Deep Learning Shows Promise for Seasonal Prediction of Antarctic Sea Ice in a Rapid Decline Scenario

The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the reforecast results of Antarctic sea-ice area and extent from December to June of the coming year with a Convolutional Long Short-Term Memory(Conv LSTM)Network. The reforecast experiments demonstrate that Conv LSTM captures the interannual and interseasonal variability of Antarctic sea ice successfully, and performs better than the European Centre for Medium-Range Weather Forecasts. Based on this, we present the prediction from December 2023 to June 2024, indicating that the Antarctic sea ice will remain at lows, but may not create a new record low. This research highlights the promising application of deep learning in Antarctic sea-ice prediction.

Seasonal Prediction of the Record-Breaking Northward Shift of the Western Pacific Subtropical High in July 2021

The unprecedented Zhengzhou heavy rainfall in July 2021 occurred under the background of a northward shift of the western Pacific subtropical high(WPSH).Although the occurrence of this extreme event could not be captured by seasonal predictions,a skillful prediction of the WPSH variation might have warned us of the increased probability of extreme weather events in Central and Northern China.However,the mechanism for the WPSH variation in July 2021 and its seasonal predictability are still unknown.Here,the observed northward shift of the WPSH in July 2021 is shown to correspond to a meridional dipole pattern of the 850-hPa geopotential height to the east of China,the amplitude of which became the strongest since 1979.The meridional dipole pattern is two nodes of the Pacific–Japan pattern.To investigate the predictability of the WPSH variation,a 21-member ensemble of seasonal predictions initiated from the end of June 2021 was conducted.The predictable and unpredictable components of the meridional dipole pattern were identified from the ensemble simulations.Its predictable component is driven by positive precipitation anomalies over the tropical western Pacific.The positive precipitation anomalies are caused by positive horizonal advection of the mean moist enthalpy by southwesterly anomalies to the northwestern flank of anticyclonic anomalies excited by the existing La Niña,which is skillfully predicted by the model.The leading mode of the unpredictable component is associated with the atmospheric internal intraseasonal oscillations,which are not initialized in the simulations.The relative contributions of the predictable and unpredictable components to the observed northward shift of the WPSH at 850 hPa are 28.0%and 72.0%,respectively.

Seasonal Prediction of Extreme High-Temperature Days in Southwestern China Based on the Physical Precursors

Extreme high temperatures frequently occur in southwestern China,significantly impacting the local ecological system and economic development.However,accurate prediction of extreme high-temperature days(EHDs)in this region is still an unresolved challenge.Based on the spatiotemporal characteristics of EHDs over China,a domain-averaged EHD index over southwestern China(SWC-EHDs)during April-May is defined.The simultaneous dynamic and thermodynamic fields associated with the increased SWC-EHDs are a local upper-level anticyclonic(high-pressure)anomaly and wavy geopotential height anomaly patterns over Eurasia.In tracing the origins of the lower boundary anomalies,two physically meaningful precursors are detected for SWC-EHDs.They are the tripolar SST change tendency from December-January to February-March in the northern Atlantic and the February-March mean snow depth in central Asia.Using these two selected predictors,a physics-based empirical model prediction was applied to the training period of 1961–2005 to obtain a skillful prediction of the EHDs index,attaining a correlation coefficient of 0.76 in the independent prediction period(2006–19),suggesting that 58%of the total SWC-EHDs variability is predictable.This study provides an estimate for the lower bound of the seasonal predictability of EHDs as well as for the hydrological drought over southwestern China.

A Correction Method Suitable for Dynamical Seasonal Prediction

Based on the hindcast results of summer rainfall anomalies over China for the period 1981-2000 by the Dynamical Climate Prediction System （IAP-DCP） developed by the Institute of Atmospheric Physics, a correction method that can account for the dependence of model＇s systematic biases on SST anomalies is proposed. It is shown that this correction method can improve the hindcast skill of the IAP-DCP for summer rainfall anomalies over China, especially in western China and southeast China, which may imply its potential application to real-time seasonal prediction.

