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经误差订正的RegCM4中国气候变化集合预估数据集 Datasets for bias corrected RegCM4 ensemble climate change projections

[发布日期: 2022-01-20 浏览量 1006]


文献引用】Citation: Tong Y, Gao XJ, Han ZY, et al. Bias correction of temperature and precipitation over China for RCM simulations using the QM and QDM methods [J]. Climate Dynamics, 2020, 57: 1425–1443. doi: 10.1007/s00382-020-05447-4.

【数据下载,请联系】Contact for download: wangjun@mail.iap.ac.cn

【数据源及方法】Data source and method

RegCM4的QDM订正数据集是使用QDM方法 (Quantile Delta Mapping, QDM, Cannon et al., 2015) ,对5个全球模式分别驱动下的RegCM4结果进行订正后的结果。由于多种因素的作用,气候模式对气候的模拟或多或少的存在着系统性的误差,这样使得气候模式的结果经常或者很难直接应用于气候变化的影响评估研究,如将模式结果用来驱动水文或者农业模式等。因此,在模式结果应用于影响评估研究前,经常会对其进行误差订正处理。

QDM方法即delta分位数映射方法,是一种保留分位数变化的分位数映射方法 (quantile mapping, QM)。QM方法是假设经验累积分布函数(CDF)不随时间的变化而变化(具体计算方法和订正原理可参见如童尧等,2017)。但这种分布实际上是会发生改变的。

QDM的具体做法是,是对应模式预估的每一个分位数先做一个去趋势处理,之后再将建模时期构造的传递函数通过QM方法对模式的模拟结果进行订正,最后再将模式的趋势预估值(气温为绝对值,降水为相对值)叠加回订正结果中(见如Tong et al., 2021)。这样QDM订正方法在减小模拟偏差的同时,较好地保留了模式预估结果中的气候变化信号。

最后需要再次说明的是,误差订正方法并不能提高气候模式本身的模拟和预估技巧,其主要目的仍在于为影响评估研究提供基础数据。

The bias corrected RegCM4 QDM dataset is bias corrected multi-RegCM4 simulations driven individualy by five diffrent GCMs using QDM (Quantile Delta Mapping, QDM, Cannon et al., 2015) method. As well known, systematic biases of climate model simulations widely exist due to various reasons. It can be very difficult and sometimes even not possible to use model outputs directly in impact assessment studies, e.g., as forcings for hydrological and agricultural models. Thus, bias correction has been widely used to postprocess the climate model output prior to application for impact studies.

The QDM is based on the quantile delta change and detrended quantile mapping (QM) method. In general, the QM method assumes that the cumulative distribution function (CDF) for a variable in the simulation and observation time series remains un-changed in the future period (see more detail in e.g., Tong et al., 2017). However, this distribution has been found to change in future projections.

To be more specific, the model projection is firstly detrended by quantiles, and the simulated value is bias corrected by QM with the transfer function constructed in the calibration period. Then the projected absolute (for temperature) or relative (for precipitation) changes in quantiles are added or multiplied to the bias corrected model outputs to obtain the final results (see more detail in e.g. Tong et a;., 2021).Thus the model simulated climate signal is well preserved by using QMD during the bias correction which effectively removes the systematic model biases.

It is finally noted that the bias correction methods cannot improve the skill of climate models either in the present day climate simulation or in the projection of future changes, but again, basically aim to provide driving data for impact studies. 

【数据简介】Data description

 区域范围 Region: 中国大陆 mainland China

 格点数 Grids:283(东西方向 west-east)×163(南北方向 north-south)

 水平分辨率 Horizontal resolution: 0.25°×0.25° (经-纬度 latitude-longitude)

 时间分辨率 Time scale: 日平均 daily mean

 时间段 Preiod: 1980年1月1日~2098年12月31日  January 1, 1980–December 31, 2098

 模式日历(各全球模式及其驱动下RegCM4的日历) Model calendar (for GCMs and thus the driven RegCM4): 

     CSIRO-Mk3-6-0 and NorESM1-M: 365d/yr; EC-EARTH and MPI-ESM-MR: 365d/yr (366 for leap years); HadGEM2-ES: 360d/yr (30d/month)

 要素 Variables: :平均气温、降水量、最高气温、最低气温 daily mean temperature, precipitation, daily maximum and minimum temperatures

 温室气体排放情景 Emission scenario: RCP4.5

【文件名和变量说明】File names and variables

◆文件名格式 Format of file names: A_day_B_rcp45_1980-2098.nc

          A为要素代码,B为订正的模式名称 A for variables, B for model names

◆要素名称 Variable names

            tas: 平均气温 mean temperature (°C)

            pr: 降水量 precipitation (mm)

            tasmax: 日最高气温 daily maximum temperature (°C)

            tasmin: 日最低气温 daily minimum temperature (°C)

【项目资助】Funding

      中国科学院战略性先导科技专项(A类)“东南亚国家气候与水资源变化”(编号:XDA20060401)和国家重点研发计划项目“中国北方地区极端气候的变化及成因研究”(编号:2016YFA0600704)共同资助 

      Jointly supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA2006040102) and National Key Research and Development Program (Grant No. 2016YFA0600704)

【主要参考文献/References】

Cannon AJ, Sobie SR, Murdock TQ. Bias correction of GCM precipitation by quantile mapping: how well do methods preserve changes in quantiles and extremes? [J]. Journal of Climate, 2015, 28: 6938–6959. doi: 10.1175/jcli-d-14-00754.1.

Tong Y, Gao XJ, Han ZY, et al. Bias correction of temperature and precipitation over China for RCM simulations using the QM and QDM methods [J]. Climate Dynamics, 2021, 57: 1425-1443. doi: 10.1007/s00382-020-05447-4.

韩振宇, 童尧, 高学杰, 等. 分位数映射法在RegCM4中国气温模拟订正中的应用 [J]. 气候变化研究进展, 2018, 14(4): 331–340. doi:10.12006/j.issn.1673-1719.2017.156. [Han ZY, Tong Y, Gao XJ. Correction based on quantile mapping for temperature simulated by the RegCM4 [J]. Climate Change Research, 2018, 14(4): 331–340.]

童尧, 高学杰, 韩振宇, 等. 基于RegCM4模式的中国区域日尺度降水模拟误差订正 [J]. 大气科学, 2017, 41(6): 1156–1166. doi:10.3878/j.issn.1006-9895.1704.16279. [Tong Y, Gao XJ, Han ZY, et al. Bias correction of daily precipitation simulated by RegCM4 model over China [J]. Chinese Journal of Atmospheric Sciences, 2017. 41(6): 1156–1166. doi:10.3878/j.issn.1006-9895.1704.16279.]

Tong Y, Gao XJ, Han ZY, et al. Bias correction of temperature and precipitation over China for RCM simulations using the QM and QDM methods [J]. Climate Dynamics, 2021, 57: 1425–1443. doi: 10.1007/s00382-020-05447-4.

童尧, 韩振宇, 高学杰. QM和QDM方法对中国极端气候高分辨率气候变化模拟的误差订正对比 [J]. 气候与环境研究, 2021, doi:10.3878/j.issn.1006-9585.2021.20000. [Tong Y, Han ZY, Gao XJ. Bias Correction in Extreme Indices over China for RegCM4 Simulations Using QM and QDM Methods [J]. Climatic and Environmental Research, 2021, doi:10.3878/j.issn.1006-9585.2021.20000.]