摘要
In this paper, we review the need for, use of, and demands on climate modeling
to support so-called ‘robust’ decision frameworks, in the context of improving the
contribution of climate information to effective decision making. Such frameworks
seek to identify policy vulnerabilities under deep uncertainty about the future
and propose strategies for minimizing regret in the event of broken assumptions.
We argue that currently there is a severe underutilization of climate models
as tools for supporting decision making, and that this is slowing progress in
developing informed adaptation and mitigation responses to climate change. This
underutilization stems from two root causes, about which there is a growing body
of literature: one, a widespread, but limiting, conception that the usefulness of
climate models in planning begins and ends with regional-scale predictions of
multidecadal climate change; two, the general failure so far to incorporate learning
from the decision and social sciences into climate-related decision support in
key sectors. We further argue that addressing these root causes will require
expanding the conception of climate models; not simply as prediction machines
within ‘predict-then-act’ decision frameworks, but as scenario generators, sources
of insight into complex system behavior, and aids to critical thinking within robust
decision frameworks. Such a shift, however, would have implications for how
users perceive and use information from climate models and, ultimately, the types
of information they will demand from these models—and thus for the types of
simulations and numerical experiments that will have the most value for informing
decision making.
中文摘要
“在这篇论文中，我们回顾了气候建模的必要性、使用和需求支持所谓的“稳健”决策框架，在改进气候信息对有效决策的贡献。此类框架在对未来充满不确定性的情况下，寻求识别政策漏洞并提出了在假设被打破的情况下尽量减少遗憾的策略。我们认为，目前气候模型的利用严重不足作为支持决策的工具，这正在减缓制定应对气候变化的知情适应和缓解措施。这利用不足源于两个根本原因，这是一个不断增长的群体文学：一，一个广泛但有限的概念，认为规划中的气候模型以区域尺度的预测开始和结束数十年气候变化；第二，到目前为止，整合学习的普遍失败从决策和社会科学到气候相关决策支持关键部门。我们进一步认为，解决这些根本原因需要扩大气候模型的概念；不仅仅是作为预测机器在“先预测后行动”的决策框架内，但作为场景生成器，来源对复杂系统行为的洞察，并有助于在robust中进行批判性思维决策框架。然而，这种转变将对如何用户感知和使用来自气候模型的信息，最终是来自类型他们将从这些模型中要求的信息，从而为最有价值的模拟和数值实验决策。"
