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NASA NTRS2023

Exploring Natural and Social Drivers of Forest Degradation in Post-Soviet Georgia

The Caucasus Mountains harbor high concentrations of endemic species and provide an abundance of ecosystem services yet are significantly understudied compared to other ecosystems in Eurasia. In the country of Georgia, at the heart of the Caucasus region, forest degradation has been the largest land change process over the last thirty years. The prevailing narrative is that legal and illegal cutting of trees for fuelwood is primarily responsible for this process. Yet, since independence from the Soviet Union in 1991, the country has undergone rapid socioeconomic and institutional changes which have not been explored as drivers of forest change. We combine newly available land-cover change estimates, Georgian statistical data, and historical institutional change data to examine socioeconomic drivers of forest degradation. Our analysis controls for concurrent changes in climate that would affect degradation and examines variation at the regional (state) level from 2011 to 2019, as well as at the national level from 1987 to 2019. We find that higher winter temperature and drought are associated with higher degradation at the regional scale, while major institutional changes and drought are associated with higher forest degradation at the national level. Access to natural gas, the major energy alternative to fuelwood, had no significant association with degradation. Our results challenge the narrative that poverty and a lack of alternative energy infrastructure drive forest degradation and suggest that government policies banning household fuelwood cutting, including the new Forest Code of 2020, may not reduce forest degradation. Given these results, improved data on wood harvesting and more research on the commercial drivers of degradation and their links to economic and political reforms is needed to better inform forest policy in the region, especially given ongoing risks from climate change.

Institutions↗
NASA NTRS2010

The Energy Budget of the Polar Atmosphere in MERRA

Components of the atmospheric energy budget from the Modern Era Retrospective-analysis for Research and Applications (MERRA) are evaluated in polar regions for the period 1979-2005 and compared with previous estimates, in situ observations, and contemporary reanalyses. Closure of the energy budget is reflected by the analysis increments term, which results from virtual enthalpy and latent heating contributions and averages -11 W/sq m over the north polar cap and -22 W/sq m over the south polar cap. Total energy tendency and energy convergence terms from MERRA agree closely with previous study for northern high latitudes but convergence exceeds previous estimates for the south polar cap by 46 percent. Discrepancies with the Southern Hemisphere transport are largest in autumn and may be related to differences in topography with earlier reanalyses. For the Arctic, differences between MERRA and other sources in TOA and surface radiative fluxes maximize in May. These differences are concurrent with the largest discrepancies between MERRA parameterized and observed surface albedo. For May, in situ observations of the upwelling shortwave flux in the Arctic are 80 W/sq m larger than MERRA, while the MERRA downwelling longwave flux is underestimated by 12 W/sq m throughout the year. Over grounded ice sheets, the annual mean net surface energy flux in MERRA is erroneously non-zero. Contemporary reanalyses from the Climate Forecast Center (CFSR) and the Interim Re-Analyses of the European Centre for Medium Range Weather Forecasts (ERA-I) are found to have better surface parameterizations, however these collections are also found to have significant discrepancies with observed surface and TOA energy fluxes. Discrepancies among available reanalyses underscore the challenge of reproducing credible estimates of the atmospheric energy budget in polar regions.

Cullather, Richard I.↗
NASA NTRS2016

Extreme Weather and Climate: Workshop Report

Extreme events are the aspects of climate to which human society is most sensitive. Due to both their severity and their rarity, extreme events can challenge the capacity of physical, social, economic and political infrastructures, turning natural events into human disasters. Yet, because they are low frequency events, the science of extreme events is very challenging. Among the challenges is the difficulty of connecting extreme events to longer-term, large-scale variability and trends in the climate system, including anthropogenic climate change. How can we best quantify the risks posed by extreme weather events, both in the current climate and in the warmer and different climates to come? How can we better predict them? What can we do to reduce the harm done by such events? In response to these questions, the Initiative on Extreme Weather and Climate has been created at Columbia University in New York City (extreme weather.columbia.edu). This Initiative is a University-wide activity focused on understanding the risks to human life, property, infrastructure, communities, institutions, ecosystems, and landscapes from extreme weather events, both in the present and future climates, and on developing solutions to mitigate those risks. In May 2015,the Initiative held its first science workshop, entitled Extreme Weather and Climate: Hazards, Impacts, Actions. The purpose of the workshop was to define the scope of the Initiative and tremendously broad intellectual footprint of the topic indicated by the titles of the presentations (see Table 1). The intent of the workshop was to stimulate thought across disciplinary lines by juxtaposing talks whose subjects differed dramatically. Each session concluded with question and answer panel sessions. Approximately, 150 people were in attendance throughout the day. Below is a brief synopsis of each presentation. The synopses collectively reflect the variety and richness of the emerging extreme event research agenda.

