Earth s Cloud Feedback
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- Merilyn McCoy
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1 Earth s Cloud Feedback Clouds are visible masses of liquid droplets and/or frozen crystals that remain suspended in the atmosphere. Molecule by molecule, water in a solid or liquid phase is 1000 times more thermally absorbent than water vapor, which is one of the key reasons clouds are such an important component of Earth s climate. While clouds warm the planet by absorbing outgoing longwave (LW, infrared) radiation from Earth s surface, they also cool the planet by reflecting incoming shortwave (SW, visible light) radiation from the Sun. Whether the warming or cooling effect dominates depends on many factors, including where on the planet clouds form; what time of day they form; cloud height, vertical profile, transparency and composition (liquid vs. ice particles and particle size); and types and concentrations of chemicals and dust particles in the cloud. Despite these complex factors, it is clear that small changes in cloud cover can lead to big changes in Earth s energy budget. Best estimates from NASA s Clouds and Earth s Radiant Energy System (CERES) experiment suggest that in the current climate, clouds have a net reflection about 47 watts per square meter (W/m 2 ) of incoming SW radiation. Clouds absorb about 27 W/m 2 of outgoing LW radiation, a reduction of 20 W/m 2 and a net cooling compared to an otherwise identical world without clouds. i For comparison, total incoming solar radiation is about 341 W/m 2.ii Impacts of Different Cloud Types by Vertical Location The temperature difference between the relatively warm surface below and cool cloud top above determines the magnitude of the LW effect: larger differences mean a greater overall warming effect. This difference is generally small for low clouds, which tend to be more opaque with bright cloud tops that reflect more sunlight than the clouds absorb thermally. The global impact of low clouds is primarily driven by the SW or reflection effect, meaning that the time of day the clouds form is particularly important for quantifying their impact on the planet s energy balance. Globally, low clouds exert a cooling effect on Earth s climate of about 15 W/m 2 from June-August and 18 W/m 2 from December-February. iii Above: A simplified diagram of the effects clouds have on Earth s climate. The marine stratocumulus clouds (right) reflect lots of incoming sunlight, leading to strong shading and an overall cooling effect. Mid-altitude clouds, like towering cumulus clouds, reflect about as much energy as they absorb, leading to a net neutral effect on temperature. Tropical cirrus clouds reflect almost none of the incoming radiation from the Sun and absorb some of the outgoing longwave radiation, giving them a warming effect. Download this image. Earth Gauge images are freely available for use on-air, online and in community outreach. Note that direct absorption by the non-cloud parts of the atmosphere is not represented. A Program of
2 Subtropical marine stratocumulus (SMS) clouds have a strong cooling effect. These clouds form in subtropical regions where a warm troposphere combines with patches of cool ocean surface waters to stabilize the atmosphere. iv This contrast means that the cloud tops of SMS clouds are normally only slightly cooler than the surface waters below, so the LW warming effect for SMS clouds is particularly small compared to their SW cooling effect. SMS clouds are made up of very small water droplets and are extremely reflective. While these cool water patches and SMS clouds occur over only two to six percent of the planet s surface area, they are important for maintaining Earth s ocean circulation patterns. Because the SMS clouds help to maintain cool conditions, they may influence global climate more than their absolute area of surface cover would suggest. High clouds do the opposite. Wispy high cirrus clouds that move away horizontally from the tops of deep convective tropical anvils (see image at right) are the best examples of clouds that radiatively warm the planet. These clouds reflect almost none of the incoming SW radiation, but reemit LW radiation at very low temperatures compared to the sea surface temperature below that drove their formation. This large temperature difference accounts for their overall warming effect. In contrast to the cooler sea surface temperatures associated with SMS clouds, cirrus clouds with the largest greenhouse effects occur over regions with the warmest sea surface temperatures. Clouds in the middle of the atmosphere can have net warming or cooling effects. While high cirrus clouds that move away from deep convective tropical anvils warm the planet, the tropical anvils themselves reflect about as much energy as they absorb and reemit. Other mid-altitude clouds have similar properties. Above right: The mechanism behind development of high altitude cirrus clouds. Note the top of the convective core where motion becomes horizontal, known as the anvil. Changes in dynamics of this layer are especially important for estimating the cloud feedback. Image NASA. The Cloud Feedback Climate change happens in two parts: first, an externally-imposed forcing such as a change in atmospheric composition, solar output, Earth s orbit, etc., triggers the second part, a series of feedbacks. Feedbacks can either amplify or dampen the impacts of the initial forcing. One important feedback is from water vapor: an initial rise in temperature increases the atmospheric moisture content (for a given