The Added Value of Large-eddy and Storm-resolving Models for Simulating Clouds and Precipitation

Abstract

More than one hundred days were simulated over very large domains with fine (0.156 km to 2.5 km) grid spacing for realistic conditions to test the hypothesis that storm (kilometer) and large-eddy (hectometer) resolving simulations would provide an improved representation of clouds and precipitation in atmospheric simulations. At scales that resolve convective storms (storm-resolving for short) scales, the vertical velocity variance becomes resolved and a better physical basis is achieved for representing clouds and precipitation. Similar to past studies we find an improved representation of precipitation at kilometer scales, as compared to models with parameterised convection. The main precipitation features (location, diurnal cycle and spatial propagation) are well captured already at kilometer scales, and refining resolution to hectometer scales does not substantially change the simulations in these respects. It does, however, lead to a reduction in the precipitation on the timescales considered-most notably over the Tropical ocean. Changes in the distribution of precipitation, with less frequent extremes are also found in simulations incorporating hecto-meter scales. Hectometer scales appear more important for the representation of clouds, and make it possible to capture many important aspects of the cloud field, from the vertical distribution of cloud cover, to the distribution of cloud sizes, to the diel (daily) cycle. Qualitative improvements, particularly in the ability to differentiate cumulus from stratiform clouds, are seen when reducing the grid spacing from kilometer to hectometer scales. At the hectometer scale new challenges arise, but the similarity of observed and simulated scales, and the more direct 1 connection between the circulation and the unconstrained degrees of freedom make these challenges less daunting. This quality, combined with an already improved simulation as compared to more parameterised models, underpins our conviction that the use and further development of storm-resolving models offers exciting opportunities for advancing understanding of climate and climate change.

Different Representation of Mesoscale Convective Systems in Convection-Permitting and Convection-Parameterizing NWP Models and Its Implications for Large-Scale Forecast Evolution

“…To do so, we used convection-permitting simulations with a grid spacing of 2.5 km that were conducted in the context of the NARVAL II field campaign (ICON-HiRes [12]; see [16] for an evaluation of bulk quantities). The simulations were daily forecasts, spanning the time period 1-31 August 2016, and covered the entire domain of the Tropical Atlantic [12].…”

“…More details on the simulation setup and an evaluation of some aspects of the simulations can be found in [12,31]. An evaluation of bulk atmospheric quantities, which were represented surprisingly well in ICON-HiRes despite being an untuned model version, as well as specific aspects like the diurnal cycle are given in [16].…”

“…However, investing resources into the development of a new generation of convection parameterizations for use in general circulation models is not the only avenue to take. The ever-increasing availability of computational resources has sparked serious interest in attenuating the "cumulus parameterization problem" (coined so by [8]) by focusing efforts on exploring effective ways of running atmospheric models at convection-permitting grid spacings on large domains, even global, and long time periods (e.g., [9][10][11][12][13][14][15][16]). Such models use grid spacings of a few kilometers, allowing them to resolve convective storms explicitly.…”

“…Two models represent current state-of-the-art operational global NWP models with grid spacing O (10 km) and convection parameterization, and the third model was run at a convection-permitting horizontal grid spacing of 2.5 km over a very large domain covering the entire tropical Atlantic (cf. Section 2.1; northwestern and southeastern corners at 20 • N, 68 • W and 10 • S, 15 • E, respectively, [12,16]). We used daily forecasts from all three models.…”

The Making of Continuous Colormaps

“…For this example, we chose a data set from a simulation with a highresolution global atmosphere model. The data we use is one timestep of the temperature at a height of 2m simulated with the icosahedral ICON model at a global resolution of 5km [53]. We remapped the data from the unstructured model grid to a regular grid with 4000 × 2000 grid points for easier use with different tools.…”