 2), significant findings and insights into the primary and regional sources of predictability arising from feedback processes among various climate components (sections 3 and 4), the effects of global warming and external forcings on predictability (section 5), developments in seasonal prediction models and techniques (section 6), the challenges and limitations of monsoon climate prediction (section 7), and emerging research trends with suggestions for future directions (section 8). We hope this review will stimulate creative activities to enhance monsoon climate prediction.
基金supported by the National Key Research and Development Program of China[grant number 2022YFE0106800]the National Natural Science Foundation of China[grant number 42230603]+1 种基金the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 311024001]supported by Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number SML2023SP209].
文摘Based on a normalized difference vegetation index(NDVI)dataset for 1982-2021,this work investigates the principal modes of interannual variability in summer NDVI over eastern Siberia using the year-to-year increment method and empirical orthogonal function(EOF)analysis.The first three principal modes(EOF1-3)of the year-to-year increment of summer NDVI(NDVI_DY)exhibit a regionally consistent mode,a western-eastern dipole mode,and a northern-southern dipole mode,respectively.Further analysis shows that sea surface temperature(SST)in the tropical Indian Ocean in February-March and western Siberian soil moisture in April could influence EOF1.EOF2 is modulated by April Northwest Pacific SST and western Siberian soil moisture in May.May North Atlantic SST and sea ice in the Kara Sea in the preceding October significantly affect EOF3.Using the year-to-year increment method and multiple linear regression analysis,prediction schemes for EOF1-3 are developed based on these predictors.To assess the predictive skill of these schemes,one-year-out cross-validation and independent hindcast methods are employed.The temporal correlation coefficients between observed EOF1-3 and the cross-validation results are 0.62,0.46,and 0.37,respectively,exceeding the 95%confidence level.In addition,reconstructed schemes for summer NDVI are developed using predicted NDVI_DY and the observed principal modes of NDVI_DY.Independent hindcasts of NDVI anomalies during 2019-2021 also present consistent distributions with the observed results.
基金the National Basic Research Program of China(973 Program)(No.2012CB956000)the Strategic Priority Project of Chinese Academy of Sciences(No.XDA11010301)+2 种基金the National Natural Science Foundation of China(Nos.41421005,U1406401)the Public Welfare Grant of China Meteorological Administration(No.GYHY201306018)the Global Change and Air-Sea Interactions of State Oceanic Administration(No.GASI-03-01-01-05)
文摘An experiment using the Community Climate System Model(CCSM4), a participant of the Coupled Model Intercomparison Project phase-5(CMIP5), is analyzed to assess the skills of this model in simulating and predicting the climate variabilities associated with the oceanic channel dynamics across the Indo-Pacific Oceans. The results of these analyses suggest that the model is able to reproduce the observed lag correlation between the oceanic anomalies in the southeastern tropical Indian Ocean and those in the cold tongue in the eastern equatorial Pacific Ocean at a time lag of 1 year. This success may be largely attributed to the successful simulation of the interannual variations of the Indonesian Throughflow, which carries the anomalies of the Indian Ocean Dipole(IOD) into the western equatorial Pacific Ocean to produce subsurface temperature anomalies, which in turn propagate to the eastern equatorial Pacific to generate ENSO. This connection is termed the "oceanic channel dynamics" and is shown to be consistent with the observational analyses. However, the model simulates a weaker connection between the IOD and the interannual variability of the Indonesian Throughflow transport than found in the observations. In addition, the model overestimates the westerly wind anomalies in the western-central equatorial Pacific in the year following the IOD, which forces unrealistic upwelling Rossby waves in the western equatorial Pacific and downwelling Kelvin waves in the east. This assessment suggests that the CCSM4 coupled climate system has underestimated the oceanic channel dynamics and overestimated the atmospheric bridge processes.