Seasonal Prediction of Monthly Precipitation in China Using Large-Scale Climate Indices

In this study, seasonal predictions were applied to precipitation in China on a monthly basis based on a multivariate linear regression with an adaptive choice of predictors drawn from regularly updated climate indices with a two to twelve month lead time. A leave-one-out cross validation was applied to obtain hindcast skill at a 1% significance level. The skill of forecast models at a monthly scale and their significance levels were evaluated using Anomaly Correlation Coefficients （ACC） and Coefficients Of Determination （COD）. The monthly ACC skill ranged between 0.43 and 0.50 in Central China, 0.41-0.57 in East China, and 0.41 0.60 in South China. The dynamic link between large-scale climate indices with lead time and the precipitation in China is also discussed based on Singular Value Decomposition Analysis （SVDA） and Correlation Analysis （CA）.

Seasonal prediction of extreme high-temperature days over the Yangtze River basin

Extreme high temperatures occur frequently over the densely populated Yangtze River basin(YRB)in China during summer,significantly impacting the local economic development and ecological system.However,accurate prediction of extreme high-temperature days in this region remains a challenge.Unfortunately,the Climate Forecast System Version 2(CFSv2)exhibits poor performance in this regard.Thus,based on the interannual increment approach,we develop a hybrid seasonal prediction model over the YRB(HM_(YRB))to improve the prediction of extreme high-temperature days in summer.The HM_(YRB)relies on the following four predictors:the observed preceding April-May snowmelt in north western Europe;the snow depth in March over the central Siberian Plateau;the CFSv2-forecasted concurrent summer sea surface temperatures around the Maritime Continent;and the 200-hPa geopotential height over the Tibetan Plateau.The HM_(YRB)indicates good capabilities in predicting the interannual variability and trend of extreme high-temperature days,with a markable correlation coefficient of 0.58 and a percentage of the same sign(PSS)of 76% during 1983-2015 in the one-year-out cross-validation.Additionally,the HM_(YRB) maintains high PSS skill(86%)and robustness in the independent prediction period(2016-2022).Furthermore,the HM_(YRB) shows a good performance for years with high occurrence of extreme high-temperature days,with a hit ratio of 40%.These predictors used in HM_(YRB)are beneficial in terms of the prediction skill for the average daily maximum temperature in summer over the YRB,albeit with biases existing in the magnitude.Our study provides promising insights into the prediction of 2022-like hot extremes over the YRB in China.

A Review of Seasonal Climate Prediction Research in China

The ultimate goal of climate research is to produce climate predictions on various time scales. In China, efforts to predict the climate started in the 1930 s. Experimental operational climate forecasts have been performed since the late 1950 s,based on historical analog circulation patterns. However, due to the inherent complexity of climate variability, the forecasts produced at that time were fairly inaccurate. Only from the late 1980 s has seasonal climate prediction experienced substantial progress, when the Tropical Ocean and Global Atmosphere project of the World Climate Research program（WCRP） was launched. This paper, following a brief description of the history of seasonal climate prediction research, provides an overview of these studies in China. Processes and factors associated with the climate variability and predictability are discussed based on the literature published by Chinese scientists. These studies in China mirror aspects of the climate research effort made in other parts of the world over the past several decades, and are particularly associated with monsoon research in East Asia. As the climate warms, climate extremes, their frequency, and intensity are projected to change, with a large possibility that they will increase. Thus, seasonal climate prediction is even more important for China in order to effectively mitigate disasters produced by climate extremes, such as frequent floods, droughts, and the heavy frozen rain events of South China.

Seasonal Prediction of Summer Temperature over Northeast China Using a Year-to-Year Incremental Approach

We present a model for predicting summertime surface air temperature in Northeast China（NESSAT） using a year-to-year incremental approach.The predicted value for each year＇s increase or decrease of NESSAT is added to the observed value within a particular year to yield the net forecast NESSAT.The seasonal forecast model for the year-to-year increments of NESSAT is constructed based on data from 1975- 2007.Five predictors are used：an index for sea ice cover over the East Siberian Sea,an index for central Pacific tropical sea surface temperature,two high latitude circulation indices,as well as a North American pressure index.All predictors are available by no later than March,which allows for compilation of a seasonal forecast with a two-month lead time.The prediction model accurately captures the interannual variations of NESSAT during 1977-2007 with a correlation coefficient between the predicted and observed NESSAT of 0.87（accounting for 76%of total variance） and a mean absolute error（MAE） of 0.3℃.A cross-validation test during 1977-2008 demonstrates that the model has good predictive skill,with MAE of 0.4℃and a correlation coefficient between the predicted and observed NESSAT of 0.76.

