Impacts of Land Process on the Onset and Evolution of Asian Summer Monsoon in the NCEP Climate Forecast System

Impacts of land models and initial land conditions （ICs） on the Asian summer monsoon, especially its onset, were investigated using the NCEP Climate Forecast System （CFS）. Two land models, the Oregon State University （OSU） land model and the NCEP, OSU, Air Force, and Hydrologic Research Laboratory （Noah） land model, were used to get parallel experiments NCEP/Department of Energy （DOE） Global Reanalysis 2 System （GLDAS）. The experiments also used land ICs from the （GR2） and the Global Land Data Assimilation Previous studies have demonstrated that, a systematic weak bias appears in the modeled monsoon, and this bias may be related to a cold bias over the Asian land mass. Results of the current study show that replacement of the OSU land model by the Noah land model improved the model＇s cold bias and produced improved monsoon precipitation and circulation patterns. The CFS predicted monsoon with greater proficiency in E1 Nifio years, compared to La Nifia years model in monsoon predictions for individual years. and the Noah model performed better than the OSU These improvements occurred not only in relation to monsoon onset in late spring but also to monsoon intensity in summer. Our analysis of the monsoon features over the India peninsula, the Indo-China peninsula, and the South Chinese Sea indicates different degrees of improvement. Furthermore, a change in the land models led to more remarkable improvement in monsoon prediction than did a change from the GR2 land ICs to the GLDAS land ICs.

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.

Flood Forecasting and Warning System: A Survey of Models and Their Applications in West Africa

Flood events occurrences and frequencies in the world are of immense worry for the stability of the economy and life safety. Africa continent is the third continent the most negatively affected by the flood events after Asia and Europe. Eastern Africa is the most hit in Africa. However, Africa continent is at the early stage in term of flood forecasting models development and implementation. Very few hydrological models for flood forecasting are available and implemented in Africa for the flood mitigation. And for the majority of the cases, they need to be improved because of the time evolution. Flash flood in Bamako (Mali) has been putting both human life and the economy in jeopardy. Studying this phenomenon, as to propose applicable solutions for its alleviation in Bamako is a great concern. Therefore, it is of upmost importance to know the existing scientific works related to this situation in Mali and elsewhere. The main aim was to point out the various solutions implemented by various local and international institutions, in order to fight against the flood events. Two types of methods are used for the flood events adaptation: the structural and non-structural methods. The structural methods are essentially based on the implementation of the structures like the dams, dykes, levees, etc. The problem of these methods is that they may reduce the volume of water that will inundate the area but are not efficient for the prediction of the coming floods and cannot alert the population with any lead time in advance. The non-structural methods are the one allowing to perform the prediction with acceptable lead time. They used the hydrological rainfall-runoff models and are the widely methods used for the flood adaptation. This review is more accentuated on the various types non-structural methods and their application in African countries in general and West African countries in particular with their strengths and weaknesses. Hydrologiska Byråns Vattenbalansavdelning (HBV), Hydrologic Engineer Center Hydrologic Model System (HEC-HMS) and Soil and Water Assessment Tool (SWAT) are the hydrological models that are the most widely used in West Africa for the purpose of flood forecasting. The easily way of calibration and the weak number of input data make these models appropriate for the West Africa region where the data are scarce and often with bad quality. These models when implemented and applied, can predict the coming floods, allow the population to adapt and mitigate the flood events and reduce considerably the impacts of floods especially in terms of loss of life.

A Study on Reconstruction of Surface Wind Speed in China Due to Various Climate Variabilities

Using European Centre for Medium-Range Weather Forecasts Reanalysis V5(ERA5)reanalysis data,this study investigated the reconstruction effects of various climate variabilities on surface wind speed in China from 1979 to 2022.The results indicated that the reconstructed annual mean wind speed and the standard deviation of the annual mean wind speed,utilizing various climate variability indices,exhibited similar spatial modes to the reanalysis data,with spatial correlation coefficients of 0.99 and 0.94,respectively.In the reconstruction of six major wind power installed capacity provinces/autonomous regions in China,the effects were notably good for Hebei and Shanxi provinces,with the correlation coefficients for the interannual regional average wind speed time series being 0.65 and 0.64,respectively.The reconstruction effects of surface wind speed differed across seasons,with spring and summer reconstructions showing the highest correlation with reanalysis data.The correlation coefficients for all seasons across most regions in China ranged between 0.4 and 0.8.Among the reconstructed seasonal wind speeds for the six provinces/autonomous regions,Shanxi Province in spring exhibited the highest correlation with the reanalysis,with a coefficient of 0.61.The large-scale climate variability indices showed good reconstruction effects on the annual mean wind speed in China,and could explain the interannual variability trends of surface wind speed in most regions of China,particularly in the main wind energy provinces/autonomous regions.

