Near-Global Observations of Low Clouds

Transcription

1 1APRIL 2000 WEARE 1255 Near-Global Observations of Low Clouds BRYAN C. WEARE Atmospheric Science Program, University of California, Davis, Davis, California (Manuscript received 15 December 1998, in final form 28 May 1999) ABSTRACT This paper analyzes several near-global datasets of low cloud cover, including the the International Satellite Cloud Climatology Project (ISCCP) satellite observations, C. J. Hahn et al. surface-derived observations, and the National Centers for Environmental Prediction (NCEP) and ECMWF reanalysis products (ERA). The magnitudes of annual-mean ISCCP and C. J. Hahn observations of low cloud fraction are found to differ by up to about 0.4 for a number of locations. These differences are largely attributable to the fact that ISCCP low clouds are only those low clouds that are not obstructed by higher cloud. Those of both the NCEP and ERA low clouds, which should be comparable to the Hahn low cloud dataset, have magnitudes up to about 0.3 less than the latter. The dominant EOFs of the seasonal variation of ISCCP and Hahn observations low cloud differ substantially over much of the Northern Hemisphere, where there is a sizable number of observations in Hahn. The pattern of the dominant seasonal EOF of NCEP low clouds has a number of qualitative similarities with that of Hahn between approximately 10 and 40 N. That of the ECMWF low clouds is less similar and has much larger amplitudes in the high latitudes of both hemispheres than any of the other datasets. The calculated regression coefficients between interannual variations of Niño-3 SST variations and low cloud departures in the equatorial central Pacific have positive magnitudes of about 0.02 ( C) 1 for the C. J. Hahn et al. and NCEP data, but negative values of similar magnitudes for the ISCCP and ECMWF low cloud fractions. These results suggest a need for improved observational estimates and model specifications of the three-dimensional structure of clouds. 1. Introduction Clouds are immensely complex three-dimensional entities that have profound interactions with weather and climate. For instance, Lau and Crane (1997) have shown that both cloud amounts and vertical structure vary considerably in the regions of composite midlatitude oceanic and continental frontal systems. Furthermore, they show that within a particular portion of the frontal zone, such as the trailing edge, cloudiness consists of a combination of several cloud types representing different heights and depths. The net effects of clouds on the overall energy budget have been summarized in recent years in terms of cloud radiative forcing (Ramanathan et al. 1989). However, the specific radiative response to a cloud field is known to be intricately related to cloud amount, height, depth, and composition. Weare and AMIP Modeling Groups (1996) discuss a comparison between observed low and high cloud cover with the output from climate models participating in the Atmospheric Model Intercomparison Project (Gates 1992). He shows that key features of the zonally av- Corresponding author address: Dr. Bryan C. Weare, Atmospheric Science Program, University of California, Davis, Davis, CA bcweare@ucdavis.edu eraged monthly statistics of satellite- and surface-derived data often do not agree even qualitatively with the model output. Unfortunately, there are relatively large uncertainties in the observations and controversies concerning the appropriate definition of fractional cloud cover for thin, partially transparent clouds and how finite cloud objects organize themselves vertically. These factors all vastly diminish our ability to make conclusive statements concerning the role of varying cloud vertical structure in weather and climate. This paper will attempt to provide a better understanding of the mean state and variability of low cloud covering most of the globe. Low cloud was chosen for investigation at this time primarily because it is generally subject to little controversy concerning an appropriate definition of fractional cloud amount, and because traditional surface observations are likely to well represent this class of cloud. There are a variety of nearglobal multiyear monthly low cloud datasets. Hahn et al. (1994, 1995, 1996) have compiled and edited nearly all of the available traditional surface observations for A number of satellite-based low cloud datasets exist. These include the International Satellite Cloud Climatology Project (ISCCP; Rossow et al. 1989, 1993) and the Nimbus-7 (Stowe et al. 1988) estimates, based fundamentally on threshold tests using measured solar and thermal radiances. Also, there are the TIROS-N Op American Meteorological Society

