Papers on cloud feedback observations

This is a list of papers on cloud feedback observations. Note that most papers listed are local analyses. The list is not complete, and will most likely be updated in the future in order to make it more thorough and more representative.

UPDATES: (August 16, 2021) Ceppi & Nowack (2021) added – thanks to climafuturo for pointing it out. (September 12, 2016): Norris et al. (2016) added. (April 3, 2012): Zelinka & Hartmann (2010) and Gehlot & Quaas (2012) added – thanks to Barry for pointing them out. (December 10, 2010): Dessler (2010) added. (December 6, 2010): Palm et al. (2010) added.

Observational evidence that cloud feedback amplifies global warming – Ceppi & Nowack (2021) “Global warming drives changes in Earth’s cloud cover, which, in turn, may amplify or dampen climate change. This “cloud feedback” is the single most important cause of uncertainty in Equilibrium Climate Sensitivity (ECS)—the equilibrium global warming following a doubling of atmospheric carbon dioxide. Using data from Earth observations and climate model simulations, we here develop a statistical learning analysis of how clouds respond to changes in the environment. We show that global cloud feedback is dominated by the sensitivity of clouds to surface temperature and tropospheric stability. Considering changes in just these two factors, we are able to constrain global cloud feedback to 0.43 ± 0.35 W⋅m⁻²⋅K⁻¹ (90% confidence), implying a robustly amplifying effect of clouds on global warming and only a 0.5% chance of ECS below 2 K. We thus anticipate that our approach will enable tighter constraints on climate change projections, including its manifold socioeconomic and ecological impacts.” Paulo Ceppi, Peer Nowack, PNAS July 27, 2021 118 (30) e2026290118; https://doi.org/10.1073/pnas.2026290118.

Evidence for climate change in the satellite cloud record – Norris et al. (2016) “Clouds substantially affect Earth’s energy budget by reflecting solar radiation back to space and by restricting emission of thermal radiation to space. They are perhaps the largest uncertainty in our understanding of climate change, owing to disagreement among climate models and observational datasets over what cloud changes have occurred during recent decades and will occur in response to global warming. This is because observational systems originally designed for monitoring weather have lacked sufficient stability to detect cloud changes reliably over decades unless they have been corrected to remove artefacts. Here we show that several independent, empirically corrected satellite records exhibit large-scale patterns of cloud change between the 1980s and the 2000s that are similar to those produced by model simulations of climate with recent historical external radiative forcing. Observed and simulated cloud change patterns are consistent with poleward retreat of mid-latitude storm tracks, expansion of subtropical dry zones, and increasing height of the highest cloud tops at all latitudes. The primary drivers of these cloud changes appear to be increasing greenhouse gas concentrations and a recovery from volcanic radiative cooling. These results indicate that the cloud changes most consistently predicted by global climate models are currently occurring in nature.” Joel R. Norris, Robert J. Allen, Amato T. Evan, Mark D. Zelinka, Christopher W. O’Dell & Stephen A. Klein, Nature 536, 72–75 (04 August 2016) doi:10.1038/nature18273.

Convection-climate feedbacks in the ECHAM5 general circulation model: Evaluation of cirrus cloud life cycles with ISCCP satellite data from a Lagrangian trajectory perspective – Gehlot & Quaas (2012) “A process-oriented climate model evaluation is presented, applying the International Satellite Cloud Climatology Project (ISCCP) simulator to pinpoint deficiencies related to the cloud processes in the ECHAM5 general circulation model. A Lagrangian trajectory analysis is performed to track the transitions of anvil cirrus originating from deep-convective detrainment to cirrostratus and thin cirrus, comparing ISCCP observations and the ECHAM5 model. Trajectories of cloudy air parcels originating from deep convection are computed for both, the ISCCP observations and the model, over which the ISCCP joint histograms are used for analyzing the cirrus life cycle over 5 days. The clouds originating from detrainment from deep-convection decay and gradually thin-out after the convective event over 3 to 4 days. The effect of the convection-cirrus transitions in a warmer climate is analyzed, in order to understand the climate feedbacks due to deep-convective cloud transitions. An idealized climate change simulation is performed using a +2K Sea Surface Temperature (SST) perturbation. The Lagrangian trajectory analysis over perturbed climate suggests that more and thicker cirrostratus and cirrus clouds occur in the warmer climate compared to the present day climate. Stronger convection is noticed in the perturbed climate which leads to an increased precipitation, especially on day-2 and -3 after the individual convective events. The shortwave and the longwave cloud forcings both increase in the warmer climate, with an increase of net cloud radiative forcing (NCRF), leading to an overall positive feedback of the increased cirrostratus and cirrus clouds from a Lagrangian transition perspective.” Swati Gehlot and Johannes Quaas, Journal of Climate 2012.

