Application of Predictive Model for Efficient Cassava (Manihot esculenta Crantz) Yield in the Face of Climate Variability in Enugu State, Nigeria

Climate variability as occasioned by conditions such as extreme rainfall and temperature, rainfall cessation, and irregular temperatures has considerable impact on crop yield and food security. This study develops a predictive model for cassava yield (Manihot esculenta Crantz) amidst climate variability in rainfed zone of Enugu State, Nigeria. This study utilized data of climate variables and tonnage of cassava yield spanning from 1971 to 2012;as well as information from a questionnaire and focus group discussion from farmers across two seasons in 2023 respectively. Regression analysis was employed to develop the predictive model equation for seasonal climate variability and cassava yield. The rainfall and temperature anomalies, decadal change in trend of cassava yield and opinion of farmers on changes in rainfall season were also computed in the study. The result shows the following relationship between cassava and all the climatic variables: R2 = 0.939;P = 0.00514;Cassava and key climatic variables: R2 = 0.560;P = 0.007. The result implies that seasonal rainfall, temperature, relative humidity, sunshine hours and radiation parameters are key climatic variables in cassava production. This is supported by computed rainfall and temperature anomalies which range from −478.5 to 517.8 mm as well as −1.2˚C to 2.3˚C over the years. The questionnaire and focus group identified that farmers experienced at one time or another, late onset of rain, early onset of rain or rainfall cessation over the years. The farmers are not particularly sure of rainfall and temperature characteristics at any point in time. The implication of the result of this study is that rainfall and temperature parameters determine the farming season and quantity of productivity. Hence, there is urgent need to address the situation through effective and quality weather forecasting network which will help stem food insecurity in the study area and Nigeria at large. The study made recommendations such as a comprehensive early warning system on climate variability incidence which can be communicated to local farmers by agro-meteorological extension officers, research on crops that can grow with little or no rain, planning irrigation scheme, and improving tree planting culture in the study area.

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）.

A Preliminary Study on the Polar Climate Predictability

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.

Predictability of US tornado outbreak seasons using ENSO and northern hemisphere geopotential height variability

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.

The ocean's role in climate variability

Because of its vast volume and heat capacity, the ocean contains most of the memory of the earth's ocean-atmosphere coupled system. It has been suggested that the ocean may delay global warming by absorbing large amounts of heat, that it may cause abrupt climate change due to its disrupted thermohaline circulation, and that it may set the time-scales for various climate oscillations. Although the slow pace and persistence of oceanic variations give hope to long-range prediction, there still exist large uncertainties in climate predictability. Presently available observations and models are generally inadequate for studying and predicting long-term climate changes. However, some short-term fluctuations such as ENSO have been well studied and shown to be highly predictable even with simplified models.

Seasonal prediction skills of FIO-ESM for North Pacific sea surface temperature and precipitation

The seasonal prediction of sea surface temperature(SST) and precipitation in the North Pacific based on the hindcast results of The First Institute of Oceanography Earth System Model(FIO-ESM) is assessed in this study.The Ensemble Adjusted Kalman Filter assimilation scheme is used to generate initial conditions, which are shown to be reliable by comparison with the observations. Based on this comparison, we analyze the FIO-ESM 6-month hindcast results starting from each month of 1993–2013. The model exhibits high SST prediction skills over most of the North Pacific for two seasons in advance. Furthermore, it remains skillful at long lead times for midlatitudes. The reliable prediction of SST can transfer fairly well to precipitation prediction via air-sea interactions.The average skill of the North Pacific variability(NPV) index from 1 to 6 months lead is as high as 0.72(0.55) when El Ni?o-Southern Oscillation and NPV are in phase(out of phase) at initial conditions. The prediction skill of the NPV index of FIO-ESM is improved by 11.6%(23.6%) over the Climate Forecast System, Version 2. For seasonal dependence, the skill of FIO-ESM is higher than the skill of persistence prediction in the later period of prediction.

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.

