Scenario and modelling uncertainty in global mean temperature change derived from emission-driven global climate models
Scenario and modelling uncertainty in global mean temperature change derived from emission-driven global climate models
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DOI：
10.5194/esd-4-95-2013
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发表时间：
2013-01-01
影响因子：
7.3
通讯作者：
Sexton, D. M. H.
中科院分区：
文献类型：
--
作者：
Booth, B. B. B.;Bernie, D.;Sexton, D. M. H.
我们比较了全球平均温度的未来变化，以应对不同的未来情景，这些情景首次由全球气候模型（GCM）的排放驱动而不是浓度驱动的扰动参数集合引起。这些新的 GCM 模拟对大气反馈、陆地碳循环、海洋物理和气溶胶硫循环过程中的不确定性进行了采样。我们发现，在考虑排放而不是浓度驱动的模拟时，会出现更广泛的预计温度响应范围（对于积极的缓解方案，10-90% 的百分位范围为 1.7K，对于高端、一切照旧的方案，最高为 3.9K）。由强大气反馈和碳循环响应组合产生的少数模拟显示温度升高超过 9K (RCP8.5)，甚至在积极缓解 (RCP2.6) 下温度超过 4 K。虽然模拟指出排放驱动实验的温度范围要大得多，但它们并没有改变对不同不确定性来源很重要的时间尺度的现有预期（基于之前的浓度驱动实验）。新的模拟对每种排放情景的未来大气浓度范围进行了采样。在 SRES A1B 和代表性浓度路径 (RCP) 的情况下，用于驱动 GCM 系综的浓度场景都位于我们模拟分布的下端。这种设计决策（先前评估的遗产）可能会导致集中驱动的实验在未来的预测中对强烈的反馈响应进行采样不足。我们的排放驱动模拟集合涵盖了 CMIP5 排放驱动模拟的全球温度响应（低端除外）。低气候敏感性和低碳循环反馈的结合导致许多 CMIP5 响应低于我们的集合范围。该系综模拟了许多高于 CMIP5 碳循环范围的高端响应。这些高端模拟可以与对一些更强的碳循环反馈进行采样以及对 4.5 K 以上的气候敏感性进行采样联系起来。后一个方面强调了确定现实世界气候敏感性约束的优先事项，如果实现了这一点，将导致预计全球平均温度变化上限的降低。这里提出的模拟集合提供了一个框架来探索当前可观察到的结果与未来变化之间的关系，而未来预测变化的大量传播凸显了对此类工作的持续需求。
We compare future changes in global mean temperature in response to different future scenarios which, for the first time, arise from emission-driven rather than concentration-driven perturbed parameter ensemble of a global climate model (GCM). These new GCM simulations sample uncertainties in atmospheric feedbacks, land carbon cycle, ocean physics and aerosol sulphur cycle processes. We find broader ranges of projected temperature responses arising when considering emission rather than concentration-driven simulations (with 10-90th percentile ranges of 1.7K for the aggressive mitigation scenario, up to 3.9K for the high-end, business as usual scenario). A small minority of simulations resulting from combinations of strong atmospheric feedbacks and carbon cycle responses show temperature increases in excess of 9K (RCP8.5) and even under aggressive mitigation (RCP2.6) temperatures in excess of 4 K. While the simulations point to much larger temperature ranges for emission-driven experiments, they do not change existing expectations (based on previous concentration-driven experiments) on the timescales over which different sources of uncertainty are important. The new simulations sample a range of future atmospheric concentrations for each emission scenario. Both in the case of SRES A1B and the Representative Concentration Pathways (RCPs), the concentration scenarios used to drive GCM ensembles, lies towards the lower end of our simulated distribution. This design decision (a legacy of previous assessments) is likely to lead concentration-driven experiments to under-sample strong feedback responses in future projections. Our ensemble of emission-driven simulations span the global temperature response of the CMIP5 emission-driven simulations, except at the low end. Combinations of low climate sensitivity and low carbon cycle feedbacks lead to a number of CMIP5 responses to lie below our ensemble range. The ensemble simulates a number of high-end responses which lie above the CMIP5 carbon cycle range. These high-end simulations can be linked to sampling a number of stronger carbon cycle feedbacks and to sampling climate sensitivities above 4.5 K. This latter aspect highlights the priority in identifying real-world climatesensitivity constraints which, if achieved, would lead to reductions on the upper bound of projected global mean temperature change. The ensembles of simulations presented here provides a framework to explore relationships between present-day observables and future changes, while the large spread of future-projected changes highlights the ongoing need for such work.