Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator

Authors

  • Stephen A. Klein,

    Corresponding author
    1. Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California, USA
    • Corresponding author: S. A. Klein, Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, 7000 East Avenue, L-103, Livermore, CA 94551, USA. (klein21@llnl.gov)

    Search for more papers by this author
  • Yuying Zhang,

    1. Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California, USA
    Search for more papers by this author
  • Mark D. Zelinka,

    1. Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California, USA
    Search for more papers by this author
  • Robert Pincus,

    1. University of Colorado and NOAA/Earth System Research Laboratory, Boulder, Colorado, USA
    Search for more papers by this author
  • James Boyle,

    1. Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California, USA
    Search for more papers by this author
  • Peter J. Gleckler

    1. Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California, USA
    Search for more papers by this author

Abstract

[1] The annual cycle climatology of cloud amount, cloud-top pressure, and optical thickness in two generations of climate models is compared to satellite observations to identify changes over time in the fidelity of simulated clouds. In more recent models, there is widespread reduction of a bias associated with too many highly reflective clouds, with the best models having eliminated this bias. With increased amounts of clouds with lesser reflectivity, the compensating errors that permit models to simulate the time-mean radiation balance have been reduced. Errors in cloud amount as a function of height or climate regime on average show little or no improvement, although greater improvement can be found in individual models.

1 Measuring Changes in the Simulations of Global Cloudiness Over Time

[2] The simulation of clouds by climate models is a key ongoing challenge in the numerical representation of Earth's climate. Due to their large impact on Earth's radiation budget, clouds are important for determining aspects of current climate, such as surface air temperatures in many regions [Ma et al., 1996; Curry et al., 1996], the strength and variability of atmospheric circulations [Slingo and Slingo, 1988], and the magnitude of climate changes that result from perturbations in the chemical composition of the atmosphere [IPCC, 2007]. While important, the modeling of clouds is very difficult because most cloud processes happen at scales far smaller than can be resolved by climate models, and thus, their bulk effects must be represented with imperfect parameterizations.

[3] Given the efforts of many scientists over several decades to understand cloud processes and improve their representation in models, it is important to ask are climate model simulations of clouds improving and, if so, by how much? Here, we analyze the ability of two generations of climate models to simulate the climatological distribution of clouds and judge fidelity by comparison to several decades of satellite observations. Because of the significant differences between the ways clouds are observed and the ways they are represented in models, we use a “satellite simulator” to increase the chances that differences between the models and observations represent actual model deficiencies. We find that significant progress in the ability of models to simulate clouds has occurred over the last decade, particularly in reducing the over-prediction of highly reflective clouds [Zhang et al., 2005].

2 Climate Models, Satellite Observations, ISCCP Simulator and Analysis Methods

2.1 Climate Models

[4] The models we analyze are those that submitted output to the first two phases of the Cloud Feedback Model Intercomparison Project [McAvaney and LeTreut, 2003; Bony et al., 2011]. Submissions to the first phase (CFMIP1) were completed by the end of 2005 from which we analyze nine models (Table 1). Submissions to the second phase (CFMIP2) began in late 2011, and as of the time of this writing, we have output from 10 models (Table 2). CFMIP2 is a subset of the much wider fifth Coupled Model Intercomparison Project (CMIP5) [Taylor et al., 2012] associated with the fifth assessment report of the Intergovernmental Panel on Climate Change. Although less formal, there was also a close connection between CFMIP1 and the corresponding third Coupled Model Intercomparison Project (CMIP3) [Meehl et al., 2007]. As some models that participated in CFMIP1 did not participate in CMIP3, we retain the more accurate label of CFMIP, instead of CMIP, when referring to the ensembles.

Table 1. CFMIP 1 Slab Ocean Models Used in this Study
Model NameModeling CenterReferenceNumber of Years in RunSymbol
AGCM4.0Canadian Centre for Climate Modeling and Analysisvon Salzen et al. [2005]20c4
CCSM3.0National Center for Atmospheric ResearchCollins et al. [2006]20n3
GFDL MLM 2.1NOAA Geophysical Fluid Dynamics LaboratoryGFDL GAMDT [2004]20g2
HadGSM1Met Office Hadley CentreMartin et al. [2006]20h1
HadSM3Met Office Hadley CentrePope et al. [2000]20h3
HadSM4Met Office Hadley CentreWebb et al. [2001]20h4
IPSL CM4Institut Pierre Simon LaplaceHourdin et al. [2006]20i
MIROC (hisens)Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global ChangeOgura et al. [2008]5m3
MIROC (losens)Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global ChangeOgura et al. [2008]5m4
Table 2. CFMIP 2 AMIP Models Used in this Study
Model NameModeling CenterReferenceNumber of Years in RunSymbol
BCC-CSM1.1(m)Beijing Climate Center, China Meteorological AdministrationWu et al. [2010]30B
CCSM4Community Earth System Model Contributors (NSF-DOE-NCAR)Gent et al., [2011]30N4
CESM1(CAM5)Community Earth System Model Contributors (NSF-DOE-NCAR)Neale et al. [2011]27N5
CanAM4Canadian Centre for Climate Modeling and Analysisvon Salzen et al. [2012]60C4
CNRM-CM5Centre National de Recherches Meteorologiques/Centre Europeen de Recherche et Formation Avancees en Calcul ScientifiqueVoldoire et al. [2012]30Q
GFDL-CM3NOAA Geophysical Fluid Dynamics LaboratoryDonner et al. [2011]30G3
HadGEM2-AHadley Centre for Climate Prediction and Research/Met OfficeCollins et al. [2008]30H2
MIROC5Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and TechnologyWatanabe et al. [2010]30M5
MPI-ESM-LRMax Planck Institute for MeteorologyStevens et al. [2013]30P
MRI-CGCM3Meteorological Research InstituteYukimoto et al. [2011]32R

