Stochastic simulation of daily air temperature and precipitation from monthly normals in North America north of Mexico

A simple, stochastic daily temperature and precipitation generator (TEMPGEN) was developed to generate inputs for the study of the effects of climate change on models driven by daily weather information when climate data are available as monthly summaries. The model uses as input only 11 sets of monthly normal statistics from individual weather stations. It needs no calibration, and was parameterized and validated for use in Canada and the continental United States. Monthly normals needed are: mean and standard deviation of daily minimum and maximum temperature, first and second order autoregressive terms for daily deviations of minimum and maximum temperatures from their daily means, correlation of deviations of daily minimum and maximum temperatures, total precipitation, and the interannual variance of total precipitation. The statistical properties and distributions of daily temperature and precipitation data produced by this generator compared quite favorably with observations from 708 stations throughout North America (north of Mexico). The algorithm generates realistic seasonal patterns, variability and extremes of temperature, precipitation, frost-free periods and hot spells. However, it predicts less accurately the daily probability of precipitation, extreme precipitation events and the duration of extreme droughts.     

Climate and climatic variability at Lake Myvatn

The climate of the Lake Myvatn region is examined through the use of weather station data, using the years from 1971 to 2000 as a reference period. Variations in mean monthly temperature and precipitation at Reykjahlid (Myvatn) are compared with variations at seven other weather stations in north east Iceland. The area is drier and colder than coastal stations and exhibits a seasonal cycle in temperature that is larger than found at the coast. The temperature is significantly influenced by the number of sunlight hours only during the summer months. During summer, the influence of a sea breeze circulation can be clearly identified.The variability of climate since 1936 is also examined in comparison with the seven other weather stations. It is found that temperature variations at the different stations are highly correlated, but for precipitation the correlation is significant but much weaker.The influence of the large scale atmospheric circulation on temperature and precipitation at Lake Myvatn is also examined. It is found that the air temperature at Lake Myvatn is most sensitive to an east-west dipole in the large scale sea surface pressure field, i.e. to a pattern that is very different from the spatial pattern associated with the North Atlantic Oscillation (NAO). Precipitation at Lake Myvatn is to some degree influenced by the NAO, but generally precipitation is associated with northerly winds and cold temperatures whereas southerly winds at Lake Myvatn are likely to be drier.     

辽宁省城市热岛强度特征及等级划分

根据气象站资料累积时间、资料连续性、站点位置、城郊站间距离、站点数量、迁站情况,对辽宁省61个气象站进行筛选,并利用选出的城郊代表站的月和年气温数据,分析了辽宁省热岛强度的变化范围及幅度,确定了热岛强度等级.结果表明:辽宁省铁岭站、大连站、鞍山站、朝阳站、丹东站、锦州站6个气象站可作为城市气温的代表站,台安站等18个气象站可作为郊区气温的代表站;1980-2009年,辽宁省6个代表城市的热岛强度年变化不同,除铁岭为微弱下降外,其他5市均表现为上升趋势,铁岭年热岛强度较强,大连年热岛强度较弱;6个代表城市的月热岛强度变化也不同,丹东、锦州、铁岭的月热岛强度呈明显的“U”型分布,6市最大热岛强度均出现在1月,最小热岛强度均出现在5-8月,具有冬季强、夏季弱的特点.6市热岛强度的年、月变化范围分别为0.57～2.15℃和-0.70～4.60℃,年和月热岛强度分别有97.8％和72.3％集中在0.5～2.0℃.辽宁省热岛强度可划分为无、弱、强和极强4个等级.     

Palaeodiversity and formation counts: redundancy or bias?

