Altered sub-seasonal predictability of Community Atmosphere Model 5 (CAM5) in CESM 1.2.1 by the choices of dynamical core

• Main Authors: Kim, Ha-Rim, Kim, Baek-Min, Jun, Sang-Yoon, Choi, Yong-Sang
• Format: Text
• Language: English
• Published: 2020
• Subjects: Arctic
• Online Access: https://doi.org/10.5194/gmd-2020-22; https://gmd.copernicus.org/preprints/gmd-2020-22/

This study investigates the prediction skill of sub-seasonal prediction models that vary based on the choice of two dynamical cores: the finite volume (FV) dynamical core on a latitude-longitude grid system and the spectral element (SE) dynamical core on a cubed-sphere grid system. Recent research showed that the SE dynamical core on a uniform grid system increases parallel scalability and removes the need for polar filters for mitigating uncertainty in climate prediction, particularly for the Arctic region. However, it still remains questionable whether the choice of dynamical cores can actually yield significant changes in prediction skill. To tackle this issue, we implemented a sub-seasonal prediction model based on the Community Atmospheric Model version 5 by incorporating the above two dynamical cores with virtually the same physics schemes. Sub-seasonal prediction skills of the SE dynamical core and FV dynamical core are verified with ERA-Interim reanalysis during the early winter (November–December) and the late winter (January–February) from 2001/2002 to 2017/2018. The prediction skills of two different dynamical cores were significantly different regardless of the similar physics scheme. In the ocean, the predictability of the SE dynamical core is similar to that of the FV dynamical core, mostly because our simulation configuration imposes the same boundary and initial conditions at the surface. Notable differences in the one-month predictability between the two cores are observed for the wintertime Arctic and mid-latitudes, particularly over North America and Eurasia continents. With a one-month lead, the SE dynamical core exhibited higher predictability over North America in late winter (r ≈ 0.45 in SE, r ≈ 0.10 in FV) whereas the FV dynamical core showed relatively higher predictability in East Asia and Eurasia in early winter (r ≈ 0.15 in SE, r ≈ 0.43 in FV). Therefore, we conclude that caution is needed when selecting the dynamical cores of sub-seasonal prediction models. Partially, these differences can be ascribed to the different manifestations of Arctic-mid-latitude linkage in the two dynamical cores; the SE dynamical core captures warmer Arctic and colder mid-latitudes relatively better than the FV dynamical core.