Search NASA⌕ Search

SEARCH · Search NASA

Results for “ocean-atmosphere interactions”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

The Diurnal Cycle in TOGA-COARE: Regional Scale Model Simulations

The diurnal variation of precipitation processes over the tropics is a well-known phenomenon and has been studied using surface rainfall data, radar reflectivity data, and satellite-derived cloudiness and precipitation. Recently, analyzed observations from Tropical Oceans and Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) in the tropical western Pacific ocean to study the relevant mechanisms producing diurnal variation of precipitation. They found that the diurnal Sea surface temperature (SST) cycle is important for afternoon showers in the undisturbed periods and diurnal radiative processes for nocturnal rainfall. Cloud resolving models (CRMS) have been used to determine the mechanisms associated with diurnal variation of precipitating processes. CRMs allow explicit cloud-radiation and air-sea interactive processes. However, CRMs can be only used for idealized simulations (i.e., no feedback between clouds and their embedded large-scale environments; cyclic lateral boundary conditions and idealized initial conditions). In this study, the Penn State/NCAR Mesoscale Model (MM5) with improved physics (i.e., cloud microphysics, radiation, land-soil-vegetation-surface processes, and TOGA COARE flux scheme) and a multiple level nesting technique (covers the TOGA COARE LSA/IFA with a 54 km grid and can nest down to 18, 6 and possibly even 2 km) will be adopted for studying the diurnal variations of rainfall. We will examine precipitation processes over open ocean and over land. We will also perform sensitivity tests to determine how the radiative forcing and diurnal SST cycle affects the development of convection.

Tao, W.-K.↗
NASA NTRS2001

A Study of the Response of Deep Tropical Clouds to Mesoscale Processes: Modeling Strategies and Simulations of TOGA-COARE Convective Systems - Part 1

Interactions between deep tropical clouds over the western Pacific warm pool and the larger-scale environment are key to understanding climate change. Cloud models are an extremely useful tool in simulating and providing statistical information on heat and moisture transfer processes between cloud systems and the environment, and can therefore be utilized to substantially improve cloud parameterizations in climate models. In this paper, the Goddard Cumulus Ensemble (GCE) cloud-resolving model is used in multi-day simulations of deep tropical convective activity over the Tropical Ocean-Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE). Large-scale temperature and moisture advective tendencies, and horizontal momentum from the TOGA-COARE Intensive Flux Array (IFA) region, are applied to the GCE version which incorporates cyclical boundary conditions. Sensitivity experiments show that grid domain size produces the largest response to domain-mean temperature and moisture deviations, as well as cloudiness, when compared to grid horizontal or vertical resolution, and advection scheme. It is found that a minimum grid-domain size of 500 km is needed to adequately resolve the convective cloud features. The control experiment shows that the atmospheric heating and moistening is primarily a response to cloud latent processes of condensation/evaporation, and deposition/sublimation, and to a lesser extent, melting of ice particles. Air-sea exchange of heat and moisture is found to be significant, but of secondary importance, while the radiational response is small. The simulated rainfall and atmospheric heating and moistening, agrees well with observations, and performs favorably to other models simulating this case.

Johnson, Daniel E.↗
NASA NTRS2005

Anticipated Improvements to Net Surface Freshwater Fluxes from GPM

Evaporation and precipitation over the oceans play very important roles in the global water cycle, upper-ocean heat budget, ocean dynamics, and coupled ocean-atmosphere dynamics. In the conventional representation of the terrestrial water cycle, the assumed role of the oceans is to act as near-infinite reservoirs of water with the main drivers of the water cycle being land- atmosphere interactions in which excess precipitation (P) over evaporation (E) is returned to the oceans as surface runoff and baseflow. Whereas this perspective is valid for short space and time scales -- fundamental principles, available observed estimates, and results from models indicate that the oceans play a far more important role in the large-scale water cycle at seasonal and longer timescales. Approximately 70-80% of the total global evaporation and precipitation occurs over oceans. Moreover, latent heat release into the atmosphere over the oceans is the major heat source driving global atmospheric circulations, with the moisture transported by circulations from oceans to continents being the major source of water precipitating over land. Notably, the major impediment in understanding and modeling the oceans role in the global water cycle is the lack of reliable net surface freshwater flux estimates (E - P fluxes) at the salient spatial and temporal resolutions, i.e., consistent coupled weekly to monthly E - P gridded datasets.