extreme weather events↗
NASA NTRS2020

Development of a Venus Surface Wind Sensor Based on a Miniature Drag-Force Anemometer

To better understand the atmospheric structure and dynamics on the Venus surface and provide input to climate models, there is a need to measure the wind velocity and direction on the surface and track changes over extended periods. A wind sensor based on a miniature drag-force anemometer is being developed to meet the challenges for wind measurements and operational requirements on the surface of Venus. The sensor materials are chosen to enhance durability and prevent reactivity with the Venus surface atmosphere. Advantages of this approach include that it is independent on variable heat transfer, has been matured in other harsh environment applications,and has a low mass and power requirement. This report describes demonstration of this miniature drag-force anemometer integrated with high temperature electronics in simulated Venus surface environment. Prototype drag-force anemometers were demonstrated integrated with an operational amplifier recording transient effects in a simulated Venus surface environment. For multidirectional wind monitoring, the sensors are small enough to be deployed orthogonally as a three-dimensional array on a small arm or mast

wind measurement↗
NASA NTRSDate not supplied

Development of a Venus Surface Wind Sensor

To better understand the atmospheric structure and dynamics on the Venus surface and provide input to climate models, there is a need to measure the wind velocity and direction on the surface and track changes over extended periods. A wind sensor based on a miniature drag-force anemometer is being developed to meet the challenges for wind measurements and operational requirements on the surface of Venus. The sensor materials are chosen to enhance durability and prevent reactivity with the Venus surface atmosphere. Advantages of this approach include that it is independent of variable heat transfer, has been matured in other harsh environment applications, and has a low mass and power requirement. Prototype drag-force anemometers were demonstrated, integrated with a high-temperature operational amplifier, recording transient effects in a simulated Venus surface environment. For multidirectional wind monitoring, the sensors are small enough to be deployed orthogonally as a three-dimensional array on a small arm or mast. This presentation describes the development and demonstration of this miniature drag-force anemometer integrated with high temperature electronics in a simulated Venus surface environment.

wind measurement↗
NASA NTRS2003

The CEOP Inter-Monsoon Studies (CIMS)

Prediction of climate relies on models, and better model prediction depends on good model physics. Improving model physics requires the maximal utilization of climate data of the past, present and future. CEOP provides the first example of a comprehensive, integrated global and regional data set, consisting of globally gridded data, reference site in-situ observations, model location time series (MOLTS), and integrated satellite data for a two-year period covering two complete annual cycles of 2003-2004. The monsoon regions are the most important socio-economically in terms of devastation by floods and droughts, and potential impacts from climate change md fluctuatinns nf the hydrologic cyc!e. Scientifically, it is most challenging, because of complex interactions of atmosphere, land and oceans, local vs. remote forcings in contributing to climate variability and change in the region. Given that many common features, and physical teleconnection exist among different monsoon regions, an international research focus on monsoon must be coordinated and sustained. Current models of the monsoon are grossly inadequate for regional predictions. For improvement, models must be confronted with relevant observations, and model physic developers must be made to be aware of the wealth of information from existing climate data, field measurements, and satellite data that can be used to improve models. Model transferability studles must be conducted. CIMS is a major initiative under CEOP to engage the modeling and the observational communities to join in a coordinated effort to study the monsoons. The objectives of CIMS are (a) To provide a better understanding of fundamental physical processes (diurnal cycle, annual cycle, and intraseasonal oscillations) in monsoon regions around the world and (b) To demonstrate the synergy and utility of CEOP data in providing a pathway for model physics evaluation and improvement. In this talk, I will present the basic concepts of CIMS and the key scientific problems facing monsoon climates and provide examples of common monsoon features, and possible monsoon induced teleconnections linking different parts of the world.