[5] A direct evaluation of model changes is complicated by the fact that the CFMIP1 output is from the control climate integrations of slab-ocean models (i.e., atmospheric models coupled with a mixed-layer model of the upper ocean), while the CFMIP2 output is from simulations of the atmosphere model with sea surface temperatures and sea-ice distributions prescribed from observations from recent decades (i.e., Atmospheric Model Intercomparison Project (AMIP) simulations [Gates et al., 1999]). This difference arises because the satellite simulator output we require is only available from the slab-ocean models of CFMIP1, while the slab-ocean model framework is not part of CFMIP2. We have examined the impact this difference might have on our study by comparing AMIP and slab-ocean model simulations for one model (CCSM4). We found that the differences between these simulations are much smaller than differences among CFMIP models. The impact of the different modeling frameworks is minor because the differences in surface boundary conditions between slab-ocean models and AMIP integrations (and hence the resulting distribution of clouds) are small, even for slab-ocean models constructed to mimic the climate of the preindustrial era.

2.2 Satellite Observations

[6] We compare simulated clouds to the climatology of observations created by the International Satellite Cloud Climatology Project (ISCCP) [Rossow and Schiffer, 1991, 1999]. ISCCP provides estimates of the area coverage of clouds stratified by ctp, the apparent cloud-top pressure of the highest cloud in a column, and by τ, the column integrated optical thickness of clouds. These estimates are the results of retrieval algorithms applied to radiance observations with typically 1–5 km resolution from the visible and infrared window channels of geostationary and polar orbiting satellites. They are accumulated for 280 × 280 km regions every 3 hours starting in July 1983; we use data from July 1983 through June 2008. Area coverage estimates are summarized in a joint histogram with six bins in τ and seven bins in ctp; bin boundaries are shown in Figures 2 and 3. We use custom-built daytime-only monthly averages that are described more fully in Pincus et al. [2012] and are available from http://climserv.ipsl.polytechnique.fr/cfmip-obs/.

[7] As a point of comparison, we also use roughly analogous observations from the MODerate resolution Imaging Spectrometer (MODIS) instruments for the period March 2000 through April 2011 [Pincus et al., 2012]. MODIS uses substantially different methods of estimating ctp than does ISCCP, so the amounts of clouds in each bin of the joint histogram of ctp and τ from MODIS are not comparable to those observed by ISCCP or the output of an ISCCP simulator applied to climate models. (MODIS observations may be compared to the output of a MODIS simulator [Pincus et al., 2012], but that was not available at the time of CFMIP1.) On the other hand, MODIS retrievals of τ are roughly equivalent to those from ISCCP, so we compare MODIS observations, aggregated over bins of ctp, to both ISCCP observations and the output of ISCCP simulators.

2.3 ISCCP Simulator

[8] A satellite simulator is a diagnostic code applied to model variables that reduces the influences of inconsistencies between the ways clouds are observed and the ways they are modeled [Bodas-Salcedo et al., 2011]. By mimicking the observational process in a simplified way, the simulator attempts to compute what a satellite would retrieve if the real-word atmosphere had the clouds of the model. Simulators increase the chances that the comparison of satellite retrievals to model output after run through a simulator is an evaluation of the fidelity of a model's simulation rather than a reflection of observational limitations or artifacts. The use of a satellite simulator also facilitates model intercomparison by minimizing the impacts of how clouds are defined in different parameterizations.

[9] The ISCCP simulator is the oldest of the satellite simulators used to evaluate clouds in models and has been widely used by most major climate modeling centers since its creation over 10 years ago [Klein and Jakob, 1999; Webb et al., 2001]. Since it was the only simulator available for CFMIP1, it is the only simulator with which one can track progress over time. The ISCCP simulator mimics the assumption of the ISCCP retrieval algorithms that radiances in cloudy satellite pixels are assumed to arise from a single homogenous layer of cloud with ctp determined from an infrared brightness temperature. In detail, the ISCCP simulator takes a model's vertical profile of grid-box mean clouds and creates a set of subgrid scale columns which are completely clear or cloudy at each level and which are consistent with the model's cloud-overlap parameterization. (This step is bypassed for models that provide to the simulator a set of previously generated subgrid scale columns.) From every subgrid scale column, one determines the single value of ctp and column-integrated τ that would be consistent with the single-layer cloud retrieval that ISCCP applies to every cloudy satellite pixel. In this step, ctp is determined by applying a simplified radiative transfer model in each subgrid scale column to determine an infrared brightness temperature, which is then converted to the temperature at cloud-top by using a cloud longwave emissivity derived from τ, as in the ISCCP retrieval algorithm. Once a cloud-top temperature has been determined, ctp is equated with the interpolated pressure that has the identical temperature according to the model's profile of temperature. The column-integrated value of τ is equated with the sum of model-reported τ from all model layers that are cloudy in a given subgrid scale column. From these subgrid scale values of ctp and τ, the grid-box mean joint histogram of ctp and τ is formed for every grid box and then subsequently averaged over time. To make the comparison with satellite retrievals of τ more fair, the ISCCP simulator is only applied to grid-boxes that are sunlit at a given model time.

[10] The ISCCP simulator itself changed between CFMIP1, which used v3.5, and CFMIP2, which used v4.1, raising the possibility that differences in the diagnostics might be mistaken for changes in simulation quality. The most significant algorithmic difference between these two versions involves the determination of ctp for clouds under atmospheric temperature inversions, such as subtropical marine stratocumulus. In these situations, ISCCP often erroneously assigns ctp to a level far higher (100–300 hPa) in the atmosphere than it should be [Garay et al., 2008]. In CFMIP1, ctp is assigned to the highest interpolated pressure (lowest altitude) with matching cloud-top temperature, but since the simulator is intended to mimic the retrieval process (even when it is faulty), the simulator was changed so that ctp is assigned to the lowest interpolated pressure (highest altitude) with matching cloud-top temperature when a temperature inversion is present in the model. We have verified that this and other simulator differences have little impact on our results by comparing the output of these two versions of the ISCCP simulator when applied to identical integrations of two CFMIP2 models (CCSM4 and HadGEM2-A) (not shown). Simulator changes primarily affect ctp with differences of up to 0.01 in the amounts of clouds annually averaged over the domain 60°N–60°S for ctp bins where ctp < 680 hPa and somewhat larger differences of up to 0.04 for ctp bins where ctp > 680 hPa.

