Erroneous Relationships among Humidity and Cloud Forcing Variables in
Three Global Climate Models
FLORIANBENNHOLD ANDSTEVENSHERWOOD
Department of Geology and Geophysics, Yale University, New Haven, Connecticut
(Manuscript received 23 March 2007, in final form 22 January 2008) ABSTRACT
Links are examined between time-averaged cloud radiative properties, particularly the longwave and shortwave components of cloud radiative forcing (CRF), and properties of the long-term averages of atmospheric soundings, in particular upper-tropospheric humidity (UTH), lower-tropospheric precipitable water (PW), and static stability (SS). The joint distributions of moisture measures and the composite or conditional mean CRF for different moisture and stability combinations are computed. This expands on previous studies that have examined cloud properties versus vertical velocity and surface temperature. These computations are done for satellite observations and for three representative coupled climate models from major modeling centers.
Aside from mean biases reported previously, several departures are identified between the modeled and observed joint distributions that are qualitative and significant. Namely, the joint distribution of PW and UTH is very compact in observations but less so in models, cloud forcings are tightly related to PW in the data but to UTH in the models, and strong negative net CRF in marine stratocumulus regions occurs only for high SS and low UTH in the data but violates one or both of these restrictions in each of the models. All three errors are preliminarily interpreted as symptoms of inadequate dependence of model convective development on ambient humidity above the boundary layer. In any case, the character of the errors suggests utility for model testing and future development. A set of scalar metrics for quantifying some of the problems is presented; these metrics can be easily applied to standard model output.
Finally, an examination of doubled-CO2simulations suggests that the errors noted here are significantly
affecting cloud feedback in at least some models. For example, in one model a strong negative feedback is found from clouds forming in model conditions that never occur in the observations.
1. Introduction
Global climate models (GCMs) are a key tool for predicting future climate change and among other things must explicitly predict cloud cover and convec-tive transports of heat and moisture throughout the tro-posphere. How to accurately represent these processes remains an unsolved problem (e.g., Arakawa 2004; Stephens 2005). Differences in cloud behavior are the dominant source of differences in how models predict overall climate sensitivity (Cess et al. 1996; Colman 2003; Bony et al. 2006), and inadequate convective pa-rameterizations have also been blamed for poor predic-tion of other phenomena such as the Madden–Julian
oscillation (e.g., Grabowski and Moncrieff 2004). More recent studies have specifically identified low cloud cover in the tropics and middle-latitude storm tracks, which has a strong net cooling effect on climate, as the dominant source of intermodel variability in climate sensitivity (Webb et al. 2006; Bony and Dufresne 2005). Miller (1997) argued that low-level clouds would stabi-lize climate via a strong negative albedo feedback, if the relationship to low-level static stability reported by Klein and Hartmann (1993) held up.
Since the 1970s, satellite observations have offered the possibility of assessing the performance of GCM present-day climate simulations using measured quan-tities on a global scale. Among those quanquan-tities mea-sured are radiative forcings, water vapor in different layers of the atmosphere, and air and sea temperatures. These variables are all likely to influence the physical processes that control cloud cover, type, and radiative impact (e.g., Ramanathan 1987).
Corresponding author address:S. Sherwood, Dept. of Geology and Geophysics, Yale University, P.O. Box 208109, New Haven, CT 6520-8109.
E-mail: [email protected]
Many studies have used such data to test models and have found systematic errors in simulated water vapor and cloud feedbacks. Current models generally simu-late too little clouds in the midtroposphere and too much high cirrus (Wyant et al. 2006), show little con-sensus on upper-tropospheric ice content (Su et al. 2006), and simulate excessively bright clouds overall (e.g., Eliseev et al. 2003; Zhang et al. 2005). Though a realistic variety of cloud types is often reproduced, their characteristics tend to differ from those observed (Wil-liams and Tselioudis 2007). Water vapor biases vary among models or even versions of the same model but are often too dry in areas that should be wet and too wet in areas that should be dry (e.g., Chen et al. 1996; Gettelman et al. 2006; Salathe and Chesters 1995). All models show strong correlations in the tropics between surface temperature, tropospheric humidity, and cloud cover that are qualitatively realistic; such correlations have occasionally been used to make various arguments about climate feedbacks, but do not show any relation-ship to the actual feedback strengths in models (John and Soden 2006). Model biases often change when con-vective or cloud schemes are changed (e.g., Chen et al. 1996). Previous studies have made the problem clear but have not led to consensus as to exactly how model parameterizations need to be changed.
One avenue is to pursue more highly directed statis-tical analyses of model and observational data. One may, for example, composite cloud data according to dynamical regimes defined in some objective way, typi-cally using analyzed vertical velocityas the dynamical index (Weaver and Ramanathan 1997; Norris and Weaver 2001; Bony et al. 2004; Williams et al. 2003, 2006). Compositing normally refers to the averaging of a set of target variables, conditional on a discrete pre-dictor or set thereof, to produce a composite picture for each possible value of the predictor. This general ap-proach has recently been expanded to two dimensions using more than one predictor variable [e.g., sea surface temperature (SST) and maximum SST advection] in composite analyses of cloud radiative forcing (CRF; Ringer and Allan 2004; Norris and Iacobellis 2005). Norris and Iacobellis (2005) inferred from composite analysis of cloud observations, surface winds, and SST measurements how a mainly stratus (St) cloud layer advected over warmer water develops into stratocumu-lus (Sc) and other convectively more active clouds. In evaluating GCM simulations, Norris and Weaver (2001) found an unrealistic sensitivity of low-level clouds to circulation changes that implied significant errors in cloud feedback arising from low-level clouds. In another bivariate predictor analysis, Ringer and Al-lan (2004) found strong underestimation of marine
stra-tocumulus (MSc) in one version of the Hadley Center model.
Beyond the question of how to represent clouds in models lies the question of how they will change in the real world as climate warms. Bony et al. (2004) pro-posed using the techniques above to discriminate be-tween cloudiness changes arising from “dynamics” ver-sus “thermodynamic” mechanisms on the basis of local SST and, the idea being that the former dominates natural variations while the latter would dominate any climate feedback. Whether this intriguing idea will ac-tually work remains to be seen; a necessary test is if it will be able to predict the forced behavior of coupled climate models.
While is attractive for this purpose owing to the useful constraint that it must average globally to zero in any climate, alternatives are worth pursuing for other reasons. First, direct measurements ofare far too rare to be of any use and one must use fields indirectly inferred from other data through the use of a model. These fields are unavoidably noisy and inaccurate, at least in the tropics, especially in regions where is highly variable and/or large in absolute magnitude (Newman et al. 2000); this may account for quantitative differences from models that have been reported under these conditions (Stowasser and Hamilton 2006). Sec-ond, since the laws of physics governing cloud develop-ment are Galilean invariant, any connection between cloud properties and the rate of large-scale vertical translation of the air mass containing them must result indirectly from either the influence of that motion on temperature and humidity or the influence of conden-sational heating on the ascent rate itself (see Emanuel et al. 1994). Relationships with more proximate predic-tors should stand a better chance of yielding clear sig-nals in the present climate and of remaining similar in a changed climate.
