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Abstract Recent advances in computer modeling have spurred the production of several global storm‐resolving models (GSRMs), which explicitly represent atmospheric circulations from convective to global scales. As a result, GSRMs simulate the formation and evolution of tropical cirrus clouds more physically than typical global climate models/general circulation models (GCMs) which use parameterizations to represent deep convection. We analyze the output from nine GSRMs from the DYAMOND initiative, focusing on the second phase of DYAMOND that simulated a period in January–February 2020. This paper is the third in a series investigating tropical cirrus clouds in GSRMs using DYAMOND model output for an intercomparison. In the tropics, models capture the mean outgoing longwave radiation within −5 to 14 W m−2of observed climatology, though most models have more convective precipitation over the 40‐day simulation period than observed. While the models represent large‐scale tropical convection with some fidelity, large regional differences in cloud properties and top‐of‐atmosphere radiation fluxes exist. We focus on a region within the Tropical Western Pacific to study the small‐scale features available with the high spatiotemporal resolution of GSRMs. Most models that participated in both phases of DYAMOND capture the seasonal differences between the two phases, yet each model exhibits unique cloud populations that are persistent across seasons. GSRMs even simulate the notoriously difficult‐to‐observe tropical tropopause layer (TTL) cirrus, providing a novel perspective on TTL cirrus even though the models have different cloud characteristics over the short 40‐days simulation.
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Comparing storm resolving models and climates via unsupervised machine learning
Abstract Global storm-resolving models (GSRMs) have gained widespread interest because of the unprecedented detail with which they resolve the global climate. However, it remains difficult to quantify objective differences in how GSRMs resolve complex atmospheric formations. This lack of comprehensive tools for comparing model similarities is a problem in many disparate fields that involve simulation tools for complex data. To address this challenge we develop methods to estimate distributional distances based on both nonlinear dimensionality reduction and vector quantization. Our approach automatically learns physically meaningful notions of similarity from low-dimensional latent data representations that the different models produce. This enables an intercomparison of nine GSRMs based on their high-dimensional simulation data (2D vertical velocity snapshots) and reveals that only six are similar in their representation of atmospheric dynamics. Furthermore, we uncover signatures of the convective response to global warming in a fully unsupervised way. Our study provides a path toward evaluating future high-resolution simulation data more objectively.
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- PAR ID:
- 10480043
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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