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Hackert, Eric

Publications and source records attributed to Hackert, Eric.

13 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

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.

Molod, Andrea↗
NASA NTRS2020

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.

Molod, Andrea↗
NASA NTRS2019

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.

Molod, Andrea↗
NASA NTRS2019

Satellite Salinity Observing System: Recent Discoveries and the Way Forward

Advances in L-band microwave satellite radiometry in the past decade, pioneered by ESA's SMOS and NASA's Aquarius and SMAP missions, have demonstrated an unprecedented capability to observe global sea surface salinity (SSS) from space. Measurements from these missions are the only means to probe the very-near surface salinity (top cm), providing a unique monitoring capability for the interfacial exchanges of water between the atmosphere and the upper-ocean, and delivering a wealth of information on various salinity processes in the ocean, linkages with the climate and water cycle, including land-sea connections, and providing constraints for ocean prediction models. The satellite SSS data are complimentary to the existing in situ systems such as Argo that provide accurate depiction of large-scale salinity variability in the open ocean but under-sample mesoscale variability, coastal oceans and marginal seas, and energetic regions such as boundary currents and fronts. In particular, salinity remote sensing has proven valuable to systematically monitor the open oceans as well as coastal regions up to approximately 40 km from the coasts. This is critical to addressing societally relevant topics, such as land-sea linkages, coastal-open ocean exchanges, research in the carbon cycle, near-surface mixing, and air-sea exchange of gas and mass. In this paper, we provide a community perspective on the major achievements of satellite SSS for the aforementioned topics, the unique capability of satellite salinity observing system and its complementarity with other platforms, uncertainty characteristics of satellite SSS, and measurement versus sampling errors in relation to in situ salinity measurements. We also discuss the need for technological innovations to improve the accuracy, resolution, and coverage of satellite SSS, and the way forward to both continue and enhance salinity remote sensing as part of the integrated Earth Observing System in order to address societal needs.

Vinogradova, Nadya↗
NASA NTRS2018

GEOS S2S-2_1: The GMAO High Resolution Seasonal Prediction System

A new version of the coupled modeling and analysis system used to produce near real time subseasonal to seasonal forecasts was recently released by the NASA/Goddard Global Modeling and Assimilation Office. The new version runs at higher atmospheric resolution than the previous, (approximately 1/2 degree globally), contains a substantially improved model description of the cryosphere, and includes additional interactive earth system model components (aerosol model). In addition, the Ocean data assimilation system has been replaced with a Local Ensemble Transform Kalman Filter, and now includes the assimilation of along-track sea surface height. Here will describe the new system, along with the plans for the future (GEOS S2S-3_0) which will include a higher resolution ocean model and more interactive earth system model components (interactive vegetation, biomass burning from fires). We will also present results from a series of retrospective seasonal forecasts. Results show significant improvements in surface temperatures over much of the northern hemisphere and a much improved prediction of sea ice extent in both hemispheres. Analysis of the ensemble spread shows improvements relative to the previous system, including generally better reliability. The precipitation forecast skill is comparable to previous S2S systems, and the only tradeoff is an increased "double ITCZ", which is expected as we go to higher atmospheric resolution.

Molod, Andrea↗
NASA NTRS2018

Impacts of NASA's Earth Observations on Subseasonal and Seasonal Forecasts

NASA's suite of Earth-observing satellites provides a unique view of many processes on Earth, with relevance on timescales ranging from hours to weeks and even years. NASA's observations span all parts of the Earth system: atmospheric, ocean, land and cryosphere, and include physical, chemical and biological components. This presentation explores use of NASA observations in extended-range prediction, from many days to months, using the Goddard Earth Observing System (GEOS) assimilation and predictive modeling capabilities. The skill of a weather forecast is linked to the fidelity of the initialization process (data assimilation) and the realistic representation of "fast" physical processes in the model. At longer time horizons, the slower "feedback" processes in the Earth System begin to take a more prominent role in the accuracy of the forecast. Simultaneously, forecast-skill attribution transitions away from feature-based metrics that emphasize smaller scales (e.g., representations of fronts and vortices) to metrics that emphasize the statistical distributions of large-scale features (e.g., ENSO diagnostics and teleconnections). This presentation will summarize studies performed using the GEOS-S2S (subseasonal to seasonal) system that explore the impacts of NASA observations on the fidelity of the forecasts. The GEOS-S2S system is configured for the atmosphere-ocean-land-ice model and is initialized using in-situ and space-based observations, including atmospheric aerosols and ozone which are not typically analyzed in such systems. The GEOS-S2S model routinely includes aerosol feedbacks, which systematically impact the realism of the forecasts, providing a first example of how suitable NASA observations impact the performance of the GEOS-S2S system. Studies in which a stratospheric chemistry module is activated in the GEOS-S2S system allow the impacts of ozone radiative feedbacks to be isolated. Space-based observations of sea-surface temperature and altimetry are routinely analyzed for the initialization of the GEOS-S2S system; recent advances allow the use of NASA's sea-surface salinity data, which are shown to impact the long-range skill of the forecasts.

