This article presents the GCM‐Oriented Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud Product (GOCCP) designed to evaluate the cloudiness simulated by general circulation models (GCMs). For this purpose, Cloud‐Aerosol Lidar with Orthogonal Polarization L1 data are processed following the same steps as in a lidar simulator used to diagnose the model cloud cover that CALIPSO would observe from space if the satellite was flying above an atmosphere similar to that predicted by the GCM. Instantaneous profiles of the lidar scattering ratio (SR) are first computed at the highest horizontal resolution of the data but at the vertical resolution typical of current GCMs, and then cloud diagnostics are inferred from these profiles: vertical distribution of cloud fraction, horizontal distribution of low, middle, high, and total cloud fractions, instantaneous SR profiles, and SR histograms as a function of height. Results are presented for different seasons (January–March 2007–2008 and June–August 2006–2008), and their sensitivity to parameters of the lidar simulator is investigated. It is shown that the choice of the vertical resolution and of the SR threshold value used for cloud detection can modify the cloud fraction by up to 0.20, particularly in the shallow cumulus regions. The tropical marine low‐level cloud fraction is larger during nighttime (by up to 0.15) than during daytime. The histograms of SR characterize the cloud types encountered in different regions. The GOCCP high‐level cloud amount is similar to that from the TIROS Operational Vertical Sounder (TOVS) and the Atmospheric Infrared Sounder (AIRS). The low‐level and middle‐level cloud fractions are larger than those derived from passive remote sensing (International Satellite Cloud Climatology Project, Moderate‐Resolution Imaging Spectroradiometer–Cloud and Earth Radiant Energy System Polarization and Directionality of Earth Reflectances, TOVS Path B, AIRS–Laboratoire de Météorologie Dynamique) because the latter only provide information on the uppermost cloud layer.

Clouds cover about 70% of the Earth's surface and playa dominant role in the energy and water cycle of our planet. Only satellite observations provide a continuous survey of the state of the atmosphere over the whole globe and across the wide range of spatial and temporal scales that comprise weather and climate variability. Satellite cloud data records now exceed more than 25 years in length. However, climatologies compiled from different satellite datasets can exhibit systematic biases. Questions therefore arise as to the accuracy and Capsule:Cloud properties derived from space observations are immensely valuable for climate studies and model evaluati~n; this assessment has revealed how their statistics may be affected by instrument capabilities and/or retrieval methods but also highlight those well determined.2

Ground‐based observations show that persistent liquid‐containing Arctic clouds occur frequently and have a dominant influence on Arctic surface radiative fluxes. Yet, without a hemispheric multi‐year perspective, the climate relevance of these intriguing Arctic cloud observations was previously unknown. In this study, Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations are used to document cloud phase over the Arctic basin (60–82°N) during a five‐year period (2006–2011). Over Arctic ocean‐covered areas, low‐level liquid‐containing clouds are prevalent in all seasons, especially in Fall. These new CALIPSO observations provide a unique and climate‐relevant constraint on Arctic cloud processes. Evaluation of one climate model using a lidar simulator suggests a lack of liquid‐containing Arctic clouds contributes to a lack of “radiatively opaque” states. The surface radiation biases found in this one model are found in multiple models, highlighting the need for improved modeling of Arctic cloud phase.

