Assessment of ocean analysis and forecast from an atmosphere–ocean coupled data assimilation operational system

Abstract

Abstract. The development of coupled atmosphere–ocean prediction systems with utility on short-range numerical weather prediction (NWP) and ocean forecasting timescales has accelerated over the last decade. This builds on a body of evidence showing the benefit, particularly for weather forecasting, of more correctly representing the feedbacks between the surface ocean and atmosphere. It prepares the way for more unified prediction systems with the capability of providing consistent surface meteorology, wave and surface ocean products to users for whom this is important. Here we describe a coupled ocean–atmosphere system, with weakly coupled data assimilation, which was operationalised at the Met Office as part of the Copernicus Marine Environment Service (CMEMS). We compare the ocean performance to that of an equivalent ocean-only system run at the Met Office and other CMEMS products. Sea surface temperatures in particular are shown to verify better than in the ocean-only systems, although other aspects including temperature profiles and surface currents are slightly degraded. We then discuss the plans to improve the current system in future as part of the development of a “coupled NWP” system at the Met Office.

Assimilating synthetic Biogeochemical-Argo and ocean colour observations into a global ocean model to inform observing system design

“…As stated in Sect. 3, OSSEs yield the most reliable conclusions when all sources of real-world error have been appropriately accounted for (Halliwell et al, 2014). This means that the errors between FREE and NATURE should, ideally, be broadly similar to the errors between FREE and the real world.…”

“…One of the keys to obtaining informative results from an OSSE is to ensure that all sources of error are appropriately accounted for (Halliwell et al, 2014(Halliwell et al, , 2017Hoffman and Atlas, 2016). If the free run is more similar to the nature run than the real forecasting system is to the real world, then it can become easier for the assimilative system to recover the truth, and the impact of the observing networks may be incorrectly estimated.…”

Assimilating synthetic Biogeochemical-Argo and ocean colour observations into a global ocean model to inform observing system design

“…FOAM is run operationally at the Met Office to produce short-range forecasts of the physical ocean and sea ice state. It is also used to initialise the ocean and sea ice components of the Met Office Global Seasonal forecasting system version 5 (GloSea5) (MacLachlan et al, 2015;Scaife et al, 2014), and short-range coupled ocean-atmosphere forecasting system (Guiavarc'h et al, 2019). The biogeochemical ocean model used in this study is version 2 of the Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification (MEDUSA) (Yool et al, 2013).…”

The impact of Argo observations in a global weakly coupled ocean–atmosphere data assimilation and short‐range prediction system

“…The experiments described in this paper have been run using the Met Office weakly coupled data assimilation system which was created to investigate the effect of weakly coupled ocean-atmosphere data assimilation on short-range coupled forecasts. A upgraded version of this system has been providing operational ocean forecasts for the Copernicus Marine Environment Monitoring Service (CMEMS) since July 2017 (Guiavarc'h et al 2019). This system is based on HadGEM3, the coupled Hadley Centre Global Environment Model version 3, with the GA4.0 physics for the atmosphere and GO3.0 for the ocean (Hewitt et al 2011).…”

“…The coupled model provides background information for separate analyses in each sub-component with the increments being added back into the coupled model. An upgraded version of this system has recently been implemented operationally to produce ocean analyses and forecasts (Guiavarc'h et al 2019) and work is ongoing to develop a higher-resolution version of that system for operational coupled numerical weather prediction (NWP).…”