Relationships between Large-Scale Vertical Velocity, Static Stability

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Relationships between Large-Scale Vertical Velocity, Static Stability, and Cloud Radiative Forcing over Northern Hemisphere Extratropical Oceans* C. P. WEAVER

AND

V. RAMANATHAN

Center for Clouds, Chemistry and Climate, and Center for Atmospheric Sciences, Scripps Institution of Oceanography, La Jolla, California (Manuscript received 2 December 1996, in final form 15 April 1997) ABSTRACT This paper identifies dynamical and thermodynamical factors that govern the seasonal and interocean differences in cloud cover and cloud radiative forcing (CRF) over the storm track regions of the northern extratropical Pacific and Atlantic Oceans. An outstanding problem of interest is the fact that cloud cover is larger in the summer than winter in the North Pacific, while the converse is true in the North Atlantic. This paper considers separately January and July in the North Pacific and North Atlantic and finds that, on daily timescales, rising motion associated with synoptic-scale events such as cyclones produces greater CRF. However, CRF does not vary much with vertical velocity in regions of subsidence. In addition, increased moist static stability is associated on daily and monthly mean timescales with increased cloud cover and shortwave CRF. These results imply that, on monthly mean timescales, if we hold moist static stability constant, CRF should increase with increasing vertical velocity variance. This effect, by itself, would tend to increase CRF during winter, since the variance of vertical velocity is much larger during winter than summer. This is consistent with what is observed in the North Atlantic. In the North Pacific, however, the mean moist static stability is much larger during summer, and this effect tends to counteract the summertime decrease in vertical velocity variance, resulting in greater summertime cloud cover. Extending the argument to explain interocean differences in cloudiness or CRF during the same season, this paper finds that the North Pacific and North Atlantic have approximately the same CRF (or cloud cover) during winter because the mean vertical velocity variance and moist static stability are approximately the same. The North Pacific is more cloudy than the North Atlantic during summer because, while the mean vertical velocity variance is approximately the same, mean moist static stability is much greater in the North Pacific. Finally, spatial variations in both parameters within a given ocean basin tend to either reinforce each other or compete in their effect on CRF.

1. Introduction This paper investigates the relationships on large spatial scales, and daily to monthly timescales, between cloudiness, dynamics, and thermodynamics on a seasonal basis for the extratropical North Pacific and North Atlantic. Specifically, we attempt to provide a consistent model, based on the relationships between clouds and two parameters, vertical velocity, and static stability, which can be used to explain differences in cloud radiative forcing (CRF) and cloud cover within and between both these ocean regions and between winter and summer. We find that, in the regions and seasons considered, variations in the vertical motion and static sta-

* NSF Center for Clouds, Chemistry and Climate Publication Number 170. Corresponding author address: Dr. C. P. Weaver, Center for Clouds, Chemistry and Climate, Scripps Institution of Oceanography, UCSD, 9500 Gilman Drive, La Jolla, CA 92093-0221. E-mail: [email protected]

q 1997 American Meteorological Society

bility fields both contribute to variations in CRF and that the variations of these two parameters can act together or in a competing sense. On an annual-average basis, the reflection of insolation by clouds over the extraropical oceans exceeds the absorption of outgoing longwave radiation (OLR) and contributes strongly to the net cooling effect of clouds on the earth’s radiation budget (Harrison et al. 1990). In the terminology of the Earth Radiation Budget Experiment (ERBE), the magnitude of shortwave cloud forcing (Cs 5 Sclear 2 Scloudy) exceeds that of longwave cloud forcing (Cl 5 OLRclear 2 OLRcloudy) (Ramanathan et al. 1989). Here, S refers to outgoing (reflected) solar radiation, and the subscripts clear and cloudy refer to the same satellite pixel during times when the scene was identified as cloud-free and cloud covered, respectively. According to these definitions, Cs is generally negative (clouds reduce the amount of solar radiation absorbed by the surface-atmosphere system), while Cl is generally positive (clouds increase the amount of longwave radiation retained by the surface-atmosphere system). Over the Northern Hemisphere extratropical oceans, during January, the magnitude of Cl is close to or some-

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TABLE 1. Area-weighted mean total cloud amount (TCA) and cloud type fraction (in %) from the Warren et al. (1988) oceanic cloud atlas for winter and summer. Included are averages over the entire box for both oceans as well as for zonal subdivisions within each box. See section 2b for definitions of the cloud-type abbreviations. North Pacific (lat 358–608N, long 1608–2208) 358–608

358–408

408–508

TCA St As Ns Cu Cb

78.0 53.1 23.2 14.1 7.3 5.1

75.8 50.0 25.7 10.9 9.1 5.2

78.8 55.1 23.2 13.7 7.1 4.7

TCA St As Ns Cu Cb

86.6 69.7 36.3 10.1 4.3 1.9

76.2 51.2 29.8 7.3 8.7 3.5

91.7 78.4 40.9 10.5 2.3 1.3

North Atlantic (lat 358–608N, long 3108–3508) 508–608

358–608

358–408

408–508

508–608

DJF 78.8 52.4 19.5 19.3 5.4 5.6

77.4 46.8 27.7 9.7 10.2 7.2

71.0 38.2 28.6 5.5 12.2 6.4

78.6 50.5 28.1 10.1 8.9 6.8

83.3 49.5 25.8 14.8 10.6 9.3

JJA 88.4 73.8 33.8 12.7 2.4 1.1

71.1 45.4 24.5 6.4 9.3 3.6

55.8 24.1 17.1 2.0 15.3 4.8

74.3 50.0 26.3 6.4 7.8 3.3

84.5 63.7 30.1 12.4 4.4 2.9

what greater than that of Cs, indicating that clouds have a near-zero, or small positive, net radiative heating effect on the total surface-atmosphere system. The reverse is true during July with the magnitude of Cs far exceeding typical Cl, often by more than 100 W m22 (Harrison et al. 1990). In other words, clouds in the extratropics are acting to oppose the seasonal warming. The magnitude of net CRF in the extratropics, and particularly of the individual Cs and Cl components, is such that relatively small percentage changes in cloud cover or cloud properties could result in an anomalous climate forcing of several W m22, comparable to the direct forcing due to a doubling of CO2 (e.g., see Ramanathan 1995). Thus, it is important to understand the physical mechanisms that control CRF in these regions. In Weaver and Ramanathan (1996), we established that for the North Pacific during July, the clouds that primarily contribute to the large magnitude of Cs are the horizontally extensive systems of stratiform clouds (at all levels in the vertical) that are associated with cyclonic disturbances in the storm tracks. Because of their large areal extent and temporal frequency, these synoptic-scale systems contribute significantly to the characteristic monthly and seasonal picture of CRF as described in Harrison et al. (1990). Though insolation, and hence Cs, is greatest during summer, extensive cloudiness is prevalent during all seasons throughout the extratropical ocean regions (Warren et al. 1988). As discussed in Weaver and Ramanathan (1996), the cloud-type climatology for this region is consistent with ERBE CRF. Table 1 shows climatological, seasonalmean cloud cover for total cloud amount (TCA) as well as for various cloud types (Warren et al. 1988). One striking feature is that, during summer (June–August; JJA), TCA in the central North Pacific (358–608N) is much greater (86.6%) than in the central North Atlantic (71.1%). During winter (December–February; DJF),

