Abstract In 2023 the NOAA Daily Optimum Interpolation Sea Surface Temperature in the Atlantic set new records, peaking above 24. 3°C for the full basin 30°S–60°N and above 25. 3°C for the North Atlantic 0–60°N, both for the first time during the satellite era. Proposed mechanisms include anomalous radiative and thermodynamic surface fluxes, horizontal transport, changing mixed layer thickness and basal entrainment rates. To test these ideas the heat budget of the ocean mixed layer was examined in a numerical simulation. The results show that the anomalous SSTs in the North Atlantic during the summer of 2023 were caused by increased shortwave warming and reduced thermodynamic cooling. In contrast, ocean mean and eddy heat transport convergence and heat exchanges along the mixed layer base dominate only regionally. A second contributor to the record SSTs was the preconditioning of mixed layer temperature following a series of anomalously warm years.

The National Centers for Environmental Prediction (NCEP) produces operational reanalysis products based on the Global Ocean Data Assimilation System (GODAS) with the Modular Ocean Model (MOM3/MOM4p0d) as a physical model and 3D-Var as the data assimilation method. The Indian National Center for Ocean Information Services (INCOIS) also produces operational ocean analysis with the same GODAS with MOM4p0d. In this study, GODAS is upgraded with MOM5. The improved reanalysis is compared with respect to the EN4 and Ocean ReAnalysis System 5 (ORAS5) for the subsurface temperature and salinity. The microwave-based satellite-derived sea surface temperature (SST) is employed for the independent evaluation of the reanalysis SST products. We have assimilated observed temperature and salinity profiles from all in situ platforms over the global ocean and also assimilated along-track sea level anomaly from altimeter (Jason1 and Jason2) to produce the improved reanalysis. There is a significant improvement in the SST with biases of temperatures reduced from 1.5 to 0.2 °C as compared to without assimilation. Moreover, a significant improvement is found in the subsurface temperature and salinity fields over the northwest Atlantic and Pacific Ocean, Nino 3.4, and Indian Ocean thermocline ridge regions. The biases in this new, improved reanalysis are even lower than ORAS5 when compared with EN4 analysis. Thermocline depth also shows improvement in terms of better representation and capturing seasonal variability. Altimeter assimilation further reduces Root Mean Square Deviation (RMSD) in SST over the global ocean. We also show the improvement in the new reanalysis with respect to the reanalysis based on MOM4p0d.

The assimilation of satellite sea surface salinity (SSS) has been acknowledged to improve the upper-ocean stratification and the relevant oceanic processes. In this study, we focus on assessing the impact of SSS assimilation on predicting the Madden-Julian oscillation (MJO) propagation across the Maritime Continent for eight representative MJO seasonal to seasonal (S2S) prediction system, version 2 (GEOS-S2S-2). Two sets of forecast experiments are performed: one initialized from the operational ocean analysis without SSS assimilation, referred to as control (CTL), and the other initialized from the ocean analysis with SSS assimilation (SSS). Evaluated with the large-scale precipitation tracking method for these eight MJO events, the SSS forecasts outperform the CTL forecasts, showing better agreement with the observed eastward propagation over the Maritime Continent. In the SSS forecasts, the deeper mixed-layer depth (MLD) and greater upper-ocean heat content (OHC) result in reduced surface cooling during MJO convection. This leads to higher sea surface temperature (SST) compared to the CTL, which enhances latent heat flux anomalies via wind convergence and further supports MJO propagation. Based on the results of these representative MJO cases, this study suggests that improved initialization of ocean stratification and OHC in the upper ocean, enabled by SSS assimilation, can help partially overcome the MJO Maritime Continent prediction barrier and thus strengthen subseasonal forecast skills.