Lau, William K. M.↗
NASA NTRS2013

Advancing Drought Understanding, Monitoring and Prediction

Having the capacity to monitor droughts in near-real time and providing accurate drought prediction from weeks to seasons in advance can greatly reduce the severity of social and economic damage caused by drought, a leading natural hazard for North America. The congressional mandate to establish the National Integrated Drought Information System (NIDIS; Public Law 109-430) in 2006 was a major impulse to develop, integrate, and provide drought information to meet the challenges posed by this hazard. Significant progress has been made on many fronts. On the research front, efforts by the broad scientific community have resulted in improved understanding of North American droughts and improved monitoring and forecasting tools. We now have a better understanding of the droughts of the twentieth century including the 1930s "Dust Bowl"; we have developed a broader array of tools and datasets that enhance the official North American Drought Monitor based on different methodologies such as state-of-the-art land surface modeling (e.g., the North American Land Data Assimilation System) and remote sensing (e.g., the evaporative stress index) to better characterize the occurrence and severity of drought in its multiple manifestations. In addition, we have new tools for drought prediction [including the new National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2, for operational prediction and an experimental National Multimodel Ensemble] and have explored diverse methodologies including ensemble hydrologic prediction approaches. Broad NIDIS-inspired progress is influencing the development of a Global Drought Information System (GDIS) under the auspices of the World Climate Research Program. Despite these advances, current drought monitoring and forecasting capabilities still fall short of users' needs, especially the need for skillful and reliable drought forecasts at regional and local scales. To tackle this outstanding challenging problem, focused and coordinated research efforts are needed, drawing from excellence across the broad drought research community. To meet this challenge, National Oceanic and Atmospheric Administration (NOAA)'s Drought Task Force was established in October 2011 with the ambitious goal of achieving significant new advances in the ability to understand, monitor, and predict drought over North America. The Task Force (duration of October 2011-September 2014) is an initiative of NOAA's Climate Program Office Modeling, Analysis, Predictions, and Projections (MAPP) program in partnership with NIDIS. It brings together over 30 leading MAPP-funded drought scientists from multiple academic and federal institutions [involves scientists from NOAA's research laboratories and centers, the National Aeronautics and Space Administration (NASA), U.S. Department of Agriculture, National Center for Atmospheric Research (NCAR), and many universities] in a concerted research effort that builds on individual MAPP research projects. These projects span the wide spectrum of drought research needed to make fundamental advances, from those aimed at the basic understanding of drought mechanisms to those aimed at testing new drought monitoring and prediction tools for operational and service purposes (as part of NCEP's Climate Test Bed). The Drought Task Force provides focus and coordination to MAPP drought research activities and also facilitates synergies with other national and international drought research efforts, including those by the GDIS.

GDIS↗
NASA NTRS2017

Comparing Proxy and Model Estimates of Hydroclimate Variability and Change over the Common Era

Water availability is fundamental to societies and ecosystems, but our understanding of variations in hydroclimate (including extreme events, flooding, and decadal periods of drought) is limited because of a paucity of modern instrumental observations that are distributed unevenly across the globe and only span parts of the 20th and 21st centuries. Such data coverage is insufficient for characterizing hydroclimate and its associated dynamics because of its multidecadal to centennial variability and highly regionalized spatial signature. High-resolution (seasonal to decadal) hydroclimatic proxies that span all or parts of the Common Era (CE) and paleoclimate simulations from climate models are therefore important tools for augmenting our understanding of hydroclimate variability. In particular, the comparison of the two sources of information is critical for addressing the uncertainties and limitations of both while enriching each of their interpretations. We review the principal proxy data available for hydroclimatic reconstructions over the CE and highlight the contemporary understanding of how these proxies are interpreted as hydroclimate indicators. We also review the available last-millennium simulations from fully coupled climate models and discuss several outstanding challenges associated with simulating hydroclimate variability and change over the CE. A specific review of simulated hydroclimatic changes forced by volcanic events is provided, as is a discussion of expected improvements in estimated radiative forcings, models, and their implementation in the future. Our review of hydroclimatic proxies and last-millennium model simulations is used as the basis for articulating a variety of considerations and best practices for how to perform proxy-model comparisons of CE hydroclimate. This discussion provides a framework for how best to evaluate hydroclimate variability and its associated dynamics using these comparisons and how they can better inform interpretations of both proxy data and model simulations.We subsequently explore means of using proxy-model comparisons to better constrain and characterize future hydroclimate risks. This is explored specifically in the context of several examples that demonstrate how proxy-model comparisons can be used to quantitatively constrain future hydroclimatic risks as estimated from climate model projections.