Smith, Eric A.↗
NASA NTRS1999

Monitoring Ocean-Atmosphere Exchanges from Space

The ocean and the atmosphere are coupled by the fluxes of momentum, heat, and water, but in situ measurements of these fluxes are sparse and uneven.

prediction (NWP) Special Sensor Microwave/Imager (↗
NASA NTRS2009

Tele-Supervised Adaptive Ocean Sensor Fleet

The Tele-supervised Adaptive Ocean Sensor Fleet (TAOSF) is a multi-robot science exploration architecture and system that uses a group of robotic boats (the Ocean-Atmosphere Sensor Integration System, or OASIS) to enable in-situ study of ocean surface and subsurface characteristics and the dynamics of such ocean phenomena as coastal pollutants, oil spills, hurricanes, or harmful algal blooms (HABs). The OASIS boats are extended- deployment, autonomous ocean surface vehicles. The TAOSF architecture provides an integrated approach to multi-vehicle coordination and sliding human-vehicle autonomy. One feature of TAOSF is the adaptive re-planning of the activities of the OASIS vessels based on sensor input ( smart sensing) and sensorial coordination among multiple assets. The architecture also incorporates Web-based communications that permit control of the assets over long distances and the sharing of data with remote experts. Autonomous hazard and assistance detection allows the automatic identification of hazards that require human intervention to ensure the safety and integrity of the robotic vehicles, or of science data that require human interpretation and response. Also, the architecture is designed for science analysis of acquired data in order to perform an initial onboard assessment of the presence of specific science signatures of immediate interest. TAOSF integrates and extends five subsystems developed by the participating institutions: Emergent Space Tech - nol ogies, Wallops Flight Facility, NASA s Goddard Space Flight Center (GSFC), Carnegie Mellon University, and Jet Propulsion Laboratory (JPL). The OASIS Autonomous Surface Vehicle (ASV) system, which includes the vessels as well as the land-based control and communications infrastructure developed for them, controls the hardware of each platform (sensors, actuators, etc.), and also provides a low-level waypoint navigation capability. The Multi-Platform Simulation Environment from GSFC is a surrogate for the OASIS ASV system and allows for independent development and testing of higher-level software components. The Platform Communicator acts as a proxy for both actual and simulated platforms. It translates platform-independent messages from the higher control systems to the device-dependent communication protocols. This enables the higher-level control systems to interact identically with heterogeneous actual or simulated platforms.

Lefes, Alberto↗
NASA NTRS2016

Western Pacific Hydroclimate Linked to Global Climate Variability Over the Past Two Millennia

Interdecadal modes of tropical Pacific ocean-atmosphere circulation have a strong influence on global temperature, yet the extent to which these phenomena influence global climate on multicentury timescales is still poorly known. Here we present a 2,000-year, multiproxy reconstruction of western Pacific hydroclimate from two speleothem records for southeastern Indonesia. The composite record shows pronounced shifts in monsoon rainfall that are antiphased with precipitation records for East Asia and the central-eastern equatorial Pacific. These meridional and zonal patterns are best explained by a poleward expansion of the Australasian Intertropical Convergence Zone and weakening of the Pacific Walker circulation (PWC) between B1000 and 1500 CE Conversely, an equatorward contraction of the Intertropical Convergence Zone and strengthened PWC occurred between B1500 and 1900 CE. Our findings, together with climate model simulations, highlight the likelihood that century-scale variations in tropical Pacific climate modes can significantly modulate radiatively forced shifts in global temperature.

