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REVIEW ARTICLE Cloud Feedbacks in the Climate System: A Critical Review

TLDR
A review of cloud-climate feedbacks can be found in this paper, where it is argued that cloud feedbacks are likely to control the bulk precipitation efficiency and associated responses of the planet's hydrological cycle to climate radiative forcings.
Abstract
This paper offers a critical review of the topic of cloud–climate feedbacks and exposes some of the underlying reasons for the inherent lack of understanding of these feedbacks and why progress might be expected on this important climate problem in the coming decade. Although many processes and related parameters come under the influence of clouds, it is argued that atmospheric processes fundamentally govern the cloud feedbacks via the relationship between the atmospheric circulations, cloudiness, and the radiative and latent heating of the atmosphere. It is also shown how perturbations to the atmospheric radiation budget that are induced by cloud changes in response to climate forcing dictate the eventual response of the global-mean hydrological cycle of the climate model to climate forcing. This suggests that cloud feedbacks are likely to control the bulk precipitation efficiency and associated responses of the planet’s hydrological cycle to climate radiative forcings. The paper provides a brief overview of the effects of clouds on the radiation budget of the earth– atmosphere system and a review of cloud feedbacks as they have been defined in simple systems, one being a system in radiative–convective equilibrium (RCE) and others relating to simple feedback ideas that regulate tropical SSTs. The systems perspective is reviewed as it has served as the basis for most feedback analyses. What emerges is the importance of being clear about the definition of the system. It is shown how different assumptions about the system produce very different conclusions about the magnitude and sign of feedbacks. Much more diligence is called for in terms of defining the system and justifying assumptions. In principle, there is also neither any theoretical basis to justify the system that defines feedbacks in terms of global–time-mean changes in surface temperature nor is there any compelling empirical evidence to do so. The lack of maturity of feedback analysis methods also suggests that progress in understanding climate feedback will require development of alternative methods of analysis. It has been argued that, in view of the complex nature of the climate system, and the cumbersome problems encountered in diagnosing feedbacks, understanding cloud feedback will be gleaned neither from observations nor proved from simple theoretical argument alone. The blueprint for progress must follow a more arduous path that requires a carefully orchestrated and systematic combination of model and observations. Models provide the tool for diagnosing processes and quantifying feedbacks while observations provide the essential test of the model’s credibility in representing these processes. While GCM climate and NWP models represent the most complete description of all the interactions between the processes that presumably establish the main cloud feedbacks, the weak link in the use of these models lies in the cloud parameterization imbedded in them. Aspects of these parameterizations remain worrisome, containing levels of empiricism and assumptions that are hard to evaluate with current global observations. Clearly observationally based methods for evaluating cloud parameterizations are an important element in the road map to progress. Although progress in understanding the cloud feedback problem has been slow and confused by past analysis, there are legitimate reasons outlined in the paper that give hope for real progress in the future.

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Citations
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Journal ArticleDOI

Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models

TL;DR: In this article, the authors analyzed the sensitivity of the tropical cloud radiative forcing to a change in sea surface temperature that is simulated by 15 coupled models simulating climate change and current interannual variability.
Journal ArticleDOI

How Well Do We Understand and Evaluate Climate Change Feedback Processes

TL;DR: In this paper, a review of recent observational, numerical, and theoretical studies of climate feedbacks is presented, showing that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of the feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and (iii) the development of methodologies of evaluation of these inputs using observations.
Journal ArticleDOI

A New Two-Moment Bulk Stratiform Cloud Microphysics Scheme in the Community Atmosphere Model, Version 3 (CAM3). Part I: Description and Numerical Tests

TL;DR: In this article, a two-moment stratiform cloud microphysics scheme in a general circulation model is described, which treats several microphysical processes, including hydrometeor collection, condensation/ evaporation, freezing, melting, and sedimentation.
Journal ArticleDOI

Dreary state of precipitation in global models

TL;DR: In this paper, the authors used CloudSat data to assess the realism of global model precipitation and found that the observed and modeled precipitation are significantly different from the character of liquid precipitation produced by global weather and climate models.
References
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LETTERS An Observationally Based Estimate of the Climate Sensitivity

TL;DR: In this article, a probability distribution for values of the effective climate sensitivity, with a lower bound of 1.6 K (5th percentile), is obtained on the basis of the increase in ocean heat content in recent decades from analyses of observed interior-ocean temperature changes, surface temperature changes measured since 1860, and estimates of anthropogenic and natural radiative forcing of the climate system.
Journal ArticleDOI

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TL;DR: In this paper, the authors compare simulations from different versions of the GFDL Atmospheric Model 2 (AM2) that have widely varying strengths of cloud feedback to illustrate the differences between the two and highlight the potential for changes in cloud radiative forcing to be misinterpreted.
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