[11] We only use models for which we are reasonably confident of a correct implementation of the ISCCP simulator. Our primary test is to verify that the sum of cloud cover over all bins of the joint histogram is consistent with the model diagnostic of total cloud cover (“clt”) which a model computes without using the ISCCP simulator [Zelinka et al., 2012].

2.4 Analysis Methods

[12] Climatological joint histograms of ctp and τ are formed for every calendar month by averaging model and observational data on a common 2° latitude by 2.5° longitude grid from every available year. Most model climatologies are based upon either 20 or 30 simulated years, whereas the observed climatologies are for 25 years for ISCCP and 11 years for MODIS, but differences in the number of years available do not materially affect our evaluation [Pincus et al., 2008]. (The scalar measures of the fidelity of model simulations [Section 4] are sensitive to this issue if the number of years used to form a climatology is very low (<5); this only affects results for the two MIROC models in CFMIP1.) To minimize issues with cloud retrievals above surfaces with snow or ice, we restrict our analysis to the domain 60°N–60°S. Because we use only monthly means, we cannot determine whether differences among models or between models and observations arise from differences in the cloud frequency of occurrence or amount when present.

[13] We evaluate changes over model generations in two ways. One considers changes in the multimodel mean from each of the CFMIP ensembles. This has the advantage of considering all available models and of highlighting common model errors. However, multimodel means are sensitive to the addition of new models (especially given the small sizes of the model ensembles), and changes in the multimodel mean may not reveal individual model error reductions when the spread of model results is centered on the observed value, as is often the case [Gleckler et al., 2008]. To address these limitations, we also track the changes over time in the models from the five modeling centers that have contributed one or more models to both ensembles. For this analysis, we use models from the Canadian Centre for Climate Modeling and Analysis (AGCM4.0 to CanAM4), the United Kingdom's Met Office Hadley Centre (HadSM3 to HadSM4 to HadGEM1 to HadGEM2-A), the Japanese effort associated with MIROC (MIROC(hisens) and MIROC(losens) to MIROC5), and the United States’ contributions from the National Oceanic and Atmospheric Administration's Geophysical Fluid Dynamics Laboratory (GFDL MLM 2.1 to GFDL-CM3) and the Community Atmosphere Model (CCSM3.0 to CCSM4 to CESM1(CAM5)).

3 Comparisons of Climate Model Simulations of Clouds to Satellite Observations

3.1 Common Improvements and Failures in the Simulation of Total Cloud Amount

[14] The ability of models to simulate the space-time distribution of total cloud amount, i.e., how often a cloud occurs with any value of ctp and τ, is perhaps the most fundamental aspect of a model's ability to simulate clouds. Unfortunately, this quantity is problematic to define from observations: satellite estimates of total cloud amount are extremely sensitive to many observational factors including the scale and sensitivity of the fundamental observations, as well as decisions made during the aggregation to larger scales [Stubenrauch et al., 2009; Mace et al., 2009; Marchand et al., 2010; Pincus et al., 2012]. We make the comparison more robust by restricting the analysis to clouds with τ exceeding some minimum threshold τmin, which we set to minimize hard-to-detect and partly cloudy observations. We select τmin = 1.3 from among the discrete choices offered by the bin boundaries of the joint histogram of ctp and τ by balancing the following desires: (a) to maximize the number of clouds that we examine, (b) to maximize agreement among the observational datasets we use, and (c) to minimize the chances that an observational platform would have missed a cloud with τ > τmin. Setting τmin = 1.3 provides the smallest relative bias and relative root-mean-square difference, as well as the maximum correlation coefficient, between the space-time distributions of the annual cycle climatologies of ISCCP and MODIS.

[15] Figure 1 illustrates the annual mean total cloud amount for the multimodel means of the CFMIP1 and CFMIP2 ensembles, the ISCCP and MODIS observations, and the difference of the CFMIP2 multimodel mean with ISCCP observations and with the CFMIP1 multimodel mean. For the domain 60°N–60°S, the annual mean total cloud amount fraction with a τ□□□ of 1.3 from ISCCP and MODIS is 0.51 and 0.47, respectively. The multimodel means of both CFMIP1 and CFMIP2 are 0.43 with more than three fourths of the models in both ensembles below the range of observational estimates. Although the multimodel mean is identical between the two ensembles, these area-averaged values have been getting closer over time to the observational estimates for four of the five model families in which we can track progress. The progress is quite striking for the Hadley Centre models, with HadSM3 having a total cloud amount of 0.33 but HadGEM2-A having a total cloud amount of 0.43.

Figure 1.

Total cloud amount (τ > 1.3) from CFMIP1 and CFMIP2 multimodel means, ISCCP and MODIS observations, and the difference of CFMIP2 multimodel mean to the ISCCP and CFMIP1 multimodel mean. The ensemble-mean distribution of total cloud amount is only slightly closer to observations in CFMIP2 than in CFMIP1, despite substantial improvement in some models.

[16] Relative to ISCCP observations, model underestimates of total cloud amount preferentially occur in regions of marine stratocumulus on the eastern sides of subtropical ocean basins and over middle latitudes. In stratocumulus regions, there is a wide variety of results in both ensembles with about three or four members in each ensemble having total cloud amount values close to observed and the reminder of models significantly below observational estimates. Although the differences between the multimodel means of ensembles are small in these regions, one finds marked improvement in three of the model families in which we can track progress, improvement motivated perhaps by the well-known importance of the low clouds in these regions for mean climate and climate sensitivity [Bony and duFresne, 2005].

