Estimation of the monthly and annual mean maximum and mean minimum air temperature values in Greece

In this study, regression equations to estimate the monthly and annual values of the mean maximum and mean minimum air temperatures in Greece are derived. For this purpose, data from 87 meteorological stations distributed all over Greece are used. Geographical parameters, i.e., altitude, latitude, longitude, minimum distance from the sea and an index of terrain morphology, are used as independent variables. These equations explain 79?C97% of the variance of the temperature values and have standard error of estimate between 0.59 and 1.20°C. Data from 37 other meteorological stations are used to validate the accuracy of the equations. Topographic or climatic factors, which could not be introduced into the equations, are responsible for most temperature residuals >0.5°C or <?0.5°C. Moreover, some particular emphasis has been given to the values of the regression coefficient for the altitude, since it is the estimator for the mean lapse rate of air temperature.     

广西耕地植被指数月变化特征分析研究

通过互联网直接下载美国地质调查局 (USGS)数据中心的全球 1km分辨率 NDVI10 d合成 NDVI资料 ,选取广西南、中、北部 5个水田监测点和 6个旱地监测点 ,分析其 NDVI月变化特征。结果表明 :NDVI的月变化与耕地上农作物生长变化有较密切的关系 ,可以利用 NDVI资料 ,开展广西农作物生长状况及农业气象灾害的气象卫星遥感监测     

Record occurrence and record values in daily and monthly temperatures

We analyze the occurrence and the values of record-breaking temperatures in daily and monthly temperature observations. Our aim is to better understand and quantify the statistics of temperature records in the context of global warming. Similar to earlier work we employ a simple mathematical model of independent and identically distributed random variables with a linearly growing expectation value. This model proved to be useful in predicting the increase (decrease) in upper (lower) temperature records in a warming climate. Using both station and re-analysis data from Europe and the United States we further investigate the statistics of temperature records and the validity of this model. The most important new contribution in this article is an analysis of the statistics of record values for our simple model and European reanalysis data. We estimate how much the mean values and the distributions of record temperatures are affected by the large scale warming trend. In this context we consider both the values of records that occur at a certain time and the values of records that have a certain record number in the series of record events. We compare the observational data both to simple analytical computations and numerical simulations. We find that it is more difficult to describe the values of record breaking temperatures within the framework of our linear drift model. Observations from the summer months fit well into the model with Gaussian random variables under the observed linear warming, in the sense that record breaking temperatures are more extreme in the summer. In winter however a significant asymmetry of the daily temperature distribution hides the effect of the slow warming trends. Therefore very extreme cold records are still possible in winter. This effect is even more pronounced if one considers only data from subpolar regions.     

Integration of synoptic and hydrodynamic monthly air temperature forecasts

The results of experiments on the integration of synoptic and hydrodynamic forecasts of monthly air temperature for the northwestern part of Eurasia for the period of 2011–2015 are presented. The comprehensive comparison of skill scores of individual and integrated forecasts is provided. It is demonstrated that during the cold season hydrodynamic forecasts have the highest skills among the other forecasts. It is revealed that empirical methods are characterized by the forecast skill increase in summer and by its decrease in winter and during the period of spring transformation of atmospheric circulation. In most cases the integration of diverse methods allows combining their benefits and improving the resultant forecast skill. The synchrony was detected in the forecast skill variations. It is shown that the forecast skill mainly depends on the persistence of macrocirculation patterns.     

Future change of extreme temperature climate indices over East Asia with uncertainties estimation in the CMIP5

How well the climate models simulate extreme temperature over East Asia and how the extreme indices would change under anthropogenic global warming are investigated. The indices studied include hot days (HD), tropical nights (TN), growing degree days (GDD), and cooling degree days (CDD) in summer and heating degree days (HDD) and frost days (FD) in winter. The representative concentration pathway 4.5 (RCP 4.5) experiments for the period of 2075–2099 are compared with historical simulations for the period of 1979–2005 from 15 coupled models that are participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5). To optimally estimate future change and its uncertainty, groups of best models are selected based on Taylor diagrams, relative entropy, and probability density function (PDF) methods previously suggested. Overall, the best models’ multi-model ensemble based on Taylor diagrams has the lowest errors in reproducing temperature extremes in the present climate among three methods. Selected best models in three methods tend to project considerably different changes in the extreme indices from each other, indicating that the selection of reliable models are of critical importance to reduce uncertainties. Three groups of best models show significant increase of summerbased indices but decrease of the winter-based indices. Over East Asia, the most significant increase is seen in the HD (336 ± 23.4% of current climate) and the most significant decrease is appeared in the HDD (82 ± 4.2%). It is suggested that the larger future change in the HD is found over in the Southeastern China region, probably due to a higher local maximum temperature in the present climate. All of the indices show the largest uncertainty over Southeastern China, particularly in the TN (~3.9 times as large as uncertainty over East Asia) and in the HD (~2.4). It is further noted that the TN reveals the largest uncertainty over three East Asian countries (~1.7 and 1.4 over Korea and Japan, respectively). These future changes in extreme temperature events have an important implication for energy-saving applications and human molarity in the future.     