基金supported by the National Science Foundation under Grant No.DGE-0947419 at Mississippi State University
文摘The predictability of dangerous atmospheric phenomena such as tornado outbreaks has generally been limited to a week or less. However, recent work has demonstrated the importance of the Rossby wavetrain phasing over the United States in establishing outbreak-favorable environments. The predictability of Rossby wavetrain phasing is strongly related to numerous climate-scale interannual variability indices, which are predictable many months in advance. To formalize the relationship between interannual variability indices and seasonal tornado outbreak frequency, indices derived from monthly mean Northern Hemisphere 500-hPa and 1000-hPa geopotential height fields and Ni？o 3.4 indices for ENSO phase were compared to annual tornado outbreak seasonal frequencies. Statistical models predicting seasonal outbreak frequency were established using linear（stepwise multivariate linear regressione SMLR） and nonlinear（support vector regressione SVR） statistical modeling techniques.The stepwise methodology revealed predictors that are important in establishing outbreak-favorable environments at long lead times. Additionally, the results of the statistical modeling revealed that the nonlinear SVR technique reduced root mean square errors produced by the control SMLR technique by 28% and provided more consistent forecasts. A preliminary physical analysis revealed that years with high outbreak frequencies were associated with the presence of 500-mb troughs over the central and western US during the peak of outbreak season, while lower frequencies were consistent with ridging over the US or northwest flow over the Plains. These patterns support the results of the statistical modeling, which demonstrate the utility of geopotential height variability as a predictability measure of outbreak frequency.
基金supported by the National Key Basic Research and Development (973) Program of China (Grant No. 2012CB955204)the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)the Research open-fund of Jiangsu Meteorology Bureau (Grant Nos. Q201205, KM201107, and K201009)
文摘Based on near-term climate simulations for IPCC-AR5 （The Fifth Assessment Report）, probabilistic multimodel ensemble prediction （PMME） of decadal variability of surface air temperature in East Asia （20°-50°N, 100°- 145°E） was conducted using the multivariate Gaussian ensemble kernel dressing （GED） methodology. The ensemble system exhibited high performance in hindcasting the deeadal （1981-2010） mean and trend of temperature anomalies with respect to 1961-90, with a RPS of 0.94 and 0.88 respectively. The interpretation of PMME for future decades （2006-35） over East Asia was made on the basis of the bivariate probability density of the mean and trend. The results showed that, under the RCP4.5 （Representative Concentration Pathway 4.5 W m-2） scenario, the annual mean temperature increases on average by about 1.1-1.2 K and the temperature trend reaches 0.6-0.7 K （30 yr）-1. The pattern for both quantities was found to be that the temperature increase will be less intense in the south. While the temperature increase in terms of the 30-yr mean was found to be virtually certain, the results for the 30-yr trend showed an almost 25% chance of a negative value. This indicated that, using a multimodel ensemble system, even if a longer-term warming exists for 2006-35 over East Asia, the trend for temperature may produce a negative value. Temperature was found to be more affected by seasonal variability, with the increase in temperature over East Asia more intense in autumn （mainly）, faster in summer to the west of 115°E, and faster still in autumn to the east of 115°E.
基金supported by the National Science Foundation of China, under grant Nos. 40890052, 40035010, 40505018, and 40940023
文摘This paper proposes a new approach which we refer to as ＂segregated prediction＂ to predict climate time series which are nonstationary. This approach is based on the empirical mode decomposition method （EMD）, which can decompose a time signal into a finite and usually small number of basic oscillatory components. To test the capabilities of this approach, some prediction experiments are carried out for several climate time series. The experimental results show that this approach can decompose the nonstationarity of the climate time series and segregate nonlinear interactions between the different mode components, which thereby is able to improve prediction accuracy of these original climate time series.