TCA is approximately the same over both basins (Table 1). We can use this kind of climatological difference in cloudiness as an example to help investigate the parameters that determine the cloud field and CRF. Other investigators have studied aspects of the extratropical oceanic cloudiness question. Klein and Hartmann (1993) investigated the seasonal cycle of low, oceanic, stratiform cloudiness. They find that the maximum in low stratiform cloud fraction occurs in the same month as the maximum lower troposphere static stability. Norris and Leovy (1994) find that marine stratiform cloudiness is strongly anticorrelated on an interannual basis with sea surface temperature (SST) anomalies over extratropical and eastern subtropical oceans, particularly in regions of strong climatological gradients in SST and cloudiness. They also find an anticorrelation between extratropical SST and nimbostratus and nonprecipitating midlevel clouds, which they attribute to displacement of the oceanic storm tracks over regions of negative temporal SST anomalies, producing characteristic increases in midlevel cloudiness. Lau and Crane (1995) examine the synoptic-scale organization of cloud type and optical depth in the wintertime storm track regions of the North and South Atlantic. They show that the satellite-inferred cloud structures associated with baroclinic eddies in the extratropical Atlantic during the cold season are consistent with accepted views of cloud organization in extratropical cyclones. The kinds of relationships between large-scale dynamics, thermodynamics, and cloud properties explored in this paper are also of interest to those who construct parameterizations for weather- and climate-prediction models. For example, in the European Centre for Medium-Range Weather Forecasts (ECMWF) general circulation model (GCM), Slingo (1987) uses an empirically derived formula to link relative humidity and vertical velocity to low-level frontal clouds. In addition,

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Slingo (1987) parameterizes low-level clouds that form beneath a boundary layer inversion using relative humidity and static stability. The inclusion of variables other than relative humidity significantly improved the ability of the GCM to predict cloud fraction. A generalization of this diagnostic scheme is currently used in the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM3) (Kiehl et al. 1996). In this paper we focus on how large-scale vertical velocity and static stability contribute to determining CRF in a statistical sense over the extratropical North Pacific and North Atlantic. Section 2 describes the data used in our analysis. We present a brief overview of the seasonal variations in CRF, vertical velocity, and static stability in sections 3a–c. In sections 3d–f, we investigate the link between these fields on daily mean timescales, while in section 3g, we use these links to understand the patterns of monthly mean CRF in the North Pacific and North Atlantic during January and July. Section 4 provides a summary and discusssion of the results.

Here, f is latitude in degrees. We will refer to normalized Cs as Csn such that Csn 5 Cs/ f. In this paper, we focus mainly on Csn, though we also discuss Cs and Cl. This normalization factor does not take into account the fact that total albedo and clear-sky albedo may have systematically different responses to seasonal solar zenith angle changes. In other words, the mean solar zenith angle is much greater in the extratropics during winter than summer, and this increases both the total and clearsky albedo, given the same cloud scene. Since Cs is proportional to the difference between the two albedos, if there is a larger increase in either clear-sky or total albedo from summer to winter, Cs will change in a way that cannot be corrected for simply by dividing by f. We performed numerical simulations, using the radiation code of the NCAR CCM2, that have indicated, however, that this effect introduces variability on the order of 10% into Csn, and we will therefore not consider it for the remainder of this paper.

2. Data

The Warren et al. (1988) oceanic cloud atlas is a shipand surface-based climatology for the period December 1951 to November 1981, derived from the Comprehensive Ocean–Atmosphere Data Set (COADS, Woodruff et al. 1987). Daily and hourly observations have been consolidated into seasonal climatologies for the 30-yr period. Cloud frequency of occurrence, amount when present, and total cloud amount are reported for a variety of cloud types at 58 3 58 resolution between 508N and 508S, with degraded longitudinal resolution poleward. The acronyms for the cloud amounts considered in this paper are TCA (total cloud), St (stratus 1 stratocumulus 1 fog), As (altostratus 1 altocumulus), Ns (nimbostratus), Cu (cumulus), and Cb (cumulonimbus). The reported cloud amounts take into account overlap of clouds at different levels and the statistical cooccurrences of different cloud types. More detailed information about the dataset characteristics can be found in the land and ocean atlases (Warren et al. 1986; Warren et al. 1988).

a. ERBE We use monthly mean ERBE Cl and Cs data for January and July of 1985–89, as well as daily mean values of ERBE outgoing solar radiation and OLR on a global 2.58 3 2.58 grid for the same time period. We estimate daily Cs and Cl by differencing the daily clear and cloudy values, when available, and by differencing the daily cloudy values and the monthly mean clear values when clear-sky values are unavailable for a given day. There are often areas of missing data, particularly at higher latitudes and particularly during January. For this reason, we consider latitudes up to 608N, though this does not solve the problem completely during January. A description of the data and its characteristics can be found in Ramanathan et al. (1989) and Harrison et al. (1990). Since much of the seasonal change in Cs is due to changes in insolation rather than cloud properties (Cess et al. 1992), we have normalized the Cs values by a factor ( f) proportional to diurnal-mean insolation at each point (e.g., see Fig. 6.4 and Eqs. 6.13–6.18 in Peixoto and Oort 1992) in order to facilitate comparisons between July and January. This factor takes into account variations in earth–sun distance, solar declination, and latitude. In the latitude range 358–608N, we have fit the value of f to latitude in the following manner: f jan 5 0.84123 2 0.00032486f 2 0.00038320f 2 1 3.0179 3 10 26f 3

(1a)

f jul 5 0.76813 1 0.0201660f 2 0.00039953f 2 1 2.2887 3 10 26f 3

(1b)

b. Surface-based cloud climatology

c. ECMWF analyzed fields Twice-daily ECMWF analyzed fields from the World Meteorological Organization (WMO) provide wind (u, v), geopotential height (z), temperature (T), relative humidity (RH), and vertical pressure velocity (v) for January and July 1985–89. The horizontal resolution is 2.58 3 2.58. Values of the fields are reported at 7 pressure levels (1000, 850, 700, 500, 300, 200, and 100 mb). A description of the WMO dataset can be found in Trenberth and Olson (1988a,b). We use these fields to calculate baroclinicity, potential temperature (u), and equivalent potential temperature (ue).

