State-of-the-art bias correction of climate models misrepresent climate science and misinform adaptation

V. Chandel, Udit Bhatia, A. Ganguly, Subimal Ghosh
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Abstract

Quantile Mapping (QM) based Bias Correction and Spatial Disaggregation (BCSD) have emerged as the de facto standard for rectifying bias and scale-mismatch in global climate models (GCMs) leading to novel climate science insights and new information for impacts and adaptation. Focusing on critical variables crucial for understanding climate dynamics in India and the United States, our evaluation challenges the premise of BCSD approach. We find that BCSD overcorrects GCM simulations to observed patterns while minimizing or even nullifying science-informed projections generated by GCMs. Furthermore, we show that BCSD incorrectly captures extremes and complex climate signals. Our evaluation in the context of the Walker Circulation suggests that this inability to adequately capture multivariate and spatial-temporal dependence patterns may at least partially explain the challenges with BCSD.
最先进的气候模型偏差修正错误地反映了气候科学并误导了适应工作
基于量子绘图(QM)的偏差校正和空间分解(BCSD)已成为纠正全球气候模型(GCMs)中的偏差和尺度不匹配的事实标准,从而带来新的气候科学见解以及影响和适应方面的新信息。我们的评估侧重于对了解印度和美国气候动态至关重要的关键变量,对 BCSD 方法的前提提出了质疑。我们发现,BCSD 过度修正了大气环流模型模拟的观测模式,同时将大气环流模型生成的有科学依据的预测降到最低甚至无效。此外,我们还发现 BCSD 无法正确捕捉极端气候和复杂的气候信号。我们在沃克环流背景下进行的评估表明,这种无法充分捕捉多变量和时空依赖模式的情况至少可以部分解释 BCSD 所面临的挑战。
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