2 1256 JOURNAL OF CLIMATE VOLUME 13 FIG. 1. Schematic of a possible two-dimensional cloud configuration identifying the fraction low cloud cover as seen from above L fa, as seen from below L fb, and at each model level f i as the ratio of the lengths of the heavy horizontal lines relative to the full width of the domain. erational Verticle Sounder (TOVS) Pathfinder (Susskind et al. 1997) estimates, based upon inversion techniques utilizing solar, thermal, and microwave radiances. The recent reanalyses by the National Centers for Environmental Prediction (NCEP) (Kalnay et al. 1996) and the European Centre for Medium-Range Weather Forecasts (ECMWF) (Gibson et al. 1997) provide estimates. These are based upon model cloud parameterizations in shortterm weather forecasts initialized with a carefully assimilated suite of thermodynamic and dynamic observations, but without a direct cloud observational input. Most recently Weare (1999) has derived hybrid analyses of high, middle, and low cloud amounts based upon a combination of the Hahn et al. surface observations and the ISCCP C2 satellite estimates. Figure 1 is designed to clarify some of the basic concepts and potential problems in using and comparing such datasets. This figure is a schematic of a two-dimensional view of a partly clouded region of the atmosphere with surface- and satellite-based observations and estimates simulated in a forecast model. Although this schematic may not represent a typical instantaneous situation, it is not unrealistic and would be more typical of a time average. The vertical coordinate in Fig. 1 shows the approximate levels separating low, middle, and high clouds in the ISCCP analysis on the left and a hypothetical model vertical grid on the right. The fraction of low cloud seen from above by a satellite L fa is defined as the cloud whose top has a pressure greater than 680 hpa that is not overlain by higher level clouds, which in this case are all assumed to be opaque. This fraction is represented by the ratio of the length of the heavy line at the top of the figure relative to the total horizontal size of the grid. The fraction of low cloud seen from below by a surface observer L fb is the fraction of clouds whose bases have pressures greater than 680 hpa; this is represented by the ratio of the sum of the heavy lines at the bottom of the figure relative to the total width. Here L fb is generally thought of as the true fraction of the sky covered by low cloud. The fraction of low cloud at each model level f i is represented by the sum of the relative lengths of the dashed lines at each model level i, having pressures greater than 680 hpa. From this figure it can be seen that: L L f, (1) fa fb i where the summation is over all model levels below 680 hpa in this example. It is important to note that as the number of model levels increases this summation must increase. Furthermore, for many typical situations where there is substantial high or middle cloud and where there are several model levels defining the low cloud domain L K L K f. (2) fa fb i In general the various cloud fractions may be directly compared to each other model only after consideration is made of the so-called overlap. This is usually done in terms of an overlap assumption (Tian and Curry 1989). The two extreme assumptions are no overlap, in which no cloud is assumed to be above or below another cloud, and full overlap, in which clouds are assumed to be stacked vertically to the maximum extent possible. The most common overlap assumptions are random overlap in which clouds at various levels are assumed to be randomly distributed in the horizontal, mixed in which convective clouds are assumed to be maximally and all other clouds randomly overlapped, and maximum-random in which clouds within broad layers are assumed maximally overlapped and the broad layers themselves are assumed to be randomly overlapped. However, as in the schematic in Fig. 1 none of these overlap assumptions may be strictly applicable, and any combination of overlap between the no and full overlap extremes is possible. Nevertheless, it is instructive to apply one of the common overlap assumptions. In the following example the random assumption is applied to the ISCCP low cloud amounts. For the three-layer atmosphere the ISCCP L fa may be defined as L fa f l f l ( f h f m f h f m ), (3) where f l, f m, and f h are the true cloud fractions for the low, middle, and high cloud layers and where by definition L fb f l. Equation (3) and a comparable equation for the middle cloud as seen from above allows one to estimate the L fb from the ISCCP estimates of low, middle, and high cloud as L fb L fa /(1 H fa M fa ), (4) where L fa, M fa, H fa refer to the ISCCP estimates of low, middle, and high cloud amount as measured from above. Note that L fb in Eq. (4) is undefined without further assumption when the sky is fully covered with high and middle cloud, that is, no part of any possible low cloud can be observed from above.