基金Specialized Science and Technology Project for Public Welfare Industry(GYHY200906015)National Basic Research Program of China(973 Program,2010CB428606)Key Technologies R&D Program of China(2009BAC51B05)
文摘Based on an analysis of the relationship between the tropical cyclone genesis frequency and large-scale circulation anomaly in NCEP reanalysis, large-scale atmosphere circulation information forecast by the JAMSTEC SINTEX-F coupled model is used to build a statistical model to predict the cyclogenesis frequency over the South China Sea and the western North Pacific. The SINTEX-F coupled model has relatively good prediction skill for some circulation features associated with the cyclogenesis frequency including sea level pressure, wind vertical shear, Intertropical Convergence Zone and cross-equatorial air flows. Predictors derived from these large-scale circulations have good relationships with the cyclogenesis frequency over the South China Sea and the western North Pacific. A multivariate linear regression(MLR) model is further designed using these predictors. This model shows good prediction skill with the anomaly correlation coefficient reaching, based on the cross validation, 0.71 between the observed and predicted cyclogenesis frequency. However, it also shows relatively large prediction errors in extreme tropical cyclone years(1994 and 1998, for example).
基金This reasearch was supported by the Science Foundation of Guangxi under grant No.0339025the Natural Sciences Foundation of China under grant No.40075021.
文摘In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the self-adaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998-2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN) model, does not occur, indicating a better practical application potential of the MFNN model.
基金supported by the National Natural Science Foundation of China [grant number 42088101]。
文摘In recent decades,the damage and economic losses caused by climate change and extreme climate events have been increasing rapidly.Although scientists all over the world have made great efforts to understand and predict climatic variations,there are still several major problems for improving climate prediction.In 2020,the Center for Climate System Prediction Research(CCSP) was established with support from the National Natural Science Foundation of China.CCSP aims to tackle three scientific problems related to climate prediction—namely,El Ni?o-Southern Oscillation(ENSO) prediction,extended-range weather forecasting,and interannual-to-decadal climate prediction—and hence provide a solid scientific basis for more reliable climate predictions and disaster prevention.In this paper,the major objectives and scientific challenges of CCSP are reported,along with related achievements of its research groups in monsoon dynamics,land-atmosphere interaction and model development,ENSO variability,intraseasonal oscillation,and climate prediction.CCSP will endeavor to tackle key scientific problems in these areas.
基金Dr.Wen Min was supported by the National Key Program for Developing Basic Sciences of China under No.2006CB403602NationalNatural Science Foundation of China under contract No.40775039the NOAA-China Meteorological Administration bilateral program
文摘The operational climate forecast system （CFS） of the US National Centers for Environmental Prediction provides climate predictions over the world, and CFS products are becoming an important source of information for regional climate predictions in many Asian countries where monsoon climate dominates. Recent studies have shown that, on monthly-to-seasonal time-scales, the CFS is highly skillful in simulating and predicting the variability of the Asian monsoon. The higher-frequency variability of the Asian summer monsoon in the CFS is analyzed, using output from a version with a spectral triangular truncation of 126 waves in horizontal and 64 sigma layers in vertical, focusing on synoptic, quasi-biweekly, and intraseasonal time-scales. The onset processes of different regional monsoon components were investigated within Asia. Although the CFS generally overestimates variability of monsoon on these time-scales, it successfully captures many major features of the variance patterns, especially for the synoptic timescale. The CFS also captures the timing of summer monsoon onsets over India and the Indo-China Peninsula. However, it encounters difficulties in simulating the onset of the South China Sea monsoon. The success and failure of the CFS in simulating the onset of monsoon precipitation can also be seen from the associated features of simulated atmospheric circulation processes. Overall, the CFS is capable of simulating the synoptic-to-intraseasonal variability of the Asian summer monsoon with skills. As for seasonal-tointerannual time-scales shown previously, the model is expected to possess a potential for skillful predictions of the high-frequency variability of the Asian monsoon.
基金The National Natural Science Foundation of China under contract No.41690124the Scientific Research Fund of the Second Institute of Oceanography,Ministry of Natural Resources under contract No.JG2007+1 种基金the National Natural Science Foundation of China under contract Nos 42006034,41690120 and 41530961the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)under contract No.311021009.
