1. Introduction
The representation of clouds in global climate models (GCMs) is critical to modeling the earth’s radiative energy budget, atmospheric circulation, and hydrological cycle, and many processes at smaller scales. Model evaluation studies consistently identify significant cloud errors (e.g., Gates et al. 1999; Zhang et al. 2005; Trenberth and Fasullo 2010) and—while GCMs are improving by some measures (Klein et al. 2013)—subsequent cloud feedbacks continue to be the greatest source of uncertainty in estimates of climate sensitivity (e.g., Cess et al. 1990, 1996; Colman 2003; Dufresne and Bony 2008). Many of the processes regulating cloud formation, composition, and behavior—and interactions with aerosols, radiation, and dynamics—occur at scales below the resolution of GCMs and must be parameterized. Errors related to parameterized cloud can compensate to match the bulk observations against which GCMs are tuned; for example, the recurring “too few, too bright” low cloud errors in many phase 3 of the Coupled Model Intercomparison Project (CMIP3) models nevertheless produced near-realistic radiative fluxes (Klein et al. 2013). To identify these compensating errors and to inform the improvement of parameterizations, there is a need for “process oriented” approaches to model evaluation (Stephens 2005; Jakob 2010).
To better understand cloud processes in observations, and to evaluate them in GCMs, we identify “cloud regimes”—classes of cloud with common physical characteristics and atmospheric contexts—and quantify both the physical and microphysical properties of clouds and the atmospheric processes to which they correspond. Cloud regimes can be identified from dynamical or thermodynamical parameters (e.g., Bony and Dufresne 2005), or directly from observed cloud characteristics by using a clustering algorithm to identify repeating patterns of cloud properties (Jakob and Tselioudis 2003; Jakob et al. 2005). The latter cloud regimes, also called “weather states,” have proved useful in associating observed cloud properties with dynamical and thermodynamical conditions in the tropics (e.g., Rossow et al. 2005; Tan et al. 2013), extratropics (e.g., Gordon and Norris 2010; Haynes et al. 2011; Oreopoulos and Rossow 2011), and globally (e.g., Tselioudis et al. 2013; Oreopoulos et al. 2014). A challenge when using cloud regimes for model evaluation is to identify them in such a way that the representation of clouds in one or more GCMs can be compared against each other and satellite observations. There have been two approaches to identifying cloud regimes for model evaluation: In the first approach (Williams and Tselioudis 2007, hereafter WT07), cloud regimes are identified from the simulated cloud properties of each GCM using the same methodology as for satellite observations. This method has the advantage of using simulated cloud directly, so that the cloud regimes accurately represent the coherent structures of cloud properties in each model. A disadvantage is that each GCM engenders a new set of cloud regimes that may be very different from those observed; without a common set of cloud regimes, evaluation between model and observations is problematic. In the WT07 approach, if simulated cloud regimes are significantly different from observations, they may be subjectively grouped into “principal” cloud regimes for evaluation. Alternatively cloud regimes can be identified from satellite observations only and then simulated clouds are assigned to cloud regimes based on average cloud properties (Williams and Webb 2009, hereafter WW09). The WW09 method has the advantage of using a consistent set of observed cloud regimes for evaluation and model intercomparison, and this approach has been widely used in subsequent studies (e.g., Tsushima et al. 2013; Bodas-Salcedo et al. 2012, 2014). A disadvantage of the WW09 methodology is that the observed cloud regimes are not necessarily representative of the coherent structures of cloud properties in the models, so that the links between cloud properties and processes in the GCM are uncertain. In this paper we aim to extend these approaches by developing a hybrid methodology that retains the structures of both observed and simulated clouds. Hybrid cloud regimes are identified from observed and simulated cloud simultaneously, ensuring the retention of observed cloud regimes to which the model must be compared, while including the cloud structures peculiar to the model—and hence the errors we aim to explore.
We apply the hybrid cloud regime methodology to a significant cloud evaluation problem for many state-of-the-art models: the shortwave (SW) radiation biases in the high-latitude Southern Ocean (50°–65°S) during the austral summer [December–February (DJF)]. An excess of absorbed SW radiation in this region—associated with a deficit of cloud or cloud reflectivity—was identified in CMIP3 (Trenberth and Fasullo 2010), and persists in the CMIP5 models (Li et al. 2013). Evaluations of the Met Office (UKMO) model and other CMIP5 models using the WW09 methodology (Bodas-Salcedo et al. 2012, 2014) have attributed the radiation biases to low and midtopped cloud regimes, especially in the postfrontal and cold-air part of extratropical cyclones. Observational studies have shown that the high-latitude Southern Ocean is dominated by near-ubiquitous low cloud, much of which is assigned by passive satellite observations to midtopped cloud regimes (Haynes et al. 2011; Bodas-Salcedo et al. 2014). While WW09 identify a single midtopped cloud regime in the Southern Ocean, observational studies distinguish between two midtopped cloud regimes with distinct dynamical contexts and radiative properties (Haynes et al. 2011), and further evaluation of these midtopped cloud regimes at high latitudes has shown that the optically thicker midtopped cloud regime includes instances of both stratiform cloud under strongly subsiding conditions and shallow frontal-type clouds, which were associated with conditions resembling the warm conveyor belt in extratropical cyclones (Mason et al. 2014). Resolving these distinct cloud processes associated with midtopped cloud in observations and GCMs is a priority for an extended cloud regime methodology.
