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Eric Hackert

Publications and source records attributed to Eric Hackert.

14 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Impacts of the Eruption of Mount Pinatubo on Surface Temperatures and Precipitation Forecasts With the NASA GEOS Subseasonal-to-Seasonal System

A contemporary seasonal forecasting system is used to study the impacts of a volcanic sulfate injection into the stratosphere on the seasonal forecasts for surface temperatures, the El Niño Southern Oscillation (ENSO), and precipitation. The focus is a case study of the June 1991 eruption of Mt. Pinatubo, Philippines and the period from July 1991 to February 1992. Version 2 of the Goddard Earth Observing System (GEOS) subseasonal-to-seasonal (S2S) forecasting system is used in this study. GEOS-S2S includes the GOddard Chemistry, Aerosols, Radiation and Transport (GOCART) aerosol module, which allows to prognostically simulate aerosol distributions. GOCART is coupled to the radiation and cloud modules to include the impact of the eruption on forecasted radiation and precipitation. The coupled GEOS-S2S system was initialized in May 1991 with fields based on observations to produce ten-member 9-month forecasts with and without the volcanic sulfur injection. The results of these ensemble experiments demonstrate that including Mt. Pinatubo in seasonal forecasts would improve the forecasts of the 1991–1992 global mean temperature and precipitation but worsen the forecast of ENSO by strengthening of El Niño beyond what showed in observations. Most significant changes in the forecasts of temperatures and precipitation are limited to the tropics. The only land area where the inclusion of Pinatubo significantly lowered the forecasted precipitation is tropical Africa.

Mt. Pinatubo↗
NASA NTRS2020

Satellite Sea Surface Salinity Observations Impact on El Niño/Southern Oscillation Predictions: Case Studies from the NASA GEOS Seasonal Forecast System

El Niño/Southern Oscillation (ENSO) has far reaching global climatic impacts and so extending useful ENSO forecasts would have great societal benefit. However, one key variable that has yet to be fully exploited within coupled forecast systems is accurate estimation of near-surface ocean salinity. Satellite sea surface salinity (SSS), combined with temperature, help to improve the estimates of ocean density changes and associated near-surface mixing. For the first time, we assess the impact of satellite SSS observations for improving near-surface dynamics within ocean reanalyses and how these initializations impact dynamical ENSO forecasts using NASA’s coupled forecast system (GEOS-S2S-2). For all initialization experiments, all available sea level and in situ temperature and salinity observations are assimilated. Separate observing system experiments (OSE) additionally assimilate Aquarius, and SMAP, SMOS, and these datasets combined. We highlight the impact of satellite SSS on ocean reanalyses by comparing experiments with and without the application of SSS assimilation. Next, we compare case studies of coupled forecasts for the big 2015 El Niño, the 2017 La Niña, and the weak El Niño in 2018 that are initialized from GEOS-S2S-2 spring reanalyses that assimilate and withhold along-track SSS. For each of these ENSO-event case studies, assimilation of satellite SSS improves the forecast validation with respect to observed NINO3.4 anomalies (or at least reduces the forecast uncertainty). Satellite SSS assimilation improved characterization of the mixed layer depth leading to more accurate coupled air/sea interaction and better forecasts. These results further underline the value of satellite SSS assimilation into operational forecast systems.

Eric Hackert↗
NASA NTRSDate not supplied

Improving Ocean Reanalyses and ENSO Forecasts By Assimilation of Rain Corrected Satellite Sea Surface Salinity Using the GMAO S2S Forecast System

During the past years, we have seen that the La Nina to El Nino transition has had a significant global socio-economic impact and so has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of satellite altimetry and subsurface temperature and salinity from (mostly) Argo help to improve the initialization of the thermocline, while satellite Sea Surface Temperature (SST) aids in constraining surface heat-fluxes, leading to improved subseasonal to seasonal forecasts of the coupled system. However, few studies have focused on improving the fresh-water flux and near-surface density and mixing through assimilation of satellite sea surface salinity (SSS). For expediency, the few ocean models that do assimilate SSS bias-correct the satellite SSS data to normalize towards the near-surface Argo data. However, in rainy regions, where buoyant water sits as a fresh lens at the surface, this assumption is likely inadequate. In previous work, we have shown that adjusting SSS data to bulk salinity (Sb) using the Rain Impact Model (RIM) of Santos-Garcia et al., 2014 has improved the near-surface density and mixed layer depth, leading to deeper thermocline and improved the NINO3.4 SST forecasts. Now we utilize the Soil Moisture/Ocean Salinity, Rain Corrected (SMOS_RC) SSS product provided by the Centre Aval de Traitement des données SMOS (CATDS CPDC) to represent the Sb more accurately at first model layer (in our case 5 m). Rather than using a diffusivity model as with RIM, SMOS_RC relies on an observed relationship between the spatial heterogeneity of SMOS SSS and instantaneous rain rate (RR) (Supply et al., 2020). In order to test the impact of SMOS_RC versus SMOS, we compare two reanalyses over the period 2014 to 2021. For both reanalysis experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF scheme (Penny et al., 2013). One reanalysis additionally assimilates SMOS SSS data as is (i.e., with the fresh bias), and a separate reanalysis is performed assimilating the SMOS_RC data. We assess the impact for near-surface and subsurface dynamics within ocean reanalyses by validating against observations and explore how SSS assimilation (SMOS versus SMOS_RC) impacts dynamical ENSO forecasts using the NASA GMAO Sub-seasonal to Seasonal coupled forecast system (GEOS S2S-3, Molod et al., 2020, Hackert et al., 2023). We will show that improved SSS estimates and near-surface density and mixing led to more accurate coupled air/sea interaction and better ENSO forecasts.

Eric Hackert↗
NASA NTRSDate not supplied

Early Comparison of Ocean Analyses Using JEDI/SOCA 3 DVar vs. GEOS-S2S- 3 ODAS LETKF

At NASA’s Global Modeling and Assimilation Office (GMAO), data assimilation (DA) for the next-generation Goddard Earth Observing System Subseasonal-to-Seasonal (GEOS-S 2 S) coupled-model forecast system will transition to the Joint Effort for DA Integration (JEDI) system, which includes the marine DA component SOCA (Sea ice, Ocean, and Coupled Assimilation). It is envisioned that incorporating SOCA into GMAO Earth System Modeling will allow a more systematic approach to assimilating new data types (e.g., SWOT KaRIn), increasing resolution (e.g., 1/12°), and facilitating weakly (and eventually strongly) coupled DA (air-sea-ice, etc.). To prepare for this transition, testing is underway to compare SOCA results at high ocean resolution against the current GEOS-S 2 S Version 3 Ocean DA System (ODAS) 1 using similar initial conditions and observations.

David Russell↗
NASA NTRSDate not supplied

Toward NASA/GMAO Coupled Data Assimilation Using JEDI

NASA’s Global Modeling and Assimilation Office (GMAO) is establishing coupled Earth system capabilities using the Joint Effort for Data Assimilation Integration (JEDI) framework, developed in collaboration with the Joint Center for Satellite Data Assimilation(JCSDA). Marine and atmospheric components will utilize a weakly-coupled data assimilation approach. The implementation of the sea-ice, ocean, and coupled analysis (SOCA) component of JEDI is highlighted here, while parallel work on atmospheric data assimilation is also in progress. Improvements to the ocean data assimilation system (ODAS) include: 1) adopting the JEDI framework, 2) a new coupled GCM, and 3) incorporation of new observation sources.

Dorukhan Ardağ↗