Why is longwave cloud feedback positive? – Zelinka & Hartmann (2010) “Longwave cloud feedback is systematically positive and nearly the same magnitude across all global climate models used in the Intergovernmental Panel on Climate Change Fourth Assessment Report (AR4). Here it is shown that this robust positive longwave cloud feedback is caused in large part by the tendency for tropical high clouds to rise in such a way as to remain at nearly the same temperature as the climate warms. Furthermore, it is shown that such a cloud response to a warming climate is consistent with well-known physics, specifically the requirement that, in equilibrium, tropospheric heating by convection can only be large in the altitude range where radiative cooling is efficient, following the fixed anvil temperature hypothesis of Hartmann and Larson (2002). Longwave cloud feedback computed assuming that high-cloud temperature follows upper tropospheric convergence-weighted temperature, which we refer to as proportionately higher anvil temperature, gives an excellent prediction of the longwave cloud feedback in the AR4 models. The ensemble-mean feedback of 0.5 W m⁻² K⁻¹ is much larger than that calculated assuming clouds remain at fixed pressure, highlighting the large contribution from rising cloud tops to the robustly positive feedback. An important result of this study is that the convergence profile computed from clear-sky energy and mass balance warms slightly as the climate warms, in proportion to the increase in stability, which results in a longwave cloud feedback that is slightly smaller than that calculated assuming clouds remain at fixed temperature.” Zelinka, M. D., and D. L. Hartmann (2010), Why is longwave cloud feedback positive?, J. Geophys. Res., 115, D16117, doi:10.1029/2010JD013817. [Full text]

A Determination of the Cloud Feedback from Climate Variations over the Past Decade – Dessler (2010) “Estimates of Earth’s climate sensitivity are uncertain, largely because of uncertainty in the long-term cloud feedback. I estimated the magnitude of the cloud feedback in response to short-term climate variations by analyzing the top-of-atmosphere radiation budget from March 2000 to February 2010. Over this period, the short-term cloud feedback had a magnitude of 0.54 ± 0.74 (2σ) watts per square meter per kelvin, meaning that it is likely positive. A small negative feedback is possible, but one large enough to cancel the climate’s positive feedbacks is not supported by these observations. Both long- and short-wave components of short-term cloud feedback are also likely positive. Calculations of short-term cloud feedback in climate models yield a similar feedback. I find no correlation in the models between the short- and long-term cloud feedbacks.” A. E. Dessler, Science 10 December 2010, Vol. 330 no. 6010 pp. 1523-1527, DOI: 10.1126/science.1192546.

Influence of Arctic sea ice extent on polar cloud fraction and vertical structure and implications for regional climate – Palm et al. (2010) “Recent satellite lidar measurements of cloud properties spanning a period of 5 years are used to examine a possible connection between Arctic sea ice amount and polar cloud fraction and vertical distribution. We find an anticorrelation between sea ice extent and cloud fraction with maximum cloudiness occurring over areas with little or no sea ice. We also find that over ice-free regions, there is greater low cloud frequency and average optical depth. Most of the optical depth increase is due to the presence of geometrically thicker clouds over water. In addition, our analysis indicates that over the last 5 years, October and March average polar cloud fraction has increased by about 7% and 10%, respectively, as year average sea ice extent has decreased by 5%–7%. The observed cloud changes are likely due to a number of effects including, but not limited to, the observed decrease in sea ice extent and thickness. Increasing cloud amount and changes in vertical distribution and optical properties have the potential to affect the radiative balance of the Arctic region by decreasing both the upwelling terrestrial longwave radiation and the downward shortwave solar radiation. Because longwave radiation dominates in the long polar winter, the overall effect of increasing low cloud cover is likely a warming of the Arctic and thus a positive climate feedback, possibly accelerating the melting of Arctic sea ice.” [Conference article (use the “extended abstract” link)]

Observational and Model Evidence for Positive Low-Level Cloud Feedback – Clement et al. (2009) “Feedbacks involving low-level clouds remain a primary cause of uncertainty in global climate model projections. This issue was addressed by examining changes in low-level clouds over the Northeast Pacific in observations and climate models. Decadal fluctuations were identified in multiple, independent cloud data sets, and changes in cloud cover appeared to be linked to changes in both local temperature structure and large-scale circulation. This observational analysis further indicated that clouds act as a positive feedback in this region on decadal time scales. … The only model that passed this test simulated a reduction in cloud cover over much of the Pacific when greenhouse gases were increased, providing modeling evidence for a positive low-level cloud feedback.” [Full text]

Cloud forcing and feedback during recent Arctic sea ice loss – Kay et al. (2009) “Recent impressive declines in Arctic sea ice extent provide new opportunities to assess the influence of cloud forcing and feedbacks on sea ice loss in observations and models. … Our results indicate large-scale atmospheric circulation patterns and sea surface temperatures primarily controlled Arctic cloud forcing. Cloud feedback on sea ice extent loss is important because it can amplify or dampen ice loss processes. … We find that the observed cloud feedbacks are not well represented in either assimilation system. We explore reasons for incorrect model cloud feedbacks and the implications for the radiative forcing on projected sea ice loss.” [Presentation material]

Evaluation of Cloud Feedback at Local Scale: Warming or Cooling? – Malek (2009) A conference paper. “To evaluate cloudiness and its feedback at local scale, a radiation station was set up, which used two CM21 Kipp & Zonen pyranometers (one inverted), and two CGI Kipp & Zonen pyrgeometers (one inverted) in Logan, Utah, USA. … As shown, due to cloudiness at the experimental site, the net radiation loss was 2791−3707 = −916 MJ m⁻² y⁻¹, which indicates cooler temperature and a negative feedback due to cloudiness.”

