多模式气候预估对华北冬小麦产量模拟的不确定性分析
Uncertainty of ensemble winter wheat yield simulation in North China based on CMIP5
查看参考文献34篇
文摘
|
基于CMIP5的多模式气候预估资料,应用集合方法,评估了未来中国华北地区冬小麦产量受气候变化影响的不确定性,并给出未来中国华北冬小麦增产或减产可能的概率。利用CMIP5的15个全球气候模式2006-2030年4种排放情景的54组逐日气候预估结果,运用CERES-Wheat模型模拟了未来华北地区冬小麦的产量。结果表明,气温的预估结果较好,降水量和太阳辐射的气候预估值的不确定性较大。河北、山东和河南的3个代表点小麦产量的模拟集合表明,未来冬小麦产量年际波动较大,以弱增产的概率为主,但是随气候变化的冬小麦产量的低产概率明显上升。最后本文还给出了2011-2030年间华北地区冬小麦产量不同等级的概率分布。 |
其他语种文摘
|
The uncertainty of the influence of climate change on the North China's winter wheat yield is estimated by using the ensemble climate projections of CMIP5 and the probability of increase or reduction of the wheat yield in main production areas is analyzed. We combined 54 runs of projections from 15 global climate models of CMIP5 under different greenhouse gas emission scenarios in 2006-2030. Meanwhile, the CERES-Wheat was employed to stimulate the North China's winter wheat yield in the future. The results indicate that the projection of precipitation and solar radiation in future climate by the climate models has the largest uncertainty. Take the three representative points as an example: although in some years the yield will increases slightly, the fluctuation of winter wheat yield from year to year can be significant. An increased risk of lower yield is inevitable. And the probabilistic distributions of winter wheat yield in Middle and Eastern China during 2011-2030 over 2000, 4000, 6000, 8000, and 10000 kg/hm2 are elaborated. |
来源
|
地理科学进展
,2013,32(4):627-636 【核心库】
|
关键词
|
概率分布
;
不确定性
;
冬小麦
;
CMIP5模式评估
;
华北
|
地址
|
1.
南京信息工程大学大气科学学院, 南京, 210044
2.
上海市气象局, 上海, 200030
3.
上海市台风所, 上海, 200030
4.
上海市气候中心, 上海, 200030
|
语种
|
中文 |
文献类型
|
研究性论文 |
ISSN
|
1007-6301 |
学科
|
农作物 |
基金
|
国家自然科学基金
;
上海市科委项目
;
上海市气象局科研项目
;
江苏省高等学校研究生创新计划项目
|
文献收藏号
|
CSCD:4824297
|
参考文献 共
34
共2页
|
1.
Bannayan M. Application of the CERES-Wheat model for within-season prediction of winter wheat yield in the United Kingdom.
Agronomy Journal,2003,95(1):114-125
|
CSCD被引
7
次
|
|
|
|
2.
Challinor A J. Ensemble yield simulations: Crop and climate uncertainties, sensitivity to temperature and genotypic adaptation to climate change.
Climate Research,2009,38(2):117-127
|
CSCD被引
12
次
|
|
|
|
3.
Chavas D R. Long-term climate change impacts on agricultural productivity in eastern China.
Agricultural and Forest Meteorology,2009,149(6/7):1118-1128
|
CSCD被引
49
次
|
|
|
|
4.
Collins M. Ensembles and probabilities: A new era in the prediction of climate change.
Philosophical Transactions of the Royal Society A,2007,365:1957-1970
|
CSCD被引
9
次
|
|
|
|
5.
Hansen J W. Translating climate forecasts into agricultural terms: advances and challenges.
Climate Research,2006,33(1):27-41
|
CSCD被引
5
次
|
|
|
|
6.
黄维. 中国气候变化对县域粮食产量影响的计量经济分析.
地理科学进展,2010,29(6):677-683
|
CSCD被引
16
次
|
|
|
|
7.
IPCC.
Climate change 2007: Impacts, adaptation and vulnerability
|
CSCD被引
1
次
|
|
|
|
8.
Jones J W. The DSSAT cropping system model.
European Journal of Agronomy,2003,18(3):235-265
|
CSCD被引
230
次
|
|
|
|
9.
Kanamitsu M. NCEP-DEO AMIP-II Reanalysis (R-2).
Bulletin of the American Meteorological Society,2002,83(11):1631-1643
|
CSCD被引
429
次
|
|
|
|
10.
Lewis J M. Roots of ensemble forecasting.
Monthly Weather Review,2005,133(7):1865-1885
|
CSCD被引
3
次
|
|
|
|
11.
Li H B. Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching.
Journal of Geophysical Research,2010,115:D10101
|
CSCD被引
44
次
|
|
|
|
12.
Lobell D B. Prioritizing climate change adaptation needs for food security in 2030.
Science,2008,319:607-610
|
CSCD被引
69
次
|
|
|
|
13.
Masutomi Y. Impact assessment of climate change on rice production in Asia in comprehensive consideration of process parameter uncertainty in general circulation models.
Agriculture, Ecosystems & Environment,2009,131(3/4):281-291
|
CSCD被引
14
次
|
|
|
|
14.
Matthias S. Translation of ensemble weather forecasts into probabilistic air traffic capacity impact.
Air Traffic Control Quarterly,2010,18(3):229-254
|
CSCD被引
2
次
|
|
|
|
15.
Moss R H. The next generation of scenarios for climate change research and assessment.
Nature,2010,463:747-756
|
CSCD被引
221
次
|
|
|
|
16.
Parry M L. Effects of climate change on global food production under SRES emissions and socioeconomic scenarios.
Global Environmental Change,2004,14(1):53-67
|
CSCD被引
48
次
|
|
|
|
17.
Piao S. The impacts of climate change on water resources and agriculture in China.
Nature,2010,467:43-51
|
CSCD被引
463
次
|
|
|
|
18.
Sheffield J. Development of a50-yr high-resolution global dataset of meteorological forcings for land surface modeling.
Journal of Climate,2006,19(13):3088-3111
|
CSCD被引
97
次
|
|
|
|
19.
Semenov M A. Use of multi-model ensembles from global climate models for assessment of climate change impacts.
Climate Research,2010,41(1):1-14
|
CSCD被引
24
次
|
|
|
|
20.
Tao F. Modelling the impacts of weather and climate variability on crop productivity over a large area: A new super-ensemble-based probabilistic projection.
Agricultural and Forest Meteorology,2009,149(8):1266-1278
|
CSCD被引
51
次
|
|
|
|
|