P3.21 Annual Cycle of Cloud Forcing of Surface Radiation Budget Anne C. Wilber1, G. Louis Smith2, Paul W. Stackhouse Jr.3 and Shashi K. Gupta1 1. Analytical Services and Materials, Hampton, Virginia 2. National Institute of Aerospace, Hampton, Virginia 3. Langley Research Center, Hampton, Virginia 1. Introduction The Surface Radiation Budget (SRB) The climate of the Earth is determined Data Set (Whitlock et al., 1995) permits the by its balance of radiation. The incoming investigation of the effects of clouds on the and outgoing radiation fluxes are strongly radiation budget at the surface of the Earth. modulated by clouds, which are not well This data set was developed in support of understood. The Earth Radiation Budget the Global Energy and Water Experiment Experiment (Barkstrom and Smith, 1986) and is based on data from the International provided data from which the effects of Satellite Cloud Climatology Project ISCCP clouds on radiation at the top of the (Rossow and Schiffer, 1991; Schiffer and atmosphere (TOA) could be computed Rossow, 1983). It includes upward and (Ramanathan, 1987). At TOA, clouds downward shortwave and longwave increase the reflected solar radiation, radiation fluxes and the total radiation fluxes tending to cool the planet, and decrease the at the surface. The initial edition used a 2.5o OLR, causing the planet to retain its heat latitude quasi-equal size grid over the globe (Ramanathan et al., 1989; Harrison et al., and covered the period July 1983 through 1990). The effects of clouds on radiation June 1991. The SRB data set has been fluxes are denoted cloud forcing. These upgraded by improvements of the algorithms shortwave and longwave forcings counter used to compute the various components of each other to various degrees, so that in the radiation and refinement of the resolution tropics the result is a near balance. Over (Stackhouse et al., 2004; Gupta et al., mid and polar latitude oceans, cloud forcing 2004). The SRB data set has recently been at TOA results in large net loss of radiation. further upgraded to Release 2.5 (Cox et al., Here, there are large areas of stratus clouds 2006) and covers a twenty-one-year period. and cloud systems associated with storms. These systems are sensitive to surface Darnell et al. (1992) used the earlier temperatures and vary strongly with the version of the SRB data set to study the annual cycle. During winter, anticyclones seasonal variation of surface radiation form over the continents and move to the budget. They presented maps of surface oceans during summer. This movement of radiation components and latitudinal plots of major cloud systems causes large changes zonal averages of the components of of surface radiation, which in turn drives the surface radiation. Gupta et al. (1993) surface temperature and sensible and latent investigated the cloud forcing for upward heat released to the atmosphere. Cloud and downward shortwave, longwave and forcing of surface radiation is thus an total radiation fluxes for July 1985 and important feedback mechanism in January 1986. They demonstrated that the atmospheric and oceanic processes. effect of clouds is to reduce downward shortwave (DSW) radiation and increase downward longwave (DLW) radiation. Clouds cool the surface in the summer hemisphere, where the reduction of DSW Corresponding author address: dominates, and warm the surface in the Anne C. Wilber, NASA-Langley Research winter hemisphere, where the longwave Center, MS 936, Hampton, VA 23681. radiation effect is greater. The global email: [email protected] average total effect of cloud is to cool the 1 surface. Gupta et al. (1999) compared the Goddard Earth Observing System (GEOS-4) SRB data set to results from general reanalysis product of Goddard Space Flight circulation models and found that the Center. This most recent release uses models computed shortwave and longwave MATCH aerosols and a higher resolution radiation which were 10 to 20 Wm-2 greater coastline. Although the algorithms have than the SRB data set for global averages. undergone several improvements, the discrimination of cloud over snow and ice As the major cloud systems move remains a problem with observations during the year with the annual cycle of currently available. insolation, the effects of clouds on the downward and upward shortwave and 3. Analysis Method longwave radiation fluxes at the surface vary also. There are a number of questions which Cloud forcing is defined as the radiation arise concerning the annual cycle of surface flux for the observed conditions of the sky radiation fluxes. The present paper uses the minus the flux for clear-sky conditions. The Release 2.5 of the GEWEX Surface SRB data set includes the clear sky flux Radiation Budget Data Set (Cox et al 2006) components computed for each 1o region as to investigate the annual cycles of cloud well as the fluxes with the observed clouds, forcing of surface radiation components. In so that the cloud forcing is simple to retrieve order to describe these annual cycles, a from the data set. The hypothetical surface principal component analysis is used temperature that would exist in the absence whereby the major cyclic effects are of clouds is not considered, so that the cloud computed as time variations with maps forcing of upward longwave radiation is revealing their geographical distributions. taken to be zero. The advantage of this approach is that it represents the time and space variations Monthly mean fluxes were each with the minimum number of terms, which averaged over the twenty-one-year period of are determined by the data. The principal the SRB data set for each calendar month to components are statistical descriptors rather form the flux components for a climatological than physical, but often have simple physical average month, and the cloud forcing for interpretations. Also, the principal each component was computed. The cloud components from the analysis of data can forcing for each component R is then written be compared with those from circulation as model results as an objective technique for CF(x,t) = CF (x) + (cid:1) PC(t) EOF(x) R RAV i i establishing the similarities and differences between the two in regard to time and space where t denotes the month and x the structure. latitude and longitude of the region, CF (x) RAV is the annual average