Single-column Model Experiments Research Articles

Representing the Subgrid Surface Heterogeneity of Precipitation in a General Circulation Model

AbstractPrecipitation is highly variable on spatial scales smaller than a typical general circulation model (GCM) grid box. Neglecting this subgrid‐scale variability can impact the simulation of the underlying land surface and its coupling with the atmosphere. In this study we present a scheme to stochastically disaggregate precipitation from an atmospheric grid to an underlying mosaic of surface tiles, using an assumed probability distribution of subgrid precipitation derived from observations. Unlike previous GCM‐based schemes, our approach includes a treatment of memory to allow persistence of subgrid features. In single column model experiments, we demonstrate the ability of the scheme to reproduce observed precipitation statistics at the spatial scale of the surface tiles. The root mean square error of hourly spatial standard deviation of precipitation is reduced from 1.90 to 0.96 mm hr−1 when disaggregation is applied. The scheme increases the spatial standard deviations of surface heat fluxes, soil moisture and temperature, and we show that incorporating memory amplifies these increases by a factor of 2–4. We also document increases in mean precipitation runoff and the Bowen ratio.

Coupling between lower‐tropospheric convective mixing and low‐level clouds: Physical mechanisms and dependence on convection scheme

Several studies have pointed out the dependence of low‐cloud feedbacks on the strength of the lower‐tropospheric convective mixing. By analyzing a series of single‐column model experiments run by a climate model using two different convective parametrizations, this study elucidates the physical mechanisms through which marine boundary‐layer clouds depend on this mixing in the present‐day climate and under surface warming. An increased lower‐tropospheric convective mixing leads to a reduction of low‐cloud fraction. However, the rate of decrease strongly depends on how the surface latent heat flux couples to the convective mixing and to boundary‐layer cloud radiative effects: (i) on the one hand, the latent heat flux is enhanced by the lower‐tropospheric drying induced by the convective mixing, which damps the reduction of the low‐cloud fraction, (ii) on the other hand, the latent heat flux is reduced as the lower troposphere stabilizes under the effect of reduced low‐cloud radiative cooling, which enhances the reduction of the low‐cloud fraction. The relative importance of these two different processes depends on the closure of the convective parameterization. The convective scheme that favors the coupling between latent heat flux and low‐cloud radiative cooling exhibits a stronger sensitivity of low‐clouds to convective mixing in the present‐day climate, and a stronger low‐cloud feedback in response to surface warming. In this model, the low‐cloud feedback is stronger when the present‐day convective mixing is weaker and when present‐day clouds are shallower and more radiatively active. The implications of these insights for constraining the strength of low‐cloud feedbacks observationally is discussed.

A PDF-Based Microphysics Parameterization for Simulation of Drizzling Boundary Layer Clouds

Abstract Formulating the contribution of subgrid-scale (SGS) variability to microphysical processes in boundary layer and deep convective cloud parameterizations is a challenging task because of the complexity of microphysical processes and the lack of subgrid-scale information. In this study, a warm-rain microphysics parameterization that is based on a joint double-Gaussian distribution of vertical velocity, liquid water potential temperature, total water mixing ratio, and perturbation of rainwater mixing ratio is developed to simulate drizzling boundary layer clouds with a single column model (SCM). The probability distribution function (PDF) is assumed, but its parameters evolve according to equations that invoke higher-order turbulence closure. These parameters are determined from the first-, second-, and third-order moments and are then used to derive analytical expressions for autoconversion, collection, and evaporation rates. The analytical expressions show that correlation between rainwater and liquid water mixing ratios of the Gaussians enhances the collection rate whereas that between saturation deficit and rainwater mixing ratios of the Gaussians enhances the evaporation rate. Cases of drizzling shallow cumulus and stratocumulus are simulated with large-eddy simulation (LES) and SCM runs (SCM-CNTL and SCM-M): LES explicitly resolves SGS variability, SCM-CNTL parameterizes SGS variability with the PDF-based scheme, but SCM-M uses the grid-mean profiles to calculate the conversion rates of microphysical processes. SCM-CNTL can well reproduce the autoconversion, collection, and evaporation rates from LES. Comparisons between the two SCM experiments showed improvements in mean profiles of potential temperature, total water mixing ratio, liquid water, and cloud amount in the simulations considering SGS variability. A 3-week integration using the PDF-based microphysics scheme indicates that the scheme is stable for long-term simulations.