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TABLE 2. Area-weighted mean ERBE Cs, Csn, and Cl (W m22) for mean January and July 1985–89. Included are averages over the entire box for both oceans as well as for zonal subdivisions within each box. North Pacific (lat 358–608N, long 1608–2208) 358–608

358–408

408–508

North Atlantic (lat 358–608N, long 3108–3508) 508–608

Cs Csn Cl

237 2114 41

251 2114 50

239 2117 42

Jan 219 2111 34

Cs Csn Cl

2129 2121 31

293 286 32

2141 2131 34

Jul 2140 2133 27

3. Results a. Seasonal variations in CRF We briefly examine the seasonality in CRF in order to provide a context for our investigation into the interactions with v and static stability. Table 2 summarizes the ERBE data for the same regions in the extratropical North Pacific and North Atlantic as in Table 1. Though the two datasets are different, and cover different time periods, there is a good correspondence between Csn (Table 2) and TCA (Table 1), such that we can consider Csn as a rough proxy for cloud cover. Figure 1 displays Csn for January and July 1985–89. The magnitude of Csn is large in the storm track regions over the North Pacific and North Atlantic, and the Csn maximum is displaced slightly poleward in the North Atlantic relative to the North Pacific during July. In the North Pacific, the position of the summertime maxima in St, As, and TCA coincide with the July maximum in Csn. As discussed in Weaver and Ramanathan (1996), the distribution of Csn and the amounts of low- and midlevel stratiform cloud cover is consistent with cyclones, and their associated cloud systems, forming preferentially

FIG. 1. The Csn (W m22; see section 2a for derivation) for (a) mean January and (b) mean July 1985–89. The contour interval is 30 W m22. Shading ranges from largest (lightest) to smallest (darkest) magnitude Csn.

358–608

358–408

408–508

508–608

234 2109 44

240 290 41

238 2116 46

221 2119 44

291 286 21

241 238 13

286 280 22

2133 2127 27

in the regions of large baroclinicity off the east coasts of Asia and North America and migrating downstream. In the North Atlantic during July, particularly in the range 358–408N, St and As amounts are small compared to other regions and seasons, and Cu amount is relatively large. Here, Cl (not shown) has many of the same features as Csn, though Cl reaches its largest values in the Tropics rather than the extratropics. Generally, Cl is larger during January than July over the storm track regions of the North Pacific and North Atlantic. Much of this difference can be attributed to the wintertime increase in lapse rate over the extratropical oceans. A comparison of Cs (rather than Csn) and Cl (Table 2) illustrates once again that during July, clouds have a large net cooling effect, particularly in the North Pacific, while during January, clouds have a slight net heating effect. b. Seasonal variations in the vertical velocity (v) field Mean v at 500 mb (Fig. 2) is organized into distinct regions of rising and sinking motion that follow cli-

FIG. 2. The 500-mb v (dp/dt in mb day21) for (a) mean January and (b) mean July 1985–89. The contour interval is 15 mb day21. Positive values correspond to downward motion. Shading ranges from strongest rising motion (lightest) to strongest sinking motion (darkest).

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TABLE 3. Area-weighted mean ECMWF 500-mb u (m s21), 850- and 500-mb v (mb day21) and 500-mb v variance [mb day21; see (2)] for mean January and July 1985–89. Included are averages over the entire box for both oceans as well as for zonal subdivisions within each box. North Pacific (lat 358–608N, long 1608–2208)

North Atlantic (lat 358–608N, long 3108–3508)

358–608

358–408

408–508

508–608

358–608

358–408

408–508

508–608

u500 v850 v500 varv500

15.8 218.7 225.8 144.2

27.8 211.0 228.3 183.2

18.4 217.2 229.9 147.6

Jan 4.1 226.3 220.4 110.5

15.2 24.3 210.7 132.3

13.9 6.3 6.2 113.2

18.6 0.2 27.3 133.6

13.1 217.4 227.6 146.0

u500 v850 v500 varv500

8.2 25.1 28.0 65.9

8.2 24.1 28.7 56.3

12.2 26.3 212.2 73.5

Jul 4.4 25.3 23.9 66.2

9.4 6.0 6.3 61.2

3.8 14.7 16.0 47.5

10.9 8.5 9.4 58.9

12.5 22.8 24.5 74.6

matological patterns such as the subtropical anticyclones and the Aleutian Low. It is interesting to note that in the North Atlantic during July, the subtropical subsidence region extends horizontally across more of the basin than in the North Pacific, leading to mean downward motion in the range 358–508N (see also Table 3). Table 3 also shows the seasonal- and regional-mean values of 500-mb zonal wind. The v variance at 500 mb (Fig. 3) indicates the intensity of synoptic-scale lifting and sinking associated with the passage of frontal systems. The variance (at a given level) is calculated in the following way: Variance 5 (v92)½.

(2)

The overbar indicates averaging over a month and the prime indicates the daily deviation from the monthly mean. This field is organized into distinctive bands, consistent with the seasonal position of the storm tracks, and reaches its largest values during January, when the band of maximum variance is shifted equatorward. The

latitudinal distribution and overall intensity in both basins and seasons is well correlated with zonal baroclinicity and other indicators of cyclone activity. A brief note is in order about our choice of level for v. We have chosen 500 mb for our analysis because, in these extratropical oceanic regions, it tends to be representative of the vertical motion in the free troposphere as a whole. In other words, large-scale rising (or sinking) motion extends throughout much of the vertical column. A description of the 850-mb v field, for example, would be very similar (see also Table 3). We have performed all the analysis in the paper (on the characteristics of the v field and the relationship between CRF and v) at other levels besides 500 mb (particularly focusing on 850 mb) and found that the relationships at 500 mb tend to be characteristic on both daily and monthly mean timescales. Thus, we have chosen those results to report here. c. Seasonal variations in static stability We have used the ECMWF temperatures and humidities to calculate ue at a given level in the following way:

ue 5 u exp

1C T2 Lq

(3a)

p

u5T

FIG. 3. Variance of 500-mb v [in mb day21; see (2) for definition] for (a) mean January and (b) mean July 1985–89. The contour interval is 30 mb day21. Shading ranges from most (lightest) to least variance (darkest).

1 2 1000 p

Rd /Cp

,

(3b)

where L is the latent heat of the phase transition, q is the specific humidity, Cp is the specific heat of air at constant pressure, T is the temperature (strictly speaking, this should be evaluated at the lifting condensation level—using the in situ temperature instead, however, introduces only slight differences), Rd is the gas constant for dry air, and p is pressure in mb. Figure 4 shows the seasonal variation of monthly mean 500- to 1000-mb ue difference, a measure of the moist static stability of

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FIG. 4. The ue (K) difference between 500 and 1000 mb for (a) mean January and (b) mean July 1985–89. The contour interval is 6 K. Shading ranges from most (lightest) to least stable (darkest).

FIG. 5. Vertical ue (K) profile for the North Pacific (lat 508–558N, 1758–1858) for mean January (solid curve) and July (dashed curve) 1985–89.

the lower-middle troposphere, while Table 4 summarizes the various thermodynamical quantities. Ocean surface temperatures are fairly equable between seasons. During winter, the oceans give up heat sparingly, compared to the land, and remain relatively warm beneath advected cold continental air. The reverse is true during summer, as the oceans stay relatively cool beneath warm air. This creates a seasonal, climatological tendency for greater dry static stability (and hence moist static stability) in the Northern Hemisphere oceanic storm tracks during summer compared to winter (Fig. 4). The seasonal difference in moist static stability tends to be less than the seasonal difference in dry static stability because of the generally larger RH aloft during Jaunuary compared to July (Table 4) and the nonlinearity of the Clausius– Clapeyron relation. In addition, it is worthwhile to point out that the North Atlantic is significantly drier than the North Pacific, particularly during July (Table 4).

