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.

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.

Long-term Energy Demand and CO_2 Problem in the PRC

The long-term energy demand in China and the-Chinese share in global CO2 emission are forecasted on the basis of scenarios of population growth and economy development up to 2050 proposed in view of the interaction of energy, economy, environment and social development. The total energy demand in 2050 will reach 4.4~ 5.4 billion tce. It is shown in energy supply analysis that coal is China’s major energy in primary energy supply. The share of CO2 emission in the future Chinese energy system will be out of proportion to its energy consumption share because of the high persentage of coal to be consumed. It will reach about 27%. The nuclear option which would replace 30.7% of coal in the total primary energy supply will reduce the share by 9.8%. So the policy considerations on the future Chinese energy system is of great importance to the global CO2 issues.

Why Are Arctic Sea Ice Concentration in September and Its Interannual Variability Well Predicted over the Barents–East Siberian Seas by CFSv2?

To further understand the prediction skill for the interannual variability of the sea ice concentration(SIC)in specific regions of the Arctic,this paper evaluates the NCEP Climate Forecast System version 2(CFSv2),in predicting the autumn SIC and its interannual variability over the Barents–East Siberian Seas(BES).It is found that CFSv2 presents much better prediction skill for the September SIC over BES than the Arctic as a whole at 1–6-month leads,and high prediction skill for the interannual variability of the SIC over BES is displayed at 1–2-month leads after removing the linear trend.CFSv2 can reasonably reproduce the relationship between the SIC over BES in September and such factors as the surface air temperature(SAT),200-hPa geopotential height,sea surface temperature(SST),and North Atlantic Oscillation.In addition,it is found that the prescribed SIC initial condition in August as an input to CFSv2 is also essential.Therefore,the above atmospheric and oceanic factors,as well as an accurate initial condition of SIC,all contribute to a high prediction skill for SIC over BES in September.Based on a statistical prediction method,the contributions from individual predictability sources are further identified.The high prediction skill of CFSv2 for the interannual variability of SIC over BES is largely attributable to its accurate predictions of the SAT and SST,as well as a better initial condition of SIC.