The GCM used in this study is the Australian Community Climate and Earth-System Simulator, version 1.3 (ACCESS1.3; Bi et al. 2013). ACCESS1.3 exhibits SW radiation errors in the high-latitude Southern Ocean during DJF typical of the persistent biases in CMIP3 and CMIP5 models. A first-order evaluation of ACCESS1.3 indicates SW cloud radiative effect (
Maps of the 
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
Maps of the
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
Maps of the
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
The mean cloud bias (
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
The mean cloud bias (
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
The mean cloud bias (
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
The purpose of this study is to develop an extended cloud regime methodology and to demonstrate its application to the evaluation of Southern Ocean cloud and radiation errors in ACCESS1.3. The satellite observations and reanalysis data used and the configuration of the GCM are described in section 2. The methodology for identifying hybrid cloud regimes is described in section 3, followed by the properties and statistics of the hybrid cloud regimes for ACCESS1.3, their contribution to the 
2. Data
a. Passive satellite observations
The International Satellite Cloud Climatology Project (ISCCP; Rossow and Schiffer 1999) combines passive observations from geostationary and polar-orbiting satellites to provide a continuous global dataset for the period July 1983–2009. Observations of cloud-top pressure (CTP) and cloud optical thickness (
To quantify the observational uncertainty, we compare 
Two summers (2006–08; DJF) of daily averages of ISCCP D1 and TOA flux data were interpolated onto a regular 2.5° grid: the CTP-
b. Reanalysis
The European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim; Dee et al. 2011) is available at 1.5° spatial and 6-h temporal resolution. Two summers (2006–08; DJF) of ERA-Interim data were reinterpolated onto a regular 2.5° grid using a linear interpolation scheme. The first and second derivatives of the ERA-Interim mean sea level pressure (MSLP) were used to identify cyclone centers as described in Field and Wood (2007). Cyclone composites are constructed by reinterpolating contemporary data onto a regular 4000 km × 4000 km grid centered at the MSLP minimum of each identified extratropical cyclone.
c. Global climate model
ACCESS1.3 (Bi et al. 2013) is a coupled climate model developed by the Centre for Australian Weather and Climate Research (CAWCR). Its atmosphere model is based on the UKMO Unified Model (UM) Global Atmosphere model, version 1.0 (GA1.0; Hewitt et al. 2011). To facilitate the consistent comparison of simulated cloud with observations, the ISCCP satellite simulator (Klein and Jakob 1999; Webb et al. 2001), part of the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP; Bodas-Salcedo et al. 2008), is integrated into ACCESS1.3 (Franklin et al. 2013a). The ISCCP simulator output differs from ISCCP observations in that subvisible cloud (
ACCESS1.3 was run in atmosphere-only mode with prescribed sea surface temperatures at N96 resolution for two years (2006–08). The output fields were daily MSLP, TOA SW radiative fluxes for full-sky and clear-sky conditions, and CTP-
3. Evaluation of ACCESS1.3
a. Hybrid cloud regime methodology
In this section we extend the existing methodologies for assigning observed and simulated cloud properties to cloud regimes for model evaluation. Previous approaches have involved either clustering on the simulated cloud properties from a model (WT07)—so that the resultant cloud regimes accurately represent the model cloud behavior, but they are not necessarily comparable with observed cloud regimes—or assigning simulated clouds directly to predefined observed cloud regimes (WW09), maintaining a consistent set of cloud regimes against which GCMs can be evaluated, at the risk of not necessarily resolving the cloud structures peculiar to each model. Based on the strengths and weaknesses of the existing approaches, we aim to combine the advantages of both so that the extended cloud regime methodology is capable of resolving both the behavior of the GCM (i.e., the repeating structures of simulated cloud properties) and the cloud regimes identified in observational studies.
To achieve this, we cluster observed and simulated CTP-
Using the method for identifying cloud regimes first described in Jakob and Tselioudis (2003), we apply the k-means clustering algorithm (Anderberg 1973) to the 42-element state vectors of the CTP-
The clustering algorithm is somewhat subjective in that the number of clusters (k) must be specified. Rossow et al. (2005) propose four criteria by which k can be objectively chosen: 1) the stability of the resulting cluster centroids across random initializations of the algorithm, and for random subsets of the data; 2) avoiding similarity between cluster centroids; 3) avoiding similarity between the frequency of occurrence patterns of the clusters in space and time; and 4) ensuring the Euclidean distances between cluster centroids exceed the distances between cluster members. As Rossow et al. (2005) note, in practice criteria 1 and 4 are automatically met: the algorithm is restarted with randomly seeded centroids a number of times and the most stable solution is selected, and the algorithm iterates until the distance between cluster centroids is greater than the spread of cluster members.
Criteria 2 and 3 are intended to avoid redundant clusters. However, for the purposes of a hybrid cloud regime analysis—simultaneously clustering on two independent populations—this is not necessarily desirable: for example, it is possible for a mostly simulated hybrid cloud regime to occur in a similar context—and therefore exhibit a similar spatial distribution—to a mostly observed cloud regime. As these cases provide information about the model errors, we would wish to resolve them.
Therefore, for the hybrid cloud regime analysis, we increase k until the set of clusters includes the observed cloud regimes identified previously. We note that the hybrid cloud regimes identified here are not identical to those identified for the broader Southern Ocean (30°–65°S; Haynes et al. 2011) and the refined analysis of midtopped cloud subregimes conducted for the high-latitude Southern Ocean (Mason et al. 2014), as the cloud regimes identified in the former study include lower-latitude clouds, and the cloud subregimes identified in the latter were derived by clustering within previously identified cloud regimes. Nevertheless, all of the features identified in the observational studies for the region of interest are represented in the hybrid cloud regimes.
