55 research outputs found
MERRA-2 Ocean: The NASA Global Modeling and Assimilation Office's Weakly Coupled Atmosphere-Ocean Reanalysis Using GEOS-S2S Version 3
The NASA Modern Era Reanalysis for Research and Applications (MERRA2) has been a respected and widely used reanalysis that has so far been restricted to the atmosphere. Now a newly released version of the atmosphere/ocean coupled data assimilation system (AODAS) has been developed by the NASA/Goddard Global Modeling and Assimilation Office to perform a retrospective ocean reanalysis from 1982 to present. In addition to assimilating all available in situ data (e.g. Argo, mooring, XBT and CTD data) and altimetry information into the ocean, the new version (GEOS-S2S Version 3) model includes a higher resolution, eddy-permitting ocean model than previous versions, a more realistic implementation of the atmosphere-ocean interface layer, and an improved coupling between glacier and ocean (among other improvements). In addition, this ocean data assimilation was expanded to include the assimilation of satellite sea surface salinity. The MERRA-2 AODAS will be described, and preliminary results will be shown from the assimilation reanalysis and from retrospective forecasts issued using a new ensemble strategy. Following the Global Ocean Data Assimilation Experiment (GODAE) protocols, we will present Class 1 through Class 4 validation results from the ocean reanalysis. Results indicate an improved ocean mixed layer depth, improved salinity near Greenland, an improved diurnal cycle of the sea surface skin temperature, an improved estimate of ocean evaporation, and better representation of western boundary currents (e.g. Gulf Stream) from our new ocean reanalysis. One of the motivations of this project is to provide optimal initial states for ENSO forecasting. Therefore, we will also present some preliminary results of retrospective ENSO forecasts. After thorough testing, it is expected that the GEOS-S2S Version 3 will replace our contributions to North American Multi-Model Ensemble (NMME), WCRP Subseasonal to Seasonal (S2S), and IRI seasonal prediction forecast projects
The role of the Indian Ocean sector and sea surface salinity for prediction of the coupled Indo-Pacific system
The purpose of this dissertation is to evaluate the potential downstream influence of the Indian Ocean (IO) on El Niño/Southern Oscillation (ENSO) forecasts through the oceanic pathway of the Indonesian Throughflow (ITF), atmospheric teleconnections between the IO and Pacific, and assimilation of IO observations. Also the impact of sea surface salinity (SSS) in the Indo-Pacific region is assessed to try to address known problems with operational coupled model precipitation forecasts. The ITF normally drains warm fresh water from the Pacific reducing the mixed layer depths (MLD). A shallower MLD amplifies large-scale oceanic Kelvin/Rossby waves thus giving ~10% larger response and more realistic ENSO sea surface temperature (SST) variability compared to observed when the ITF is open. In order to isolate the impact of the IO sector atmospheric teleconnections to ENSO, experiments are contrasted that selectively couple/decouple the interannual forcing in the IO. The interannual variability of IO SST forcing is responsible for 3 month lagged widespread downwelling in the Pacific, assisted by off-equatorial curl, leading to warmer NINO3 SST anomaly and improved ENSO validation (significant from 3-9 months). Isolating the impact of observations in the IO sector using regional assimilation identifies large-scale warming in the IO that acts to intensify the easterlies of the Walker circulation and increases pervasive upwelling across the Pacific, cooling the eastern Pacific, and improving ENSO validation (r ~ 0.05, RMS~0.08C). Lastly, the positive impact of more accurate fresh water forcing is demonstrated to address inadequate precipitation forecasts in operational coupled models. Aquarius SSS assimilation improves the mixed layer density and enhances mixing, setting off upwelling that eventually cools the eastern Pacific after 6 months, counteracting the pervasive warming of most coupled models and significantly improving ENSO validation from 5-11 months. In summary, the ITF oceanic pathway, the atmospheric teleconnection, the impact of observations in the IO, and improved Indo-Pacific SSS are all responsible for ENSO forecast improvements, and so each aspect of this study contributes to a better overall understanding of ENSO. Therefore, the upstream influence of the IO should be thought of as integral to the functioning of ENSO phenomenon
Database of Observations: Ocean/Marine Perspectives
NASA GMAO is one of the contributing agencies in the Joint Center for Satellite Data Assimilation (JCSDA). One of the projects of the JCSDA is the Joint Effort for Data Assimilation Integration (JEDI). The JEDI framework needs a database of observations of the earth system. This talk is about planning for the ocean observations to be used in the JEDI based assimilation system at GMAO, NASA. We present preliminary requirements of such an observational database and scope out issues that need multi-agency attention in future
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Sea surface topography fields of the tropical Pacific from data assimilation
Time series of maps of monthly tropical Pacific dynamic topography
anomalies from 1979 through 1985 were constructed by means of assimilation of tide
gauge and expendable bathythermograph (XBT) data into a linear model driven by
observed winds. Estimates of error statistics were calculated and compared to actual
differences between hindcasts and observations. Four experiments were performed
as follows: one with no assimilation, one with assimiation of sea level anomaly data
from eight selected island tide gauge stations, one with assimilation of dynamic
height anomalies derived from XBT data, and one with both XBT and tide gauge
data assimilated. Data from seven additional tide gauge stations were withheld from
the assimilation process and used for verification in all four experiments. Statistical
objective maps based on data alone were also constructed for comparison purposes.
