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

Abstract

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.

Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0)

“…The current implementation of AWI-CM-PDAF only contains assimilation for the ocean component, while assimilation for the atmosphere is technically prepared. First studies (Mu et al, 2020;Tang et al, 2020) based on this implementation have been published. In future work, we plan to add the…”

“…PDAF (Nerger and Hiller, 2013, http://pdaf.awi.de, last access: 14 September 2020) is a free open-source software that was developed to simplify the implementation and application of ensemble DA methods. PDAF provides a generic framework containing fully implemented and parallelized ensemble filter and smoother algorithms like the LETKF (Hunt et al, 2007), the ESTKF (Nerger et al, 2012b), or the nonlinear NETF method (Tödter and Ahrens, 2015) and related smoothers (e.g., Nerger et al, 2014;Kirchgessner et al, 2017). Further, it provides functionality to adapt a model parallelization for parallel ensemble forecasts as well as routines for parallel communication linking the model and filters.…”

“…Applications based on the AWI-CM-PDAF 1.0 code are given in Mu et al (2020), where the focus is on the effect L. Nerger et al: Building a coupled data assimilation system on sea ice, and Tang et al (2020), who discuss the reaction of the atmosphere on assimilating ocean observations.…”

“…The same SST observations as in Sect. 4.1 are assimilated, which are treated as in Tang et al (2020). The resolution of the observations is 0.1 • and hence higher than the resolution of the model in most regions.…”

“…By now the weakly coupled assimilation is the common choice for assimilation into L. Nerger et al: Building a coupled data assimilation system coupled models and recent studies assess the effect of this assimilation approach. For atmosphere-ocean coupled models, different studies either assimilated observations of one compartment into the observed compartment (e.g., Kunii et al, 2017;Mu et al, 2020) or assimilated observations of each compartment into the corresponding observed compartments (e.g., Zhang et al, 2007;Liu et al, 2013;Han et al, 2013;Chang et al, 2013;Lea et al, 2015;Karspeck et al, 2018;Browne et al, 2019). The research question considered in these studies is usually to what extent the assimilation into a coupled model can improve predictions in comparison to the assimilation into uncoupled models.…”