Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth

2012 ◽  
Vol 25 (11) ◽  
pp. 3736-3754 ◽  
Author(s):  
Mark D. Zelinka ◽  
Stephen A. Klein ◽  
Dennis L. Hartmann
Keyword(s):  
Optical Depth ◽  
Climate Models ◽  
Negative Impact ◽  
Positive Impact ◽  
Cloud Amount ◽  
Cloud Feedback ◽  
Global Climate Models ◽  
Positive Contribution ◽  
Total Cloud Amount ◽  
Cloud Feedbacks

Cloud radiative kernels and histograms of cloud fraction, both as functions of cloud-top pressure and optical depth, are used to quantify cloud amount, altitude, and optical depth feedbacks. The analysis is applied to doubled-CO2 simulations from 11 global climate models in the Cloud Feedback Model Intercomparison Project. Global, annual, and ensemble mean longwave (LW) and shortwave (SW) cloud feedbacks are positive, with the latter nearly twice as large as the former. The robust increase in cloud-top altitude in both the tropics and extratropics is the dominant contributor to the positive LW cloud feedback. The negative impact of reductions in cloud amount offsets more than half of the positive impact of rising clouds on LW cloud feedback, but the magnitude of compensation varies considerably across the models. In contrast, robust reductions in cloud amount make a large and virtually unopposed positive contribution to SW cloud feedback, though the intermodel spread is greater than for any other individual feedback component. Overall reductions in cloud amount have twice as large an impact on SW fluxes as on LW fluxes, such that the net cloud amount feedback is moderately positive, with no models exhibiting a negative value. As a consequence of large but partially offsetting effects of cloud amount reductions on LW and SW feedbacks, both the mean and intermodel spread in net cloud amount feedback are smaller than those of the net cloud altitude feedback. Finally, the study finds that the large negative cloud feedback at high latitudes results from robust increases in cloud optical depth, not from increases in total cloud amount as is commonly assumed.

scholarly journals Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels

2012 ◽  
Vol 25 (11) ◽  
pp. 3715-3735 ◽  
Author(s):  
Mark D. Zelinka ◽  
Stephen A. Klein ◽  
Dennis L. Hartmann
Keyword(s):  
Radiative Forcing ◽  
Climate Models ◽  
Grid Point ◽  
Cloud Fraction ◽  
Cloud Feedback ◽  
Global Climate Models ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Cloud Feedbacks ◽  
Low Clouds

This study proposes a novel technique for computing cloud feedbacks using histograms of cloud fraction as a joint function of cloud-top pressure (CTP) and optical depth (τ). These histograms were generated by the International Satellite Cloud Climatology Project (ISCCP) simulator that was incorporated into doubled-CO2 simulations from 11 global climate models in the Cloud Feedback Model Intercomparison Project. The authors use a radiative transfer model to compute top of atmosphere flux sensitivities to cloud fraction perturbations in each bin of the histogram for each month and latitude. Multiplying these cloud radiative kernels with histograms of modeled cloud fraction changes at each grid point per unit of global warming produces an estimate of cloud feedback. Spatial structures and globally integrated cloud feedbacks computed in this manner agree remarkably well with the adjusted change in cloud radiative forcing. The global and annual mean model-simulated cloud feedback is dominated by contributions from medium thickness (3.6 < τ ≤ 23) cloud changes, but thick (τ > 23) cloud changes cause the rapid transition of cloud feedback values from positive in midlatitudes to negative poleward of 50°S and 70°N. High (CTP ≤ 440 hPa) cloud changes are the dominant contributor to longwave (LW) cloud feedback, but because their LW and shortwave (SW) impacts are in opposition, they contribute less to the net cloud feedback than do the positive contributions from low (CTP > 680 hPa) cloud changes. Midlevel (440 < CTP ≤ 680 hPa) cloud changes cause positive SW cloud feedbacks that are 80% as large as those due to low clouds. Finally, high cloud changes induce wider ranges of LW and SW cloud feedbacks across models than do low clouds.

scholarly journals Insights into low-latitude cloud feedbacks from high-resolution models

2015 ◽  
Vol 373 (2054) ◽  
pp. 20140415 ◽  
Author(s):  
Christopher S. Bretherton
Keyword(s):  
Boundary Layer ◽  
Climate Models ◽  
Global Climate ◽  
Cloud Feedback ◽  
Global Climate Models ◽  
Limited Area ◽  
Slight Reduction ◽  
Low Latitude ◽  
Cloud Feedbacks ◽  
Cloud Regimes

