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NASA NTRS2022

Satellite-Based Evaluation of AeroCom Model Bias in Biomass Burning Regions

Global models are widely used to simulate biomass burning aerosol (BBA). Exhaustive evaluations on model representation of aerosol distributions and properties are fundamental to assess health and climate impacts of BBA. Here we conducted a comprehensive comparison of Aerosol Comparisons between Observations and Models (AeroCom) project model simulations with satellite observations. A total of 59 runs by 18 models from three AeroCom Phase-III experiments (i.e., biomass burning emissions, CTRL16, and CTRL19) and 14 satellite products of aerosols were used in the study. Aerosol optical depth (AOD) at 550 nm was investigated during the fire season over three key fire regions reflecting different fire dynamics (i.e., deforestation-dominated Amazon, Southern Hemisphere Africa where savannas are the key source of emissions, and boreal forest burning in boreal North America). The 14 satellite products were first evaluated against AErosol RObotic NETwork (AERONET) observations, with large uncertainties found. But these uncertainties had small impacts on the model evaluation that was dominated by modeling bias. Through a comparison with Polarization and Directionality of the Earth’s Reflectances measurements with the Generalized Retrieval of Aerosol and Surface Properties algorithm (POLDER-GRASP), we found that the modeled AOD values were biased by −93 % to 152 %, with most models showing significant underestimations even for the state-of-the-art aerosol modeling techniques (i.e., CTRL19). By scaling up BBA emissions, the negative biases in modeled AOD were significantly mitigated, although it yielded only negligible improvements in the correlation between models and observations, and the spatial and temporal variations in AOD biases did not change much. For models in CTRL16 and CTRL19, the large diversity in modeled AOD was in almost equal measures caused by diversity in emissions, lifetime, and the mass extinction coefficient (MEC). We found that in the AeroCom ensemble, BBA lifetime correlated significantly with particle deposition (as expected) and in turn correlated strongly with precipitation. Additional analysis based on Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) aerosol profiles suggested that the altitude of the aerosol layer in the current models was generally too low, which also contributed to the bias in modeled lifetime. Modeled MECs exhibited significant correlations with the Ångström exponent (AE, an indicator of particle size). Comparisons with the POLDER-GRASP-observed AE suggested that the models tended to overestimate the AE (underestimated particle size), indicating a possible underestimation of MECs in models. The hygroscopic growth in most models generally agreed with observations and might not explain the overall underestimation of modeled AOD. Our results imply that current global models contain biases in important aerosol processes for BBA (e.g., emissions, removal, and optical properties) that remain to be addressed in future research.

biomass burning aerosols↗

NASA NTRS2016

Regional Atmospheric CO2 Inversion Reveals Seasonal and Geographic Differences in Amazon Net Biome Exchange

Understanding tropical rainforest carbon exchange and its response to heat and drought is critical for quantifying the effects of climate change on tropical ecosystems, including global climate carbon feedbacks. Of particular importance for the global carbon budget is net biome exchange of CO2 with the atmosphere (NBE), which represents nonfire carbon fluxes into and out of biomass and soils. Subannual and sub-Basin Amazon NBE estimates have relied heavily on process-based biosphere models, despite lack of model agreement with plot-scale observations. We present a new analysis of airborne measurements that reveals monthly, regional-scale (Approx.1-8 x 10(exp -6) km2) NBE variations. We develop a regional atmospheric CO2 inversion that provides the first analysis of geographic and temporal variability in Amazon biosphere-atmosphere carbon exchange and that is minimally influenced by biosphere model-based first guesses of seasonal and annual mean fluxes. We find little evidence for a clear seasonal cycle in Amazon NBE but do find NBE sensitivity to aberrations from long-term mean climate. In particular, we observe increased NBE (more carbon emitted to the atmosphere) associated with heat and drought in 2010, and correlations between wet season NBE and precipitation (negative correlation) and temperature (positive correlation). In the eastern Amazon, pulses of increased NBE persisted through 2011, suggesting legacy effects of 2010 heat and drought. We also identify regional differences in postdrought NBE that appear related to long-term water availability. We examine satellite proxies and find evidence for higher gross primary productivity (GPP) during a pulse of increased carbon uptake in 2011, and lower GPP during a period of increased NBE in the 2010 dry season drought, but links between GPP and NBE changes are not conclusive. These results provide novel evidence of NBE sensitivity to short-term temperature and moisture extremes in the Amazon, where monthly and sub-Basin estimates have not been previously available.

