Climate Sensitivity of a One-Dimensional Radiative-Convective Model with Cloud Feedback

Wang, Wei‐Chyung; Rossow, William B.; Yao, Mao‐Sung; Wolfson, Marilyn M.

doi:10.1175/1520-0469(1981)038<1167:csoaod>2.0.co;2

Cited by 58 publications

(32 citation statements)

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“…Radiative properties depend on the liquid water path not cloud amount (e.g. Wang et al, 1981;Charlock, 1982;Somerville and Remer, 1984). The observational data such as those described here offer little information about these other characteristics.…”

Section: Gfdlmentioning

confidence: 83%

Increasing cloud in a warming world

Henderson‐Sellers

1986

Climatic Change

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Cloud amount records for the U.S.A. have been analyzed in the context of the 'warming world' analogue model described by Lough et al. (1983). Cloud amount increases over practically the entire U.S.A. in all seasons. This result considerably strengthens the more tentative conclusion of HendersonSellers (1986) that clotid amount increases over Europe in the same warming world scenario. These results are in contrast to the few numerical model predictions of cloud changes in warming world experiments. A possible, rather tantalizing, conclusion is that current GCM cloud prediction schemes tend to enhance temperature increases through cloud-climate feedback whereas the historical data could suggest a negative feedback. Part, possibly all, of this difference may be the result of the fundamental distinction between the two experimental scenarios: the equilibrium change modelled by GCMs as compared to the smaller transient change represented by the historical analogue. On the other hand the current 'real-world' experiment is, itself, a transient change in boundary and atmospheric conditions. At the least, surface-observed cloudiness seems to offer a useful and complementary data source with which to examine one aspect of the performance of numerical climate models.

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Section: Gfdlmentioning

confidence: 83%

Increasing cloud in a warming world

Henderson‐Sellers

1986

Climatic Change

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“…Cloud feedback concepts were also specifically pursued using RCE models. Paltridge (1980), Wang et al (1981), Charlock (1982), Sommerville and Remer (1984), Sommerville and Iacobellis (1986), and Stephens et al (1990) are all examples of cloud feedback ideas that involve cloud optical depth and equivalent water and ice path information. A more systematic analysis of the water path-optical depth feedback is provided below.…”

Section: A Rce and Cloud Feedbackmentioning

confidence: 99%

Cloud Feedbacks in the Climate System: A Critical Review

2005

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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 earthatmosphere 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 ...

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“…But the general circulation of the atmosphere is the large-scale, thermally driven field of motion in which Table 2. Surface temperature change induced by a doubled CO 2 concentration as calculated by selected radiative-convective models Study AT s (K) Manabe and Wetherald (1967) 1.33~.92 Manabe (1971) 1.9 Augustsson and Ramanathan (1977) 1.98-3.2 Rowntree and Walker (1978) 0.78-2.76 Hunt and Wells (1979) 1.82-2.2 Wang and Stone (1980) 2.004.20 Charlock (1981) 1.58-2.25 Hansen et al (1981) 1.22-3.5 Hummel and Kuhn (1981 a) 0.79-1.94 Hummel and Kuhn (1981 b) 0.8 -1.2 Hummel and Reck (1981) 1.71-2.05 Hunt (1981) 0.69-1.82 Wang et al (1981) 1.47-2.80 Hummel (1982) 1.29-1.83 Lindzen et al (1982) 1.46-1.93 Lal and Ramanathan (1984) 1.8 -2.4 Somerville and Remer (1984) 0. 48-1.74 there are interactions between the heating and motion fields.…”

Section: General Circulation Modelsmentioning

confidence: 99%

“…(1) written as f = 1 (AT~)o ATs Manabe and Wetherald (1967) . d With the moist adiabatic lapse rate e Determined with fixed cloud temperature prescribed equal to that of the 1 • CO 2 simulation with no feedback f With variable cloud eover prescribed as in Wang et al (1981) g With variable optical depth ~ prescribed as in Wang et al (1981) and cloud albedo, absorptivity and transmissivity parameterized in terms of following Stephens et al (1984) h With variable surface albedo prescribed as in Wang and Stone (1980) and our estimate of(ATs)o = GoAQ --1.2 K based on AQ = 4 Wm 2 and Go = 0.3 K/(Wm-2). Thus, for the RCMs (Table 2), -1.5 < f < 0.7, and for the GCMs (Table 3), 0 < f < 0.7.…”

Section: Zero-feedback Responsementioning

confidence: 99%

Equilibrium and transient climatic warming induced by increased atmospheric CO2

Schlesinger

1986

Climate Dynamics

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The change in the Earth's equilibrium global mean surface temperature induced by a doubling of the CO2 concentration has been estimated as 0.2 to 10 K by surface energy balance models, 0.5 to 4.2 K by radiativeconvective models, and 1.3 to 4.2 K by general circulation models. These wide ranges are interpreted and quantified here in terms of the direct radiative forcing of the increased CO2, the response of the climate system in the absence of feedback processes, and the feedbacks of the climate system. It is the range in the values of these feedbacks that leads to the ranges in the projections of the 340 global mean surface warming. The time required for a CO2-induced climate change to reach equilibrium has been characterized by an e-folding time ze with values estimated by a variety of climate/ocean models as 10 to 100 years. Analytical and numerical studies show that this 335

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Climate Sensitivity of a One-Dimensional Radiative-Convective Model with Cloud Feedback

Cited by 58 publications

References 0 publications

Increasing cloud in a warming world

Increasing cloud in a warming world

Cloud Feedbacks in the Climate System: A Critical Review

Equilibrium and transient climatic warming induced by increased atmospheric CO2

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