Cloud feedbacks are a leading source of uncertainty in the climate sensitivity simulated by global climate models (GCMs). Low-latitude boundary-layer and cumulus cloud regimes are particularly problematic, because they are sustained by tight interactions between clouds and unresolved turbulent circulations. Turbulence-resolving models better simulate such cloud regimes and support the GCM consensus that they contribute to positive global cloud feedbacks. Large-eddy simulations using sub-100 m grid spacings over small computational domains elucidate marine boundary-layer cloud response to greenhouse warming. Four observationally supported mechanisms contribute: ‘thermodynamic’ cloudiness reduction from warming of the atmosphere–ocean column, ‘radiative’ cloudiness reduction from CO 2 - and H 2 O-induced increase in atmospheric emissivity aloft, ‘stability-induced’ cloud increase from increased lower tropospheric stratification, and ‘dynamical’ cloudiness increase from reduced subsidence. The cloudiness reduction mechanisms typically dominate, giving positive shortwave cloud feedback. Cloud-resolving models with horizontal grid spacings of a few kilometres illuminate how cumulonimbus cloud systems affect climate feedbacks. Limited-area simulations and superparameterized GCMs show upward shift and slight reduction of cloud cover in a warmer climate, implying positive cloud feedbacks. A global cloud-resolving model suggests tropical cirrus increases in a warmer climate, producing positive longwave cloud feedback, but results are sensitive to subgrid turbulence and ice microphysics schemes.

scholarly journals Cloud feedbacks in extratropical cyclones: insight from long-term satellite data and high-resolution global simulations

2018 ◽  
Author(s):  
Daniel T. McCoy ◽  
Paul R. Field ◽  
Gregory S. Elsaesser ◽  
Alejandro Bodas-Salcedo ◽  
Brian H. Kahn ◽  
...  
Keyword(s):  
Water Vapor ◽  
Liquid Water ◽  
Climate Models ◽  
Moisture Flux ◽  
Cloud Feedback ◽  
Global Climate Models ◽  
Extratropical Cyclones ◽  
Liquid Water Path ◽  
Cloud Feedbacks ◽  
Water Path

Abstract. Extratropical cyclones provide a unique set of challenges and opportunities in understanding variability in cloudiness over the extratropics (poleward of 30°). We can gain insight into the shortwave cloud feedback from examining cyclone variability. Here we contrast global climate models (GCMs) with horizontal resolutions from 7 km up to hundreds of kilometers with Multi-Sensor Advanced Climatology Liquid Water Path (MAC-LWP) microwave observations of cyclone properties from the period 1992–2015. We find that inter-cyclone variability in both observations and models is strongly driven by moisture flux along the cyclone's warm conveyor belt (WCB). Stronger WCB moisture flux enhances liquid water path (LWP) within cyclones. This relationship is replicated in GCMs, although its strength varies substantially across models. In the southern hemisphere (SH) oceans 28–42 % of the observed interannual variability in cyclone LWP may be explained by WCB moisture flux variability. This relationship is used to propose two cloud feedbacks acting within extratropical cyclones: a negative feedback driven by Clausius-Clapeyron increasing water vapor path (WVP), which enhances the amount of water vapor available to be fluxed into the cyclone; and a feedback moderated by changes in the life cycle and vorticity of cyclones under warming, which changes the rate at which existing moisture is imported into the cyclone. We show that changes in moisture flux drive can explain the observed trend in Southern Ocean cyclone LWP over the last two decades. Transient warming simulations show that the majority of the change in cyclone LWP can be explained by changes in WCB moisture flux, as opposed to changes in cloud phase. The variability within cyclone composites is examined to understand what cyclonic regimes the mixed phase cloud feedback is relevant to. At a fixed WCB moisture flux cyclone LWP increases with increasing SST in the half of the composite poleward of the low and decreases in the half equatorward of the low in both GCMs and observations. Cloud-top phase partitioning observed by the Atmospheric Infrared Sounder (AIRS) indicates that phase transitions may be driving increases in LWP in the poleward half of cyclones.

scholarly journals A cloud feedback emulator (CFE, version 1.0) for an intermediate complexity model

2017 ◽  
Vol 10 (2) ◽  
pp. 945-958 ◽  
Author(s):  
David J. Ullman ◽  
Andreas Schmittner
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Cloud Feedback ◽  
Global Climate Models ◽  
Cloud Feedbacks ◽  
Intermediate Complexity ◽  
Model Cloud ◽  
Radiative Feedbacks ◽  
The University

Abstract. The dominant source of inter-model differences in comprehensive global climate models (GCMs) are cloud radiative effects on Earth's energy budget. Intermediate complexity models, while able to run more efficiently, often lack cloud feedbacks. Here, we describe and evaluate a method for applying GCM-derived shortwave and longwave cloud feedbacks from 4 × CO2 and Last Glacial Maximum experiments to the University of Victoria Earth System Climate Model. The method generally captures the spread in top-of-the-atmosphere radiative feedbacks between the original GCMs, which impacts the magnitude and spatial distribution of surface temperature changes and climate sensitivity. These results suggest that the method is suitable to incorporate multi-model cloud feedback uncertainties in ensemble simulations with a single intermediate complexity model.