Previous studies have found that, notwithstanding some quantitative differences noted above, relation-ships between, SST, and cloud properties are quali-tatively consistent between models and observations (Williams et al. 2006). In analyzing different variables, we have found several instances where this is not the case. Below, we describe these instances and propose some simple metrics to quantify them. We hope these discrepancies and metrics will be useful to future model development efforts.
2. Data
We have used several satellite observation datasets, the output of five coupled GCMs from three modeling centers, and some reanalysis data. We first present a
general analysis of cloud forcing over tropical and mid-latitude oceans, then a second one focused on the sub-tropical and midlatitude regions of maritime low cloud cover noted by Klein and Hartmann (1993, see Table 3). Only maritime regions (which dominate feedback in models) are included here to avoid complications asso-ciated with orography, and polar regions are omitted because of uncertainties in data quality. Both restric-tions have been common in previous studies. All data shown, unless otherwise indicated, come from long-term averages (2–10 yr, over all months, for observa-tions and models, respectively). This is not common. We have applied our analysis to monthly data as well, but found that the long-term average shows the discrep-ancies between models and observations more clearly. This suggests a particular relevance of our findings to climate rather than just short-term weather phenom-ena.
a. Observations
Our general approach will follow that of previous studies mentioned above; the principal innovation is to use different predictor variables, and to focus on long-term rather than monthly means. The relevant quanti-ties for this study are CRF [as defined in Ramanathan (1987)], upper-tropospheric humidity (UTH), lower-tropospheric dry static stability (SS), and precipitable water (PW).
1) CLOUD RADIATIVE FORCING
We employ the usual definition of cloud radiative forcing (or CRF) as the difference between actual and cloud-cleared outgoing radiant fluxes at the top of the atmosphere. The observations come from Earth Radia-tion Budget Experiment (ERBE) data obtained from the National Aeronautics and Space Administration (NASA) Langley Research Center. We are using the monthly scanner data product (E-9), available on a 2.5° ⫻ 2.5° horizontal grid. The shortwave (SW) and longwave (LW) components of the CRF are reported separately. The errors for the different sensors for re-flected and emitted radiances on a 2.5°⫻2.5° grid are estimated to be about 10 W m⫺2(Barkstrom 1984). The
dataset has also been found to underestimate the nega-tive SW CRF and overestimate the LW CRF because
The LW CRF caused by a cloud of given area is controlled primarily by the height of the cloud top above the surface (or more specifically, the tempera-ture difference between the level of unit optical depth and the surface), though it is also affected by the amount of water vapor present since this reduces the clear-sky radiance. The SW CRF on the other hand is controlled mainly by the total water content of the cloud, the incident solar flux, and the surface albedo. Thus, both components of the CRF depend not only on what kind of cloud occurs, but where. This effect is particularly severe for SW CRF: the same middle-latitude cloud will reflect much more radiation in sum-mer just because of increased insolation (about 20 W m⫺2). For the LW CRF, this effect is not as important,
especially over the oceans where the temperature does not vary as significantly with the seasons.
To eliminate the influence of location and season on the SW CRF, we consider a “normalized” SW CRF that depends only on the cloud’s own properties. Following the definition of SW CRF (Ramanathan 1987), we re-place S(x,y, t), the solar irradiation at the top of the atmosphere, with an average insolation of S ⫽
341.75 W m⫺2 ⫽ S
0/4 (with S0 being the “solar
con-stant”) and define a normalized SW CRF:
CSW,norm⫽S共␣C⫺␣兲, 共1兲
where␣Cand␣are the albedos of “clear skies” and “all skies,” respectively; CSW,normis the SW CRF that would
have been exerted with average insolation everywhere on the globe and only carries the time and location information from the albedo measurements. For the computation of the net forcing (sum of both compo-nents), we used the trueCSWrather than this
normal-ized value.
2) UPPER-TROPOSPHERIC HUMIDITY
Channel 12 of the High Resolution Infrared Sounder (HIRS) on board the Television Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS), located around 6.7 m, is sensitive to tem-perature and humidity in the upper troposphere. Through the “radiance to humidity” transformation (Soden and Bretherton 1993; Stephens et al. 1996; Lan-zante and Gahrs 2000; Jackson and Bates 2001; Bates et
During the analysis, overcast pixels have to be dis-carded since clouds are opaque in the infrared and thus obstruct the retrieval. This process is known as “cloud clearing” (Soden and Bretherton 1996). A necessary but not sufficient condition for clouds to form is super-saturation of the air with water vapor. This means that generally RH and clouds are positively correlated— where clouds exist, RH is high. Unfortunately, because of this dependence, the cloud clearing introduces a dry bias. In the tropics this “clear-sky bias” may reach 15% RH but generally it is of the order of 5%–10% RH, lower when RH itself is lower because of reduced cloud formation (Lanzante and Gahrs 2000; Jackson and Bates 2001). In presenting the data we will remind read-ers of this using arrows when presenting the results (see section 3). The random error from diverse sources is estimated to be of the order of 10% of the RH value (not 10 percentage points of RH) (Soden and Brether-ton 1996).
3) STATIC STABILITY
Static stability is defined as the difference in potential temperature between two layers. It is an important fac-tor in regulating the vertical humidity transport in the atmosphere. Klein and Hartmann (1993) found the po-tential temperature difference between 700 hPa and sea level to be a good predictor of MSc and low cloud cover, although Weaver (1999) reported little differ-ence if 500 hPa was used. We therefore rely on satellite data, which represent broadly variations in lower-tropospheric temperature but give important global coverage not available from radiosondes. We again use data only from the ERBE time period. Since we are constructing a proxy from a bulk measurement, abso-lute values of SS are not necessarily comparable to those reported by Klein and Hartmann (1993) and oth-ers based on a temperature at a particular level.
For the above purpose we used the lower-tropospheric temperature (TLT) product derived from Microwave Sounder Unit (MSU) measurements (Christy et al. 2003; Spencer and Christy 1992). Previ-ous comparisons of the monthly product with data from radiosondes at several U.S. stations revealed that the error at the 95% confidence level in a 2.5° pixel was less than⫾1.3 K (Christy et al. 2003). The TLT is sensitive to the temperature of a broad layer in the atmosphere lying mostly below 600 hPa, so it should serve ad-equately as a rough proxy for the 700-hPa temperature albeit with some influence from surface and mixed-layer emissions that will slightly reduce the range of the estimated stabilities.
For SST we use National Oceanic and Atmospheric Administration’s (NOAA’s) blended monthly SST
analysis (Reynolds et al. 2002), version OI.v2. We lin-early interpolated this dataset from its native 1° grid to the 2.5° grid used for the ERBE data. Computing a potential temperature from SST requires the surface pressure, which was obtained from the National Cen-ters for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) monthly re-analysis (information online at www.cdc.noaa.gov). The surface pressure is the only variable in our analysis that did not come from satellite observations, but its influ-ence on estimated static stability is not large, with a 1-hPa error producing approximately a 0.1-K error in SS.
