Evaluation of the Planetary Boundary Layer Height From ERA5 Reanalysis With MOSAiC Observations Over the Arctic Ocean

Author:

Xi Xingya1ORCID,Yang Qinghua1ORCID,Liu Changwei1ORCID,Shupe Matthew D.23ORCID,Han Bo1ORCID,Peng Shijie1,Zhou Shaohui4,Chen Dake1ORCID

Affiliation:

1. School of Atmospheric Sciences Sun Yat‐sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Zhuhai China

2. Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder Boulder CO USA

3. NOAA Physical Science Laboratory Boulder CO USA

4. Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters Key Laboratory for Aerosol‐Cloud‐Precipitation of China Meteorological Administration School of Atmospheric Physics Nanjing University of Information Science and Technology Nanjing China

Abstract

AbstractThe planetary boundary layer height (PBLH) is a crucial indicator reflecting the region of the atmosphere characterized by continuous turbulence. Here, we use radiosonde and surface meteorological observations (4–7 times per day, year‐round measurements) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition to derive the PBLH (PBLHMOSAiC), and further evaluate the PBLH from the ERA5 reanalysis (PBLHERA5). Comparisons between PBLHMOSAiC and PBLHERA5 from different perspectives reveal that: (a) The overestimation of PBLHERA5 when the sea ice concentration is >90% is significant with the centered root mean squared error reaching up to 201 m; (b) The difference between the two products is notably pronounced in cold seasons, while it is comparatively diminished in warm seasons; (c) In neutral boundary layers, differences in PBLHERA5 are larger compared with stable and convective boundary layers. In addition, the analysis of error sources indicates that the bias of PBLHERA5 is sensitive to the bias of vertical thermal structure and wind speed profiles in ERA5 data sets in all conditions. Finally, we find a Random Forest model effectively reduces the bias of PBLHERA5 with the index of agreement reaching up to 0.71 in the test data set, while a multiple linear regression demonstrates comparable performance to the Random Forest model.

Publisher

American Geophysical Union (AGU)

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