precipitation↗

NASA NTRS2018

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

In this paper we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

Earth observations↗

NASA NTRS2018

Impact of Aquarius and SMAP Sea Surface Salinity Observations on Seasonal Predictions of the 2015 El Nino

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, allows a unique opportunity to compare and contrast coupled 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 (World Ocean Atlas 2013) SSS), 3) Aquarius and 4) SMAP initialization. Coupled hindcasts are 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.

Kovach, R.↗

NASA NTRS2021

AeroCom Phase III Multi-Model Evaluation of the Aerosol Life Cycle and Optical Properties Using Ground and Space-Based Remote Sensing as Well as Surface In Situ Observations

Within the framework of the AeroCom (Aerosol Comparisons between Observations and Models) initiative, the state-of-the-art modelling of aerosol optical properties is assessed from 14 global models participating in the phase III control experiment (AP3). The models are similar to CMIP6/AerChemMIP Earth System Models (ESMs) and provide a robust multi-model ensemble. Inter-model spread of aerosol species lifetimes and emissions appears to be similar to that of mass extinction coefficients (MECs), suggesting that aerosol optical depth (AOD) uncertainties are associated with a broad spectrum of parameterised aerosol processes. Total AOD is approximately the same as in AeroCom phase I (AP1) simulations. However, we find a 50 % decrease in the optical depth (OD) of black carbon (BC), attributable to a combination of decreased emissions and lifetimes. Relative contributions from sea salt (SS) and dust (DU) have shifted from being approximately equal in AP1 to SS contributing about 2∕3 of the natural AOD in AP3. This shift is linked with a decrease in DU mass burden, a lower DU MEC, and a slight decrease in DU lifetime, suggesting coarser DU particle sizes in AP3 compared to AP1. Relative to observations, the AP3 ensemble median and most of the participating models underestimate all aerosol optical properties investigated, that is, total AOD as well as fine and coarse AOD (AODf, AODc), Ångström exponent (AE), dry surface scattering (SCdry), and absorption (ACdry) coefficients. Compared to AERONET, the models underestimate total AOD by ca. 21 % ± 20 % (as inferred from the ensemble median and interquartile range). Against satellite data, the ensemble AOD biases range from −37 % (MODIS-Terra) to −16 % (MERGED-FMI, a multi-satellite AOD product), which we explain by differences between individual satellites and AERONET measurements themselves. Correlation coefficients (R) between model and observation AOD records are generally high (R>0.75), suggesting that the models are capable of capturing spatio-temporal variations in AOD. We find a much larger underestimate in coarse AODc (∼ −45 % ± 25 %) than in fine AODf (∼ −15 % ± 25 %) with slightly increased inter-model spread compared to total AOD. These results indicate problems in the modelling of DU and SS. The AODc bias is likely due to missing DU over continental land masses (particularly over the United States, SE Asia, and S. America), while marine AERONET sites and the AATSR SU satellite data suggest more moderate oceanic biases in AODc. Column AEs are underestimated by about 10 % ± 16 %. For situations in which measurements show AE > 2, models underestimate AERONET AE by ca. 35 %. In contrast, all models (but one) exhibit large overestimates in AE when coarse aerosol dominates (bias ca. +140 % if observed AE < 0.5). Simulated AE does not span the observed AE variability. These results indicate that models overestimate particle size (or underestimate the fine-mode fraction) for fine-dominated aerosol and underestimate size (or overestimate the fine-mode fraction) for coarse-dominated aerosol. This must have implications for lifetime, water uptake, scattering enhancement, and the aerosol radiative effect, which we can not quantify at this moment. Comparison against Global Atmosphere Watch (GAW) in situ data results in mean bias and inter-model variations of −35 % ± 25 % and −20 % ± 18 % for SCdry and ACdry, respectively. The larger underestimate of SCdry than ACdry suggests the models will simulate an aerosol single scattering albedo that is too low. The larger underestimate of SCdry than ambient air AOD is consistent with recent findings that models overestimate scattering enhancement due to hygroscopic growth. The broadly consistent negative bias in AOD and surface scattering suggests an underestimate of aerosol radiative effects in current global aerosol models. Considerable inter-model diversity in the simulated optical properties is often found in regions that are, unfortunately, not or only sparsely covered by ground-based observations. This includes, for instance, the Sahara, Amazonia, central Australia, and the South Pacific. This highlights the need for a better site coverage in the observations, which would enable us to better assess the models, but also the performance of satellite products in these regions. Using fine-mode AOD as a proxy for present-day aerosol forcing estimates, our results suggest that models underestimate aerosol forcing by ca. −15 %, however, with a considerably large interquartile range, suggesting a spread between −35 % and +10 %.

