From reliable weather forecasts to skilful climate response: A dynamical systems approach
From reliable weather forecasts to skilful climate response: A dynamical systems approach
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从可靠的天气预报到熟练的气候响应：动力系统方法
DOI：
10.1002/qj.3476
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发表时间：
2019
影响因子：
8.9
通讯作者：
Christensen H
中科院分区：
文献类型：
--
作者：
Christensen H
虽然可以通过执行和评估许多后报来测试天气预报模型，但有限的观测记录限制了评估气候预测的程度。因此，一个有趣的问题是：通过评估由同一模式产生的短期天气和季节预报的可靠性，我们可以在多大程度上评估气候模式对强迫响应的潜在技巧？我们使用动态系统框架来解决这个问题。我们使用线性响应理论来提供一个一般的动力系统的平均气候响应一个小的外部强迫。我们将这种反应与初始值预测的可靠性联系起来。我们发现，为了捕捉平均气候响应，预测模型必须正确地代表系统中变化最慢的演变模式。因此，季节性和较长时间尺度的预报的可靠性对这些慢模态的代表性敏感，可以表明预报模式是否具有正确的气候敏感性，从而对所施加的外部强迫做出正确的反应。通过这种方式，初始化预测的技能可以作为对气候敏感性的“紧急约束”。然而，我们也强调，不可靠的季节性预测并不一定表明气候预测不正确。这是因为正确地表示快速演变的模式也是可靠的季节预报所必需的。
While weather forecasting models can be tested by performing and evaluating many hindcasts, the limited observational record restricts the degree to which climate projections can be evaluated. Therefore a question of interest is: to what degree can we evaluate the potential skill of a climate model's response to forcing by assessing the reliability of short‐range weather and seasonal forecasts produced by the same model? We address this question using a dynamical systems framework. We use linear response theory to provide the mean climate response of a general dynamical system to a small external forcing. We relate this response to the reliability of initial value forecasts. We find that, in order to capture the mean climate response, the forecast model must correctly represent the slowest evolving modes of variability in the system. The reliability of forecasts on seasonal and longer time‐scales, which is sensitive to the representation of these slow modes, could therefore indicate if the forecast model has the correct climate sensitivity and so will respond correctly to an applied external forcing. In this way, the skill of initialized forecasts could act as an ‘emergent constraint’ on climate sensitivity. However, we also highlight that unreliable seasonal forecasts do not necessarily indicate an incorrect climate projection. This is because correctly representing rapidly evolving modes is also necessary for reliable seasonal forecasts.
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DOI：
10.1016/j.physd.2017.02.015
发表时间：
2016-04
期刊：
Physica D: Nonlinear Phenomena
影响因子：
--
作者：
A. Gritsun;V. Lucarini
通讯作者：
A. Gritsun;V. Lucarini
接近平衡的非平衡统计力学：计算高阶项
影响因子：
1.7
作者：
D. Ruelle
通讯作者：
D. Ruelle
影响因子：
8
作者：
G. Brunet;M. Shapiro;B. Hoskins;M. Moncrieff;R. Dole;G. Kiladis;B. Kirtman;A. Lorenc;Brian Mills;R. Morss;S. Polavarapu;D. Rogers;J. Schaake;J. Shukla
通讯作者：
G. Brunet;M. Shapiro;B. Hoskins;M. Moncrieff;R. Dole;G. Kiladis;B. Kirtman;A. Lorenc;Brian Mills;R. Morss;S. Polavarapu;D. Rogers;J. Schaake;J. Shukla
DOI：
10.1002/qj.49712757202
发表时间：
2001-01-01
影响因子：
8.9
作者：
Palmer, TN
通讯作者：
Palmer, TN
大西洋海面观测温度的预报技巧和可预测性
影响因子：
4.9
作者：
L. Zanna
通讯作者：
L. Zanna