Evaluation and Correction for the Deviation of the Surface Air Temperature based on 24 CMIP5 Models over China for 2006-2015

ZHANG Bei; DAI Xingang

doi:10.7522/j.issn.1000-0534.2016.00136

2017 , Vol. 36 >Issue 6: 1619 - 1629

DOI: https://doi.org/10.7522/j.issn.1000-0534.2016.00136

Evaluation and Correction for the Deviation of the Surface Air Temperature based on 24 CMIP5 Models over China for 2006-2015

ZHANG Bei ,
DAI Xingang

Expand

College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, Gansu, China;Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;Tianshui Meteorology Bureau, Tianshui 741000, Gansu, China

Received date: 2016-06-08

Online published: 2017-12-28

Fold

Abstract

The deviation of surface air temperature projected by 24 models of the Coupled Model Inter-comparison Project phase five (CMIP5) over China from 2006 to 2015 under the Representative Concentration Pathways (RCPs) scenarios was revealed and corrected. Compared with the data from NCEP/NCAR, the differences of projected temperature among the 24 models are more obvious in the east than the west over China. The annual and seasonal mean model-ensemble temperature are both underestimated in most parts of China, while the deviation is larger in the western region and it is largest in Tibet Plateau. In summer, the uncertainty of projected temperature is lower than other seasons. However, the phenomenon of underestimating temperature is enhanced obviously in winter. In addition, the higher emission intensity would enhance the underestimation of temperature in the most parts of China and the impact is more evident to the western regions, but the deviation would be lower in Tibet Plateau and Yunnan-Kweichow Plateau. Furthermore, the inter-annual differences of the temperature deviations are similar, except in the northeastern regions, and the patterns of the deviations are similar between El Niño years and La Niña years. After the terrain correction, the deviation of model temperature could reduce about 20%, and the deviation of the complex terrain region is effectively correction. The EOF indicates that historical simulation temperature by CMIP5 models has climate drift and the deviation is associated with topographic deviation. By deducting the climate drift, the effect of topographic and interpolation can be eliminated. The deviation of annual temperature would decrease about 72%. The models temperature is still underestimated and the deviations are about ±1℃ in most parts of China. However, this method makes the deviation turn to warmer in western region in summer and it is still colder in eastern region in winter. This suggests that the areas where the temperature deviations of models are lower would be vulnerable to the impact of deviation rate. Therefore, except the climate drift of models, the temporal evolution characteristics of deviations must be considered in the more detailed error correction methods.

Key words： CMIP5; RCPs scenarios; surface air temperat; deviation correction

Cite this article

ZHANG Bei , DAI Xingang . Evaluation and Correction for the Deviation of the Surface Air Temperature based on 24 CMIP5 Models over China for 2006-2015[J]. Plateau Meteorology, 2017 , 36(6) : 1619 -1629 . DOI: 10.7522/j.issn.1000-0534.2016.00136

