设为首页
加入收藏
Simulation and Projection of Temperature in China with BCC_CSM1.1 Model
-
摘要: 利用我国541个测站1960—2010年气温资料以及国家气候中心参加第5次耦合模式比较计划 (CMIP5) 的气候系统模式BCC_CSM1.1的历史试验和年代际试验结果，评估了该模式对我国近50年气温变化特征的模拟能力, 对模式的年代际试验结果进行了误差订正，并给出未来10~20年我国气温变化的预估。结果表明：历史试验和年代际试验均模拟出了与观测较为一致的增暖趋势，但均没有观测资料的增暖幅度大。其中，历史试验比年代际试验更接近于观测。年代际尺度上，模式对我国东部的模拟要好于西部；年际尺度上，模式的高预报技巧区在我国西北地区西南部和东部、西南地区北部。历史试验和年代际试验对我国气温空间场整体分布模拟较好，误差订正后的年代际试验结果对空间气温场的模拟有更好把握。相对于观测资料得到的1960—2010年0.27℃/10 a的增温速率，模式预估我国2011—2030年平均气温变化速率达到0.48℃/10 a, 上升趋势更加明显。
-
关键词:
- CMIP5;
- BCC_CSM1.1;
- 气温;
- 误差订正;
- 预估
Abstract: Inter-annual and inter-decadal variability are two kinds of different timescale variability existing at the same time in climate system found in previous studies. Affected by the global warming, the inter-decadal signal of climate change becomes more and more significant. The next 10 to 30 years of climate change, namely inter-decadal time scales climate change and their impacts on the global environment, society and economic development, draw more and more attention. Climate change features of inter-decadal scale become one of the most important content of the IPCC AR5. The 10 to 30 years' timescale of inter-decadal forecast experiment which is listed as one of the main experiment content has joined the 5th Coupled Model Inter-comparison Project (CMIP5). More in-depth research will be carried out on predictability of inter-decadal timescale.The air temperature data of 541 stations in China from 1960 to 2010 as well as the CMIP5 historical and decadal experiment results of Beijing Climate Center Climate System Model (BCC_CSM1.1) are utilized to evaluate the simulation ability of the model. The model results are interpolated to the corresponding latitude and longitude of 541 stations use bilinear interpolation method. Whether the pattern of regional prediction ability could improve by the decadal experiment of BCC_CSM1.1 which initialed the SST (sea surface temperature) is discussed. Bias corrections to the decadal experiment results are done and the preliminary projection of the changes of the air temperature of China for the next 10—20 years is presented. Results show that both historical and decadal experiments can capture the warming trend in accordance with the observations, but the warming tendency of the experiments are less significant than those of observations. Results of historical experiments are slightly better than those of decadal experiments of the model. On the inter-decadal timescales, simulations in the eastern part of China are better than those in the western part of China. On the inter-annual timescales, the high prediction skills are located in the southwestern and eastern parts of northwest region, and southwest of China. Distributions of temperature in China are well simulated in both of historical and decadal experiments, such as the spatial correlation coefficients of 0.9 or above. After bias correction, results