of R for region x, 2. Data Set PC(t) is the i-th principal component and i EOF(x) is the i-th empirical orthogonal i The Release 2.5 Surface Radiation function (EOF). The principal component Budget data set includes the downward and thus describes a time history and the EOF is upward reflected solar radiation flux at the the corresponding geographical distribution surface, the upward longwave radiation flux at the surface and the longwave radiation 4. Results flux from the atmosphere to the surface. These fluxes are provided on a 1o grid for The annual-mean cloud forcings are daily and monthly means for July 1983 considered first and then the annual cycles through December 2004. These fluxes are of the cloud forcing. computed by use of a number of data The global-average annual-mean of products. Cloud properties are derived from DSW is 184 Wm-2, so that the cloud forcing ISCCP pixel level (DX) data. Temperature of DSW is –59 Wm-2, i.e. the effect of clouds and humidity profiles come from the 2 is to reduce surface DSW. Figure 1a is a The downward longwave radiation flux map of annual mean downward shortwave for all-sky conditions global-average annual- DSW cloud forcing. mean radiation flux is 349 Wm-2, so that the cloud forcing of DLW is 34 Wm-2. Figure 1b shows the annual-mean downward longwave DLW cloud forcing. The map of annual-mean net total cloud forcing is shown by fig. 1c and is very similar to that for DSW in fig. 1a. The root-mean-square (RMS) of shortwave cloud forcing at the surface is listed in table 1 and is 24.7 Wm-2. This may be compared with the RMS of the annual cycle of downward shortwave radiative flux at the surface for all sky conditions (Wilber et al., 2006), which is 60.6 Wm-2. The eigenvalues are normalized so as to sum to one and are listed in table 1 also. Table 1: RMS and eigenvalues for cloud forcing. SW CF LW CF Total CF RMS,Wm-2 24.7 3.87 25.0 (cid:1) 0.880 0.710 0.901 1 (cid:1) 0.063 0.154 0.048 2 (cid:1) 0.028 0.067 0.027 3 (cid:1) 0.015 0.029 0.004 4 Sum of first 0.986 0.960 0.980 4 e-values Figure 2 shows the first three principal components of shortwave cloud forcing. The first principal component is very nearly a sine and is an annual cycle with amplitude of 35 Wm-2. The maximum is in June and the minimum is in December, so that it is in phase with the insolation. The first principal component for all-sky DSW has amplitude of 80 Wm-2. Figure 1: (a) Map of the annual mean of cloud forcing of downward surface shortwave flux (b) Same for downward longwave flux. (c) Same for downward total flux. Figure 1: (a) Map of the annual mean of cloud forcing of (Wm-2) downward surface shortwave flux (b) Same for Figure 2: Principal components for downward downward longwave flux. (c) Same for downward shortwave flux at surface, Wm-2. total flux. (Wm-2) 3 Figure 4 shows the zonal mean of EOF-1 for DSW cloud forcing as a function of latitude. The zonal mean has extrema at 60o north and south latitudes. The maximum in the Southern Hemisphere is 2 standard deviations, whereas in the Northern Hemisphere the extreme value is only -1 because of the land-sea differences. Figure 4 shows that the zonal mean of EOF-2 for DSW cloud forcing is small except for the local maximum and minimum beside the Equator due to the ITCZ movements and the maximum near 40o N. Figure 4: Zonal means of empirical orthogonal functions for downward shortwave flux at surface as functions of latitude, dimensionless. The third principal component describes 2.8% of the variance and is a semiannual cycle of about 4 W-m-2 with maxima in June and December. Figure 3c shows EOF-3 for DSW cloud forcing is largest near the poles due to the semi-annual cycle of insolation. Figure 4 shows that the zonal mean of EOF-3 is small except for the near-polar extrema. Figure 3: Maps of empirical orthogonal functions for The RMS for downward longwave DLW downward shortwave flux at surface, cloud forcing is 3.87 Wm -2, smaller than the dimensionless. a. EOF-1, b. EOF-2, c. EOF-3. DSW cloud forcing by a factor of 6. The first eigenvalue, 0.710, is smaller and the remaining eigenvalues are larger than for Figure 3a shows EOF-1, which is the DSW cloud forcing, indicating greater variety geographical distribution of the DSW cloud of the DLW than the DSW case. forcing corresponding to the first principal component. The EOFs are normalized with Figure 5 shows that the first principal a RMS of unity and are measured as component for DLW cloud forcing is an standard deviations. 4 annual cycle with a maximum in August and amplitude of 4 to 5 Wm-2. The shape is close to a sine, but has a flatter decrease from September to December than a sine wave. Whereas the first principal component of DSW cloud forcing is in phase with the insolation, the DLW cloud forcing lags insolation by two months. This variation of DLW cloud forcing could be due to changes of cloud amount or of cloud base height. Figure 5: Principal components for downward longwave flux at surface, Wm-2. Figure 6a is the map of EOF-1 for DLW cloud forcing. Figure 7 shows the zonal means of the first three EOFs of DLW cloud forcing as a function of latitude. The zonal mean of these two bands are 1.3 standard deviations at 30oS and -2 standard deviations at 35oN, or 4.2 and -6.5 Wm-2 respectively. Figure 6: Maps of empirical orthogonal functions for downward longwave flux at surface, dimensionless. a. EOF-1, b. EOF-2, c. EOF-3. Figure 7: Zonal means of empirical orthogonal functions for downward Table 1 shows that the RMS for total longwave flux at surface as functions of downward radiative cloud forcing is latitude, dimensionless. 25.0(cid:1)Wm-2, slightly greater than for DSW cloud forcing. The first four eigenvalues for Figure 6b shows EOF-2 for DLW cloud net total cloud forcing are close to those for forcing. DSW cloud forcing. Plots of the first three 5 principal components are indistinguishable the National Institute of Aerospace. Data from those for DSW cloud forcing and are were provided by the Langley Atmospheric not shown. Likewise, the maps of the first Sciences Data Center. two EOFs for net total cloud forcing are indistinguishable from those for DSW cloud REFERENCES forcing and the EOF-3 differ only in small details. The close similarity of the net total Barkstrom, B. R. and G. L. 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