[17] Models also typically underestimate total cloud amount at middle latitudes over both land and ocean (Figure 1). While a few models are close to observed over the middle latitude oceans, all models underestimate total cloud amount over the middle latitudes of Eurasia and North America. Examination of level-by-level cloud amount indicates that these underestimates, over both land and ocean, are primarily of lower level clouds (ctp > 560 hPa). When examining results within model families, one finds no consistent sign of progress for this bias.

3.2 Improvements as a Function of Cloud-Top Pressure and Cloud Optical Depth

[18] In addition to getting clouds to occur in the right places and times, correctly simulating ctp and τ is essential to getting the correct longwave and shortwave impacts of a cloud on the top-of-atmosphere radiation budget. Figure 2 illustrates the amount of clouds with τ > 1.3 as a function of ctp averaged over 60°N–60°S. Models tend to underestimate the amount of middle- (440 hPa < ctp < 680 hPa) and low-level (ctp > 680 hPa) clouds while having about the right amount of high-level (ctp < 440 hPa) clouds [Zhang et al., 2005]. The general underestimate of low-level clouds is consistent with the lack of clouds in marine stratocumulus and middle latitudes mentioned above. Differences in middle-level clouds are somewhat hard to interpret as many middle-level clouds observed by ISCCP are in fact multilayer cloud scenes of cirrus above boundary layer cloud [Marchand et al., 2010; Mace et al., 2011]. Although the ISCCP simulator is capable of reproducing this artifact [Mace et al., 2011], it will do so only if a model produces thin cirrus over boundary layer clouds. Thus, underestimates of middle-level cloud may actually indicate a lack of cirrus above boundary layer cloud.

Figure 2.

Fractional area in the domain 60ºS–60ºN covered by clouds as a function of cloud-top pressure from models and ISCCP observations. CFMIP1 (2) ensemble means are plotted with a blue (red) line. The area is computed only for clouds with τ > 1.3. The symbol key for models is provided in Tables 1 and 2. Slight improvement in the vertical distribution of cloudiness is found in CFMIP2, although underestimates of the amount of low and middle level clouds generally persist.

[19] Relative to that of the CFMIP1 ensemble, the CFMIP2 multimodel mean is closer to the observed amounts for six of seven bins of ctp, suggesting some improvement. This improvement is noticeable in the relative amounts of low-level clouds in the two lowest ctp bins. While a large part of this improvement is due to the change in the simulator's determination of ctp for clouds under an inversion, improvement can be found in the models from centers that contribute more than one model to a given ensemble (compare HadSM3 to HadGSM1 and CCSM4 to CESM1(CAM5)). Because the ISCCP simulator version does not change within these two pairs, we can conclude that these models have improved their simulation of low-level clouds. For middle-level clouds, there is also a reduction in the model underestimate, particularly for the 560–680 hPa ctp bin. In fact, the perfect agreement of CESM1(CAM5) with ISCCP for this bin can partially be attributed to the fact that snow is now radiatively active, and thus, the simulator counts the contribution of snow to τ and the infrared-brightness temperature used to determine ctp [Kay et al., 2012].

[20] Figure 3 illustrates the amount of clouds as a function of τ regardless of ctp and averaged over 60°N–60°S. More so than for ctp, rather marked improvement can be seen for τ bins where ISCCP and MODIS agree fairly well (τ > 3.6). In particular, the amounts of optically thick clouds (τ  > 23) are significantly closer to observed in the CFMIP2 ensemble relative to the CFMIP1 ensemble with a marked reduction in the previously identified overestimate of highly reflective clouds [Zhang et al., 2005]. This bias reduction is widespread enough that it is present for each of the five model families in which we can track progress (Figure 4).

Figure 3.

Fractional area in the domain 60ºS–60ºN covered by clouds as a function of optical thickness from models and ISCCP and MODIS observations. CFMIP1 (2) ensemble means are plotted with a blue (red) line. The symbol key for models is provided in Tables 1 and 2. The CFMIP2 ensemble is in better agreement with observations than the CFMIP1 ensemble for the amount of clouds in different ranges of optical depth where those observations are robust (τ > 3.6).

Figure 4.

Fractional area in the domain 60ºS–60ºN covered by clouds with τ > 23 for selected model families and observations. Models are plotted so as to illustrate progress in reducing the overestimate of optically thick cloud over time by ordering models from earliest to latest (left to right) within families. In models for which progress can be tracked, the amount of optically thick cloud has been reduced between model generations, making them more consistent with observations.

[21] The fraction of the 60°N–60°S area covered by optically thick cloud is 0.18 for the CFMIP1 ensemble mean but is 0.13 for the CFMIP2 ensemble mean. The CFMIP2 ensemble mean is still larger than the observational estimates of 0.06 for ISCCP and 0.08 for MODIS, although for HadGEM2-A and MRI-CGCM3, the amount of optically thick cloud is within the range of the two observational estimates. The reduction between ensembles in optically thick clouds is larger for lower-level (ctp > 560 hPa) clouds than it is for upper-level (ctp < 560 hPa) clouds, 0.04 versus 0.01 respectively, for the 60°N–60°S mean (not shown). With the greater reduction in lower-level optically thick clouds, 8 of 10 CFMIP2 models as opposed to 5 of 9 CFMIP1 models reproduce the fact that in ISCCP observations, optically thick clouds occur more frequently with ctp at upper levels than at lower levels.