space-based remote sensing↗

NASA NTRS2021

Western Montana Ecological Forecasting: Modeling Habitat Suitability of Mustelid Species to Guide Detection Dog Surveys for Contaminants Monitoring, via Collected Scats in River Systems of Western Montana

Environmental contaminants are becoming increasingly prevalent in riverine ecosystems. The status of contaminants in western Montana’s relatively pristine river systems is largely unknown. Monitoring for heavy metals, brominated flame-retardants (BFRs), and pharmaceuticals is important due to their negative effects on ecosystems. Exposure to these contaminants can have significant endocrine, neurological, and reproductive effects. Contaminants easily travel up the food chain and bioaccumulate in apex predators. As predators with a largely aquatic diet, American mink (Mustela vison) and North American river otter (Lontra canadensis) serve as reliable indicator species of environmental health and the status of contaminants. Analysis of scat from these species is a noninvasive method to measure contaminant levels, and detection dogs from Working Dogs for Conservation (WD4C) have been used to locate these scat samples. To aid in the search of these samples, habitat suitability models were created for mink and otter for the years 2013-2020 and projected to 2040 using the random forest algorithm in the Software for Assisted Habitat Modeling (SAHM). Predictor variable data were acquired from Landsat 8 Operational Land Imager (OLI), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Global Precipitation Measurement Integrated Multi-satellite Retrievals for GPM (GPM IMERG), Shuttle Radar Topography Mission (SRTM), and Soil Moisture Active Passive (SMAP). Within these models, the most important variable for mink and otter habitat was distance to river. Suitable habitat also corresponded with emergent herbaceous land cover and deeper river locations. These habitat suitability models will inform sampling site section for further contaminant analysis.

Anna Winter↗

NASA NTRS2016

The Global Methane Budget 2000-2012

The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase, making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (approximately biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modeling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations).For the 2003-2012 decade, global methane emissions are estimated by top-down inversions at 558 TgCH4 yr(exp -1), range 540-568. About 60 of global emissions are anthropogenic (range 50-65%). Since 2010, the bottom-up global emission inventories have been closer to methane emissions in the most carbon-intensive Representative Concentrations Pathway (RCP8.5) and higher than all other RCP scenarios. Bottom-up approaches suggest larger global emissions (736 TgCH4 yr(exp -1), range 596-884) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the top-down budget, it is likely that some of the individual emissions reported by the bottom-up approaches are overestimated, leading to too large global emissions. Latitudinal data from top-down emissions indicate a predominance of tropical emissions (approximately 64% of the global budget, less than 30deg N) as compared to mid (approximately 32%, 30-60deg N) and high northern latitudes (approximately 4%, 60-90deg N). Top-down inversions consistently infer lower emissions in China (approximately 58 TgCH4 yr(exp -1), range 51-72, minus14% ) and higher emissions in Africa (86 TgCH4 yr(exp -1), range 73-108, plus 19% ) than bottom-up values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for top-down inversions than for bottom-up inventories and models. The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30-40% on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions. The data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (http://doi.org/10.3334/CDIAC/GLOBAL_ METHANE_BUDGET_2016_V1.1) and the Global Carbon Project.