A comparison of mean, vertical ue profiles for January and July in the central North Pacific (Fig. 5) shows the vertical structure of this seasonality. During January, the difference in ue between 1000 and 850 mb is close to zero, with ue increasing more rapidly with height farther above the boundary layer. During July, there is a strong inversion immediately above the boundary layer with less stable air above. We might expect that cumulus convection originating at low levels would be inhibited during July compared to January. The 500- to 850-mb ue difference is larger during January than July in both the North Pacific and North Atlantic (Table 4). We consider the 500- to 1000-mb ue difference as an indicator of the moist static stability in the lower and mid-troposphere as a whole. However, variations in 500–1000-mb ue are usually dominated by the presence or absence of the strong boundary layer inversion and are, therefore, well correlated with 850–1000-mb vari-

TABLE 4. Area-weighted mean ECMWF potential temperature ( u) difference (K; between 500 and 1000 mb), equivalent potential temperature (ue) difference (K; between 500 and 1000 mb, 850 and 1000 mb, and 850 and 500 mb), and RH at 850 and 500 mb (%) for mean January and July 1985–89. Included are averages over the entire box for both oceans as well as for zonal subdivisions within each box. North Pacific (lat 358–608N, long 1608–2208)

North Atlantic (lat 358–608N, long 3108–3508)

358–608

358–408

408–508

508–608

358–608

358–408

408–508

508–608

u500–u1000 ue500–ue1000 ue850–ue1000 ue500–ue850 RH850 RH500

21.5 11.2 0.2 11.0 81.0 58.0

23.2 9.1 22.2 11.3 77.6 51.2

20.8 10.6 20.2 10.7 83.2 58.4

Jan 20.8 13.4 2.4 11.0 81.7 63.2

24.3 11.2 20.1 11.3 74.8 52.9

25.8 8.5 21.5 10.0 69.0 46.4

25.1 11.7 0.3 11.5 74.0 52.2

22.4 12.7 0.5 12.2 80.3 58.6

u500–u1000 ue500–ue1000 ue850–ue1000 ue500–ue850 RH850 RH500

34.3 19.4 12.2 7.2 77.9 52.4

33.1 11.1 6.2 4.9 74.9 48.2

36.0 22.8 15.6 7.2 78.8 53.8

Jul 33.6 22.8 13.9 8.9 79.6 54.6

32.2 13.6 7.1 6.5 72.8 44.3

31.5 6.7 3.1 3.6 67.3 34.7

32.9 13.5 7.7 5.8 72.8 42.9

32.1 19.1 9.6 9.4 7.1 53.1

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FIG. 6. Time series of Csn in the North Pacific at lat 43.758N, long 146.258 for five Julys (1985–89).

ations on daily and monthly mean timescales (e.g., see Table 4). The results of most of our analysis are similar regardless of whether we use 500–1000 or 850–1000 mb. Somewhat arbitrarily, therefore, we use 500– 1000-mb ue difference as an index of stability throughout the remainder of this paper. d. A summary of cyclone-scale dynamics and CRF Here we provide some context for interpreting the statistical relationships between daily mean CRF, v, and moist static stability, which we present in later sections. In Weaver and Ramanathan (1996), we examined the relationship between CRF and the 1000-mb geopotential height field for July 1985 in the North Pacific. In order to gain a greater understanding of the correspondence between CRF and cyclone dynamics on daily timescales, we have examined CRF in conjunction with z, v, and moist static stability for many case study days in both January and July during 1985–89 in the North Pacific and North Atlantic. From this we have noted certain common features relating to the distribution of CRF in cyclones. A convenient way to summarize this information is by creating composite maps. Figure 6 shows a time series of Csn for July 1985–89 at a point in the North Pacific (latitude 43.758N, longitude 146.25) that experiences a large number of cyclones. We have averaged the ECMWF fields over days when Csn , 2200 W m22 (9 points) in order to construct the composites. In a similar manner, Lau and Crane (1995) used time series of International Satellite Cloud Climatology Project (ISCCP) cloud optical depth to infer details about cloud-dynamical interactions in coldseason cyclones. The resulting picture is not perfect, but we can clearly pick out many features typical of individual cyclones. The composite maps of 1000-mb z, 500-mb v, 500- to 1000-mb ue difference, Csn, and Cl, respectively, are shown in Figs. 7a–e. There is a strong low near the averaging point (Fig. 7a) with a weaker

FIG. 7. Composite maps for July of (a)1000-mb z (m), where the contour interval is 20 m, and (b) 500-mb v (mb day21), where the contour interval is 40 mb day21 (upward motion for v , 0). Contours range from strongest rising motion (lightest shading) to strongest sinking motion (darkest shading). (c), 500- to 1000-mb ue difference (K), where the contour interval is 5 K. Contours range from most (lightest) to least stable (darkest). (d) Csn (W m22), where the contours are 2150 (lightest), 2100, 250, and 0 W m22 (darkest). (e) C1 (W m22), where the contours are 0 (darkest), 20, 40, and 60 W m 22 (lightest). These maps are averages over the days where Csn , 2200 W m22 in Fig. 6.

trough one wavelength downstream. There is intense rising motion in the poleward-moving airstream around the composite low near Japan (Fig. 7b), and since this air is relatively warm and moist compared to the colder ocean over which it has moved (poleward), the lower and midtroposphere is, therefore, more stable (Fig. 7c). Sinking motion is located to the east of this rising motion and, to a lesser extent, to the west in the cold, equatorward-moving air. A shield of highly reflective (Csn , 2150 W m22) and high-level (Cl . 60 W m22) cloud coincides with the rising air (Figs. 7d–e). These are the clouds associated with the ‘‘warm conveyer belt’’ as discussed in Lau and Crane (1995). In addition, there is an indication of a comma-like tail extending to the southwest in the composite of Cl. The regions of sinking motion are characterized by smaller values of Csn and Cl. These findings are broadly consistent with the findings of Lau and Crane (1995) and with classical ideas about circulations and cloud structure in extratropical cyclones. Weaker rising motion and an area of highly reflective, lower-level cloud is associated with the downstream trough. These features represent a com-

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FIG. 8. The distribution of 1985–89 500-mb v (mb day21) in the latitude range 358–608N for (a) January in the North Pacific (longitude 1608–2208), (b) January in the North Atlantic (longitude 3108–3508), (c) July in the North Pacific, and (d) July in the North Atlantic. Each plot is a normalized histogram with an v interval of 0.01 Pa s21 (or 8.64 mb day21). For a given month and region (e.g., July 1985, North Pacific) we used the daily mean, 2.58 3 2.58 v values to construct a histogram. We created a separate histogram for each of the 5 yr (1985–89) and then averaged the five histograms to produce the plots shown.