EXTRA-SEASONAL PREDICTIONS OF SUMMER RAINFALL IN CHINA AND ENSO IN 2001 BY CLIMATE MODELS

China is a monsoon country.The most rainfalls in China concentrate on the summer seasons. More frequent floods or droughts occur in some parts of China.Therefore,the prediction of summer rainfall in China is a significant issue.As we know,the obvious impacts of the sea surface temperature anomalies(SSTA)on the summer rainfall over China have been noticed.The predictions of the SSTA have been involved in the research. The key project on short-term climate modeling prediction system has been finished in 2000. The system included an atmospheric general circulation model named AGCM95,a coupled atmospheric-oceanic general circulation model named AOGCM95,a regional climate model over China named RegCM95,a high-resolution Indian-Pacific OGCM named IPOGCM95,and a simplified atmosphere-ocean dynamic model system named SAOMS95.They became the operational prediction models of National Climate Center(NCC). Extra-seasonal predictions in 2001 have been conducted by several climate models,which were the AGCM95,AOGCM95,RegCM95,IPOGCM95,AIPOGCM95,OSU/NCC,SAOMS95,IAP APOGCM and CAMS/ZS.All of those models predicted the summer precipitation over China and/ or the annual SSTA over the tropical Pacific Ocean in the Modeling Prediction Workshop held in March 2001. The assessments have shown that the most models predicted the distributions of main rain belt over Huanan and parts of Jiangnan and droughts over Huabei-Hetao and Huaihe River Valley reasonably.The most models predicted successfully that a weaker cold phase of the SSTA over the central and eastern tropical Pacific Ocean would continue in 2001. The evaluations of extra-seasonal predictions have also indicated that the models had a certain capability of predicting the SSTA over the tropical Pacific Ocean and the summer rainfall over China.The assessment also showed that multi-model ensemble(super ensembles)predictions provided the better forecasts for both SSTA and summer rainfall in 2001,compared with the single model. It is a preliminary assessment for the extra-seasonal predictions by the climate models.The further investigations will be carried out.The model system should be developed and improved.

The Predictability Limit of Oceanic Mesoscale Eddy Tracks in the South China Sea

Employing the nonlinear local Lyapunov exponent (NLLE) technique, this study assesses the quantitative predictability limit of oceanic mesoscale eddy (OME) tracks utilizing three eddy datasets for both annual and seasonal means. Our findings reveal a discernible predictability limit of approximately 39 days for cyclonic eddies (CEs) and 44 days for anticyclonic eddies (AEs) within the South China Sea (SCS). The predictability limit is related to the OME properties and seasons. The long-lived, large-amplitude, and large-radius OMEs tend to have a higher predictability limit. The predictability limit of AE (CE) tracks is highest in autumn (winter) with 52 (53) days and lowest in spring (summer) with 40 (30) days. The spatial distribution of the predictability limit of OME tracks also has seasonal variations, further finding that the area of higher predictability limits often overlaps with periodic OMEs. Additionally, the predictability limit of periodic OME tracks is about 49 days for both CEs and AEs, which is 5-10 days higher than the mean values. Usually, in the SCS, OMEs characterized by high predictability limit values exhibit more extended and smoother trajectories and often move along the northern slope of the SCS.

Case Study: ENSO Events, Rainfall Variability and the Potential of SOI for the Seasonal Precipitation Predictions in Iran

The studies in recent decades show that many natural disasters such as tropical severe storms, hurricanes development, torrential rain, river flooding, and landslides in some regions of the world and severe droughts and wildfires in other areas are due to El Nino-Southern Oscillation (ENSO). This research aims to contribute to an improved definition of the relation between ENSO and seasonal (autumn and winter) variability of rainfall over Iran. The results show that during autumn, the positive phase of SOI is associated with decrease in the rainfall amount in most part of the country;negative phase of SOI is associated with a significant increase in the rainfall amount. It is also found that, during the winter time when positive phase of SOI is dominant, winter precipitation increases in most areas of the eastern part of the country while at the same time the decreases in the amount of rainfall in other parts is not significant. Moreover, with negative phase of SOI in winter season the amount of rainfall in most areas except south shores of Caspian Sea in the north decreases, so that the decrease of rainfall amount in the eastern part is statistically significant.

