Assessing relevant climate data for agricultural applications
Assessing relevant climate data for agricultural applications
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评估农业应用的相关气候数据
DOI：
10.1016/j.agrformet.2012.03.015
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发表时间：
2012
影响因子：
6.2
通讯作者：
A. Challinor
中科院分区：
文献类型：
--
作者：
J. Ramirez;A. Challinor
正如政府间气候变化专门委员会（气专委）报告所述，气候变化预计将大幅度减少农业产量。在撒哈拉以南非洲和南亚（程度较轻），数据可用性和机构网络有限制约了农业研究和发展。在这里，我们进行了相关方面的审查，农业气候预测耦合，和三个步骤分析气候数据的重要性，农业影响评估。首先，使用科学文献中的元数据，我们研究了农业研究中使用气候和天气数据的趋势，我们发现，尽管农业研究人员倾向于现场规模的天气数据（在汇编的文献中占50.4%），但大规模的数据集加上天气发生器在农业环境中可能是有用的。使用众所周知的插值技术，我们然后评估了气象站网络对数据缺乏的敏感性，发现只有尼泊尔和埃塞俄比亚的山区对数据丢失的敏感性很高（随机删除数据影响降水估计± 1300毫米/年，温度估计±3°C）。最后，我们数值比较了IPCC第四次评估报告（4AR）气候模式的平均气候和年际变率与不同的观测数据集的代表性。在西非和南亚，气候模型不足以进行实地规模的农业研究，因为它们表示平均气候和气候变异性的能力有限：超过50%的国家模式组合显示年平均降雨量调整<50%（平均气候），GCM输出的降雨量存在较大偏差（1000- 2500毫米/年），尽管这在GCM基础上有所不同（气候变率）。某些地区的温度偏差也很大（喜马拉雅山和萨赫勒地区5-10°C）。所有这一切都有望在IPCC的第五次评估报告中得到改善;因此，即使是这些新的气候模型也仍然需要适当使用。这一改进的使用需要减少偏差（气候模型的加权或气候变化信号的偏差校正），实施与空间尺度相匹配的方法，并尽可能量化不确定性。
Climate change is expected to substantially reduce agricultural yields, as reported in the by the Intergovernmental Panel on Climate Change (IPCC). In Sub-Saharan Africa and (to a lesser extent) in South Asia, limited data availability and institutional networking constrain agricultural research and development. Here we performed a review of relevant aspects in relation to coupling agriculture–climate predictions, and a three-step analysis of the importance of climate data for agricultural impact assessment. First, using meta-data from the scientific literature we examined trends in the use of climate and weather data in agricultural research, and we found that despite agricultural researchers’ preference for field-scale weather data (50.4% of cases in the assembled literature), large-scale datasets coupled with weather generators can be useful in the agricultural context. Using well-known interpolation techniques, we then assessed the sensitivities of the weather station network to the lack of data and found high sensitivities to data loss only over mountainous areas in Nepal and Ethiopia (random removal of data impacted precipitation estimates by ±1300mm/year and temperature estimates by ±3°C). Finally, we numerically compared IPCC Fourth Assessment Report (4AR) climate models’ representation of mean climates and interannual variability with different observational datasets. Climate models were found inadequate for field-scale agricultural studies in West Africa and South Asia, as their ability to represent mean climates and climate variability was limited: more than 50% of the country-model combinations showed <50% adjustment for annual mean rainfall (mean climates), and there were large rainfall biases in GCM outputs (1000–2500mm/year), although this varied on a GCM basis (climate variability). Temperature biases were also large for certain areas (5–10°C in the Himalayas and Sahel). All this is expected to improve with IPCC's Fifth Assessment Report; hence, appropriate usage of even these new climate models is still required. This improved usage entails bias reduction (weighting of climate models or bias-correcting the climate change signals), the implementation of methods to match the spatial scales, and the quantification of uncertainties to the maximum extent possible.
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影响因子：
4.8
作者：
M. Funke;M. Paetz
通讯作者：
M. Funke;M. Paetz
影响因子：
6.2
作者：
Hawkins, Ed;Osborne, Thomas M.;Challinor, Andrew J.
通讯作者：
Challinor, Andrew J.
影响因子：
6.7
作者：
Challinor, Andrew J.;Simelton, Elisabeth S.;Collins, Mathew
通讯作者：
Collins, Mathew
影响因子：
6.9
作者：
Challinor, Andrew J.;Ewert, Frank;Fraser, Evan
通讯作者：
Fraser, Evan