A key question in palaeontology is whether the fossil record taken at face value is adequate to represent true patterns of diversity through time. Some methods of assessing data quality have depended on the commonly observed covariation of palaeodiversity and fossiliferous formation counts through time, based on the assumption that the count of formations containing fossils, to a greater or lesser extent, drives diversity; but what if diversity drives formations? Close study of two fossil records, early tetrapods (Devonian–Jurassic) and dinosaurs, shows how the relationship between new taxa and new fossiliferous formations varies through research time. Initially, each new find represents a new fossiliferous formation and discovery follows the ‘bonanza’ model (fossils drive formations). In unexplored parts of the world, new taxa are identified frequently in new regions/formations. Only after time, in well‐explored continents such as Europe and North America, does collecting style switch to a mix of exploration for new formations and re‐sampling of known fossiliferous formations. Data are most striking for dinosaurs, where the Triassic–Jurassic record largely comprises finds from Europe and North America, where new formation discoveries reached their half‐life in 1914. This contrasts with the Cretaceous, which is dominated by rapidly rising discoveries from regions outside Europe and North America and the formation half‐life for these ‘new’ lands is 1986, showing that 50% of new Cretaceous dinosaur‐bearing formations were identified only in the past 30 years. The relationship between dinosaur‐bearing formations and palaeodiversity then combines three signals in variable amounts, reflecting the original diversity (relative abundances of particular taxa in different formations), redundancy (new fossiliferous formations accruing because of new fossil finds) and sampling (intensity of exploration for new fossiliferous formations, and of search within already‐sampled formations). For fossil vertebrates at least, formation counts of various kinds are poor predictors of sampling, missing, for example, the bonanza samples of Lagerstätten such as the Yixian Formation in China: thousands of specimens, dozens of species, but counted as one formation. These observations suggest that formation count cannot be regarded as an unbiased metric of sampling.     

Prediction of Peromyscus maniculatus (deer mouse) population dynamics in Montana, USA, using satellite-driven vegetation productivity and weather data

Deer mice (Peromyscus maniculatus) are the main reservoir host for Sin Nombre virus, the primary etiologic agent of hantavirus pulmonary syndrome in North America. Sequential changes in weather and plant productivity (trophic cascades) have been noted as likely catalysts of deer mouse population irruptions, and monitoring and modeling of these phenomena may allow for development of early-warning systems for disease risk. Relationships among weather variables, satellite-derived vegetation productivity, and deer mouse populations were examined for a grassland site east of the Continental Divide and a sage-steppe site west of the Continental Divide in Montana, USA. We acquired monthly deer mouse population data for mid-1994 through 2007 from long-term study sites maintained for monitoring changes in hantavirus reservoir populations, and we compared these with monthly bioclimatology data from the same period and gross primary productivity data from the Moderate Resolution Imaging Spectroradiometer sensor for 2000-06. We used the Random Forests statistical learning technique to fit a series of predictive models based on temperature, precipitation, and vegetation productivity variables. Although we attempted several iterations of models, including incorporating lag effects and classifying rodent density by seasonal thresholds, our results showed no ability to predict rodent populations using vegetation productivity or weather data. We concluded that trophic cascade connections to rodent population levels may be weaker than originally supposed, may be specific to only certain climatic regions, or may not be detectable using remotely sensed vegetation productivity measures, although weather patterns and vegetation dynamics were positively correlated.     

Effects of temperature variation on suicide in five U.S. counties, 1991–2001

Effects of weather variables on suicide are well-documented, but there is still little consistency among the results of most studies. Nevertheless, most studies show a peak in suicides during the spring season, and this is often attributed to increased temperatures. The purpose of this study is to test the relationship between monthly temperature and monthly suicide, independent of months or seasons, for five counties located across the United States. Harmonic analysis shows that four of the five counties display some seasonal components in the suicide data. However, simple linear regression shows no correlation between suicide and temperature, and discriminant analysis shows that monthly departure from mean annual suicide rates is not a useful tool for identifying months with temperatures that are colder or warmer than the annual average. Therefore, it appears that the seasonality of suicides is due to factors other than temperature.     

What parts of the US mainland are climatically suitable for invasive alien pythons spreading from Everglades National Park?