4) PRECIPITABLE WATER
Precipitable water is the vertically integrated mass of water vapor in the atmosphere in kilograms per square meter. Because water vapor mixing ratios drop so much above the planetary boundary layer (PBL), PW is de-termined primarily by the absolute humidity and thick-ness of the PBL. This distinguishes it from UTH, which reflects the relative humidity above the PBL.
We have obtained precipitable water from the NASA Water Vapor Project (NVAP) reanalysis (Ran-del et al. 1996), a blend of measurements from the Spe-cial Sensor Microwave Imager (SSM/I) and in situ ob-servations gathered primarily from radiosondes. The NVAP product discriminates water vapor in several layers. We include here only the precipitable water be-low 700 hPa, designated PW. This typically constitutes the bulk of the total precipitable water, but by elimi-nating any overlap with UTH, we fully ensure the in-dependence of the two observables.
The NVAP data were obtained from the NASA Langley Research Center and bilinearly interpolated to the ERBE grid. Unfortunately, the product does not start until 1989, which gives an overlap of only 24 months with the ERBE data. However, the amount of data proved to be more than sufficient to reveal key results, as will be seen below.
Recently, Trenberth et al. (2005) analyzed different precipitable water datasets from satellite observations, reanalyses, and blended products including the NVAP product. They found that especially over mountain ar-eas and coastal arar-eas the NVAP dataset shows some erroneous behavior. Trenberth et al. also noted that for trend analysis a change in data treatment in 1993 cre-ated a discontinuity. Neither inconsistency affects our results since we only use data over oceans and before 1990 and are not looking at trends.
To summarize, Fig. 1 shows climatological maps of each variable over the retrieval period. The maps are
discussed in the next section along with scatterplots of the data.
b. Models
We have chosen three models that have been run for the Intergovernmental Panel on Climate Change’s (IPCC’s) Fourth Assessment Report (AR4) and ar-chived for the Coupled Model Intercomparison Project round 3 (CMIP3) and/or the Cloud Feedback Model Intercomparison Project (CFMIP), as indicated in Table 1. Our choice was motivated by a preference for a subset of models that have a broad range of climate sensitivities (Boer and Yu 2003) that are relatively well known, and for which all needed simulations were available. The climate sensitivities are based on slab-ocean model versions of the respective GCMs. The sen-sitivity ranges by somewhat less than a factor of 2, com-pared to nearly a factor of 3 for all AR4 models.
⌻he Community Climate System Model (CCSM) by NCAR, the Hadley Center Coupled Model (HadCM) and the Hadley Center Slab Ocean Model (HadSM) by the Hadley Center, and the Canadian Global Climate Model (CGCM) by the Canadian Climate Center
(CCC) are fully coupled atmosphere–ocean models. Coupled runs are used for the comparison with the sat-ellite data, whereas some of the runs for the climate change analysis use slab oceans.
The IPCC specified certain predefined runs that all participating modeling groups were asked to perform. Table 2 summarizes the runs used in this study.
The Community Climate System Model developed by NCAR (Collins et al. 2006) at the lower (T42) reso-lution (CCSM3) is among the least sensitive to climate forcings. We also considered coupled and slab versions of the CGCM, a model of moderate climate sensitivity (Kim et al. 2002; Flato and Boer 2001). Finally, we used two versions of the relatively sensitive Hadley Centre climate model: the HadCM3 (Gordon et al. 2000), a fully coupled version from CMIP3, and the HadSM4, a 50-m slab-ocean development version used in CFMIP. The output from these models was obtained from the following sources: for CCSM3, the Earth System Grid (information online at www.earthsystemgrid.org); for CGCM and HadCM4, the PCMDI archival site (infor-mation online at esg.llnl.gov:8443/index.jsp); and for HadSM4, the CFMIP Web site.
FIG. 1. Averaged plots of CRF, UTH, SS, and PW from the respective retrieval periods.
Our study is concerned primarily with comparisons between present-day atmospheric behavior in models and observations. For this purpose, we used 10-yr mean climatologies, from a present-day (1990) run for CCSM3 and from a preindustrial run for each of the other models. Results were not sensitive to the length of the run (not shown).
Toward the end of the paper we also briefly consider the sensitivity of cloud forcing in a climate change sce-nario. For this we compare equilibrium runs at a low CO2concentration (280 or 355 ppmv depending on the
model; see Table 2) with those at double these initial CO2concentrations. To obtain the latter, we averaged
the last 10 yr of the relevant high-CO2 simulation
(which for the coupled version of CGCM was a 1% yr⫺1to doubling run). Among the two Hadley Centre
models, only the slab-ocean HadSM4 was available at doubled CO2.
To compare with the observational data, we have estimated quantities from models in a manner similar to that used by the observation systems to measure the real atmosphere. We have not gone so far as to apply the satellite data treatment algorithms on radiative transfer output from the model runs, which would be optimal. More simply, to estimate SS we applied the static (i.e., only dependent on pressure) weighting func-tion for TLT (provided by RSS on their Web site; see section 3) to the model temperature, SST, and surface pressure data. Estimating UTH is less straightforward since the weighting profile depends on the mixing ratio and temperature profile of the troposphere. To ap-proximately capture this effect, we applied a tempera-ture-dependent weighting function (Soden and Bretherton 1996) to model the relative humidity (with respect to ice). While in principle a specific humidity– dependent profile would be even better, model biases make this problematic. The key results changed little even if a static weighting function was used (not shown). 3. Analysis and results
a. 1D distributions and composites
We begin by examining the distributions [probability density functions (PDFs) or densities] of individual
en-vironmental variables in isolation. Figure 2 shows dis-tributions (left panels) of UTH and PW from the ob-servations and the models. The distributions are of the long-term mean over all ocean horizontal grid locations within 40°N–40°S.
Striking biases are immediately evident, though not unexpected. The observed UTH is much drier than in any of the models. A bias of up to⬃10%–15% in this direction in cloudy and moist regions is expected be-cause of clear-sky sampling biases (see section 2, indi-cated by the black arrow in the upper-left panel), but should diminish significantly at low UTH; compensat-ing for this would make the observed distribution wet-ter and broader. Even considering this bias, however, all models appear too wet and their distributions too wide: in CCSM3, where the bias is strongest, mean UTH values of up to 60% are common while in the observations the distribution declines rapidly above 35%. This is consistent with the conclusions of Iacono et al. (2003), who reported that for the CCSM3 the discrepancy in UTH remains very striking even when using a radiative transfer model considering only clear-sky radiances and comparing brightness temperatures. Looking at dry regions (dominant in the midlatitudes), where the clear sky bias is weak, the wet bias persists, although it is less strong than for high UTH values. Biases are less extreme in HadCM3 and CGCM3 and may be reconcilable with the data in the CGCM3 model if one makes very generous allowances for clear-sky sampling biases.