[22] Geographically, the amount of optically thick clouds is preferentially reduced over both the middle-latitude oceans and the portions of the subtropical oceans where stratocumulus typically transitions to trade cumulus (Figure 5). However, there is no improvement in the multimodel mean overestimate of optically thick clouds over tropical continents, a bias present in 7 of 9 CFMIP1 models and 8 of 10 CFMIP2 models. We suspect that the common model bias in the diurnal cycle precipitation over tropical land [Yang and Slingo, 2001; Dai, 2006] contributes to this error by producing too many optically thick anvil clouds near mid-day, when they are visible to the ISCCP simulator, rather than at night.

Figure 5.

Fractional area covered by optically thick clouds (τ > 23) from CFMIP1 and CFMIP2 multimodel means, ISCCP and MODIS observations, and the difference of the CFMIP2 multi-model mean to ISCCP and the CFMIP1 multimodel mean. The overprediction of optically thick cloud has been alleviated mostly over the subtropical stratocumulus-to-cumulus transition and in middle latitudes, while biases over tropical continents have not been reduced.

[23] The decrease in optically thick clouds has been accompanied by an increase in the amount of clouds with intermediate optical depths (3.6 < τ  < 23) (Figures 3 and 6). This increase is present in each of the five model families in which we can track progress, and the amount of clouds with intermediate optical depths lies in between the values from ISCCP and MODIS for four CFMIP2 models.

Figure 6.

Scatterplot of the fractional area in the domain 60ºS–60ºN covered by clouds with τ > 23 and clouds with 3.6 < τ < 23. Observations from MODIS and ISCCP are represented by “M” and “I,” respectively. The symbol key for models is provided in Tables 1 and 2. Generally, any decrease in the amount of optically thick cloud has been compensated by an increase in the amount of optically intermediate cloud.

[24] Observational estimates of the amount of cloud with 0.3 < τ  < 3.6 disagree sharply, in part because many of the observations which produce clouds in this optical thickness range are partly cloudy [Pincus et al., 2012]. Furthermore, the impact of clouds with τ  < 0.3 on the top-of-atmosphere radiation budget is too small for passive sensors to detect. Assessment of optically thin clouds requires the use of observations from an active sensor such as CALIPSO [Winker et al., 2009] and could be performed using the output of the CALIPSO simulator applied to CFMIP2 models [Cessana and Chepfer, 2012].

3.3 Radiative Impact of Model Errors in Cloud Properties

[25] As in nature, clouds in climate models strongly affect the radiation balance as a function of space and time. Model tuning guarantees that the global and annual average of the top-of-atmosphere net radiation is close to zero, but significant regional errors in the radiation field may persist, and correct regional fluxes can be achieved through compensating errors in cloud properties. One common error is to have clouds which are too few but too bright, that is, to have lower-than-observed cloud amounts with larger-than-observed values of τ, such that the average shortwave radiation budget is about right [Zhang et al., 2005; Nam et al., 2012].

[26] We explore these issues by using cloud radiative kernels [Zelinka et al., 2012] to compute the radiative effects of errors in cloud properties. A cloud kernel KSW,LW is the result of a radiative transfer calculation that computes the impact on the top-of-atmosphere shortwave and longwave fluxes, relative to clear-sky, of the addition of a unit area covered by a cloud with a given ctp and τ. Our kernels are computed as a function of latitude, longitude, and calendar month. Multiplying the kernels by the bias, relative to ISCCP, in cloud amount in each bin of the joint ctp - τ histogram yields an estimate of the error in top-of-atmosphere radiation budget due to errors in the simulated distribution of clouds. However, evaluating differences with observations for each bin of ctp and τ is not warranted for two reasons. First, comparisons with clouds retrieved from ground-based remote sensors and passed through the ISCCP simulator [Figures 2c and 3c of Mace et al., 2011] suggest that the uncertainty of ISCCP retrievals is about ±200 hPa for ctp and a factor of 3 for τ. Thus, we aggregate differences into a reduced-resolution joint histogram of ctp and τ with bin boundaries in ctp of 440 hPa and 680 hPa and in τ of 3.6 and 23. (This is equivalent to the reduced-resolution joint histogram available in the monthly averaged ISCCP data archives.) Second, the large observational uncertainties for thin clouds suggest that differences with observations for bins of low τ may not reflect model errors. Thus, from the reduced-resolution joint histogram, we do not examine differences for τ < 3.6.

[27] In the first two columns, Figure 7 shows the annually and 60°N–60°S averaged bias relative to ISCCP in cloud amount fraction in the reduced-resolution joint histograms of ctp and τ for the five model families in which we can track progress and the multimodel means for CFMIP1 and CFMIP2. The rightmost column of Figure 7 shows the absolute values of the biases after summing over ctp bins. Figures 8 and 9 show the corresponding biases in W m-2 for the shortwave and longwave radiation of the same models. (The Canadian model pairing is absent from Figures 8, 9 because we cannot perform accurate cloud kernel calculations for AGCM4.0 for the reasons discussed in the Appendix of Zelinka et al. [2012].) The oldest models are in the left column and the most recent models in the center column. The prominent overestimate of optically thick clouds occurs in all ctp bins in the earlier models (left column) but is much reduced in the later models (center column). Likewise, the underestimate of optically intermediate clouds present in nearly all ctp bins has been reduced in the more recent model versions.

Figure 7.

(Left two columns) Area-averaged biases in the domain 60ºS–60ºN with respect to ISCCP observations of fractional area covered by clouds in bins of cloud-top pressure and optical depth. Results are plotted for the five model families in which we can track progress and the ensemble mean. Models are ordered with the oldest models on the left and the newest models on the right. The sum of the histogram (denoted by Σ) and the range (maximum minus minimum value in the histogram, denoted by R) are shown in the title of each panel. Positive values indicate model overestimates relative to observations. The fact that the recent models have fewer bins with color as well as reduced intensity in the bins with color indicates improvements with time. (Right column) The same biases summed over cloud-top pressure bins and plotted as a function of optical depth for the oldest (gray-shading) and most recent (black) model of the same row. The absolute value of the summed biases are plotted with positive biases indicated by solid lines and negative biases indicated by dashed lines. In every model for which progress can be tracked, the coarse-grained joint distribution of optical thickness and cloud-top pressure is more consistent with observations in later model generations.