of decadal experiments are much better. By the corrected result of decadal experiments, the result of temperature spatial distribution simulation is better. The model projects that the rising rate of the mean temperature of China will be 0.48℃/10 a during 2011—2030, which is more significant than the warming rate of 0.27℃/10 a during 1960—2010 on the basis of observations. And the forecast results of the model show that the air temperature of China during 2001—2010 grows more slowly and fluctuate less compared with the period of 2011—2030.-
Key words:
- CMIP5;
- BCC_CSM1.1;
- air temperature;
- bias correction;
- projection
-
图 1 我国10年平均气温 (a) 及其距平 (b) 时间序列
Fig. 1 The 10-year mean of China temperature (a) and their anomalies (b)
图 2 年代际试验订正前 (a)、订正后 (b) 及历史试验 (c) 的10年平均气温与相应观测气温的相关系数
Fig. 2 Correlation coefficients between 10-year means of 9 experiments and corresponding observations (a) decadal experiment, (b) bias-revised decadal experiment, (c) historical experiment
图 3 年代际试验的10年平均气温及气温距平与相应的观测气温及气温距平的均方根误差
(a) 订正前的年代际试验气温结果, (b) 订正后的年代际试验气温结果, (c) 历史试验气温结果, (d) 订正前的年代际试验气温距平结果, (e) 订正后的年代际试验气温距平结果, (f) 历史试验气温距平结果
Fig. 3 Root mean square error 10-year mean of temperature and its anomalies from 9-group experiments and corresponding observations
(a) temperature from decadal experiment, (b) temperature from bias-revised decadal experiment, (c) temperature from historical experiment, (d) temperature anomalies from decadal experiment, (e) temperature anomalies from bias-revised decadal experiment, (f) temperature anomalies from historical experiment
图 4 5组订正前、订正后的年代际试验结果与对应年份观测资料相关系数的空间分布
(a)1961—1990年年代际试验结果，(b)1961—1990年年代际试验误差订正后的结果，(c)1966—1995年年代际试验结果，(d)1966—1995年年代际试验误差订正后的结果，(e)1971—2000年年代际试验结果，(f)1971—2000年年代际试验误差订正后的结果，(g)1976—2005年年代际试验结果，(h)1976—2005年年代际试验误差订正后的结果，(i)1981—2010年年代际试验结果，(j)1981—2010年年代际试验误差订正后的结果
Fig. 4 Correlation coefficients between 5-group decadal experiments and corresponding observations
(a)1961—1990 group decadal experiment, (b) bias-revised 1961—1990 group decadal experiment, (c)1966—1995 group decadal experiment, (d) bias-revised 1966—1995 group decadal experiment, (e)1971—2000 group decadal experiment, (f) bias-revised 1971—2000 group decadal experiment, (g)1976—2005 group decadal experiment, (h) bias-revised 1976—2005 group decadal experiment, (i)1981—2010 group decadal experiment, (j) bias-revised 1981—2010 group decadal experiment
图 5 订正前、订正后的5组年代际试验和历史试验模拟气温与观测气温的空间相关系数随时间变化
Fig. 5 The time series of correlation of 5-group decadal experiments and historical experiment to their corresponding
图 6 1960—2010年观测和误差订正前、订正后模式预估的2001—2030年我国年平均气温时间序列
Fig. 6 Annual mean temperature of 1960—2010 observations and 2001—2030 model forecast and model bias-revised forecast results
表 1 年代际试验订正前、订正后与相应观测正相关及显著正相关站点数比较
Table 1 Positive correlation and significantly correlation of stations number contrast between 5-group decadal experiments and their bias-revised results