Recent Advances in Dynamical Extra-Seasonal to Annual Climate Prediction at IAP/CAS

Recent advances in dynamical climate prediction at the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP/CAS) during the last five years have been briefly described in this paper. Firstly, the second generation of the IAP dynamical climate prediction system (IAP DCP-Ⅱ) has been described, and two sets of hindcast experiments of the summer rainfall anomalies over China for the periods of 1980-1994 with different versions of the IAP AGCM have been conducted. The comparison results show that the predictive skill of summer rainfall anomalies over China is improved with the improved IAP AGCM in which the surface albedo parameterization is modified. Furthermore, IAP DCP-II has been applied to the real-time prediction of summer rainfall anomalies over China since 1998, and the verification results show that IAP DCP-II can quite well capture the large scale patterns of the summer flood/drought situations over China during the last five years (1998-2002). Meanwhile, an investigation has demonstrated the importance of the atmospheric initial conditions on the seasonal climate prediction, along with studies on the influences from surface boundary conditions (e.g., land surface characteristics, sea surface temperature). Certain conclusions have been reached, such as, the initial atmospheric anomalies in spring may play an important role in the summer climate anomalies, and soil moisture anomalies in spring can also have a significant impact on the summer climate anomalies over East Asia. Finally, several practical techniques (e.g., ensemble technique, correction method, etc.), which lead to the increase of the prediction skill for summer rainfall anomalies over China, have also been illustrated. The paper concludes with a list of critical requirements needed for the further improvement of dynamical seasonal climate prediction.

Probabilistic Seasonal Prediction of Summer Rainfall over East China Based on Multi-Model Ensemble Schemes

The skill of probability density function （PDF） prediction of summer rainfall over East China using optimal ensemble schemes is evaluated based on the precipitation data from five coupled atmosphere-ocean general circulation models that participate in the ENSEMBLES project. The optimal ensemble scheme in each region is the scheme with the highest skill among the four commonly-used ones： the equally-weighted ensemble （EE）, EE for calibrated model-simulations （Cali-EE）, the ensemble scheme based on multiple linear regression analysis （MLR）, and the Bayesian ensemble scheme （Bayes）. The results show that the optimal ensemble scheme is the Bayes in the southern part of East China; the Cali-EE in the Yangtze River valley, the Yangtze-Huaihe River basin, and the central part of northern China; and the MLR in the eastern part of northern China. Their PDF predictions are well calibrated, and are sharper than or have approximately equal interval-width to the climatology prediction. In all regions, these optimal ensemble schemes outperform the climatology prediction, indicating that current commonly-used multi-model ensemble schemes are able to produce skillful PDF prediction of summer rainfall over East China, even though more information for other model variables is not derived.

Seasonal Prediction of the Global Precipitation Annual Modes with the Grid-Point Atmospheric Model of IAP LASG

A right annual cycle is of critical importance for a model to improve its seasonal prediction skill. This work assesses the performance of the Grid-point Atmospheric Model of IAP LASG （GAMIL） in retrospective prediction of the global precipitation annual modes for the 1980 2004 period. The annual modes are gauged by a three-parameter metrics： the long-term annual mean and two major modes of annual cycle （AC）, namely, a solstitial mode and an equinoctial asymmetric mode. The results demonstrate that the GAMIL one-month lead prediction is basically able to capture the major patterns of the long-term annual mean as well as the first AC mode （the solstitial monsoon mode）. The GAMIL has deficiencies in reproducing the second AC mode （the equinoctial asymmetric mode）. The magnitude of the GAMIL prediction tends to be greater than the observed precipitation, especially in the sea areas including the Arabian Sea, the Bay of Bengal （BOB）, and the western North Pacific （WNP）. These biases may be due to underestimation of the convective activity predicted in the tropics, especially over the western Pacific warm pool （WPWP） and its neighboring areas. It is suggested that a more accurate parameterization of convection in the tropics, especially in the Maritime Continent, the WPWP and its neighboring areas, may be critical for reproducing the more realistic annual modes, since the enhancement of convective activity over the WPWP and its vicinity can induce suppressed convection over the WNP, the BOB, and the South Indian Ocean where the GAMIL produces falsely vigorous convections. More efforts are needed to improve the simulation not only in monsoon seasons but also in transitional seasons when the second AC mode takes place. Selection of the one-tier or coupled atmosphere-ocean system may also reduce the systematic error of the GAMIL prediction. These results offer some references for improvement of the GAMIL seasonal prediction skill.