Pacific Ocean↗
NASA NTRS2018

Comparison of MERRA-2 and ECCO-V4 Ocean Surface Heat Fluxes: Consequences of Different Forcing Feedbacks on Ocean Circulation and Implications for Climate Data Assimilation

Forcing ocean models with reanalysis data is a common practice in ocean modeling. As part of this practice, prescribed atmospheric state variables and interactive ocean SST (Sea Surface Temperature) are used to calculate fluxes between the ocean and the atmosphere. When forcing an ocean model with reanalysis fields, errors in the reanalysis data, errors in the ocean model and errors in the forcing formulation will generate a different solution compared to other ocean reanalysis solutions (which also have their own errors). As a first step towards a consistent coupled ocean-atmosphere reanalysis, we compare surface heat fluxes from a state-of-the-art atmospheric reanalysis, the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), to heat fluxes from a state-of-the-art oceanic reanalysis, the Estimating the Circulation and Climate of the Ocean Version 4, Release 2 (ECCO-v4). Then, we investigate the errors associated with the MITgcm (Massachusetts Institute of Technology general circulation model) ocean model in its ECCO-v4 ocean reanalysis configuration (1992-2011) when it is forced with MERRA- 2 atmospheric reanalysis fields instead of with the ECCO-v4 adjoint optimized ERA-interim state variables. This is done by forcing ECCO-v4 ocean with and without feedbacks from MERRA-2 related to turbulent fluxes of heat and moisture and the outgoing long wave radiation. In addition, we introduce an intermediate forcing method that includes only the feedback from the interactive outgoing long wave radiation. The resulting ocean circulation is compared with ECCO-v4 reanalysis and in-situ observations. We show that, without feedbacks, imbalances in the energy and the hydrological cycles of MERRA-2 (which are directly related to the fact it was created without interactive ocean) result in considerable SST drifts and a large reduction in sea level. The bulk formulae and interactive outgoing long wave radiation, although providing air-sea feedbacks and reducing model-data misfit, strongly relax the ocean to observed SST and may result in unwanted features such as large change in the water budget. These features have implications in a desired forcing recipe to be used. The results strongly and unambiguously argue for next generation data assimilation climate studies to involve fully coupled systems.

MERRA-2↗
NASA NTRS2018

Development of the GEOS-MIT gcm Atmosphere-Ocean Model for Coupled Data Assimilation

During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MIT-GCM (General Circulation Model)) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies. Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (approximately 1/8 degrees) configuration of the GEOS atmosphere to a latitude-longitude-cap-1080 (approximately 1/12 degrees) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.

Strobach, Ehud↗
NASA NTRS2018

The Development of the New GEOS-MITgcm Atmosphere-Ocean Model for Coupled Data Assimilation System

During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MITgcm) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies.Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (~ 1/8 deg) configuration of the GEOS atmosphere to a lat-lon-cap-1080 (~ 1/12 deg) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.

Strobach, Ehud↗
NASA NTRS2018

A New Atmosphere-Ocean Model for Studying Air-Sea Interactions and Coupled Data Assimilation

During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MITgcm) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies.Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (~ 1/8°) configuration of the GEOS atmosphere to a lat-lon-cap-1080 (~ 1/12°) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.

Strobach, Ehud↗
NASA NTRS2018

The Development of the New GEOS-MITgcm Atmosphere-Ocean Model for Coupled Data Assimilation System

During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MITgcm) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies.Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (~ 1/8°) configuration of the GEOS atmosphere to a lat-lon-cap-1080 (~ 1/12°) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.

Strobach, Ehud↗
NASA NTRS2018

Consequences of Different Air-Sea Feedbacks on Ocean Using MITgcm and MERRA-2 Forcing: Implications for Coupled Data Assimilation Systems

