由卫星辐射测量资料计算候、旬和月平均海面温度场

本文介绍了一个从NOAA卫星AVHRR红外窗区通道辐射测量资料获取候、旬和月平均海面温度场的计算机软件系统,并提供了用1987年6月的NOAA-9卫星AVHRR资料,对软件系统进行测试得到的旬和月平均海面温度场。     

基于卫星观测的南海表层温度气候学特征及长期变化

利用高分辨率AVHRR Pathfinder卫星海表温度资料,分析了1982-2012年南海及其毗邻海区海表温度（SST）的变化趋势,并给出了近30年该海域SST的气候学特征。结果表明：南海年平均SST随纬度的增加而降低,且越靠近陆地海温梯度越大,等温线呈西南-东北向分布;南海最高、最低SST分别出现在夏季和冬季;夏季中南半岛和海南岛东侧存在相对低温区,应与西南季风和地转偏向力共同作用引起的深层冷水涌升有关;近30年南海及毗邻海区年平均SST增温趋势为0.100℃/10a,20世纪90年代末到21世纪初年平均SST处于高值期,最高值出现在1998年;南海海区四季均存在变暖趋势,冬季增温趋势最大,为0.194℃/10a,夏、春季次之,分别为0.121℃/10a和0.107℃/10a,秋季最小,为0.086℃/10a;近30年台湾海峡和中国大陆东南沿海增温最显著,最大增温值达到0.7℃/10a以上。     

PREDICTABILITY LEVELS OF MONTHLY FORECAST BASED ON TIME-AVERAGED OCEAN/ATMOSPHERE VARIABLES —A NATURALLY OCCURRING ANALOGUE STUDY

Naturally occurring analogues between the monthly averaged data of 1000,500 and 100 hPa geopotential height and the sea surface temperature (SST) in the Pacific,Atlantic and Indian Oceans during the period January 1956-December 1972 are used to study the potential predictability levels of forecasting the monthly mean ocean/atmosphere variables.It is found that in the ocean-atmosphere system the forecast of geopotential height may be more difficult than SST,and that the predictability level of monthly mean geopotential height anomaly calculated from the corresponding monthly mean SST appears relatively poor,but it can be improved by using the past observational data of monthly mean SST/geopotential fields.     

Correlation analyses between sea surface temperature anomalies in the eastern equatorial Pacific and the world ocean

Based on data from the Comprehensive Ocean-Atmosphere Data Set (COADS), objective analyses of the monthly mean sea surface temperature (SST) were prepared at GFDL for each month of the 110-year period 1870–1979. Time series of various indices characterizing the SST anomalies averaged over the eastern equatorial Pacific (EEP), the tropical oceans and the world ocean are presented for monthly, yearly and decadal time-averaging periods. Global correlations maps are given for each decade of the 1870–1979 period. They show the spatial connections between the monthly SST anomalies in the EEP and in other parts of the world ocean and how these connections vary for the different decades. On the intermonthly time scale the SST anomalies in the EEP and those in the tropical and world oceans are found to be highly correlated, with maximum correlations values of 0.91 at zero lag for the tropical oceans during the 1950–1959 decade and 0.81 for the world ocean during the 1970–1979 decade. Positive correlation values of r0.36 persist on average from about 4 months before to about 8 months after the EEP anomalies occur. There is a clear tendency for the tropical and world ocean anomalies to lag behind the EEP anomalies. Comparing different oceans, we find the tendency for the tropical SST anomalies in the Indian and Atlantic Oceans to lag behind those in the EEP region by about 1 and 3 months, respectively. On the interannual time scale the EEP anomalies are also well correlated with those in the other regions, having an average correlation of 0.84 for the tropical oceans and of about 0.7 for the world ocean.     

依据月平均资料作月预报——利用自然相似的探讨

本文利用1956年1月—1972年12月的月平均1000,500,100 hPa位势高度和太平洋、大西洋、印度洋的海表温度资料中存在的自然相似,对依据月平均资料作的海洋—大气变量月平均值的预报可能达到的水平进行了分析研究。结果表明在一个海—气耦合系统中,大气变量的预报是比海温预报更为困难的一环。根据月平均海表温度决定相应的月平均位势高度场的准确度较低,但在考虑了过去的海温和高度场资料后能有所改进。     