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.

A Short-Term Climate Prediction Model Based on a Modular Fuzzy Neural Network

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.

Subseasonal features of the Asian summer monsoon in the NCEP climate forecast system

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.

Seasonal Forecasts of Precipitation during the First Rainy Season in South China Based on NUIST-CFS1.0

Current dynamical models experience great difficulties providing reliable seasonal forecasts of regional/local rainfall in South China.This study evaluates seasonal forecast skill for precipitation in the first rainy season(FRS,i.e.,April–June)over South China from 1982 to 2020 based on the global real-time Climate Forecast System of Nanjing University of Information Science and Technology(NUIST-CFS1.0,previously known as SINTEX-F).The potential predictability and the practical forecast skill of NUIST-CFS1.0 for FRS precipitation remain low in general.But NUIST-CFS1.0 still performs better than the average of nine international models in terms of correlation coefficient skill in predicting the interannual precipitation anomaly and its related circulation index.NUIST-CFS1.0 captures the anomalous Philippines anticyclone,which transports moisture and heat northward to South China,favoring more precipitation in South China during the FRS.By examining the correlations between sea surface temperature(SST)and FRS precipitation and the Philippines anticyclone,we find that the model reasonably captures SST-associated precipitation and circulation anomalies,which partly explains the predictability of FRS precipitation.A dynamical downscaling model with 30-km resolution forced by the large-scale circulations of the NUIST-CFS1.0 predictions could improve forecasts of the climatological states and extreme precipitation events.Our results also reveal interesting interdecadal changes in the predictive skill for FRS precipitation in South China based on the NUIST-CFS1.0 hindcasts.These results help improve the understanding and forecasts for FRS precipitation in South China.

MEPM模型:基于深度学习的多变量厄尔尼诺-南方涛动预测模型

厄尔尼诺-南方涛动(ENSO)是发生在热带太平洋年际时间尺度的海-气相互作用的异常现象,并由Nino3.4指数表征其发生情况;除此之外,ENSO与众多极端气候事件密切相关。因此,有效的ENSO预测对于预防极端气候事件和深入研究全球气候变化具有重要意义。然而,目前基于深度学习的ENSO预测大多数是预测一个指数或者单一变量,对于模拟多气候要素下的ENSO预测研究较少。通过提出一种利用多气候变量的ENSO预测模型——MEPM模型,其中包括多变量信息提取模块(MIEM)和时空融合模块(STFM),捕获不同气候变量在时空上的相互依赖性,进而提高ENSO预测的准确性。选取了纬向风应力异常(τ_(x))、经向风应力异常(τ_(y))、海表温度异常(SSTA)和海表下150 m温度异常(SSTA150)4个变量的距平值进行ENSO预测。结果表明:MEPM模型在提前11个月的Nino3.4指数相关技巧上分别比北美多模型集合中的动力预报系统CanCM4、CCSM3和GFDL-aer04高10%、20%和14%。此外,MEPM模型在中期Nino3.4指数相关技巧上显著优于其他深度学习模型,并可提供长达17个月的有效预测。