posite of systems that have moved downstream and are in the process of dissipating. We note that Walcek (1994) has found significant correlations between large-scale cloud cover and vertical velocity at most levels in the troposphere for an individual springtime cyclone over the continental United States. A similar analysis of January 1985–89 in the North Pacific (not shown) illustrates similar relationships, though the dynamics (variations in z and v) are more intense. Both the background Csn level, as well as the Csn spikes, are of larger magnitude during July than January, however. In addition, the background level of stability is much lower during January than during July. e. Relationship between daily mean CRF and v As discussed in the previous section, we find strong correspondences between cyclone-scale motions and the CRF field. Next, we investigate the statistical relationships between daily variations in vertical motion and CRF (this section), and static stability and CRF (section 3f). In section 3g, we will use these results to try to understand variations in monthly mean CRF. Figure 8 shows the frequency distribution (in the form of normalized histograms) of daily 500-mb v for January and July (averaged over the period 1985–89) in the North Pacific (latitude 358–608N, longitude 1608– 2208) and in the North Atlantic (latitude 358–608N, longitude 3108–3508). The distributions are close to Gaus-

sian, with means near zero relative to the overall range in v (recall Table 3), which implies a near-balance between rising and sinking motion on ocean basin scales, though the tail of extreme rising motion extends farther than that of extreme sinking motion. Comparing Fig. 8 with Fig. 2, we see that the monthly mean v field is actually a small residual resulting from averaging over many large v events of opposite sign. For each month, the distributions are very similar over both oceans. Seasonally, the January distributions are flattened relative to July, with more extreme rising and sinking events at the expense of gentler motions. This is consistent with enhanced cyclone activity during January and with the seasonal maps of v variance (Fig. 3). Similar distributions hold in individual latitude bands as well as for the entire basin. Since the resolution of the ECMWF analyzed fields is 2.58 3 2.58, each point represents an average of larger-scale motions as well as individual cloud-scale updrafts and downdrafts. We have averaged the area-weighted, daily mean Csn and Cl values into 500-mb v bins (0.02 Pa s21 or approximately 17-mb day21 bin interval) for each January and each July from 1985 to 1989 in the North Pacific and North Atlantic. The latitude and longitude ranges are the same as in Fig. 8. Figure 9 shows the results of averaging over the curves for all 5 yr for each month and ocean basin. Consistent with the findings of Lau and Crane (1995), as well as our composite analysis in

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FIG. 9. Daily mean 500-mb v (mb day21) vs (a) Csn (W m22) for January 1985–89, (b) Csn for July 1985–89, (c) C1 (in W m22) for January 1985–89, and (d) C1 for July 1985–89 in the lat range 358–608N. In each plot, the solid line refers to the North Pacific (long 1608–2208) and the dashed line refers to the North Atlantic (long 3108–3508). The daily CRF data has been averaged (area weighted) into v bins (0.02 Pa s21 or approximately 17-mb day21 wide).

section 3d, rising motion (v , 0) is associated with thick cloud decks and high cloud tops (large magnitude Csn and Cl, respectively), while sinking motion (v . 0) is associated with thinner cloud decks and low cloud tops. As discussed in section 3b, we do not mean to imply that rising motion at 500 mb produces clouds only at that level. Rather, we consider rising motion at 500 mb as an indicator of general rising motion throughout

the free troposphere that favors cloud formation at many levels. Binning CRF by v at other levels in the free troposphere produces similar results. The standard deviation in each bin is fairly large (;40–60 W m22 for Csn; ;10–20 W m22 for Cl) indicating a high degree of scatter between the two datasets. At the extreme rising and sinking ends of the curves, the number of points in each bin decreases (see Fig. 8) and the impact of this

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scatter becomes more noticeable. This behavior is exacerbated by potential discrepancies between the ERBE and ECMWF datasets. Though one cannot expect the correspondence between such different datasets to be perfect, the fact that dynamical features from the ECMWF fields seem to agree fairly well with ERBE CRF on a daily basis (recall Fig. 7) is quite encouraging. From our inspection of case study cyclones, we found that the regions of strongest upward and downward motion are often adjacent to each other, as organized by the three-dimensional flow field within each cyclone. Slight displacements between the ECMWF and ERBE data would lead to mixing of values from strongly positive and strongly negative v bins, increasing the variability within each bin. Advection of clouds by strong horizontal winds could also contribute to this displacement (Lau and Crane 1995). The relationship between CRF and v is distinctly different between rising and sinking regimes (Fig. 9). In regions of rising motion, the magnitudes of Csn and Cl increase approximately linearly with increasing intensity of rising motion. In regions of sinking motion, however, the slopes of the Csn and Cl curves tend to plateau at a much more constant (nonzero) level. During January in the North Pacific, there even seems to be a tendency for increased subsidence leading to larger magnitude Csn values. While it is not surprising that subsidence leads to scenes with only low-level clouds, the fact that, on average, the strength of the downward motion does not alter CRF much indicates that parameters besides v are playing an important role in subsidence regions, and/or there is some compensation between cloud fraction and cloud optical thickness. This behavior, as discussed below, has important implications for the influence of variations in vertical velocity on the longer-timescale CRF field. As noted above, the curves for Csn (Figs. 9a–b) and Cl (Figs. 9c–d) look very similar (taking into account the fact that Csn is negative and Cl is positive). A plausible interpretation of Fig. 9 in the most general sense is that low-level clouds are present much of the time, in both rising and sinking regimes, but that in conditions of rising motion they are associated with clouds at higher levels (i.e., Cl is larger). The fact that scenes with higher cloud tops are also more highly reflective (larger magnitude Csn) is consistent with the idea that the layering of mid- and high-level clouds above low clouds in extratropical cyclones increases the total column optical depth (Weaver and Ramanathan 1996). Since, according to Figs. 9a–b, the magnitude of daily mean Csn increases with increasing rising motion but does not decrease with increasing sinking motion, increasing the amplitude of vertical velocity variations (i.e., increasing v variance) should increase the magnitude of Csn integrated over a time longer than several individual upward and downward motion events (if all other factors remain the same). This is particularly true because increasing the v variance does not seem to

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systematically shift the approximately equal proportions of rising and sinking events. For example, in Fig. 8, though the January histograms (Figs. 8a–b) show a larger percentage of more intense upward and downward motion than the July historgrams (Figs. 8c–d), the relative proportions of rising and sinking motion stay roughly the same. This inferred relationship between v variance and Csn is interesting in light of the results of Roads (1978a,b) who used numerical models to find that fractional cloud cover and RH tend to 50% with increasing eddy vertical velocity and increasing static stability. A further examination of Fig. 9 shows that we cannot use v alone to predict CRF between ocean basins or seasons. The Csn curves for the North Pacific and North Atlantic lie nearly on top of each other during January, while during July the North Atlantic is offset toward smaller magnitude Csn values, particularly in sinking regions: This is consistent with similar monthly or seasonal-mean TCA and Csn between the North Pacific and North Atlantic during January and smaller magnitude TCA and Csn in the North Atlantic compared to the North Pacific during July (Tables 1 and 2; Fig. 1). In addition, in regions of rising motion, the slope of v versus cloud forcing is steeper for July compared to January. Other factors are influencing the value of CRF associated with a particular v in a given region or season. We discuss the relationship between CRF and one such important factor, moist static stability, in the next section. One possible contributor to the seasonal difference in slope in Fig. 9 is the fact that the zonal wind is much stronger during winter. Lau and Crane (1995) find that, in wintertime cyclones over the extratropical North Atlantic, the cloud shield of mid- and high-level clouds ahead of the surface low is displaced eastward from the region of most intense rising motion. They suggest that the strong westerly winds in the upper troposphere lead to cloud advection. During January, zonal wind is largest where v variance is also largest (Table 3), that is, where air is moving up and down most vigorously is also where the most rapid horizontal mixing is potentially taking place. While the presence or absence of greater cloud cover could be initially associated with updrafts or downdrafts, respectively, strong horizontal winds would tend to smear out and homogenize the cloud field, moving cloud cover away from localized regions of intense rising motion. This would tend to decrease the slope of the v–CRF relationship shown in Fig. 9. Following this line of argument, it is appropriate to ask if the timescales of vertical cloud formation and horizontal cloud advection are similar. From Fig. 8, we see that a typical magnitude of daily 500-mb v (rising or sinking motion) is 100 mb day21. At this rate, it would take on the order of 1 week to ventilate the entire troposphere. The horizontal scale of alternating regions of rising and sinking motion associated with cyclones is