For PW, on the other hand, the observations indicate a wetter lower atmosphere than the models predict. Now, the CCSM3—consistently the wettest model—is
TABLE2. Model runs and CO2concentrations.
Runs CO2properties
Present-day climate 355 ppm
Preindustrial 280 ppm
CO2doubling 560 ppm
CO2 doubling for CCSM 710 ppm
1% increase yr⫺1in CO
2to doubling 280 ppm plus 1% until
560 ppm 1% increase yr⫺1in CO 2to doubling for CCSM 355 ppm plus 1% until 710 ppm
TABLE1. Model resolution and sensitivity.
Model Module Horizontal resolution (at equator) Vertical levels Climate sensitivity
CCSM3 Atmosphere T42 (2.8°⫻2.8°) 26
Ocean 1° 26 0.68 K m⫺2W⫺1
CGCM3 Atmosphere T47 (2.5°⫻2.5°) 31
Ocean 1.85° 29 1.04 K m⫺2W⫺1
HadCM3 Atmosphere Equivalent T47 (2.5°⫻3.75°) 19
closest to the observations, while CCMA is farthest away with biases of about 10 kg m⫺2 in PW. As with
UTH, the worst biases are about as big as the spread of the observed values. We know of no systematic obser-vation errors in PW that could explain the differences between the models and the observations. All models show a distribution with two pronounced maxima (one at 10–15 kg m⫺2and another at 25–35 kg m⫺2) whereas
in the observed distribution the two peaks are less pro-nounced and much closer together (28 and 35 kg m⫺2). This error in the models could indicate weak horizontal moisture transport between low- and high-PW regions.
Also shown in Fig. 2 (right panels) are the composite projections of the net CRF onto these two moisture variables. The first thing to note is the overall negative bias in model net CRF, with model clouds cooling the climate system by at least 10 W m⫺2more on average
than indicated by ERBE. Allowing for ERBE’s clear-sky bias makes the discrepancy smaller, but since the biases from SW and LW CRF work against each other, a significant difference remains. Interestingly, the maxi-mum net CRF is similar in the models and observations at 30–40 W m⫺2and similarly occurs under the driest conditions; the main difference arises because this FIG. 2. (left) Normalized distributions (PDFs) of PW and UTH and (right) average CRF as a function of each,
from observed and model data within 40°N–40°S. Simulations are preindustial for HadCM3 and CGCM3, and 1990 control for CCSM3. Each dot represents a sample of equal size. The error bars represent⫾one standard deviation of the entire set for the PDFs and one standard deviation of each bin for the distributions. The arrow in the top-left panel indicates the anticipated bias at high UTH.
maximum is attained rarely in the observations but commonly in the models. This also argues against ERBE biases being the main problem. The models vary somewhat in the relationship of net CRF to moisture, with CCSM showing relatively little relationship be-tween net CRF and PW, and CCMA showing a par-ticularly strong one between net CRF and UTH.
It is well known that UTH, PW, and SST, as well as cloud amount and height, all vary similarly in the trop-ics (see Fig. 1). Strongly negative net CRF indicates low-level clouds, whose (warming) LW CRF is weak but whose (cooling) SW CRF can still be considerable if the cloud cover and water content are sufficient. High and persistent coverage by such clouds tends to coin-cide with low SST and subsiding (hence dry) free-tropospheric air. This certainly contributes to the ten-dency of negative net CRF to occur at low UTH. More-over, low SST tends to produce low PW because of a lower saturation mixing ratio, providing a plausible ex-planation for the negative relationship between the net CRF and PW. As is evident from Figs. 1 and 3, the regions of marine Sc (Table 3) show very low values of UTH. In fact, a negative net CRF appears to become significant below a certain UTH threshold (approxi-mately 20%–25%), a nonlinearity less evident in the models. CCSM and HadCM3 show a slight increase in the lowest UTH regimes but stay practically stable after that, in contrast to the observations.
b. Joint distributions and bivariate composites
We now move to the central results, which involve the joint distributions of pairs of environmental vari-ables and the composite projection of net CRF onto such pairs. In comparing models to data we now over-look the biases identified above and concentrate on the shapes of the distributions, examining in effect whether relative extrema of different variables have the right relationships. In the spirit of overlooking mean biases, we will plot each dataset on axes shifted so as to re-center the data.
1) TROPICAL AND MIDLATITUDE OCEANS: ALL CLOUDS
In the observations, the two humidity variables UTH and PW exhibit a compact, sickle-shaped distribution (Fig. 3). As evident in the figure, points on the “handle” of the sickle (PW⬍25 kg m⫺2but UTH⬎25%) come
from midlatitudes and show a negative correlation be-tween UTH and PW, while points on the curved “blade” come from the tropics and show the well-known positive correlation between PW and UTH (each also highly correlated with SST; not shown). The section in the bottom-left corner (low UTH and low PW) corresponds to the subtropics. Subtropical points tend to straddle the two parts of the distribution. The same data are plotted in a binned form in the top-left panel of Fig. 4.
Compositing of net CRF according to these two vari-ables (remaining panels in Fig. 4) reveals much that was hidden in the univariate composites. Highly reflective clouds (strongly negative SW CRF) occur in the ERBE data at both extremes of the PW range, but mostly for midlatitude clouds at low PW. The subtropics (low UTH and low PW) do not show such a pronounced strong negative forcing and especially the PDF reveals that strong forcing does not occur consistently here. Clouds exert stronger LW forcing as UTH increases, but for UTH above 30% this forcing becomes almost entirely dependent on PW rather than UTH. The stron-gest forcing, at the highest PW, is for deep convective FIG. 3. Scatterplot of UTH and PW for observational data
av-eraged over 24 months. Red dots are used within 20°N and 20°S, blue dots within 20°–30°N and S, and black dots within 30°–60°N and S.
TABLE3. Selection of relevant MSc regions and types (based on Hanson 1991; Klein and Hartmann 1993).
Region with subtropical MSc
over cold ocean currents Location
Peruvian 5°–35°S, 74°–114°W Namibian 5°–35°S, 12°W–18°E Californian 15°–45°N, 112°–142°W Australian 25°–35°S, 95°–105°E Canarian 15°–45°N, 4°–34°W Fig 3 live 4/C
anvil-type clouds, which are also highly reflective. The net forcing ends up being surprisingly simple, depend-ing almost exclusively on PW rather than UTH per se (i.e., gradients of the net forcing are nearly everywhere horizontal in the plot). In other words, net CRF is nearly conditionally independent of UTH given PW.
An exception occurs for middle-latitude clouds for UTH between 35% and 55%, where the lowest values of both UTH (locally) and PW seem to favor the stron-gest net CRF. Furthermore, the net forcing is remark-ably weak for PW⬎20 kg m⫺2, as has been noted in
many other studies (see, e.g., Ramanathan et al. 1989). Conversely, strong negative forcing (⫺35 W m⫺2)
co-incides with low PW (20 kg m⫺2; Fig. 4). Since these PW
values occur mainly in midlatitudes (Fig. 3), midlatitude clouds are exerting the most net cooling on today’s cli-mate, and this effect (unlike that in the tropics) does correlate with UTH.