STUDY OF THE EFFECTS OF REDUCING SYSTEMATIC ERRORS ON MONTHLY REGIONAL CLIMATE DYNAMICAL FORECAST

A nested-model system is constructed by embedding the regional climate model RegCM3 into a general circulation model for monthly-scale regional climate forecast over East China. The systematic errors are formulated for the region on the basis of 10-yr (1991-2000) results of the nested-model system, and of the datasets of the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) and the temperature analysis of the National Meteorological Center (NMC), U.S.A., which are then used for correcting the original forecast by the system for the period 2001-2005. After the assessment of the original and corrected forecasts for monthly precipitation and surface air temperature, it is found that the corrected forecast is apparently better than the original, suggesting that the approach can be applied for improving monthly-scale regional climate dynamical forecast.

Study on Fog Climate Characteristic and Forecasting Method in East of Hexi Corridor

Using fog meteorological data of five stations of Wuwei in east of Hexi Corridor from 1961 to 2008,geography distribution and climate characteristic of fog were analyzed with statistical method.The results showed that fog had the obvious region characteristic,fog days were more in mountainous area than Sichuan area and were more in south than north.Fog assumed reducing tendency year by year.Fog occurring frequency was the highest from July to October in one year.Fog occurring centralized time was form 20:00 to next day 08:00 in one day.Selecting of ECMWF numerical forecast grid field,factor was initially elected with Press criterion,factor was selected with stepwise regression forecast method.The fog forecasting equation was built with optimal subset regression.The overall situation and the most superior significance equations of fog forecasting were ascertained finally for spring,summer and autumn.Fitting rate three seasonal forecasting equation were 85.5%,82.1% and 81.2% respectively,which would provide objective and effective instruction products for forecasting service.

Sea level change and forecast in the future — climate of the past,today and the future

The sea-level change is resulted from superposition of sun, moon and other planeries, and earth itself, biological process, atmosphere and oceanography, as well as artificial actions. As a result, the sea level change is really a sensitive integral variation value of many variations, or a combined function of coupling effects of various big systems. Therefore the above mentioned superposed action of different systems and the coupling effect of sun earth and biological aspects may be called as sun earth biological coupling effect system. Based on this hypothesis, the corresponding sun dynamic, air dynamic, water dynamic and earth dynamic conceptional models are established in order to research the multiple coupling effects and feedback machsnism between these big systems. In order to determine the relations, effectness and coherent relation of different variations, the quantity, analysis is conducted through collective variation and stage division. The quantity analysis indicates that the earths spindle rotation speed is the dynamic mechanism controlling the sea level change of fluctuation. The change rate of sea level in the world is ＋1.32 ＋ 0.22 mm/a, while the sea level change rate in China is only＋1.39 ＋ 0.26 mm/a in average. If take the CO2 content as the climate marker, eight cold stages （periods） are grouped out since two hundreds years AC. The extreme cold of the eighth cold stage started approximately at 1850 years AC. and if the stage from the extreme cold to extreme warm is determined as long as 200 years, the present ongoing warm stage will end at about 2050 years, there after the temperature will begin to tower. If the stage between cold and warm extremes lasts for 250 years, then the temperature will become lower at about 2100 year. Until to that time, the sea-level is estimated to raise ＋7 - ＋11 ＋ 3.5 cm again, and there after, the sea level will begin the new lowering trend. In the same time, the climate will enter into next new cold stage subsequently.

Analyzing and Forecasting Climate Change in Harbin City,Northeast China

Based on sounding data from 1975 to 2005 and TM/ETM+ remote sensing images in 1989, 2001 and 2007, the climate changes in Harbin City, Northeast China in recent 30 years were analyzed and forecasted. Results show that in the lower troposphere the meridional wind speed and mean annual wind speed decrease, and in the lower stratosphere the temperature decreases while the meridional wind speed increases significantly. In the study area, the climate is becoming warmer and wetter in the middle lower troposphere. The expansion of urban area has great effects on the surface air temperature and the wind speed, leading to the increase of the surface air temperature, the decrease of the surface wind speed, and the increase of the area of urban high temperature zone. The quantitative equations have been established among the surface air temperature, the carbon dioxide (CO2) concentration and the specific humidity (the water vapor content). It is predicted that the future increasing rate of the surface air temperature is 0.85℃/10yr if emission concentration of CO2 remains unchanged; if emission concentration of CO2 decreases to 75%, 50% and 25%, respectively, the surface air temperature will increase 0.65℃/10yr, 0.46℃/10yr and 0.27℃/10yr, respectively. The rise of the surface air temperature in the study area is higher than that of the global mean temperature forecasted by IPCC.