试验组别 正相关站点百分比/% 0.05显著性水平正相关站点百分比/% 订正后 订正前 差值 订正后 订正前 差值 1961—1990年 84.7 60.4 24.3 36.4 1.7 34.7 1966—1995年 98.0 42.0 56.0 41.9 0.6 41.3 1971—2000年 98.3 35.5 62.8 57.1 2.8 54.3 1976—2005年 100.0 53.8 46.2 90.0 2.2 87.8 1981—2010年 99.1 78.7 20.4 90.7 19.4 71.3 
下载: 导出CSV
表 2 历史试验和误差订正前、订正后5组年代际试验与观测资料30年平均空间场的相关系数和均方根误差
Table 2 The correlation and root mean square error of historical experiment and 5 decadal experiments (un-revised and bias-revised) with their corresponding observations for 30-year means
试验组别 历史试验 订正前的年代际试验 订正后的年代际试验 相关系数 均方根误差/℃ 相关系数 均方根误差/℃ 相关系数 均方根误差/℃ 1961—1990年 0.912 4.24 0.912 4.25 0.999 0.31 1966—1995年 0.912 4.25 0.913 4.24 0.999 0.27 1971—2000年 0.912 4.25 0.913 4.29 0.999 0.18 1976—2005年 0.912 4.25 0.913 4.31 0.999 0.11 1981—2010年 0.912 4.26 0.913 4.32 0.999 0.05
下载: 导出CSV
-
[1] Lambert S J, Boer G J.Climate Dynamics:CMIP1 evaluation and intercomparison of coupled climate models.Springer-Verlag, 2001, 17:83-106. doi: 10.1007/PL00013736 [2] 丑纪范, 任宏利.数值天气预报——另类途径的必要性和可行性.应用气象学报, 2006, 17(2):240-244. doi: 10.11898/1001-7313.20060216 [3] Zhang Y, Wallace J M, Battisti D S.ENSO-like interdecadal variability:1900—1931.J Climate, 1997, 10:1004-1020. doi: 10.1175/1520-0442(1997)010<1004:ELIV>2.0.CO;2 [4] Mantua N J, Hare S R, Zhang Y.A Pacific interdecadal climate oscillation with impacts on salmon production.Bull Amer Meteor Soc, 1997, 78:1069-1079. doi: 10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2 [5] Meehl G A, Goddard L, Murphy J, et al.Decadal prediction:Can it be skillful? Bull Amer Meteor Soc, 2009, 90:1467-1485. doi: 10.1175/2009BAMS2778.1 [6] Hurrell J, Meehl G A, Bader D, et al.A unified modeling approach to climate system prediction.Bull Amer Meteor Soc, 2009, 90:1819-1832. doi: 10.1175/2009BAMS2752.1 [7] 魏凤英.我国短期气候预测的物理基础及其预测思路.应用气象学报, 2011, 22(1):1-11. doi: 10.11898/1001-7313.20110101 [8] Meehl G A, Stocker T F, Collins W D, et al.Climate change 2007:The Physical Science Basis//Solomon S, Qin D, Manning M, et al.Contribution of Working Group Ⅰ to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.Cambridge:Cambridge University Press, 2007:747-845. [9] Taylor K E, Stouffer R J, Meehl G A.An Overview of CMIP5 and the experiment design.Bull Amer Meteor Soc, 2012, 93, doi: 10.1175/BAMS-D-11-00094.1. [10] Latif M, Collins M, Pohlmann H, et al.A review of predictability studies of Atlantic sector climate on decadal scales.J Climate, 2006, 19:5971-5987. doi: 10.1175/JCLI3945.1 [11] Branstator G, Teng H Y, Gerald A M.Systematic estimates of initial-value decadal predictability for six AOGCMs.J Climate, 2012, 25:1827-1846. doi: 10.1175/JCLI-D-11-00227.1 [12] Keenlyside N S, Latif M, Jungclaus J, et al.Advancing decadal-scale climate prediction in the North Atlantic sector.Nature, 2008, 453:84-88. doi: 10.1038/nature06921 [13] Mochizuki T, Ishii M, Kimoto M, et al.Pacific Decadal Oscillation Hindcasts Relevant to Near-term Climate Prediction.Proc Natl Acad Sci USA, 2010, 107:1833-1837. doi: 10.1073/pnas.0906531107 [14] 吴波, 周天军.IAP/LASG气候系统模式FGOALS_gl预测的海表面温度年代际尺度的演变.