德国气候预测系统中东亚冬季风的季节预测及可预报性

东亚冬季风(EAWM)作为北半球冬季最强的中纬度环流系统之一,主导着东亚的冬季气候。因此,开展东亚冬季风季节预测和可预报性研究具有十分重要的意义。本研究使用德国气候预测系统(German Climate Forecast System,简称GCFS2)输出的回报数据(1993~2016年)对EAWM的预测性能进行全面评估。GCFS2很好地预测了EAWM气候态的主要特征,包括西伯利亚高压、阿留申低压、东亚大槽、东亚高空急流及东亚上空的地表气温和降水,并可以熟练地预测东亚大槽及东亚地表气温的年际变化。GCFS2对一个海平面气压定义的EAWM指数(EAWMI)显示出了预测技巧,同时可以很好地预测与EAWM相关的位于海洋上的大气环流、地表气温及降水异常。GCFS2中EAWM的预测技巧主要得益于对观测中的EAWM–ENSO关系及ENSO遥相关的成功再现,模式中增强的EAWM–ENSO[强于观测,观测中整个24年(1993~2016)EAWM与ENSO的相关系数为-0.46]关系,有助于提前2个月或更长时间预测EAWM。GCFS2中12月初始化的EAWMI在去除ENSO信号后仍有0.42的预测技巧,说明有另一预测源,为冬季巴伦支—喀拉海区域海冰覆盖度(BK_SIC)。观测中BK_SIC减少,增强西伯利亚高压,EAWM从而增强;模式中BK_SIC的变化可以增加西伯利亚高压东北部的可预测性,使得12月初始化的EAWM预测技巧增加。

中国东北极端持续大—暴雪事件的个例成因及可预测性

2013年11月东北大—暴雪持续日数为1982—2020年同期最多的一年。其中,2013年11月17—20日和25日先后发生两次强降雪过程,其中第一次过程降雪持续时间较长,而第二次过程日降雪强度强。基于此,从2013年11月月际异常气候背景和两次强降雪过程的角度,开展其成因和可预测性研究。研究结果表明,2013年11月北极涛动(AO)正位相异常偏强、类北太平洋涛动(NPO)负位相、巴伦支海以北的海冰月增长量(11月减9月)异常偏多和热带-南印度洋海温异常偏暖的气候背景有利于这次东北持续性大—暴雪事件的发生。其中,2013年11月巴伦支海以北的海冰月增长量偏多,意味着季节性海冰生长量增加使得向大气中释放的潜热通量增加,气温偏高,有利于AO正位相加强并激发罗斯贝(Rossby)波列,使得阿留申低压减弱;同时11月热带-南印度洋海温异常偏暖,热带印度洋对流加强和热带西太平洋对流减弱,有利于西北太平洋-阿留申地区维持“气旋-反气旋”式环流异常,呈现类NPO负位相。这样的环流形势有利于从北太平洋向东北地区持续输送水汽。第一次强降雪过程(17—20日)发生前5 d(12—16日)到降雪过程结束(20日),北大西洋涛动(NAO)维持正位相并且强度达到冬半年最强,由此激发持续东传Rossby波,使得西北太平洋-阿留申地区为持续性的南北向“气旋-反气旋”式环流异常,有利于北太平洋水汽持续输送至东北地区。在2013年11月25日第二次强降雪过程中,乌拉尔山阻塞高压显著加强,东北低涡加深,有利于热带西太平洋更为暖湿的水汽输送至东北地区,与北太平洋输送的水汽共同导致第二次强降雪过程的日降雪强度大。最后,利用CFSv2评估2013年11月异常气候背景的可预测性,结果表明CFSv2可提前1个月预测2013年11月热带-南印度洋海温异常偏暖,但对热带印度洋和西太平洋对流异常、NAO以及热带-中高纬大气遥相关预测能力较弱。在次季节尺度上,ECMWF(CMA)能提前29(12)d和13(16)d合理预测出两次过程降雪量的空间分布,这可能是由于模式能合理再现NAO和乌拉尔山阻塞高压等关键环流系统的逐日变化。因此未来还有待提升热带-中高纬大气遥相关、水汽输送以及平流层极涡的次季节-季节预测效能。