Climate and Forecast Mode Simulations for Antarctica:Implications for Temperature and Wind

Climate and forecast mode simulations with the regional climate model HIRlam-ECHAM（HIRHAM） are evaluated over a pan-Antarctic domain. The ability of the model to simulate temperature and wind profiles in the troposphere is quantified by comparing its results with radiosonde data acquired from the Davis station for January and July 2007. Compared to the climate mode, the forecast mode was found to deliver improved results for temperature and wind simulations at the near surface and in the lower troposphere. The main remaining model bias found was the under-representation of low-level wind jets. Based on ensemble simulations, it is shown that a distinct internal variability is inherent in the climate mode simulations, and associated areas of reduced predictability over Antarctica are identified.

Climate Features of Regional Fog in Liaoxi Area and Forecast Analysis

By using the fog data from 1995 to 2004 of four selected observation stations,the weather features of foggy days in Liaoxi area have been studied in this paper.The favorable surface and upper circulation for fog and its frequency have also been concluded from the statistic.In this paper,the forecasting index of fog,proposed on the basis of the condition and mechanism of the fog occurrence,has been tested by the 10-year analysis.Another test conducted by using the data of 1st July-31st December,2004 also gives a good result which has a vacancy rate of 22.2% and a miss rate of 5.1%.

Forecasting method of monthly wind power generation based on climate model and long short-term memory neural network

Predicting wind power gen eration over the medium and long term is helpful for dispatchi ng departme nts,as it aids in constructing generation plans and electricity market transactions.This study presents a monthly wind power gen eration forecast!ng method based on a climate model and long short-term memory(LSTM)n eural n etwork.A non linear mappi ng model is established between the meteorological elements and wind power monthly utilization hours.After considering the meteorological data(as predicted for the future)and new installed capacity planning,the monthly wind power gen eration forecast results are output.A case study shows the effectiveness of the prediction method.

Progress in Climate Prediction and Weather Forecast Operations in China

The current status of weather forecasting and climate prediction, and the main progress China has made in recent years, are summarized in this paper. The characteristics and requirements of modern weather forecast operations are described briefly, and the significance of Numerical Weather Prediction （NWP） for future development is emphasized. Tile objectives and critical tasks for seamless short-term climate predic- tion that covers the extended-range （15 30 days）, monthly, seasonal, annual, interannual and interdecadal timescales, are proposed.

Study of Holocene glacier degradation in central Asia by isotopic methods for long-term forecast of climate changes

This article presents a summary of our studies of Holocene moraines and glaciers of the Tien-Shan, Pamir, and Himalaya moun- mills with the purpose of providing pattern regularity of the Holocene glaciation decomposition. We developed a method for ob- taining reliable radiocarbon dating of moraines with the use of autochthonous organic matter dispersed in fine-grained morainic material, as well there were shown new possibilities of isotope-oxygen and isotope-uranium analysis for the Holocene glaciations dynamics. We found that Holocene glaciations disintegrate stadiaUy according to the decaying principle, and seven main stages may be distinguished. We achieved the absolute dating of the first three stages, identifying these periods as 8,000, 5,000, and 3,400 years ago. The application of the above-mentioned isotope methods of the Holocene glaciations and moraines study will allow re- searchers to improve the offered model of the Holocene glaciations disintegration; it will be great contribution to salvation of the problem of long-term climatic and glaciations forecast.