References

[1]Fan Y, Dool H V D, 2008. A global monthly land surface air temperature analysis for 1948-present[J] Geophys Res, 113, D01103. DOI:10.1029/2007JD008470.
[2]Gao X J, Wang M L, Giorgi F, 2013. Climate change over China in the 21st century as simulated by BCC_CSM1. 1-RegCM4. 0[J]. Atmos Oceanic Sci Lett, 6(5):381-386. DOI:10.3878/j.issn.1674-2834.13.0029.
[3]IPCC, 2007. Climat change 2007:The Physical Science Basis[R]. Solomon S, Qin D, Manning M, et al, Eds. Cambridge, United Kingdom and New York, NY, USA:Cambridge University Press, 996.
[4]IPCC, 2013a. Climate Change 2013a:The Physical Science Basis. Final Draft Underlying Scientific-Technical Assessment[R]. Stocker T F, Qin D, Plattner G K, et al, eds. Cambridge, UK and New York, NY, USA:Cambridge University Press, 5.
[5]IPCC, 2013b. Climate Change 2013b:The Physical Science Basis. Summary for Policymakers[R]. Stocker T F, Qin D, Plattner G K, et al., eds. Cambridge, UK and New York, NY, USA:Cambridge University Press, 19.
[6]IPCC, 2014. Climate Change 2014:Impacts, adaptation and vulnerability. Contribution of Working Group Ⅱ to the fifth assessment report of the Intergovernmental Panel on Climate Change[R]. Cambridge & New York:Cambridge University Press.
[7]Lin Z D, Lu R Y, 2009. The ENSO's effect on eastern China rainfall in the following early summer[J]. Adv Atmos Sci, 26(2):333-342.
[8]Liu Y H, Feng J M, Ma Z G, 2014. An analysis of historical and future temperature fluctuations over China based on CMIP5 simulations[J]. Adv Atmos Sci, 31(2):457-467. DOI:10.1007/s00376-013-3093-0.
[9]Masson D, Knutti R, 2011. Spatial-scale dependence of climate model performance in the CMIP3 ensemble[J]. J Climate, 24(11):2680-2692. DOI:10.1175/2011JCLI3513.1.
[10]Meehl G A, Tebaldi C, 2004. More intense, more frequent, and longer lasting heat waves in the 21st century[J]. Science, 305:994-997. DOI:10.1126/science.1098704.
[11]Meinshausen M, Smith S J, Calvin K, et al, 2011. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300[J]. Climatic Change, 109(1-2):213-241. DOI:10.1007/s10584-011-0156-z.
[12]Riahi K, Rao S, Krey V, et al, 2011. RCP8. 5:A scenario of comparatively high greenhouse gas emissions[J]. Climatic Change, 109(1/2):33-57. DOI:10.1007/s10584-011-0149-y.
[13]Semenov M A, Stratonovitch P, 2010. Use of multi-model ensembles from global climate models for assessment of climate change impacts[J]. Climate Res, 41(1):1-14.
[14]Sillmann J, Kharin V V, Zhang X, et al, 2013. Climate extremes indices in the CMIP5 multi-model ensemble:part 1. Model evaluation in the present climate[J]. J Geophys Res:Atmospheres, 118:1-18.
[15]Su F G, Duan X L, Chen D L, et al, 2013. Evaluation of the global climate models in the CMIP5 over the Tibetan Plateau[J]. J Climate, 26:3187-3208. DOI:10.1175/JCLI-D-12-00321.1.
[16]Taylor K E, Stouffer R J, Meehl G A, 2012. An overview of CMIP5 and the experiment design[J]. Bull Amer Meteor Soc, 93(4):485-498. DOI:10.1175/BAMS-D-11-00094.1.
[17]Thomson A M, Calvin K V, Smith S J, et al, 2011. RCP4. 5:A pathway for stabilization of radiative forcing by 2100[J]. Climatic Change, 109(1/2):77-94. DOI:10.1007/s10584-011-0151-4.
[18]Tian D, Guo Y, Dong W J, 2015. Future changes and uncertainties in temperature and precipitation over China based on CMIP5 models[J]. Adv Atmos Sci, 32(4), 487-496. DOI:10.1007/s00376-014-4102-7.