蛙壶菌潜在分布区及其风险因素预测与分析

[目的]壶菌病是一种由蛙壶菌(Batrachochytrium dendrobatidis)感染导致的疾病,在近半个多世纪的时间里,是导致两栖动物种群数量大量减少甚至灭绝的主要原因之一。目前,壶菌病尚没有行之有效的治疗方法,因此,对世界范围内蛙壶菌的分布与传播进行调查研究,分析蛙壶菌在世界和区域尺度的分布风险及影响因素,可为其预防措施的制定提供基础研究资料。[方法]使用蛙壶菌在世界范围内的分布数据,共考虑了20个气候环境变量,经过对蛙壶菌分布数据和变量进行筛选,建立最大熵模型,比较纳入模型的6个变量与蛙壶菌分布风险的关系,并预测蛙壶菌在世界范围和中国大陆的分布风险。[结果]变量中对蛙壶菌分布概率贡献度最高的前4位依次为年平均气温、年降水量、温度季节性、降水季节性。蛙壶菌分布概率与年平均气温和温度季节性总体均呈先正相关后负相关,与年降水量和降水季节性分别呈正相关和负相关。蛙壶菌全球分布的高风险地区主要在中国大陆南部、澳大利亚东南部、巴布亚新几内亚中部、瑞典南部、德国、波兰、罗马尼亚、英国南部、爱尔兰、法国、马达加斯加东部、苏丹、美国南部、秘鲁东部、埃塞俄比亚和日本等。其在中国大陆分布的高风险区域主要集中在广东北部、广西中部和北部、云南、贵州、湖南、江西、福建、浙江、江苏南部、安徽南部、湖北南部、重庆、四川东部及南部、陕西南部等地区。[结论]蛙壶菌的分布与气温和降水关系密切,其在全球分布的高风险地区主要在欧洲、非洲南部和北美洲南部,在中国大陆分布的高风险区域主要集中在华中、华东、华南地区和西南地区的东南部。

Dynamic Downscaling of Summer Precipitation Prediction over China in 1998 Using WRF and CCSM4

To study the prediction of the anomalous precipitation and general circulation for the summer（June–July–August） of1998, the Community Climate System Model Version 4.0（CCSM4.0） integrations were used to drive version 3.2 of the Weather Research and Forecasting（WRF3.2） regional climate model to produce hindcasts at 60 km resolution. The results showed that the WRF model produced improved summer precipitation simulations. The systematic errors in the east of the Tibetan Plateau were removed, while in North China and Northeast China the systematic errors still existed. The improvements in summer precipitation interannual increment prediction also had regional characteristics. There was a marked improvement over the south of the Yangtze River basin and South China, but no obvious improvement over North China and Northeast China. Further analysis showed that the improvement was present not only for the seasonal mean precipitation, but also on a sub-seasonal timescale. The two occurrences of the Mei-yu rainfall agreed better with the observations in the WRF model,but were not resolved in CCSM. These improvements resulted from both the higher resolution and better topography of the WRF model.

Progress and Prospects of Research on Subseasonal to Seasonal Variability and Prediction of the East Asian Monsoon

Subseasonal to seasonal(S2S)variability represents the atmospheric disturbance on the 10–90-day timescale,which is an important bridge linking weather and climate.In 2015,China Meteorological Administration(CMA)listed the S2S prediction project that was initiated by WMO programs three years ago as one of its key tasks.After five years of research,significant progress has been made on the mechanisms of the East Asian monsoon(EAM)S2S variability,related impact of climate change,as well as the predictability on the S2S timescale of numerical models.The S2S variability of the EAM is closely linked to extreme persistent climate events in China and is an important target for seasonal climate prediction.However,under the influence of global warming and the interactions among climate systems,the S2S variability of the EAM is so complex that its prediction remains a great challenge.This paper reviews the past achievement and summarizes the recent progress in research of the EAM S2S variability and prediction,including characteristics of the main S2S modes of the EAM,their impact on the extreme events in China,effects of external and internal forcing on the S2S variability,as well as uncertainties of climate models in predicting the S2S variability,with a focus on the progress achieved by the S2S research team of the Chinese Academy of Meteorological Sciences.The present bottlenecks,future directions,and critical research recommendations are also analyzed and presented.