Common Era↗
NASA NTRS2014

Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

Shukla, Shraddhanand↗
NASA NTRS2015

Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

Shukla, Shraddhanand↗
NASA NTRS2017

H31G-1596: DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites

Cloud and cloud shadow detection has important applications in weather and climate studies. It is even more crucial when we introduce geostationary satellites into the field of terrestrial remote sensing. With the challenges associated with data acquired in very high frequency (10-15 mins per scan), the ability to derive an accurate cloud shadow mask from geostationary satellite data is critical. The key to the success for most of the existing algorithms depends on spatially and temporally varying thresholds,which better capture local atmospheric and surface effects.However, the selection of proper threshold is difficult and may lead to erroneous results. In this work, we propose a deep neural network based approach called CloudCNN to classify cloudshadow from Himawari-8 AHI and GOES-16 ABI multispectral data. DeepSAT's CloudCNN consists of an encoderdecoder based architecture for binary-class pixel wise segmentation. We train CloudCNN on multi-GPU Nvidia Devbox cluster, and deploy the prediction pipeline on NASA Earth Exchange (NEX) Pleiades supercomputer. We achieved an overall accuracy of 93.29% on test samples. Since, the predictions take only a few seconds to segment a full multispectral GOES-16 or Himawari-8 Full Disk image, the developed framework can be used for real-time cloud detection, cyclone detection, or extreme weather event predictions.

GOES-1↗
NASA NTRS2002

Venus Atmospheric Exploration by Solar Aircraft

The Venus atmosphere is a favorable environment for flying powered aircraft. The atmospheric pressure makes flight much easier than on planets such as Mars. Above the clouds, solar energy is available in abundance on Venus, and the slow rotation of Venus allows a solar airplane to be designed for flight within continuous sunlight. The atmosphere between 50 km and 75 km on Venus is one of the most dynamic and interesting regions of the planet. The challenge for a Venus aircraft will be the fierce winds and caustic atmosphere. In order to remain on the sunlit side of Venus, an exploration aircraft will have to be capable of sustained flight at or above the wind speed. An aircraft would be a powerful tool for exploration. By learning how Venus can be so similar to Earth, and yet so different, we will learn to better understand the climate and geological history of the Earth.

Landis, Geoffrey A.↗
NASA NTRS2006

Three Decades of Precision Orbit Determination Progress, Achievements, Future Challenges and its Vital Contribution to Oceanography and Climate Research

Although satellite altimetry has been around for thirty years, the last fifteen beginning with the launch of TOPEX/Poseidon (TP) have yielded an abundance of significant results including: monitoring of ENS0 events, detection of internal tides, determination of accurate global tides, unambiguous delineation of Rossby waves and their propagation characteristics, accurate determination of geostrophic currents, and a multi-decadal time series of mean sea level trend and dynamic ocean topography variability. While the high level of accuracy being achieved is a result of both instrument maturity and the quality of models and correction algorithms applied to the data, improving the quality of the Climate Data Records produced from altimetry is highly dependent on concurrent progress being made in fields such as orbit determination. The precision orbits form the reference frame from which the radar altimeter observations are made. Therefore, the accuracy of the altimetric mapping is limited to a great extent by the accuracy to which a satellite orbit can be computed. The TP mission represents the first time that the radial component of an altimeter orbit was routinely computed with an accuracy of 2-cm. Recently it has been demonstrated that it is possible to compute the radial component of Jason orbits with an accuracy of better than 1-cm. Additionally, still further improvements in TP orbits are being achieved with new techniques and algorithms largely developed from combined Jason and TP data analysis. While these recent POD achievements are impressive, the new accuracies are now revealing subtle systematic orbit error that manifest as both intra and inter annual ocean topography errors. Additionally the construction of inter-decadal time series of climate data records requires the removal of systematic differences across multiple missions. Current and future efforts must focus on the understanding and reduction of these errors in order to generate a complete and consistent time series of improved orbits across multiple missions and decades required for the most stringent climate-related research. This presentation discusses the POD progress and achievements made over nearly three decades, and presents the future challenges, goals and their impact on altimetric derived ocean sciences.