A Dual Mass Flux Framework for Boundary Layer Convection. Part I: Transport

Abstract This study considers the question of what is the least complex bulk mass flux framework that can still conceptually reproduce the smoothly varying coupling between the shallow convective cloud layer and the subcloud mixed layer. To this end, the model complexity of the classic single bulk mass flux scheme is enhanced. Inspired by recent large-eddy simulation results, the authors argue that two relatively minor but key conceptual modifications are already sufficient to achieve this goal: (i) retaining a dry transporting updraft in the moist limit and (ii) applying continuous updraft area partitioning to this dual mass flux (DualM) framework. The dry updraft represents all internal mixed layer updrafts that terminate near the mixed layer top, whereas the moist updraft represents all updrafts that condense and rise out of the mixed layer as buoyant cumulus clouds. The continuous area partitioning between the dry and moist updraft is a function of moist convective inhibition above the mixed layer top. Updraft initialization is a function of the updraft area fraction and is therefore consistent with the updraft definition. It is argued that the model complexity thus enhanced is sufficient to allow reproduction of various phenomena involved in the cloud–subcloud coupling, namely (i) dry countergradient transport within the mixed layer that is independent of the moist updraft, (ii) soft triggering of moist convective flux throughout the boundary layer, and (iii) a smooth response to smoothly varying forcings, including the reproduction of gradual transitions to and from shallow cumulus convection. The DualM framework is evaluated by implementing in the Eddy Diffusivity Mass Flux (EDMF) boundary layer scheme of the ECMWF’s Integrated Forecasting System. Single column model experiments are evaluated against large-eddy simulation results for a range of different cases that span a broad parameter space of cloud–subcloud coupling intensities. The results illustrate that also in numerical practice the DualM framework can reproduce gradual transitions to and from shallow cumulus convection. Model behavior is further explored through experiments in which model complexity is purposely reduced, thus mimicking a single bulk updraft setup. This gives more insight into the new model-internal interactions and explains the obtained case results.

Evaluation of the NCEP Global Forecast System at the ARM SGP Site

AbstractThis study evaluates the performance of the National Centers for Environmental Prediction Global Forecast System (GFS) against observations made by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program at the southern Great Plains site for the years 2001–04. The spatial and temporal scales of the observations are examined to search for an optimum approach for comparing grid-mean model forecasts with single-point observations. A single-column model (SCM) based upon the GFS was also used to aid in understanding certain forecast errors. The investigation is focused on the surface energy fluxes and clouds. Results show that the overall performance of the GFS model has been improving, although certain forecast errors remain. The model overestimated the daily maximum latent heat flux by 76 W m−2 and the daily maximum surface downward solar flux by 44 W m−2, and underestimated the daily maximum sensible heat flux by 44 W m−2. The model’s surface energy balance was reached by a cancellation of errors. For clouds, the GFS was able to capture the observed evolutions of cloud systems during major synoptic events. However, on average, the model largely underestimated cloud fraction in the lower and midtroposphere, especially for daytime nonprecipitating low clouds because shallow convection in the GFS does not produce clouds. Analyses of surface radiative fluxes revealed that the diurnal cycle of the model’s surface downward longwave flux (SDLW) was not in phase with that of the ARM-observed SDLW. SCM experiments showed that this error was caused by an inaccurate scaling factor, which was a function of ground skin temperature and was used to adjust the SDLW at each model time step to that computed by the model’s longwave radiative transfer routine once every 3 h. A method has been proposed to correct this error in the operational forecast model. It was also noticed that the SDLW biases changed from mostly negative in 2003 to slightly positive in 2004. This change was traced back to errors in the near-surface air temperature. In addition, the SDLW simulated with the newly implemented Rapid Radiative Transfer Model longwave routine in the GFS is usually 5–10 W m−2 larger than that simulated with the previous routine. The forecasts of surface downward shortwave flux (SDSW) were relatively accurate under clear-sky conditions. The errors in SDSW were primarily caused by inaccurate forecasts of cloud properties. Results from this study can be used as guidance for the further development of the GFS.

Implications of Microphysics for Cloud-Radiation Parameterizations: Lessons from TOGA COARE

Abstract A single-column model (SCM) and observational data collected during TOGA COARE were used to investigate the sensitivity of model-produced cloud properties and radiative fluxes to the representation of cloud microphysics in the cloud-radiation parameterizations. Four 78-day SCM numerical experiments were conducted for the atmospheric column overlying the COARE Intensive Flux Array. Each SCM experiment used a different cloud-radiation parameterization with a different representation of cloud microphysics. All the SCM experiments successfully reproduced most of the observed temporal variability in precipitation, cloud fraction, shortwave and longwave cloud forcing, and downwelling surface shortwave flux. The magnitude and temporal variability of the downward surface longwave flux was overestimated by all the SCM experiments. This bias is probably due to clouds forming too low in the model atmosphere. Time-averaged model results were used to examine the sensitivity of model performance to the differe...