THE POTENTIAL TO NARROW UNCERTAINTY IN REGIONAL CLIMATE PREDICTIONS
THE POTENTIAL TO NARROW UNCERTAINTY IN REGIONAL CLIMATE PREDICTIONS
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DOI：
10.1175/2009bams2607.1
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发表时间：
2009-08-01
影响因子：
8
通讯作者：
Sutton, Rowan
中科院分区：
文献类型：
--
作者：
Hawkins, Ed;Sutton, Rowan
面对气候变化的现实，各种组织的决策者越来越多地寻求对区域和当地气候的定量预测。对于这些决策者和资助气候研究的组织来说，一个重要的问题是，气候科学在这种预测中带来改进--特别是减少不确定性--的潜力有多大？气候预测中的不确定性来自三个不同的来源：内部可变性、模型不确定性和情景不确定性。使用一套气候模型的数据，我们分离并量化了这些来源。对于在十年时间尺度和区域空间尺度上预测地表气温变化，我们表明，未来几十年的不确定性由来源(模型不确定性和内部变异性)主导，这些来源可能通过气候科学的进展而减少。此外，我们发现模型的不确定性比内部变量更重要。我们的发现对管理对气候变化的适应具有启示意义。由于适应的成本非常高，而且未来气候的更大不确定性可能与更昂贵的适应相关，因此减少气候预测的不确定性可能具有巨大的经济价值。我们强调需要做更多的工作来比较：(A)考虑到目前的不确定性水平，不同程度适应的成本；(B)为减少目前不确定性水平而对气候科学进行新投资的成本。我们的研究还强调了将气候科学投资瞄准最有希望的机会以减少预测不确定性的重要性。
Faced by the realities of a changing climate, decision makers in a wide variety of organizations are increasingly seeking quantitative predictions of regional and local climate. An important issue for these decision makers, and for organizations that fund climate research, is what is the potential for climate science to deliver improvements-especially reductions in uncertainty-in such predictions? Uncertainty in climate predictions arises from three distinct sources: internal variability, model uncertainty, and scenario uncertainty. Using data from a suite of climate models, we separate and quantify these sources. For predictions of changes in surface air temperature on decadal timescales and regional spatial scales, we show that uncertainty for the next few decades is dominated by sources (model uncertainty and internal variability) that are potentially reducible through progress in climate science. Furthermore, we find that model uncertainty is of greater importance than internal variability.Our findings have implications for managing adaptation to a changing climate. Because the costs of adaptation are very large, and greater uncertainty about future climate is likely to be associated with more expensive adaptation, reducing uncertainty in climate predictions is potentially of enormous economic value. We highlight the need for much more work to compare (a) the cost of various degrees of adaptation, given current levels of uncertainty and (b) the cost of new investments in climate science to reduce current levels of uncertainty. Our study also highlights the importance of targeting climate science investments on the most promising opportunities to reduce prediction uncertainty.