Previous research has shown that assimilating satellite sea surface salinity (SSS) has improved initialization of coupled El Niño/Southern Oscillation (ENSO) forecasts. However, most of these assimilation techniques have either removed the freshwater bias by correcting to monthly mean fields of subsurface observations or ignored it altogether. In this paper, we explore the impact of accounting for the satellite SSS fresh bias by first estimating, then removing the near‐surface salinity gradient from the satellite SSS using the Rain Impact Model (RIM [Santos‐Garcia et al., 2014, https://doi.org/10.1002/2014jc010137 ]). This diffusivity model is calculated using collocated satellite rainfall and SSS estimates. Two ocean reanalyses are produced, one assimilating RIM data, which removes the fresh bias at the surface (SSS_RIM), and the other experiment retains this bias (CONTROL). Both reanalyses additionally assimilate all conventional ocean observations. Comparison of SSS_RIM versus CONTROL shows that the thermocline is deeper for the SSS_RIM, allowing this reanalysis to store more heat. Removing the fresh bias destabilizes the water column for the SSS_RIM experiment, allowing enhanced mixing, and more heat storage. ENSO forecasts initiated from April reanalyses from 2015 to 2021 are consistently warmer for SSS_RIM than for the CONTROL. For all but one instance (2017), these SSS_RIM forecasts are closer to observations than the CONTROL. These results argue that operational coupled forecast centers should reevaluate bias‐correcting the satellite SSS using monthly gridded fields of in situ salinity, but rather they should utilize observed rainfall to estimate coincident near surface salinity gradients.

The National Centers for Environmental Prediction (NCEP) produces operational ocean reanalysis products based on the Global Ocean Data Assimilation System (GODAS) with the Modular Ocean Model (MOM3) as a physical model and 3D-Var as the data assimilation method. Subsequently, the Indian National Centre for Ocean Information Services (INCOIS) has also operationalized the same GODAS system with MOM4p0d as a physical model to produce ocean analysis since 2013. In this study, we upgraded GODAS with MOM5 to produce ocean reanalysis with NCEP-R2 atmospheric forcing. The improved reanalysis is compared with respect to the EN4 analysis and Ocean Reanalysis System 5 (ORAS5) for the subsurface temperature and salinity. The microwave-based satellite-derived sea surface temperature (SST) is employed for the independent evaluation of the reanalysis SST products. We have assimilated observed temperature and salinity profiles from all in situ platforms over the global ocean and assimilated along-track sea level anomaly from altimeter (Jason1 and Jason2) to produce the improved reanalysis. There is a significant improvement in the sea surface temperature (SST) with biases of temperatures reduced from 1.5 °C to ~0.2 °C as compared to without assimilation. Moreover, a significant improvement is found in the subsurface temperature and salinity fields over the northwest Atlantic and Pacific Ocean, Nino 3.4, and Indian Ocean thermocline ridge regions as well. The biases in this new, improved reanalysis are even lower than ORAS5 when compared with EN4 analysis. Thermocline depth also shows improvement in terms of better representation and capturing seasonal variability. We also show the improvement in the new reanalysis with respect to the old reanalysis based on MOM4p0d.

The National Centers for Environmental Prediction (NCEP) produces operational ocean reanalysis products based on the Global Ocean Data Assimilation System (GODAS) with the Modular Ocean Model (MOM3) as a physical model and 3D-Var as the data assimilation method. Subsequently, the Indian National Centre for Ocean Information Services (INCOIS) has also operationalized the same GODAS system with MOM4p0d as a physical model to produce ocean analysis since 2013. In this study, we upgraded GODAS with MOM5 to produce ocean reanalysis with NCEP-R2 atmospheric forcing. The improved reanalysis is compared with respect to the EN4 analysis and Ocean Reanalysis System 5 (ORAS5) for the subsurface temperature and salinity. The microwave-based satellite-derived sea surface temperature (SST) is employed for the independent evaluation of the reanalysis SST products. We have assimilated observed temperature and salinity profiles from all in situ platforms over the global ocean and assimilated along-track sea level anomaly from altimeter (Jason1 and Jason2) to produce the improved reanalysis. There is a significant improvement in the sea surface temperature (SST) with biases of temperatures reduced from 1.5 °C to ~0.2 °C as compared to without assimilation. Moreover, a significant improvement is found in the subsurface temperature and salinity fields over the northwest Atlantic and Pacific Ocean, Nino 3.4, and Indian Ocean thermocline ridge regions as well. The biases in this new, improved reanalysis are even lower than ORAS5 when compared with EN4 analysis. Thermocline depth also shows improvement in terms of better representation and capturing seasonal variability. We also show the improvement in the new reanalysis with respect to the old reanalysis based on MOM4p0d.