Three subsequent figures (Figs. 5, 6, and 7) show the
same quantities calculated from the models. The first thing that strikes the eye is the much broader joint distributions of the two moisture variables. While the maxima in the joint distributions occur at roughly simi-lar places in the models and the observations, combi-nations of the two variables far from these maxima oc-cur much more often in the models than in the obser-vations. CCSM (Fig. 5) in particular shows a plentiful population of points at combinations of low UTH and high PW that never occur in the observed data. While the scatter in the other models is slightly less drastic, no model comes close to producing the compact distribu-tion seen in the data.
While biases have been noted in the observational data, it is unlikely that these have much to do with the model–data differences noted here. For one thing, some of the clearest discrepancies are seen where the data show only high UTH while the models show some low UTH; known observational biases should, if any-FIG. 4. (top left) Normalized joint distribution of UTH and PW, and average net CRF for each combination of
these variables: (top right) net, (bottom left) normalized SW, and (bottom right) LW. Data are climatological means from 60°N to 60°S over ocean pixels.
thing, lead to the opposite problem. Second, while all models show broader distributions than do the data, they do not agree as to how much broader and where. Thus, it is unlikely that the problem here lies in the data.
The simplicity of the net CRF behavior in the data is not reproduced by any model either. The most striking systematic difference (aside from the excessive SW CRF overall; previously noted) is that UTH has an ex-aggerated impact on the appearance of high thick clouds. This is evident in both the LW and SW plots of all three models, where forcing gradients are nearly vertical while in the observations they were nearly hori-zontal. Thus, in the models, deep cloud characteristics are seen to be tightly related to UTH while in reality they are evidently tightly related to PW.
The unrealistic combinations of high PW and low UTH noted earlier, particularly in CCSM, become a greater cause for concern when net CRF is examined. There is a strong negative forcing for these points
(of-ten in excess of 50 W m⫺2
), indicating that “clouds from bogus states,” which are forming under unrealistic con-ditions, are cooling the planet in the model.
2) LOW CLOUD COVER REGIONS
Because of the obvious importance of low-level clouds, we also show some results for regions identified by Hanson (1991) and Klein and Hartmann (1993). The regions that we used are derived mainly from the former study and are similar to the MSc regions over cold ocean currents examined in Klein and Hartmann (1993). The considered regions are summarized in Table 3. These regions support persistent low strati-form cloud layers (generally referred to as MSc in this paper) of high coverage and relatively low altitude. Be-cause of these characteristics, the net CRF is strongly negative in these regions (see Fig. 1). While low clouds outside these regions (e.g., trade cumulus or the north-ern and southnorth-ern oceans) may be equally or even more FIG. 5. As in Fig. 4 but for CCSM3.
important for climate sensitivity, we have not yet con-sidered broader areas.
Cloud cover in these regions is widely thought to be regulated mainly by dry static stability (SS) (Klein and Hartmann 1993). We thus expect the net CRF to be-come increasingly negative with increasing SS. We ex-amined the relation between net CRF, SS, and UTH (Fig. 8) from the defined MSc regions. The expected trend does occur—but only for low to moderate UTH. At high UTH, the net CRF is small regardless of SS.
There are again marked differences between the models and observations, although in this case the mod-els depart from each other in a unique way. Only the CCSM3 reproduces the observed relationship between SS and net CRF at low UTH, with one model actually producing a trend in the opposite direction. The success of CCSM3 in this regard may be a consequence of its use of a parameterization based on the empirical rela-tionship between low-level cloud cover and SS as docu-mented by Klein and Hartmann. Only the CCMA
model shows the observed small net CRF at high UTH, with the others sometimes showing strong cooling at high UTH. Thus, no model gets both features of the data right.
Williams et al. (2006) recently found that all available CFMIP models showed some low stratiform clouds in the observed “MSc regions.” This will presumably oc-cur as long as the cold SSTs are roughly reproduced. We thus do not believe these discrepancies result from a geographical misplacement of the low-level cloud but, rather, from errors in the cloud and moisture processes.
c. Feedback analysis
Following the general strategy of Bony et al. (2004), we may calculate for any given predictor⌶the antici-pated change in the net cloud forcing⌿ in a new cli-mate given the change in the distribution of ⌶. First note that the average⌿in a given climate can be writ-ten as
FIG. 6. As in Fig. 4 but for HadCM3.
具⌿典⫽
冕
E共⌿|⌶兲P⌶d⌶, 共2兲 where E(⌿|⌶) is the conditional expectation (mean) value of⌿given⌶,P⌶is the probability density of⌶, and the integral is over all⌶. Bony et al. (2004) decom-posed the climate-induced change in 具⌿典 into an “in-duced” part arising from changes in P⌶ given fixedE(⌿|⌶), an “internal” part arising from changes in
E(⌿|⌶) given fixed P⌶, and the remaining “covaria-tion” part due to the nonlinear interaction of these terms when both change (employingas the predictor variable, Bony et al. used the terms “dynamic,” “ther-modynamic,” and “covariation,” respectively, for the three terms). Thus, if the net CRF intrinsically in-creases with (say) SS, and SS inin-creases in a warmer climate, an “induced” increase in net CRF is predicted as noted by Miller (1997). On the other hand, if the relationship between net CRF and SS itself were to shift, this would produce harder-to-predict “internal” and covariation changes.
In Table 4 we present the calculations of each com-ponent of change in net CRF with respect to SS for the three models in the region 40°N–40°S. The stabilizing, induced component is present in each model and is especially strong in CGCM3, where in terms of net CRF it is roughly equal (and opposite) to the applied greenhouse forcing. Interestingly, however, the only model in which this predicted feedback actually pre-vailed was the CCSM; in the two other models, it was canceled by the other two components. The CCSM be-havior is not unexpected, given that the model physics strictly enforces a relationship between SS and cloud cover, unlike the other two models.
Based on the findings in the previous section we ex-amined the changes in net CRF in the CCSM model more closely. First, we computed relative (normalized) values of each variable, scaled to the range occurring in the particular dataset; for example, for UTH,
UTHrel⫽
UTH⫺min共UTH兲
关max共UTH兲⫺min共UTH兲兴100. 共3兲 FIG. 7. As in Fig. 4 but for CGCM3.
