Dreary state of precipitation in global models

Abstract

[1] New, definitive measures of precipitation frequency provided by CloudSat are used to assess the realism of global model precipitation. The character of liquid precipitation (defined as a combination of accumulation, frequency, and intensity) over the global oceans is significantly different from the character of liquid precipitation produced by global weather and climate models. Five different models are used in this comparison representing state-of-the-art weather prediction models, state-of-the-art climate models, and the emerging high-resolution global cloud "resolving" models. The differences between observed and modeled precipitation are larger than can be explained by observational retrieval errors or by the inherent sampling differences between observations and models. We show that the time integrated accumulations of precipitation produced by models closely match observations when globally composited. However, these models produce precipitation approximately twice as often as that observed and make rainfall far too lightly. This finding reinforces similar findings from other studies based on surface accumulated rainfall measurements. The implications of this dreary state of model depiction of the real world are discussed.

Interpreting an evaluation of the ECMWF global model with CloudSat observations: ambiguities due to radar reflectivity forward operator uncertainties

“…The overestimate of reflectivity in the lower troposphere across all latitudes, but most prominent at lower latitudes, suggests too frequent occurrence of rain, leading to reflectivities that are much too high compared with the equivalent reflectivity for non-precipitating cloud (as seen in Figure 12). This is consistent with other studies that show many GCMs, including the IFS, tend to overestimate the occurrence of light rain (Stephens et al, 2010). There is also an issue in representing the PSD for light rain shown by the rain PSD sensitivity experiment in Figure 11, where the reflectivity bias was reduced at low altitudes when a modified PSD more appropriate for light rain was used.…”

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

“…The shape of the diurnal cycle is a more-regular sinusoid over sea points for all models and, to a lesser extent, for TRMM as well. The fact that the parametrized and explicit convection models become slightly closer to each other in terms of mean precipitation when land points are excluded agrees with the findings in Stephens et al (2010).…”

“…Probability densities are much higher at high rain rates for the 4 km model runs and TRMM, while below about 1 mm h −1 the 12 km param model probability densities are much higher than those of the observations and other models. The fact that the 12 km param model has far too much light rain agrees with many earlier analyses of weather and climate models using convective parametrizations (Dai and Trenberth, 2004;Sun et al, 2006;Wilcox and Donner, 2007;Stephens et al, 2010).…”

“…Many studies have shown that convective parametrizations generally lead to rainfall occurring at rates that are too small and over durations that are too long (Dai and Trenberth, 2004;Sun et al, 2006;Stephens et al, 2010), though there have been efforts to improve this behaviour (Wilcox and Donner, 2007). 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).…”

Improved rain rate and drop size retrievals from airborne Doppler radar

“…Even light rain and drizzle make significant contributions to global precipitation at the surface (Haynes et al, 2009;Berg et al, 2010;Behrangi et al, 2012), while the vertical profile of precipitation can be used to estimate the transfer of latent heat (Nelson et al, 2016) and microphysical processes . The intensity and drop size distribution (DSD) of rain are subject to persistent errors in weather and climate models, which frequently produce excess drizzle from shallow maritime clouds (Stephens et al, 2010;Abel and Boutle, 2012). Improved instrumentation and retrieval algorithms for the satellite remote sensing of rain are therefore priorities for earth observation, model evaluation, and an understanding of cloud and precipitation processes.…”