Pawson, Steven↗
NASA NTRS2018

NASA GMAO GEOS S2S Prediction System: Metrics, Post-Processing and Products

In this presentation we present an overview of the GMAO Sub-Seasonal and Seasonal Prediction System, current users and products, and methods for validation and evaluation of the system. Methods for evaluation include baseline evaluations metrics, the ability to simulate key modes of variability, and evaluation of new development areas.

Molod, Andrea M.↗
NASA NTRS2018

NASA GMAO S2S Prediction System Hindcast and Near-Real Time Operations Strategy

In this presentation we present an overview of the GMAO Sub-Seasonal and Seasonal Prediction System with a focus on the computing time and resources and actual time it takes to complete a full set of hindcasts. The goal is to come up with some solutions to allow us to run more ensemble members for the next version of the system which will be higher resolution and take many more resources.

Kovach, Robin↗
NASA NTRS2018

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.

El Nino↗
NASA NTRS2018

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.

SST↗
NASA NTRS2017

GEOS S2S-2_1: GMAO's New High Resolution Seasonal Prediction System

A new version of the modeling and analysis system used to produce sub-seasonal to seasonal forecasts has just been released by the NASA Goddard Global Modeling and Assimilation Office. The new version runs at higher atmospheric resolution (approximately 12 degree globally), contains a substantially improved model description of the cryosphere, and includes additional interactive earth system model components (aerosol model). In addition, the Ocean data assimilation system has been replaced with a Local Ensemble Transform Kalman Filter. Here will describe the new system, along with the plans for the future (GEOS S2S-3_0) which will include a higher resolution ocean model and more interactive earth system model components (interactive vegetation, biomass burning from fires). We will also present results from a free-running coupled simulation with the new system and results from a series of retrospective seasonal forecasts. Results from retrospective forecasts show significant improvements in surface temperatures over much of the northern hemisphere and a much improved prediction of sea ice extent in both hemispheres. The precipitation forecast skill is comparable to previous S2S systems, and the only trade off is an increased double ITCZ, which is expected as we go to higher atmospheric resolution.

Molod, Andrea↗
NASA NTRS2000

The Child's Tantrum: El Nino. The Origin of the El Nino-Southern Oscillation

In 1997, a child's tantrums caught the world's attention. These tantrums took the form not of crying and foot stamping, but of droughts and floods. Obviously, this was no ordinary child. It was, in fact, The Child, or El Nino, as it was, named in the late 1800s by South American observers, who noted that its timing coincided with the Christmas holiday. El Nino is a reversal in sea surface temperature (SST) distributions that occurs once every few years in the tropical Pacific. When it coincides with a cyclical shift in air pressure, known as the Southern Oscillation, normal weather patterns are drastically altered. The combined phenomenon is known as El Nino-Southern Oscillation (ENSO). Although ENSO is a regular phenomenon, it was unusually strong in 1997. It produced heavy rainfall and floods in California and bestowed spring-like temperatures on the Midwest during the winter. These drastic changes in normal weather patterns captured the public imagination, from news reports to jokes on late-night talk shows. Naturally, people wanted to. know as much, about El Nino as possible. Fortunately, scientists had at their disposal new satellites and ocean sensors that provided an unprecedented level of information. Consequently, not only was the 1997 ENSO the strongest in recent memory, but it was also the most thoroughly studied. Prominent groups such as the NASA Seasonalto-Interannual Prediction Project (NSIPP) combined numerous aspects of climate modeling into a single, predictive endeavor.

Picault, Joel↗