The Burmese Python (Python molurus bivittatus) is now well established in southern Florida and spreading northward. The factors likely to limit this spread are unknown, but presumably include climate or are correlated with climate. We compiled monthly rainfall and temperature statistics from 149 stations located near the edge of the python’s native range in Asia (Pakistan east to China and south to Indonesia). The southern and eastern native range limits extend to saltwater, leaving unresolved the species’ climatic tolerances in those areas. The northern and western limits are associated with cold and aridity respectively. We plotted mean monthly rainfall against mean monthly temperature for the 149 native range weather stations to identify the climate conditions inhabited by pythons in their native range, and mapped areas of the coterminous United States with the same climate today and projected for the year 2100. We accounted for both dry-season aestivation and winter hibernation (under two scenarios of hibernation duration). The potential distribution was relatively insensitive to choice of scenario for hibernation duration. US areas climatically matched at present ranged up the coasts and across the south from Delaware to Oregon, and included most of California, Texas, Oklahoma, Arkansas, Louisiana, Mississippi, Alabama, Florida, Georgia, and South and North Carolina. By the year 2100, projected areas of potential suitable climate extend northward beyond the current limit to include parts of the states of Washington, Colorado, Illinois, Indiana, Ohio, West Virginia, Pennsylvania, New Jersey, and New York. Thus a substantial portion of the mainland US is potentially vulnerable to this ostensibly tropical invader.     

基于SPEI指数的华北冬麦区干旱时空分布特征分析

气候变化的背景下,华北地区干旱化趋势不断加剧。利用华北冬麦区45个气象站1961—2010逐月温度与降水数据,选取标准化降水蒸散指数SPEI(Standardized Precipitation Evapotranspiration Index)作为区域干旱指数进行华北冬麦区近50年干旱时空特征分析。研究表明:(1)近50年来华北地区平均温度明显上升,研究区整体呈现干旱化加剧趋势。华北地区平均SPEI指数对于典型干旱年份的表征准确,与历史资料相符合。(2)华北不同区域之间增温率不同,导致干旱化趋势存在差异。通过对典型站点的分析,发现增温率越大的区域干旱化趋势越严重。(3)不同等级干旱发生的站次比能够较好地反映不同年型干旱的发生特点。对SPEI指数矩阵的EOF分析结果显示出华北地区典型的干旱时空分布特征,第一模态呈现全区旱涝变化一致型的分布形式,高值区包括山东西部、河南北部、河北南部地区,表明这些地区对干旱的反应最为敏感。时间系数序列未显示出明显的变化趋势;第二模态呈现南北相反的分布型,河北及山东的大部分地区空间系数均为正值,而河南大部分地区为负值。时间系数序列整体呈下降趋势,表明研究区北部干旱化趋势加剧,南部干旱化有所缓解;第三模态呈现东西相反的分布形式,这种分布特征的变化趋势不明显。     

The influences of weather and topography on water bird migration in the southwestern United States

Summary Radar and visual observations were made on water bird migration throuh the Southwest at six stations from February 1973 through November 1974. The influence of weather on nocturnal non-passerine migration in the Southwest is less significant than reported for passerine migration elsewhere. Both seasonality and weather factors influence water bird migration, but the relative contribution of each is strongly dependent on the specific migration season. Spring migration, which has more extreme weather conditions, shows a stronger correlation to meteorological factors. Autumn migration in the Southwest rarely experiences severe weather, and consequently shows a stronger correlation to seasonality than to weather.The intensity of spring non-passerine migration was highly correlated with height of freezing level, temperature at sunset and midnight, humidity at sunset, cloud height at sunset and midnight, following winds aloft, and inversely related to change in temperature and day of the year. The most important variables listed by the stepwise linear multiple regression analysis were freezing level, following winds aloft, day of the year, and surface wind speed. When the height of the lowest cloud layer was allowed to enter the regression analysis, freezing level and cloud height were the most significant variables, with the intensity of migration higher with higher freezing level and higher cloud base. The most important variables predicting the occurrence or absence of migration were freezing level, barometric pressure and dew point.Autumn water bird migration was most highly correlated with cloud height and day of the year, and inversely correlated with humidity. The most important variables from the stepwise regression analysis and discriminant function analysis were day of the year, 670 m following-wind speed, and temperature. When cloud height was allowed to enter the model, it was the only significant factor influencing migration. As in spring, higher rates of autumn migration occurred with higher freezing levels.More migration occurred on the Great Plains than in the Rocky Mountains; but within the mountains, geography had little influence on the intensity of migration. Indirect evidence indicates that waterfowl and shorebirds move over the mountains (up to 2000 m above the surrounding terrain and 3500 m above sea level), rather than around them.     

