[1] Lakes can influence regional climate, yet most general circulation models have, at best, simple and largely untested representations of lakes. We developed the Lake, Ice, Snow, and Sediment Simulator (LISSS) for inclusion in the land-surface component (CLM4) of an earth system model (CESM1). The existing CLM4 lake model performed poorly at all sites tested; for temperate lakes, summer surface water temperature predictions were 10-25uC lower than observations. CLM4-LISSS modifies the existing model by including (1) a treatment of snow; (2) freezing, melting, and ice physics; (3) a sediment thermal submodel; (4) spatially variable prescribed lake depth; (5) improved parameterizations of lake surface properties; (6) increased mixing under ice and in deep lakes; and (7) correction of previous errors. We evaluated the lake model predictions of water temperature and surface fluxes at three small temperate and boreal lakes where extensive observational data was available. We also evaluated the predicted water temperature and/or ice and snow thicknesses for ten other lakes where less comprehensive forcing observations were available. CLM4-LISSS performed very well compared to observations for shallow to medium-depth small lakes. For large, deep lakes, the under-prediction of mixing was improved by increasing the lake eddy diffusivity by a factor of 10, consistent with previous published analyses. Surface temperature and surface flux predictions were improved when the aerodynamic roughness lengths were calculated as a function of friction velocity, rather than using a constant value of 1 mm or greater. We evaluated the sensitivity of surface energy fluxes to modeled lake processes and parameters. Large changes in monthly-averaged surface fluxes (up to 30 W m 22 ) were found when excluding snow insulation or phase change physics and when varying the opacity, depth, albedo of melting lake ice, and mixing strength across ranges commonly found in real lakes. Typical variation among model parameterization choices can therefore cause persistent local surface flux changes much larger than expected changes in greenhouse forcing. We conclude that CLM4-LISSS adequately simulates lake water temperature and surface energy fluxes, with errors comparable in magnitude to those resulting from uncertainty in global lake properties, and is suitable for inclusion in global and regional climate studies.Citation: Subin, Riley and Mironov (2012), Improved lake model for climate simulations, J. Adv. Model.

Entrainment and detrainment processes have been recognised for a long time as key processes for cumulus convection and have recently witnessed a regrowth of interest mainly due to the capability of large-eddy simulations (LES) to diagnose these processes in more detail. This article has a twofold purpose. Firstly, it provides a historical overview of the past research on these mixing processes, and secondly, it highlights more recent important developments. These include both fundamental process studies using LES aiming to improve our understanding of the mixing process, but also more practical studies targeted toward an improved parametrised representation of entrainment and detrainment in large-scale models. A highlight of the fundamental studies resolves a long-lasting controversy by showing that lateral entrainment is the dominant mixing mechanism in comparison with the cloud-top entrainment in shallow cumulus convection. The more practical studies provide a wide variety of new parametrisations with sometimes conflicting approaches to the way in which the effect of the free tropospheric humidity on the lateral mixing is taken into account. An important new insight that will be highlighted is that, despite the focus in the literature on entrainment, it appears that it is rather the detrainment process that determines the vertical structure of the convection in general and the mass flux especially. Finally, in order to speed up progress and stimulate convergence in future parametrisations, stronger and more systematic use of LES is advocated.

Relationships between parameters of convective entrainment into a shear-free, linearly stratified atmosphere predicted by the zero-order jump and general-structure bulk models of entrainment are reexamined using data from large eddy simulations (LESs). Relevant data from other numerical simulations, water tank experiments, and atmospheric measurements are also incorporated in the analysis. Simulations have been performed for 10 values of the buoyancy gradient in the free atmosphere covering a typical atmospheric stability range. The entrainment parameters derived from LES and relationships between them are found to be sensitive to the model framework employed for their interpretation. Methods of determining bulk model entrainment parameters from the LES output are proposed and discussed. Within the range of investigated free-atmosphere stratifications, the LES predictions of the inversion height and buoyancy increment across the inversion are found to be close to the analytical solutions for the equilibrium entrainment regime, which is realized when the rate of time change of the CBL-mean turbulence kinetic energy and the energy drain from the CBL top are both negligibly small. The zero-order model entrainment ratio of about 0.2 for this regime is generally supported by the LES data. However, the zero-order parameterization of the entrainment layer thickness is found insufficient. A set of relationships between the general-structure entrainment parameters for typical atmospheric stability conditions is retrieved from the LES. Dimensionless constants in these relationships are estimated from the LES and laboratory data. Power-law approximations for relationships between the entrainment parameters in the zero-order jump and general-structure bulk models are evaluated based on the conducted LES. In the regime of equilibrium entrainment, the stratification parameter of the entrainment layer, which is the ratio of the buoyancy gradient in the free atmosphere to the overall buoyancy gradient across the entrainment layer, appears to be a constant of about 1.2.