Figure 8.

As in Figure 7, but for the contributions to shortwave radiation reflected to space by clouds in W m-2 stratified into bins of cloud-top pressure and optical depth (left two columns) and then summed over bins of cloud-top pressure (right column). Positive values in the left two columns indicate a bias toward too much reflected radiation due to a positive bias in cloud amount. Most models have reduced the compensating error of too much shortwave radiation reflected to space by optically thick clouds and too little reflection by optically intermediate clouds.

Figure 9.

As in Figure 7, but for the contributions to reductions of outgoing longwave radiation (relative to clear-sky) by clouds in W m-2 stratified into bins of cloud-top pressure and optical depth (left two columns) and then summed over bins of cloud-top pressure (right column). Positive values in the left two columns indicate a bias toward too much longwave radiation emitted to space due to a negative bias in cloud amount. Most models have reduced the compensating error of too much reduction of the outgoing longwave radiation by optically thick clouds and too little reduction by optically intermediate clouds.

[28] The impact of these biases on the shortwave radiation quantifies the nature of compensating errors (Figure 8), with the overestimates of reflected shortwave by clouds with τ > 23 compensating for a lack of reflection by clouds with intermediate optical depths. The figure is similar to that of the cloud biases (Figure 7) except that weighting by the shortwave radiative kernel reduces the impact of the underestimate of optically intermediate clouds relative to the overestimate of optically thick clouds. The degree of compensation is markedly reduced in the more recent models. For example, in HadSM3, clouds with τ  > 23 reflected approximately 30 W m-2 too much shortwave radiation which compensated for a 20 W m-2 underestimate of the amount of shortwave radiation reflected by clouds with intermediate optical depths. This compensating error is nearly eliminated in HadGEM2-A and significantly reduced in the other models in which we can track progress as well as for the multimodel mean.

[29] In the longwave spectrum, the nature of compensating biases is similar but with emphasis on upper level clouds (Figure 9). In general, there is too much reduction of outgoing longwave radiation by high clouds with τ  > 23, which compensates for a lack of reduction of outgoing longwave radiation by optically intermediate clouds at all levels of the troposphere. Progress is clearly identifiable for the Community Atmosphere and Hadley Centre models but somewhat less for the MIROC and GFDL models and the multimodel mean.

4 Scalar Measures of the Fidelity of Model Simulations

[30] While the evidence above supports the notion that the simulation of clouds in climate models has been improving, it is helpful to provide scalar measures of the fidelity of model simulations that can quantitatively demonstrate progress. Here we present a few such quantities chosen to measure different aspects of cloud simulations and for which observational uncertainty is less than the differences between models and observations and among models themselves. These measures may be useful as metrics for assessing the skill of climate models in reproducing the present-day distribution clouds and their properties [Gleckler et al., 2008; Pincus et al., 2008; Williams and Webb, 2009].

[31] In the following, c(ctp,τ,X) is the amount of cloud in a given bin of the ISCCP histogram and is a function of cloud-top pressure ctp, optical depth τ, and generalized position X, including latitude, longitude, and month. Total cloud amount C(τmin) is the sum of the cloud amounts of all bins with τ greater than the minimum optical thickness τmin:

display math(1)

[32] We compute the normalized root-mean-square error ETCA in the space–time distribution of total cloud amount, as

display math(2)

[33] The integral in (2) denotes the area-weighted space–time average of squared differences between the model and ISCCP observations. The root-mean-square differences are normalized by the space–time standard deviation of the observed total cloud amount, given by

display math(3)

[34] As in section 3.1, we set τmin = 1.3.

[35] Equation (1) uses the ISCCP simulator to ensure that model definitions of cloudiness are comparable with what is robustly observable but ignores the wealth of information provided by the joint histogram of ctp and τ. We evaluate the error Ectp-τ in this more finely resolved distribution as the sum over a finite number of cloud-top pressure (Nctp) and optical thickness (Nτ) bins of squared differences between the model and ISCCP observations:

display math(4)

[36] Considering the issues with thin-cloud retrievals and the uncertainty of the ISCCP observations, Ectp-τ is evaluated for the six bins of the reduced-resolution joint histogram shown in Figures 7-9 and is normalized by σctp-τ , the accumulated space–time standard deviation of observed cloud amounts in the reduced bin set. This makes Ectp-τ the normalized root-mean-square error in the amount of optically intermediate and thick clouds at low, middle, and high levels of the atmosphere.

[37] We compute radiatively relevant errors ESW, LW in the distribution of clouds by using the radiative kernels to weight bin-by-bin errors by their radiative impact on top-of-atmosphere radiation fluxes:

display math(5)

[38] Multiplication by radiative kernel is performed for each bin of the original ISCCP histogram before aggregation to the reduced resolution histogram. ESW, LW are computed separately for shortwave and longwave radiation and are normalized by the accumulated space–time standard deviation σSW,LW of the radiative impacts of observed clouds from the reduced resolution histogram.

[39] Figure 10 shows ETCA, Ectp-τ, ELW, and ESW for each model stratified into two rows according to the model ensemble. Arrows from earlier to later models indicate the change with time in the fidelity of model simulations; left-pointing arrows indicate smaller errors over time. The arrows connect the earliest and latest models from the modeling centers in which we track progress as well as the mean measure of each model ensemble, which is computed using only the earliest CFMIP1 (latest CFMIP2) models from modeling centers that contribute more than one model to a given ensemble.

Figure 10.