Sampling errors in estimation of the small scales of monthly mean climate

Abstract

We examine the sampling errors in the estimation of the small scales of monthly average mean atmospheric climate as seen in mean kinetic energy. The relationships between the small‐scale mean and transient kinetic energy in the atmosphere and atmospheric flow simulations are discussed. We elucidate how the estimation of the mean depends on the number of realizations or the length of the time period of the data. Studies based on both a barotropic model and on the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mark 3 general circulation model (GCM) are performed focusing on 500 hPa and vertically averaged spectra. Results for perpetual January simulations are presented for 32, 62 and 1500 member ensembles within the barotropic model and for 1, 10 and 60 month integrations with the GCM. We find that, with too few realizations in the ensemble or averaging over just one month, the mean kinetic energy has a spurious spectrum with similar power law to the transient kinetic energy but with smaller values by about two orders of magnitude. For larger ensembles or longer averaging periods, the mean kinetic energy falls off more rapidly than the transient kinetic energy. Our results lead to the conclusion that mean kinetic energy spectra based on just one month of data, such as reported in the literature, most recently by Boer (2003), are dominated by sampling errors at the small scales.     

华东地区月平均气温统计降尺度方法比较

用中国地面气象观测站的逐日气温观测资料和NCEP/NCAR再分析资料,分别使用基于多元线性回归(MLR)和3种主成分分析(PCA)的统计降尺度方法,对1959-2008年的华东地区的月平均气温分两个时段进行统计降尺度分析并加以检验,比较了不同降尺度方法的结果。结果表明:对于华东地区气温的统计降尺度预报,基于MLR的统计降尺度方法相对于3种PCA方法而言,对单站年际变化模拟方面有一定优势。PCA方法应用于统计降尺度时,预报因子的区域选择是影响统计降尺度结果的重要因素之一。对于温度进行统计降尺度分析时,预报因子中包含温度因子是非常必要的;所试验的4种降尺度方法,对各站点多年平均情况的模拟要好于对区域平均的年际变化的模拟。     

西北地区月季气温变化研究

利用西北五省 (区 ) 89个测站 196 1～ 1990年月平均气温资料 ,通过EOF分解等方法 ,分析了月 (季 )平均气温的年际变化特征及其基本空间分布型 ,最后将该地区气温按年际变化分为 :陕西区、青藏高原东部边缘区、青海高原区、河西走廊区、南疆区和北疆区等 6个区 ,这与实况较吻合。     

On the mean monthly equivalent potential temperature and rainfall in West Africa

Summary Rainfall in West Africa is examined in relation to monthly mean equivalent potential temperature ( _e )at the earth's surface. The study revealed that monthly mean equivalent potential temperature ( _e ) and monthly rainfall (R) generally decreased northwards from the equator.A good relationship existed betweenR and _e in the northern zone of West Africa (i.e., north of 7.5° N). No definite relationship existed in the southern zone. In the northern zone, the departure of _e from its annual mean ( ) first became positive about a month before the onset of the rains. Positive departures from ) generally resulted in more than normal (or average) rainfall in this zone. In general, little or no rainfall occurred in West Africa whenever _e was less than 320 K.

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Zusammenfassung Der Niederschlag (MonatssummeR) in Westafrika wird in Zusammenhang mit der mittleren monatlichen Äquivalent-temperatur ( _e ) an der Erdoberfläche untersucht. Es zeigte sich, daß die Monatswerte beider Elemente im allgemeinen vom Äquator nach Norden abnehmen.ZwischenR und _e ergab sich für das nördliche Westafrika (nördlich von 7.5° N) eine gute, für die südliche Zone jedoch keine beweisbare Übereinstimmung. In der nördlichen Zone übertraf _e das Jahresmittel erstmals etwa einen Monat vor Beginn der Regenzeit. Positive Abweichungen vom mittleren _e hatten immer übernormalen Niederschlag in dieser Zone zur Folge. Dagegen gab es wenig oder keinen Niederschlag in Westafrika, wenn _e unter 320 K lag.