3 1APRIL 2000 WEARE 1257 FIG. 2. Monthly mean low cloud fraction for a grid in the North Pacific at 40 N, 140 W: ISCCP L fa low cloud from above from ISCCP; L fb Hahn low cloud from below from Hahn et al.; L fb ISCCP low cloud from below calculated from the ISCCP L fa using the random overlap assumption; and L fb NCEP low cloud from below from the NCEP reanalysis. Figure 2 shows monthly means of ISCCP low cloud as measured from above L fa, Hahn et al. surface-observed low cloud as measured from below L fb, NCEP reanalysis L fb (using the NCEP overlap assumption to combine the nine model layers with low cloud), and an estimate of the ISCCP derived L fb using Eq. (4) for a point in the North Pacific (40 N, 140 W). This sample point was chosen as a location in which more than 100 observations/month of traditional observations exist, where the satellite observations do not have any special difficulties in determining low cloud, and where the denominator in Eq. (4) is never zero. Several features of this figure should be pointed out. First, the Hahn et al. values are about larger than the original ISCCP L fa. On the other hand the magnitudes of the ISCCP L fb, estimated using the random overlap assumption, are very similar to the directly measured Hahn L fb. However, the NCEP L fb, which should be equivalent to the Hahn and ISCCP L fb, have smaller magnitudes by about 0.2. Second, the original ISCCP L fa observations have a relatively clear and large annual cycle, which is not evident in the other curves. This is largely related to the basic difference in the definition of low cloud as observed from below or above. In particular the original ISCCP observations identify only low clouds that are not overlain by opaque middle or high cloud, which are numerous in this region. Therefore, seasonal and other variations of the original ISCCP low cloud amounts are greatly influenced by variations in upper clouds, which alternately expose or cover underlying low cloud. This reflects a fundamental aspect of satellite-derived low clouds. Their magnitudes and variations are functions not only of the cloud in the lower layers of the atmosphere but also of all of the cloud above. Finally, there are only moderate correlations between the variations of any pairs of observations representing low cloud as measured from below. This is in part because of the considerable uncertainties and biases in each of the basic datasets, and because the true overlap may not be random as has been assumed. In fact the true overlap may vary from month to month, which could account for most of the differences between the Hahn and ISCCP L fb. On the other hand the large systematic differences between the NCEP model-derived low cloud values and the other observational estimates of low cloud as seen from below are likely too large to be solely related to an incorrect choice of overlap assumption. The general goal of this paper is to illustrate for most of the globe the basic properties of several of the available observations of low clouds to gain a better under-

4 1258 JOURNAL OF CLIMATE VOLUME 13 standing of what we do and do not know about this important parameter. This will be preceded by a basic review of the various observations and their uncertainties and potential biases. Then, maps of annual means of low cloudiness from the various sources will be compared and contrasted. This will be followed by comparisons of empirical orthogonal analyses of the monthly departures from these annual means, and diagnoses of the relationships between interannual variations of sea surface temperature (SST) and low clouds for selected regions of the globe. In all these cases the emphasis will be placed upon understanding the common features, which are most likely representative of the true state of the climatology of the earth s low cloud field. 2. Data The most comprehensive and carefully developed set of traditional surface-observer low cloud observations is that summarized by Hahn et al. (1996). These data, which are compilations of subjective estimates by observers at land stations and aboard ships-of-opportunity, represent the visible cloud with bases between the surface and about 2500 m. The available compilations of edited individual observations include separate files for land and ocean observations for each month. Individual monthly means of low cloud amounts for January 1984 through December 1988 were calculated for each grid for the globe from 70 S to70 N. A mean was calculated for each grid-month in which there was at least one observation containing both total and low cloud data. Only grids with adequate illumination (see Hahn et al. 1995) were included to eliminate possible biases introduced by inadequate moonlight to observe the sky. For grids with both oceanic and land data only the land-based observations were retained, since they were usually much better sampled in time. Figure 1 in Weare (1999) shows the average number of observations in each grid. The mean number of observations for most ocean grids north of 20 N generally exceeds 10; the number for other oceanic points is often less than 6. Over land areas with data there are usually more than 100 observations per grid-month, and many parts of Europe and Asia have more than 600 observations per grid-month. However, the mountainous regions of Asia and North America have few grids with observations as do the interiors of Africa, Australia, and South