The Obtaining and Verification of Meticulous Climate Forecast Product

[Objective] The aim was to study the obtaining method of meticulous climate forecast product and test its forecast effect. [Method] Based on the national standard surface observation data and secondary data bank obtained by means of distance weight interpolation method, the model of climate forecast was established, and the timing, fixed-point and quantitative meticulous climate forecast of meteorological elements was obtained by using many forecast methods and artificial revision, finally the forecast effect was tested. [Result] At present, meticulous climate forecast system was used to predict the daily, five-day, ten-day, monthly, seasonal and annual variation of six meteorological elements (including average temperature, maximum temperature, minimum temperature, precipitation, average pressure and sunshine) in 10 meteorological stations in Guangzhou City. The forecast effect of meteorological elements in 10 stations in Guangzhou City from 2006 to 2010 was tested, and the average scores of monthly precipitation, average temperature, maximum temperature and minimum temperature were 75.0, 94.5, 98.6 and 88.9, respectively, while the scores of five-day meteorological elements were 77.1, 90.6, 90.1 and 82.8, which met the requirement of objective management of Guangdong Meteorological Observatory. [Conclusion] Meticulous climate forecast system could be widely used in the forecast of agricultural meteorological disasters and fixed-point forecast in large reservoir, new airport and nuclear power station.

Skill Assessment of Copernicus Climate Change Service Seasonal Ensemble Precipitation Forecasts over Iran

Medium to long-term precipitation forecasting plays a pivotal role in water resource management and development of warning systems.Recently,the Copernicus Climate Change Service(C3S)database has been releasing monthly forecasts for lead times of up to three months for public use.This study evaluated the ensemble forecasts of three C3S models over the period 1993-2017 in Iran’s eight classified precipitation clusters for one-to three-month lead times.Probabilistic and non-probabilistic criteria were used for evaluation.Furthermore,the skill of selected models was analyzed in dry and wet periods in different precipitation clusters.The results indicated that the models performed best in western precipitation clusters,while in the northern humid cluster the models had negative skill scores.All models were better at forecasting upper-tercile events in dry seasons and lower-tercile events in wet seasons.Moreover,with increasing lead time,the forecast skill of the models worsened.In terms of forecasting in dry and wet years,the forecasts of the models were generally close to observations,albeit they underestimated several severe dry periods and overestimated a few wet periods.Moreover,the multi-model forecasts generated via multivariate regression of the forecasts of the three models yielded better results compared with those of individual models.In general,the ECMWF and UKMO models were found to be appropriate for one-month-ahead precipitation forecasting in most clusters of Iran.For the clusters considered in Iran and for the long-range system versions considered,the Météo France model had lower skill than the other models.

Focus on Climate Impact Assessment and Forecast Service in Liaocheng City in 2015

Detailed statistics of characteristics of climatic factors in Liaocheng in 2015 were carried out. The main points of meteorological service＇ were summed up to im- prove the ability of meteorological forecast, thereby meeting the new requirements of the public for meteorological cause. The results showed that the annual average temperature in various Counties was in the range of 13.6-14.5 ℃, which was slight- ly higher than that in an average year and lower than that in the past year; the average annual precipitation of various counties ranged from 376.9 to 592.1 mm, which was slightly less than those in an average year and the past year; the aver- age sunshine duration across the city was 2 026 h, which was less than that in an average year and slightly more than that in the past year. The main climatic events included fog and haze, heavy snowfall, thunderstorm, gale, hail, high temperature, heavy rain and rainy sparse sunlight, which produced a negative impact on the agri- culture, tourism, transportation and people＇s daily life. This is the focus of future services.

The Potential of Hydro-Climate Forecast Strategies in Sustaining Agricultural Productivity amongst Rainfed Farmers in West Africa: A Review

Several studies have reported a significant yearly decrease in forest cover globally, using satellite images. This is especially true in West Africa, where rapid urbanisation acerbates the problem, and both of these changes lead to alterations in rainfall regime and other changes in climatic parameters. Several studies reveal that adaptation which reduces vulnerability to adverse climatic variation effects is the key to developing resilience against climate change. In this region, over 90% of farmers are engaged in small-scale rainfed crop cultivation and rely on their own weather perception, instincts and what they observe from the surrounding biota (flora and fauna) to forecast the weather and plan their agricultural activities. The pressing nature of the problem of climate variability in Africa had provoked a lot of research into developing and testing several adaptation strategies to control the situation. Various strategies to improve and localize global, regional and national climate services (Local Forecast, Scientific Forecast and Integrated Forecast) needed by the farmers are being developed to build resilience against climate change. This review illustrates the situation in Ghana and identifies various forecast strategies developed to mitigate the adverse effects of climate variability on agricultural productivity. These mitigation methods include the development of climate services to provide users with forecast information in order to make climate-smart decisions to minimize the risk.

