中国农业气象 ›› 2021, Vol. 42 ›› Issue (06): 447-462.doi: 10.3969/j.issn.1000-6362.2021.06.001

• 农业气候资源与气候变化栏目 •    下一篇

基于格网的GCM数据修订分析未来海南岛农业水热资源的变化特征

李宁,白蕤,李玮,陈淼,杨桂生,陈歆,范长华,张文   

  1. 1.中国热带农业科学院环境与植物保护研究所/海南儋州热带农业生态系统国家野外科学观测研究站/国家农业环境儋州观测实验站/农业农村部儋州农业环境科学观测实验站/海南省热带生态循环农业重点实验室,海口 571101;2.海南省气候中心, 海口 570203
  • 收稿日期:2020-10-12 出版日期:2021-06-20 发布日期:2021-06-20
  • 通讯作者: 白蕤,工程师,研究方向为农业气象灾害,E-mail: 1138522640@qq.com E-mail:1138522640@qq.com
  • 作者简介:李宁,E-mail: n.li@catas.cn
  • 基金资助:
    海南省自然科学基金(420QN371);中国热带农业科学院基本科研业务费专项(1630042021010);海南省气象局技术提升项目(HNQXJS202007);海南省南海气象防灾减灾重点实验室开放基金(SCSF202011)

Analysis of the Change of Agricultural Heat and Precipitation Resources Based on Grid Revision of GCM Outputs in Hainan Island

LI Ning, BAI Rui, LI Wei, CHEN Miao, YANG Gui-sheng, CHEN Xin, FAN Chang-hua, ZHANG Wen   

  1. 1. Environment and Plant Protection Institute, Chinese Academy of Tropical Agricultural Sciences/ Danzhou Hainan,Tropical Agro-ecosystem,National Observation and Research Station/ National Agricultural Experimental Station for Agricultural-Environment/ Danzhou Scientific Observing and Experimental Station of Agro-Environment, Ministry of Agriculture and Rural Affairs/Hainan Key Laboratory of Tropical Eco-circular Agriculture, Haikou 571101, China; 2. Hainan Climate Center, Haikou 570203
  • Received:2020-10-12 Online:2021-06-20 Published:2021-06-20

摘要: 以海南岛为研究区域,选用5个大气环流模式(GCMs)1970−1999年的逐日输出数据和同期地面气象观测数据,使用空间插值降尺度到0.5°×0.5°格网。以格网单元为基础,应用系统误差修订(修正值法或比值法)和多模式集合平均方法(贝叶斯模型平均法BMA或等权重平均法EW),训练与验证GCMs输出值并进行综合修订。在此基础上,分析RCP2.6、RCP4.5和RCP8.5情景下,未来海南岛近期(2020−2059年)和远期(2060−2099年)农业水热资源,包括年平均气温、1月平均气温、≥10℃积温、≥20℃积温、年降水量、1月降水量和≥20℃界限温度生长期间降水量的变化特征。结果表明:GCMs输出值的系统误差和BMA权重系数在格网间存在较大的空间差异,且GCMs输出值低估逐日最高气温约3.55℃,高估逐日最低气温约1.19℃,逐日降水量仅为观测值的54.35%。基于格网的综合修订,可有效降低GCMs输出值在空间上的不确定性,BMA与EW的修订结果相似,均优于单一GCM模式。通过格网BMA综合修订后,最高气温、最低气温和降水量在验证期的相关系数r分别约提升0.10、0.07和0.06;均方根误差RMSE分别约降低2.38℃、1.01℃和1.01mm;较单一GCM相对观测值的偏差平均约减少3.25℃、1.13℃和25.67mm。未来海南岛农业热量资源在空间上主要表现为从中部向外围逐渐升高,高温主要分布在南部至西部沿海地区,年平均气温的增幅全岛较为接近,1月平均气温、≥10℃积温和≥20℃积温的增幅分别表现为由东向西、由北向南和由中部向外围递减。在时间上,RCP8.5情景下所有农业热量资源均为极显著增加且增温最快,RCP4.5情景为先增加后平缓,RCP2.6情景较为平缓,远期无显著增温。未来海南岛降水资源在空间上转为由东向西逐步递减的格局,南部和北部沿海地区降水变率增加,西部和中部降水变率减少,在时间上无显著变化趋势。随着未来海南岛气候变暖和降水格局的改变,农作物适宜种植面积扩大,会对农业生产带来巨大挑战,应提前布局,做好趋利避害。