Ocean surface flux estimates from atmospheric and oceanic reanalyses contain errors that compensate for inaccuracies in the respective atmosphere and ocean models used to generate these reanalyses. A conundrum for climate studies is the discrepancy between surface fluxes that minimize model-data differences for an atmosphere-only model vs surface fluxes that minimize model-data differences for an ocean model. As a first step towards a consistent coupled ocean-atmosphere data-assimilation (DA) system, we compare surface net heat flux from a state-of-the-art atmospheric reanalysis, the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), to net heat flux from a state-of-the-art ocean state estimate, the Estimating the Circulation and Climate of the Ocean Version 4 (ECCO-v4). The possible impacts of the MERRA-2 and ECCO-v4 air-sea net heat flux difference in a coupled DA system were assessed using a set of experiments designed to imitate different “flavors” of a coupled DA system in an ocean-only setup. This was done by forcing the ECCO-v4 underlying ocean model - the Massachusetts Institute of Technology general circulation model (MITgcm) - with different sets of MERRA-2 fields and utilizing different forcing methods. By doing so we were able to turn off different air-sea feedbacks which, in a coupled DA setup, are partially muted by the constraining observations. The set of experiments, therefore, represents a range of active feedbacks in different “flavors” of coupled data-assimilation systems. For the period 1992–2011, MERRA-2 net heat flux has a global mean difference of -4.9 Wm(exp -2) relative to ECCO-v4. When MERRA-2 surface fields are used to force MITgcm, imbalances in the energy and the hydrological cycles of MERRA-2, which are directly related to the fact that MERRA-2 was created without an interactive ocean, propagate to the ocean. The experiment in which MITgcm is forced with MERRA-2 fluxes (MERRA-2-flux experiment) results in a 2.5°C global mean Sea Surface Temperature (SST) cooling, a 1m reduction in global mean sea level, and other drastic changes in the large scale ocean circulation relative to those resulting when the MITgcm is forced with the optimized ECCO-v4 net heat flux (the ECCO-v4 experiment itself). When MITgcm is forced with MERRA-2 state variables (MERRA-2-state experiment), the SST is somewhat restored to the observed SST, but the errors are shifted to the water cycle, resulting in a global mean sea level increase of 2.7 m. To further explore the pros and cons of these two approaches, we introduce a new intermediate forcing method in which the ocean is forced with turbulent fluxes but has a long wave feedback. This method, unlike MERRA-2 state, preserves the MERRA-2 water and salinity cycles, and it reduces the SST error compared to the MERRA-2-flux experiment, but the SST is not as good as that in the MERRA-2-state experiment. Our results have implications for ocean-model forcing recipes and clearly reveal the undesirable consequences of limiting the feedbacks in either these types of experiments or in coupled DA.

Ehud Strobach↗
NASA NTRSDate not supplied

Effect of Rain-Adjusted Satellite Sea Surface Salinity on ENSO Predictions from the GMAO S2S Forecast System

ENSO has a significant impact on climate variability throughout the world and so has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of altimetry and in situ data lead to improved short-term forecasts of the coupled system. However, few studies have focused on improving the near-surface density/mixing through assimilation of satellite sea surface salinity (SSS). For expediency, most projects that do assimilate SSS do so as if these data were observed at the top model layer instead of at the surface. In rainy regions where buoyant water sits as a fresh lens at the surface, this assumption is likely invalid. Therefore, we adjust SSS so that it more accurately represents the salinity at the first model layer. The Rain Impact Model (RIM) uses a simple diffusion model to determine the near surface salinity gradient (i.e., 1 cm to 5 m). Satellite SSS data are modified using this near-surface salinity gradient, so the salinity values are now valid at the first model layer (we call this SSS@5m). We assess the impact of satellite SSS observations for near-surface dynamics within ocean reanalyses and how these impact dynamical ENSO forecasts using the NASA GMAO S2S coupled system. For all reanalysis experiments, all available along-track sea level and in situ observations are assimilated for 2011 to 2020 using the LETKF scheme (Penny et al., 2013). One reanalysis assimilates Aquarius /SMAP SSS as before. An additional reanalysis is performed assimilating the SSS@5m data. Validation statistics are compared for experiments that assimilate SSS (sub-optimally as before) versus the SSS@5m. We also compare results of coupled forecasts that are initialized from these reanalyses in spring. For all but the big La Niña in 2017, all NINO3.4 forecasts were improved by using the RIM. We will show that improved SSS estimates upgrades density and near-surface mixing leading to more accurate coupled air/sea interaction and better forecasts.