We then defined by the relationship Ab ⫽ (⌶ ∈
R2| UTHrel⬍PWrel⫺40%) the main region of UTH–
PW space not inhabited by observations but sometimes inhabited in models (see Figs. 4 and 5, shaded area in the top-left panel). To quantify the feedback impor-tance of the “clouds from bogus states” forming inAb, we recalculated the change in net CRF with only this region included—specifically⌬ 具⌿典⬘: ⌬具⌿典⬘⫽具⌿典pre ⫺
冉
冕
⌶∉Ab E共⌿|⌶兲preP⌶pred⌶ ⫹冕
⌶∈Ab E共⌿|⌶兲CCP⌶CCd⌶冊
, 共4兲 where the superscripts pre and CC indicate the control and climate change runs, respectively. For CCM3 this yields⌬具⌿典⬘ ⫽ ⫺0.74 W m⫺2. Comparing this figure tothe overall change of⫺1.75 W m⫺2(see Table 4), we conclude that 40% of the negative cloud feedback in CCSM3 coming from the tropics and subtropics over oceans can be attributed to clouds forming under mois-ture conditions that should not exist. This analysis is crude but indicates that the problems noted here are important for the climate sensitivity of the models.
TABLE4. Different components of the cloud forcing sensitivity to doubling of CO2with respect to SS in W m⫺
2 from 40°N to
40°S. (Note: Values for HadSM4 are from a slab model run to equilibrium, while CCSM3 and CGCM3 data are from fully coupled model versions, which are not fully equilibrated.)
SS具␦CRF典 CCSM3 HadSM4 CGCM3
Total ⫺1.746 0.391 0.951
Induced ⫺2.408 ⫺2.466 ⫺4.091
Internal 0.068 1.534 0.417
Covariation 0.595 1.323 4.624
FIG. 8. Joint distributions of UTH and SS and composite projection of net CRF onto these, for observations and models, within MSc regions as defined in Table 3.
d. Proposed simple proxy metrics
We have identified several qualitative errors that persist in the climatologies of all three models exam-ined, and we have shown their potential importance. We argue in the final section that all of these errors may be symptoms of a single problem in the model physics. Whether or not this proves correct, the qualitative na-ture of these differences demands attention. The fact that they appear in relationships between different moisture and cloud variables (rather than mean values) suggests a relatively direct connection to moist pro-cesses in models that we hope can be exploited. To facilitate this, we propose a few easy-to-compute scalar metrics for quantifying the errors. For the models we used the 1990 (CCSM) and preindustrial runs and we used long-term averages for the calculations.
1) COHERENCE METRIC
To measure the compactness of the PW–UTH rela-tion, we propose the simple linear correlation coeffi-cient [ln(UTH), P] applied to tropical (20°S–20°N) data. Since the locus of tropical points (see Fig. 3) is curved, to better measure the scatter about this relation with a linear correlation analysis, we first take the loga-rithm of UTH, whose relation to PW is nearly linear (not shown). Both ln(UTH) and PW have then been averaged over the respective analysis periods for the models and observations.
For ease of computation we also calculated the measures using a simpler proxy of UTH: RH^⫽0.5[RH(300hPa)⫹ RH(500hPa)]. The correlation coefficients for the ob-servations and the three models are listed in Table 5, and show that this simpler-to-calculate proxy behaves very similarly to UTH. The metrics are significantly higher in the observations than in the three models, especially CCSM, quantifying the characteristics noted earlier.
2) CRFMOISTURE-DEPENDENCE METRICr50
To measure the way in which the CRF components depend on moisture, we have derived another simple metric,r50. We are focusing on the convectively active
regions in the tropics and subtropics by examining the slope of the bivariate distribution over UTH and PW. To single out the areas of deeper convection, we used LW and examined in particular the slopes for the 50 W m⫺2level in LW CRF (thus,r50). First, we define two
LW intervalsA⫽[30 W m⫺2, 50 W m⫺2] andB⫽[50 W m⫺2, 70 W m⫺2]. For the models we have once more
used the proxy RH^instead of UTH. Here,r50 (W m⫺ 2
%⫺1) is defined as follows:
r50共UTH, PW兲⫽
具PW共B兲典⫺具PW共A兲典
具UTH共B兲典⫺具UTH共A兲典, 共5兲
where具 典is the averaging operator. The calculated val-ues ofr50 are listed alongside thevalues in Table 5.
The much greaterr50in the observations indicates the
mentioned stronger dependence on PW versus the models for deeper cloud regions.
At this time we do not propose a simple metric for quantifying the behavior in the MSc regions because the divergent model behavior and possible dependence of the result on the region definitions suggests that this should await further study.
4. Summary and discussion
As a primary result of this work, we have docu-mented the mean biases between models and products derived from satellite observations, namely, upper-tropospheric humidity, precipitable water, and net cloud radiative forcing, which are too high, too low, and too strong, respectively, in all models. In addition, we have documented several significant and systematic dis-crepancies between modeled and observed relation-ships among cloud- and moisture-related variables. These discrepancies are documented here using long-term averages (10 yr for the models and 2–4 yr for the observations). This may seem to be an unconventional way of examining the data, but it accentuates biases by averaging out the synoptic and seasonal variability. The analysis turns out to reveal most clearly the important features, although these also appear in monthly data (not shown). The appearance of the discrepancies in the long-term average fields highlights their likely im-portance for climate, as well as making it easier for modeling groups to replicate the analyses.
The first discrepancy is that UTH and PW are not nearly as tightly connected in the models as in the ob-servations. This represents a disconnect between the boundary layer and the upper-tropospheric water vapor in the models. We propose the linear correlation of TABLE 5. Correlation coefficient for UTH and PW and RH^ and PW in the tropics (20°N–20°S) and moisture dependence met-ricr50. Source (UTH, PW) 共RH^, PW兲 r50共RH ^, PW兲* (W m⫺2%⫺1) Observation 0.850881 — 4.35 CCSM3 0.592615 0.580893 2.24 HadCM3 0.786503 0.771387 1.33 CGCM3 0.754892 0.72912 0.97
* For observations,r50uses ln(UTH) in RH
ln(UTH) and PW [where ln(UTH) and PW are long-term means] as a proxy that quantifies this error. Fur-thermore, model locations exhibiting unrealistic combi-nations of high PW and low UTH tended also to con-tain clouds with a particularly strong net cooling effect. With a crude calculation, we found that these “clouds from bogus states” have a strong impact on the climate sensitivity and substantially increase the negative cloud feedback in at least one model.
A second problem is that in the models the tropical cloud properties did not vary correctly with respect to moisture, especially in the case of thick, high clouds. While models produced these clouds in a manner more tightly related to UTH, observations show the proxi-mate relationship being to PW. This applies to both the longwave and shortwave forcing properties. This dis-crepancy was quantified using a simple finite-difference parameter ratio.
The final problem was documented in the particu-larly interesting marine stratocumulus (MSc) regions. Here, observations show that strongly negative cloud forcing (indicating low cloud cover) occurs only when UTH is relatively low and SS is relatively high. In each model at least one of these two conditions was violated, with the SS requirement holding only in CCSM3 and the UTH requirement holding only in CCMA. Wood and Bretherton (2006) and Williams et al. (2006) have recently shown that vertical gradients ofespredict low cloud better than does , on which our SS measure is based; Wood and Bretherton (2006) argued that this was caused by increasing specific humidity at higher temperatures decreasing the effective stability near the cloud layer. If this moisture dependence were incorpo-rated into a modified SS predictor of low-cloud amount, most of the “induced” negative feedback documented here would vanish. Our finding from Fig. 8 that high UTH inhibits (or, at least, does not coincide with) negative net CRF in the observations may be related to this.