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up to a few tens of degrees latitude and longitude (Fig. 7). Assuming that an arc of 208 longitude separates the centers of rising and sinking motion at 408N (i.e., 1.7 3 106 m), a horizontal velocity of 25 m s21 would move a parcel across this distance in 6.8 3 104 s, or 0.79 days, comparable to the time it would take a typical value of rising motion (see Fig. 8) to form a 100-mb thick cloud layer. Thus, it might be reasonable to assume that stronger horizontal advection of mid- and high-level clouds during January relative to July could play a role in decoupling the wintertime cloud cover from localized areas of cloud formation and could, therefore, contribute to changes in the slope of Csn, or C1, versus v. This highly simplified discussion, however, considers cloud formation as purely vertical and advection as purely horizontal, and it does not take into account the fact that clouds in cyclones often form along broad sloping surfaces with significant horizontal as well as vertical components of motion. Nor does it consider the advection of the cyclones themselves along with their associated dynamical fields. In addition, as we shall see in the following section, variations in moist static stability also influence the v–CRF relationship and could also contribute to altering the slope. Some issues relating to horizontal advection and cloud cover are discussed for a simple numerical model in Roads (1978b), who found that increased horizontal advection tends to decrease cloud cover by mixing dry air with saturated (cloudy) air. f. Relationship between daily mean CRF and moist static stability Climatological changes in static stability can have a large impact on the types of clouds that form in response to a given degree of vertical motion (Wallace and Hobbs 1977; Cotton and Anthes 1989; Klein and Hartmann 1993). Illustrating this point, Fig. 10a shows our measure of moist static stability (500- to 1000-mb ue difference) for 1985–89 versus Warren et al. (1988) St, while Fig. 10b shows a similar plot for Cu. Each of Fig. 10a and 10b include both winter and summer points (averaged into 58 3 108 boxes) in the North Pacific and North Atlantic. In interpreting Fig. 10, it is important to keep in mind that the cloud data is for DJF and JJA (rather than January and July) and covers a different time period than the stability data (1952–81 versus 1985–89, respectively). We are attempting to compare estimates of the climatologies of both moist static stability and cloud type fraction. Figure 10 demonstrates that moist static stability is positively correlated with St and negatively correlated with Cu in a nearly opposite manner. In more stable conditions, such as occur in the North Pacific between 408 and 608N during July (recall Fig. 4 and Table 4), increased amounts of layered (stratiform) clouds tend to form, while in less stable conditions, such as occur during January, or in the North Atlantic between 358 and 408N during July, convective

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FIG. 10. (a) 500- to 1000-mb ue difference (K) vs Warren et al. (1988) St amount (%). (b) 500- to 1000-mb ue difference (K) vs Warren et al. (1988) Cu amount (%). Each point represents an average in a 58 lat 3 108 long box in the North Pacific (lat 358–608N, long 1608–2208) or North Atlantic (lat 358–608N, long 3108–3508) over climatological mean winter (January 1985–89 for ECMWF or DJF, 1952–81 for Warren et al. 1988) and summer (July 1985–89 for ECMWF or JJA, 1952–81 for Warren et al. 1988). In each plot, the four symbols refer separately to North Pacific winter, North Atlantic winter, North Pacific summer, and North Atlantic summer, respectively.

(cumuliform) clouds often occur preferentially. We note that the largest seasonal increases in low-level stability (i.e., 850- to 1000-mb ue difference; see Table 4) occur in the North Pacific from January to July and that these increases are associated with corresponding increases in low-level stratiform cloud amount (St; see Table 1). This is consistent with the findings of Klein and Hartmann (1993). In addition, midlevel stratiform cloud amount (As; Table 1) also increases from winter to summer in the North Pacific following this strong increase in lowlevel stability. Figure 10 implies that cloud formation in a more stable environment could lead to larger overall cloud cover than in a less stable environment because of the relative change in the proportions of horizontally extensive stratiform and horizontally limited cumuliform clouds (Sellers 1976). As is evident from Table 1, St is well correlated with TCA. Thus, we might expect that moist static stability is positively correlated with the magnitude of Csn. It is important to note that we have not attempted to address the underlying physical cause that is responsible for the observed correlations between increased stability and increased St amount (and decreased Cu). This is a topic of great interest and considerable ongoing research (e.g., see the discussion in Klein and Hartmann 1993), and a detailed investigation is beyond the scope of this paper. In order to investigate the relationship between CRF and stability on daily mean timescales, we bin Csn and C1 by the 500- to 1000-mb ue difference (Fig. 11). We find that Csn is well correlated with moist static stability

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FIG. 11. Daily mean 500- to 1000-mb ue difference (K) vs (a) Csn (W m22) for January 1985– 89, (b) Csn for July 1985–89, (c) C1 (W m22) for January 1985–89, and (d) C1 for July 1985– 89 in the lat range 358–608N. In each plot, the solid line refers to the North Pacific (long 1608– 2208) and the dashed line refers to the North Atlantic (long 3108–3508). The daily CRF data has been averaged (area weighted) into daily bins of 500- to 1000-mb ue difference (2 K wide).

during January and July in both the North Pacific and North Atlantic, such that larger magnitude Csn values are associated with more stable air (Figs. 11a–b). This is consistent with our discussion in the previous two paragraphs. When the troposphere tends to be more stable on daily mean timescales, we tend to have larger magnitudes of Csn. By contrast, daily mean C1 is not, in general, well

correlated with daily mean moist static stability (Figs. 11c–d), except in the North Atlantic during July (Fig. 11d). This is different than what we found for the relationship between v and CRF in the previous section (see Fig. 9) as both Csn and C1 varied with v in very similar ways. This indicates that, while greater stability produces more (or more highly reflective) clouds, it doesn’t necessarily produce higher cloud tops, whereas