Starting in the early 1990's, the Tropical Atmosphere Ocean (TAO)/TRIangle Trans Ocean buoy Network (TRITON) array has been the pervasive source for observing large‐scale equatorial wave propagation which is key for El Nino/Southern Oscillation (ENSO) predictions. However, removal of western TRITON moorings, the plan to reorganize the array (i.e., TPOS 2020), and availability of other sources of in situ data (e.g., Argo) have highlighted the need to rigorously assess the impact of TAO/TRITON data on ENSO predictions. Therefore, we evaluate TAO/TRITON array using data denial assimilation experiments and assess the impact on coupled atmosphere/ocean predictions of the big 2015 El Niño. Validation of the CONTROL (assimilates all available data) and NOTAO (withholds TAO/TRITON data) reanalyses shows that assimilating TAO/TRITON data generally improves comparisons versus gridded and pointwise in situ observations. This is especially true across the entire basin above and in the eastern half of the Pacific just below the thermocline for temperature. Even with relatively few observations, salinity is generally improved except near 120°W near the surface. To evaluate the impact of TAO/TRITON data on ENSO initialization, seasonal forecasts were initialized from the CONTROL and NOTAO experiments. For the 9‐month forecasts which were initialized in January, July, and October 2015, both the amplitude and the accuracy of the ensembles initialized with TAO/TRITON data were closer to observations. Through the analysis of Kelvin and Rossby waves, we show that the impact of TAO/TRITON is to generally shoal the mixed layer depth, leading to amplification of the El Niño downwelling signal, and improving the amplitude of the ENSO signal.

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.

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.

El Nino/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 additionally assimilate Aquarius, SMAP, SMOS, and these data sets 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 Nino, the 2017 La Nina, and the weak El Nino 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. Plain Language Summary Improving the prediction of El Nino/Southern Oscillation (ENSO) is important because of the global impacts of ENSO and the associated socioeconomic implications. Only recently has satellite sea surface salinity (SSS) become available for improving our characterization of the global hydrological cycle. SSS, combined with temperature, helps to improve the estimates of near-surface density changes and associated ocean mixing. Here we show results of experiments designed to highlight the impact of SSS on ENSO forecasts. In the control experiment, we assimilate a comprehensive set of in situ oceanographic information and satellite altimetry, as typically done in operational ocean data assimilation, but exclude satellite SSS. In the second set of reanalyses, we add different satellite SSS products to our assimilation. Air/sea coupled model hindcasts are then initialized for various case studies including the big El Nino (2015), the moderate La Nina (2017), and a weak El Nino (2018). For each example, satellite SSS assimilation improves coupled forecasts by adjusting the large-scale equatorial waves that are integral to ENSO development. For 2015, SSS damps these waves resulting in a more realistic ENSO prediction. In 2017 and 2018, SSS assimilation acts to change the sign of ENSO forecasts, again leading to more realistic ENSO forecasts.

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.

The Global Modeling and Assimilation Office (GMAO) has recently released a new version of the Goddard Earth Observing System (GEOS) Subseasonal to Seasonal prediction (S2S) system, GEOS‐S2S‐2, that represents a substantial improvement in performance and infrastructure over the previous system. The system is described here in detail, and results are presented from forecasts, climate equillibrium simulations, and data assimilation experiments. The climate or equillibrium state of the atmosphere and ocean showed a substantial reduction in bias relative to GEOS‐S2S‐1. The GEOS‐S2S‐2 coupled reanalysis also showed substantial improvements, attributed to the assimilation of along‐track absolute dynamic topography. The forecast skill on subseasonal scales showed a much improved prediction of the Madden‐Julian Oscillation in GEOS‐S2S‐2, and on a seasonal scale the tropical Pacific forecasts show substantial improvement in the east and comparable skill to GEOS‐S2S‐1 in the central Pacific. GEOS‐S2S‐2 anomaly correlations of both land surface temperature and precipitation were comparable to GEOS‐S2S‐1 and showed substantially reduced root‐mean‐square error of surface temperature. The remaining issues described here are being addressed in the development of GEOS‐S2S Version 3, and with that system GMAO will continue its tradition of maintaining a state‐of‐the‐art seasonal prediction system for use in evaluating the impact on seasonal and decadal forecasts of assimilating newly available satellite observations, as well as evaluating additional sources of predictability in the Earth system through the expanded coupling of the Earth system model and assimilation components.

