Instructions: --------------------- Current Climate Change Reports: --------------------- Articles should include the following: • New and interesting findings • Significant trends or developments • Headings and subheadings to break up the text You may also highlight your own experiences, describe your own approaches, and report critically on current debates in the field. Word allocation: Do not significantly exceed 4000 words, not including references, figures, figure legends, or tables. Introduction & Conclusions: Provide an introductory section titled “Introduction” and a conclusion section titled “Conclusions.” These should not be one and the same. Figures: You should not submit more than 10 figures without alerting the editorial office first. A legend should accompany each figure. Tables: You should not submit more than 2 tables. --------------------- Processes Responsible for Cloud Feedbacks Andrew Gettelman 1. Introduction --------------------- Cloud Feedbacks are the Largest Uncertainty in climate sensitivity. Clouds are a big cooling effect, if their radiative effects change in a small way, it has a big impact on how much energy the earth absorbs. We know that low clouds cool, high clouds net warm. Changes to these radiative properties in response to climate change, specifically a surface temperature change (dTs), constitute cloud feedback. What cloud regimes change? How might clouds change? What processes are responsible? Cloud radiative effects can change because of cloud microphysical processes: clouds can be brighter or dimmer depending on droplet size, aerosol composition, and their lifetime depends on precipitation. In addition, clouds are sensitive to the large scale environment. Drop concentrations and ambient humidity are strongly affected by the cloud environment convergence, entrainment, humidity and turbulence. Traditionally Feedbacks are a response to global temperature changes ( Feedback = dR/dTs). Thus they cannot be observed. But cloud processes can be observed, so if dTs is local, then observations can be used as ‘analogs’. Goal has been also to find which processes are responsible, and then models that simulate these processes may be more ‘reliable’. Can either be done from process level, or from top down correlation level. Often called emergent constraints, and they proliferate (e.g.: Fasullo 2015 review). Goals of this review: Review basic constraints (sec 2). Discuss what we know about clouds and how they might change (sec 3), cloud regimes and which clouds may change (sec 4). Observational evidence and constraints (sec 5) simulation methods and results (sec 6), summary and conclusions (sec 7). [Or wrap sec 5-obs and sec 6-models into sec 3 & 4?] 2. Basic Constraints on cloud feedback --------------------- There are arguments that high clouds will remain at the same temperature, meaning their effect gets larger: radiative effect is OLR_cld - OLR_clr. The reduction of OLR is a warming. as Ts goes up OLR_clr goes up, and the difference gets larger: more warming. Low Clouds: extent or brightness can change. There are several regions where differences are seen in models. Sub-tropical cumulus regions. What is seen in observations? 3. Cloud Processes: Sensitivity (v. Emergent Constraints) --------------------- 3.1 Cloud Microphysics Big idea: microphysics of clouds seems to have a small effect. Can claim it is a small effect? If so, it is really the forcing of the cloud environment. Separate the two? Determine what is sensitive. Microphysics are the precipitation process. Interesting that clouds are not sensitive to this. Cloud lifetime is not sensitive to environment? Humidity stays constant, RH thresholds constant? This might be larger scale. Microphysics is onset of precip. But precip itself doesn't change very much. Interesting: different drop numbers (diff precip) yields similar feedbacks. Aquaplanet 3.2 Cloud Dynamics What processes: moisture transport, shallow cumulus in sub-tropics. Some mixed phase effects (quantify: do across models). Humidification. If clouds are not sensitive to microphysics, then changes are from large scale. Or is this because our models do not represent clouds correctly? Bretherton work on CIGLS. 