About A New Complex Method of Climates Reconstruction and Forecasting of Climate Changes in the Future

This paper deals with a new integrated method of reconstruction and forecasting of climatic changes in future. The method is based on proxy data pollen-spore analysis method, 14C analysis method, nowadays meteorological data, and data about of solar activity expressed in numbers of W （Wolf）. Here we present the results of investigation of sediments of the 2nd Fomich River terrace, Taymyr Peninsula, Russia. The formation of the peat bog started 10500 ± 140 years BP and continued during the entire Holocene. The pollen analysis of the sediment samples of the 2nd Fomich River terrace and the analysis of surface samples from a larch forest, typical of this region, reveals two phytochrones： both climatically preconditioned--tundra phytochrone （I1-4） and forest phytochrone （Ⅱ1-4）. The techniques of reconstruction and forecasting of basic elements of climate are presented and discussed in details.

Knowledge flow analysis of knowledge co-production-based climate change adaptation for lowland rice farmers in Bulukumba Regency,Indonesia

To increase the resilience of farmers’livelihood systems,detailed knowledge of adaptation strategies for dealing with the impacts of climate change is required.Knowledge co-production approach is an adaptation strategy that is considered appropriate in the context of the increasing frequency of disasters caused by climate change.Previous research of knowledge co-production on climate change adaptation in Indonesia is insufficient,particularly at local level,so we examined the flow of climate change adaptation knowledge in the knowledge co-production process through climate field school(CFS)activities in this study.We interviewed 120 people living in Bulukumba Regency,South Sulawesi Province,Indonesia,involving 12 crowds including male and female farmers participated in CFS and not participated in CFS,local government officials,agriculture extension workers,agricultural traders,farmers’family members and neighbors,etc.In brief,the 12 groups of people mainly include two categories of people,i.e.,people involved in CFS activities and outside CFS.We applied descriptive method and Social network analysis(SNA)to determine how knowledge flow in the community network and which groups of actors are important for knowledge flow.The findings of this study reveal that participants in CFS activities convey the knowledge they acquired formally(i.e.,from TV,radio,government,etc.)and informally(i.e.,from market,friends,relatives,etc.)to other actors,especially to their families and neighbors.The results also show that the acquisition and sharing of knowledge facilitate the flow of climate change adaptation knowledge based on knowledge co-operation.In addition,the findings highlight the key role of actors in the knowledge transfer process,and key actors involved in disseminating information about climate change adaptation.To be specific,among all the actors,family member and neighbor of CFS actor are the most common actors in disseminating climate knowledge information and closest to other actors in the network;agricultural trader and family member of CFS actor collaborate most with other actors in the community network;and farmers participated in CFS,including those heads of farmer groups,agricultural extension workers,and local government officials are more willing to contact with other actors in the network.To facilitate the flow of knowledge on climate change adaptation,CFS activities should be conducted regularly and CFS models that fit the situation of farmers’vulnerability to climate change should be developed.

CMA-GEPS极端温度预报指数及2022年夏季极端高温预报检验评估

极端预报指数(EFI)是利用集合预报获取极端天气信息的有效工具之一。为提升CMA全球集合预报系统(CMAGEPS)对极端天气的预报能力,针对CMA-GEPS历史预报数据少且再预报数据缺乏、难以合理统计模式气候分布的难题,研究利用小样本确定性预报数据形成EFI所需模式气候分布的方法。采用2020年6月15日—2022年7月22日CMA全球高分辨率(0.25°×0.25°)确定性业务预报数据,通过一种时间、空间样本扩展方法建立了与较低分辨率(0.5°×0.5°)的CMA-GEPS预报模式版本匹配的各预报时效(1—10 d)逐月模式气候分布。使用CMA-GEPS业务预报和ERA5再分析数据评估了CMA-GEPS 2 m气温EFI对2022年夏季(6—8月)中外4个代表性区域极端高温的预报能力。基于相对作用特征曲线的检验结果表明,CMA-GEPS EFI在1—10 d的短、中期预报时效上均具备区分极端高温的能力。以最大TS评分为准则,确定了用于发布极端高温预警信号的EFI临界阈值。EFI的预报能力随预报时效延长呈下降趋势,且在不同区域的表现存在差异:对中国长江中下游地区极端高温的预报能力在各时效上均优于华北地区;欧洲西部地区1—7 d时效上的EFI预报能力高于欧洲中部地区,而欧洲中部地区8—10 d时效上的EFI预报能力更好。上述结果与2 m气温的集合预报质量随预报时效与空间位置而变化有关。经济价值模型的评估结果表明,基于EFI预报信息的风险决策存在一定的经济价值和参考价值。个例分析结果进一步展现了CMA-GEPS EFI能够在中期预报时效上发出极端高温早期预警的能力。