[1] New, definitive measures of precipitation frequency provided by CloudSat are used to assess the realism of global model precipitation. The character of liquid precipitation (defined as a combination of accumulation, frequency, and intensity) over the global oceans is significantly different from the character of liquid precipitation produced by global weather and climate models. Five different models are used in this comparison representing state-of-the-art weather prediction models, state-of-the-art climate models, and the emerging high-resolution global cloud "resolving" models. The differences between observed and modeled precipitation are larger than can be explained by observational retrieval errors or by the inherent sampling differences between observations and models. We show that the time integrated accumulations of precipitation produced by models closely match observations when globally composited. However, these models produce precipitation approximately twice as often as that observed and make rainfall far too lightly. This finding reinforces similar findings from other studies based on surface accumulated rainfall measurements. The implications of this dreary state of model depiction of the real world are discussed.

By simulating the observations of multiple satellite instruments, COSP enables quantitative evaluation of clouds, humidity, and precipitation processes in diverse numerical models.G eneral circulation models (GCMs) of the atmosphere, including those used for numerical weather prediction (NWP) and climate projections, operate with resolutions from a few kilometers to hundreds of kilometers. Many atmospheric processes, such as turbulence and microphysical processes within clouds, operate at smaller scales and hence cannot be resolved by current model resolutions. These processes are included by means of parameterizations, which are semiempirical or statistical models that relate gridbox mean variables to these subgrid processes. For instance, some cloud parameterizations diagnose the amount of cloud condensate and the fraction of the grid box that a cloud occupies (cloud area fraction) as a function of the relative humidity (RH) of the grid box (Slingo 1980;Smith 1990). The formulation of these parameterizations is very important for the model evolution because they modify the three-dimensional structure of temperature and humidity directly (e.g., condensation/evaporation) or indirectly by interacting with other parameterizations (e.g., radiation) and the large-scale dynamics. Therefore, the evaluation of these parameterizations is crucial to improving our weather forecasts or increasing our confidence in climate projections.Satellites have proven to be very helpful tools for this purpose because they provide global or nearglobal coverage, thereby giving a representative sample of all meteorological conditions. However, satellites do not measure directly those geophysical quantities of interest, such as the amount or phase of cloud condensate. They measure the intensity of radiation coming from a particular area and direction in a particular wavelength range (radiances). The range of wavelengths covered by past and current systems spans several orders of magnitude, from COSP

The Global Coupled 3 (GC3) configuration of the Met Office Unified Model is presented. Among other applications, GC3 is the basis of the United Kingdom's submission to the Coupled Model Intercomparison Project 6 (CMIP6). This paper documents the model components that make up the configuration (although the scientific descriptions of these components are in companion papers) and details the coupling between them. The performance of GC3 is assessed in terms of mean biases and variability in long climate simulations using present‐day forcing. The suitability of the configuration for predictability on shorter time scales (weather and seasonal forecasting) is also briefly discussed. The performance of GC3 is compared against GC2, the previous Met Office coupled model configuration, and against an older configuration (HadGEM2‐AO) which was the submission to CMIP5. In many respects, the performance of GC3 is comparable with GC2, however, there is a notable improvement in the Southern Ocean warm sea surface temperature bias which has been reduced by 75%, and there are improvements in cloud amount and some aspects of tropical variability. Relative to HadGEM2‐AO, many aspects of the present‐day climate are improved in GC3 including tropospheric and stratospheric temperature structure, most aspects of tropical and extratropical variability and top‐of‐atmosphere and surface fluxes. A number of outstanding errors are identified including a residual asymmetric sea surface temperature bias (cool northern hemisphere, warm Southern Ocean), an overly strong global hydrological cycle and insufficient European blocking.

The concept of feedback is key in assessing whether a perturbation to a system is amplified or damped by mechanisms internal to the system. In polar regions, climate dynamics are controlled by both radiative and non-radiative interactions between the atmosphere, ocean, sea ice, ice sheets and land surfaces. Precisely quantifying polar feedbacks is required for a process-oriented evaluation of climate models, a clear understanding of the processes responsible for polar climate changes, and a reduction in uncertainty associated with model projections. This quantification can be performed using a simple and consistent approach that is valid for a wide range of feedbacks, offering the opportunity for more systematic feedback analyses and a better understanding of polar climate changes.

