09 Jan 2023
09 Jan 2023
Status: this preprint is open for discussion.

Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM5.4-MPAS4.0 variable-resolution model

Koichi Sakaguchi1, L. Ruby Leung1, Colin M. Zarzycki2, Jihyeon Jang3, Seth McGinnis4, Bryce E. Harrop1, William C. Skamarock3, Andrew Gettelman5, Chun Zhao6, William J. Gutowski7, Stephen Leak8, and Linda Mearns4 Koichi Sakaguchi et al.
  • 1Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
  • 2Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA, USA
  • 3Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
  • 4Research Application Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
  • 5Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
  • 6School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui, China
  • 7Geological And Atmospheric Sciences Department, Iowa State University, Ames, IA, USA
  • 8The National Energy Research Scientific Computing Center, Berkeley, CA, USA

Abstract. Comprehensive assessment of climate datasets is important for communicating to stakeholders model projections and associated uncertainties. Uncertainties can arise not only from assumptions and biases within the model but also from external factors such as computational constraint and data processing. To understand sources of uncertainties in global variable-resolution (VR) dynamical downscaling, we produced a regional climate dataset using the Model for Prediction Across Scales dynamical core coupled to the Community Atmosphere Model version 5.4 (CAM-MPAS). This document provides technical details of the model configuration, simulations, computational requirements, post-processing, and data archive of the experimental CAM-MPAS downscaling data.

The CAM-MPAS model is configured with VR meshes featuring higher resolutions over North America, as well as quasi-uniform resolution meshes across the globe. The dataset includes multiple uniform- (240 and 120 km) and variable-resolution (50–200, 25–100, and 12–46 km) simulations for both the present-day (1990–2010) and future (2080–2100) periods, closely following the protocol of the North American Coordinated Regional Climate Downscaling Experiment. A deviation from the protocol is the pseudo-warming experiment for the future period, using the ocean boundary conditions produced by adding the sea surface temperature and sea ice changes from the low resolution version of the Max Planck Institute Earth System Model in the Coupled Model Intercomparison Project phase five to the present-day ocean state from a reanalysis product.

Some unique aspects of global VR models are evaluated to provide background knowledge to data users and to explore good practices for modelers who use VR models for regional downscaling. In the coarse-resolution domain, strong resolution-sensitivity of the hydrological cycles exists over the tropics but does not appear to affect the mid-latitude circulations in the Northern Hemisphere including the downscaling target of North America. The pseudo-warming experiment leads to similar responses of large-scale circulations to the imposed radiative and boundary forcings in the CAM-MPAS and MPI models, but their climatological states in the historical period differ over various regions including North America. Such differences are carried to the future period, suggesting the importance of the base state climatology. Within the refined domain, precipitation statistics improve with higher resolutions, and such statistical inference is verified to be negligibly influenced by horizontal remapping during post-processing. Limited (≈ 50 % slower) throughput of the current code is found on a recent many-core/wide-vector High Performance Computing system, which limits the lengths of the 12–46 km simulations and indirectly affects the uncertainty from sampling. Our experience shows that global and technical aspects of VR downscaling framework require further investigations to reduce uncertainties for regional refinement.

Koichi Sakaguchi et al.

Status: open (until 06 Mar 2023)

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Koichi Sakaguchi et al.

Koichi Sakaguchi et al.


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Short summary
We document details of the regional climate downscaling dataset produced by a global variable-resolution model. The experiment is unique for its following a standard protocol designed for coordinated experiments of regional models. Negligible influence of post-processing on statistical analysis, importance of simulation quality outside of the target region, and computational challenges that our model code faced under rapidly changing super computer systems are illustrated.