Climate pattern

Abstract: 90 © Kluwer Academic Publishers Challenges to Food Production and Nutrition Current and future energy use from burning of fossil fuels and clearing of forests for cultivation can have profound effects on the global environment, agriculture, and the availability of low-cost, highquality food for humans. Individual farmers and consumers are expected to be affected by changes in global and regional climate. The agricultural sector in both developing and developed areas needs to understand what is at stake and to prepare for the potential for change wisely. Despite tremendous improvements in technology and crop yield potential, food production remains highly dependent on climate, because solar radiation, temperature, and precipitation are the main drivers of crop growth. Plant diseases and pest infestations, as well as the supply of and demand for irrigation water are infl uenced by climate. For example, in recent decades, the persistent drought in the Sahelian region of Africa has caused continuing deterioration of food production[1,2]; the 1988 Midwest drought led to a 30% reduction in U.S. corn production and cost taxpayers $3 billion in direct relief payments to farmers[3] and, weather anomalies associated with the 1997-98 El Niño affected agriculture adversely in Nordeste, Brazil and Indonesia[4]. Earlier in the century, the 1930s U.S. Southern Great Plains drought caused some 200,000 farm bankruptcies in the Dust Bowl; yields of wheat and corn were reduced by as much as 50%[5]. The aim of this article is to discuss the effects of climate variability and change on food production, risk of malnutrition, and incidence of weeds, insects, and diseases. It focuses on the effects of extreme weather events on agriculture, looking at examples from the recent past and to future projections. Major incidents of climate variability are contrasted, including the effects of the El NiñoSouthern Oscillation. Finally, projected scenarios of future climate change impacts on crop production and risk of hunger in major agricultural regions are presented. Altered weather patterns can increase crop vulnerability to infection, pest infestations, and choking weeds. Ranges of crop weeds, insects, and diseases are projected to expand to higher latitudes[6,7]. Shifts in climate in different world regions may have different and contrasting effects. Some parts of the world may benefi t from global climate change (at least in the short term), but large regions of the developing world may experience reduced food supplies and potential increase in malnutrition[2,3]. Changes in food supply could lead to permanent or semi-permanent displacement of populations in developing countries, consequent overcrowding and associated diseases, such as tuberculosis[8]. Climate change and extreme weather events

Abstract: Whereas the El Nino Southern Oscillation (ENSO) affects weather and climate variability worldwide, the North Atlantic Oscillation (NAO) represents the dominant climate pattern in the North Atlantic region. Both climate systems have been demonstrated to considerably influence ecological processes. Several other large-scale climate patterns also exist. Although less well known outside the field of climatology, these patterns are also likely to be of ecological interest. We provide an overview of these climate patterns within the context of the ecological effects of climate variability. The application of climate indices by definition reduces complex space and time variability into simple measures, 'packages of weather'. The disadvantages of using global climate indices are all related to the fact that another level of problems are added to the ecology-climate interface, namely the link between global climate indices and local climate. We identify issues related to: (i) spatial variation; (ii) seasonality; (iii) non-stationarity; (iv) nonlinearity; and (v) lack of correlation in the relationship between global and local climate. The main advantages of using global climate indices are: (i) biological effects may be related more strongly to global indices than to any single local climate variable; (ii) it helps to avoid problems of model selection; (iii) it opens the possibility for ecologists to make predictions; and (iv) they are typically readily available on Internet.

Abstract: The rapidly increasing atmospheric concentrations of greenhouse gases may lead to significant changes in regional and seasonal climate patterns. Such changes can strongly influence the diversity and distribution of species and, therefore, affect ecosystems and biodiversity. To assess these changes we developed a model, called euromove. The model uses climate data from 1990 to 2050 as compiled from the image 2 model, and determines climate envelopes for about 1400 plant species by multiple logistic regression analysis. The climate envelopes were applied to the projected climate to obtain predictions about plant diversity and distributions by 2050. For each European grid cell, euromove calculates which species would still occur in forecasted future climate conditions and which not. The results show major changes in biodiversity by 2050. On average, 32␘f the European plant species that were present in a cell in 1990 would disappear from that cell. The area, in which 32␘r more of the 1990 species will disappear, takes up 44␘f the modelled European area. Individual responses of the plant species to the forecasted climate change were diverse. In reviewing possible future trends, we found that plant species, in general, would find their current climate envelopes further northeast by 2050, shifting ranges that were comparable with those ranges in other studies.

Abstract: Spatial climate data are often key drivers of computer models and statistical analyses, which form the basis for scientific conclusions, management decisions, and other important outcomes. The recent availability of very high-resolution climate data sets raises important questions about the tendency to equate resolution with realism. This paper discusses the relationship between scale and spatial climate-forcing factors, and provides background and advice on assessing the suitability of data sets. Spatial climate patterns are most affected by terrain and water bodies, primarily through the direct effects of elevation, terrain-induced climate transitions, cold air drainage and inversions, and coastal effects. The importance of these factors is generally lowest at scales of 100 km and greater, and becomes greatest at less than 10 km. Except in densely populated regions of developed countries, typical station spacing is on the order of 100 km. Regions without major terrain features and which are at least 100 km from climatically important coastlines can be handled adequately by most interpolation techniques. Situations characterized by significant terrain features, but with no climatically important coastlines, no rain shadows, and a well-mixed atmosphere can be reasonably handled by methods that explicitly account for elevation effects. Regions having significant terrain features, and also significant coastal effects, rain shadows, or cold air drainage and inversions are best handled by sophisticated systems that are configured and evaluated by experienced climatologists. There is no one satisfactory method for quantitatively estimating errors in spatial climate data sets, because the field that is being estimated is unknown between data points. Perhaps the best overall way to assess errors is to use a combination of approaches, involve data that are as independent from those used in the analysis as possible, and use common sense in the interpretation of results. Data set developers are encouraged to conduct expert reviews of their draft data sets, which is probably the single most effective way to improve data set quality. Copyright  2006 Royal Meteorological Society.