For the area of interest in this study, two seasons (6 months) of data provide a large enough dataset that the hybrid cloud regimes are reliably identified. A test of robustness was made by clustering on randomly selected subsets of the data; the resulting cluster centroids were found to be substantially similar for even one month of data. We note that the relative frequencies of occurrence of the cloud regimes are more sensitive to interannual variability; we present these values here for evaluation of the GCM over the period in question, and not as a climatology of the identified cloud types.
Once the hybrid cloud regimes have been identified, each CTP-
b. Identification and properties of hybrid cloud regimes
Eleven hybrid cloud regimes (H1–H11, ranked from high to low CTP and from low to high 
The CTP-τ joint histograms representing the cluster centroids of the hybrid cloud regimes. The hybrid cloud regimes are arranged according to the dominant features of the joint histograms with the optically thinnest and lowest cloud regime in the bottom-left corner, and the optically thickest and highest cloud regime in the top-right corner, such that clouds of similar CTPs are comparably along the horizontal axis and similar optical thicknesses along the vertical axis. The mean observed and simulated RFO over the regions of interest are indicated in the top-right corner of each histogram. Where the cloud regime is underrepresented (underrepresented) in ACCESS1.3 by more than 50% with respect to the observed value, the histogram is bordered in blue (red). The TCC for each cloud regime (the sum of each joint histogram) is indicated at top right of each histogram.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
The CTP-τ joint histograms representing the cluster centroids of the hybrid cloud regimes. The hybrid cloud regimes are arranged according to the dominant features of the joint histograms with the optically thinnest and lowest cloud regime in the bottom-left corner, and the optically thickest and highest cloud regime in the top-right corner, such that clouds of similar CTPs are comparably along the horizontal axis and similar optical thicknesses along the vertical axis. The mean observed and simulated RFO over the regions of interest are indicated in the top-right corner of each histogram. Where the cloud regime is underrepresented (underrepresented) in ACCESS1.3 by more than 50% with respect to the observed value, the histogram is bordered in blue (red). The TCC for each cloud regime (the sum of each joint histogram) is indicated at top right of each histogram.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
The CTP-τ joint histograms representing the cluster centroids of the hybrid cloud regimes. The hybrid cloud regimes are arranged according to the dominant features of the joint histograms with the optically thinnest and lowest cloud regime in the bottom-left corner, and the optically thickest and highest cloud regime in the top-right corner, such that clouds of similar CTPs are comparably along the horizontal axis and similar optical thicknesses along the vertical axis. The mean observed and simulated RFO over the regions of interest are indicated in the top-right corner of each histogram. Where the cloud regime is underrepresented (underrepresented) in ACCESS1.3 by more than 50% with respect to the observed value, the histogram is bordered in blue (red). The TCC for each cloud regime (the sum of each joint histogram) is indicated at top right of each histogram.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
By assigning each daily CTP-
The observed (obs) and simulated (sim) properties of the hybrid cloud regimes.
Maps of the observed and simulated RFO of the hybrid cloud regimes over the region of interest. As in Fig. 3, the hybrid cloud regimes are arranged according to the dominant features of the CTP-τ histogram, with the optically thinnest and lowest regime in the bottom-left map and the optically thickest and highest cloud regime in the top-right map. Note that the simulated RFO of H1 is on a different color scale to resolve the significant overproduction of this hybrid cloud regime in ACCESS1.3.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
Maps of the observed and simulated RFO of the hybrid cloud regimes over the region of interest. As in Fig. 3, the hybrid cloud regimes are arranged according to the dominant features of the CTP-τ histogram, with the optically thinnest and lowest regime in the bottom-left map and the optically thickest and highest cloud regime in the top-right map. Note that the simulated RFO of H1 is on a different color scale to resolve the significant overproduction of this hybrid cloud regime in ACCESS1.3.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
Maps of the observed and simulated RFO of the hybrid cloud regimes over the region of interest. As in Fig. 3, the hybrid cloud regimes are arranged according to the dominant features of the CTP-τ histogram, with the optically thinnest and lowest regime in the bottom-left map and the optically thickest and highest cloud regime in the top-right map. Note that the simulated RFO of H1 is on a different color scale to resolve the significant overproduction of this hybrid cloud regime in ACCESS1.3.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
Cloud fractions are underestimated in ACCESS1.3 by 10%–20% for almost all cloud regimes, consistent with the overall deficit of TCC in the model. The tendency of the model to produce low cloud fractions is most apparent in the overproduction of the lowest cloud fraction, lowest optical thickness cloud regime (H1) throughout the area of interest (RFO of 42% compared with 9% observed). The CTP-
ACCESS1.3 has a systematic bias toward optically thin (low 
The warm frontal cloud regime H9 is almost absent in ACCESS1.3, compared with an observed RFO of 16%, and has no clear compensating cloud regimes as identified above. As distinct from a systematic deficiency in cloud properties, this cloud error may be related to the representation of a dynamical process in the model. The higher frontal cloud regimes H10 and H11 are simulated with similar RFO to observations, but with a less cohesive distribution around the midlatitude storm track.