Seasonal and Extraseasonal Predictions of Summer Monsoon Precipitation by Gcms

A semi-operational real time short-term climate prediction system has been developed in the Center of Climate and Environment Prediction Research (CCEPRE), Institute of Atmospheric Physics/Chinese Academy of Sciences. The system consists of the following components: the AGCM and OGCM and their coupling, initial conditions and initialization, practical schemes of anomaly prediction, ensemble prediction and its standard deviation, correction of GCM output, and verification of prediction. The experiences of semi-operational real-time prediction by using this system for six years (1989-1994) and of hindcasting for 1980-1989 are reported. It is shown that in most cases large positive and negative anomalies of summer precipitation resulting in disastrous climate events such as severe flood or drought over East Asia can be well predicted for two seasons in advance, although the quantitatively statistical skill scores are only satisfactory due to the difficulty in correctly predicting the signs of small anomalies. Some methods for removing the systematic errors and introducing corrections to the GCM output are suggested. The sensitivity of prediction to the initial conditions and the problem of ensemble prediction are also discussed in the paper.

Analysis of Causes and Seasonal Prediction of the Severe Floods in Yangtze／Huaihe Basins during Summer 1991

The present paper shows that a seasonal prediction for the large scale flooding and waterlogging of the mid-lower Yangtze/ Huaihe River basins in the summer of 1991 made successfully in early April 1991.The seasonal forecasting method and some predictors are also introduced and analyzed herein. Because the extra extent of the abnormally early onset of the plum rain period in 1991 was unexpected,great efforts have been made to find out the causes of this abnormality. These causes are mainly associated with the large scale warming of SST surrounding the south and east part of Asia during the preceding winter,while the ENSO-like pattern of SSTA occurred in the North Pacific.In addition,the possible influence of strong solar proton events is analyzed.In order to improve the seasonal pre4iction the usage of the predicted SOl in following spring/summer is also introduced.The author believes thatthe regional climate anomaly can be correctly predicted for one season ahead only on the basis of physical understanding of the interactions of many preceding factors.

POSSIBILITY TO USE SCH(?)RDINGER EQUATION TO DESCRIBE LARGE-SCALE PROBABILITY WAVES AND ITS APPLICATION IN SEASONAL PREDICTION

Under the influence of a one-dimensional stationary outfield with the equilibrium between kinetic and potential energy produced by it,a modified Sch(?)rdinger equation in the form i((?)ψ/(?)t)t=a (?)~2ψ/ax^2-ib (?),where b=b_o(?)T/(?)x,is used to describe the behavior of the probability wave on the six-month departure charts at the 500 hPa level.It is found that C=2πa/L-b_o(?)T/ax and when L→∞,then C= -b_o(?)T/(?)x,where C is wave velocity,a and b are constants,and L is wavelength.The motion direction of probability waves is against the outfield temperature gradient,and their velocity is related to the absolute value of temperature gradient.The motion of waves shrinks in heat sinks and expands in heat sources,which have been verified in practice.Finally the six-month departure probability wave and the modified Sch(?)rdinger equation are used in the MOS predictions of temperature and rainfall in spring-summer 1981-1985 in Jilin Province and the accuracy for trend predictions is equal to 80%.

Seasonal Prediction Experiments of the Summer Droughts and Floods during the Early 1990′s in East Asia with Numerical Models

It has been shown by the observed data that during the early 1990′s, the severe disastrous climate occurred in East Asia. In the summer of 1991, severe flood occurred in the Yangtze River and the Huaihe River basin of China and in South Korea, and it also appeared in South Korea in the summer of 1993. However, in the summer of 1994, a dry and hot summer was caused in the Huaihe River basin of China and in R. O. K.. In order to investigate the seasonal predictability of the summer droughts and floods during the early 1990′s in East Asia, the seasonal prediction experiments of the summer droughts and floods in the summers of 1991-1994 in East Asia have been made by using the Institute of Atmopsheric Physics-Two-Level General Circulation Model (IAP-L2 AGCM), the IAP-Atmosphere/Ocean Coupled Model (IAP-CGCM) and the IAP-L2 AGCM including a filtering scheme, respectively. Compared with the observational facts, it is shown that the IAP-L2 AGCM or IAP-CGCM has some predictability for the summer droughts and floods during the early 1990′s in East Asia, especially for the severe droughts and floods in China and R. O. K.. In this study, a filtering scheme is used to improve the seasonal prediction experiments of the summer droughts and floods during the early 1990′s in East Asia. The predicted results show that the filtering scheme to remain the planetary-scale disturbances is an effective method for the improvement of the seasonal prediction of the summer droughts and floods in East Asia.