[19]van Vuuren D P, Stehfest E, den Elzen M G J, et al, 2011. RCP2. 6:Exploring the possibility to keep global mean temperature increase below 2℃[J]. Climatic Change, 109(1/2):95-116. DOI:10.1007/s10584-011-0152-3.
[20]Verdin K L, 2011. ISLSCP Ⅱ HYDRO1k Elevation-derived Products; ORNL Distributed Active Archive Center. <a href="http://dx.doi.org/10.3334/ORNLDAAC/1007" target="_blank">http://dx.doi.org/10.3334/ORNLDAAC/1007</a>.
[21]Xu C H, Xu Y, 2012. The projection of temperature and precipitation over China under RCP scenarios using a CMIP5 multi-model ensemble[J]. Atmos Oceanic Sci Lett, 5(6):527-533.
[22]Xu Y, Gao X J, Giorgi F, 2010. Upgrades to the reliability ensemble averaging method for producing probabilistic climate-change projections[J]. Climate Res, 41:61-81.
[23]Xu Y, Xu C H, Gao X J, et al, 2009. Projected changes in temperature and precipitation extremes over the Yangtze River basin of China in the 21st century[J]. Quat Int, 208:44-52.
[24]Xu Y, Xu C H, 2012. Preliminary assessment of simulations of climate changes over china by CMIP5 multi-models[J]. Atmos Oceanic Sci Lett, 5 (6):489-494.
[25]Yao Y, Luo Y, Huang J, et al, 2013. Comparison of monthly temperature extremes simulated by CMIP3 and CMIP5 models[J]. J Climate, 26:7692-7707. DOI:10.1175/JCLI-D-12-00560.1.
[26]Zhao T B, Guo W, Fu C, 2008. Calibrating and Evaluating Reanalysis Surface Temperature Error by Topographic Correction[J]. J Climate, 21(6):1440-1446. DOI:10.1175/2007JCLI1463.1.
[27]Zhou T J, Yu R C, 2006. Twentieth century surface air temperature over china and the globe simulated by coupled climate models[J]. J Climate, 19(22):5843-5858. DOI:10.1175/JCLI3952.1.
[28]Zou L, Zhou T, 2013. Near future (2016-40) summer precipitation changes over China as projected by a regional climate model (RCM) under the RCP8. 5 emissions scenario:comparison between RCM downscaling and the driving GCM[J]. Adv Atmos Sci, 30(3):806-818. DOI:10.1007/s00376-013-2209-x.
[29]Chen J, Zhang Y C, 2016. Regional differences of land extreme precipitation and temperature changes under climate change condition[J]. Plateau Meteor, 35(4):955-968. DOI:10.7522/j.issn.1000-0534.2015.00075. 陈姣, 张耀存, 2016.气候变化背景下陆地极端降水和温度变化区域差异[J].高原气象, 35(4):955-968.
[30]Chen A F, Feng Q, Zhang J K, et al, 2015. A review of climate change scenario for impacts process study[J]. Sci Geograp Sinica, 35(1):84-90. DOI:10.13249/j.cnki.sgs.2015.01.84. 成爱芳, 冯起, 张健恺, 等, 2015.未来气候情景下气候变化响应过程研究综述[J].地理科学, 35(1):84-90.
[31]Dong W J, Zhou T J, Guo Y, et al, 2014. Climate phenomena and their relevance for future regional climate change[J]. Adv Climate Change Res, 10(1):60-64. DOI:10.3969/j.issn.1673-1719.2014.01.013. 董文杰, 周天军, 郭彦, 等, 2014.气候现象及其与未来区域气候变化的联系[J].气候变化研究进展, 10(1):60-64.
[32]Guo Y, Dong W J, Ren F M, et al, 2013. Assessment of CMIP5 simulations for China annual average surface temperature and its comparison with CMIP3 simulations[J]. Adv Climate Change Res, 9(3):181-186. DOI:10.3969/j.issn.1673-1719.2013.03.004. 郭彦, 董文杰, 任福民, 等, 2013. CMIP5模式对中国年平均气温模拟及其与CMIP3模式的比较[J].气候变化研究进展, 9(3):181-186.
[33]Hu Q, Jian D B, Fan G Z, 2014. Evaluation of CMIP5 models over the Qinghai-Tibetan Plateau[J]. Chinese J Atmos Sci, 38 (5):924-938. DOI:10.3878/j.issn.1006-9895.2013.13197. 胡芩, 姜大膀, 范广洲, 2014. CMIP5全球气候模式对青藏高原地区气候模拟能力评估[J].大气科学, 38(5):924-938.