Saunois, Marielle↗

NASA NTRS2021

Retrieving Aerosol Optical Depth and High Spatial Resolution Ocean Surface Wind Speed from CALIPSO: A Neural Network Approach

An innovative neural network retrieval algorithm is developed for retrieving ocean surface wind speed from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements. The neural network is trained with CALIPSO ocean surface and atmospheric backscatter measurements together with collocated Advanced Microwave Scanning Radiometer for EOS(AMSR-E) ocean surface wind speed. Ocean surface wind speeds are derived by applying the neural network algorithm to CALIPSO measurements between 2008 and 2020. CALIPSO wind speed measurement of 2015 also compare with Advanced Microwave Scanning Radiometer 2 (AMSR-2) measurements on the Global Change Observation Mission –Water "Shizuku"(GCOM-W) satellite. Aerosol optical depths are then derived from CALIPSO’s ocean surface backscatter signal and theoretical ocean surface reflectance calculated from CALIPSO wind speed and Cox-Munk wind –surface slope variance relation. This CALIPSO wind speed retrieval technique is an improvement from Hu et al. (2008), as it can be applied to most clear skies with optical depths up to 1.5 without making assumptions about aerosol lidar ratio.

wind aerosols CALIPSO↗

NASA NTRS2022

Disturbances in North American Boreal Forest and Arctic Tundra: Impacts, Interactions, and Responses

Ecosystems in the North American Arctic-Boreal Zone (ABZ) experience a diverse set of disturbances associated with wildfire, permafrost dynamics, geomorphic processes, insect outbreaks and pathogens, extreme weather events, and human activity. Climate warming in the ABZ is occurring at over twice the rate of the global average, and as a result the extent, frequency, and severity of these disturbances are increasing rapidly. Disturbances in the ABZ span a wide gradient of spatiotemporal scales and have varying impacts on ecosystem properties and function. However, many ABZ disturbances are relatively understudied and have different sensitivities to climate and trajectories of recovery, resulting in considerable uncertainty in the impacts of climate warming and human land use on ABZ vegetation dynamics and in the interactions between disturbance types. Here we review the current knowledge of ABZ disturbances and their precursors, ecosystem impacts, temporal frequencies, spatial extents, and severity. We also summarize current knowledge of interactions and feedbacks among ABZ disturbances and characterize typical trajectories of vegetation loss and recovery in response to ecosystem disturbance using satellite time-series. We conclude with a summary of critical data and knowledge gaps and identify priorities for future study.

high-latitude↗

NASA NTRSDate not supplied

Identifying Indicators of Harmful Algal Blooms in Coastal Atacama Using Satellite Image Processing Techniques to Improve Industry and Authority Response

