郭悦, 张文青, 刘浏, 周雪婷. 青藏高原未来气候变化预估的不确定性来源及其降低途径[J]. 北京师范大学学报(自然科学版), 2024, 60(1): 87-98. DOI: 10.12202/j.0476-0301.2023133
引用本文: 郭悦, 张文青, 刘浏, 周雪婷. 青藏高原未来气候变化预估的不确定性来源及其降低途径[J]. 北京师范大学学报(自然科学版), 2024, 60(1): 87-98. DOI: 10.12202/j.0476-0301.2023133
GUO Yue, ZHANG Wenqing, LIU Liu, ZHOU Xueting. Quantifying uncertainty sources of future climate projections of Qinghai-Xizang Plateau and narrowing uncertainties with bias correction techniques[J]. Journal of Beijing Normal University(Natural Science), 2024, 60(1): 87-98. DOI: 10.12202/j.0476-0301.2023133
Citation: GUO Yue, ZHANG Wenqing, LIU Liu, ZHOU Xueting. Quantifying uncertainty sources of future climate projections of Qinghai-Xizang Plateau and narrowing uncertainties with bias correction techniques[J]. Journal of Beijing Normal University(Natural Science), 2024, 60(1): 87-98. DOI: 10.12202/j.0476-0301.2023133

青藏高原未来气候变化预估的不确定性来源及其降低途径

Quantifying uncertainty sources of future climate projections of Qinghai-Xizang Plateau and narrowing uncertainties with bias correction techniques

  • 摘要: 在全球变暖加剧的背景下,准确预估未来时期气候变化至关重要.本文基于最新发布的CMIP6(coupled model intercomparison project 6)中的15个全球气候模式(global climate models,GCMs),在未来4种共享社会经济路径和典型浓度路径组合情景下,量化青藏高原地区气温和降水预估的不确定性.然后使用DT(daily translation)方法,对CMIP6模式输出数据进行偏差校正.通过对比DT方法前后不确定性的大小,讨论偏差校正方法在降低气温和降水预估不确定性方面的潜力.结果显示:DT方法校正结果与实测值更为接近;对于气候预估不确定性,从长期来看模式不确定性占据主导地位,而情景不确定性和内部变异性的贡献相对较低;DT方法对于降低降水预估不确定性的效果较好,其中模式不确定性降低最多,对情景不确定性的大小影响不大,但影响了不确定性的相对贡献大小.综合来看,利用DT方法进行偏差校正能在一定程度上降低预估不确定性.研究结果可为揭示气候变化对高寒区水循环的影响机制提供科学依据.

     

    Abstract: In the context of intensifying global warming, accurate projections of climate changes in the coming period is essential. The 15 global climate models (GCMs) in the newly released coupled model intercomparison project 6 (CMIP6) are used to quantify uncertainty in temperature and precipitation estimates in the Qinghai-Xizang Plateau under the next four shared socioeconomic pathways and typical concentration pathway combination scenarios. The CMIP6 model output data is bias-corrected using the DT method. The magnitude of the uncertainty before and after DT is compared, the potential of bias correction in reducing uncertainty in temperature and precipitation estimates was discussed. The correction results of the DT method are found closer to measured values. In the long run, model uncertainty dominates, while contribution of scenario uncertainty and internal variability is relatively low. DT method is effective to reduce uncertainty in precipitation prediction, reducing the model uncertainty by 80%; this has little effect on the size of scenario uncertainty but affects the relative contribution of uncertainty. It is concluded that DT method for deviation correction reduces uncertainty in estimation, with important implications for the impact of climate change on water cycles in alpine regions.

     

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