[34]Jiang D B, Wang H J, Lang X M, 2004. Multimodel ensemble prediction for climate change trend of China under SRESA2 scenario[J]. Chinese J Geophys, 47(5):776-784. 姜大膀, 王会军, 郎咸梅, 2004. SRES A2情景下中国气候未来变化的多模式集合预测结果[J].地球物理学报, 47(5):776-784.
[35]Jiang Y M, Huang A N, Wu H M, 2015. Evaluation of the performance of Beijing climate center climate system model with different horizontal resolution in simulating the annual surface temperature over Central Asia[J]. Chinese J Atmos Sci, 39(3):535-547. DOI:10.3878/j.issn.1006-9895.1408.14133. 姜燕敏, 黄安宁, 吴昊旻, 2015.不同水平分辨率BCC_CSM模式对中亚地面气温模拟能力评估[J].大气科学, 39(3):535-547.
[36]Li Z C, Wei Z G, Lu S H, et al, 2013. Verifications of surface air temperature and precipitation from CMIP5 Model in Northern Hemisphere and Qinghai-Xizang Plateau[J]. Plateau Meteor, 32(4):921-928. DOI:10.7522/j.issn.1000-0534.2012.00088. 李振朝, 韦志刚, 吕世华, 等, 2013. CMIP5部分模式气温和降水模拟结果在北半球及青藏高原的检验[J].高原气象, 32(4):921-928.
[37]Qian Z H, Hou W, Yang P, et al, 2011. Characteristics of spatiotemporal distribution of extreme temperature events over China mainland in different climate states against the backdrop of most probable temperature[J]. Acta Phys Sinica, 60(10):819-830. DOI:10.7498/aps.60.109204. 钱忠华, 侯威, 杨萍, 等, 2011.最概然温度背景下不同气候态中国夏冬季极端温度事件时空分布特征[J].物理学报, 60(10):819-830.
[38]Qin D H, Stocker T, 2014. Highlights of the IPCC working group Ⅰ Fifth Assessment Report[J]. Adv Climate Change Res, 10(1):1-6. DOI:10.3969/j.issn.1673-1719.2014.01.001. 秦大河, Stocker T, 2014. IPCC第五次评估报告第一工作组报告的亮点结论[J].气候变化研究进展, 10(1):1-6.
[39]Tian Z P, Jiang D B, 2013. Evaluation of the performance of low-to high-resolution CCSM4 over East Asia and China[J]. Chinese J Atmos Sci, 37(1):171-186. DOI:10.3878/j.issn.1006-9895.2012.12050. 田芝平, 姜大膀, 2013.不同分辨率CCSM4对东亚和中国气候模拟能力分析[J].大气科学, 37(1):171-186.
[40]Wang M L, Gao X J, Shi Y, et al, 2015. Projection of the future drought over Yunnan and its surrounding areas by RegCM4[J]. Plateau Meteor, 34(3):706-713. DOI:10.7522/j.issn.100-0531.2014.00013. 王美丽, 高学杰, 石英, 等, 2015. RegCM4模式对云南及周边地区干旱化趋势的预估[J].高原气象, 34(3):706-713.
[41]Wang X L, Song W L, 2009. A study on relationships between ENSO and land falling tropical cyclones in China[J]. J Trop Meteor, 25(5):576-580. DOI:10.3969/j.issn.1004-4965.2009.05.008. 王小玲, 宋文玲, 2009. ENSO与登陆我国热带气旋的关系研究[J].热带气象学报, 25(5):576-580.
[42]Yao Y, Luo Y, Huang J B, 2012. Evaluation and projection of temperature extremes over China Based on 8 Modeling Data from CMIP5[J]. Adv Climate Change Res, 8(4):250-256. DOI:10.3969/j.issn.1673-1719.2012.04.003. 姚遥, 罗勇, 黄建斌, 2012. 8个CMIP5模式对中国极端气温的模拟和预估[J].气候变化研究进展, 8(4):250-256.
[43]Zhai Y J, Li Y H, Xu Y, 2016. Aridity change characteristics over Northern Region of China under RCPs Scenario[J]. Plateau Meteor, 35(1):94-106. DOI:10.7522/j.issn.1000-0534.2014.00078. 翟颖佳, 李耀辉, 徐影, 2016. RCPs情景下中国北方地区干旱气候变化特征[J].高原气象, 35(1):94-106.
[44]Zhao J H, Feng G L, Zhang S X, et al, 2011. Seasonal changes in China during recent 48 years and their relationship with temperature extremes[J]. Acta Phys Sinica (in Chinese), 60(9):842-850. 赵俊虎, 封国林, 张世轩, 等, 2011.近48年中国的季节变化与极端温度事件的联系[J].物理学报, 60(9):842-850.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References