level of relative humidity), increasing the greenhouse effect and amplifying the warming. This is an example of a positive feedback. On the other hand, rising temperatures mean increased heat loss to space, leading to cooling that counteracts the initial warming a negative feedback. Feedbacks drive many of the nonlinear properties of Earth s climate: a one percent increase in solar output, for example, will not necessarily correspond to a one percent increase in Earth s temperature. Because feedbacks can cumulatively have a bigger impact on climate than the initial forcing, identifying and quantifying feedbacks is a major focus of climate research. The water vapor feedback is arguably the largest feedback and is always positive. The cloud feedback is clearly important, but much less well understood: determining its most likely magnitude and its sign (positive or negative) is an active area of research. Many general circulation models (GCMs) characterize the cloud feedback as positive (reinforcing warming), yet this remains the largest source of model uncertainty. The frequency of cloud occurrence at different levels of the atmosphere and how it changes as the global climate system responds to forcings is an important component of the cloud feedback. Why is the Cloud Feedback Difficult to Model? While it is almost certain that the magnitude of the cooling effect clouds have will change with the current global warming trend, estimating this magnitude is difficult. Reasons for this difficulty include: Clouds are crucial components of other feedbacks. Other feedbacks such as changes in snow/ice albedo, soil moisture, ocean temperature, temperature lapse rate and biology often involve clouds. For example, rises in sea surface temperatures can influence cloud formation. This then affects how much sunlight hits the oceans, which in turn impacts the very sea surface temperature distributions that influence cloud formation. Feedbacks couple clouds to the global circulation system. Changes in cloud cover and ocean temperature distributions resulting from changes in overall ocean heat content are almost certain to be more pronounced in some regions than others. Regional changes create other changes in the global circulation system, affecting local cloud formation processes in all regions.
3 Clouds are small. The 50 kilometer spatial scales used by more sophisticated GCMs are too coarse to resolve many of the processes needed to precisely understand how and when clouds form. Instead, parameterizations, approximations based on observations and experimental data, are used by the models to represent clouds and bound their possible effects on Earth s energy budget and circulation. Parameterizations at too-coarse scales account for many of the inconsistencies between different climate models. Connecting Theory with Observations Most climate models predict a small increase in high cirrus clouds in response to warming a positive feedback. This is supported by the Fixed Anvil Temperature (FAT) Hypothesis and its slight modification, the Proportionally- Higher Anvil Temperature (PHAT) Hypothesis. These hypotheses state that even as the surface warms, the temperature at the top of anvil clouds is likely to remain the same because of the specific emissivity properties of water vapor. Because the cloud top temperature is likely to remain fixed and cirrus anvil clouds dominate the LW radiative effects of tropical convection, the ability of Earth s tropics to increase their LW emissions in response to a temperature rise (a negative feedback) is inherently limited. These same models predict a wide range of low cloud cover scenarios, ranging from slightly negative (cooling) to significantly positive (warming) feedbacks. This is prompting current research to focus on better representations of low level clouds. One technique for evaluating the validity of model treatment of clouds is to compare simulations to historic observations. One such comparison suggests that marine stratocumulus clouds in the northeastern Pacific may have been less frequent during recent (second half of the 20 th century) periods of warmer sea surface temperatures. v Unfortunately, regular observations of cloud cover rarely go back beyond a few decades and observations of cloud vertical distribution are even less available. In 2006, the first space-borne instrument capable of inferring the vertical distribution of cloud cover, a millimeter-wavelength cloud radar, began its global survey. Other satellite and ground-based remote sensing tools are also being used to make better observations of cloud occurrence and the microphysics driving their formation. Additional techniques for evaluating the climate-relevant cloud processes include modeling at scales small enough to resolve turbulence and mixing (~100 meters or less) and considering simple theoretical models that help deepen our understanding. All these tools are likely necessary for a consensus to develop on the role clouds play in climate. Above: Image taken by the MODIS instrument on NASA s Terra satellite in September 2003, featuring open/closed cell stratocumulus clouds off the coast of Peru. The open cells are areas where rain is occurring, reducing cloud fraction and changing Earth s energy budget. How precipitation changes this cloud fraction illustrates the connection between the cloud feedback and the water cycle. Also, whether or not it rains is linked to the properties of the aerosols within the clouds. How aerosols affect clouds, and therefore Earth s water cycle and energy budget, is another active area of study.