With an evaluation of the occurrence of the hybrid cloud regimes in the model and observations, the major cloud errors in the GCM are made explicit in a process-oriented way. The tendencies toward low TCC and low-
c. Contributions to SW radiation bias
Eleven hybrid cloud regimes have been identified from passive satellite observations and simulated cloud properties in ACCESS1.3. The most significant and recurring deviations from observed cloud properties give rise to hybrid cloud regimes that are not frequently found in the observations: these predominantly simulated hybrid cloud regimes provide an initial indication of the major cloud errors in the GCM. By associating the hybrid cloud regimes with radiation errors, we can quantify the relative contributions of these major cloud errors to the total SW cloud radiative effect bias in the high-latitude Southern Ocean in ACCESS1.3.
The cloud radiative effect (CRE) is the difference between clear-sky and cloudy-sky TOA radiative fluxes. Since outgoing fluxes at TOA are defined as positive and cloud typically has the effect of increasing reflected SW radiation, values of 
The mean SW cloud radiative effect bias 
The 
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
The
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
The
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
At each CTP level, the optically thinner cloud regimes make a small or negative contribution to the 
The strongest compensating (negative) contribution to 
The greatest positive contributor to the net 
d. Dynamical contexts of the hybrid cloud regimes
We have used hybrid cloud regimes to identify the major shortcomings in the simulation of clouds in the high-latitude Southern Ocean in ACCESS1.3 and have quantified their contributions to the SW radiation errors. It remains to investigate if the hybrid cloud regimes are associated with consistent dynamical and thermodynamical processes. In observational studies it is common to characterize cloud regimes by their contemporary meteorology derived from reanalyses (e.g., Gordon and Norris 2010; Haynes et al. 2011; Mason et al. 2014). This approach should be well suited to GCMs, wherein these fields are directly available; however, direct comparisons of dynamical fields in GCMs and reanalysis are frustrated by possible errors in model dynamics. An alternative approach is to consider cloud regimes in the context of a composite extratropical cyclone, the structure of which is both well understood in terms of observed dynamical and thermodynamical structure and well resolved by climate models (Catto et al. 2010). An evaluation of Southern Hemisphere extratropical cyclones in an earlier version of the ACCESS model, modified to use the same cloud scheme as ACCESS1.3 (Govekar et al. 2014), found that the circulation and dynamical variables were significantly weaker than in reanalyses and showed that the deficits of low cloud in this context are consistent with the broader evaluation of clouds in ACCESS1.3 (Franklin et al. 2013a) and in other models. Extratropical cyclones are the dominant synoptic-scale feature in the high-latitude Southern Ocean in terms of both cloud and precipitation (Bodas-Salcedo et al. 2014; Papritz et al. 2014), and cloud regimes have been used effectively to evaluate cloud in this context (e.g., Bodas-Salcedo et al. 2012, 2014). However, we note that not all dynamical contexts relevant to the high-latitude Southern Ocean are necessarily represented within the composite extratropical cyclone. The clouds associated with other features in the high-latitude Southern Ocean, such as anticyclones and mesoscale cyclones, are of considerable interest for model evaluation but are not considered here.
We identify extratropical cyclones in observations and simulations as described in Field and Wood (2007) using MSLP from ERA-Interim to identify cyclone centers contemporary to the satellite observations. Cloud regime occurrence and TOA radiative flux fields from observations and ACCESS1.3 are reinterpolated on to a 2000 km × 2000 km grid centered at each MSLP minimum, and candidates are filtered to select only cyclones with centers from 50° to 65°S latitude.
The observed RFOs of the hybrid cloud regimes in the context of the composite cyclone (Fig. 6) agree well with other composite cyclone studies (e.g., Bodas-Salcedo et al. 2012, 2014), with some key differences. The most physically important difference is that the midtopped cloud regimes identified in this study are located in separate and coherent parts of the extratropical cyclone. This reinforces the distinction between optically thin and optically thick midtopped, and shallow frontal cloud regimes made in Mason et al. (2014). The profiles of dynamical and thermodynamical properties from reanalysis (not shown) are consistent with those presented in previous studies (e.g., Gordon and Norris 2010; Haynes et al. 2011; Mason et al. 2014). The fronts associated with the composite extratropical cyclone are characterized by the occurrence of the cirrus (H10) and deep frontal (H11) cloud regimes. H10 is observed farther from the cyclone center and appears to be associated with both prefrontal cirrus and other high and thin cloud in other contexts. H11 is found along the warm and cold fronts. H9 occurs predominantly near the cyclone center and into the cold sector, and resembles the warm conveyor belt (WCB) flow that overshoots the warm front; we note that a similar midtopped cloud subregime identified in Mason et al. (2014) was associated with conditions resembling that of the WCB. The warm sector is also associated with the shallow cloud regime H3. The cold sector of the extratropical cyclone consists of easterly flow ahead of the warm front turning equatorward behind the storm center, where it meets the descending dry sector. The cold sector is dominated by low and midtopped stratiform cloud regimes H5 and H8, which form under subsiding conditions ahead of the warm front. These stratiform clouds transition to H7 in the postfrontal region of cold-air advection, and finally in the driest section the shallow cumulus (H2) dominates.
