Does Model Calibration Reduce Uncertainty in Climate Projections?
Does Model Calibration Reduce Uncertainty in Climate Projections?
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模型校准是否会降低气候预测的不确定性？
DOI：
10.1175/jcli-d-21-0434.1
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发表时间：
2022
影响因子：
4.9
通讯作者：
Tett S
中科院分区：
文献类型：
--
作者：
Tett S
气候预测的不确定性很大，如2.5-4 K的平衡气候敏感性（ECS）和1.4-2.2 K的瞬态气候响应（TCR）的可能不确定性范围所示。模型预测的不确定性可能来自未解决过程的表示方式、使用的参数值或模型校准的目标。我们发现，在两个气候模式合奏，客观地校准，以尽量减少观测到的大尺度大气气候学的差异，ECS和TCR的不确定性约2-6倍小于CMIP 5或CMIP 6多模式合奏。我们还发现，预计在地表温度，降水和年度极端的不确定性相对较小。剩余的不确定性主要来自于无约束的海冰反馈。超过20年的历史HadAM 3标准模式配置模拟观测到的半球尺度观测和工业化前的表面温度以及CMIP 5和CMIP 6合奏的中位数，而优化的配置模拟这些更好地比几乎所有的CMIP 5和CMIP 6模式。半球尺度的观测和工业化前的温度没有系统地更好地模拟CMIP 6比CMIP 5，虽然CMIP 6合奏似乎更好地模拟模式的大规模观测比CMIP 5合奏和优化的HadAM 3配置。我们的研究结果表明，大多数CMIP模式可以通过系统校准来改进它们对大尺度观测的模拟。然而，在气候预测（对于一个给定的情况下）的不确定性可能主要来自未解决的过程（“结构不确定性”）的参数化方案的选择，与不同的调整目标另一个可能的contributor.Significance StatementClimate模式代表未解决的现象控制的不确定参数。这些参数的变化会影响气候模式模拟当前气候及其气候预测的效果。利用客观方法对单一气候模式进行了大尺度大气观测的多重校准。这些模式在全球和区域尺度上都产生了非常相似的气候预测。将观测的不确定性与模拟的对观测和气候响应的敏感性相结合的分析也具有很小的不确定性，这表明，对于这个模型，当前的观测限制了气候预测。最近开发的气候模式具有广泛的模拟大尺度气候的能力，尽管经过了十年的模式开发，但在模拟大尺度气候的能力方面只有一些改进。
Uncertainty in climate projections is large as shown by the likely uncertainty ranges in equilibrium climate sensitivity (ECS) of 2.5–4 K and in the transient climate response (TCR) of 1.4–2.2 K. Uncertainty in model projections could arise from the way in which unresolved processes are represented, the parameter values used, or the targets for model calibration. We show that, in two climate model ensembles that were objectively calibrated to minimize differences from observed large-scale atmospheric climatology, uncertainties in ECS and TCR are about 2–6 times smaller than in the CMIP5 or CMIP6 multimodel ensemble. We also find that projected uncertainties in surface temperature, precipitation, and annual extremes are relatively small. Residual uncertainty largely arises from unconstrained sea ice feedbacks. The more than 20-year-old HadAM3 standard model configuration simulates observed hemispheric-scale observations and preindustrial surface temperatures about as well as the median CMIP5 and CMIP6 ensembles while the optimized configurations simulate these better than almost all the CMIP5 and CMIP6 models. Hemispheric-scale observations and preindustrial temperatures are not systematically better simulated in CMIP6 than in CMIP5 although the CMIP6 ensemble seems to better simulate patterns of large-scale observations than the CMIP5 ensemble and the optimized HadAM3 configurations. Our results suggest that most CMIP models could be improved in their simulation of large-scale observations by systematic calibration. However, the uncertainty in climate projections (for a given scenario) likely largely arises from the choice of parameterization schemes for unresolved processes (“structural uncertainty”), with different tuning targets another possible contributor.Significance StatementClimate models represent unresolved phenomena controlled by uncertain parameters. Changes in these parameters impact how well a climate model simulates current climate and its climate projections. Multiple calibrations of a single climate model, using an objective method, to large-scale atmospheric observations are performed. These models produce very similar climate projections at both global and regional scales. An analysis that combines uncertainties in observations with simulated sensitivity to observations and climate response also has small uncertainty showing that, for this model, current observations constrain climate projections. Recently developed climate models have a broad range of abilities to simulate large-scale climate with only some improvement in their ability to simulate this despite a decade of model development.
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大气层顶部辐射测量可以限制气候预测吗？
影响因子：
4.9
作者：
S. Tett;D. Rowlands;M. Mineter;C. Cartis
通讯作者：
C. Cartis
在不使用通量调整的情况下获得气候模型中的不同行为
DOI：
10.1002/jgrd.50304
发表时间：
2013
期刊：
Journal of Geophysical Research: Atmospheres
影响因子：
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作者：
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通讯作者：
M. Allen
影响因子：
18.3
作者：
Rowlands, Daniel J.;Frame, David J.;Allen, Myles R.
通讯作者：
Allen, Myles R.
将参数、软件和硬件变化与 57,000 个气候模型的大规模行为关联起来
DOI：
--
发表时间：
2007
影响因子：
11.1
作者：
Chris G. Knight;Sylvia Knight;N. Massey;T. Aina;C. Christensen;D. Frame;J. Kettleborough;Andrew Martin;S. Pascoe;B. Sanderson;D. Stainforth;M. Allen
通讯作者：
M. Allen
大气层顶部辐射测量可以限制气候预测吗？
影响因子：
4.9
作者：
S. Tett;M. Mineter;C. Cartis;D. Rowlands;Ping Liu
通讯作者：
Ping Liu