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Impact of Boundary Layer Processes on Systematic Winter Temperature Bias in a Regional Climate Model

  • Seok Woo Shin
  • , Dong Hyun Cha
  • , Minkyu Lee
  • , Donghyuck Yoon
  • , Hyeyum Hailey Shin
  • , Changyong Park
  • , Sang Hun Park
  • , Jae Bum Lee
  • , Sung Chul Hong
  • Ulsan National Institute of Science and Technology
  • Korea Institute of Energy Research
  • Princeton University
  • Yonsei University
  • National Institute of Environmental Research

Research output: Contribution to journal › Article › peer-review

Abstract

This study investigated the source of systematic errors in air temperature at 2 m [surface air temperature (SAT)] in Far East Asia during years with strong East Asian winter monsoon (EAWM) using the Weather Research and Forecasting (WRF) Model regional climate model. Three planetary boundary layer (PBL) parameterizations were employed}Mellor–Yamada–Janjić (MYJ), Yonsei University (YSU), and Asymmetric Convective Model, version 2 (ACM2). The results showed notable nighttime SAT biases over Manchuria (MC) for the strong East Asian winter monsoon years, with the ACM2 scheme producing smaller cold biases than the MYJ and YSU schemes. The analysis of cold biases in lower-level air temperature indicated that the PBL schemes influenced the vertical temperature distribution along with nocturnal radiative cooling, indirectly leading to SAT errors. Mechanically driven PBL turbulent mixing, influenced by wind shear and overestimated synoptic variables, was identified as a significant factor influencing lower-layer air temperature and SAT errors, along with land surface processes. In essence, weaker PBL mixing reduced the nighttime SAT (;21%) and skin temperature (;19%) errors over MC, particularly the nighttime SAT (;26%) errors on cold wave days, even if it still has the systematic winter temperature bias.

Original languageEnglish
Pages (from-to)6153-6173
Number of pages21
JournalJournal of Climate
Volume38
Issue number21
DOIs
StatePublished - Nov 2025

Keywords

  • Bias
  • Boundary layer
  • Climate models
  • Model errors
  • Regional models
  • Temperature

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