基于CMDAS驱动SWAT模式的精博河流域水文相关分量模拟、验证及分析

利用大气和水文模型定量描述陆表相关变量变化规律一直是大气科学和水文学界的研究热点。然而,由于我国西部地区站点匮乏,传统气象观测站点已不能满足大尺度地表分量高精度模拟分析的需求。建立SWAT模型中国大气同化驱动数据集(China Meteorological Assimilation Driving Datasets for the SWAT model,CMADS)驱动SWAT(Soil and Water Assessment Tool)模型(简称为CMADS+SWAT模式),选取传统气象站点稀缺的新疆精博河流域为靶区,完成流域各地表分量(如土壤湿度、雪深、融雪)校准、验证及其时空关系提取与分析。分析发现:CMADS数据集可很好地驱动、率定SWAT模式完成本地化工作。其中,CMADS+SWAT模式在月尺度上总体NSE效率系数均在0.659—0.942,日尺度也均在0.526—0.815。对流域内土壤湿度和融雪过程进行相关分析发现:精博河流域土壤湿度在年内3—4月份达到其第一次峰值,主要贡献来自于流域内高山融雪现象;融雪期结束后,流域降水量增加,伴随气温上升等现象导致土壤温度呈现波动态势,至10月中旬冷空气过境产生较大降水(雪),最终使土壤水转变为冻土,直至次年接近融雪期,土壤水再次增加直到融雪过程结束。一方面证明CMADS+SWAT模式可有效提高SWAT水文模型在我国西北干旱区(站点稀缺区域)的表现能力,另一方面理清了精博河流域相关地表分量(土壤湿度、蒸发等)时空演变规律。本研究对我国大气水文学科发展将起到一定的科学促进作用。     

Imported Dengue Cases,Weather Variation and Autochthonous Dengue Incidence in Cairns,Australia

Background

Dengue fever (DF) outbreaks often arise from imported DF cases in Cairns, Australia. Few studies have incorporated imported DF cases in the estimation of the relationship between weather variability and incidence of autochthonous DF. The study aimed to examine the impact of weather variability on autochthonous DF infection after accounting for imported DF cases and then to explore the possibility of developing an empirical forecast system.

Methodology/principal finds

Data on weather variables, notified DF cases (including those acquired locally and overseas), and population size in Cairns were supplied by the Australian Bureau of Meteorology, Queensland Health, and Australian Bureau of Statistics. A time-series negative-binomial hurdle model was used to assess the effects of imported DF cases and weather variability on autochthonous DF incidence. Our results showed that monthly autochthonous DF incidences were significantly associated with monthly imported DF cases (Relative Risk (RR):1.52; 95% confidence interval (CI): 1.01–2.28), monthly minimum temperature (^oC) (RR: 2.28; 95% CI: 1.77–2.93), monthly relative humidity (%) (RR: 1.21; 95% CI: 1.06–1.37), monthly rainfall (mm) (RR: 0.50; 95% CI: 0.31–0.81) and monthly standard deviation of daily relative humidity (%) (RR: 1.27; 95% CI: 1.08–1.50). In the zero hurdle component, the occurrence of monthly autochthonous DF cases was significantly associated with monthly minimum temperature (Odds Ratio (OR): 1.64; 95% CI: 1.01–2.67).

Conclusions/significance

Our research suggested that incidences of monthly autochthonous DF were strongly positively associated with monthly imported DF cases, local minimum temperature and inter-month relative humidity variability in Cairns. Moreover, DF outbreak in Cairns was driven by imported DF cases only under favourable seasons and weather conditions in the study.     