Over the past decade, Chile has experienced a significant rise in both the frequency and intensity of coastal Harmful Algal Blooms (HABs). These bloom events are a growing concern for the Atacama Region especially, with potential impacts on human health, aquaculture, and the environment. HABs are caused by an excess proliferation of microalgae, with certain algae species commonly found in Chilean HABs capable of producing toxins which can poison fish, toxify shellfish, and cause illness or death when ingested by humans. Collaborating with the Ministry of Health of Chile, Centro de Información de Recursos Naturales, the University of Atacama, and the Embassy of Chile’s Agricultural Office, this study identifies potential indicators of HABs in the Coastal Atacama Region using NASA Earth observations. Satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard NASA’s Aqua satellite was used for the identification of chlorophyll-a which provides direct estimates of the algae pigment’s concentration in water bodies. In addition, the spatiotemporal patterns of several other parameters were examined to identify correlations with occurrences of HABs, including sea surface temperature and normalized fluorescence line height using Aqua MODIS, Normalized Difference Turbidity Index (NDTI) using Landsat 8, and ocean wind speed measurements from the NOAA Joint Polar Satellite System. Analysis of these patterns revealed hotspots of high chlorophyll-a concentrations from the years 2014 to 2022, the most major of which occurring in the coastal zones of Chañaral, Bahía Inglesa, and Huasco. Also, through the use of a similarity search conducted, the seasonal variation of these indicators was mapped to highlight areas with great likelihood of experiencing HABs in the future. The results can guide future efforts for in-situ water sampling, toxin analysis and assessment, and HAB monitoring and prevention.

Cody O'Ferrall↗

NASA NTRS2021

Heterodyne Receiver for Origins

The Heterodyne Receiver for Origins (HERO) is the first detailed study of a heterodyne focal plane array receiver for space applications. HERO gives the Origins Space Telescope the capability to observe at very high spectral resolution (R ¼ 107) over an unprecedentedly large far-infrared (FIR) wavelengths range (111 to 617 μm) with high sensitivity, with simultaneous dual polarization and dual-frequency band operation. The design is based on prior successful heterodyne receivers, such as Heterodyne Instrument for the Far-Infrared /Herschel, but surpasses it by one to two orders of magnitude by exploiting the latest technological developments. Innovative components are used to keep the required satellite resources low and thus allowing for the first time a convincing design of a large format heterodyne array receiver for space. HERO on Origins is a unique tool to explore the FIR universe and extends the enormous potential of submillimeter astronomical spectroscopy into new areas of astronomical research.

Martina C Wiedner↗

DOE OSTI2025

The AGORA High-Resolution Galaxy Simulations Comparison Project

Context. Satellite galaxies experience multiple physical processes when interacting with their host halos, often leading to the quenching of star formation. In the Local Group, satellite quenching has been shown to be highly efficient, affecting nearly all satellites except the most massive ones. While recent surveys study Milky Way-analogs to assess how representative our Local Group is, the dominant physical mechanisms behind satellite quenching in Milky Way-mass halos remain under debate. Aims. We analyze satellite quenching within the same Milky Way-mass halo simulated using various widely used astrophysical codes, each using different hydrodynamic methods and implementing different supernovae feedback recipes. The goal is to determine whether quenched fractions, quenching timescales, and the dominant quenching mechanisms are consistent across codes or if they show sensitivity to the specific hydrodynamic method and supernovae feedback physics employed. Methods. We used a subset of high-resolution cosmological zoom-in simulations of a Milky Way-mass halo from the multiple-code AGORA CosmoRun suite. Our analysis focuses on comparing satellite quenching across the different models and against observational data. We also analyzed the dominant mechanisms driving satellite quenching in each model. Results. We find that the quenched fraction is consistent with the latest SAGA Survey results within its 1σ host-to-host scatter across all the models. Regarding quenching timescales, all the models reproduce the trend observed in the ELVES survey, Local Group observations, and previous simulations: The less massive the satellite, the shorter its quenching timescale. All of our models converge on the dominant quenching mechanisms: Strangulation halts cold gas accretion in all satellites, while ram pressure stripping is the predominant mechanism for gas removal, and it is particularly effective in satellites with M * <10 8 M ⊙ . Nevertheless, the efficiency of the stripping mechanisms differs among the codes, showing a strong sensitivity to the different supernovae feedback implementations and/or hydrodynamic methods employed.