Observed (above) and simulated (below) relative RFO of each hybrid cloud regime in the context of the composite extratropical cyclone. The cloud regimes are organized according to optical thickness (horizontal axis) and CTP (vertical axis). Note that the simulated RFO of H1 is on a different color scale to resolve the significant overproduction of this hybrid cloud regime in ACCESS1.3. A cartoon illustrating the orientation of warm and cold fronts and warm and cold sectors in Southern Hemisphere extratropical cyclones is shown at top left.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
Observed (above) and simulated (below) relative RFO of each hybrid cloud regime in the context of the composite extratropical cyclone. The cloud regimes are organized according to optical thickness (horizontal axis) and CTP (vertical axis). Note that the simulated RFO of H1 is on a different color scale to resolve the significant overproduction of this hybrid cloud regime in ACCESS1.3. A cartoon illustrating the orientation of warm and cold fronts and warm and cold sectors in Southern Hemisphere extratropical cyclones is shown at top left.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
Observed (above) and simulated (below) relative RFO of each hybrid cloud regime in the context of the composite extratropical cyclone. The cloud regimes are organized according to optical thickness (horizontal axis) and CTP (vertical axis). Note that the simulated RFO of H1 is on a different color scale to resolve the significant overproduction of this hybrid cloud regime in ACCESS1.3. A cartoon illustrating the orientation of warm and cold fronts and warm and cold sectors in Southern Hemisphere extratropical cyclones is shown at top left.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
The ACCESS1.3 cloud errors in the context of the extratropical cyclone are consistent with those identified above. The frontal cloud regime H11 is found closest to the storm center: the shallow frontal cloud regime H9 is not present near the warm front, while the optically thinner H10 is found throughout the storm center and inner frontal region. The compensating relationships between predominantly simulated and predominantly observed hybrid cloud regimes are evident, indicating the consistent low-
The occurrence of the hybrid cloud regimes in the context of the extratropical cyclone illustrate consistent relationships between cloud properties and dynamical and thermodynamical conditions in both the observations and the model, reinforcing the corresponding spatial distributions of the mostly observed and mostly simulated hybrid cloud regimes over the high-latitude Southern Ocean. In both the composite cyclone and Southern Ocean maps, the significant overproduction of the low-TCC cloud regime H1 and the compensation of low-
In contrast to the cloud regimes used in Bodas-Salcedo et al. (2012), wherein a single midtopped cloud regime was identified more than 30% of the time in both the cold-air sector and the warm front part of the composite extratropical cyclone, the distinction between shallow frontal (H9), optically thin (H7), and stratiform (H8) midtopped hybrid cloud regimes are confirmed by their distributions through the extratropical cyclone. The single largest contributor to the 
4. Evaluation of parameterization changes
We have used the hybrid cloud regimes to associate cloud and radiation errors in ACCESS1.3 in a process-oriented way. The identification of hybrid cloud regimes is a diagnostic of the simulated cloud properties in a GCM, making this approach suited to quantifying the sensitivity of the errors to changes made to the model. In this section we illustrate the use of the hybrid cloud regimes to evaluate the sensitivity of the radiation errors to targeted changes to cloud parameterizations in ACCESS1.3 and to quantify their effects on the hybrid cloud regimes.
ACCESS1.3 is characterized by systematic deficits of both TCC and 
To target the optical thickness biases, we make three changes to the representation of clouds intended to reduce the Southern Ocean cloud and radiative biases in ACCESS1.3. A new autoconversion scheme (Franklin 2008) is implemented; it was shown by Franklin et al. (2013b) to increase the occurrence of optically thicker low clouds and to reduce the overestimate of drizzle in tropical boundary layer clouds. The fall speeds of the ice aggregate category are reduced by one-third; Franklin et al. (2013a) demonstrated that by reducing these fall speed, the occurrence of optically thicker low- and midlevel clouds in ACCESS1.3 was increased over the Southern Ocean. A change is also made to the erosion time-scale parameter that controls the rate at which the liquid cloud fraction is reduced by the mixing of cloudy air with drier environmental air. This parameter takes the value of −4 × 10−5 s−1 in the control version of ACCESS1.3 and is reduced by half in the modified cloud parameterizations experiment. While this change directly affects the cloud fraction, it also indirectly affects the microphysical processes by changing the in-cloud water contents that are used in the microphysical parameterizations, such as the autoconversion scheme. We note that the autoconversion, ice fall speed, and erosion rate are not the only possible microphysics changes that could be made in order to increase cloud brightness. Changes to ice particle size, ice deposition rate, and heterogeneous nucleation temperature were also considered; the effects of the selected changes compare most favorably in a bulk evaluation of cloud properties against CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) observations (not shown). Testing the three selected changes independently shows that the largest change in both the ISCCP diagnostics and 
While our interest is in the sensitivity of the Southern Ocean cloud and radiation errors to these microphysics changes, their global impacts have also been investigated individually and in combination. Franklin et al. (2013b) showed that the Franklin (2008) autoconversion scheme led to more stratocumulus and less drizzle in the tropics, and an increase in stratocumulus and fair-weather cumulus cloud-top height attributed to a stronger cloud radiative effect driving enhanced entrainment at cloud top. Franklin et al. (2013a) showed that a reduction in ice fall speeds resulted in increased midlevel cloud fraction in the tropical warm pool and optically thicker high cloud. A reduction in the erosion rate leads to increased low- and midlevel liquid cloud fraction. The combined changes have a significant impact on the global DJF 
ACCESS1.3 is run in atmosphere-only mode with the modified cloud microphysics in the same way as for the initial model evaluation. Two years of DJF data, including CTP-
The decomposed 
As in Fig. 5, but comparing the control simulation (solid) against the modified microphysics case (hatched). Thick (thin) bars indicate the total 
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
As in Fig. 5, but comparing the control simulation (solid) against the modified microphysics case (hatched). Thick (thin) bars indicate the total
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
As in Fig. 5, but comparing the control simulation (solid) against the modified microphysics case (hatched). Thick (thin) bars indicate the total
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00846.1
Where the changes in the optical thickness of a cloud regime are large enough, instances of cloud previously belonging to one hybrid cloud regime may be assigned to an optically thicker hybrid cloud regime. These changes in cloud regime assignations lead to changes in the RFO-related error of both the prior and subsequent cloud regime: this is most apparent in the compensating RFO-related errors associated with the optically thin midtopped hybrid cloud regimes H6 and H7—each is reduced in magnitude by around 5 W m−2, while the net bias associated with midtopped cloud overall is largely unchanged. The RFO of H1 decreases in a similar manner, corresponding to a 3 W m−2 improvement in the RFO-related bias—a net increase in the overall 
The hybrid cloud regime approach allows us to resolve the contexts in which the changes to the microphysics had the most—and least—effect on the radiation errors. This evaluation illustrates the benefits of the methodology: major model errors can be explicitly identified, rather than assigned to an adjacent observed cloud regime. We can quantify how the model errors compensate for, or contribute to, the total radiation error, and we can evaluate changes to the model to determine whether an improvement to the total radiation error is the result of an increased compensating bias or from a shift toward the observed cloud state.