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With 7 Figures     

Climatic noise and potential predictability of monthly mean temperature over China

Summary In this paper, based on the data at 162 stations selected over China from 1960 to 1991 the climatic noise and potential predictability of monthly mean temperature have been studied. The method of estimating climatic noise is based on the idea of Yamamoto et al. (1985) and the potential predictability is expressed by the ratio of the estimated inter-annual variation to the estimated natural variation (or climatic noise). Generally the climatic noise of monthly mean temperature increases with latitude and altitude and varies with season. The continental air from Siberia and Mongolia plays a significant role and the ocean acts as an adjustor and a reductor in the climatic noise except for the tropical Pacific ocean in transitional season. The potential predictability is diversified from month to month and one station to another, but generally the monthly mean temperature over China is potentially predictable at statistical significance level 0.10. The results suggest that we could not ask a climate model to predict the climate with satisfactory results worldwide in all seasons and that the regional model could be a hopeful way to predict the climate.With 3 Figures     

概率密度分布法在“一带一路”地区极端月气温评估和预估中的应用

“一带一路”地区人口众多,气候类型复杂,亟待加强区域气候变化风险的认识。文中将该区分成10个区域,基于第五次耦合模式比较计划（CMIP5）中的31个全球模式模拟结果,应用概率密度分布（PDF）方法评估历史阶段（1986—2005年）各模式模拟暖月和冷月气温的能力,挑选并建立较优模式集合,用以预估21世纪中叶（2041—2060年）和21世纪末（2081—2100年）的极端月气温。结果表明,模式对观测中冷月气温距平PDF的模拟水平整体较暖月高。与多模式平均以及中位值相比,较优模式集合方法更适于极端暖/冷月气温的评估。在中等排放RCP4.5情景下,与低纬度地区相比,较优模式模拟中高纬地区未来极端暖/冷月气温的增温幅度的不确定性范围较大。21世纪中叶和21世纪末较优模式模拟的极端暖月气温在地中海增幅整体最大,东南亚增幅整体最小。对较优模式集合预估的极端冷月气温而言,无论是21世纪中叶还是世纪末,北欧增幅整体最大,东南亚增幅整体最小。     

南方涛动与我国大尺度季、月气温的关系

南方涛动与我国月平均气温的相关,在当年2月、4月、9月以正相关为主,9月以后出现持续的负相关。南方涛动与我国季平均气温相关最显著的季节是当年秋季,其次是次年春季。南方涛动与次年长江下游、广东、福建、山东的年平均气温有良好的负相关。这些关系均可在预报中利用。此外,还指出,在我国4、5月,9、10月的大范围气温记录中存在早期识别厄尔尼诺的信号。     

四川省气温空间分布回归模型及月变化特征分析

在利用G is进行立体农业气候区划时,常用气候要素与地理参数的多元回归方程推估地表栅格尺度气候要素的空间分布。在多元回归方程中,气候要素随地理参数经度、纬度和高度变化的系数,即要素在三维地球表面不同方向上的变化梯度,对气候要素的空间分布起着决定性作用。本文利用四川省157个气象站的各月累年平均气温,建立了它们与地理参数的多元回归模型,分析了月平均气温空间变化梯度随季节变化的特点,这对利用G is建立气温空间分布的栅格图层,有着十分重要的作用。     

四川省气温空间分布回归模型及月变化特征分析

在利用Gis进行立体农业气候区划时,常用气候要素与地理参数的多元回归方程推估地表栅格尺度气候要素的空间分布.在多元回归方程中,气候要素随地理参数经度、纬度和高度变化的系数,即要素在三维地球表面不同方向上的变化梯度,对气候要素的空间分布起着决定性作用.本文利用四川省157个气象站的各月累年平均气温,建立了它们与地理参数的多元回归模型,分析了月平均气温空间变化梯度随季节变化的特点,这对利用Gis建立气温空间分布的栅格图层,有着十分重要的作用.     

北太平洋700毫巴月平均环流形势与海温的相互关系

北太平洋700 mb环流形势具有相当鲜明的季节性与地域性特征.北太平洋的海、气相互作用是这些特征以及东亚季风形成的一个原因.从海、气相互作用的角度出发,对比分析北太平洋700 mb环流形势和海表温度距平的分布,可以看出:有些情况下,大气对海洋的影响比较明显;在另一些情况下,海洋对700 mb环流形势的影响较为明显.它们之间的矛盾与矛盾的主要方面是不断地、经常地在转化以及相互影响着.它们的转化、演变对我国副热带高压及旱涝天气的变化有一定的关系。     

Spatiotemporal modeling of monthly soil temperature using artificial neural networks

Soil temperature data are critical for understanding land–atmosphere interactions. However, in many cases, they are limited at both spatial and temporal scales. In the current study, an attempt was made to predict monthly mean soil temperature at a depth of 10 cm using artificial neural networks (ANNs) over a large region with complex terrain. Gridded independent variables, including latitude, longitude, elevation, topographic wetness index, and normalized difference vegetation index, were derived from a digital elevation model and remote sensing images with a resolution of 1 km. The good performance and robustness of the proposed ANNs were demonstrated by comparisons with multiple linear regressions. On average, the developed ANNs presented a relative improvement of about 44 % in root mean square error, 70 % in mean absolute percentage error, and 18 % in coefficient of determination over classical linear models. The proposed ANN models were then applied to predict soil temperatures at unsampled locations across the study area. Spatiotemporal variability of soil temperature was investigated based on the obtained database. Future work will be needed to test the applicability of ANNs for estimating soil temperature at finer scales.