4. Cloud regimes: which clouds matter. --------------------- Some maps (Gettelman, more likely Zelinka) 4.1 Role of high clouds (cirrus) 4.2 Sub-tropical Stratocumulus (Bony, Sherwood, etc) 4.3 Mixed Phase Clouds (esp. S. Ocean) 5. Observations of Cloud Feedback and ‘emergent constraints’ --------------------- How can we do this from observations? Emergent Constraints Sherwood 2014 Fasullo 2014 6. Simulations from the small scale to global --------------------- Bretherton work Gettelman, Webb Zelinka 7. Conclusions/Summary --------------------- References/Sources: --------------------- Bony, S., and J. L. Dufresne. “Marine Boundary Layer Clouds at the Heart of Tropical Cloud Feedback Uncertainties in Climate Models.” Geophys. Res. Lett. 32, no. L20806 (2005). doi:10.1029/2005GL023851. -Segmented CRE by omega500: show spread in models in moderate subsidence regions. Bony, S., and others. “How Well Do We Understand and Evaluate Climate Change Feedback Processes.” Joc 19 (2006): 3445–82. -Segmented feedbacks and discussed mechanisms for analysis. Clouds largest uncertainty. Goes through cloud feedback ‘processes’ -Dynamics and Thermodynamics : large scale circulation and cloud properties [How much are cloud radiative properties dependent on the large scale circ?] Tropics: dynamics controls cloudiness and CRE. Hadley circa, area of up and downwelling. -Deep convection/Cirrus: Is near cancellation of CRE a feature (Hartmann 2001) FAT hypothesis = positive cloud feedback. Role of aerosols? Hartmann and Larson 2002 -Low Lat PBL: what happens to cloud microphysics with warming? Or environment -Extratropics: Studies on individual storms. How cloud effects: re and lwp, how respond to warming. Also possible shift of storm track (more below: Kay et al). -Polar Clouds -convective and PBL clouds most important: changes in PBL clouds are the largest changes (Webb, Gettelman) Methods: satellite simulators, clustering/compositing Bony, Sandrine, Bjorn Stevens, Dargan M. W. Frierson, Christian Jakob, Masa Kageyama, Robert Pincus, Theodore G. Shepherd, et al. “Clouds, Circulation and Climate Sensitivity.” Nature Geoscience 8, no. 4 (April 2015): 261–68. doi:10.1038/ngeo2398. Bretherton, Christopher S. “Insights into Low-Latitude Cloud Feedbacks from High-Resolution Models.” Phil. Trans. R. Soc. A 373, no. 2054 (November 13, 2015): 20140415. doi:10.1098/rsta.2014.0415. -Focus on marine boundary layer clouds. LES: 4 mechanisms. (From Bretherton 2013) Thermodynamic cloud reduction. Rad cloud reduction from emissivity aloft Stability induced cloud increase from +stratification Dynamical cloud increase rom reduced subsidence. Reduction dominates. CRM: deep convection affects cloud feedbacks? SPCAM: upward shift and reduction of cloud cover. Global CRM: Cirrus increases (or gets higher). Sensitive to ice microphysics. CRMs not that different than GCMs in cloud feedback magnitude, despite parameterizing turbulence. SPCAM not robust (not great at 4km resolution). Microphysics: matters only on margin... Bretherton, Christopher S., Peter N. Blossey, and Christopher R. Jones. “Mechanisms of Marine Low Cloud Sensitivity to Idealized Climate Perturbations: A Single-LES Exploration Extending the CGILS Cases.” Journal of Advances in Modeling Earth Systems 5, no. 2 (June 1, 2013): 316–37. doi:10.1002/jame.20019. Ceppi, Paulo, Daniel T. McCoy, and Dennis L. Hartmann. “Observational Evidence for a Negative Shortwave Cloud Feedback in Middle to High Latitudes.” Geophysical Research Letters, January 1, 2016, 2015GL067499. doi:10.1002/2015GL067499. Choi, Yong-Sang, Chang-Hoi Ho, Chang-Eui Park, Trude Storelvmo, and Ivy Tan. “Influence of Cloud Phase Composition on Climate Feedbacks.” Journal of Geophysical Research: Atmospheres 119, no. 7 (April 16, 2014): 3687–3700. doi:10.1002/2013JD020582. -Change cloud phase in CAM3 with temperature ramp. More liquid to lower temps changes climate feedbacks positive and negative. Overall change in cloud feedbacks is small (net +0.05Wm-2K-1), even though different clouds compensated for by altering solar constant to get back to similar Ts. Radiative kernels used to compute feedbacks. Difference in ECS is ~0.15K (on 2.07K, CAM 3). Clement, A., R. Burgman, and J. R. Norris. “Observational and Model Evidence for Positive Low-Level Cloud Feedback.” Science 325 (2009): 460–64. -NE Pacific cloud cover negatively correlated with SST: warmer SST = fewer clouds. Observational constraint for positive low cloud feedback. Mechanisms not quite well described. Warmer SST and fewer clouds = weaker circulation (lower LTS, less cloud?) Fasullo, J. T., B. M. Sanderson, and K. E. Trenberth. “Recent Progress in Constraining Climate Sensitivity With Model Ensembles.” Current Climate Change Reports 1, no. 4 (2015): 268–75. Gettelman, A., J. T. Fasullo, and J. E. 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Zhai, Chengxing, Jonathan H. Jiang, and Hui Su. “Long-Term Cloud Change Imprinted in Seasonal Cloud Variation: More Evidence of High Climate Sensitivity.” Geophysical Research Letters, January 1, 2015, 2015GL065911. doi:10.1002/2015GL065911. Zhang, M., and C. Bretherton. “Mechanisms of Low Cloud-Climate Feedback in Idealized Single-Column Simulations with the Community Atmospheric Model, Version 3 (CAM3).” Journal of Climate 21, no. 18 (2008): 4859–78. Note: Papers in: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Discussion meeting issue ‘Feedbacks on climate in the Earth system’ organised and edited by Eric W. Wolff , John G. Shepherd, Emily Shuckburgh and Andrew J. Watson November 13, 2015; Vol. 373, No. 2054