How Well Do We Understand and Evaluate Climate Change Feedback Processes?

Bony, Sandrine; Colman, Robert; Kattsov, Vladimir; Allan, Richard P.; Bretherton, Christopher S.; Dufresne, Jean‐Louis; Hall, Alex; Hallegatte, Stéphane; Holland, Marika M.; Ingram, William; Randall, David A.; Soden, Brian J.; Tselioudis, George; Webb, Mark J.

doi:10.1175/jcli3819.1

Cited by 888 publications

(778 citation statements)

References 223 publications

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“…There is another school of approach in evaluating feedbacks called the online feedback suppression method (OFSM; Bony et al 2006). In this approach one or more feedback loops are artificially forced to be turned off (Graversen and Wang 2009;Hall 2004;Hall and Manabe 1999;Schneider et al 1999;Vavrus 2004).…”

Section: Discussionmentioning

confidence: 99%

Robust Seasonality of Arctic Warming Processes in Two Different Versions of the MIROC GCM

et al. 2014

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It is one of the most robust projected responses of climate models to the increase of atmospheric CO 2 concentration that the Arctic experiences a rapid warming with a magnitude larger than the rest of the world. While many processes are proposed as important, the relative contribution of individual processes to the Arctic warming is not often investigated systematically. Feedbacks are quantified in two different versions of an atmosphere-ocean GCM under idealized transient experiments based on an energy balance analysis that extends from the surface to the top of the atmosphere. The emphasis is placed on the largest warming from late autumn to early winter (October-December) and the difference from other seasons. It is confirmed that dominating processes vary with season. In autumn, the largest contribution to the Arctic surface warming is made by a reduction of ocean heat storage and cloud radiative feedback. In the annual mean, on the other hand, it is the albedo feedback that contributes the most, with increasing ocean heat uptake to the deeper layers working as a negative feedback. While the qualitative results are robust between the two models, they differ quantitatively, indicating the need for further constraint on each process. Ocean heat uptake, lower tropospheric stability, and low-level cloud response probably require special attention.

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Section: Discussionmentioning

confidence: 99%

Robust Seasonality of Arctic Warming Processes in Two Different Versions of the MIROC GCM

et al. 2014

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“…Based on the ClausiusClapeyron equation, the saturation water-vapor pressure rises with temperature at approximately 7%/K [1], which implies an increase of precipitable water at the same rate regarding the marginal change in relative humidity predicted in global climate models [33]. Figure 7(b) compares the precipitation amount for each category in the two coldest years (1984 and 1985) and two warmest years (1998 and 2005) averaged over 60°S-60°N.…”

Section: Global Precipitation Spectral Changementioning

confidence: 99%

Change of precipitation intensity spectra at different spatial scales under warming conditions

2013

Chin. Sci. Bull.

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The long-term change of the whole spectra of precipitation intensity in China is examined using observed daily data recorded at 477 surface stations for the period from 1961 to 2008. The results show a spatially coherent decrease of trace precipitation despite different reduction magnitudes among the regions. For measurable precipitation, significant regional and seasonal characteristics are observed. In autumn, the whole measurable precipitation decreased over Eastern China (east of 98°E). In summer and winter, a significant increase of heavy precipitation and decrease of light precipitation are detected south of Eastern China. In Western China, measurable precipitation is found to have increased in all four seasons. Composite analysis reveals a quasi-linear relationship between increasing surface temperature and precipitation on a global scale. The responses of precipitation at different intensities to the increased temperature are distinct, with a significant spectra-shifting from light to heavy precipitation. Compared with precipitation over the ocean, the amplification of heavy precipitation over land is relatively less, most likely constrained by the limited water supply. The response of regional precipitation to global warming shows greater uncertainties compared with those on the global scale, perhaps due to interference by more complex topography and land cover, as well as human activities, among other factors. precipitation spectra, global warming, spatial scale, extreme precipitation, composite analysis Citation:Wu F T, Fu C B. Change of precipitation intensity spectra at different spatial scales under warming conditions.

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“…The magnitude of cloud 7116 Z. Zhang et al: Influence of ice particle model on satellite ice cloud retrieval feedbacks remains very uncertain (Bony et al, 2006). The need for a better understanding of ice clouds is evident.…”

Section: Introductionmentioning

confidence: 99%

Influence of ice particle model on satellite ice cloud retrieval: lessons learned from MODIS and POLDER cloud product comparison

et al. 2009

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Abstract. The influence is investigated of the assumed ice particle microphysical and optical model on inferring ice cloud optical thickness (τ ) from satellite measurements of the Earth's reflected shortwave radiance. Ice cloud τ are inferred, and subsequently compared, using products from MODIS (MODerate resolution Imaging Spectroradiometer) and POLDER (POLarization and Directionality of the Earth's Reflectances). POLDER τ values are found to be substantially smaller than those from collocated MODIS data. It is shown that this difference is caused primarily by the use of different ice particle bulk scattering models in the two retrievals, and more specifically, the scattering phase function. Furthermore, the influence of the ice particle model on the derivation of ice cloud radiative forcing (CRF) from satellite retrievals is studied. Three sets of shortwave CRF are calculated using different combinations of the retrieval and associated ice particle models. It is shown that the uncertainty associated with an ice particle model may lead to two types of errors in estimating CRF from satellite retrievals. One stems from the retrieval itself and the other is due to the optical properties, such as the asymmetry factor, used for CRF calculations. Although a comparison of the CRFs reveals that these two types of errors tend to cancel each other, significant differences are still found between the three CRFs, which indicates that the ice particle model affects notCorrespondence to: Z. Zhang (zzbatmos@umbc.edu) only optical thickness retrievals but also CRF calculations. In addition to CRF, the effect of the ice particle model on the derivation of seasonal variation of τ from satellite measurements is discussed. It is shown that optical thickness retrievals based on the same MODIS observations, but derived using different assumptions of the ice particle model, can be substantially different. These differences can be divided into two parts. The first-order difference is mainly caused by the differences in the asymmetry factor. The second-order difference is related to seasonal changes in the sampled scattering angles and therefore dependent on the sun-satellite viewing geometry. Because of this second-order difference, the use of different ice particle models may lead to a different understanding of the seasonal variation of τ .

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How Well Do We Understand and Evaluate Climate Change Feedback Processes?

Cited by 888 publications

References 223 publications

Robust Seasonality of Arctic Warming Processes in Two Different Versions of the MIROC GCM

Robust Seasonality of Arctic Warming Processes in Two Different Versions of the MIROC GCM

Change of precipitation intensity spectra at different spatial scales under warming conditions

Influence of ice particle model on satellite ice cloud retrieval: lessons learned from MODIS and POLDER cloud product comparison

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