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

An ensemble-based data assimilation framework for a coupled oceanatmosphere model is applied to investigate the influence of assimilating different types of ocean observations on the ocean and atmosphere simulation. The data assimilation is performed with the parallel data assimilation framework (PDAF) for the climate model AWI-CM. Observations of the ocean, namely satellite sea-surface temperature (SST) and temperature and salinity profiles, are assimilated into the ocean component. The atmospheric state is only influenced by the model dynamics. Different assimilation scenarios were carried out with different combinations of observations to investigate to what extent the assimilation into the coupled model leads to a better estimation of the state of the ocean as well as the atmosphere. The influence of the data assimilation is assessed by comparing the ocean prediction with dependent and independent ocean observations. For the atmosphere, the assimilation result is compared with the ERA-Interim atmospheric reanalysis data. The ocean temperature and salinity are improved by all the assimilation scenarios in the coupled system. The assimilation leads to a response of the atmosphere throughout the troposphere and impacts the global atmospheric circulation. Globally the temperature and wind speed are improved in the atmosphere on average. K E Y W O R D S coupled model, data assimilation, sea-surface temperature, temperature and salinity profiles 1 INTRODUCTION Traditionally, different components of the Earth system such as the ocean and the atmosphere are simulated by separate models with influences of other components being modelled as boundary conditions or forcings. However, the oceans and the atmosphere are connected and interact with each other. A consistent initial condition for these different components is required and is expected to provide a better forecast for both the ocean and the atmosphere. Earth system models simulate different components like the ocean, atmosphere, sea ice and land This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Section: Introductionmentioning

confidence: 99%

Improving the ocean and atmosphere in a coupled ocean–atmosphere model by assimilating satellite sea‐surface temperature and subsurface profile data

Tang

Sidorenko

et al. 2020

“…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 . ). 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 .…”

Section: Model and Data Assimilation Systemmentioning

confidence: 97%

“…An upgraded version of this system has recently been implemented operationally to produce ocean analyses and forecasts (Guiavarc'h et al . ) and work is ongoing to develop a higher‐resolution version of that system for operational coupled numerical weather prediction (NWP).…”

Section: Introductionmentioning

confidence: 99%

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

King

Lea

Martin

et al. 2019

Self Cite

The Argo array is a large component of the ocean observing system on which operational ocean forecasts rely. Global observations of the sub‐surface temperature and salinity from Argo enables the accurate initialisation of ocean forecasts, improving the position of currents and the overall energy available for air–sea interactions. Such constraints on the sub‐surface ocean are important for coupled initialisation for seasonal forecasting where the atmosphere is coupled to the upper ocean. As operational centres move toward using coupled atmosphere–ocean models for numerical weather prediction (NWP), it is useful to understand how ocean observations contribute to short‐range coupled forecasts. Using the Met Office Weakly Coupled Data Assimilation system, we have run Observing System Experiments (OSEs) to investigate the effect of withholding Argo observations on short‐range coupled ocean–atmosphere analyses and forecasts. Withholding Argo significantly increases the sub‐surface temperature and salinity RMS errors with consequent changes in the upper ocean heat content and positions of currents. Overall, there is little systematic impact of these ocean differences on global atmosphere analyses. However, in a case‐study over the period of hurricane Sandy (October 2012), we do see that Argo observations have some impact on atmospheric forecasts. The assimilation of Argo observations affects the position of the Gulf Stream leading to a small effect on the position and intensity of hurricane Sandy as it made landfall. As the resolution of the system is quite low compared to operational NWP systems, seeing even a small impact is encouraging. We might expect with future increased‐resolution coupled models that this influence of ocean observations on the atmosphere would only increase.

“…Bauer et al ., 2016; Uotila et al ., 2019). Due to satellite observations dating back to 1979, the sea ice concentration (SIC) is one of the few sea ice variables that have been well exploited by data assimilation studies into standalone sea‐ice models (Thomas et al ., 1996), coupled ocean–sea ice models (Lisæter et al ., 2003; Peterson et al ., 2015; Posey et al ., 2015; Yang et al ., 2015; 2016) and coupled ocean–atmosphere–sea ice models (Lea et al ., 2015; Guiavarc'h et al ., 2019; Mu et al ., 2020; Barton et al ., 2021). Therefore, SIC assimilation is well established and routine at many operational centres (e.g.…”

Section: Introductionmentioning

confidence: 99%

Improving the Met Office's Forecast Ocean Assimilation Model (FOAM) with the assimilation of satellite‐derived sea‐ice thickness data from CryoSat‐2 and SMOS in the Arctic

Mignac

Martin

Fiedler

et al. 2022

Derived from two complementary satellites, CryoSat-2 and Soil Moisture and Ocean Salinity (SMOS), sea ice thickness (SIT) data are assimilated into the Met Office's global ocean-sea ice forecasting system, FOAM, using a 3D-Var assimilation scheme, NEMOVAR. CryoSat-2 along-track SITs, which are converted from freeboard measurements using the model snow depth, and a daily, gridded SMOS SIT product are used in the assimilation to constrain the Arctic sea ice thickness. When using only CryoSat-2 assimilation, SIT forecast fields within the ice pack are greatly improved with respect to independent airborne measurements. However, the positive impacts of CryoSat-2 assimilation in thick ice regions are counteracted by an SIT overestimation in areas of thin ice, due to biased freeboard measurements there. Adding the SMOS assimilation results in much thinner SITs in those regions, which performs better than the control when compared to SIT objective analyses and mooring measurements in the Beaufort and Barents Seas. Furthermore, SMOS assimilation enhances the short-term predictive skill of the marginal sea-ice concentration relative to the control. This is translated into a consistent retreat of the sea-ice covered areas in the 5-day forecasts during March 2017, which is in better agreement with independent ice edge products. This work successfully demonstrates improvements in FOAM sea ice when SIT observations from both CryoSat-2 and SMOS are assimilated, representing an important step towards the operational implementation of SIT assimilation within Met Office forecasting systems.