Supercooled liquid water (SLW) layers in boundary layer clouds are abundantly observed in the atmosphere at high latitudes, but remain a challenge to represent in numerical weather prediction (NWP) and climate models. Unresolved processes such as small-scale turbulence and mixed-phase microphysics act to maintain the liquid layer at cloud top, directly affecting cloud radiative properties and prolonging cloud lifetimes. This paper describes the representation of supercooled liquid water in boundary layer clouds in the European Centre for Medium-Range Weather Forecasts (ECMWF) global NWP model and in particular the change from a diagnostic temperature-dependent mixed phase to a prognostic representation with separate cloud liquid and ice variables. Data from the Atmospheric Radiation Measurement site in Alaska and from the CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite missions are used to evaluate the model parameterizations. The prognostic scheme shows a more realistic cloud structure, with an SLW layer at cloud top and ice falling out below. However, because of the limited vertical and horizontal resolution and uncertainties in the parameterization of physical processes near cloud top, the change leads to an overall reduction in SLW water with a detrimental impact on shortwave and longwave radiative fluxes, and increased 2-m temperature errors over land. A reduction in the ice deposition rate at cloud top significantly improves the SLW occurrence and radiative impacts, and highlights the need for improved understanding and parameterization of physical processes for mixed-phase cloud in large-scale models.

Many weather forecast and climate models simulate warm surface air temperature (T2m) biases over midlatitude continents during the summertime, especially over the Great Plains. We present here one of a series of papers from a multimodel intercomparison project (CAUSES: Cloud Above the United States and Errors at the Surface), which aims to evaluate the role of cloud, radiation, and precipitation biases in contributing to the T2m bias using a short‐term hindcast approach during the spring and summer of 2011. Observations are mainly from the Atmospheric Radiation Measurement Southern Great Plains sites. The present study examines the contributions of surface energy budget errors. All participating models simulate too much net shortwave and longwave fluxes at the surface but with no consistent mean bias sign in turbulent fluxes over the Central United States and Southern Great Plains. Nevertheless, biases in the net shortwave and downward longwave fluxes as well as surface evaporative fraction (EF) are contributors to T2m bias. Radiation biases are largely affected by cloud simulations, while EF bias is largely affected by soil moisture modulated by seasonal accumulated precipitation and evaporation. An approximate equation based upon the surface energy budget is derived to further quantify the magnitudes of radiation and EF contributions to T2m bias. Our analysis ascribes that a large EF underestimate is the dominant source of error in all models with a large positive temperature bias, whereas an EF overestimate compensates for an excess of absorbed shortwave radiation in nearly all the models with the smallest temperature bias.

Guided by ground-based radar and lidar profiling at the Barbados Cloud Observatory (BCO), this study evaluates trade-wind cloudiness in ECMWF's Integrated Forecast System (IFS) and nine CMIP5 models using their single-timestep output at selected grid points. The observed profile of cloudiness is relatively evenly distributed between two important height levels: the lifting condensation level (LCL) and the tops of the deepest cumuli near the trade-wind inversion (2-3 km). Cloudiness at the LCL dominates the total cloud cover, but is relatively invariant. Variance in cloudiness instead peaks at the inversion. The IFS reproduces the depth of the cloud field and its variability, but underestimates cloudiness at the LCL and the inversion. A few CMIP5 models produce a single stratocumulus-like layer near the LCL, but more than half of the CMIP5 models reproduce the observed cloud layer depth in long-term mean profiles. At single-time steps, however, half of the models do not produce cloudiness near cloud tops along with the (almost everpresent) cloudiness near the LCL. In seven models, cloudiness is zero at both levels 10 to 65% of the time, compared to 3% in the observations. Models therefore tend to overestimate variance in cloudiness near the LCL. This variance is associated with longer time scales than in observations, which suggests that modeled cloudiness is too sensitive to large-scale processes. To conclude, many models do not appear to capture the processes that underlie changes in cloudiness, which is relevant for cloud feedbacks and climate prediction.

Abstract. This study describes the characteristics of largescale vertical velocity, apparent heating source (Q 1 ) and apparent moisture sink (Q 2 ) profiles associated with seasonal and diurnal variations of convective systems observed during the two intensive operational periods (IOPs) that were conducted from 15 February to 26 March 2014 (wet season) and from 1 September to 10 October 2014 (dry season) near Manaus, Brazil, during the Green Ocean Amazon (GoAmazon2014/5) experiment. The derived large-scale fields have large diurnal variations according to convective activity in the GoAmazon region and the morning profiles show distinct differences between the dry and wet seasons. In the wet season, propagating convective systems originating far from the GoAmazon region are often seen in the early morning, while in the dry season they are rarely observed. Afternoon convective systems due to solar heating are frequently seen in both seasons. Accordingly, in the morning, there is strong upward motion and associated heating and drying throughout the entire troposphere in the wet season, which is limited to lower levels in the dry season. In the afternoon, both seasons exhibit weak heating and strong moistening in the boundary layer related to the vertical convergence of eddy fluxes.A set of case studies of three typical types of convective systems occurring in Amazonia -i.e., locally occurring systems, coastal-occurring systems and basin-occurring systems -is also conducted to investigate the variability of the large-scale environment with different types of convective systems.

Many Numerical Weather Prediction (NWP) and climate models exhibit too warm lower tropospheres near the midlatitude continents. The warm bias has been shown to coincide with important surface radiation biases that likely play a critical role in the inception or the growth of the warm bias. This paper presents an attribution study on the net radiation biases in nine model simulations, performed in the framework of the CAUSES project (Clouds Above the United States and Errors at the Surface). Contributions from deficiencies in the surface properties, clouds, water vapor, and aerosols are quantified, using an array of radiation measurement stations near the Atmospheric Radiation Measurement Southern Great Plains site. Furthermore, an in‐depth analysis is shown to attribute the radiation errors to specific cloud regimes. The net surface shortwave radiation is overestimated in all models throughout most of the simulation period. Cloud errors are shown to contribute most to this overestimation, although nonnegligible contributions from the surface albedo exist in most models. Missing deep cloud events and/or simulating deep clouds with too weak cloud radiative effects dominate in the cloud‐related radiation errors. Some models have compensating errors between excessive occurrence of deep cloud but largely underestimating their radiative effect, while other models miss deep cloud events altogether. Surprisingly, even the latter models tend to produce too much and too frequent afternoon surface precipitation. This suggests that rather than issues with the triggering of deep convection, cloud radiative deficiencies are related to too weak convective cloud detrainment and too large precipitation efficiencies.