Coupled assimilation strategies

The Community Earth System Model (CESM) has been interfaced to a community facility for ensemble data assimilation (Data Assimilation Research Testbed – DART). In the CESM-DART framework, data is assimilated into each of the respective atmosphere/ocean/land model

components during the assimilation step, and information is exchanged between the model components during the forecast step.

As a first step towards the development of an ocean-atmosphere coupled data assimilation system, the NASA-Global Modeling and Assimilation Office (GMAO) atmospheric system has been

extended to model and analyze skin sea surface temperature (SST) using a simple air-sea interface layer. This layer modifies the bulk SST to include near-surface effects, such as diurnal warming due to solar insolation and cool-skin that were previously not felt by the atmosphere. The impact of surface waves is parametrized in this initial version of interface layer. By directly assimilating

infrared and microwave satellite radiance observations that include SST sensitive channels and in situ data from ships and buoys, using the Gridpoint Statistical Interpolation (co-developed with NCEP) realistic diurnally varying skin SST can be estimated.

At the University of Washington, strategies in fully coupled data assimilation have been considered in an idealized low-dimensional analogue of the coupled atmosphere-ocean North Atlantic climate system, featuring the Atlantic meridional overturning circulation (AMOC). The ability to initialize the multi-frequency AMOC with an EnKF has been assessed over a range of experiments with varying levels of observations available for assimilation (atmosphere, upper and deep ocean).

Scarcity of observations of the ocean interior is a key barrier to further improvements in ocean state estimation and forecasting. Coupled data assimilation has the potential to ameliorate this problem by extracting information from atmospheric observations to correct the ocean state using coupled atmosphere-ocean covariances. This is being investigated at NRL by evaluating the impact of scatterometer wind measurements on ocean analyses in the Mediterranean Sea (Frolov 2014, personal communication).

3.4 SUMMARY AND FUTURE PROSPECTS

The application of data assimilation in the context of forecast models and observing systems that are both becoming increasingly complex represents a significant challenge. To address this, research to improve data assimilation methodologies, diagnostic tools and their application to a wide range of geophysical systems will continue.

Increases in computing power allow for the use of forecast models with improved temporal and spatial resolution that are also increasingly coupled with other components of the Earth system.

Computing power has become sufficient to also facilitate the use of ensembles of increasing size with the goal of enabling the use of more advanced data assimilation procedures. Existing data assimilation methods and diagnostic tools must be improved and new methods developed to take full advantage of these increases in the complexity of prediction systems. Methods must be highly computationally efficient and readily parallelize over a very large number of processors. In addition, the explicit inclusion of additional physical processes, increases in spatial resolution and the

coupling of multiple components of the Earth system require data assimilation methods that can better account for nonlinearity and non-Gaussian uncertainties.

In summary, the development of data assimilation systems will progress in the future towards systems that have higher resolution, larger ensemble size, higher degree of coupling, and a greater volume and variety of the types of assimilated observations.

3.5 ACKNOWLEDGEMENTS

The authors thank Arlindo da Silva (GMAO, NASA) and an anonymous reviewer for their assistance in summarizing issues related to the assimilation of atmospheric constituents in global reanalyses.

The notes taken by the young scientists during the Open Science Conference in Montreal during the Observations and Data Assimilation Theme Sessions helped to contribute and clarify various aspects of this paper.

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