Convective Precipitation Variability as a Tool for General Circulation Model Analysis

Abstract

Precipitation variability is analyzed in two versions of the Community Atmospheric Model (CAM), the standard model, CAM, and a "multiscale modeling framework" (MMF), in which the cumulus parameterization has been replaced with a cloud-resolving model. Probability distribution functions (PDFs) of daily mean rainfall in three geographic locations [the Amazon Basin and western Pacific in December-February (DJF) and the North American Great Plains in June-August (JJA)] indicate that the CAM produces too much light-moderate rainfall (10 ϳ 20 mm day Ϫ1 ), and not enough heavy rainfall, compared to observations. The MMF underestimates rain contributions from the lightest rainfall rates but correctly simulates more intense rainfall events. These differences are not always apparent in seasonal mean rainfall totals.Analysis of 3-6-hourly rainfall and sounding data in the same locations reveals that the CAM produces moderately intense rainfall as soon as the boundary layer energizes. Precipitation is also concurrent with tropospheric relative humidity and lifted parcel buoyancy increases. In contrast, the MMF and observations are characterized by a lag of several hours between boundary layer energy buildup and precipitation, and a gradual increase in the depth of low-level relative humidity maximum prior to rainfall.The environmental entrainment rate selection in the CAM cumulus parameterization influences CAM precipitation timing and intensity, and may contribute to the midlevel dry bias in that model. The resulting low-intensity rainfall in the CAM leads to rainfall-canopy vegetation interactions that are different from those simulated by the MMF. The authors present evidence suggesting that this interaction may artificially inflate North American Great Plains summertime rainfall totals in the CAM.

Precipitation distributions for explicit versus parametrized convection in a large‐domain high‐resolution tropical case study

“…When the same 15 min data are plotted using 1 • values (not shown), the plots look very similar to the 3 h average 1 • grid, showing that this is a horizontal and not a temporal resolution dependence. A likely explanation for this is that, although on larger scales the average precipitation is constrained by radiative-convective equilibrium (and therefore agrees well with both M in the model and the preferred rain rate in the warm pool region in DeMott et al (2007)), on smaller scales there is some mesoscale circulation feedback that allows some grid cells to rain at a consistently heavier rate at the expense of moredistant cells (typically, the heavier rain in the model occurs along lines of low-level convergence). Field and Shutts (2009) found that instantaneous rain rates from UM operational forecasts with parametrized convection actually had too many instances of heavy rain relative to an idealized CSRM and satellite data.…”

“…The 4 km 3Dsmag model exhibits cooling and moistening at mid-levels and warming and drying at low levels, while the 12 km param model shows much less, with perhaps slight warming and moistening at low levels for subgrid terms. Looking only at what convection does directly (the subgrid terms), the 4 km 3Dsmag model would seem to take longer in the transition to deeper convection, while the 12 km param model might transition to deep convection more quickly (especially given its tendency to rain at profiles with dryer mid-levels anyway, likely due to small entrainment rates) and therefore might consume CAPE more quickly and not maintain extended heavy precipitation because the CAPE never builds up, similar to arguments from DeMott et al (2007). However, it is not as simple as arguing that rainfall occurs only in moister tropospheric conditions, since the 4 km 2Dsmag model still gets the rainfall distribution right.…”

“…(2007), which shows scale-free rain amounts for observations (GPCP) and the superparametrized Multi-scale Modeling Framework (MMF) but not for the regular Community Atmosphere Model (CAM) in the western Pacific, which has a bump around 10 mm day −1 similar to our 12 km param distribution. A possible explanation for similarity in the preferred rain rate for the CAM in the western Pacific in DeMott et al (2007) and our 12 km param model is that the parametrized convection in both models is too tightly constrained to balance the large-scale forcing. Note that over the Amazon basin, however, even the MMF and observations have a bump around this precipitation rate.…”

“…A mechanism suggested by DeMott et al (2007) to explain the CAM behaviour relates to the fact that the parametrized plumes in that model's convective parametrization (Zhang and McFarlane, 1995) must detrain above the level of minimum saturated moist static energy. Since the deep convection scheme must consume CAPE, the entrainment values for the plumes are forced to be very small in order for the plumes to maintain buoyancy while satisfying this constraint on the height of detrainment.…”

“…Observations over tropical regions show roughly power-law behaviour of precipitation distributions at lower rain rates, with faster, exponential decreases as very high rain rates are approached (DeMott et al, 2007;Field and Shutts, 2009;Peters et al, 2010). In this article, we compare different runs of the same model over different horizontal resolutions with both parametrized and explicit convection for a real case over a large domain covering the tropical Indian and Western Pacific oceans.…”