Sparse and inconsistent coverage of ocean observations makes analysis of climate impacts on ocean physics and marine ecosystems challenging. As a result, ocean reanalyses (i.e., ocean models created by observations through data assimilation) were developed to provide historical ocean state estimates that are spatially and temporally uniform. Recent advances in high-performance computing and the number and quality of observations have led to the development of high-resolution ocean reanalyses, which offer an opportunity to investigate coastal ocean variability with enhanced detail. In a new Progress in Oceanography article, MAPP-funded authors Dillon Amaya, Michael Alexander, James Scott, and Michael Jacox evaluate and compare the ability of three high-resolution ocean reanalyses; the Global Ocean Reanalysis and Simulations (GLORYS), the Ocean Reanalysis System version 5 (ORAS5), and the California Current System Reanalysis (CCSRA), to accurately represent ocean temperature and salinity (from the surface to the bottom), sea surface height, and mesoscale activity in the California Current Large Marine Ecosystem (CCLME). It was found that the reanalyses generally reproduced the large-scale variability in temperature and sea surface height within the CCLME, including effects of major ENSO events and recent marine heatwaves. In addition, GLORYS and CCSRA, with their finer horizontal resolution, have the ability to simulate nearshore ocean parameters such as coastal sea level and bottom temperature along the continental shelf. Lead author Dillon Amaya explains the use of renalyses in this study — “Because reanalyses are gridded and continuous in time and space, they are very attractive datasets for researchers looking to understand observed ocean variability in the past couple of decades. The fact that these reanalyses are also high-resolution is a huge plus for those working in the California Current System because the continental shelf along the US west coast is very narrow (~20-30km). Most reanalyses don’t resolve the shelf, but these do. That said, just because these products resolve finer details doesn’t mean they are always reliable on those scales. They are first and foremost models, constrained by observations. When you get to finer and finer scales, you often have fewer and fewer observations and therefore rely more heavily on the underlying model, which can introduce errors. The goal of this work was to really push these reanalyses and see how well they compare to raw observations on a variety of time and space scales. In doing so, we are able to make some recommendations for where and when these products are reliable.” Read the full paper here. Funding for this project was provided in part by the NOAA Climate Program Office, MAPP program. ———————————————————————————————– About MAPP The Modeling, Analysis, Predictions, and Projections (MAPP) Program is a competitive research program in NOAA Research’s Climate Program Office. MAPP’s mission is to enhance the Nation’s and NOAA’s capability to understand, predict, and project variability and long-term changes in Earth’s system and mitigate human and economic impacts. To achieve its mission, MAPP supports foundational research, transition of research to applications, and engagement across other parts of NOAA, among partner agencies, and with the external research community. MAPP plays a crucial role in enabling national preparedness for extreme events like drought and longer-term climate changes. For more information, please visit www.cpo.noaa.gov/MAPP.