Scalar measures of fidelity of CFMIP model simulations in reproducing the space-time distribution of several cloud measures, with greater fidelity indicated by lower E values. ETCA measures fidelity in simulating total cloud amount, whereas Ectp-τ measures fidelity in simulating cloud-top pressure and optical depth in different categories of optically intermediate and thick clouds at high, middle, and low-levels of the atmosphere. The impacts on top-of-atmosphere shortwave and longwave radiation in the same categories used for Ectp-τ are measured by ESW (lower left) and ELW (lower right), respectively. Models are stratified vertically into the two ensembles and are plotted according to the symbol key in Tables 1 and 2. For the modeling centers in which we can track progress, the arrow connects the oldest model in the family (arrow base) to the most recent model (arrow tip). The thick black arrow connects the average measure of CFMIP1 models (arrow base) to that of CFMIP2 models (arrow tip). Arrows pointing to the left indicate improvements with time. Most individual models and the ensembles as a whole show progress over time in most measures of simulation fidelity, with small improvement for the prediction of total cloud amount and large improvements for the distribution of cloud optical properties and their impact on shortwave radiation.

[40] The values of the total cloud amount measure ETCA range from 0.65 to 1.18, indicating that the standard deviation of biases in total cloud amount relative to ISCCP are generally comparable in size to the space–time standard deviation of observed total cloud amount. To put this number into context, the ETCA measure between the MODIS and ISCCP climatologies is 0.47. All model differences with ISCCP exceed this value, so it is likely that errors in the climatology of total cloud amount are robustly determined. Consistent with Figure 1, there is not a clear sign of improvement when considering the ensemble as a whole with the CFMIP1 ensemble mean value of ETCA equal to 0.86 and the CFMIP2 ensemble mean value of ETCA equal to 0.81. However, significantly larger improvement is found for the Hadley Centre and Community Atmosphere models.

[41] For the cloud property measure Ectp-τ, much more widespread progress can be found. For four of the five models in which we can track progress (Hadley Centre, Community Atmosphere, Canadian Centre, and GFDL models), errors relative to ISCCP has been reduced by 20%–45% (relative), from 115%–175% to 80%–105% of the standard deviation of the ISCCP amounts of the six intermediate and thick cloud types. For the ensemble mean measure, moderate progress can be found with 25% (relative) reduction in Ectp-τ. Separate calculations reveal that the majority of the improvement in Ectp-τ comes from a better simulation of the cloud optical thickness rather than from a better simulation of the vertical distribution of clouds (figures not shown). For the equivalent error measure calculated using only two bins for optically intermediate and thick clouds regardless of ctp, the value for the best model HadGEM2-A is close to that calculated for differences between the observed ISCCP and MODIS distributions (0.71 vs. 0.59).

[42] Radiatively relevant cloud property measures ESW and ELW are shown in the bottom row of Figure 10. Similar to the cloud property measure Ectp-τ, both measures show significant error reductions of 20%–30% for the ensemble mean measure with larger 40%–50% error reductions for the Hadley Centre and Community Atmosphere models. Again, the majority of this error reduction comes from improvement in the simulation of τ, indicating that models are better simulating the amount of shortwave radiation reflected and longwave radiation trapped by optically intermediate and thick clouds. Although it may appear that there is a redundancy among Ectp-τ, ESW, and ELW, only Ectp-τ and ESW are highly correlated; all other possible pairings, including those with ETCA, have statistically insignificant intermodel correlations.

5 Why are Simulations of Clouds Improving, and What Impacts Might This have?

[43] The agreement between satellite observations and simulations by climate models of the climatological annual cycle of cloud amount, cloud-top pressure, and optical thickness has improved over the last decade. The improvement is most striking in the simulation of τ, where a bias of having too many optically thick clouds (τ  > 23) has been reduced by about 50% in the multimodel mean, with the best models having eliminated this bias. With a corresponding increase in the simulated amount of clouds with intermediate optical depth (3.6 < τ  < 23), this reduces the tendency for climate models to simulate approximately the right amount of shortwave radiation reflected by clouds but with the compensating errors of having too few clouds that are too bright.

[44] Improvement in the amount or height distribution of clouds is not clear in the ensemble as a whole, although progress can be found in individual models. For example, the simulations of total cloud amount in the Hadley Centre and Community Atmosphere models do show noticeable improvement (see ETCA of Figure 10); in part, this improvement results from better simulations of the amount of clouds in the climatically important subtropical marine stratocumulus regions, where the amount of cloud is close to the observed value in their most recent models. Other aspects show no improvement in the majority of climate models such as the underestimate of cloud over middle-latitude land and ocean and an overestimate in the amount of optically thick cloud over tropical land. Incremental progress by climate models in simulating clouds has also been reported in Jiang et al. [2012] and Lauer and Hamilton [2012].

[45] Pinpointing the reasons for model improvement is difficult without testing individual modifications from among the myriad of changes that modeling centers have implemented in the last decade, and it is likely that many factors have contributed. Even apart from parameterization changes, the incorporation of ISCCP simulator diagnostics in the routine evaluation of developmental model versions (as was done at the Hadley Centre for much of the last decade [Martin et al., 2006]) can have a subtle but persistent influence on the choices made in the model-development process in such a way as to lead to improved simulation of clouds. However, at most modeling centers, the ISCCP simulator was not routinely run, and the improvements in the simulation of optically thick clouds came as a surprise to some model developers we contacted.

[46] With regard to parameterizations, the improved boundary layer turbulence and shallow convection parameterizations in the Hadley Centre and Community Atmosphere models [Lock et al., 2000; Bretherton and Park, 2009; Park and Bretherton, 2009] are critical for the improved simulations in marine stratocumulus clouds. However, an improved simulation would not have been realized without also increasing the vertical resolution and, in the case of the Hadley Centre, incorporating a new semi-Lagrangian dynamical core [Martin et al., 2006].