The dynamic response of the model without assimilation was, in general, weaker
than the observed response. Assimilation resulted in enhanced signal amplitude in
all three assimilation experiments. RMS amplitudes of statistical objective maps
were only strong near observing points. In large data-void regions these maps show
amplitudes even weaker than the wind-driven model without assimilation. With
few exceptions the error estimates generated by the Kalman filter appeared quite
reasonable. Since the error processes cannot be assumed to be white or stationary,
we could find no straightforward way to test the formal statistical hypothesis that
the time series of differences between the filter output and the actual observations
were drawn from a population with statistics given by the Kalman filter estimates.
The autocovariance of the innovation sequence, i.e., the sequence of differences
between forecasts before assimilation and observations, has long been used as an
indicator of how close a filter is to optimality. We found that the best filter we
could devise was still short of the goal of producing a white innovation sequence.
In this and earlier studies, little sensitivity has been found to the parameters under
our direct control. Extensive changes in the assumed error statistics make only
marginal differences. The same is true for long time and space scale behavior of
different models with richer physics and finer resolution. Better data assimilation
results will probably require relaxation of the assumptions of stationarity and serial
independence of the errors. Formulation of such detailed noise models will require
longer time series, with the attendant problems of matching very different data sets
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An optimized design for a moored instrument array in the tropical Atlantic Ocean
This paper presents a series of observing system simulation experiments (OSSEs) which are intended as a design study for a proposed array of instrumented moorings in the tropical Atlantic Ocean. Fields of TOPEX/Poseidon sea surface height anomalies are subsampled with the goal being reconstruction of the original fields through the use of reduced-space Kalman filter data assimilation at a restricted number of locations. Our approach differs from typical identical and fraternal twin experiments in that real observed data (i.e., TOPEX/Poseidon data) are subsampled and used in place of synthetic data in all phases of the OSSEs. In this way the question of how closely a particular model-generated data set resembles nature is avoided. Several data assimilation runs are performed in order to optimize the location of a limited number of moorings for the proposed Pilot Research Moored Array in the Tropical Atlantic (PIRATA). Results of experiments in which data are assimilated at 2°N, 2°S and the equator and the longitude is systematically varied by 5° show that the greatest impact of the assimilated data occurs when the observations are taken between 15°W and 30°W. Next, a more systematic technique is presented which allows us to determine optimal points in an objective fashion by applying a least squares regression approach to reconstruct the errors on a dense array of points from the data misfits at any three selected points. The forecast error structure from the Kalman filter is used in a novel way to assess the optimality of mooring locations. From a large sample of triads of points, the optimal mooring locations are found to be along the equator at 35°W, 20°W, and 10°W. Additional experiments are performed to demonstrate the efficacy of the initial and final PIRATA configurations and the added value that can be expected from PIRATA observations beyond existing expendable bathythermograph observations
The Impact of Satellite Sea Surface Salinity for Prediction of the Coupled Indo-Pacific System
We assess the impact of satellite sea surface salinity (SSS) observations on seasonal to interannual variability of tropical Indo-Pacific Ocean dynamics as well as on dynamical ENSO forecasts. Our coupled model is composed of a primitive equation ocean model for the tropical Indo-Pacific region that is coupled with the global SPEEDY atmospheric model (Molteni, 2003). The Ensemble Reduced Order Kalman Filter is used to assimilate observations to constrain dynamics and thermodynamics for initialization of the coupled model. The baseline experiment assimilates satellite sea level, SST, and in situ subsurface temperature and salinity observations. This baseline is then compared with experiments that additionally assimilate Aquarius (version 4.0) and SMAP (version 2.0) SSS. Twelve-month