Simulating the diurnal cycle of rainfall in global climate models: resolution versus parameterization

Abstract

The effects of horizontal resolution and the treatment of convection on simulation of the diurnal cycle of precipitation during boreal summer are analyzed in several innovative weather and climate model integrations. The simulations include: season-long integrations of the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) with explicit clouds and convection; year-long integrations of the operational Integrated Forecast System (IFS) from the European Centre for Medium-range Weather Forecasts at three resolutions (125, 39 and 16 km); seasonal simulations of the same model at 10 km resolution; and seasonal simulations of the National Center for Atmospheric Research (NCAR) low-resolution climate model with and without an embedded two-dimensional cloud-resolving model in each grid box. NICAM with explicit convection simulates best the phase of the diurnal cycle, as well as many regional features such as rainfall triggered by advancing sea breezes or high topography. However, NICAM greatly overestimates mean rainfall and the magnitude of the diurnal cycle. Introduction of an embedded cloud model within the NCAR model significantly improves global statistics of the seasonal mean and diurnal cycle of rainfall, as well as many regional features. However, errors often remain larger than for the other higher-resolution models. Increasing resolution alone has little impact on the timing of daily rainfall in IFS with parameterized convection, yet the amplitude of the diurnal cycle does improve along with the representation of mean rainfall. Variations during the day in atmospheric prognostic fields appear quite similar among models, suggesting that the distinctive treatments of model physics account for the differences in representing the diurnal cycle of precipitation.

Fog and rain in the Amazon

“…This increase, in turn, moistens the land surface, an important factor leading to the differences in cloud between the wet and dry seasons. In agreement with observations [here Climate Prediction Center Morphing Technique (CMORPH) (26,27) data averaged over 10 y (2004−2014) for the months of February and September for the wet and dry seasons, respectively], our simulated precipitation maxima occur in the early to late afternoon in both seasons, with greater absolute amplitude in the wet season.…”

Effects of explicit convection on global land‐atmosphere coupling in the superparameterized CAM

“…These effects of superparameterization on rainfall have been noted before [Khairoutdinov et al, 2005;DeMott et al, 2007;Somerville, 2009a, 2009b;Pritchard et al, 2011;Dirmeyer et al, 2012] and are improvements given the observed nocturnal peak of rainfall in the U.S. Midwest [Dai et al, 1999] and the persistent drizzle problem in conventional GCMs [Dai, 2006]. It is reassuring that similar improvements of diurnal rainfall associated with superparameterization occur even in the micro-CRM configuration that we have employed for computational efficiency, which emphasizes that mesoscale organization on the 32-128 km scale is not a critical factor for the diurnal cycle, as has also been noted for SPCAM's Madden-Julian Oscillation [Pritchard et al, 2014].…”

“…Despite the large diversity of coupling signals in different global circulation models (GCMs), they tend to agree on several regions of strong coupling signals, initially identified in the Global Land-Atmosphere Coupling Experiment (GLACE) [Koster et al, 2004[Koster et al, , 2006, which has inspired a series of global land-atmosphere coupling studies in the past decade. These studies range in spatial and temporal scales and use a combination of models, reanalysis or satellite products [e.g., Dirmeyer, 2006Dirmeyer, , 2011Dirmeyer et al 2012;Findell et al, 2011;Taylor et al, 2012], with associated trade-offs. For instance, observational-based analyses are limited by an inability to explore the relationship of causality and short temporal and sparse spatial coverage, while reanalysis and models are impaired by their PBL and cloud parameterization schemes [Seneviratne et al, 2010;Ferguson et al, 2012;Guillod et al, 2015], though this can be mitigated to some degree by assimilating rainfall data [Findell et al, 2011].…”

The heated condensation framework as a convective trigger in the NCEP Climate Forecast System version 2

“…The largest phase differences between simulations and observations occur over the Great Plains in the U.S. (Figures 6b, 6d, and 6f), which has a maximum during nighttime (Figure 6c). The latter error is common in atmospheric models, because they do not accurately represent mesoscale convective complexes (MCS) that originate over the Rocky Mountains in early afternoon and propagate across the Great Plains over a period of several hours, which are responsible for most warm season rainfall in that region [Fritsch et al, 1986;Dirmeyer et al, 2012]. There is a remarkable difference between the phase simulated by CTRL and HCFv2 (Figures 6d and 6f).…”

“…Recent advancements in high performance computing represent an exciting opportunity for global climate model development, pushing the limits of representing clouds and convection in a global atmospheric model [Chen and Lin, 2011;Dirmeyer et al, 2012;Manganello et al, 2012;Murakami et al, 2012;Bacmeister et al, 2014;Wehner et al, 2014]. As a consequence, atmospheric moist convection can now be represented in sophisticated schemes such as global cloud-resolving models and super parameterization schemes [Grabowski, 2001;Khairoutdinov and Randall, 2001;Randall et al, 2003;Tomita and Satoh, 2004;Satoh et al, 2008Satoh et al, , 2014Stan et al, 2010].…”