America. The ISCCP C2 cloud observations (Rossow et al. 1993) for the same months were obtained from the National Center for Atmospheric Research (NCAR) data archive. The ISCCP C2 dataset includes monthly means for all daylight hours of a large variety of cloud variables. The ISCCP low cloud amounts were calculated from the cloud amounts for the two ISCCP C2 low cloud types (C2 VAR 32 and 36) corresponding to clouds with tops having pressures greater than 680 hpa. This demarcation was chosen to approximately mimic the variations in top elevation of the morphological low cloud types of the surface observations. However, this elevation refers to the maximum height of the low cloud tops, whereas that for the surface observed clouds is the maximum for the low cloud bases. In addition in the surface observations the maximum base is relative to the height of the station, but the ISCCP low cloud-top minimum pressure is relative to sea level. Thus, care must be taken in interpreting any comparisons over mountainous regions. The ISCCP data were interpolated to the same grid as the surface observations using the algorithm provided by ISCCP. The NCEP low cloud fields are from the reanalysis undertaken by NCEP and NCAR, which have been described by Kalnay et al. (1996). Monthly means on a global 2.5 lat 2.5 long grid are available for the period January 1982 December 1994 on the NCEP/ NCAR (1996) CD-ROM, distributed with the above article. These low clouds represent the cloud in model levels below approximately 680 hpa, utilizing an overlap assumption to combine model-layer fractions. However, NCEP (1998) to the Bulletin Reanalysis article (//wesley.wwb.noaa. gov/update bams) states that: When there are no convective clouds present, these cloud fractions are as described. However, when convective clouds are present, these quantities are a nonlinear function of the convective and large-scale clouds. The resultant cloud fraction can be as much as 70% less than the cloud fraction as seen by the model. The magnitude of the effect of the improper specification of the cloud overlap in the NCEP NCAR low cloud amounts was explored utilizing the NCEP monthly reanalyses for This is the first year processed in which the low, middle, and high cloud amounts have been calculated with a self-consistent overlap scheme (W. Ebisuzaki 1998, personal communication). As a preliminary assessment the monthly low cloud amounts for the correct 1968 data were compared with the monthly means of the incorrect data for each of the years In comparing these two datasets, the fact that 1968 precedes the inclusion in the analyses of satellite temperature sounding data must be accounted for. In general poleward of about 20 the low cloud amounts seem to be very realistic, whereas at lower latitudes, especially over convective zones, the low cloud amounts apparently are underestimated by about 25% compared to the 1968 correct year (not shown). The ECMWF reanalysis (ERA; Gibson et al. 1997) low clouds are derived from forecast fields of the T106 resolution, 31-hybrid-layer ERA model. This model utilizes the ECMWF prognostic cloud scheme to derive cloud fractions and liquid and ice contents at each level. Global monthly means for low, middle, high, and total cloud cover for were made available through the cooperation of the Laboratory for Dynamic Meteorology on a regular lat long grid (L. Li 1998, personal communication). The maximum-random overlap assumption was used to combine model-

5 1APRIL 2000 WEARE 1259 FIG. 3. Annual-mean low cloud cover (cloud fraction): (a) ISCCP low cloud as observed from above L fa and (b) Hahn et al. low cloud as observed from below L fb. Note that the scale for (b) is 0.3 greater than that for (a). layer cloud fractions into the low, middle, and high cloud categories (Jakob and Klein 1999). Weare (1999) introduced combined analyses of the Hahn et al. surface observations and the ISCCP satellite estimates of cloud fields. The method derives the probabilities of all possible cloud states, which are consistent with both sets of observations. Adjusted ISCCP and Hahn et al. cloud amounts are then calculated from these probabilities. This method is based on the fact that given a finite set of probabilities p (p 1, p 2,...),anyobservational dataset n may be described by the expression A p n. (5) The matrix A is a set of fixed rules, which relate the cloud configuration probabilities to a given set of observations. The appropriate specification of A allows the distinction to be made between the different viewpoints of satellite and surface observers without an overlap assumption. Although the solution for p is mathematically underconstrained, Eq. (5) can be solved given: 1) the requirement that the desired solution fit as well as possible the data, and 2) the assumption that the desired solution requires the smallest possible corrections from a set of first guesses. This method was applied to a three-layer atmosphere using monthly cloud observations. Reconstructions of the observed fields from the calculated probabilities p usually lead to modifications of the Hahn et al. low cloud amounts of less than 0.01 fractional cloud cover. Over ocean the ISCCP low cloud amounts are usually decreased by between 0.06 and 0.12 for most of the middle latitudes and southeastern tropical Pacific. Over land the adjustments in the satellite low cloud amounts are generally smaller. In the following analyses