Approximating the Internal Variability of Bias-Corrected Global Temperature Projections with Spatial Stochastic Generators
Approximating the Internal Variability of Bias-Corrected Global Temperature Projections with Spatial Stochastic Generators
复制标题
使用空间随机生成器逼近偏差校正的全球温度预测的内部变异性
DOI：
10.1175/jcli-d-21-0083.1
复制
发表时间：
2021
影响因子：
4.9
通讯作者：
S. Castruccio
中科院分区：
文献类型：
--
作者：
Wenjing Hu;S. Castruccio
气候变化方面的决策，从脆弱性评估到适应和减缓，都需要对未来气候的不确定性进行准确的量化。在既有观测又有气候模拟的情况下，可以通过建立历史时期的经验关系来获得物理约束的预测，并用它来纠正未来模拟的偏差。传统的偏差校正方法没有考虑气候模拟中的不确定性，并侧重于没有空间依赖性的区域汇总变量，从而丢失了有用的信息，例如跨区域梯度的变化。我们提出了一个新的统计模型，每月的表面温度与稀疏和可解释的空间结构的偏差校正，我们用它来获得未来的再分析预测与相关的不确定性，只使用一个小的合奏全球模拟。
Decision making under climate change, from vulnerability assessments to adaptation and mitigation, requires an accurate quantification of the uncertainty in the future climate. Physically constrained projections, in the presence of both observations and climate simulations, can be obtained by establishing an empirical relationship in the historical time period, and use it to correct the bias of future simulations. Traditional bias correction approaches do not account for the uncertainty in the climate simulation, and focus on regionally aggregated variables without spatial dependence, with loss of useful information such as the variability of gradients across regions. We propose a new statistical model for bias correction of monthly surface temperatures with sparse and interpretable spatial structure, and we use it to obtain future reanalysis projections with associated uncertainty, using only a small ensemble of global simulations.
影响因子：
6.2
作者：
Hawkins, Ed;Osborne, Thomas M.;Challinor, Andrew J.
通讯作者：
Challinor, Andrew J.