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Abstract The response of zonal-mean precipitation minus evaporation ( P − E ) to global warming is investigated using a moist energy balance model (MEBM) with a simple Hadley cell parameterization. The MEBM accurately emulates zonal-mean P − E change simulated by a suite of global climate models (GCMs) under greenhouse gas forcing. The MEBM also accounts for most of the intermodel differences in GCM P − E change and better emulates GCM P − E change when compared to the “wet-gets-wetter, dry-gets-drier” thermodynamic mechanism. The intermodel spread in P − E change is attributed to intermodel differences in radiative feedbacks, which account for 60%–70% of the intermodel variance, with smaller contributions from radiative forcing and ocean heat uptake. Isolating the intermodel spread of feedbacks to specific regions shows that tropical feedbacks are the primary source of intermodel spread in zonal-mean P − E change. The ability of the MEBM to emulate GCM P − E change is further investigated using idealized feedback patterns. A less negative and narrowly peaked feedback pattern near the equator results in more atmospheric heating, which strengthens the Hadley cell circulation in the deep tropics through an enhanced poleward heat flux. This pattern also increases gross moist stability, which weakens the subtropical Hadley cell circulation. These two processes in unison increase P − E in the deep tropics, decrease P − E in the subtropics, and narrow the intertropical convergence zone. Additionally, a feedback pattern that produces polar-amplified warming partially reduces the poleward moisture flux by weakening the meridional temperature gradient. It is shown that changes to the Hadley cell circulation and the poleward moisture flux are crucial for understanding the pattern of GCM P − E change under warming. Significance Statement Changes to the hydrological cycle over the twenty-first century are predicted to impact ecosystems and socioeconomic activities throughout the world. While it is broadly expected that dry regions will get drier and wet regions will get wetter, the magnitude and spatial structure of these changes remains uncertain. In this study, we use an idealized climate model, which assumes how energy is transported in the atmosphere, to understand the processes setting the pattern of precipitation and evaporation under global warming. We first use the idealized climate model to explain why comprehensive climate models predict different changes to precipitation and evaporation across a range of latitudes. We show this arises primarily from climate feedbacks, which act to amplify or dampen the amount of warming. Ocean heat uptake and radiative forcing play secondary roles but can account for a significant amount of the uncertainty in regions where ocean circulation influences the rate of warming. We further show that uncertainty in tropical feedbacks (mainly from clouds) affects changes to the hydrological cycle across a range of latitudes. We then show how the pattern of climate feedbacks affects how the patterns of precipitation and evaporation respond to climate change through a set of idealized experiments. These results show how the pattern of climate feedbacks impacts tropical hydrological changes by affecting the strength of the Hadley circulation and polar hydrological changes by affecting the transport of moisture to the high latitudes.
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Tropical High Cloud Feedback Relationships to Climate Sensitivity
Abstract Clouds constitute a large portion of uncertainty in predictions of equilibrium climate sensitivity (ECS). While low cloud feedbacks have been the focus of intermodel studies due to their high variability among global climate models, tropical high cloud feedbacks also exhibit considerable uncertainty. Here, we apply the cloud radiative kernel technique of Zelinka et al. to 22 models across the CMIP5 and CMIP6 ensembles to survey tropical high cloud feedbacks and analyze their relationship to ECS. We find that the net high cloud feedback and its altitude and optical depth feedback components are significantly positively correlated with ECS in the tropical mean. On the other hand, the tropical mean high cloud amount feedback is not correlated with ECS. These relationships are most pronounced outside of areas of strong climatological ascent, suggesting the importance of thin cirrus feedbacks. Finally, we explore connections between high cloud feedbacks, climate sensitivity, and mean state high cloud properties. In general, high ECS models are cloudier in the upper troposphere but have a thinner high cloud population. Moreover, we find that having more thin cirrus in the mean state relates to more positive high cloud altitude and optical depth feedbacks, and it either amplifies or dampens the high cloud amount feedback depending on the large-scale dynamical regime (amplifying in descent and dampening in ascent). In summary, our analysis highlights the importance of tropical high cloud feedbacks for driving intermodel spread in ECS and suggests that mean state high cloud characteristics might provide a unique opportunity for observationally constraining high cloud feedbacks. Significance StatementClouds play an important role in modulating the effects of climate change through feedback processes involving changes to their amount, altitude, and opacity. In this study, we seek to understand how changes to tropical high clouds under warming are related to the magnitude of warming that global climate models simulate. We find that tropical high cloud feedbacks robustly relate to the amount of warming a model predicts and that warmer models tend to have a thinner tropical high cloud climatology. Our results highlight a potential opportunity to form a new constraint using these relationships in order to narrow the spread of warming estimates among global climate models.
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- Award ID(s):
- 2225954
- PAR ID:
- 10562554
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of Climate
- Volume:
- 38
- Issue:
- 2
- ISSN:
- 0894-8755
- Format(s):
- Medium: X Size: p. 583-596
- Size(s):
- p. 583-596
- Sponsoring Org:
- National Science Foundation
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