grid-months without data were interpolated using regression equations of the form I I L Lfa 0 1 Hfa 2 T stovs, (6) where L is the reconstructed ISCCP L fa or Hahn et al. L fb low cloud amounts, T stovs are the ISCCP reported TOVS surface temperatures, and the s are regression coefficients derived from all available grid-months north of the equator, computed separately for land and sea. The regression models explain around 30% of the variance for both low cloud types over both land and sea. In general the interpolated values create smooth transitions between the available data. However, over areas of high topography care must be taken in their interpretation because the Weare (1999) methodology does not account for the fact that the tops of Hahn et al. low clouds are a fixed level above the surface rather than above sea level as is assumed in the ISCCP analysis. 3. Annual means Figure 3 illustrates the annual means of the ISCCP and Hahn et al. low cloud amounts. Clearly, the Hahn et al. low cloud observations are nearly everywhere greater. However, it must be emphasized once again that these two maps are not expected to be identical, or even similar, since as explained in the introduction the ISCCP analysis refers to low clouds that

6 1260 JOURNAL OF CLIMATE VOLUME 13 FIG. 4. Annual-mean reconstructed low cloud cover (cloud fraction) using the methodology of Weare (1999) and with interpolation for points with no data using Eq. (6): (a) ISCCP low cloud as observed from above L fa and (b) Hahn et al. low cloud as observed from below L fb. Note that the scale for (b) is 0.3 greater than that for (a). are observable through normally opaque upper clouds and the Hahn et al. analysis refers to low clouds seen from below by a surface observer. The differences in the definitions of these two fields mean that they will likely be most different in regions of considerable high and middle cloud cover such as the equatorial western Pacific. However, these two fields differ considerably elsewhere as well. Figure 4 illustrates 5-yr annual means of the interpolated, reconstructed ISCCP and Hahn et al. low cloud amounts based upon the method of Weare (1999) described above. The major differences between the reconstructed (Fig. 4a) and original ISCCP (Fig. 3a) low cloud amounts are that the reconstructed magnitudes are up to about 0.1 smaller, especially over the eastern oceans. Weare (1999) shows that these differences are associated with increases in middle and high clouds in the reconstructions. Thus, this analysis suggests that ISCCP methods actually overestimate in many ocean regions the amount of low cloud visible from above. The largest differences are over the subtropical highs, where ISCCP optical depths are small, suggesting that some thin high clouds may be misinterpreted as middle or low cloud. The differences between reconstructed (Fig. 4b) and original Hahn et al. (Fig. 3b) low cloud amounts are much smaller than for the corresponding ISCCP, but generally of the same sign. That is the reconstructed values are often slightly smaller than the original values, suggesting that surface observers may often slightly overestimate low cloud amounts. Figure 5 illustrates the annual means of low clouds derived from the NCEP and ECMWF reanalyses. Both analyses represent the low cloud as seen from below with an appropriate overlap assumption to account for the fact that the reanalysis models have several layers below 680 hpa. Thus they should be most equivalent to the Hahn et al. analyses. However, the means from the two reanalyses have magnitudes similar to those of the ISCCP low cloud amounts, but much more resemble each other than they resemble either of the observations shown in Figs. 3 or 4. Important differences between the two reanalysis products do exist, however. Overall, the NCEP amounts are at least 0.05 larger nearly everywhere. In addition, in the eastern

7 1APRIL 2000 WEARE 1261 FIG. 5. Annual-mean low cloud cover (cloud fraction): (a) NCEP reanalysis low cloud and (b) ERA low cloud. North and South Pacific and Atlantic Oceans the maximum in ERA low clouds is farther west of the continents than in the NCEP product. No such offset is apparent on the western sides of the ocean basins. Also the NCEP analysis has about 0.2 more cloud cover in the convective regions of the Tropics. 4. Seasonal empirical orthogonal functions Empirical orthogonal functions (EOFs) for monthly departures of low cloudiness from the annual means were calculated for the period for each of the datasets just discussed. The region of analysis was approximately from 60 S to60 Nwith data at a 2.5 spacing for all but the ERA analysis in which the grid spacing was In all cases the first EOF was separable from all of the others using the North et al. (1982) statistical criteria. Time coefficients of these functions (Fig. 6) have a very strong seasonal cycle with a maximum usually in January or February. The second EOFs are also separable from the remaining ones and are in approximate quadrature in time with the first. The most striking feature of these time coefficients, which have been normalized to have peak magnitudes of about 6, is that the ERA values lead the others by about 2 months. This is primarily due to the fact that the spatial pattern of the ERA EOF (Fig. 8b later) is relatively very large at the high latitudes of both hemispheres. Figure 7 shows the first EOFs of the interpolated, reconstructed