We offer here a hypothetical, unified explanation for the three errors in the covariance noted above: the model convective and/or boundary layer schemes are underestimating the role of midtropospheric moisture in regulating the depth of the convection. Many studies have investigated the role of moisture above the bound-ary layer, and some have already proposed its
impor-of the deep convection to bypass a dry lower and midtroposphere. This could explain the loose connec-tion noted above between PW and UTH in the models, compared to a tight relationship in the observations, suggesting that moisture at all levels must be high for vigorous deep convection to occur in the real atmo-sphere. Finally, it would explain the noted lack of a sufficiently strong relationship between high cloud and PW, which was particularly evident in the most convec-tively active regions (high PW and UTH, or top-right panels in Figs. 4–7). The strong observed connection suggests the importance of moisture near and above the boundary layer top in the real atmosphere. An alterna-tive explanation for the relaalterna-tively strong connection be-tween UTH and thick, deep cloud cover could, how-ever, be excessive moisture detrainment in the upper troposphere or associated errors in the diabatic circu-lation, raising the UTH of deep convective areas too high relative to others.
Most deep convective schemes today include entrain-ment of ambient air into updrafts, which allows dry air to inhibit to some extent the development of deep con-vection. Recognition of the potential importance of this has increased, particularly with respect to the correct simulation of the diurnal cycle, and several modeling centers are now changing their schemes to increase the role of entrainment (C. Senior and A. Gettelman 2007, personal communication). However, even the entrain-ment of dry air in such a calculation can be overcome by sufficient convective available potential energy (CAPE), while the observations cast significant doubt on the ability of real-world deep convection to develop in dry environments even with substantial CAPE (Sher-wood 1999; Jensen and Del Genio 2006). Further, the entrainment mechanism has been found to be quanti-tatively inadequate to explain the large impact of midtropospheric moisture on convective penetration (Sherwood et al. 2004), indicating other microphysical or turbulent mechanisms. While our hypothesis is not original, we hope that the new evidence presented here will be sufficient to spur further modifications of model physics in the appropriate direction. As our results make clear, this problem (if our explanation is indeed correct) affects not only the details of storm develop-ment and the diurnal cycle, but also the water climatol-ogy and cloud feedbacks in the model.
sored by the NOAA Climate and Global Change Pro-gram. We thank Darren Jackson for providing the UTH data and the results of radiative calculations as well as information on the weighting function for UTH. Finally, we thank the anonymous reviewers for their comments. This work was supported by NSF Grant ATM-0453639.
REFERENCES
Arakawa, A., 2004: The cumulus parameterization problem: Past, present, and future.J. Climate,17,2493–2525.
Barkstrom, B. R., 1984: The Earth Radiation Budget Experiment (ERBE).Bull. Amer. Meteor. Soc.,65,1170–1185.
Bates, J. J., D. L. Jackson, F.-M. Bréon, and Z. D. Bergen, 2001: Variability of tropical upper tropospheric humidity 1979– 1998.J. Geophys. Res.,106,32 271–32 282.
Boer, G. J., and B. Yu, 2003: Climate sensitivity and climate state.
Climate Dyn.,21,167–176, doi:10.1007/s00382-003-0323-7. Bony, S., and J. L. Dufresne, 2005: Marine boundary layer clouds
at the heart of tropical cloud feedback uncertainties in cli-mate models.Geophys. Res. Lett., 32,L20806, doi:10.1029/ 2005GL023851.
——, ——, H. Le Treut, J.-J. Morcrette, and C. Senior, 2004: On dynamic and thermodynamic components of cloud changes.
Climate Dyn.,22,71–86, doi:10.1007/s00382-003-0369-6. ——, and Coauthors, 2006: How well do we understand and
evalu-ate climevalu-ate change feedback processes?J. Climate,19,3445– 3482.
Cess, R. D., and Coauthors, 1996: Cloud feedback in atmospheric general circulation models: An update.J. Geophys. Res.,101, 12 791–12 794.
Chen, C. T., E. Roeckner, and B. J. Soden, 1996: A comparison of satellite observations and model simulations of column-integrated moisture and upper-tropospheric humidity.J. Cli-mate,9,1561–1585.
Christy, J. R., R. W. Spencer, W. B. Norris, W. D. Braswell, and D. E. Parker, 2003: Error estimates of version 5.0 of MSUAMSU bulk atmospheric temperatures.J. Atmos. Oce-anic Technol.,20,613–629.
Collins, W. D., and Coauthors, 2006: The Community Climate System Model: CCSM3.J. Climate,19,2122–2143.
Colman, R., 2003: A comparison of climate feedbacks in general circulation models.Climate Dyn.,20,865–873, doi:10.1007/ s00382-003-0310-z.
Eliseev, A. V., H. Le Treut, I. I. Mokhov, M. Doutriaux-Boucher, and A. V. Chernokulsky, 2003: Validation of TOA radiation and clouds simulated by different versions of the LMD gen-eral circulation model in comparison to satellite and ground-based data.Izv. Atmos. Oceanic Phys.,39,S15–S26. Emanuel, K. A., J. D. Neelin, and C. S. Bretherton, 1994: On
large-scale circulations in convecting atmospheres.Quart. J. Roy. Meteor. Soc.,120,1111–1143.
Flato, G. M., and G. J. Boer, 2001: Warming asymmetry in climate change simulations.Geophys. Res. Lett.,28,195–198. Gettelman, A., W. D. Collins, E. J. Fetzer, A. Eldering, F. W.
Irion, P. B. Duffy, and G. Bala, 2006: Climatology of upper-tropospheric relative humidity from the Atmospheric Infra-red Sounder and implications for climate. J. Climate, 19, 6104–6121.
Gordon, C., C. Cooper, C. A. Senior, H. Banks, J. M. Gregory, T. C. Johns, J. F. B. Mitchell, and R. A. Wood, 2000: The
simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments.Climate Dyn.,16,147–168.
Grabowski, W. W., and M. W. Moncrieff, 2004: Moisture– convection feedback in the tropics.Quart. J. Roy. Meteor. Soc.,130,3081–3104.
Hanson, H. P., 1991: Marine stratocumulus climatologies.Int. J. Climatol.,11,147–161.
Iacono, M. J., J. S. Delamere, E. J. Mlawer, and S. A. Clough, 2003: Evaluation of upper tropospheric water vapor in the NCAR Community Climate Model (CCM3) using modeled and observed HIRS radiances.J. Geophys. Res., 108,4037, doi:10.1029/2002JD002539.