Local Group↗

NASA NTRS2021

Hawai‘i Island Disasters: Using NASA Earth Observations to Assess Coastal Flood Risk with Measures of Land Cover Change, Flood Extent, and Vulnerability for Adaptation and Mitigation Planning on Hawai‘i Island

As the County of Hawai‘i faces an increased risk of extreme flooding events, sea-level rise, and other hazards associated with climate change, the need for building geospatial capacity to make better-informed decisions is critical. The County of Hawai‘i and Arizona State University partnered with NASA DEVELOP to complete a macro-scale risk analysis for the island of Hawai‘i analyzing flooding, land cover, vulnerability, and exposure factors using Earth observations and socio-economic data. The team assessed the variation in urban coastal vulnerability around the entire island of Hawai‘i, using satellite imagery of coastal land cover typology from satellite products such as Landsat 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Sentinel-1 Synthetic Aperture Radar (SAR). The team made a sharable geodatabase containing datasets modeling vulnerability to coastal flooding as well as the Hawai‘i Flood Risk Toolbox (HiFloRT) which contains multiple tools for the County to map land cover, extreme rainfall and flood extent across the Island. The end products will allow the County of Hawai‘i to establish a protocol and standard framework for the utilization of Earth observations in future planning.

Garren Kalter↗

NASA NTRS2021

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.

Ghada Y.H. El Serafy↗

NASA NTRSDate not supplied

PBL Thermodynamic Characterization and Its Variability From NAST-I During the Wh2yMSIE Field Campaign

The National Airborne Sounder Testbed-Interferometer (NAST‑I) suborbital system (<2.6 km IFOV; 0.25 cm-1 within 645–2700 cm‑1) serves as a spaceborne instrument simulator and pathfinder for future satellite capabilities and airborne science experiments. The NAST‑I measurements are made to advance understanding of science critical for weather, climate, chemistry, and radiation applications. Here we present some groundbreaking capabilities of NAST-I measurements and corresponding geophysical retrievals and their potential benefits toward improved atmospheric state and composition characterizations needed for science applications of particular importance toward enhancing characterization and understanding of the Planetary Boundary Layer (PBL). Initial results of PBL height estimation and thermodynamic characterization and their time evolution from NAST-I measurements during the WH2yMSIE field campaign are presented.

Daniel K Zhou↗

NASA NTRS2010

A Geostatistical Data Fusion Technique for Merging Remote Sensing and Ground-Based Observations of Aerosol Optical Thickness

Particles in the atmosphere reflect incoming sunlight, tending to cool the Earth below. Some particles, such as soot, also absorb sunlight, which tens to warm the ambient atmosphere. Aerosol optical depth (AOD) is a measure of the amount of particulate matter in the atmosphere, and is a key input to computer models that simulate and predict Earth's changing climate. The global AOD products from the Multi-angle Imaging SpectroRadiometer (MISR) and the MODerate resolution Imaging Spectroradiometer (MODIS), both of which fly on the NASA Earth Observing System's Terra satellite, provide complementary views of the particles in the atmosphere. Whereas MODIS offers global coverage about four times as frequent as MISR, the multi-angle data makes it possible to separate the surface and atmospheric contributions to the observed top-of-atmosphere radiances, and also to more effectively discriminate particle type. Surface-based AERONET sun photometers retrieve AOD with smaller uncertainties than the satellite instruments, but only at a few fixed locations. So there are clear reasons to combine these data sets in a way that takes advantage of their respective strengths. This paper represents an effort at combining MISR, MODIS and AERONET AOD products over the continental US, using a common spatial statistical technique called kriging. The technique uses the correlation between the satellite data and the "ground-truth" sun photometer observations to assign uncertainty to the satellite data on a region-by-region basis. The larger fraction of the sun photometer variance that is duplicated by the satellite data, the higher the confidence assigned to the satellite data in that region. In the Western and Central US, MISR AOD correlation with AERONET are significantly higher than those with MODIS, likely due to bright surfaces in these regions, which pose greater challenges for the single-view MODIS retrievals. In the east, MODIS correlations are higher, due to more frequent sampling of the varying AOD. These results demonstrate how the MISR and MODIS aerosol products are complementary. The underlying technique also provides one method for combining these products in such a way that takes advantage of the strengths of each, in the places and times when they are maximal, and in addition, yields an estimate of the associated uncertainties in space and time.