ISCCP and Hahn et al. low cloud amounts. The corresponding functions for the original ISCCP and Hahn et al. have slightly larger amplitudes, but nearly identical spatial patterns. Figure 7 indicates that there are dramatic differences between the seasonal variations of low cloud as seen from a satellite (Fig. 7a) and the surface (Fig. 7b). For instance in the North Pacific the variations of ISCCP low cloud largely reflect an east west contrast such that in January there are maxima in low cloudiness over most of the eastern ocean and minima in the west. On the other hand the dominant seasonal function of Hahn et al. low cloud in the North Pacific has a strong three-band zonal pattern over most of this region such that in January there is a maxima near 30 N and minima near 15 N and 50 N. In the Tropics the ISCCP function has broad maxima north and minima south of the equator, whereas the Hahn et al. function

8 1262 JOURNAL OF CLIMATE VOLUME 13 FIG. 6. Time coefficients of the dominant empirical orthogonal functions of departures of monthly mean low cloud cover from their respective annual means for the ISCCP and Hahn reconstructions (as in Fig. 4) and the NCEP reanalysis and ERA (as in Fig 5). has this pattern concentrated over the continents and maritime continent. Many other sizable differences exist. Since Weare (1999) shows that the monthly mean Hahn low clouds are nearly always accompanied by higher cloud, most of these differences are likely due to the fundamental differences between the low cloud as seen from below and that seen from above. Figure 8 shows the dominant seasonal empirical orthogonal functions of the low clouds of the NCEP and ECMWF reanalyses. Although these functions share some common features in the Tropics, the patterns are remarkably different from each other. For instance, in January the ERA indicates minima in low cloud over most of the North Pacific and North Atlantic, whereas the NCEP reanalysis suggests maxima. In the Tropics the ERA shows a relatively weak cross-equatorial contrast across the Indian-Maritime Continent region compared to that in the NCEP reanalysis. On the other hand the ERA indicates a stronger seasonal cycle in Southern Ocean low cloudiness. Numerous other substantial differences are evident. These patterns of seasonal variation should be comparable to those of Hahn et al. Overall the dominant seasonal EOF of NCEP low cloud is indeed in modest agreement with the Hahn et al. patterns, especially between about 10 N and 40 N, where surface observations are relatively plentiful. Figure 7b and 8a both show in this region maxima across eastern Europe and the central North Pacific and Atlantic Oceans, and mimina over southern Asia, central America, and the Sahel. The major differences between the dominant EOFs of NCEP and Hahn low cloud are the positive weights in the NCEP in the north Pacific and Atlantic Oceans and that just south of the equator in the eastern Pacific. The EOF pattern of the dominant EOF of the seasonal variations of ERA low clouds has its best agreement with that of Hahn et al. in the Tropics. Areas of disagreement with the Hahn low cloud dominant EOF include the large amplitudes at high latitudes in both hemispheres and the positive amplitudes off the coast of Mexico. The paucity of surface observations in the Southern Ocean does not allow definitive statements concerning the veracity of the strong amplitudes in the ERA in this region. As expected neither reanalysis product has a pattern of variations in low cloud very similar to that of the dominant ISCCP low cloud EOF. 5. Variations associated with Niño-3 SST changes To understand how observed cloud are modified by the substantial atmospheric and oceanic changes asso-

9 1APRIL 2000 WEARE 1263 FIG. 7. Dominant intraannual EOFs of low cloud cover: (a) reconstructed ISCCP, explaining 14.3% of the total variance; and (b) reconstructed Hahn, explaining 13.3%. ciated with El Niño, regression coefficients were calculated relating interannual departures in the SST variations in the Niño-3 region of the tropical Pacific (5 S 5 N, W) to the corresponding variations in low cloud. The monthly SST variations for were taken from the observational data on the NCEP/NCAR (1996) CD-ROM, which are derived from the NCEP operational analyses. After these data were averaged over the Niño-3 domain, 5-yr monthly means and departures were calculated. Similar interannual departures of low cloud were calculated at each point from the departures of monthly values from the corresponding 5-yr monthly means. Figure 9 illustrates the mean change of low cloud fraction per degree fluctuation in Niño-3 SST for the estimated ISCCP and Hahn et al. low clouds amounts, whose annual means are illustrated in Fig. 4. Figure 9, especially across the tropical western and central Pacific, again illustrates the dramatic differences between the low cloud as seen from above (Fig. 9a) and low cloud as seen from below (Fig. 9b). In the equatorial western and central Pacific region the predicted ISCCP and Hahn low cloud responses to the SST changes are of similar magnitude [( 0.02 ( C) 1 ], but often of the opposite sign. These differences may be explained if one assumes that during warm events low-level trade cumuli are being replaced by more plentiful deeper clouds, or vice versa (Bajuk and Leovy 1998). On the other hand in the eastern tropical Pacific and Atlantic regions, which are typically dominated by low clouds, variations in the ISCCP and Hahn et al. low clouds are similar. Thus variations in low cloud cover in these regions seem not to be accompanied by changes in higher cloud amounts. It should also be noted that the estimated variations in the Hahn et al. low clouds are generally spatially noisier than those of ISCCP, primarily due to the relatively small number of surface-observed cloud observations over much of the Tropics. Figure 10 illustrates the corresponding regression coefficients associated with the interannual variations of the NCEP reanalysis and ERA low cloud as seen from below. Again, the low cloud variations suggested by these two reanalysis products are quite different. Overall, the NCEP values are in better agreement with the