forecasts are initialized for each month from Sep. 2011 to Dec. 2016. We find that including satellite SSS significantly improves NINO3.4 sea surface temperature anomaly validation after 1 out to 12 month forecast lead times. For initialization of the coupled forecast, the positive impact of SSS assimilation is brought about by surface freshening near the eastern edge of the western Pacific warm pool and density changes that lead to shallower mixed layer between 10S-5N. SST differences at initialization force wide-spread downwelling favorable curl over most of the tropical Pacific. Over an average forecast, SST remains warmer with SSS assimilation at the eastern edge of the warm pool. This warm SST propagates into the eastern Pacific and drags westerly wind anomalies eastward into the NINO3.4 region. In addition, salting near the ITCZ leads to a deepening of the mixed layer and thermocline near 8N. These patterns together lead to a funneling effect that provides the background state to amplify equatorial Kelvin waves. We show that the downwelling Kelvin waves are amplified by assimilating satellite SSS and lead to significantly improved forecasts particularly for the 2015 El Nino
The Impact of Satellite Sea Surface Salinity for Prediction of the Coupled Indo-Pacific System
We assess the impact of satellite sea surface salinity (SSS) observations on seasonal to interannual variability of tropical Indo-Pacific Ocean dynamics as well as on dynamical ENSO forecasts. Our coupled model is composed of a primitive equation ocean model for the tropical Indo-Pacific region that is coupled with the global SPEEDY atmospheric model (Molteni, 2003). The Ensemble Reduced Order Kalman Filter is used to assimilate observations to constrain dynamics and thermodynamics for initialization of the coupled model. The baseline experiment assimilates satellite sea level, SST, and in situ subsurface temperature and salinity observations. This baseline is then compared with experiments that additionally assimilate Aquarius (version 4.0) and SMAP (version 2.0) SSS. Twelve-month forecasts are initialized for each month from Sep. 2011 to Dec. 2016. We find that including satellite SSS significantly improves NINO 3.4 sea surface temperature anomaly validation after 1 out to 12 month forecast lead times. For initialization of the coupled forecast, the positive impact of SSS assimilation is brought about by surface freshening near the eastern edge of the western Pacific warm pool and density changes that lead to shallower mixed layer between 10 degrees South latitude-5 degrees North latitude. SST differences at initialization force wide-spread downwelling favorable curl over most of the tropical Pacific. Over an average forecast, SST remains warmer with SSS assimilation at the eastern edge of the warm pool. This warm SST propagates into the eastern Pacific and drags westerly wind anomalies eastward into the NINO 3.4 region. In addition, salting near the ITCZ (Intertropical Convergence Zone) leads to a deepening of the mixed layer and thermocline near 8 degrees North latitude. These patterns together lead to a funneling effect that provides the background state to amplify equatorial Kelvin waves. We show that the downwelling Kelvin waves are amplified by assimilating satellite SSS and lead to significantly improved forecasts particularly for the 2015 El Nino
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Mapping tropical Pacific sea level: Data assimilation via a reduced state space Kalman filter
The well-known fact that tropical sea level can be usefully simulated by linear wind driven models recommends it as a realistic test problem for data assimilation schemes. Here we report on an assimilation of monthly data for the period 1975-1992 from 34 tropical Pacific tide gauges into such a model using a Kalman filter. We present an approach to the Kalman filter that uses a reduced state space representation for the required error covariance matrices. This reduction makes the calculation highly feasible. We argue that a more complete representation will be of no value in typical oceanographic practice, that in principle it is unlikely to be helpful, and that it may even be harmful if the data coverage is sparse, the usual case in oceanography. This is in part a consequence of ignorance of the correct error statistics for the data and model, but only in part. The reduced state space is obtained from a truncated set of multivariate empirical orthogonal functions (EOFs) derived from a long model run without assimilation. The reduced state space filter is compared with a full grid point Kalman filter using the same dynamical model for the