关键词: 气候变化, GCM, 格网BMA, 水热资源, 海南岛

Abstract: Tropics are more fragile to climate change, especially in tropical island. It’s has not been investigated the change of agricultural heat and precipitation resources in future in tropical island like Hainan island, China. Because there are a lot of space biases between the raw CMIP5 data set and the observed values in Hainan island. Daily maximum temperature, minimum temperature and precipitation were obtained from the ground weather stations and the GCMs include FGOALS-g2, GFDL-ESM2G, HadGEM2-ES, MPI-ESM-MR and MRI-CGCM3 in Hainan island and its nearby waters. The observations and the raw GCMs outputs for the historical (1970-1999), RCP2.6, RCP4.5 and RCP8.5 (2020−2099) scenarios were processed and interpolated to a spatial resolution of 0.5°×0.5° as grid cells using the bilinear method. We used both systematic residuals revision methods (corrected value method or ratio method) and multi-mode ensemble averaging methods include the Bayesian model averaging (BMA) method and the equal weight averaging (EW) method in each grid cells to reduce the spatial uncertainty of the raw GCMs in the training and verification period. And then, we used the revised GCMs outputs and the agro-climatic index computing software to analysis the change of agricultural heat and precipitation resources under the scenarios of RCP2.6, RCP4.5 and RCP8.5 in both short-term (2020−2059) and long-term (2060−2099). These sources include annual mean temperature, mean temperature in January, ≥10℃ and ≥20℃ integrated temperature, annual precipitation, precipitation in January and precipitation in ≥20℃ integrated temperature period.The results showed that the correct coefficients of the raw GCMs outputs from both systematic residuals revision and the BMA method all have large spatial differences among the grid cells. The raw GCMs outputs underestimate the daily maximum temperature about 3.55℃, overestimate the daily minimum temperature about 1.19℃ and underestimate the daily precipitation which only 54.35% of the observations. It can effectively reduce the spatial uncertainty of the raw GCMs outputs by the above revision methods. The revised results of the BMA and the EW are similar and both are better than a single GCM for simulate historical climate variables. After comprehensive revision of the BMA in each grid cells, the correlation coefficients of maximum temperature, minimum temperature and precipitation are increased about 0.10, 0.07 and 0.06 respectively, and the root mean square error are reduced about 2.38℃, 1.01℃ and 1.01mm respectively, in the verification period. There are decreased about 3.25℃, 1.13℃ and 25.67mm compared with the average biases of a single GCM and closer to the observed value. In the future, the agricultural heat resources will generally show a gradual increase from the central mountains to the coast in spatial. The high temperature will distribute mainly range from the southern to the western coastal areas. The annual mean temperature will increase evenly in the whole island. The increasing amplitude of mean temperature in January, ≥10℃ and ≥20℃ integrated temperature has different patterns that will decrease from the eastern to the western, from the northern to the southern, and from the central mountains to the coast, respectively. It will increase significantly with the fastest climate trend rate under the RCP8.5, or increase first in short-term and then level off in long-term under the RCP4.5, or relatively flat without increase significantly under the RCP2.6. The precipitation resources are transforming into a pattern of gradually decreasing from the eastern to the western and with no significant trend in temporal. The precipitation variability will increase in the southern and the northern coastal areas, while decrease in the western and the central areas. With climate warming and the changes of precipitation pattern in future, the expansion of suitable crop cultivation areas will face huge challenges to agricultural production. It is necessary to arrange in advance to seek advantages and avoid disadvantages.

Key words: Climate change, GCM, Grid BMA, Heat and precipitation resources, Hainan island