Much work has gone into revising and updating algorithms for converting satellite-measured radiances to useful ocean variables like sea surface salinity (e.g. SMOS - Boutin et al., 2017, SMAP - Fore et al., 2016 and Aquarius - Meissner et al., 2018). As part of our Ocean Salinity Science Team work, we utilize an intermediate-complexity air/sea coupled model as a transfer function to test if more mature satellite SSS model algorithms actually improve ENSO forecast skill. For initialization of the coupled forecast, we demonstrate that 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. In addition, salting near the ITCZ leads to a deepening of the mixed layer and thermocline near 8N. These patterns together provide the background state to amplify equatorial Kelvin waves and improve ENSO hindcasts (Hackert et al., 2019). Here we extend this work to compare the impact of various pairs of original and improved satellite SSS algorithms. For instance we compare SMAP V4.1 with the latest, SMAP V4.2, to see what impact algorithm improvements may have on ENSO forecasts. SSS observations are tested on seasonal to interannual variability of tropical Indo-Pacific Ocean dynamics as well as on dynamical ENSO forecasts by initializing twelve-month forecasts for each month of available data. All experiments assimilate satellite sea level (SL), sea surface temperature (SST), and in situ subsurface temperature and salinity observations (Tz, Sz). Additionally various satellite, blended, and in-situ SSS products are assimilated. We find that including satellite SSS significantly improves NiA±o3.4 sea surface temperature anomaly validation, more mature SSS model algorithms are generally improving ENSO forecasts over time, and more satellite SSS data coverage helps to extend useful forecasts.

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 water cycle and climate, and 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.

This study demonstrates the positive impact of including gridded Aquarius and Soil Moisture, Active/Passive (SMAP) sea surface salinity (SSS) into initialization of intermediate complexity coupled model forecasts for the tropical Indo-Pacific. An experiment that assimilates conventional ocean observations serves as the control. In a separate experiment, Aquarius and SMAP satellite SSS are additionally assimilated into the coupled model initialization. Analysis of the initialization differences with the control indicates that SSS assimilation causes a freshening and shallowing of the mixed layer depth near the equator and enhanced Kelvin wave amplitude. For each month from September 2011 to September 2017, 12-month-coupled ENSO forecasts are initialized from both the control and satellite SSS assimilation experiments. The experiment assimilating Aquarius and SMAP SSS significantly outperforms the control relative to observed NINO3.4 sea surface temperature anomalies. This work highlights the potential importance of inclusion of satellite SSS for improving the initialization of operational ENSO coupled forecasts. Plain Language Summary El Nino/Southern Oscillation (ENSO) has far reaching climatic impacts over the globe so extending useful ENSO forecasts would be of great benefit for society. In response, NASA has developed satellite technology to observe the global hydrological cycle by measuring ocean sea surface salinity (SSS) from space. SSS, combined with temperature, helps to identify density changes and associated mixing near the ocean surface. Here we show results of two intermediate complexity coupled experiments designed to highlight the positive impact of SSS on ENSO forecasts. In the control, we assimilate all conventional satellite and in situ oceanographic information including satellite altimetry (matching current operational data assimilation schemes) but exclude SSS. In the second experiment, we add satellite SSS to our assimilation suite. Air/sea coupled model retrospective forecasts are then initialized from these two experiments and they show that satellite SSS assimilation improves coupled forecasts. For all lead times, the experiment with SSS assimilation has better correlation and root-mean-square difference with the ENSO metric (i.e., NINO 3.4 observed sea surface temperature anomalies). Density changes associated with SSS assimilation shoal the mixed layer near the equator and enhance the impact of large-scale ocean wave and wind changes that are associated with ENSO.

We assess the impact of satellite sea surface salinity (SSS) observations on dynamical ENSO forecasts for the big 2015 El Nino event. From March to June 2015, the availability of two overlapping satellite SSS instruments, Aquarius and SMAP (Soil Moisture Active Passive Mission), allows a unique opportunity to compare and contrast forecasts generated with the benefit of these two satellite SSS observation types. Four distinct experiments are presented that include 1) freely evolving model SSS (i.e. no satellite SSS), relaxation to 2) climatological SSS (i.e. WOA13 SSS), 3) Aquarius, and 4) SMAP initialization. Coupled hindcasts are then generated from these initial conditions for March 2015. These forecasts are then validated against observations and evaluated with respect to the observed El Nino development.

Hovmöller (2 o N-2 o S) for average March forecasts for SSS (top), ADT (middle) and SST (bottom) and Observed anomalies (far right). All forecasts clearly underestimate the observed ENSO signal all are too fresh in the fresh pool, too salty near the eastern edge of the fresh pool (~180 o ) and show a predominance of upwelling (with respect to observations) and enhanced warming in the western half of the Pacific. Note that the salty IC and shallower MLD for SMAP makes this product more susceptible to upwelling and cooler eastern Pacific SST anomalies.

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.