FY-3B/VIRR海表温度算法改进及精度评估

该文介绍了卫星观测海表温度 (SST) 算法的发展历程,给出了所用SST算法的回归模型,并在FY-3B/VIRR业务SST算法的基础上进行了改进。基于NOAA-19/AVHRR匹配数据集,进行多算法建模分析及精度评估,白天最优算法为非线性SST (NL) 算法,夜间最优算法为三通道SST (TC) 算法,最优算法的确定与NESDIS/STAR一致。建立2012年8月—2013年3月FY-3B/VIRR匹配数据集,并在此基础上进行多算法回归建模及精度评估,白天和夜间的最优均为NL算法,分析发现夜间TC算法采用匹配数据集版本2(MDB_V2) 时,3.7 μm通道存在类似百叶窗的条带现象。以2012年10—12月FY-3B/VIRR匹配数据集计算回归系数,以2013年1—3月独立样本进行精度评估,与浮标SST相比,NL算法白天和夜间的均方根误差分别为0.41℃和0.43℃。与日平均最优插值海温 (OISST) 相比,NL算法白天和夜间的均方根误差分别为1.45℃和1.5℃; 选择与OISST偏差在2℃以内的样本,NL算法白天和夜间均方根误差分别为0.82℃和0.84℃。     

An analysis of prediction skill of monthly mean climate variability

In this paper, lead-time and spatial dependence in skill for prediction of monthly mean climate variability is analyzed. The analysis is based on a set of extensive hindcasts from the Climate Forecast System at the National Centers for Environmental Prediction. The skill characteristics of initialized predictions is also compared with the AMIP simulations forced with the observed sea surface temperature (SST) to quantify the role of initial versus boundary conditions in the prediction of monthly means. The analysis is for prediction of monthly mean SST, precipitation, and 200-hPa height. The results show a rapid decay in skill with lead time for the atmospheric variables in the extratropical latitudes. Further, after a lead-time of approximately 30?C40?days, the skill of monthly mean prediction is essentially a boundary forced problem, with SST anomalies in the tropical central/eastern Pacific playing a dominant role. Because of the larger contribution from the atmospheric internal variability to monthly time-averages (compared to seasonal averages), skill for monthly mean prediction associated with boundary forcing is also lower. The analysis indicates that the prospects of skillful prediction of monthly means may remain a challenging problem, and may be limited by inherent limits in predictability.     

用统计模式重建热带太平洋环流场资料的可行性试验

在一种恢复总辐射场序列的统计模式的基础上,建立由海温场推算同期热带太平洋环流场的统计模式,对1982至1989年期间冬季(1、2、3月)月平均500hPa高度场资料验证表明,恢复效果稳定。于是利用该地区现有的月平均海温场资料,外推重建了1966年至1978年间冬季(1、2、3月)月平均500hPa高度场资料。这一试验证明,可进一步利用海温场资料恢复更长时期的热带环流场资料。      

近年秋季南美沿岸海表温度周期性下降对ＥＮＳＯ事件的可能影响

文章分析了1950年以来赤道南美沿岸海表温度变化特点，发现1999年以来，Nino1＋2区海表温度的年变化振幅呈增大趋势，使得赤道东太平洋东北地区每年出现周期性降温，这主要是由秋季月平均SST最低值偏低造成，这种长时间持续偏低是1950年以来首次出现。进一步的分析表明，近年来南美沿岸海表温度在秋季的周期性下降可能对ENS0暖事件发生产生了影响，使得2002／2003ENS0暖事件成为20世纪50年代以粜由玲事件向瞎事件转换历时最长的一次．     

Spatiotemporal Variations of Cloud Amount over the Yangtze River Delta, China

Based on the NOAA's Advanced Very High Resolution Radiometer(AVHRR) Pathfinder Atmospheres Extended(PATMOS-x) monthly mean cloud amount data, variations of annual and seasonal mean cloud amount over the Yangtze River Delta(YRD), China were examined for the period 1982–2006 by using a linear regression analysis. Both total and high-level cloud amounts peak in June and reach minimum in December, mid-level clouds have a peak during winter months and reach a minimum in summer, and lowlevel clouds vary weakly throughout the year with a weak maximum from August to October. For the annual mean cloud amount, a slightly decreasing tendency(–0.6% sky cover per decade) of total cloud amount is observed during the studying period, which is mainly due to the reduction of annual mean high-level cloud amount(–2.2% sky cover per decade). Mid-level clouds occur least(approximately 15% sky cover) and remain invariant, while the low-level cloud amount shows a significant increase during spring(1.5% sky cover per decade) and summer(3.0% sky cover per decade). Further analysis has revealed that the increased low-level clouds during the summer season are mainly impacted by the local environment. For example,compared to the low-level cloud amounts over the adjacent rural areas(e.g., cropland, large water body, and mountain areas covered by forest), those over and around urban agglomerations rise more dramatically.