Introduction

Clouds are an extremely important element of Earth's climate system. They are highly reflective in the solar spectrum, yet strongly absorbing in the thermal infrared; consequently, they produce a large impact on Earth's radiation budget. This impact, termed cloud radiative forcing (CRF), has been quantified through satellite observations: globally, on average, clouds decrease the absorption of solar radiation by about 50 W m-2 (shortwave CRF) and decrease the upwelling thermal infrared radiation by 30 W m-2 (longwave CRF), thus exerting a net CRF of about -20 W m-2 (Kiehl and Trenberth 1997). Locally and instantaneously, clouds can reduce absorbed shortwave radiation by as much as 700 W m-2. In addition, clouds play a central role in Earth's hydro-logical cycle, which is coupled to the energy budget through the release of latent heat that results from water condensation or evaporation. This, in turn, influences atmospheric circulation on a variety of scales.

The nature and extent of these cloud processes may be expected to change in the future in response to changes in concentrations and properties of trace gases and aerosols and resulting changes in climate. Thus, it is imperative for clouds, as well as their radiative and hydrological properties, to be represented accurately in climate models. However, for a variety of reasons, accurate representation of clouds and cloud influences on radiation and hydrology in climate models remains particularly challenging. Key among these reasons are:

• the small fraction of the total water in the cloud that is present in condensed (solid or liquid) phase; this necessitates an accurate representation of both the total water content and temperature governing saturation vapor concentration;

• the complexities associated with the presence of several forms of condensed phase water (liquid, supercooled liquid, ice, mixed);

• the spatial and temporal diversity of cloud microphysical structure, as refl ected in the number concentration and size distribution of cloud hydrometeors and the crystal habit of ice clouds; and

• the numerous varieties and morphologies of clouds as well as the resultant complexity of their three-dimensional structure on many scales (see Figure 23.1).

Small changes to macrophysical (coverage, structure, altitude) or microphysi-cal properties (droplet size, phase) can exert substantial effects on climate. For example, a 5% increase of the shortwave cloud forcing, which could result from changes in the nature or amount of the atmospheric aerosol, would be enough to compensate for the increase in greenhouse gases between 17502000 (Ramaswamy et al. 2001). Recognition of this has stimulated the development of improved physically based representations of cloud processes, in general, and of aerosol influences on clouds, in particular, for inclusion in climate models. However, despite intensified research, the feedbacks on clouds and cloud processes that result from forcings by increasing greenhouse gases and aerosols remain among the greatest uncertainties in climate modeling projections of future and climate change (Randall et al. 2007). Similarly, understanding the radiative forcing by aerosols through their influences on clouds remains the greatest uncertainty in radiative forcing of climate change over the industrial period (IPCC 2007).

The principal tools for examining prospective consequences of future emissions of greenhouse gases and aerosols on Earth's climate are general circulation models (GCMs). The acronym GCM is also used to denote global climate model, and the terms are often used interchangeably. Global climate models are

Figure 23.1 Complexity of three-dimensional structure of clouds; note penetration of cumulonimbus clouds through thin cirrus layer (courtesy of Y.-N. Lee, Brookhaven National Laboratory).

not only the primary tool for simulating global climate change; they are also used to evaluate the regional effects of anthropogenic emissions on modifying precipitation amounts and distribution. By integrating atmospheric, radiative, oceanic, and land-surface processes on a global scale, global climate models can provide an indication of expected changes in the coupled system, including possible consequences of coupled increases in greenhouse gases and aerosols on atmospheric radiation, clouds, precipitation, and the climate system in general. Here we examine the current state of understanding of aerosol and cloud processes that must be represented in GCMs and the state of such representation. In addition, we identify recent advances and further developments that are needed.

When used to examine aerosol influences on clouds and precipitation, GCMs must accurately represent the macrophysical properties of clouds and precipitation, including their geographical and seasonal variation. Although GCMs have been used to examine the influence of widespread anthropogenic sources of cloud condensation nuclei (CCN) on global climate (i.e., aerosol particles that serve as the nuclei on which cloud droplets form), this has presented numerous problems. First is the issue of scales. Typically, GCM grid cells have a horizontal dimension of 150-250 km and a vertical dimension of hundreds to thousands of meters, over which there can be substantial spatial inhomogeneity. For example, clouds cover often only a small fraction of the volume of a grid cell, necessitating rather ad hoc parameterizations, and the average vertical velocities in a grid cell are very small (~0.01 m s-1), whereas actual vertical velocities, which control cloud formation and the activation of aerosol particles to cloud droplets, might be 1 m s-1 or greater. The poor representation of convection is likely a major source of error in modeled liquid and solid water in clouds.

It is clear that there are major disparities among GCMs (see Figure 23.2), even in zonal averages of cloud albedo, which is a major determinant of Earth's

CGCM3.1(T47) CNRM-CM3 CSIRO-Mk3.0 GFDL-CM2.0

CGCM3.1(T47) CNRM-CM3 CSIRO-Mk3.0 GFDL-CM2.0

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ECHAM5/MPI-0M MRI-CGCM2.3.2 CCSM3 PCM

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ECHAM5/MPI-0M MRI-CGCM2.3.2 CCSM3 PCM

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Figure 23.2 Difference between cloud albedo as determined by satellite measurements (ERBE) and twenty global climate models as a function of latitude and time (November 1984 to February 1990). Positive anomalies, where ERBE is higher, are indicated with red, and negative anomalies, where ERBE is lower, with blue colors. Courtesy of Frida Bender; modified from Bender at al. (2006).

radiation budget. In Figure 23.2, each panel corresponds to a different climate model. The model output was obtained from coordinated simulations with twenty different coupled ocean-atmosphere climate models, performed in support of the IPCC Fourth Assessment Report. Clearly these models cannot all be correct. Although space-based measurements can identify models that are doing better or worse, relative to this important cloud variable, such measurements are also difficult, although uncertainties in observations are smaller than intermodel differences.

Cloud microphysical properties are determined by processes such as droplet and crystal nucleation, condensation, evaporation, gravitational settling, and precipitation, all of which operate at the scale of the individual cloud particles or local populations. In contrast, the spatial distribution of clouds is determined by dynamic processes (e.g., turbulence, updrafts, downdrafts, and frontal circulations) and radiative cooling, which operate across meter to global scales. However, these scales are coupled by a variety of processes (e.g., microphysi-cal influences on precipitation development), which in turn affect the release of latent heat below cloud that affects atmospheric stability and vertical motions. Treatment of these processes in climate models and the confidence in this treatment are limited by a lack of understanding and computational resources to represent these on all relevant scales; the latter necessitates development and application of parameterizations, which are inherently scale-dependent. The requirement of accurately representing the many roles of clouds in the climate system and more generally in the biogeochemistry of the planet applies not only to the present atmosphere but also to prior atmospheres (necessary for evaluation of performance of climate models over the instrumental record of the past 150 years or so) and to future atmospheres (necessary for evaluation of the influences of different projected emissions scenarios of greenhouse gases and aerosols). A concern is that each role's common dependence on many of the same cloud properties and processes suggests that errors in simulating one role would produce errors in other roles. Conversely, improving cloud treatment to reduce uncertainty in one role may also reduce uncertainty in other roles. Hence, improving representations and parameterizations of cloud processes in climate models will produce benefits well beyond the simulation of cloud feedbacks and aerosol indirect effects.

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