Applying global warming levels of emergence to highlight the increasing population exposure to temperature and precipitation extremes

David Gampe, Clemens Schwingshackl, A. Böhnisch, Magdalena Mittermeier, M. Sandstad, R. R. Wood
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Abstract

Abstract. The swift and ongoing rise of global temperatures over the past decades led to an increasing number of climate variables showing statistically significant changes compared to their pre-industrial state. Determining when these climate signals emerge from the noise of internal climate variability (i.e., estimating the time of emergence, ToE) is crucial for climate risk assessments and adaptation planning. However, robustly disentangling the climate signal from internal variability represents a challenging task. While climate projections are communicated increasingly frequently through global warming levels (GWLs), the ToE is usually still expressed in terms of time horizons. Here, we present a framework to robustly derive global warming levels of emergence (GWLoE) using five single-model initial-condition large ensembles (SMILEs) and apply it to four selected temperature and precipitation indices. We show that the concept of GWLoE is particularly promising to constrain temperature projections and that it proves a viable tool to communicate scientific results. We find that > 85 % of the global population is exposed to emerged signals of nighttime temperatures at a GWL of 1.5 °C, increasing to > 95 % at 2.0 °C. Daily maximum temperature follows a similar yet less pronounced path. Emerged signals for mean and extreme precipitation start appearing at current GWLs and increase steadily with further warming (∼ 10 % population exposed at 2.0 °C). Related probability ratios for the occurrence of extremes indicate a strong increase with widespread saturation of temperature extremes (extremes relative to historical conditions occur every year) reached below 2.5 °C warming particularly in (sub)tropical regions. These results indicate that we are in a critical period for climate action as every fraction of additional warming substantially increases the adverse effects on human wellbeing.
应用全球变暖的出现程度,强调人口受极端气温和降水影响的程度不断增加
摘要过去几十年来,全球气温迅速持续上升,导致越来越多的气候变量与工业化前的状态相比出现了统计意义上的显著变化。确定这些气候信号何时从内部气候变率的噪声中出现(即估计出现时间,ToE)对于气候风险评估和适应规划至关重要。然而,将气候信号与内部变异性有力地分离开来是一项具有挑战性的任务。虽然气候预测越来越多地通过全球变暖水平(GWLs)来传达,但 ToE 通常仍以时间跨度来表示。在这里,我们提出了一个框架,利用五个单一模式初始条件大集合(SMILEs)稳健地推导出全球变暖出现水平(GWLoE),并将其应用于四个选定的温度和降水指数。我们的研究表明,GWLoE 的概念在制约气温预测方面特别有前途,而且证明它是传播科学成果的可行工具。我们发现,在全球升温潜能值为 1.5 ℃ 时,> 85% 的全球人口受到夜间气温信号的影响,而在 2.0 ℃ 时,这一比例增加到> 95%。日最高气温的变化趋势类似,但不太明显。平均降水量和极端降水量的新信号在当前全球升温潜能值时开始出现,并随着进一步变暖而稳步增加(2.0 °C时,10%的人口受到影响)。极端事件发生的相关概率比表明,随着极端气温的广泛饱和(相对于历史条件的极端事件每年都会发生),特别是在(亚)热带地区,气温升至2.5 °C以下时,极端事件发生的概率比会显著增加。这些结果表明,我们正处于采取气候行动的关键时期,因为每增加一小部分升温都会大大增加对人类福祉的不利影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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