We reiterate that this experiment is intended to illustrate the use of the hybrid cloud regimes to evaluate model changes in a process-oriented way. The microphysics changes targeted the optical thickness biases in low and midtopped clouds, with the overall result of a 20%–25% improvement in the total Southern Ocean 
5. Discussion and conclusions
We have presented a hybrid methodology for identifying cloud regimes from satellite observations and model-simulated cloud properties simultaneously. This approach expands on previous methodologies for identifying cloud regimes for model evaluation, with the advantage that the cloud regimes include a fixed reference to the observed cloud properties against which the models are evaluated, while also permitting cloud regimes that are peculiar to the model. The emergent hybrid cloud regimes include pairs of cloud regimes with similar spatial distributions and dynamical contexts, where one hybrid cloud regime is mostly simulated and the other is mostly observed; the differences between these pairs of hybrid cloud regimes relate to the major cloud errors in the GCM.
Based on two DJFs of simulated and observed cloud data, we identify 11 hybrid cloud regimes with which to evaluate high-latitude Southern Ocean clouds in ACCESS1.3. We use the cloud regimes to associate errors in cloud properties with SW radiation errors and to describe the dynamical context of the cloud regimes as inferred from composite extratropical cyclones.
Consistent with Franklin et al. (2013a), total cloud fraction in ACCESS1.3 is underpredicted, which contributes to the weak 
Low 
The largest contributor to the SW radiation bias over the Southern Ocean is the shallow frontal cloud regime observed at high latitudes and in the warm fronts of extratropical cyclones. These clouds were very rarely identified in the model, and a significant compensating relationship with an optically thinner cloud regime was not evident. The other frontal cloud regimes are also generally too optically thin: the cirrus cloud regime is simulated too frequently, especially close to the center of extratropical cyclones; and the frontal cloud regime is simulated infrequently and without the distinct midlatitude storm-track distribution found in observations. While the structure and frequency of Southern Ocean storms were sufficiently well represented in ACCESS1.3 to carry out a comparison of composite extratropical cyclones, we note that the significant errors in frontal cloud structure, especially relating to shallow frontal cloud at high latitudes, are consistent with the dynamically weak midlatitude storms identified in Govekar et al. (2014) for a related version of the ACCESS model. The insignificant impact on the shallow frontal cloud regime of the changes to the erosion rate and ice fall speeds—which could be expected to increase midlevel cloud fraction—suggests further work is required to evaluate the thermodynamics and dynamics of fronts.
We note that considering the Southern Ocean cloud regimes only in the context of extratropical cyclones is not necessarily representative. For example, while the shallow frontal cloud regime makes the largest net contribution to the SW bias, the warm front does not correspond to the region of greatest SW bias in the context of the composite extratropical cyclone—the cold-air advection in the cold and dry sectors (e.g., Bodas-Salcedo et al. 2012, 2014) where errors in total cloud cover and optical thickness dominate, and where modifications to the diagnosis of the shear-dominated boundary layer have had some success in mitigating the total error (Bodas-Salcedo et al. 2012). These two distinct dynamical contexts—cold-air advection and shallow frontal conditions—are each associated with midtopped cloud and are assigned to the same cloud regime in studies using WW09 cloud regime identification. We have demonstrated that the hybrid cloud regimes in this study are capable of distinguishing between midtopped cloud in these contexts, but further work is required to explore the representation of these distinct cloud processes: more detailed considerations of cold-air outbreaks and high-latitude storms may be an effective approach.
The approach of identifying pairs of mostly simulated and mostly observed hybrid cloud regimes has proved useful: the pairs were shown to be simulated with similar spatial distributions to their observed counterparts, and these relationships were also found in the dynamical context of a composite extratropical cyclone. However, more work is required to quantify the strength of the associations between these cloud regimes with dynamical contexts in the model and observations, perhaps by using the model in hindcast mode.
The hybrid cloud regimes have been identified for a single GCM, and this approach is not immediately suited to the evaluation of multiple models. However, the hybrid cloud regimes provide a fixed reference to cloud errors in the model, making this a promising tool for quantifying the effects of changes to the model on the properties and statistics in a process-oriented way.