[47] In the case of the improved optical depth distribution, the causes for improvement are less clear, but there are some clues from what has happened at the individual modeling centers whose progress we can track. These clues were developed in part through correspondence with a number of model developers (see Acknowledgments). We present our speculations in two categories: the parameterizations of stratiform cloud microphysics and macrophysics.

[48] The improvements to cloud microphysics incorporated into a number of models seems to have been important, particularly for middle latitude storm-track clouds. The separation of liquid and ice into separate prognostic variables permits a more complete treatment of microphysics, particularly for mixed phase clouds, where the inclusion of the Bergeron process may reduce the amount of super-cooled liquid in deep frontal clouds. Improved microphysics [Wilson and Ballard, 1999; Morrison and Gettelman, 2008] was important for cloud changes in the Hadley Centre (HadSM3 to HadSM4), Japanese (MIROC(hisens) and MIROC(losens) to MIROC5), and Community Atmosphere Models (CCSM4 to CESM1(CAM5)). In the CAM, the new microphysics is directly responsible for a substantial reduction in liquid water path over middle-latitudes that contributes to its reduction of optically thick clouds [see Figure 12f of Gettelman et al., 2008].

[49] With regard to stratiform cloud macrophysics, the specification of cloud radiative properties seems to have been particularly important. For the Canadian model, the likeliest cause for the reduction of optically thick cloud is the introduction of the Monte Carlo Independent Column Approximation (McICA) [Pincus et al., 2003], which affects a model's radiation budget by removing biases in the treatment of subgrid scale variability in cloud optical properties due to overlap and internal variability. Upon model retuning, a significant reduction in liquid water path occurred which is apparently responsible for the reduction in optically thick cloud in this model. McICA has also been incorporated to the GFDL-CM3 and CESM1(CAM5) and is likely partially responsible for the reduction of optically thick cloud in these models. Indeed, a sensitivity study using McICA in the GFDL model [see Figure 4 of Zhang et al., 2005] shows a reduction of 0.03 in the 60°N–60°S mean amount of optically thick cloud. In summary, the improved treatment of the radiative impact of clouds by McICA permitted better cloud properties to be simulated in models that are tuned to the observed radiation budget.

[50] Other aspects of cloud macrophysics are likely important. Because the geometric thickness of many observed stratiform clouds are thinner than the typical thickness of model levels, the increased vertical resolution of many models permits simulation of geometrically and optically thinner clouds (at fixed water contents and particle sizes). In the Hadley Centre model, the introduction of a subgrid (in the vertical) treatment of clouds is also thought to have helped in this regard.

[51] One may wonder if there is any connection between improved cloud simulations in climate models and the response to greenhouse gases in the climate changes these models simulate. We examined the relationships between our scalar measures of the fidelity of model simulations and various climate change measures from the available CFMIP1 slab-ocean model simulations of the equilibrium response to an abrupt doubling of carbon dioxide and the available CFMIP2 coupled-ocean atmosphere model simulations of the response to an abrupt quadrupling of carbon dioxide. The measures include the equilibrium climate sensitivity, the global-mean net radiative forcing, and the global-mean net, shortwave and longwave cloud feedbacks and rapid adjustments to carbon dioxide calculated according the methods of Gregory and Webb [2008], Andrews et al. [2012] and Webb et al. [2012]. Boot-strapping methods suggest that only two relationships are potentially significant, both of which are displayed in Figure 11. Within each ensemble, models with smaller Ectp-τ have larger shortwave and net cloud feedbacks. Similar to the results of Pincus et al. [2008] for CMIP3 models, we did not find a significant relationship between climate sensitivity and ETCA. However, the relationships of net and shortwave cloud feedbacks with Ectp-τ for the combined ensembles are not significant, which cannot be explained by the different simulation types as there is no known systematic difference in cloud feedbacks between slab-ocean and coupled ocean-atmosphere models [Yokohata et al., 2008]. Without a physical basis to these relationships, we cannot eliminate the possibility that these correlations arise by chance. One implication of the reduction of cloud optical depths is that the magnitude of cloud feedbacks resulting per unit change in cloud optical depth can be larger if the current climate's cloud albedo is lower [Stephens 2010].

Figure 11.

Scatterplot of Ectp-τ versus the global and annual mean net (left) and shortwave (right) cloud feedback for six CFMIP1 (blue) and nine CFMIP2 models (red). Linear regression lines and correlation coefficients are shown separately for CFMIP1 (blue) and CFMIP2 (red) model ensembles and as well for the combined ensemble (black). The symbol key for models is provided in Tables 1 and 2. Of all the measures examined, only Ectp-τ is correlated with global-mean cloud feedbacks, and this correlation applies within but not between model ensembles, suggesting that it may be a statistical artifact.

Acknowledgments

[52] We acknowledge the World Climate Research Program's Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Tables 1 and 2 of this paper) for producing and making available their model output. For CMIP, the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. The efforts of authors from Lawrence Livermore National Laboratory were supported by the Regional and Global Climate and Earth System Modeling programs of the United States Department of Energy's Office of Science and were performed under the auspices of the United States Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344. Robert Pincus appreciates support from NASA under grant NNX11AF09G and from NSF under grant AGS 1138394. We thank Ben Sanderson for providing ISCCP simulator output from the CCSM4 slab-ocean model; Alejandro Bodas-Salcedo for providing additional ISCCP simulator output from the Hadley Center models; and Tim Andrews and Mark Webb for providing estimates of cloud feedbacks, adjustments, and climate sensitivities for several models. We thank a number of individuals for helping us to understand the reasons for changes in their models, specifically Jason Cole, Leo Donner, Andrew Gettelman, Chris Golaz, Johannes Quaas, Masahiro Watanabe, and Mark Webb. We also thank Shaocheng Xie for conversations.

Ancillary