A less cloudy picture of the inter-model spread in future global warming projections

Hu, Xiaoming; Fan, Hanjie; Cai, Ming; Sejas, Sergio A.; Taylor, Patrick C.; Yang, Song

doi:10.1038/s41467-020-18227-9

Cited by 25 publications

(15 citation statements)

References 31 publications

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“…Our results point to somewhat divergent conclusions. Similarly to Hu et al (2020), our results point to the water vapor feedback as the main mechanism leading to model spread. If the model spread is only attributed using feedback analysis, model differences in the forcing and adjustments may counteract some of the differences.…”

Section: Model-to-model Spread In Regional Effective Temperature Responses For Different Forcerssupporting

confidence: 67%

Understanding the surface temperature response and its uncertainty to CO2, CH4, black carbon, and sulfate

et al. 2021

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Abstract. Understanding the regional surface temperature responses to different anthropogenic climate forcing agents, such as greenhouse gases and aerosols, is crucial for understanding past and future regional climate changes. In modern climate models, the regional temperature responses vary greatly for all major forcing agents, but the causes of this variability are poorly understood. Here, we analyze how changes in atmospheric and oceanic energy fluxes due to perturbations in different anthropogenic climate forcing agents lead to changes in global and regional surface temperatures. We use climate model data on idealized perturbations in four major anthropogenic climate forcing agents (CO2, CH4, sulfate, and black carbon aerosols) from Precipitation Driver Response Model Intercomparison Project (PDRMIP) climate experiments for six climate models (CanESM2, HadGEM2-ES, NCAR-CESM1-CAM4, NorESM1, MIROC-SPRINTARS, GISS-E2). Particularly, we decompose the regional energy budget contributions to the surface temperature responses due to changes in longwave and shortwave fluxes under clear-sky and cloudy conditions, surface albedo changes, and oceanic and atmospheric energy transport. We also analyze the regional model-to-model temperature response spread due to each of these components. The global surface temperature response stems from changes in longwave emissivity for greenhouse gases (CO2 and CH4) and mainly from changes in shortwave clear-sky fluxes for aerosols (sulfate and black carbon). The global surface temperature response normalized by effective radiative forcing is nearly the same for all forcing agents (0.63, 0.54, 0.57, 0.61 K W−1 m2). While the main physical processes driving global temperature responses vary between forcing agents, for all forcing agents the model-to-model spread in temperature responses is dominated by differences in modeled changes in longwave clear-sky emissivity. Furthermore, in polar regions for all forcing agents the differences in surface albedo change is a key contributor to temperature responses and its spread. For black carbon, the modeled differences in temperature response due to shortwave clear-sky radiation are also important in the Arctic. Regional model-to-model differences due to changes in shortwave and longwave cloud radiative effect strongly modulate each other. For aerosols, clouds play a major role in the model spread of regional surface temperature responses. In regions with strong aerosol forcing, the model-to-model differences arise from shortwave clear-sky responses and are strongly modulated by combined temperature responses to oceanic and atmospheric heat transport in the models.

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Section: Model-to-model Spread In Regional Effective Temperature Responses For Different Forcerssupporting

confidence: 67%

Understanding the surface temperature response and its uncertainty to CO2, CH4, black carbon, and sulfate

et al. 2021

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“…Evidence from coupled climate models suggests that feedbacks local to the Arctic are the dominant drivers of amplification (e.g., Stuecker et al, 2018) although interactions with lower latitudes through changing atmospheric and oceanic heat transport for example can play some role (e.g., Mahlstein and Knutti, 2011). There are considerable discrepancies across models in the magnitude and relative importance of various feedbacks which contributes to uncertainty in Arctic warming (e.g., Pithan and Mauritsen, 2014;Bonan et al, 2018;Hu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

The Emergence and Transient Nature of Arctic Amplification in Coupled Climate Models

Holland

Landrum

2021

Front. Earth Sci.