10 1264 JOURNAL OF CLIMATE VOLUME 13 FIG. 8. Dominant intraanuual EOFs of low cloud cover: (a) NCEP reanalysis, explaining 23.9% of the total variance; and (b) ERA, explaining 40.7%. Hahn et al. values than are those of the ERA. For instance positive Niño-3 SST departures are associated with increased NCEP, but decreased ERA, low cloud over the central tropical Pacific. Similarly, in the areas poleward and westward of the Niño-3 region the NCEP and Hahn et al. analyses suggest decreases in low cloud associated with high SSTs, whereas the ERA suggests the opposite. On the other hand there are regions in which the NCEP reanalysis and ERA low cloud changes are quite similar. These include much of the western Pacific south of 20 S, the eastern tropical Pacific, and the Atlantic south of the equator. Some of these areas of agreement are similar to those of agreement in Fig. 9a and 9b, which suggests that the reanalysis models both give relatively good low cloud estimates in areas dominated by low clouds. 6. Discussion This paper has reviewed several near-global datasets of low cloud cover. Several important conclusions include the following. 1) The magnitudes of annual-mean ISCCP and Hahn et al. low cloud differ by up to about 0.4 for a number of locations. 2) The annual mean of both the NCEP and ERA low clouds, which should be comparable to the low cloud as measured from below in the Hahn et al. dataset, have magnitudes that are up to about 0.3 less than the latter. 3) However, unlike for the ISCCP low clouds, the spatial patterns of variation of the annual mean NCEP and ERA low clouds are qualitatively similar to that of Hahn et al. 4) The time coefficients of the first EOF of the seasonal cycle of ERA low clouds leads those of all of the other datasets by about 2 months. 5) The patterns of the dominant EOFs of seasonal variation of ISCCP and Hahn et al. low cloud differ substantially over much of the Northern Hemisphere. 6) The pattern of the dominant seasonal EOF of NCEP low clouds has a number of qualitative similarities with those of the Hahn et al. set between approximately 10 and 40 N.

11 1APRIL 2000 WEARE 1265 FIG. 9. Regression coefficients relating interannual departures of low cloud fractions with those of Niño-3 SST [cloud fraction ( C) 1 ] using: (a) reconstructed ISCCP and (b) reconstructed Hahn low cloud. All shaded regions are significant at better than the 95% confidence interval. 7) The dominant seasonal EOF of the ERA low clouds has much higher amplitudes in the high latitudes of both hemispheres than any of the other datasets. The spatial patterns also differ considerably from those of either the Hahn et al. or NCEP datasets. 8) The calculated regression coefficients between interannual variations of Niño-3 SST variations and low cloud departures in the equatorial central Pacific have positive magnitudes of about 0.02 ( C) 1 for the Hahn et al. and NCEP data, but negative values of similar magnitudes for the ISCCP and ERA low cloud fractions. Figure 2 suggests that most of the differences between the ISCCP and Hahn et al. low cloud datasets may be attributed to the basic differences in the definition of low clouds as viewed from above and below. However, there are a large number of grid-months, when no low cloud is observed by the satellite, for which Eq. (4) cannot be readily utilized. On the other hand the large differences between the Hahn et al., NCEP, and ERA low clouds are more difficult to explain. Weare (1992) has estimated that the sampling errors in the monthly mean estimates of surface-observed cloud range between about 0.05 and 0.14 for grids having between 50 and 10 observations, which is true for most oceanic regions north of 20 N. Assuming that the sufficiently sampled Hahn low cloud amounts are true, this implies that at least over most of the Northern Hemisphere the large systematic differences between the Hahn and reanalysis low cloud amounts must be primarily related to deficiencies in the latter. The possible sources of biases and errors in the NCEP and ERA low cloud sets are myriad. The errors may be partially separated into three classes: (a) those associated with analysis model forecasts of basic thermodynamic and dynamic variables, (b) those associated with the model cloud parameterization itself, and (c) those associated with the overlap that is assumed in combining the cloudiness in the various model layers that make up the low cloud region. A simple estimate of the effects of the use of different overlap assumptions can be made. For instance, assume that there are five model levels with low cloud each