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FIG. 12. (a) Daily mean 500- to 1000-mb ue difference (K) vs Csn (W m22) for January 1988 in the North Pacific (lat 358–608N and long 1608–2208). (c) Daily mean 500- to 1000-mb ue difference vs C1 (W m22) for the same region and month. Diamonds indicate points where v . 0 (sinking motion), while plus signs indicate points where v , 0 (rising motion). The daily CRF data has been averaged into bins of 500- to 1000-mb ue difference (2 K wide).

more rising motion produces more (or more highly reflective) clouds as well as higher cloud tops. Therefore, perhaps boundary layer cloud cover is what increases most uniformly with increasing stability, and the population of higher-level clouds is highly variable in each moist static stability bin (e.g., large values of C1 might be associated with deep convection in relatively unstable air and also with slantwise convection in more stable air ahead of a warm front). It is possible to compare the relationship between daily mean Csn and moist static stability in regions of rising versus sinking motion. Figure 12 shows Csn binned by 500–1000-mb ue difference for January and July 1988 in the North Pacific. The plus signs indicate Csn averages where 500-mb v , 0, while the diamonds indicate Csn averages where 500-mb v . 0. During both January and particularly July, we find that the magnitude of Csn increases with increasing stability in both rising and sinking regions, though the correspondence is not perfect, particularly at the smallest and largest stabilities. If we recall Fig. 9, Csn increases with increasing rising motion and remains relatively constant with increasing sinking motion. Taken together, Fig. 9 and 12 raise questions about which parameter (v or stability) tends to control CRF to the greatest degree in subsidence regions and what the nature of the coupling is between the v and static stability fields. These issues require further study (see the discussion section).

g. Variations in mean monthly CRF Now we are ready to examine how the relationships between Csn, v, and moist static stability on short timescales help explain longer-timescale (monthly or seasonal-mean) interseasonal and interocean variations in Csn. We first consider stability and then consider the monthly mean variance of the v field [as defined by (2)]. Figure 13 shows the spatial correlation between mean January (1985–89) and mean July (1985–89) Csn and 500- to 1000-mb ue difference for the North Pacific and North Atlantic. Each point represents an average over a 58 latitude 3 108 longitude box in the latitude range 358–608N and the longitude range 1608–2208 (North Pacific) and 3108–3508 (North Atlantic). The linear correlation coefficient for each scatter plot is also shown (recall that Csn is typically a negative quantity, so the description ‘‘well-correlated’’ should be taken to indicate a value close to negative one). Moist static stability has a larger range in July than in January, particularly in the North Atlantic (recall Table 4). Stability and Csn are well correlated over both oceans during July, and over the North Atlantic during January (Figs. 13b–d; r has values of 20.82, 20.87, and 20.92, respectively), with Csn magnitude increasing with increasing stability, though the slope of the relationship is different for each case. They are essentially uncorrelated over the North Pacific during January (Fig. 13a; r 5 20.03), however. Though the moist static stability varies over a reasonable

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FIG. 13. Mean monthly 500- to 1000-mb ue difference (K) vs Csn for (a) the North Pacific (lat 358–608N, long 1608–2208) during January 1985–89, (b) the North Atlantic (lat 358–608N, long 3108–3508) during January 1985–89, (c) the North Pacific during July 1985–89, and (d) the North Atlantic during July 1985–89. Each point represents an average of monthly mean data in a 58 lat 3 108 long box over all five Januarys or Julys. Linear correlation coefficients are included for each scatter plot.

range during January in the North Pacific, Csn does not vary much. Now we ask, how does Csn vary with v variance? As discussed in section 3e, the fact that increases in rising motion on short timescales lead to enhanced Csn, while, conversely, Csn does not vary much with in-

creased sinking motion implies that, averaged over longer time periods, increases in the variance of the vertical velocity field should lead to increases in the time-averaged Csn magnitude (all other factors being equal). In Fig. 14, we show the correlation between Csn and v variance at 500 mb in a manner similar to Fig. 13. As

FIG. 14. Same as Fig. 13, but for 500-mb v variance (mb day21) vs Csn.

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FIG. 15. Same as Fig. 13, but for 500- to 1000-mb ue difference vs 500-mb v variance.

shown in Fig. 3 and Table 3, mean 500-mb v variance is larger during January than during July. As with moist static stability as shown in Fig. 13, Csn and v variance are reasonably well correlated over the North Atlantic in both seasons, and over the North Pacific during July (Figs. 14b–d; r takes values of 20.89, 20.70, and 20.91, respectively), with Csn magnitude increasing with increasing v variance, but they are poorly correlated during January over the North Pacific (Fig. 14a; r 5 20.23). Again, even in the three cases where a reasonable correlation exists, the slope of the v variance-Csn relationship is quite different for each one. The fact that we observe distinct correlations and slopes for different seasons and regions between monthly mean moist static stability and Csn, and v variance and Csn, indicates clearly that neither moist static stability nor v variance alone control Csn, but that both could play an important role. In particular, a possible explanation for the lack of correlation between Csn and either moist static stability or v variance in the North Pacific during January is that these two parameters are varying in opposite senses there, and their effects on Csn, as presented in sections 3e–f, are, therefore, compensating for each other. In Fig. 15, we show a scatter plot of 500- to 1000-mb ue difference versus 500-mb v variance for the same regions and months as in Figs. 13–14. Consistent with this idea, these two positive quantities are positively correlated in the North Atlantic during January and July, and in the North Pacific during July, but are negatively correlated in the North Pacific during January (r 5 20.74). Where static stability and v variance are both small, the magnitude of Csn is also small, and vice versa. When one parameter is large and

the other small, Csn takes on intermediate values. If we recall Tables 2, 3, and 4, in the North Pacific during January, zonal-mean moist static stability increases poleward (Table 4), but 500-mb v variance decreases poleward (Table 3) resulting in a relatively small latitudinal variation in Csn (Table 2). This information can be summarized in a phase diagram for monthly mean Csn as a function of moist static stability and v variance (Table 5). Here, Csn values from each month (January, July), in each year (1985–89), and in each 58 3 108 box for both the North Pacific and North Atlantic have been averaged into bins characterized by a given range of both 500- to 1000-mb ue difference and 500-mb v variance. Though the correspondence in Table 5 is not perfect, the picture presented in this section seems to hold in a qualitative sense such that variability in monthly mean Csn within and across the regions and seasons considered can largely be accounted for by variability in both moist static stability and vertical velocity variance. 4. Discussion For the extratropical North Pacific and North Atlantic, we have attempted to link interseasonal and interbasin differences in cloud cover and CRF to differences in the characteristics of the large-scale vertical motion field and the large-scale static stability field. Our results indicate the following points: In general, on short (daily or synoptic) timescales, the magnitudes of Csn and C1 increase with v in regions of rising motion (v , 0) and are relatively insensitive to v in regions of sinking motion (v . 0). As a result, with all other factors being

