Abstract ID: 090
Assessing opportunities for improved coastal data assimilation in ocean model analyses and forecasting systems
Lead Author: Matthew Widlansky
Cooperative Institute for Marine and Atmospheric Research, School of Ocean and Earth Science and Technology, University of Hawai’i at Mānoa, United States of America
Keywords: monthly ocean forecasting, sea level variability, ocean heat content, ocean data assimilation
Abstract: Satellite altimetry measurements of sea surface height provide near-global ocean state observations on sub-monthly time scales, which are not always utilized by climate forecasting systems. As early as the mid-1990s, attempts were made to assimilate altimetry observations to initialize climate models. These experiments demonstrated improved ocean forecasting skill, especially compared to experiments that did not assimilate subsurface ocean temperature information. Nowadays, some operational climate forecasting models utilize altimetry in their assimilation systems, whereas others do not. Here, we assess the impact of altimetry assimilation on analyses and monthly prediction skill of ocean variables in two climate forecasting systems that are from the European Centre for Medium-Range Weather Forecasts (SEAS5) and the Australian Bureau of Meteorology (ACCESS-S). We show that assimilating altimetry improves the initialization of subsurface ocean temperatures, as well as forecasts of monthly variability in upper-ocean heat content and sea level. Skill improvements are largest in the subtropics, especially away from the shallow waters over coastal shelves. Elsewhere, the skill improvements are minimal. In summary, we find that utilizing altimetry observations improves the overall global ocean forecasting skill, at least for upper-ocean heat content and sea level in the open ocean. We compare these results with the coastal ocean depicted in the Copernicus GLORYS12 reanalysis as well as a new coastal water level reanalysis from NOAA, both of which have much higher spatial resolutions and utilize different data assimilation methods compared to our experiment set (i.e., SEAS5 and ACCESS-S). We will conclude by discussing potential opportunities for improved ocean data assimilation in many shallow-self regions, such as for much of the U.S. East Coast, which have typically been the most challenging to initialize and forecast well.
Co-authors:
Xiaoyu Long (Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA),
Magdalena Balmaseda (European Centre for Medium-Range Weather Forecasts, Reading, UK),
Claire Spillman (Bureau of Meteorology, Melbourne, VIC, AUS)>,
Grant Smith (Bureau of Meteorology, Melbourne, VIC, AUS),
Hao Zuo (European Centre for Medium-Range Weather Forecasts, Reading, UK),
Yonghong Yin (Bureau of Meteorology, Melbourne, VIC, AUS),
Oscar Alves (Bureau of Meteorology, Melbourne, VIC, AUS),
Arun Kumar (Climate Prediction Center, NCEP/NWS/NOAA, College Park, MD, USA),
Xue Feng (Cooperative Institute for Marine and Atmospheric Research, School of Ocean and Earth Science and Technology, University of Hawai’i at Mānoa, Honolulu, HI, USA),
Linta Rose (Cooperative Institute for Marine and Atmospheric Research, School of Ocean and Earth Science and Technology, University of Hawai’i at Mānoa, Honolulu, HI, USA),
Gregory Dusek (Center for Operational Oceanographic Products and Services, NOS/NOAA, Silver Spring, MD, USA)