12 1266 JOURNAL OF CLIMATE VOLUME 13 FIG. 10. Regression coefficients [cloud fraction ( C) 1 ] as Fig. 9 using: (a) NCEP reanalysis and (b) ERA low cloud. having a fraction of 0.2. If these layers are maximally overlapped, the low cloud cover must be 0.2; if they are minimally overlapped, the low cloud cover is 1.0; if they are randomly overlapped, the low cloud cover is Thus, if a model creates cloud at several lowlevel layers, the choice of overlap assumption itself can have a very large effect on the reported low cloud cover. It is also important to note that the random overlap assumption always leads to greater cloud cover as the number of cloud containing layers increases; this is not true for the maximum overlap assumption, for instance. What is the appropriate overlap assumption? There have been very few published reports to answer this question. Tian and Curry (1989) used the U.S. Air Force Three-Dimensional Nephanalysis to test overlap assumptions for daily data for a single month over the North Atlantic. They concluded that the random overlap assumption performs reasonably well on average, but that systematic bias and random discrepancies could result in significant errors. Weare (1999) evaluated the random and mixed overlap assumptions for a three-layer atmosphere using monthly means of combined ISCCP and Hahn et al. observations. He concluded that the mixed assumption generally better fits the observations. Unfortunately, the Weare (1999) methodology is not easily extrapolated to the many layers now common in climate or forecast models. In addition to errors in the overlap of the low clouds the analysis models almost certainly have sizable errors in the forecast of individual layer cloud amounts. Although the calculated regression coefficients (Figs. 9 and 10) associated with Niño-3 SST variations strongly suggest a problem in the ERA in relating tropical SST variations to those in low cloudiness, clear elucidation of such an error can be made only with a detailed analysis of a number of reanalysis fields, which is beyond the scope of this paper. One important question is what influence does predicted low cloud cover have on the other climatic fields such as radiative fluxes at the surface or the top of the atmosphere. The Column Radiation Model (CRM) of the NCAR Community Climate Model Version 3 (CCM3) (Kiehl et al. 1998) has been used to estimate the possible influences of the fluxes near 0, 180 associated with the 1987 ENSO event, which had a maximum departure of about 2.0 C in the Niño-3 SST. The primary meteorological input for the CRM was mean temperature and humidity profiles for the region

13 1APRIL 2000 WEARE 1267 TABLE 1. Daily average radiative fluxes and perturbations from the reference state (W m 2 ) calculated with the CCM3 CRM using the mean conditions described in the text and the variations in low cloud cover f 1 shown in the table. Shortwave up: Top Shortwave down: Surface Longwave up: Top Longwave down: Surface Reference fluxes f perturbations f perturbations near 0, 180 from the NCEP reanalysis. Mean cloud amounts for three layers were derived from the Weare (1999) analysis such that f h, f m, and f l are 0.192, 0.198, and 0.378, respectively; these clouds were placed at the 250-, 500-, and 850-hPa levels. The CRM assumes a random overlap between layers. The required cloud liquid water paths were specified as 10 g m 2 for all three clouds. The solar angle was specified to simulate the annual mean daily averaged downward solar radiation at the top of the atmosphere for this region. These specifications give solar and infrared fluxes at the top of the atmosphere that matched the annual mean Earth Radiation Budget Experiment (ERBE; Ramanathan et al. 1989) values at 0, 180 within about 5 W m 2. Perturbations about this mean were calculated for two cases. The first corresponds to the change predicted in the NCEP low cloud cover for the assumed increase in Niño-3 SST based on the regression results in Fig. 10a, which is about The second is the comparable predicted ERA low cloud perturbation of about The results are summarized in Table 1; similar results are obtained (not shown) using a reference with twice the mean high cloud amount. The different predicted cloud amounts lead to about an 8 W m 2 difference in the solar fluxes and a smaller shift in the longwave. The magnitudes of these differences, especially the solar, are large enough to lead to sizable differences in feedbacks within both the atmosphere and ocean during an ENSO event. The observed changes in ERBE top-of-atmosphere fluxes during the 1987 El Niño were of the same sign as those for the predicted NCEP low cloud perturbation, but several times larger due in large part to the known changes in high cloud. Overall, these results suggest that care must be taken in utilizing any of the available low cloud datasets. Careful distinction must be made between satellite-view, surface-view, and model-layer estimates of low cloud. At present the means and seasonal variability of low cloud simulated by the NCEP or ECMWF analysis differ substantially from each other and from the most directly comparable surface and satellite surface observations. These conclusions suggest a strong need to make improved near-global estimates of observed three-dimensional cloudiness, as well as to develop and verify enhanced cloud models for incorporation in forecast and climate models. Acknowledgments. 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