Robin Kovach

Error Covariance Modeling in the GMAO Ocean Ensemble Kalman Filter

Abstract In practical applications of the ensemble Kalman filter (EnKF) for ocean data assimilation, the computational burden and memory limitations usually require a trade-off between ensemble size and model resolution. This is certainly true for the NASA Global Modeling and Assimilation Office (GMAO) ocean EnKF used for ocean climate analyses. The importance of resolution for the adequate representation of the dominant current systems means that small ensembles, with their concomitant sampling biases, have to be used. Hence, strategies have been sought to address sampling problems and to improve the performance of the EnKF for a given ensemble size. Approaches assessed herein consist of spatiotemporal filtering of background-error covariances, improving the system-noise representation, imposing a steady-state error covariance model, and speeding up the analysis by performing the most expensive operation of the analysis on a coarser computational grid. A judicious combination of these approaches leads to...

Comparison and Sensitivity of ODASI Ocean Analyses in the Tropical Pacific

Abstract Two global ocean analyses from 1993 to 2001 have been generated by the Global Modeling and Assimilation Office (GMAO) and Geophysical Fluid Dynamics Laboratory (GFDL), as part of the Ocean Data Assimilation for Seasonal-to-Interannual Prediction (ODASI) consortium efforts. The ocean general circulation models (OGCM) and assimilation methods in the analyses are different, but the forcing and observations are the same as designed for ODASI experiments. Global expendable bathythermograph and Tropical Atmosphere Ocean (TAO) temperature profile observations are assimilated. The GMAO analysis also assimilates synthetic salinity profiles based on climatological T–S relationships from observations (denoted “TS scheme”). The quality of the two ocean analyses in the tropical Pacific is examined here. Questions such as the following are addressed: How do different assimilation methods impact the analyses, including ancillary fields such as salinity and currents? Is there a significant difference in interpre...

Intercomparison of the Arctic sea ice cover in global ocean–sea ice reanalyses from the ORA-IP project

AbstractOcean–sea ice reanalyses are crucial for assessing the variability and recent trends in the Arctic sea ice cover. This is especially true for sea ice volume, as long-term and large scale sea ice thickness observations are inexistent. Results from the Ocean ReAnalyses Intercomparison Project (ORA-IP) are presented, with a focus on Arctic sea ice fields reconstructed by state-of-the-art global ocean reanalyses. Differences between the various reanalyses are explored in terms of the effects of data assimilation, model physics and atmospheric forcing on properties of the sea ice cover, including concentration, thickness, velocity and snow. Amongst the 14 reanalyses studied here, 9 assimilate sea ice concentration, and none assimilate sea ice thickness data. The comparison reveals an overall agreement in the reconstructed concentration fields, mainly because of the constraints in surface temperature imposed by direct assimilation of ocean observations, prescribed or assimilated atmospheric forcing and assimilation of sea ice concentration. However, some spread still exists amongst the reanalyses, due to a variety of factors. In particular, a large spread in sea ice thickness is found within the ensemble of reanalyses, partially caused by the biases inherited from their sea ice model components. Biases are also affected by the assimilation of sea ice concentration and the treatment of sea ice thickness in the data assimilation process. An important outcome of this study is that the spatial distribution of ice volume varies widely between products, with no reanalysis standing out as clearly superior as compared to altimetry estimates. The ice thickness from systems without assimilation of sea ice concentration is not worse than that from systems constrained with sea ice observations. An evaluation of the sea ice velocity fields reveals that ice drifts too fast in most systems. As an ensemble, the ORA-IP reanalyses capture trends in Arctic sea ice area and extent relatively well. However, the ensemble can not be used to get a robust estimate of recent trends in the Arctic sea ice volume. Biases in the reanalyses certainly impact the simulated air–sea fluxes in the polar regions, and questions the suitability of current sea ice reanalyses to initialize seasonal forecasts.

