首页> 外文期刊>International Journal of Climatology: A Journal of the Royal Meteorological Society >Seasonal trends in air temperature and precipitation in IPCC AR4 GCM output for Kansas, USA: Evaluation and implications
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Seasonal trends in air temperature and precipitation in IPCC AR4 GCM output for Kansas, USA: Evaluation and implications

机译:美国堪萨斯州IPCC AR4 GCM产出中气温和降水的季节性趋势:评估和意义

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Understanding the impacts of future climate change in Kansas is important for agricultural and other socioeconomic sectors in the region. To quantify these impacts, seasonal trends in air temperature and precipitation patterns from decadally averaged monthly output of 21 global climate models under the Special Report on Emissions Scenarios A1B scenario used in the Intergovernmental Panel of Climate Change Assessment Report 4 are examined for six grid cells representing Kansas. To ascertain the performance of the models, we compared model output to kriged meteorological data from stations in the Global Historical Climate Network for the period from 1950 to 2000. Agreement between multimodel ensemble mean output and observations is very good for temperature (r~2 all more than 0.99, root mean square errors range from 0.84 to 1.48°C) and good for precipitation (r~2 ranging between 0.64 and 0.89, root mean square errors range from 322 to 1144 mm). Seasonal trends for the second half of the 20th century are generally not observed except in modelled temperature trends. Linear trends for the 21st century are significant for all seasons in all grid cells for temperature and many for precipitation. Results indicate that temperatures are likely to warm in all seasons, with the largest trends being on the order of 0.04°C/year in summer and fall. Precipitation is likely to increase slightly in winter and decrease in summer and fall. These changes have profound implications for both natural ecosystems and agricultural land uses in the region.
机译:了解堪萨斯州未来气候变化的影响对于该地区的农业和其他社会经济部门至关重要。为了量化这些影响,根据政府间气候变化专门委员会评估报告4中使用的“排放情景​​特别报告” A1B情景,对21种全球气候模型的十年平均月产量的气温和降水模式的季节性趋势进行了研究,调查了六个网格单元,分别代表堪萨斯州。为了确定模型的性能,我们将模型输出与1950年至2000年期间全球历史气候网络中各站的kriged气象数据进行了比较。多模型集合平均输出与观测值之间的一致性非常适合温度(r〜2全部大于0.99时,均方根误差在0.84至1.48°C范围内),适合降水(r〜2在0.64至0.89之间,均方根误差在322至1144 mm之间)。除了模拟的温度趋势外,通常未观察到20世纪下半叶的季节性趋势。在21世纪的线性趋势中,所有季节的所有温度单元中的线性趋势都非常重要,而降水量的线性趋势则非常重要。结果表明温度可能在所有季节都变暖,夏季和秋季的最大趋势约为每年0.04°C。冬季降水可能会略有增加,夏季和秋季可能会减少。这些变化对该地区的自然生态系统和农业土地利用都具有深远的影响。

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