The nighttime high-latitude stably stratified atmospheric boundary layer (SBL) is computationally simulated using high-Reynolds number large-eddy simulation on meshes varying from 200 3 to 1024 3 over 9 physical hours for surface cooling rates C r 5 [0.25, 1] K h 21 . Continuous weakly stratified turbulence is maintained for this range of cooling, and the SBL splits into two regions depending on the location of the lowlevel jet (LLJ) and C r . Above the LLJ, turbulence is very weak and the gradient Richardson number is nearly constant: Ri ; 0:25. Below the LLJ, small scales are dynamically important as the shear and buoyancy frequencies vary with mesh resolution. The heights of the SBL and Ri noticeably decrease as the mesh is varied from 200 3 to 1024 3 . Vertical profiles of the Ozmidov scale L o show its rapid decrease with increasing C r , with L o , 2 m over a large fraction of the SBL for high cooling. Flow visualization identifies ubiquitous warm-cool temperature fronts populating the SBL. The fronts span a large vertical extent, tilt forward more so as the surface cooling increases, and propagate coherently. In a height-time reference frame, an instantaneous vertical profile of temperature appears intermittent, exhibiting a staircase pattern with increasing distance from the surface. Observations from CASES-99 also display these features. Conditional sampling based on linear stochastic estimation is used to identify coherent structures. Vortical structures are found upstream and downstream of a temperature front, similar to those in neutrally stratified boundary layers, and their dynamics are central to the front formation.

A model evaluation approach is proposed in which weather and climate prediction models are analyzed along a Pacific Ocean cross section, from the stratocumulus regions off the coast of California, across the shallow convection dominated trade winds, to the deep convection regions of the ITCZ-the Global Energy and Water Cycle Experiment Cloud System Study/Working Group on Numerical Experimentation (GCSS/ WGNE) Pacific Cross-Section Intercomparison (GPCI). The main goal of GPCI is to evaluate and help understand and improve the representation of tropical and subtropical cloud processes in weather and climate prediction models. In this paper, a detailed analysis of cloud regime transitions along the cross section from the subtropics to the tropics for the season June-July-August of 1998 is presented. This GPCI study confirms many of the typical weather and climate prediction model problems in the representation of clouds: underestimation of clouds in the stratocumulus regime by most models with the corresponding consequences in terms of shortwave radiation biases; overestimation of clouds by the 40-yr ECMWF Re-Analysis (ERA-40) in the deep tropics (in particular) with the corresponding impact in the outgoing longwave radiation; large spread between the different models in terms of cloud cover, liquid water path and shortwave radiation; significant differences between the models in terms of vertical cross sections of cloud properties (in particular), vertical velocity, and relative humidity. An alternative analysis of cloud cover mean statistics is proposed where sharp gradients in cloud cover along the GPCI transect are taken into account. This analysis shows that the negative cloud bias of some models and ERA-40 in the stratocumulus regions [as compared to the first International Satellite Cloud Climatology Project (ISCCP)] is associated not only with lower values of cloud cover in these regimes, but also with a stratocumulus-to-cumulus transition that occurs too early along the trade wind Lagrangian trajectory. Histograms of cloud cover along the cross section differ significantly between models. Some models exhibit a quasi-bimodal structure with cloud cover being either very large (close to 100%) or very small, while other models show a more continuous transition. The ISCCP observations suggest that reality is in-between these two extreme examples. These different patterns reflect the diverse nature of the cloud, boundary layer, and convection parameterizations in the participating weather and climate prediction models.