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We take advantage of climate simulations from two multimodel experiments to characterize and evaluate the cloud phase partitioning in 16 general circulation models (GCMs), specifically the vertical structure of the transition between liquid and ice in clouds. We base our analysis on the ratio of ice condensates to the total condensates (phase ratio, PR). Its transition at 90% (PR90) and its links with other relevant variables are evaluated using the GCM-Oriented Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation Cloud Product climatology, reanalysis data, and other satellite observations. In 13 of 16 models, the PR90 transition height occurs too low (6 km to 8.4 km) and at temperatures too warm (À13.9°C to À32.5°C) compared to observations (8.6 km, À33.7°C); features consistent with a lack of supercooled liquid with respect to ice above 6.5 km. However, this bias would be slightly reduced by using the lidar simulator. In convective regimes (more humid air and precipitation), the observed cloud phase transition occurs at a warmer temperature than for subsidence regimes (less humid air and precipitation). Only few models manage to roughly replicate the observed correlations with humidity (5/16), vertical velocity (5/16), and precipitation (4/16); 3/16 perform well for all these parameters (MPI-ESM, NCAR-CAM5, and NCHU). Using an observation-based Clausius-Clapeyron phase diagram, we illustrate that the Bergeron-Findeisen process is a necessary condition for models to represent the observed features. Finally, the best models are those that include more complex microphysics.

[1] Level 1 measurements, including cross-polarized backscatter, from the Cloud-Aerosol Lidar with Orthogonal Polarization lidar, have been used to document the vertical structure of the cloud thermodynamic phase at global scale. We built a cloud phase identification (liquid, ice, or undefined) in the Global Climate Model (GCM)-oriented Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud Product (GOCCP) and analyzed the spatial distribution of liquid and ice clouds in five January, February, March (JFM) seasons of global-scale observations (2007)(2008)(2009)(2010)(2011). We developed a cloud phase diagnosis in the Cloud Feedback Model Intercomparison Program Observation Simulator Package to evaluate the cloud phase description in the LMDZ5B climate model. The diagnosis in the simulator is fully consistent with the CALIPSO-GOCCP observations to ensure that differences between the observations and the "model + simulator" ensemble outputs can be attributed to model biases. We compared the liquid and ice cloud vertical distributions simulated by the model with and without the simulator to quantify the impact of the simulator. The model does not produce liquid clouds above 3 km and produces ice instead of liquid at low and middle altitudes in polar regions, as well as along the Intertropical Convergence Zone. The model is unable to replicate the observed coexistence of liquid and ice cloud between 0 C and À40 C. Liquid clouds dominate T > À21 C in the observations, T > À12 C in the model + simulator, and T > À7.5 C in the model parameterization. Even if the simulator shifts the model cloud phase parameterization to colder temperature because of the lidar instrument peculiarities, the cloud phase transition remains too warm compared to the observations. Citation: Cesana, G., and H. Chepfer (2013), Evaluation of the cloud thermodynamic phase in a climate model using CALIPSO-GOCCP,

This paper describes the GISS‐E2.1 contribution to the Coupled Model Intercomparison Project, Phase 6 (CMIP6). This model version differs from the predecessor model (GISS‐E2) chiefly due to parameterization improvements to the atmospheric and ocean model components, while keeping atmospheric resolution the same. Model skill when compared to modern era climatologies is significantly higher than in previous versions. Additionally, updates in forcings have a material impact on the results. In particular, there have been specific improvements in representations of modes of variability (such as the Madden‐Julian Oscillation and other modes in the Pacific) and significant improvements in the simulation of the climate of the Southern Oceans, including sea ice. The effective climate sensitivity to 2 × CO2 is slightly higher than previously at 2.7–3.1°C (depending on version) and is a result of lower CO2 radiative forcing and stronger positive feedbacks.

While the representation of clouds in climate models has become more sophisticated over the last 30+ years, the vertical and seasonal fingerprints of Arctic greenhouse warming have not changed. Are the models right? Observations in recent decades show the same fingerprints: surface amplified warming especially in late fall and winter. Recent observations show no summer cloud response to Arctic sea ice loss but increased cloud cover and a deepening atmospheric boundary layer in fall. Taken together, clouds appear to not affect the fingerprints of Arctic warming. Yet, the magnitude of warming depends strongly on the representation of clouds. Can we check the models? Having observations alone does not enable robust model evaluation and model improvement. Comparing models and observations is hard enough, but to improve models, one must both understand why models and observations differ and fix the parameterizations. It is all a tall order, but recent progress is summarized here.