A Review on Seasonal Climate Prediction

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

Dynamical and Machine Learning Hybrid Seasonal Prediction of Summer Rainfall in China

Seasonal prediction of summer rainfall is crucial to reduction of regional disasters,but currently it has a low prediction skill.We developed a dynamical and machine learning hybrid(MLD)seasonal prediction method for summer rainfall in China based on circulation fields from the Chinese Academy of Sciences(CAS)Flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2)operational dynamical prediction model.Through selecting optimum hyperparameters for three machine learning methods to obtain the best fit and least overfitting,an ensemble mean of the random forest and gradient boosting regression tree methods was shown to have the highest prediction skill measured by the anomalous correlation coefficient.The skill has an average value of 0.34 in the historical cross-validation period(1981-2010)and 0.20 in the 10-yr period(2011-2020)of independent prediction,which significantly improves the dynamical prediction skill by 400%.Both reducing overfitting and using the best dynamical prediction are important in applications of the MLD method and in-depth analysis of these warrants a further investigation.

Improving Multi-model Ensemble Probabilistic Prediction of Yangtze River Valley Summer Rainfall

Seasonal prediction of summer rainfall over the Yangtze River valley（YRV） is valuable for agricultural and industrial production and freshwater resource management in China, but remains a major challenge. Earlier multi-model ensemble（MME） prediction schemes for summer rainfall over China focus on single-value prediction, which cannot provide the necessary uncertainty information, while commonly-used ensemble schemes for probability density function（PDF） prediction are not adapted to YRV summer rainfall prediction. In the present study, an MME PDF prediction scheme is proposed based on the ENSEMBLES hindcasts. It is similar to the earlier Bayesian ensemble prediction scheme, but with optimization of ensemble members and a revision of the variance modeling of the likelihood function. The optimized ensemble members are regressed YRV summer rainfall with factors selected from model outputs of synchronous 500-h Pa geopotential height as predictors. The revised variance modeling of the likelihood function is a simple linear regression with ensemble spread as the predictor. The cross-validation skill of 1960–2002 YRV summer rainfall prediction shows that the new scheme produces a skillful PDF prediction, and is much better-calibrated, sharper, and more accurate than the earlier Bayesian ensemble and raw ensemble.

A Statistical Scheme for the Seasonal Forecasting of North China's Surface Air Temperature during Winter

This paper presents a statistical scheme for the seasonal forecasting of North China's surface air temperature (NCSAT) during winter. Firstly, a prediction model for an decrease or increase of winter NCSAT is established, whose predictors are available for no later than the previous September, as this is the most favorable month for seasonal forecasting up to two months ahead.The predicted NCSAT is then derived as the sum of the predicted increment of NCSAT and the previous NCSAT. The scheme successfully predicts the interannual and the decadal variability of NCSAT. Additionally, the advantages of the prediction scheme are discussed.

A Potential High-Score Scheme for the Prediction of Atlantic Storm Activity

A new empirical approach for the seasonal prediction of annual Atlantic tropical storm number (ATSN) was developed using precipitation and 500 hPa geopotential height data from the preceding January February and April May.The 2.5°×2.5° resolution reanalysis data from both the US National Center for Environmental Prediction/the National Center for Atmospheric Research (NCEP/NCAR) and the European Center for Medium-Range Weather Forecasting (ECMWF) were applied.The model was cross-validated using data from 1979 2002.The ATSN predictions from the two reanalysis models were correlated with the observations with the anomaly correlation coefficients (ACC) of 0.79 (NCEP/NCAR) and 0.78 (ECMWF) and the multi-year mean absolute prediction errors (MAE) of 1.85 and 1.76,respectively.When the predictions of the two models were averaged,the ACC increased to 0.90 and the MAE decreased to 1.18,an exceptionally high score.Therefore,this new empirical approach has the potential to improve the operational prediction of the annual tropical Atlantic storm frequency.