文摘A new nudging scheme is proposed for the operational prediction system of the National Marine Environmental Forecasting Center(NMEFC)of China,mainly aimed at improving El Niño–Southern Oscillation(ENSO)and Indian Ocean Dipole(IOD)predictions.Compared with the origin nudging scheme of NMEFC,the new scheme adds a nudge assimilation for wind components,and increases the nudging weight at the subsurface.Increasing the nudging weight at the subsurface directly improved the simulation performance of the ocean component,while assimilating low-level wind components not only affected the atmospheric component but also benefited the oceanic simulation.Hindcast experiments showed that the new scheme remarkably improved both ENSO and IOD prediction skills.The skillful prediction lead time of ENSO was up to 11 months,1 month longer than a hindcast using the original nudging scheme.Skillful prediction of IOD could be made 4–5 months ahead by the new scheme,with a 0.2 higher correlation at a 3-month lead time.These prediction skills approach the level of some of the best state-of-the-art coupled general circulation models.Improved ENSO and IOD predictions occurred across all seasons,but mainly for target months in the boreal spring for the ENSO and the boreal spring and summer for the IOD.
基金supported by the National Key Program for Developing Bas ic Sciences in China(Grant No.G1999043405)the National Natural Science Foundation of China(Grant No.40035010).
文摘Based on the theory of reconstructing state space, a technique for spatiotemporal series prediction is presented. By means of this technique and NCEP/NCAR data of the monthly mean geopotential height anomaly of the 500-hPa isobaric surface in the Northern Hemisphere, a regional prediction experiment is also carried out. If using the correlation coefficient R between the observed field and the prediction field to measure the prediction accuracy, the averaged R given by 48 prediction samples reaches 21%, which corresponds to the current prediction level for the short range climate process.
基金Supported by " National Key Programme for Developing Basic Sciences" -Research on the Forma-tion Mechanism and Prediction Theory
文摘The characters of experiments of prediction on monthly mean atmospheric circulation, seasonal predic-tion and seasonal forecast of summer rainfall over China are summarized in the present paper. The results demonstrate that climate prediction can be made only if the time average is taken. However, the improvement of the skill score of seasonal forecasts depends on the studies on physical parameters and mechanisms that are responsible for seasonal anomaly. Finally, the predictability of seasonal forecast of temperature and precipitation is discussed, including effectiveness and accuracy. Key words Seasonal climate prediction - Summer rainfall over China - Predictability Supported by “ National Key Programme for Developing Basic Sciences”—Research on the Forma tion Mechanism and Prediction Theory of Severe Climate Disasters in China (G199804900) and “ National Key Project”—Studies on Short Term Climate Prediction System in China
文摘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.
文摘In this work a neural network model for climate forecasting is presented. The model is built by training a neural network with available reanalysis data. In order to assess the model, the development methodology considers the use of data reduction strategies that eliminate data redundancy thus reducing the complexity of the models. The results presented in this paper considered the use of Rough Sets Theory principles in extracting relevant information from the available data to achieve the reduction of redundancy among the variables used for forecasting purposes. The paper presents results of climate prediction made with the use of the neural network based model. The results obtained in the conducted experiments show the effectiveness of the methodology, presenting estimates similar to observations.
基金This research was supported Jointly by the Chinese Academy of Sciences key program The Eurasiamid-and-high latitude atmospheri
文摘Studies on the seasonal to extraseasonal climate prediction at the Institute of Atmospheric Physics (IAP) in recent years were reviewed. The first short-term climate prediction experiment was carried out based on the atmospheric general circulation model (AGCM) coupled to a tropical Pacific oceanic general circulation model (OGCM) In 1997, an ENSO prediction system including an oceanic initialization scheme was set up. At the same time, researches on the SST-induced climate predictability over East Asia were made. Based on the blennial signal in the interannual climate variability, an effective method was proposed for correcting the model predicted results recently In order to consider the impacts of the initial soil mois- ture anomalies, an empirical scheme was designed to compute the soil moisture by use of the atmospheric quantities like temperature, precipitation, and so on. Sets of prediction experiments were carried out to study the impacts of SST and the initial atmospheric conditinns on the flood occurring over China in 1998.
