Kilometer-Scale Climate Models: Prospects and Challenges

Abstract

Currently major efforts are underway toward refining the horizontal resolution (or grid spacing) of climate models to about 1 km, using both global and regional climate models (GCMs and RCMs). Several groups have succeeded in conducting kilometer-scale multiweek GCM simulations and decadelong continental-scale RCM simulations. There is the well-founded hope that this increase in resolution represents a quantum jump in climate modeling, as it enables replacing the parameterization of moist convection by an explicit treatment. It is expected that this will improve the simulation of the water cycle and extreme events and reduce uncertainties in climate change projections. While kilometer-scale resolution is commonly employed in limitedarea numerical weather prediction, enabling it on global scales for extended climate simulations requires a concerted effort. In this paper, we exploit an RCM that runs entirely on graphics processing units (GPUs) and show examples that highlight the prospects of this approach. A particular challenge addressed in this paper relates to the growth in output volumes. It is argued that the data avalanche of high-resolution simulations will make it impractical or impossible to store the data. Rather, repeating the simulation and conducting online analysis will become more efficient. A prototype of this methodology is presented. It makes use of a bit-reproducible model version that ensures reproducible simulations across hardware architectures, in conjunction with a data virtualization layer as a common interface for output analyses. An assessment of the potential of these novel approaches will be provided.

European daily precipitation according to EURO-CORDEX regional climate models (RCMs) and high-resolution global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP)

“…Minobe et al, 2008;Shaffrey et al, 2009;Roberts et al, 2016). Synoptic-scale dynamics are better resolved in GCMs with increasing resolution, which improves the representation of midlatitude eddy-driven jet variability, extratropical cyclones, and associated extreme precipitation (Catto et al, 2010;Haarsma et al, 2013;Schiemann et al, 2018;Baker et al, 2019), as well as blocking events (Matsueda and Palmer, 2011;Berckmans et al, 2013). The intensity of tropical cyclones in GCMs also increases with resolution, and their inter-annual variability is better captured (e.g.…”

High-resolution monthly precipitation and temperature time series from 2006 to 2100

“…Such a coarse resolution is usually not capable of capturing orographic precipitation in complex terrain [1][2][3] . Although global circulation and weather models such as WRF-ARF 4 , or ICON 5,6 , for example, can be run at high horizontal resolutions close to 1 km, they are still heavily constrained by computational limits 7 . Currently, global kilometre-scale models only achieve a simulation throughput of 0.043 SYPD (simulated years per day) 8 , which amounts to an 25 x shortfall compared to what would be computationally efficient simulations of 1 SYPD 7,9 .…”

“…Although global circulation and weather models such as WRF-ARF 4 , or ICON 5,6 , for example, can be run at high horizontal resolutions close to 1 km, they are still heavily constrained by computational limits 7 . Currently, global kilometre-scale models only achieve a simulation throughput of 0.043 SYPD (simulated years per day) 8 , which amounts to an 25 x shortfall compared to what would be computationally efficient simulations of 1 SYPD 7,9 . Even with the largest supercomputers and state-of-the-art climate models, as well as large financial investments, such a shortfall can currently only be reduced by a factor of 20 (ref.…”

Attribution of precipitation to cyclones and fronts over Europe in a kilometer-scale regional climate simulation

“…Fronts are characterized by strong horizontal contrasts in low-level temperature and humidity, which makes equivalent potential temperature θ e at 850 hPa a suitable field for front detection (specifically, the modulus |∇θ e | of the θ e gradient). Schemm et al (2018) discuss this choice in detail and provide a historical context. Following the general approach proposed by Hewson (1998), the front identification method developed by Jenkner et al (2010) is based on applying the thermal front parameter (TFP; Renard and Clarke, 1965) to θ e and using the cross-frontal wind component to distinguish between cold, warm, and quasistationary fronts.…”

“…While such methods are in principle objective, choosing specific approaches and configurations involves many ultimately subjective choices. Lacking a universally accepted definition of fronts, it is not inherently clear how to identify them, and consequently, many different approaches exist, as discussed in detail by Schemm et al (2018) and Thomas and Schultz (2019). Another subjective choice is involved when attributing precipitation to a front within a certain distance, which might also depend on the resolution of the available datasets.…”