气候模型的未来:天气细节,宏观天气随机性,还是两者兼而有之?

S. Lovejoy
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引用次数: 7

摘要

自20世纪70年代第一个气候模式出现以来,算法和计算机速度提高了约1017倍,从而能够以越来越精细的分辨率模拟越来越多的过程。然而,去年IPCC第六次评估报告中使用的气候模式比对项目(CMIP)的多模式集合(MME)成员的分布比以往任何时候都大:模式不确定性,即MME不确定性的意义上,增加了。即使“圣杯”仍然是千米尺度的模型,也不一定越大越好。为什么对寿命约为15分钟的结构进行建模,只是为了在几十万个因子上取平均值,以产生十年气候预测?在这篇评论中,我认为,在开发“无缝”(独特)天气气候模型的同时,社会应该认真投资于随机宏观天气模型的开发,这些模型追求更小的——而且大多是不相关的——细节。这些模型利用了比行星尺度结构寿命更长尺度上遵循的统计规律,超出了确定性预测极限(≈10天)。我认为,传统的大气环流模式和这些新的宏观天气模式是互补的,就像统计力学和连续介质力学一样,在由应用决定的选择方法上同样有效。随机宏观天气模型的候选模型正在出现,那些基于分数能量平衡方程(FEBE)的模型尤其有前途。FEBE是经典Budyko-Sellers能量平衡模型的更新和推广,它尊重尺度和节能的对称性,并且已经允许最先进的月度和季节性,年际温度预测和多年预测。我用21世纪FEBE对全球平均温度的气候预测来证明这一点。总体而言,预估结果与CMIP5和CMIP6多模式集合一致,FEBE参数不确定性约为MME结构不确定性的一半。如果没有FEBE,不确定性是如此之大,以至于气候政策(缓解)在很大程度上与气候后果(变暖)脱钩,给政策制定者留下了太多的“回旋余地”。较低的FEBE不确定性将有助于克服当前的“不确定性危机”。这两种模式类型是互补的,事实证明,CMIP全球平均温度可以使用这种随机宏观天气模式准确地预测(验证两种方法)。因此,毫不奇怪,它们可以结合起来产生一个最优的混合模型,其中两种模型类型被用作预测器:当结合起来时,各种不确定性进一步减少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Future of Climate Modelling: Weather Details, Macroweather Stochastics—Or Both?
Since the first climate models in the 1970s, algorithms and computer speeds have increased by a factor of ≈1017 allowing the simulation of more and more processes at finer and finer resolutions. Yet, the spread of the members of the multi-model ensemble (MME) of the Climate Model Intercomparison Project (CMIP) used in last year’s 6th IPCC Assessment Report was larger than ever: model uncertainty, in the sense of MME uncertainty, has increased. Even if the holy grail is still kilometric scale models, bigger may not be better. Why model structures that live for ≈15 min only to average them over factors of several hundred thousand in order to produce decadal climate projections? In this commentary, I argue that alongside the development of “seamless” (unique) weather-climate models that chase ever smaller—and mostly irrelevant—details, the community should seriously invest in the development of stochastic macroweather models. Such models exploit the statistical laws that are obeyed at scales longer than the lifetimes of planetary scale structures, beyond the deterministic prediction limit (≈10 days). I argue that the conventional General Circulation Models and these new macroweather models are complementary in the same way that statistical mechanics and continuum mechanics are equally valid with the method of choice determined by the application. Candidates for stochastic macroweather models are now emerging, those based on the Fractional Energy Balance Equation (FEBE) are particularly promising. The FEBE is an update and generalization of the classical Budyko–Sellers energy balance models, it respects the symmetries of scaling and energy conservation and it already allows for both state-of-the-art monthly and seasonal, interannual temperature forecasts and multidecadal projections. I demonstrate this with 21st century FEBE climate projections for global mean temperatures. Overall, the projections agree with the CMIP5 and CMIP6 multi-model ensembles and the FEBE parametric uncertainty is about half of the MME structural uncertainty. Without the FEBE, uncertainties are so large that climate policies (mitigation) are largely decoupled from climate consequences (warming) allowing policy makers too much “wiggle room”. The lower FEBE uncertainties will help overcome the current “uncertainty crisis”. Both model types are complementary, a fact demonstrated by showing that CMIP global mean temperatures can be accurately projected using such stochastic macroweather models (validating both approaches). Unsurprisingly, they can therefore be combined to produce an optimum hybrid model in which the two model types are used as copredictors: when combined, the various uncertainties are reduced even further.
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