Cloud radiative forcing of subtropical low level clouds in global models – Karlsson et al. (2008) “Simulations of subtropical marine low clouds and their radiative properties by nine coupled ocean-atmosphere climate models participating in the fourth assesment report (AR4) of the intergovernmental panel on climate change (IPCC) are analyzed. Satellite observations of cloudiness and radiative fluxes at the top of the atmosphere (TOA) are utilized for comparison. … As a consequence of the combination of these two biases, this study suggests that all investigated models are likely to overestimate the radiative response to changes in low level subtropical cloudiness.”

Cloud Feedbacks in the Climate System: A Critical Review – Stephens (2005) A review article. “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. … 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. … 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.” [Full text]

The Iris Hypothesis: A Negative or Positive Cloud Feedback? – Lin et al. (2002) “Using the Tropical Rainfall Measuring Mission (TRMM) satellite measurements over tropical oceans, this study evaluates the iris hypothesis recently proposed by Lindzen et al. that tropical upper-tropospheric anvils act as a strong negative feedback in the global climate system. The modeled radiative fluxes of Lindzen et al. are replaced by the Clouds and the Earth’s Radiant Energy System (CERES) directly observed broadband radiation fields. The observations show that the clouds have much higher albedos and moderately larger longwave fluxes than those assumed by Lindzen et al. As a result, decreases in these clouds would cause a significant but weak positive feedback to the climate system, instead of providing a strong negative feedback.” [Full text]

Influence of cloud feedback on annual variation of global mean surface temperature – Tsushima & Manabe (2001) “The goal of this study is to estimate the cloud radiative feedback effect on the annual variation of the global mean surface temperature using radiative flux data from the Earth Radiation Budget Experiment. We found that the influence of the cloud feedback upon the change of the global mean surface temperature is quite small, though the increase of the temperature is as much as 3.3 K from January to July. On a global scale, we found no significant relationship between either solar reflectivity of clouds or effective cloud top height and the annual cycle of surface temperature.” [Full text]

Cloud Feedbacks – Randall et al. (2000) A symposium paper. “The observational literature contains some evidence of cloud feedbacks on decadal time scales. Recent work shows that global atmospheric circulation models (AGCMs) are capable of simulating many observed fluctuations of cloudiness.” [Full text]

Interannual Variability in Stratiform Cloudiness and Sea Surface Temperature – Norris & Leovy (1994) “Long-term datasets of cloudiness and sea surface temperature (SST) from surface observations from 1952 to 1981 are used to examine interannual variations in MSC and SST. Linear correlations of anomalies in seasonal MSC amount with seasonal SST anomalies are negative and significant in midlatitude and eastern subtropical oceans, especially during summer. Significant negative correlations between SST and nimbostratus and nonprecipitating midlevel cloudiness are also observed at midlatitudes during summer, suggesting that summer storm tracks shift from year to year following year-to-year meridional shifts in the SST gradient. Over the 30-yr period, there are significant upward trends in MSC amount over the northern midlatitude oceans and a significant downward trend off the coast of California. The highest correlations and trends occur where gradients in MSC and SST are strongest.” [Full text]

Cirrus-cloud thermostat for tropical sea surface temperatures tested using satellite data – Fu et al. (1992) “Ramanathan and Collins have suggested cirrus clouds associated with tropical convection might act as a ‘thermostat’ to limit tropical sea surface temperatures (SSTs) to less than 305 K by shielding the ocean from sunlight. Here we use satellite radiance data to test this hypothesis. We find that changes in the properties of cirrus clouds do not seem to be related to changes in SSTs. During the 1987 El Niño event, large-scale perturbations to the radiative effects of cirrus clouds were controlled by changes in large-scale atmospheric circulation rather than directly by SSTs. If they are averaged over the entire tropical Pacific, increases in surface evaporative cooling are stronger than decreases in solar heating owing to cirrus cloud variations. Thus we conclude that there is no ‘cirrus cloud thermostat’ to tropical SSTs.” [Full text]

Thermodynamic regulation of ocean warming by cirrus clouds deduced from observations of the 1987 El Niño – Ramanathan & Collins (1991) “Observations made during the 1987 El Niño show that in the upper range of sea surface temperatures, the greenhouse effect increases with surface temperature at a rate which exceeds the rate at which radiation is being emitted from the surface. In response to this ‘super greenhouse effect’, highly reflective cirrus clouds are produced which act like a thermostat shielding the ocean from solar radiation. The regulatory effect of these cirrus clouds may limit sea surface temperatures to less than 305 K.” [Full text]

Closely related

Papers on global cloud cover trends
Papers on the albedo of the Earth