ENSO↗
NASA NTRS2022

The Turning Point of the Aerosol Era

Over the CMIP6 historical period (1850–2014), aerosols provided the largest negative forcing compared to all other climate forcings via their ability to absorb or scatter solar radiation and alter clouds. Aerosols played an important role in counterbalancing warming by greenhouse gases (GHGs). Here we study aerosol forcing trends in the CMIP6 simulations of the NASA Goddard Institute for Space Studies (GISS) ocean-atmosphere ModelE version 2.1 (GISS-E2.1-G) using a fully coupled atmospheric composition configuration, including interactive gas-phase chemistry, and either an aerosol microphysical (MATRIX) or a mass-based aerosol (OMA) module. Simulations of the CMIP6 historical period are analyzed as well as four Shared Socioeconomic Pathway (SSP) future scenarios for 2015–2100: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The main conclusion of this study is that aerosol forcing in the GISS model has reached its turning point, switching from globally increasing to a decreasing trend in the first decade of the 21st century. This result is robust, independent of which aerosol module or SSP scenario is used. Non-linear aerosol-cloud interactions dominate as a forcing agent over aerosol-radiation interactions. Aerosols' ability to counterbalance GHG forcing on the global scale is today at a level comparable to that at the beginning of the last century. In the 1980s, the decade of largest global aerosol loads, aerosols balanced up to 80% of GHG forcing. As a consequence, global warming of the last decades, which is primarily driven by greenhouse gases, has been augmented by the effect of decreasing aerosol cooling in our model. By the end of this century, following the SSP scenarios, aerosols will only counterbalance 0%–20% of GHG forcing, depending on model and on scenario.

Aerosol forcing↗
NASA NTRS2022

A Primordial Atmospheric Origin of Hydrospheric Deuterium Enrichment on Mars

The deuterium-to-hydrogen (D/H or 2H/1H) ratio of Martian atmospheric water (~6× standard mean ocean water, SMOW) is higher than that of known sources, requiring planetary enrichment. A recent measurement by NASA’s Mars Science Laboratory rover Curiosity of Hesperian-era (>3 Ga) clays yields a D/H ratio ~3×SMOW, demonstrating that most of the enrichment occurs early in Mars’s history, reinforcing the conclusions of Martian meteorite studies. As on Venus, Mars’s D/H enrichment is widely thought to reflect preferential loss to space of 1H (protium) relative to 2H (deuterium), but both the cause and the global environmental context of large and early hydrogen losses remain to be determined. Here, we apply a recent model of primordial atmosphere evolution to Mars, link the magma ocean of the accretion epoch with a subsequent water-ocean epoch, and calculate the behavior of deuterium for comparison with the observed record. In contrast to earlier works that consider Martian D/H fractionation in atmospheres in which hydrogen reservoirs are present exclusively as H2O or H2, here we consider 2-component (H2O-H2) outgassed atmospheres in which both condensed (H2O) and escaping (H2) components – and their interaction – are explicitly calculated. We find that a a ≈2-3× hydrospheric deuterium-enrichment is produced rapidly if the Martian magma ocean is chemically reducing at last equilibration with the primordial atmosphere, making H2 and CO the initially dominant species, with minor abundances of H2O and CO2. Reducing gases – in particular H2 – can cause substantial greenhouse warming and prevent a water ocean from freezing immediately after the magma ocean epoch. We find that greenhouse warming due to plausible H2 inventories (pH2=1-102 bars) yields surface temperatures high enough (Ts=290-560 K) to stabilize a water ocean and produce an early hydrological cycle through which surface water can be circulated. Moreover, the pressure-temperature conditions are high enough to produce ocean-atmosphere H2O-H2 isotopic equilibrium through gas-phase deuterium exchange such that surface H2O strongly concentrates deuterium relative to H2, which preferentially takes up protium and escapes from the primordial atmosphere. The efficient physical separation of deuterium-rich (H2O) and deuterium-poor (H2) species via condensation permits equilibrium isotopic partitioning and early atmospheric escape to be recorded in modern crustal reservoirs. The proposed scenario of primordial H2-CO-rich outgassing and escape suggests significant durations (>Myr) of chemical conditions on the Martian surface conducive to prebiotic chemistry immediately following magma ocean crystallization.