The amounts and properties of transported air at two norwegian stations as functions of wind direction and weather type

Based on monthly means of climatic variables during the period 1937–65, the climate at two Norwegian meteorological stations in January and July was classified by principal component analysis. The new variables were interpreted as pairs of alternating weather types, or airmasses. Four airmasses were identified, covering 20–30% of the transported air at each station. The significance, properties and amounts of air transported by each airmass was calculated and distributed according to wind direction. Differences between air circulation pattern and airmass distribution and properties during the summer and winter season were pointed out. One of the major differences is the tendency to form high pressure over inland areas in winter vs. low pressure in the summer season.     

利用GIS和多变量分析估算青藏高原月降水

随着空间降水信息需求的日益增加,空间降水插值已被广泛应用。降水区域不同,插值方法不同;时间尺度不同,插值方法也不相同。适合于所有地区的通用降水插值模型是不存在的。青藏高原自然地理特征独特,分析高原降水的时空格局意义重要。以青藏高原及其周边地区140个气象站点的月降水信息及其该地区的数字高程数据(DEM)为基础,利用G IS工具,对比分析了五种插值方法在青藏高原不同降水年份(以1998年、1997年分别代表丰水及欠水年份)的干湿季(1998年的干湿季分别以12月份和8月份为代表,1997年的干湿季分别以1月份和7月份为代表)月降水插值中的应用,并对整个高原地区的干季和湿季的月降水进行制图。这5种插值方法分别是:克里金插值法、反距离加权法、样条法、混合插值法Ⅰ和混合插值法Ⅱ,前3种插值方法未考虑海拔高度对降水的影响,而混合插值法则将高程作为降水的重要影响因子。结果表明:①在干季,无论是丰水还是欠水年份,月降水量都比较少,高程对降水量的影响较小,在月降水插值时可不考虑高程的影响,克里金法的月降水插值精度最高。②在湿季,月降水量较多,高程的影响较大,混合插值法比局部插值法及克里金插值法的精度高,尤以混合插值法Ⅱ(多元回归和样条法的综合)的精度最高。③干季,整个高原的月降水很少,西部和北部降水最少,东部和南部相对较多;湿季,高原的月降水较多,空间格局表现为由东南向西北递减。     

Geographic patterns and dynamics of Alaskan climate interpolated from a sparse station record

Data from a sparse network of climate stations in Alaska were interpolated to provide 1‐km resolution maps of mean monthly temperature and precipitation–‐variables that are required at high spatial resolution for input into regional models of ecological processes and resource management. The interpolation model is based on thin‐plate smoothing splines, which uses the spatial data along with a digital elevation model to incorporate local topography. The model provides maps that are consistent with regional climatology and with patterns recognized by experienced weather forecasters. The broad patterns of Alaskan climate are well represented and include latitudinal and altitudinal trends in temperature and precipitation and gradients in continentality. Variations within these broad patterns reflect both the weakening and reduction in frequency of low‐pressure centres in their eastward movement across southern Alaska during the summer, and the shift of the storm tracks into central and northern Alaska in late summer. Not surprisingly, apparent artifacts of the interpolated climate occur primarily in regions with few or no stations. The interpolation model did not accurately represent low‐level winter temperature inversions that occur within large valleys and basins. Along with well‐recognized climate patterns, the model captures local topographic effects that would not be depicted using standard interpolation techniques. This suggests that similar procedures could be used to generate high‐ resolution maps for other high‐latitude regions with a sparse density of data.     