The 2015/16 El Niño Event in Context of the MERRA-2 Reanalysis: A Comparison of the Tropical Pacific with 1982/83 and 1997/98

The 2015/2016 El Niño is analyzed using atmospheric/oceanic analysis produced using the Goddard Earth Observing System (GEOS) data assimilation systems. As well as describing the structure of the event, a theme of the work is to compare and contrast it with two other strong El Niños, in 1982/1983 and 1997/1998. These three El Niño events are included in the Modern-Era Retrospective analysis for Research and Applications (MERRA) and in the more recent MERRA-2 reanalyses. MERRA-2 allows a comparison of fields derived from the underlying GEOS model, facilitating a more detailed comparison of physical forcing mechanisms in the El Niño events. Various atmospheric/oceanic structures indicate that the 2015/2016 El Niño maximized in the Niño3.4 region, with the large region of warming over most of the Pacific and Indian Ocean. The eastern tropical Indian Ocean, Maritime Continent, and western tropical Pacific are found to be less dry in boreal winter, compared to the earlier two strong events. While the 2015/2016 El Niño had an earlier occurrence of the equatorial Pacific warming and was the strongest event on record in the central Pacific, the 1997/1998 event exhibited a more rapid growth due to stronger westerly wind bursts and Madden-Julian Oscillation during spring, making it the strongest El Niño in the eastern Pacific. Compared to 1982/1983 and 1997/1998, the 2015/2016 event has a shallower thermocline over the eastern Pacific with a weaker zonal contrast of sub-surface water temperatures along the equatorial Pacific. While the three major ENSO events have similarities, each are unique when looking at the atmosphere and ocean surface and sub-surface.

The impact of the assimilation of Aquarius sea surface salinity data in the GEOS ocean data assimilation system

Ocean salinity and temperature differences drive thermohaline circulation. These properties also play a key role in the ocean-atmosphere coupling. With the availability of L-band space-borne observations, it becomes possible to provide global scale sea surface salinity (SSS) distribution. This study analyzes globally the along-track (Level 2) Aquarius SSS retrievals obtained using both passive and active L-band observations. Aquarius along-track retrieved SSS are assimilated into the ocean data assimilation component of Version 5 of the Goddard Earth Observing System (GEOS-5) assimilation and forecast model. We present a methodology to correct the large biases and errors apparent in Version 2.0 of the Aquarius SSS retrieval algorithm and map the observed Aquarius SSS retrieval into the ocean models bulk salinity in the topmost layer. The impact of the assimilation of the corrected SSS on the salinity analysis is evaluated by comparisons with in situ salinity measurements from Argo. The results show a significant reduction of the global biases and RMS of observations-minus-forecast differences at in situ locations. The most striking results are found in the tropics and southern latitudes. Our results highlight the complementary role and problems that arise during the assimilation of salinity information from in situ (Argo) and space-borne SSS retrievals.

The Roles of Climate Change and Climate Variability in the 2017 Atlantic Hurricane Season

The 2017 Atlantic hurricane season was extremely active with six major hurricanes, the third most on record. The sea-surface temperatures (SSTs) over the eastern Main Development Region (EMDR), where many tropical cyclones (TCs) developed during active months of August/September, were ~0.96 °C above the 1901–2017 average (warmest on record): about ~0.42 °C from a long-term upward trend and the rest (~80%) attributed to the Atlantic Meridional Mode (AMM). The contribution to the SST from the North Atlantic Oscillation (NAO) over the EMDR was a weak warming, while that from El Niño–Southern Oscillation (ENSO) was negligible. Nevertheless, ENSO, the NAO, and the AMM all contributed to favorable wind shear conditions, while the AMM also produced enhanced atmospheric instability. Compared with the strong hurricane years of 2005/2010, the ocean heat content (OHC) during 2017 was larger across the tropics, with higher SST anomalies over the EMDR and Caribbean Sea. On the other hand, the dynamical/thermodynamical atmospheric conditions, while favorable for enhanced TC activity, were less prominent than in 2005/2010 across the tropics. The results suggest that unusually warm SST in the EMDR together with the long fetch of the resulting storms in the presence of record-breaking OHC may be key factors in driving the strong TC activity in 2017.