科学通报, 2012, 57(13):1168-1175. http://www.cnki.com.cn/Article/CJFDTOTAL-KXTB201213015.htm [15] Mehta V, Meehl G, Goddard L, et al.Decadal climate predictabitily and prediction.Bull Amer Meteor Soc, 2011, 92(5):637-640. doi: 10.1175/2010BAMS3025.1 [16] Zhou Tianjun, Yu Rucong.Twentieth century surface air temperature over China and the globe simulated by coupled climate models.J Climate, 2006, 19(22):5843-5858. doi: 10.1175/JCLI3952.1 [17] 刘敏, 江志红.13个IPCC AR4模式对中国区域近40a气候模拟能力的评估.南京气象学院学报, 2009, 32(2):256-268. http://www.cnki.com.cn/Article/CJFDTOTAL-NJQX200902013.htm [18] 罗勇, 赵宗慈.NCAR RegCM2对东亚区域气候的模拟试验.应用气象学报, 1997, 8(增刊):124-133. http://www.cnki.com.cn/Article/CJFDTOTAL-YYQX7S1.016.htm [19] 施晓晖, 徐祥德.东亚冬季风年代际变化可能成因的模拟研究.应用气象学报, 2007, 18(6):776-782. doi: 10.11898/1001-7313.200706119 [20] 张勇, 曹丽娟, 许吟隆, 等.未来我国极端温度事件变化情景分析.应用气象学报, 2008, 19(6):655-660. doi: 10.11898/1001-7313.20080603 [21] 石英, 高学杰, 吴佳, 等.华北地区未来气候变化的高分辨率数值模拟.应用气象学报, 2010, 21(5):580-589. doi: 10.11898/1001-7313.20100507 [22] 姜大膀, 王会军, 郎咸梅.SRE SA2情景下中国气候未来变化的多模式集合预测结果.地球物理学报, 2004, 47(5):776-784. [23] 李博, 周天军.基于IPCC A1B情景的中国未来气候变化预估:多模式集合结果及其不确定性.气候变化研究进展, 2010, 6(4):270-276. http://www.cnki.com.cn/Article/CJFDTOTAL-QHBH201004012.htm [24] Li Hongmei, Feng Lei, Zhou Tianjun.Multi-model projection of July-August climate extreme changes over China under CO2 doubling.Part Ⅰ:Precipitation.Adv Atmos Sci, 2011, 28(2):433-447. doi: 10.1007/s00376-010-0013-4 [25] Li Hongmei, Feng Lei, Zhou Tianjun.Multi-model projection of July-August climate extreme changes over China under CO2 doubling.Part Ⅱ:Temperature.Adv Atmos Sci, 2011, 28(2):448-463. doi: 10.1007/s00376-010-0052-x [26] 辛晓歌, 吴统文, 张洁.BCC气候系统模式开展的CMIP5试验介绍.气候变化研究进展, 2012, 8(5):378-382. http://www.cnki.com.cn/Article/CJFDTOTAL-QHBH201205012.htm [27] ICPO.Data and Bias Correction for Decadal Climate Predictions.CLIVAR Publication Series No.150, 2012. [28] Xu Ying, Xu Chonghai.Preliminary assessment of simulations of climate changes over China by CMIP5 multi-models.Atmospheric and Oceanic Science Letters, 2012, 5(6):489-494. http://mall.cnki.net/magazine/article/AOSL201206010.htm [29] Kim H, Webster P J, Curry J A.Evaluation of short-term climate change prediction in multi-model CMIP5 decadal hindcasts.Geophys Res Lett, 2012, 39, L10701, doi: 10.1029/2012GL051644. [30] 余锦华, 唐盛, 吴立广, 等.IPCC AR4模式对热带气旋热力控制因子的模拟评估.海洋学报, 2011, 33(6):39-54. http://www.cnki.com.cn/Article/CJFDTOTAL-SEAC201106006.htm [31] van Oldenborgh G J, Doblas-Reyes F J, Bert W, et al.Decadal prediction skill in a multi-model ensemble.Clim Dyn, 2012(38):1263-1280. https://www.researchgate.net/publication/257411640_Decadal_prediction_skill_in_a_multi-model_ensemble [32] Zhao Zongci.Issues on Current Research of Climate Change.IU-GG 2003 Conference, 2003. -
计量
- 摘要浏览量: 4654
- HTML全文浏览量: 2502
- PDF下载量: 551
- 被引次数: 0