While the Southern Ocean SW radiation errors in ACCESS1.3 are representative of radiation biases in many state-of-the-art GCMs, the nature and causes of these biases are almost certainly not the same in each model.
Acknowledgments
This research was supported by ARC Discovery and Linkage Grant Schemes (DP130100869 and LP0883961), and by the ARC Centre of Excellence for Climate System Science (CE110001028). We are grateful to Alejandro Bodas-Salcedo and Keith Williams at the UKMO for their generous cooperation and feedback.
REFERENCES
Anderberg, M. R., 1973: Cluster Analysis for Applications. Probability and Mathematical Statistics Series, Vol. 19, Academic Press, 359 pp.
Bi, D., and Coauthors, 2013: The ACCESS coupled model: Description, control climate and evaluation. Aust. Meteor. Oceanogr. J., 63, 41–64.
Bodas-Salcedo, A., M. J. Webb, M. E. Brooks, M. A. Ringer, K. D. Williams, S. F. Milton, and D. R. Wilson, 2008: Evaluating cloud systems in the Met Office global forecast model using simulated CloudSat radar reflectivities. J. Geophys. Res.,113, D00A13, doi:10.1029/2007JD009620.
Bodas-Salcedo, A., K. D. Williams, P. R. Field, and A. P. Lock, 2012: The surface downwelling solar radiation surplus over the Southern Ocean in the Met Office model: The role of midlatitude cyclone clouds. J. Climate, 25, 7467–7486, doi:10.1175/JCLI-D-11-00702.1.
Bodas-Salcedo, A., and Coauthors, 2014: Origins of the solar radiation biases over the Southern Ocean in CFMIP2 models. J. Climate, 27, 41–56, doi:10.1175/JCLI-D-13-00169.1.
Bony, S., and J.-L. Dufresne, 2005: Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys. Res. Lett.,32, L20806, doi:10.1029/2005GL023851.
Catto, J. L., L. C. Shaffrey, and K. I. Hodges, 2010: Can climate models capture the structure of extratropical cyclones? J. Climate, 23, 1621–1635, doi:10.1175/2009JCLI3318.1.
Cess, R. D., and Coauthors, 1990: Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. J. Geophys. Res.,95,16 601–16 615, doi:10.1029/JD095iD10p16601.
Cess, R. D., and Coauthors, 1996: Cloud feedback in atmospheric general circulation models: An update. J. Geophys. Res., 101, 12 791–12 794, doi:10.1029/96JD00822.
Colman, R., 2003: A comparison of climate feedbacks in general circulation models. Climate Dyn., 20, 865–873, doi:10.1007/s00382-003-0310-z.
Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828.
Dufresne, J.-L., and S. Bony, 2008: An assessment of the primary sources of spread of global warming estimates from coupled atmosphere–ocean models. J. Climate, 21, 5135–5144, doi:10.1175/2008JCLI2239.1.
Field, P. R., and R. Wood, 2007: Precipitation and cloud structure in midlatitude cyclones. J. Climate, 20, 233–254, doi:10.1175/JCLI3998.1.
Franklin, C. N., 2008: A warm rain microphysics parameterization that includes the effect of turbulence. J. Atmos. Sci., 65, 1795–1816, doi:10.1175/2007JAS2556.1.
Franklin, C. N., Z. Sun, D. Bi, M. Dix, H. Yan, and A. Bodas-Salcedo, 2013a: Evaluation of clouds in ACCESS using the satellite simulator package COSP: Global, seasonal, and regional cloud properties. J. Geophys. Res. Atmos.,118, 732–748, doi:10.1029/2012JD018469.
Franklin, C. N., Z. Sun, D. Bi, M. Dix, H. Yan, and A. Bodas-Salcedo, 2013b: Evaluation of clouds in ACCESS using the satellite simulator package COSP: Regime-sorted tropical cloud properties. J. Geophys. Res. Atmos.,118, 6663–6679, doi:10.1002/jgrd.50496.
Gates, W. L., and Coauthors, 1999: An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I). Bull. Amer. Meteor. Soc., 80, 29–55, doi:10.1175/1520-0477(1999)080<0029:AOOTRO>2.0.CO;2.
Gordon, N., and J. Norris, 2010: Cluster analysis of midlatitude oceanic cloud regimes: Mean properties and temperature sensitivity. Atmos. Chem. Phys., 10, 6435–6459, doi:10.5194/acp-10-6435-2010.
Govekar, P. D., C. Jakob, and J. Catto, 2014: The relationship between clouds and dynamics in Southern Hemisphere extratropical cyclones in the real world and a climate model. J. Geophys. Res. Atmos.,119, 6609–6628, doi:10.1002/2013JD020699.
Haynes, J. M., C. Jakob, W. B. Rossow, G. Tselioudis, and J. Brown, 2011: Major characteristics of Southern Ocean cloud regimes and their effects on the energy budget. J. Climate, 24, 5061–5080, doi:10.1175/2011JCLI4052.1.
Hewitt, H. T., D. Copsey, I. D. Culverwell, C. M. Harris, R. S. R. Hill, A. B. Keen, A. J. McLaren, and E. C. Hunke, 2011: Design and implementation of the infrastructure of HadGEM3: The next-generation Met Office climate modelling system. Geosci. Model Dev., 4, 223–253, doi:10.5194/gmd-4-223-2011.