Jackson, D. L., and J. J. Bates, 2001: Upper tropospheric humidity algorithm assessment.J. Geophys. Res.,106,32 259–32 270. Jensen, M. P., and A. D. Del Genio, 2006: Factors limiting
con-vective cloud-top height at the ARM Nauru Island climate research facility.J. Climate,19,2105–2117.
John, V. O., and B. J. Soden, 2006: Does convectively-detrained cloud ice enhance water vapor feedback?Geophys. Res. Lett.,
33,L20701, doi:10.1029/2006GL027260.
Kim, S.-J., G. M. Flato, and G. J. Boer, 2002: A coupled climate model simulation of the Last Glacial Maximum, Part 2: Ap-proach to equilibrium. Climate Dyn., 20, 635–661, doi:10.1007/s00382-002-0292-2.
Klein, S. A., and D. L. Hartmann, 1993: The seasonal cycle of low stratiform clouds.J. Climate,6,1587–1606.
Lanzante, J. R., and G. E. Gahrs, 2000: The “clear-sky bias” of TOVS upper-tropospheric humidity. J. Climate, 13, 4034– 4041.
Miller, R. L., 1997: Tropical thermostats and low cloud cover.J. Climate,10,409–440.
Newman, M., P. D. Sardeshmukh, and J. W. Bergman, 2000: An assessment of the NCEP, NASA, and ECMWF reanalyses over the tropical west Pacific warm pool.Bull. Amer. Meteor. Soc.,81,41–48.
Norris, J. R., and C. P. Weaver, 2001: Improved techniques for evaluating GCM cloudiness applied to the NCAR CCM3.J. Climate,14,2540–2550.
——, and S. F. Iacobellis, 2005: North Pacific cloud feedbacks inferred from synoptic-scale dynamic and thermodynamic re-lationships.J. Climate,18,4862–4878.
Ramanathan, V., 1987: The role of earth radiation budget studies in climate and general.J. Geophys. Res.,92,4075–4096. ——, R. D. Cess, E. F. Harrison, P. Minnis, B. R. Barkstrom, E.
Ahmad, and D. Hartmann, 1989: Cloud–radiative forcing and climate: Results from the Earth Radiation Budget Experi-ment.Science,243,57–63.
Randel, D. L., T. J. Greenwald, T. H. Vonder Haar, G. L. Stephens, M. A. Ringerud, and C. L. Combs, 1996: A new global water vapor dataset. Bull. Amer. Meteor. Soc., 77, 1233–1254.
Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Wang, 2002: An improved in situ and satellite SST analysis for climate.J. Climate,15,1609–1625.
Ringer, M. A., and R. P. Allan, 2004: Evaluating climate model simulations of tropical cloud.Tellus,56A,308–327. Salathe, E. P., and D. Chesters, 1995: Variability of moisture in
the upper troposphere as inferred from TOVS satellite ob-servations and the ECMWF model analyses in 1989.J. Cli-mate,8,120–132.
Sherwood, S. C., 1999: Convective precursors and predictability in the tropical western Pacific.Mon. Wea. Rev.,127,2977–2991.
——, P. Minnis, M. McGill, and J. Chae, 2004: Underestimation of deep convective cloud tops by thermal imagery. Geophys. Res. Lett.,31,L11102, doi:10.1029/2004GL019699.
Soden, B. J., and F. P. Bretherton, 1993: Upper tropospheric rela-tive humidity from the GOES 6.7m channel: Method and climatology for July 1987.J. Geophys. Res.,98,16 669–16 688. ——, and ——, 1996: Interpretation of TOVS water vapor radi-ances in terms of layer-average relative humidities: Method and climatology for the upper, middle, and lower tropo-sphere.J. Geophys. Res.,101,9333–9344.
Spencer, R. W., and J. R. Christy, 1992: Precision and radiosonde validation of satellite gridpoint temperature anomalies. Part II: A tropospheric retrieval and trends during 1979–90. J. Climate,5,858–866.
Stephens, G. L., 2005: Cloud feedbacks in the climate system: A critical review.J. Climate,18,237–273.
——, D. L. Jackson, and I. Wittmeyer, 1996: Global observations of upper-tropospheric water vapor derived from TOVS radi-ance data.J. Climate,9,305–326.
Stowasser, M., and K. Hamilton, 2006: Relationship between shortwave cloud radiative forcing and local meteorological variables compared in observations and several global cli-mate models.J. Climate,19,4344–4359.
Stubenrauch, C. J., V. Briand, and W. B. Rossow, 2002: The role of clear-sky identification in the study of cloud radiative ef-fects: Combined analysis from ISCCP and the scanner of radiation budget.J. Appl. Meteor.,41,396–412.
Su, H., D. E. Waliser, J. H. Jiang, J. L. Li, W. G. Read, J. W. Waters, and A. M. Tompkins, 2006: Relationships of upper tropospheric water vapor, clouds and SST: MLS observa-tions, ECMWF analyses and GCM simulations. Geophys. Res. Lett.,33,L22802, doi:10.1029/2006GL027582.
Trenberth, K. E., J. Fasullo, and L. Smith, 2005: Trends and vari-ability in column-integrated atmospheric water vapor. Cli-mate Dyn.,24,741–758, doi:10.1007/s00382-005-0017-4.
Weaver, C. P., 1999: The interactions among cyclone dynamics, vertical thermodynamic structure, and cloud radiative forcing in the North Atlantic summertime storm track.J. Climate,12, 2625–2642.
——, and V. Ramanathan, 1997: Relationships between large-scale vertical velocity, static stability, and cloud radiative forcing over Northern Hemisphere extratropical oceans.J. Climate,10,2871–2887.
Webb, M. J., and Coauthors, 2006: On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles.Climate Dyn.,27,17–38.
Williams, K. D., and G. Tselioudis, 2007: GCM intercomparison of global cloud regimes: Present-day evaluation and climate change response. Climate Dyn., 29, 231–250, doi:10.1007/ s00382-007-0232-2.
——, M. A. Ringer, and C. A. Senior, 2003: Evaluating the cloud response to climate change and current climate variability.
Climate Dyn.,20,705–721, doi:10.1007/s00382-002-0303-3. ——, and Coauthors, 2006: Evaluation of a component of the
cloud response to climate change in an intercomparison of climate models. Climate Dyn., 26, 145–165, doi:10.1007/ s00382-005-0067-7.
Wood, R., and C. S. Bretherton, 2006: On the relationship be-tween stratiform low cloud cover and lower-tropospheric sta-bility.J. Climate,19,6425–6432.
Wyant, M. C., C. S. Bretherton, J. T. Bacmeister, J. T. Kiehl, I. M. Held, M. Zhao, S. A. Klein, and B. J. Soden, 2006: A com-parison of low-latitude cloud properties and their response to climate change in three AGCMs sorted into regimes using mid-tropospheric vertical velocity.Climate Dyn.,27,261–279. Zhang, M. H., and Coauthors, 2005: Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements.J. Geophys. Res.,110, D15S02, doi:10.1029/2004JD005021.