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TABLE 5. Phase diagram showing variation of monthly mean C sn (W m22) with 500- to 1000-mb ue difference [K; as defined in (3); along the top and increasing to the right] and 500-mb v variance [mb day21; as defined in (2); along the left-hand side and increasing toward the bottom]. The tabulated Csn values represent averages of monthly mean data (previously averaged into 58 3 108 boxes) in the North Pacific (lat 358–608N, long 1608–2208) and North Atlantic (lat 358–608N, long 3108–3508) for each January and each July from 1985 to 1989. 500 mb v variance 0–30 30–60 60–90 90–120 120–150 150–180 180–210

ue (500 mb)–ue (1000 mb) 0–4

289.8 2115.1 2116.9

4–8 260.9 294.8 2102.1 2115.3

8–12 264.3 287.3 2100.6 2113.6 2115.8 2113.2

12–16 262.2 275.7 2104.8 2110.6 2122.7 2126.4

equal, the monthly mean CRF (or cloudiness) in a given area would tend to increase with the time-variance of vertical velocity. Another way of thinking about this is that the net effect of synoptic-scale disturbances such as cyclones is to increase the total cloudiness over what it would be if the disturbances were not present. This dependence on v variance would tend to make the wintertime more cloudy (on monthly mean timescales) because of the more than factor of two increase in v variance from summer to winter (in both oceans; see Table 3). This is what happens in the North Atlantic, but it is not the case in the North Pacific. To get a more complete picture, we have to consider another important factor. Here, Csn, which is a measure of cloudiness, increases with lower–midtropospheric moist static stability on both daily and monthly time scales. This positive coupling between cloudiness and static stability is valid in regimes of rising as well as sinking motions and can be plausibly linked to increasing stratiform cloud amount under more stable conditions. In the North Pacific, the mean moist static stability between 500 and 1000 mb during July is significantly larger than during January (by a factor of approximately 1.7; see Table 4), which tends to produce larger magnitude values of summertime Csn. Indeed, the static stability effect seems to dominate the seasonal variation in North Pacific cloudiness, counteracting the effect of increased v variance during winter and leading to more cloud cover during summer (Tables 1 and 2). In the North Atlantic, the seasonal difference in moist static stability is much smaller than in the North Pacific (Table 4), therefore changes in v variance seem to dominate the seasonal variation in North Atlantic cloudiness, and the cloud cover is correspondingly larger during winter than summer (Tables 1 and 2). Extending the argument to explain interbasin differences in cloudiness, the North Pacific and North Atlantic have similar cloud cover during winter because both mean v variance and mean moist static stability are approximately the same. The North Pacific is more cloudy than the North Atlantic during summer because, while the mean v variance is similar, the mean moist static stability is much greater in the North Pacific. This effect is apparent even on daily mean timescales, as we

16–20

20–24

24–28

28–32

2106.8 2123.7 2116.6 2129.2 2138.7 2150.6

2121.5 2131.4 2129.8 2136.1 2118.9

2132.4 2139.5 2121.0

2147.2

see that the North Atlantic curve (dashed) in Fig. 9b is shifted to significantly smaller values of Csn magnitude compared to the North Pacific curve (solid). Within an individual ocean basin, the monthly mean effects of moist static stability and v variance compete in their influence on Csn. In the North Pacific during January, these two fields are regionally anticorrelated such that their effects on clouds seem to cancel each other. All other cases considered here (North Pacific July, North Atlantic January and July) reveal a positive spatial correlation between v variance and moist static stability. We propose that this compensation is the reason for the much smaller spatial variation in cloud cover in the wintertime North Pacific compared to the other three cases (Tables 1 and 2). It is worthwhile to point out that a preliminary analysis of the South Pacific has yielded results that are generally consistent with those presented in this paper (Weaver 1996). One further factor that might contribute to interseasonal and interbasin differences in cloud cover is the monthly mean value of v, as opposed to the variance. For example, Table 3 shows that July-mean 500-mb v is negative (rising motion) in the North Pacific, while it is positive (sinking motion) in the North Atlantic. Though the magnitude of monthly mean v is small compared to synoptic-scale rising and sinking events (recall Fig. 8), this difference may also contribute to the smaller observed values of cloud cover and CRF in the North Atlantic, compared to the North Pacific during July, and could be linked to the overall dryness of the North Atlantic relative to the North Pacific (e.g., see RH in Table 4). A few points require further discussion. The results of this paper indicate that upward motion favors larger magnitude Csn values on daily mean timescales. The physics of why Csn magnitude does not vary much with variations in the strength of subsidence seems less well understood. While subsidence can lead to conditions favorable for low cloud formation (e.g., producing a capping inversion above the boundary layer) (Cotton and Anthes 1989; Klein and Hartmann 1993), subsidence that is very strong can potentially contribute to the breakup of a stratiform cloud deck (Roach et al 1982;

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Chen and Cotton 1987). Some of our analysis (e.g., Fig. 12) suggests that, in the regions considered, Csn, under conditions of subsidence, may depend strongly on static stability. Our observed lack of correlation between Csn and the strength of subsidence (recall Fig. 9) would, therefore, depend on coupling between the vertical velocity and static stability fields in subsidence regions. The nature of the relationship between the vertical velocity and moist static stability fields, as well as the physical mechanisms linking increased cloud cover (and increased stratiform cloud cover) to increased stability, are topics that require further study. In addition, we have not considered how synopticscale transport processes, both horizontal and vertical, influence the distribution of water vapor over the extratropical Northern Hemisphere oceans and how this in turn influences cloud cover and CRF. In particular, the causes of the overall dryness of the North Atlantic relative to the North Pacific, especially during summer, require additional and comprehensive investigation. Finally, while the results of this study tend to show a general pattern, the picture remains complex, and in order to approach a more fundamental understanding, it may be necessary to work out further aspects using a sophisticated dynamical model. Acknowledgments. We obtained the ECMWF analyses from the Scientific Computing Division of the National Center for Atmospheric Research (NCAR). NCAR and the Carbon Dioxide Information Analysis Center (CDIAC) in Oak Ridge, Tennessee, provided the cloud atlas data. The research for this paper was supported by NASA CERES NAG 1-1259 and the NSF Center for Clouds, Chemistry and Climate (C4) ATM 94-05024. We would like to thank J. M. Wallace, N.-C. Lau, and D. Rogers for useful discussions. In addition, we would like to thank two anonymous reviewers whose comments led directly to a significantly improved and more readable paper. The material in this paper is included as part of chapter 3 of C. P. Weaver’s Ph.D. thesis, a document that is C4 publication number 171. REFERENCES Cess, R. D., E. F. Harrison, P. Minnis, B. R. Barkstrom, V. Ramanathan, and T. Y. Kwon, 1992: Interpretation of seasonal cloudclimate interactions using Earth Radiation Budget Experiment data. J. Geophys. Res., 97, 7613–7617. Chen, C., and W. R. Cotton, 1987: The physics of the marine stratocumulus-capped mixed layer. J. Atmos. Sci., 44, 2951–2977. Cotton, W. R., and R. A. Anthes, 1989: Storm and Cloud Dynamics. Academic Press, 883 pp. Harrison, E. F., P. Minnis, B. R. Barkstrom, V. Ramanathan, R. D. Cess, and G. G. Gibson, 1990: Seasonal variation of cloud radiative forcing derived from the Earth Radiation Budget Experiment. J. Geophys. Res., 95, 18 687–18 703.

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