scholarly journals The University of Victoria Cloud Feedback Emulator (UVic-CFE): cloud radiative feedbacks in an intermediate complexity model

2016 ◽  
Author(s):  
David Ullman ◽  
Andreas Schmittner
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Cloud Feedback ◽  
Global Climate Models ◽  
Cloud Feedbacks ◽  
Intermediate Complexity ◽  
Model Cloud ◽  
Radiative Feedbacks ◽  
The University

Abstract. The dominant source of inter-model differences in comprehensive global climate models (GCMs) are cloud radiative effects on Earth's energy budget. Intermediate complexity models, while able to run more efficiently, often lack cloud feedbacks. Here, we describe and evaluate a method for applying GCM-derived shortwave and longwave cloud feedbacks from 4xCO2 and Last Glacial Maximum experiments to the University of Victoria Earth System Climate Model. The method generally captures the spread in top-of-the-atmosphere radiative feedbacks between the original GCMs, which impacts the magnitude and spatial distribution of surface temperature changes and climate sensitivity. These results suggest that the method is suitable to incorporate multi-model cloud feedback uncertainties in ensemble simulations with a single intermediate complexity model.

scholarly journals Elucidating ice formation pathways in the aerosol–climate model ECHAM6-HAM2

2019 ◽  
Vol 19 (14) ◽  
pp. 9061-9080 ◽  
Author(s):  
Remo Dietlicher ◽  
David Neubauer ◽  
Ulrike Lohmann
Keyword(s):  
Temperature Regime ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Cloud Amount ◽  
Supercooled Liquid ◽  
Cloud Microphysics ◽  
Global Climate Models ◽  
Ice Formation ◽  
Total Cloud Amount

Abstract. Cloud microphysics schemes in global climate models have long suffered from a lack of reliable satellite observations of cloud ice. At the same time there is a broad consensus that the correct simulation of cloud phase is imperative for a reliable assessment of Earth's climate sensitivity. At the core of this problem is understanding the causes for the inter-model spread of the predicted cloud phase partitioning. This work introduces a new method to build a sound cause-and-effect relation between the microphysical parameterizations employed in our model and the resulting cloud field by analysing ice formation pathways. We find that freezing processes in supercooled liquid clouds only dominate ice formation in roughly 6 % of the simulated clouds, a small fraction compared to roughly 63 % of the clouds governed by freezing in the cirrus temperature regime below −35 ∘C. This pathway analysis further reveals that even in the mixed-phase temperature regime between −35 and 0 ∘C, the dominant source of ice is the sedimentation of ice crystals that originated in the cirrus regime. The simulated fraction of ice cloud to total cloud amount in our model is lower than that reported by the CALIPSO-GOCCP satellite product. This is most likely caused by structural differences of the cloud and aerosol fields in our model rather than the microphysical parametrizations employed.

scholarly journals Contributions of Different Cloud Types to Feedbacks and Rapid Adjustments in CMIP5*

2013 ◽  
Vol 26 (14) ◽  
pp. 5007-5027 ◽  
Author(s):  
Mark D. Zelinka ◽  
Stephen A. Klein ◽  
Karl E. Taylor ◽  
Timothy Andrews ◽  
Mark J. Webb ◽  
...  
Keyword(s):  
Optical Depth ◽  
Climate Model ◽  
Cloud Amount ◽  
Longwave Radiation ◽  
Cloud Feedback ◽  
Shortwave Radiation ◽  
Rapid Reduction ◽  
Cloud Feedbacks ◽  
Cloud Properties ◽  
Climate Model Simulations

Abstract Using five climate model simulations of the response to an abrupt quadrupling of CO2, the authors perform the first simultaneous model intercomparison of cloud feedbacks and rapid radiative adjustments with cloud masking effects removed, partitioned among changes in cloud types and gross cloud properties. Upon CO2 quadrupling, clouds exhibit a rapid reduction in fractional coverage, cloud-top pressure, and optical depth, with each contributing equally to a 1.1 W m−2 net cloud radiative adjustment, primarily from shortwave radiation. Rapid reductions in midlevel clouds and optically thick clouds are important in reducing planetary albedo in every model. As the planet warms, clouds become fewer, higher, and thicker, and global mean net cloud feedback is positive in all but one model and results primarily from increased trapping of longwave radiation. As was true for earlier models, high cloud changes are the largest contributor to intermodel spread in longwave and shortwave cloud feedbacks, but low cloud changes are the largest contributor to the mean and spread in net cloud feedback. The importance of the negative optical depth feedback relative to the amount feedback at high latitudes is even more marked than in earlier models. The authors show that the negative longwave cloud adjustment inferred in previous studies is primarily caused by a 1.3 W m−2 cloud masking of CO2 forcing. Properly accounting for cloud masking increases net cloud feedback by 0.3 W m−2 K−1, whereas accounting for rapid adjustments reduces by 0.14 W m−2 K−1 the ensemble mean net cloud feedback through a combination of smaller positive cloud amount and altitude feedbacks and larger negative optical depth feedbacks.

Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data

2016 ◽  
Vol 29 (17) ◽  
pp. 6065-6083 ◽  
Author(s):  
Yinghui Liu ◽  
Jeffrey R. Key
Keyword(s):  
Cloud Cover ◽  
Climate Models ◽  
Global Climate ◽  
Correlation Coefficients ◽  
Cloud Amount ◽  
Satellite Observation ◽  
Skill Score ◽  
Global Climate Models ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Cloud cover is one of the largest uncertainties in model predictions of the future Arctic climate. Previous studies have shown that cloud amounts in global climate models and atmospheric reanalyses vary widely and may have large biases. However, many climate studies are based on anomalies rather than absolute values, for which biases are less important. This study examines the performance of five atmospheric reanalysis products—ERA-Interim, MERRA, MERRA-2, NCEP R1, and NCEP R2—in depicting monthly mean Arctic cloud amount anomalies against Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations from 2000 to 2014 and against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations from 2006 to 2014. All five reanalysis products exhibit biases in the mean cloud amount, especially in winter. The Gerrity skill score (GSS) and correlation analysis are used to quantify their performance in terms of interannual variations. Results show that ERA-Interim, MERRA, MERRA-2, and NCEP R2 perform similarly, with annual mean GSSs of 0.36/0.22, 0.31/0.24, 0.32/0.23, and 0.32/0.23 and annual mean correlation coefficients of 0.50/0.51, 0.43/0.54, 0.44/0.53, and 0.50/0.52 against MODIS/CALIPSO, indicating that the reanalysis datasets do exhibit some capability for depicting the monthly mean cloud amount anomalies. There are no significant differences in the overall performance of reanalysis products. They all perform best in July, August, and September and worst in November, December, and January. All reanalysis datasets have better performance over land than over ocean. This study identifies the magnitudes of errors in Arctic mean cloud amounts and anomalies and provides a useful tool for evaluating future improvements in the cloud schemes of reanalysis products.

Diagnosing Cloud Feedbacks in General Circulation Models

2007 ◽  
Vol 20 (11) ◽  
pp. 2602-2622 ◽  
Author(s):  
Ping Zhu ◽  
James J. Hack ◽  
Jeffrey T. Kiehl
Keyword(s):  
Radiative Forcing ◽  
General Circulation ◽  
Cloud Amount ◽  
General Circulation Models ◽  
Cloud Feedback ◽  
Radiative Fluxes ◽  
Cloud Feedbacks ◽  
Flux Change ◽  
Gfdl Model ◽  
The Individual

Abstract In this study, it is shown that the NCAR and GFDL GCMs exhibit a marked difference in climate sensitivity of clouds and radiative fluxes in response to doubled CO2 and ±2-K SST perturbations. The GFDL model predicted a substantial decrease in cloud amount and an increase in cloud condensate in the warmer climate, but produced a much weaker change in net cloud radiative forcing (CRF) than the NCAR model. Using a multiple linear regression (MLR) method, the full-sky radiative flux change at the top of the atmosphere was successfully decomposed into individual components associated with the clear sky and different types of clouds. The authors specifically examined the cloud feedbacks due to the cloud amount and cloud condensate changes involving low, mid-, and high clouds between 60°S and 60°N. It was found that the NCAR and GFDL models predicted the same sign of individual longwave and shortwave feedbacks resulting from the change in cloud amount and cloud condensate for all three types of clouds (low, mid, and high) despite the different cloud and radiation schemes used in the models. However, since the individual longwave and shortwave feedbacks resulting from the change in cloud amount and cloud condensate generally have the opposite signs, the net cloud feedback is a subtle residual of all. Strong cancellations between individual cloud feedbacks may result in a weak net cloud feedback. This result is consistent with the findings of the previous studies, which used different approaches to diagnose cloud feedbacks. This study indicates that the proposed MLR approach provides an easy way to efficiently expose the similarity and discrepancy of individual cloud feedback processes between GCMs, which are hidden in the total cloud feedback measured by CRF. Most importantly, this method has the potential to be applied to satellite measurements. Thus, it may serve as a reliable and efficient method to investigate cloud feedback mechanisms on short-term scales by comparing simulations with available observations, which may provide a useful way to identify the cause for the wide spread of cloud feedbacks in GCMs.

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