不同区域森林火灾对生态因子的响应及其概率模型

火灾是影响森林生态系统过程的重要干扰之一,其对森林生态系统内各生态因子的响应各不相同.由于植被状况及生态环境的不同,森林火灾的时空分布特征在中国不同植被气候类型内表现不同,根据植被气候类型分类系统,将中国主要森林火灾地区划分为4个区域:东北(冷温带松林)、华北(落叶阔叶林)、东南(常绿阔叶林)和西南(热带雨林),应用遥感监测数据和地面环境数据,以时空变量、生态因子(植被生长变化指数、湿度等)为可选自变量,应用半参数化Logistic回归模型,就森林火险对不同生态影响因子的响应规律进行了分析,建立了基于生态因子的着火概率模型和大火蔓延概率模型,通过模拟及实际数据散点图、火险概率图,评估了模型应用价值.结果表明,土壤湿度及植被含水量在落叶阔叶林、常绿阔叶林、热带雨林地区对着火概率影响显著.在4个植被气候区内,土壤及凋落物湿度对大火蔓延的作用较小.在冷温带松林、落叶阔叶林、常绿阔叶林地区,植被生长的年内变化对火灾发生的影响显著,在常绿阔叶林地区,年内植被生长变化对大火蔓延的作用较小.森林火险概率与各生态因子的相关关系主要呈现出非线性.不同植被气候区内,火险概率受不同生态因子组合的影响,这与不同区域的植被状况及生态环境不同有关.在不同植被气候类型,应用时空变量、生态因子建立半参数化logistic回归模型,进行着火概率和大火蔓延概率的模拟具有可行性和实际应用能力.为进一步分析森林生态系统与火灾之间的动态关系、展开生态系统火灾干扰研究提供了理论基础.     

Quantitative digital and palmar dermatoglyphics among Sardinian linguistic groups

Discriminant analyses of 23 digital and 15 palmar quantitative dermatoglyphic variables of 1364 Sardinians, 689 males and 675 females, were performed to identify biological relationships among five Sardinian linguistic groups of both sexes. By various subsets of dermatoglyphic variables (23 and 20 digital, 15 and 14 palmar, 4 summary traits) MANOVA revealed high intergroup heterogeneity among the groups of both sexes and within each sex. In the latter case the males are an exception when 15 and 14 (MLI removed) palmar variables are used. Standard discriminant analysis of the 23 digital variables, i.e. the radial and ulnar ridge counts on each of the 10 fingers plus total finger ridge count (TFRC), absolute finger ridge count (AFRC) and pattern intensity (PI), resulted in imperfect separation of males and females and an unclear picture of the biological relationships among the groups. In contrast, standard discriminant analysis of 20 digital variables (TFRC, AFRC and PI were removed from the analysis) resulted in separation of the sexes and a pattern of relationships among the populations consistent with their ethno-historical backgrounds. Standard discriminant analysis of 15 palmar dermatoglyphic variables failed to provide separation of the sexes and produced a pattern of relationships in disagreement with both the linguistic and ethno-historical backgrounds, even removing MLI (Main Line Index). Standard discriminant analysis of 4 summary dermatoglyphic variables (TFRC, AFRC, PI and MLI) yielded imperfect separation of males and females and an unclear pattern of relationships. By stepwise discriminant analysis with p < or = 0.01 as F-to-enter and p < or = 0.05 as F-to-remove, only 4 of the 38 digital and palmar variables were in the model (URC R5, RRC L5, RRC R5, URC R4). The pattern of inter-population biological relationships was conceptually similar to the one produced by the 20 digital variables. It showed a clear separation of the Gallurian group (both males and females), which speaks an Italian dialect. The properly Sardinian linguistic groups (Campidanian and Logudorian), the Sassarian group (which speaks an Italian dialect) and the Alghero group (which speaks Catalan) were close to one another. This picture agrees with the ethno-historical background rather than with the linguistic one.     

Urban climate and air quality in Trier Germany

To conceptualize strategies for regional environmental management in the Trier region, extensive urban meteorological measurements were undertaken. Weather stations from the German Weather Service and the state Pollution Monitoring Network were used as well as a number of our automatic meteorological stations and a mobile platform (instrumented van). The bioclimatic conditions in the city of Trier are affected by the valley of the Moselle River. Both the wind field and the thermal stratification in the urban boundary layer showed local characteristics especially marked in the diurnal variation and monthly mean concentrations of the air pollutants nitrogen and sulfurdioxide (NO(x), SO(2)), ozone (O(3)) and particle matter (PM10). Catabatic flows from the side valleys partially reduce the urban heat island and increase the ozone concentration in the city in the evening during calm weather conditions. The impact-based air-quality index is mostly determined by a high PM10 concentration. Strategies to reduce air pollutions in the Trier region are discussed.     

