BackgroundConsidering the lack of research over this region the Statistical Downscaling Model (SDSM) was used as a tool for downscaling meteorological data statistically over four representative regions in the eastern side of Colombia. Data from the two Global Climate Models CanESM2 and IPSL-CM5A-MR, which are part of the CMIP5-project have been used to project future maximum and minimum temperature, precipitation and relative humidity for the periods 2021–2050 and 2071–2100. For both models, the Representative Concentration Pathways RCP2.6 and RCP8.5 were considered, representing two different possible future emission trajectories and radiative forcings. Predictor variables from the National Centre for Environmental Prediction (NCEP-DOE 2) reanalysis dataset, together with analyzed correlation coefficient (R) and root mean square error (RMSE) were used as performance indicators during the calibration and validation process.ResultsResults indicate that Maximum and minimum temperature is projected to increase for both Global Climate Models and both Representative Concentration Pathways; relative humidity shows a decreasing trend for all scenarios and all regions; and precipitation shows a slight decrease over three regions and an increase over the warmest region. As expected, the results of the simulation for the period 2071–2100 show a more drastic change when compared to the baseline period of observations.ConclusionsThe SDSM model proves to be efficient in the downscaling of maximum/minimum temperature as well as relative humidity over the studied regions; while showing a lower performance for precipitation, agreeing with the results for other statistical downscaling studies. The results of the projections offer good information for the evaluation of possible future-case scenarios and decision-making management.

中文翻译：

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哥伦比亚四个不同地区的预计气候变化

背景考虑到对该地区缺乏研究，统计降尺度模型 (SDSM) 被用作统计降尺度哥伦比亚东部四个代表性区域的气象数据的工具。来自作为 CMIP5 项目一部分的两个全球气候模型 CanESM2 和 IPSL-CM5A-MR 的数据已用于预测 2021-2050 年和 2071-2100 年期间的未来最高和最低温度、降水和相对湿度。对于这两种模型，都考虑了代表性浓度路径 RCP2.6 和 RCP8.5，代表两种不同的未来可能的排放轨迹和辐射强迫。来自国家环境预测中心 (NCEP-DOE 2) 再分析数据集的预测变量，在校准和验证过程中，将分析的相关系数 (R) 和均方根误差 (RMSE) 与分析的相关系数 (R) 和均方根误差 (RMSE) 一起用作性能指标。所有情景和所有地区的相对湿度均呈下降趋势；三个地区的降水量略有减少，最暖地区的降水量有所增加。正如预期的那样，与观测的基线期相比，2071-2100 年期间的模拟结果显示出更剧烈的变化。结论 SDSM 模型证明在降低最高/最低温度以及相对湿度方面是有效的。研究区域；同时显示出较低的降水性能，与其他统计降尺度研究的结果一致。预测结果为评估未来可能的案例情景和决策管理提供了很好的信息。