Evaluation of the atmosphere–land–ocean–sea ice interface processes in the Regional Arctic System Model version 1 (RASM1) using local and globally gridded observations

Details

• Journal Title:
  Geoscientific Model Development
• Personal Author:
  Brunke, Michael A. ; Cassano, John J. ; Dawson, Nicholas ; DuVivier, Alice K. ; Gutowski Jr., William J. ; Hamman, Joseph ; Maslowski, Wieslaw ; Nijssen, Bart ; Reeves Eyre, J. E. Jack ; Renteria, José C. ; Roberts, Andrew ; Zeng, Xubin
• NOAA Program & Office:
  CIRES (Cooperative Institute for Research in Environmental Sciences)
• Description:
  The Regional Arctic System Model version 1 (RASM1) has been developed to provide high-resolution simulations of the Arctic atmosphere–ocean–sea ice–land system. Here, we provide a baseline for the capability of RASM to simulate interface processes by comparing retrospective simulations from RASM1 for 1990–2014 with the Community Earth System Model version 1 (CESM1) and the spread across three recent reanalyses. Evaluations of surface and 2 m air temperature, surface radiative and turbulent fluxes, precipitation, and snow depth in the various models and reanalyses are performed using global and regional datasets and a variety of in situ datasets, including flux towers over land, ship cruises over oceans, and a field experiment over sea ice. These evaluations reveal that RASM1 simulates precipitation that is similar to CESM1, reanalyses, and satellite gauge combined precipitation datasets over all river basins within the RASM domain. Snow depth in RASM is closer to upscaled surface observations over a flatter region than in more mountainous terrain in Alaska. The sea ice–atmosphere interface is well simulated in regards to radiation fluxes, which generally fall within observational uncertainty. RASM1 monthly mean surface temperature and radiation biases are shown to be due to biases in the simulated mean diurnal cycle. At some locations, a minimal monthly mean bias is shown to be due to the compensation of roughly equal but opposite biases between daytime and nighttime, whereas this is not the case at locations where the monthly mean bias is higher in magnitude. These biases are derived from errors in the diurnal cycle of the energy balance (radiative and turbulent flux) components. Therefore, the key to advancing the simulation of SAT and the surface energy budget would be to improve the representation of the diurnal cycle of radiative and turbulent fluxes. The development of RASM2 aims to address these biases. Still, an advantage of RASM1 is that it captures the interannual and interdecadal variability in the climate of the Arctic region, which global models like CESM cannot do.
• Content Notes:
  CFSR data were downloaded from NOAA National Centers for Environmental Information National Operational Model Archive and Distribution System (https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/climate-forecast-system-version2-cfsv2#CFSReanalysis (CFSR), last access: 7 November 2018).
  
  NOAA sea ice concentration CDR was downloaded from the National Snow and Ice Data Center (http://nsidc.org/data/g02202, last access: 7 November 2018).
  
  CMAP was downloaded from NOAA's Earth System Research Laboratory (https://www.esrl.noaa.gov/psd/data/gridded/data.cmap.html and https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html, last access: 8 November 2018).
  
  GC-Net weather station data were obtained from the Cooperative Institute for Research in Environmental Sciences (http://cires1.colorado.edu/steffen/gcnet/, last access: 8 November 2018).
  
  
• Keywords:
  Atmospheric Science Mathematical Models Atmospheric Science Mathematical Models
• Source:
  Geosci. Model Dev., 11, 4817–4841, 2018
• DOI:
  https://doi.org/10.5194/gmd-11-4817-2018
• Document Type:
  Journal Article
• Rights Information:
  CC BY
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:cbafbe6651096d985d50a19ca76313d80e6596ecde0b435bb55400fbcac67b725cf08f9530ee5f9592dfa0366d642ff9403effc330462030b4895674ae7e3ec0
• Download URL:
  https://repository.library.noaa.gov/view/noaa/26415/noaa_26415_DS1.pdf
• File Type:
  [PDF - 7.64 MB ]