Jakob, C., 2010: Accelerating progress in global atmospheric model development through improved parameterizations: Challenges, opportunities, and strategies. Bull. Amer. Meteor. Soc., 91, 869–875, doi:10.1175/2009BAMS2898.1.
Jakob, C., and G. Tselioudis, 2003: Objective identification of cloud regimes in the tropical western Pacific. Geophys. Res. Lett., 30, 2082, doi:10.1029/2003GL018367.
Jakob, C., G. Tselioudis, and T. Hume, 2005: The radiative, cloud, and thermodynamic properties of the major tropical western Pacific cloud regimes. J. Climate, 18, 1203–1215, doi:10.1175/JCLI3326.1.
Klein, S. A., and C. Jakob, 1999: Validation and sensitivities of frontal clouds simulated by the ECMWF model. Mon. Wea. Rev., 127, 2514–2531, doi:10.1175/1520-0493(1999)127<2514:VASOFC>2.0.CO;2.
Klein, S. A., Y. Zhang, M. D. Zelinka, R. Pincus, J. Boyle, and P. J. Gleckler, 2013: Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator. J. Geophys. Res. Atmos.,118, 1329–1342, doi:10.1002/jgrd.50141.
Li, J.-L. F., D. E. Waliser, G. Stephens, S. Lee, T. L’Ecuyer, S. Kato, N. Loeb, and H.-Y. Ma, 2013: Characterizing and understanding radiation budget biases in CMIP3/CMIP5 GCMs, contemporary GCM, and reanalysis. J. Geophys. Res. Atmos.,118, 8166–8184, doi:10.1002/jgrd.50378.
Mace, G. G., and F. J. Wrenn, 2013: Evaluation of the hydrometeor layers in the east and west Pacific within ISCCP cloud-top pressure–optical depth bins using merged CloudSat and CALIPSO data. J. Climate, 26, 9429–9444, doi:10.1175/JCLI-D-12-00207.1.
Mason, S., C. Jakob, A. Protat, and J. Delanoë, 2014: Characterizing observed midtopped cloud regimes associated with Southern Ocean shortwave radiation biases. J. Climate, 27, 6189–6203, doi:10.1175/JCLI-D-14-00139.1.
Oreopoulos, L., and W. B. Rossow, 2011: The cloud radiative effects of International Satellite Cloud Climatology Project weather states. J. Geophys. Res.,116, D12202, doi:10.1029/2010JD015472.
Oreopoulos, L., N. Cho, D. Lee, S. Kato, and G. J. Huffman, 2014: An examination of the nature of global MODIS cloud regimes. J. Geophys. Res. Atmos.,119, 8362–8383, doi:10.1002/2013JD021409.
Papritz, L., S. Pfahl, I. Rudeva, I. Simmonds, H. Sodemann, and H. Wernli, 2014: The role of extratropical cyclones and fronts for Southern Ocean freshwater fluxes. J. Climate, 27, 6205–6224, doi:10.1175/JCLI-D-13-00409.1.
Rossow, W., and R. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80, 2261–2288, doi:10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2.
Rossow, W., G. Tselioudis, A. Polak, and C. Jakob, 2005: Tropical climate described as a distribution of weather states indicated by distinct mesoscale cloud property mixtures. Geophys. Res. Lett.,32, L21812, doi:10.1029/2005GL024584.
Stephens, G. L., 2005: Cloud feedbacks in the climate system: A critical review. J. Climate, 18, 237–273, doi:10.1175/JCLI-3243.1.
Tan, J., C. Jakob, and T. P. Lane, 2013: On the identification of the large-scale properties of tropical convection using cloud regimes. J. Climate, 26, 6618–6632, doi:10.1175/JCLI-D-12-00624.1.
Trenberth, K., and J. T. Fasullo, 2010: Simulation of present-day and twenty-first-century energy budgets of the southern oceans. J. Climate, 23, 440–454, doi:10.1175/2009JCLI3152.1.
Tselioudis, G., W. Rossow, Y. Zhang, and D. Konsta, 2013: Global weather states and their properties from passive and active satellite cloud retrievals. J. Climate, 26, 7734–7746, doi:10.1175/JCLI-D-13-00024.1.
Tsushima, Y., M. A. Ringer, M. J. Webb, and K. D. Williams, 2013: Quantitative evaluation of the seasonal variations in climate model cloud regimes. Climate Dyn., 41, 2679–2696, doi:10.1007/s00382-012-1609-4.
Walters, D. N., and Coauthors, 2011: The Met Office Unified Model Global Atmosphere 3.0/3.1 and JULES Global Land 3.0/3.1 configurations. Geosci. Model Dev. Discuss., 4, 1213–1271, doi:10.5194/gmdd-4-1213-2011.
Webb, M. J., C. Senior, S. Bony, and J.-J. Morcrette, 2001: Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models. Climate Dyn., 17, 905–922, doi:10.1007/s003820100157.
Wielicki, B. A., B. R. Barkstrom, and E. F. Harrison, 1996: Clouds and the Earth’s Radiant Energy System (CERES): An Earth Observing System Experiment. Bull. Amer. Meteor. Soc.,77, 853–868, doi:10.1175/1520-0477(1996)077<0853:CATERE>2.0.CO;2.
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.
Williams, K. D., and M. J. Webb, 2009: A quantitative performance assessment of cloud regimes in climate models. Climate Dyn., 33, 141–157, doi:10.1007/s00382-008-0443-1.
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.