4 Special thanks to Dr. Paquita Zuidema at the University of Miami's Rosenstiel School of Marine and Atmospheric Science for her contributions to this paper Sources: i Loeb, NG et al. Toward Optimal Closure of the Earth s Top-of-Atmosphere Radiation Budget. Journal of Climate 22 (2009): ii Trenberth, KE et al. Earth s Global Energy Budget. BAMS 90 (2009): iii Kubar, TL et al. Boundary Layer and Cloud Structure Controls on Tropical Low Cloud Cover Using A-Train Satellite Data and ECMWF Analyses. Journal of Climate 24 (2011): iv Klein, SA and Hartmann, DL. The Seasonal Cycle of Low Stratiform Clouds. Journal of Climate 6 (1993): v Clement, AC et al. Observational and Model Evidence for Positive Low-Level Cloud Feedback. Science 325 (2009): Other Sources: Abel, SJ and Boutle, IA. An improved representation of the raindrop size distribution for single-moment microphysical schemes. Quarterly Journal of the Royal Meteorological Society 1 MAY 2012, DOI: /qj Allan, RP. The Role of Water Vapour in Earth s Energy Flows. Suveys in Geophysics 33 (2012): Andrews, T et al. Cloud Adjustment and its Role in CO 2 Radiative Forcing and Climate Sensitivity: A Review. Survey of Geophysics 33 (2012): Bodas-Salcedo, A et al. Evaluating cloud system in the Met Office global forecast model using simulated CloudSat radar reflectivities. Journal of Geophysical Research: Atmospheres 113 (2008): D00A13. Bouniol, D et al. Using Continuous Ground-Based Radar and Lidar Measurements for Evaluating the Representation of Clouds in Four Operational Models. Journal of Applied Meteorology and Climatology 49 (2010): Chen, WT et al. Partitioning CloudSat ice water content for comparison with upper tropospheric ice in global atmosphere models. Journal of Geophysical Research: Atmospheres 116 (2011): D Chepfer, H et al. The GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP). Journal of Geophysical Research 115 (2010): D00H16. Del Genio, AD. Representing the Sensitivity of Convective Cloud Systems to Tropospheric Humidity in General Circulation Models. Survey of Geophysics 33 (2011): Delanoë, J et al. Evaluation of ice cloud representation in the ECMWF and UK Met Office models using CloudSat and CALIPSO data. Quarterly Journal of the Royal Meteorological Society 137 (2011): Dessler, AE. A Determination of the Cloud Feedback from Climate Variations over the Past Decade. Science 330 (2010): Di Michele, S et al. Interpreting an evaluation of the ECMWF global model with CloudSat observations: ambiguities due to radar reflectivity forward operator uncertainties. Quarterly Journal of the Royal Meteorological Society 23 April 2012: DOI: /qj Gettelman, A et al. The Evolution of Climate Sensitivity and Climate Feedbacks in the Community Atmosphere Model. Journal of Climate 25 (2012): Hannay, C et al. Evaluation of Forecasted Southeast Pacific Stratocumulus in the NCAR, GFDL and ECMWF Models. Journal of Climate 22 (2009): Hartmann, DL and Larson, K. An important constraint on tropical cloud climate feedback. Geophysical Research Letters 29 (2002): doi: /2002gl Intergovernmental Panel on Climate Change. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.). Cambridge University Press. Cambridge, United Kingdom and New York, NY, USA, 996 pp. Lebsock, MD et al. Detecting the Ratio of Rain and Cloud Water in Low-Latitude Shallow Marine Clouds. Journal of Climate 50 (2011): Loeb, NG et al. Advances in Understanding Top-of-Atmosphere Radiation Variability from Satellite Observations. Survey of Geophysics 33 (2012): Raymond, DJ et al. EPIC2001 and the Coupled Ocean-Atmosphere System of the Tropical East Pacific. Bulletin of the American Meteorological Society 85 (2004): Satoh, M et al. Response of Upper Clouds in Global Warming Experiments Obtained Using a Global Nonhydrostatic Model with Explicit Cloud Processes. Journal of Climate 25 (2012):
5 Slawinska, J et al. Optical Properties of Shallow Convective Clouds Diagnosed from a Bulk-Microphysics Large-Eddy Simulation. Journal of Climate 21 (2008): Soden, BJ and Vecchi, GA. The vertical distribution of cloud feedback in coupled ocean-atmosphere models. Geophysical Research Letters 38 (2011): L Stephens, GL. Cloud Feedbacks in the Climate System: A Critical Review. Journal of Climate 18 (2005): Webb, MJ et al. On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles. Climate Dynamics 27 (2006): Yorks, JE et al. Airborne validation of cirrus cloud properties derived from CALIPSO lidar measurements: Spatial properties. Journal of Geophysical Research: Atmospheres 116 (2011): D Zelinka, MD and Hartmann, DL. Why is longwave cloud feedback positive. Journal of Geophysical Research: Atmospheres 115 (2010): D Zelinka, MD and Hartmann, DL. The observed sensitivity of high clouds to mean surface temperature anomalies in the tropics. Journal of Geophysical Research: Atmospheres 116 (2011): D Zelinka, MD and Hartman, DL. Climate Feedbacks and Their Implications for Poleward Energy Flux Changes in a Warming Climate. Journal of Climate 25 (2012): Zhao, C et al. Toward understanding of differences in current cloud retrievals of ARM ground-based measurements. Journal of Geophysical Research 117 (2012): D10206.
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