Predicting Malaria occurrence in Southwest and North central Nigeria using Meteorological parameters

Malaria is a major public health problem especially in the tropics with the potential to significantly increase in response to changing weather and climate. This study explored the impact of weather and climate and its variability on the occurrence and transmission of malaria in Akure, the tropical rain forest area of southwest and Kaduna, in the savanna area of Nigeria. We investigate this supposition by looking at the relationship between rainfall, relative humidity, minimum and maximum temperature, and malaria at the two stations. This study uses monthly data of 7 years (2001–2007) for both meteorological data and record of reported cases of malaria infection. Autoregressive integrated moving average (ARIMA) models were used to evaluate the relationship between weather factors and malaria incidence. Of all the models tested, the ARIMA (1, 0, 1) model fits the malaria incidence data best for Akure and Kaduna according to normalized Bayesian information criterion (BIC) and goodness-of-fit criteria. Humidity and rainfall have almost the same trend of association in all the stations while maximum temperature share the same negative association at southwestern stations and positive in the northern station. Rainfall and humidity have a positive association with malaria incidence at lag of 1 month. In all, we found that minimum temperature is not a limiting factor for malaria transmission in Akure but otherwise in the other stations.     

Linkages between common wheat yields and climate in Morocco (1982–2008)

In Morocco, wheat production shows a high inter-annual variability due to uncertain rainfall. In view of the importance of this resource to the country’s economy, it is important to gain a better understanding of the natural large-scale climate oscillation governing this variability. In this study, we analyzed de-trended (1) time series of common wheat yields (1983–2008) from 11 agricultural provinces that account for 80 % of national wheat production; (2) monthly rainfall and 10-day temperature from ten meteorological stations; (3) 10-day normalized difference vegetation index (NDVI) from the AVHRR sensor; (4) monthly atmospheric climate indices [North Atlantic Oscillation (NAO) and Scandinavian Pattern (SCA)] and monthly 500 hPa geopotentials fields; and (5) monthly sea surface temperature (SST) fields and indices (NIÑO3, Tropical North Atlantic and Tropical South Atlantic). The relationship between rainfall and temperature during tillering in early winter and grain filling in early spring and wheat yields already observed at the plot scale was also found to be significant at the provincial scale. The linkages between wheat yields and large scale climate have been analyzed for the first time over Morocco. In agreement with previous studies, results show a complex and competing influence of different climate phenomena. The NAO is found to be significantly related to yields during the early stage of wheat growth in December, whereas the SCA correlates with yields later in the season, in January and February. Interesting lagged correlations with higher lead time are also highlighted, with the leading modes of SST variability in the equatorial Atlantic during October (the “Atlantic Niño” mode) and in the North Atlantic (the “Atlantic tripole” mode) in February. Our conclusion is that regional climate indices and variables represent valuable information with which to increase lead time and skill regarding wheat yield predictions in Morocco.     

The association between temperature and mortality in tropical middle income Thailand from 1999 to 2008

We have investigated the association between tropical weather condition and age-sex adjusted death rates (ADR) in Thailand over a 10-year period from 1999 to 2008. Population, mortality, weather and air pollution data were obtained from four national databases. Alternating multivariable fractional polynomial (MFP) regression and stepwise multivariable linear regression analysis were used to sequentially build models of the associations between temperature variable and deaths, adjusted for the effects and interactions of age, sex, weather (6 variables), and air pollution (10 variables). The associations are explored and compared among three seasons (cold, hot and wet months) and four weather zones of Thailand (the North, Northeast, Central, and South regions). We found statistically significant associations between temperature and mortality in Thailand. The maximum temperature is the most important variable in predicting mortality. Overall, the association is nonlinear U-shape and 31 °C is the minimum-mortality temperature in Thailand. The death rates increase when maximum temperature increase with the highest rates in the North and Central during hot months. The final equation used in this study allowed estimation of the impact of a 4 °C increase in temperature as projected for Thailand by 2100; this analysis revealed that the heat-related deaths will increase more than the cold-related deaths avoided in the hot and wet months, and overall the net increase in expected mortality by region ranges from 5 to 13 % unless preventive measures were adopted. Overall, these results are useful for health impact assessment for the present situation and future public health implication of global climate change for tropical Thailand.