Chatterjee, Abhishek↗

NASA NTRSDate not supplied

A Preliminary Comparison of GEOS-GSI with GEOS-JEDI Using the Complete GEOS Observing System

Transitioning the NASA GMAO GEOS data assimilation capabilities to JEDI involves replacing the GEOS Gridpoint Statistical Interpolation (GSI) with a JEDI-based analysis. Performing fair comparisons between the corresponding GEOS-JEDI system and the present GEOS-GSI has involved downgrading the latter's capabilities to compensate for missing capabilities in the former. As the capabilities in JEDI mature and start matching those of GSI’s, experimentation has evolved from simple 3DVAR using only radiosondes, to adding satellite observations without VarBC, to now having GEOS-JEDI use hybrid 4DEnVar and full observing system, VarBC and most of the knobs typically used in the present operational (and experimental) version of GEOS-GSI. This presentation provides a discussion of the process and pathway taken to replace GSI with JEDI in GEOS at GMAO. The presentation also shows latest results of testing in the still relatively simply context of 3D-FGAT, but now having JEDI use the whole of the typical observing system used in GEOS. Results are encouraging but preliminary, with various caveats having been identified during the attempt to cycle JEDI analyses. Solutions to most of the identified problems have been worked out, but a few pending issues are still being worked out.

Ricardo Todling↗

DOE OSTI2024

Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model

Abstract Ice formation remains one of the most poorly represented microphysical processes in climate models. While primary ice production (PIP) parameterizations are known to have a large influence on the modeled cloud properties, the representation of secondary ice production (SIP) is incomplete and its corresponding impact is therefore largely unquantified. Furthermore, ice aggregation is another important process for the total cloud ice budget, which also remains largely unconstrained. In this study, we examine the impact of PIP, SIP, and ice aggregation on Arctic clouds, using the Norwegian Earth System Model, version 2 (NorESM2). Simulations with both prognostic and diagnostic PIP show that heterogeneous freezing alone cannot reproduce the observed cloud ice content. The implementation of missing SIP mechanisms (collisional breakup, drop shattering, and sublimation breakup) in NorESM2 improves the modeled ice properties, while improvements in liquid content occur only in simulations with prognostic PIP. However, results are sensitive to the description of collisional breakup. This mechanism, which dominates SIP in the examined conditions, is very sensitive to the treatment of the sublimation correction factor, a poorly constrained parameter that is included in the utilized parameterization. Finally, variations in ice aggregation treatment can also significantly impact cloud properties, mainly through their impact on collisional breakup efficiency. Overall, enhancement in ice production through the addition of SIP mechanisms and the reduction in ice aggregation (in line with radar observations of shallow Arctic clouds) result in enhanced cloud cover and decreased TOA radiation biases, compared to satellite measurements, especially during the cold months. Significance Statement Arctic clouds remain a large source of uncertainty in projections of the future climate due to the poor representation of the microphysical processes that govern their life cycle. Ice formation is among the least understood processes. While it is widely recognized that better constraints on primary ice production (PIP) are needed to improve existing parameterizations, we show that secondary ice production (SIP) and ice aggregation can have also a significant impact on ice number concentrations. Constraining ice formation through the addition of missing SIP mechanisms and reducing ice aggregation can improve the representation of the cloud macrophysical properties and enhance total cloud cover in the Arctic region, which in turn contributes to decreased TOA radiation biases in the cold months.

Meteorology & Atmospheric Sciences↗