Mars↗
NASA NTRS2023

PACE Technical Report Series, Volume 11: The PACE Postlaunch Airborne eXperiment (PACE-PAX)

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE; https://pace.gsfc.nasa.gov) mission is NASA's latest and greatest effort to study the ocean-atmosphere system and build upon the legacy of previous satellite missions that have revolutionized our understanding of our home planet. By using advanced radiometric and polarimetric instruments to measure ocean color and key atmospheric variables, PACE aims to provide new insights into the complex interactions between the ocean, atmosphere, and biosphere. This information will be critical for improving our ability to model and predict changes in the Earth's climate, as well as to manage and conserve our planet's valuable natural resources. Performance assessments and science data product validation is a critical process for any Earth Science space-borne mission, as it involves assessing and verifying the quality of the data products generated by the mission while it is in orbit, as well as assigning uncertainties to these retrievals. More specifically, validation efforts are essential for assessing the accuracy of instrument data and the derived science products across varied spatial and temporal scales and ensuring their reliability and consistency with the mission's scientific goals and objectives. The overall plan for validation of PACE data is described in “PACE Science Data Product Validation Plan” (see, e.g., https://pace.gsfc.nasa.gov), and among range of post-launch activities, it describes the need and requirements for a post-launch airborne field campaign in support of PACE validation. This volume outlines the plans and requirements of the PACE Postlaunch Airborne eXperiment (PACE-PAX). The main goal of PACE-PAX is to gather validation and assessment data for the PACE mission, using a variety of airborne and ground-based assets. These assets will be coordinated to obtain complementary measurements of key ocean and atmospheric variables, such as ocean color, aerosols, and clouds. By collecting data from multiple sources, PACE-PAX will help to validate and refine the data products generated by the PACE mission, as well as to identify any potential errors or biases in the measurements. This will be essential for ensuring the accuracy and reliability of the PACE data products, and for maximizing the scientific value of the mission.

Kirk D Knobelspiesse↗
NASA NTRSDate not supplied

Improving Ocean Reanalyses and ENSO Forecasts By Assimilation of Rain-Corrected Satellite Sea Surface Salinity Using the GMOA S2s Forecast System

The ENSO phenomenon has a significant global socio-economic impact and has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of satellite altimetry and subsurface temperature and salinity from (mostly) Argo help improve the initialization of the thermocline, while satellite SST aids in constraining surface heat-fluxes, leading to improved coupled system sub-seasonal to seasonal forecasts. However, few studies have focused on improving the near-surface density and mixing through satellite sea surface salinity (SSS) assimilation. The few ocean models that assimilate satellite SSS, bias correct to normalize towards the near-surface Argo data for expediency. This assumption is likely inadequate in rainy regions, where buoyant water forms a fresh surface lens. In previous work, we showed that adjusting SSS to bulk salinity (Sb) using the Rain Impact Model (RIM) of Santos-Garcia et al., 2014 improves the near-surface density and mixed layer depth, leading to deeper thermocline and improved NINO3.4 SST forecasts. We now utilize the Soil Moisture and Ocean Salinity rain-corrected (SMOS_RC) SSS, available in SMOS-CATDS products, to represent Sb more accurately at the first model layer (e.g., 5 m). Rather than a diffusivity model as RIM, SMOS_RC uses a statistical correction dependent on Integrated Multi-satellitE Retrievals for GPM (IMERG) rain rates, established on observed SMOS SSS decreases related to Sb in the presence of rain (Supply et al., 2020). For all 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, and a separate reanalysis assimilates SMOS_RC. We assess the impact on near-surface and subsurface dynamics by validating against observations and explore how SSS assimilation (SMOS vs SMOS_RC) impacts ENSO forecasts using the NASA GMAO Sub-seasonal to Seasonal coupled forecast system (S2S-v3, Molod et al. 2020). We show that improved estimates of density and near-surface mixing led to more accurate coupled air/sea interaction and better ENSO forecasts. The increased SSS, resulting from the removal of the instantaneous rain effect, modifies the ocean state by enhancing mixing and deepening the thermocline.

Veronica Ruiz Xomchuk↗
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↗