A new coupled general circulation climate model developed at the Met Office's Hadley Centre is presented, and aspects of its performance in climate simulations run for the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) documented with reference to previous models. The Hadley Centre Global Environmental Model version 1 (HadGEM1) is built around a new atmospheric dynamical core; uses higher resolution than the previous Hadley Centre model, HadCM3; and contains several improvements in its formulation including interactive atmospheric aerosols (sulphate, black carbon, biomass burning, and sea salt) plus their direct and indirect effects. The ocean component also has higher resolution and incorporates a sea ice component more advanced than HadCM3 in terms of both dynamics and thermodynamics. HadGEM1 thus permits experiments including some interactive processes not feasible with HadCM3. The simulation of present-day mean climate in HadGEM1 is significantly better overall in comparison to HadCM3, although some deficiencies exist in the simulation of tropical climate and El Niño variability. We quantify the overall improvement using a quasi-objective climate index encompassing a range of atmospheric, oceanic, and sea ice variables. It arises partly from higher resolution but also from greater fidelity in modeling dynamical and physical processes, for example, in the representation of clouds and sea ice. HadGEM1 has a similar effective climate sensitivity (2.8 K) to a CO2 doubling as HadCM3 (3.1 K), although there are significant regional differences in their response patterns, especially in the Tropics. HadGEM1 is anticipated to be used as the basis both for higher-resolution and higher-complexity Earth System studies in the near future.

Current climate models generally reflect too little solar radiation over the Southern Ocean, which may be the leading cause of the prevalent sea surface temperature biases in climate models. The authors study the role of clouds on the radiation biases in atmosphere-only simulations of the Cloud Feedback Model Intercomparison Project phase 2 (CFMIP2), as clouds have a leading role in controlling the solar radiation absorbed at those latitudes. The authors composite daily data around cyclone centers in the latitude band between 40° and 70°S during the summer. They use cloud property estimates from satellite to classify clouds into different regimes, which allow them to relate the cloud regimes and their associated radiative biases to the meteorological conditions in which they occur. The cloud regimes are defined using cloud properties retrieved using passive sensors and may suffer from the errors associated with this type of retrievals. The authors use information from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar to investigate in more detail the properties of the “midlevel” cloud regime. Most of the model biases occur in the cold-air side of the cyclone composite, and the cyclone composite accounts for most of the climatological error in that latitudinal band. The midlevel regime is the main contributor to reflected shortwave radiation biases. CALIPSO data show that the midlevel cloud regime is dominated by two main cloud types: cloud with tops actually at midlevel and low-level cloud. Improving the simulation of these cloud types should help reduce the biases in the simulation of the solar radiation budget in the Southern Ocean in climate models.

Abstract. The latest coupled configuration of the Met Office Unified Model (Global Coupled configuration 2, GC2) is presented. This paper documents the model components which make up the configuration (although the scientific description of these components is detailed elsewhere) and provides a description of the coupling between the components. The performance of GC2 in terms of its systematic errors is assessed using a variety of diagnostic techniques. The configuration is intended to be used by the Met Office and collaborating institutes across a range of timescales, with the seasonal forecast system (GloSea5) and climate projection system (HadGEM) being the initial users. In this paper GC2 is compared against the model currently used operationally in those two systems. Overall GC2 is shown to be an improvement on the configurations used currently, particularly in terms of modes of variability (e.g. mid-latitude and tropical cyclone intensities, the Madden–Julian Oscillation and El Niño Southern Oscillation). A number of outstanding errors are identified with the most significant being a considerable warm bias over the Southern Ocean and a dry precipitation bias in the Indian and West African summer monsoons. Research to address these is ongoing.

The Transpose-Atmospheric Model Intercomparison Project (AMIP) is an international model intercomparison project in which climate models are run in ''weather forecast mode.'' The Transpose-AMIP II experiment is run alongside phase 5 of the Coupled Model Intercomparison Project (CMIP5) and allows processes operating in climate models to be evaluated, and the origin of climatological biases to be explored, by examining the evolution of the model from a state in which the large-scale dynamics, temperature, and humidity structures are constrained through use of common analyses.The Transpose-AMIP II experimental design is presented. The project requests participants to submit a comprehensive set of diagnostics to enable detailed investigation of the models to be performed. An example of the type of analysis that may be undertaken using these diagnostics is illustrated through a study of the development of cloud biases over the Southern Ocean, a region that is problematic for many models. Several models share a climatological bias for too little reflected shortwave radiation from cloud across the region. This is found to mainly occur behind cold fronts and/or on the leading side of transient ridges and to be associated with more stable lower-tropospheric profiles. Investigation of a case study that is typical of the bias and associated meteorological conditions reveals the models to typically simulate cloud that is too optically and physically thin with an inversion that is too low. The evolution of the models within the first few hours suggests that these conditions are particularly sensitive and a positive feedback can develop between the thinning of the cloud layer and boundary layer structure.