period 1979-1985, assimilating eight tide gauge stations and using an additional seven for verification [Miller et al., 1995]. Results are not inferior to the full grid point filter, even when the reduced filter retains only nine EOFs. Five sets of reduced space filter assimilations are run with all tide gauge data for the period 1975-1992. In each set a different number of EOFs is retained: 5, 9, 17, 32, and 93, accounting for 60, 70, 80, 90, and 99% of the model variance, respectively. Each set consists of 34 runs, in each of which one station is withheld for verification. Comparing each set to the nonassimilation run, the average rms error at the withheld stations decreases by more than 1 cm. The improvement is generally larger for the stations at lowest latitudes. Increasing the number of EOFs increases agreement with data at locations where data are assimilated; the added structures allow better fits locally. In contrast, results at withheld stations are almost insensitive to the number of EOFs retained. We also compare the Kalman filter theoretical error estimates with the actual errors of the assimilations. Features agree on average, but not in detail, a reminder of the fact that the quality of theoretical estimates is limited by the quality of error models they assume. We briefly discuss the implications of our work for future studies, including the application of the method to full ocean general circulation models and coupled models.Copyrighted by American Geophysical Union
GEOS S2S Version 3: The New NASA/GMAO High Resolution Seasonal Prediction System
The NASA/Goddard Global Modeling and Assimilation Office (GMAO) released Version 2 of the Subseasonal to Seasonal (GEOS-S2S) forecast system in the fall of 2017, and it has been producing near-real time subseasonal to seasonal forecasts and a weakly coupled atmosphere-ocean data assimilation record since then. A new version of the coupled modeling and analysis system (Version 3) was released by the GMAO at the end of 2019. The new version runs at higher oceanic resolution than the previous (approximately 1/2 degree for the atmosphere, 1/4 degree for the ocean), and includes interactive earth system model components not typically present in seasonal prediction systems (two moment cloud microphysics for aerosol indirect effect and an interactive aerosol model). The weakly coupled atmosphere-ocean data assimilation system now includes assimilation of sea surface salinity, that has been shown to result in improved ocean mixed layer simulation and ENSO prediction skill
An Introduction to the NASA GMAO Coupled Atmosphere-Ocean System - GEOS-S2S Version 3
Recently NASA's Global Modeling and Assimilation Office (GMAO) has developed a new Subseasonal to Seasonal Prediction system Version 3 (GEOS-S2S-3). This upgrade replaces the GEOS-S2S-2 which is NASA's current contribution to the North American Multi-Model Experiment seasonal prediction project (Kirtman et al., 2014). The main improvements for our S2S-3 system include 1) a higher resolution MOM5 (Griffies et al., 2005) ocean model (now 0.25o x 0.25o x 50 layers), 2) an improved atmospheric/ocean interface layer (Akella and Suarez, 2018), and 3) assimilation of a long-track satellite salinity into the ocean model (Hackert et al, 2019). Atmospheric forcing is provided by the NASA MERRA-2 reanalysis (Gelaro et al., 2017). Initialization for the ocean relies on the GMAO ocean reanalysis system which assimilates all available in situ temperature and salinity, satellite sea surface salinity, and sea level using the Local Ensemble Transform Kalman Filter (LETKF) implementation of (Penny et al., 2013) on a 5 day assimilation cycle with 20 fixed ensemble members.In this presentation, we will authenticate our new S2S-3 ocean reanalysis using standard GODAE validation metrics. For example, we will compare gridded fields of mean and standard deviation of the ocean reanalysis versus observed fields. We will show correlation/RMS of model versus observations and temperature and salinity mean profiles for the various basins and latitude bands. Basin-scale volume transports, such as the Atlantic Meridional Overturning Circulation and the Indonesian Throughflow will be validated. Equatorial ocean waves will be compared by decomposing sea level into Kelvin and Rossby components. For each of these metrics, we plan to validate the results and then compare our new S2S-3 against the current production version, S2S-2. Finally, we will compare 9-month seasonal forecasts initialized from these two systems for the tropical Pacific NINO3.4 region over the period 1981-present
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