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Under rising atmospheric greenhouse gas concentrations, the Arctic exhibits amplified warming relative to the globe. This Arctic amplification is a defining feature of global warming. However, the Arctic is also home to large internal variability, which can make the detection of a forced climate response difficult. Here we use results from seven model large ensembles, which have different rates of Arctic warming and sea ice loss, to assess the time of emergence of anthropogenically-forced Arctic amplification. We find that this time of emergence occurs at the turn of the century in all models, ranging across the models by a decade from 1994–2005. We also assess transient changes in this amplified signal across the 21st century and beyond. Over the 21st century, the projections indicate that the maximum Arctic warming will transition from fall to winter due to sea ice reductions that extend further into the fall. Additionally, the magnitude of the annual amplification signal declines over the 21st century associated in part with a weakening albedo feedback strength. In a simulation that extends to the 23rd century, we find that as sea ice cover is completely lost, there is little further reduction in the surface albedo and Arctic amplification saturates at a level that is reduced from its 21st century value.

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“…Therefore, if using a TOA approach, it is important to understand that the differences in interpretation are due to process contributions hidden within the lapse-rate feedback. Alternatively, one could solely use the CFRAM to study radiative and non-radiative process contributions to surface and atmospheric warming and the inter-model warming spread (Cai and Tung 2012;Taylor et al, 2013;Sejas et al, 2014;Song et al, 2014a;Yoshimori et al, 2014;Hu et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Understanding the Differences Between TOA and Surface Energy Budget Attributions of Surface Warming

Sejas

Cai

et al. 2021

Front. Earth Sci.

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Energy budget decompositions have widely been used to evaluate individual process contributions to surface warming. Conventionally, the top-of-atmosphere (TOA) energy budget has been used to carry out such attribution, while other studies use the surface energy budget instead. However, the two perspectives do not provide the same interpretation of process contributions to surface warming, particularly when executing a spatial analysis. These differences cloud our understanding and inhibit our ability to shrink the inter-model spread. Changes to the TOA energy budget are equivalent to the sum of the changes in the atmospheric and surface energy budgets. Therefore, we show that the major discrepancies between the surface and TOA perspectives are due to non-negligible changes in the atmospheric energy budget that differ from their counterparts at the surface. The TOA lapse-rate feedback is the manifestation of multiple processes that produce a vertically non-uniform warming response such that it accounts for the asymmetry between the changes in the atmospheric and surface energy budgets. Using the climate feedback-response analysis method, we are able to decompose the lapse-rate feedback into contributions by individual processes. Combining the process contributions that are hidden within the lapse-rate feedback with their respective direct impacts on the TOA energy budget allows for a very consistent picture of process contributions to surface warming and its inter-model spread as that given by the surface energy budget approach.

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A less cloudy picture of the inter-model spread in future global warming projections

Cited by 25 publications

References 31 publications

Understanding the surface temperature response and its uncertainty to CO<sub>2</sub>, CH<sub>4</sub>, black carbon, and sulfate

Understanding the surface temperature response and its uncertainty to CO<sub>2</sub>, CH<sub>4</sub>, black carbon, and sulfate

The Emergence and Transient Nature of Arctic Amplification in Coupled Climate Models

Understanding the Differences Between TOA and Surface Energy Budget Attributions of Surface Warming

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A less cloudy picture of the inter-model spread in future global warming projections

Cited by 25 publications

References 31 publications

Understanding the surface temperature response and its uncertainty to CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt;, black carbon, and sulfate

Understanding the surface temperature response and its uncertainty to CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt;, black carbon, and sulfate

The Emergence and Transient Nature of Arctic Amplification in Coupled Climate Models

Understanding the Differences Between TOA and Surface Energy Budget Attributions of Surface Warming

Contact Info

Product

Resources

About

Understanding the surface temperature response and its uncertainty to CO<sub>2</sub>, CH<sub>4</sub>, black carbon, and sulfate

Understanding the surface temperature response and its uncertainty to CO<sub>2</sub>, CH<sub>4</sub>, black carbon, and sulfate