Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period

被引：5
|
作者：
Heinemann, Gunther [1 ]
Schefczyk, Lukas [2 ]
Willmes, Sascha [1 ]
Shupe, Matthew D. [3 ,4 ]
机构：
[1] Univ Trier, Environm Meteorol, Trier, Germany
[2] State Environm Agcy Rhineland Palatinate, Mainz, Germany
[3] Univ Colorado, Cooperat Inst Res Environm Sci, Boulder, CO 80309 USA
[4] NOAA, Phys Sci Lab, Boulder, CO USA
来源：
ELEMENTA-SCIENCE OF THE ANTHROPOCENE | 2022年 / 10卷 / 01期
基金：
美国国家科学基金会;
关键词：
Arctic; Sea ice; Regional climate model; Verification; Leads; MODIS; SEA-ICE; TRANSFER-COEFFICIENTS; THICKNESS; MOMENTUM; POLYNYAS; HEAT; SNOW;
D O I：
10.1525/elementa.2022.00033
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic and yielded an excellent data set to test the parameterizations of ocean/sea-ice/atmosphere interaction processes in regional climate models (RCMs). In the present paper, near-surface data during MOSAiC are used for the verification of the RCM COnsortium for Small-scale MOdel-Climate Limited area Mode (COSMO-CLM or CCLM). CCLM is used in a forecast mode (nested in ERA5) for the whole Arctic with 15 km resolution and is run with different configurations of sea ice data. These include the standard sea ice concentration taken from passive microwave data with around 6 km resolution, sea ice concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data and MODIS sea ice lead fraction data for the winter period. CCLM simulations show a good agreement with the measurements. Relatively large negative biases for temperature occur for November and December, which are likely associated with a too large ice thickness used by CCLM. The consideration of sea ice leads in the sub-grid parameterization in CCLM yields improved results for the near-surface temperature. ERA5 data show a large warm bias of about 2.5 degrees C and an underestimation of the temperature variability.
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页数：22
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