Luthcke, Scott↗
NASA NTRSDate not supplied

Comprehensive Analysis of Thirty Years of Land Change in Georgia: Forest Degradation, Land-Use Patterns, and Drivers

In this talk, we present results from a study, funded by the NASA Land-Cover and Land-Use Change program, of thirty years of environmental change in the country of Georgia. The country, a former Soviet Union Republic, has experienced more political, economic and social change in the last 30 years than most countries. But the environmental implications of these extreme events remain largely unknown – the region is understudied, no national forest inventory has been completed in 20 years, and previous research in the area conducted by the authors were not successful. The latter statement is because of forest degradation. After the collapse of the Soviet Union, much of the forestry infrastructure broke down and people resorted to chopping down individual trees. The result was in many parts of the country a reduction in forest biomass but not to the point of a change in land cover. This type of forest degradation, which was either gradual over decades or abrupt, could not be readily identified using the methods and data at our disposal at the time. In the recent study, we had full access to the Landsat archive in combination with computing power. By using monitoring methods that are based on time series analysis, we were able to capture and identify the gradual processes driven by small scale activities that are so characteristic of the post-Soviet Georgian landscape. We developed an approach (“CCDC-SMA”) that combines time series analysis and spectral mixture analysis running on Google Earth Engine for monitoring abrupt and gradual forest degradation. By using this approach, we found that forest degradation was significantly larger than the area estimate of deforestation; 3,541 ± 556 km2 (11% of the forest area in 1987) compared to 158 ± 98 km2 from 1987 to 2020. The prevailing narrative is that legal and illegal cutting of trees for fuelwood is primarily responsible for this process. Yet, since independence from the Soviet Union in 1991, the country has undergone rapid socioeconomic and institutional changes which have not been explored as drivers of forest change. To further our understanding of the underlying causes, we combined forest disturbance estimates, Georgian statistical data, and historical institutional change data to examine socioeconomic drivers of forest degradation. We found that higher winter temperature and drought were associated with higher degradation at the regional scale, while major institutional changes and drought were associated with higher forest degradation at the national level. Access to natural gas, the major energy alternative to fuelwood, had no significant association with degradation. Our results challenge the narrative that poverty and a lack of alternative energy infrastructure drive forest degradation and suggest that government policies banning household fuelwood cutting, including the new Forest Code of 2020, may not reduce forest degradation. Given these results, improved data on wood harvesting and more research on the commercial drivers of degradation and their links to economic and political reforms is needed to better inform forest policy in the region, especially given ongoing risks from climate change.

Pontus Olofsson↗
NASA NTRS2003

Measuring Aerosol Optical Properties with the Ozone Monitoring Instrument (OMI)

The Ozone Monitoring Instrument (OMI) is the Dutch-Finnish contribution to the NASA EOS-Aura mission scheduled for launch in January 2004. OM1 is an imaging spectrometer that will measure the back-scattered Solar radiance between 270 an 500 nm. With its relatively high spatial resolution (13x24 sq km at nadir) and daily global coverage. OM1 will make a major contribution to our understanding of atmospheric chemistry and to climate research. OM1 will provide data continuity with the TOMS instruments. One of the pleasant surprises of the TOMS data record was its information on aerosol properties. First, only the absorbing aerosol index, which is sensitive to elevated lay- ers of aerosols such as desert dust and smoke aerosols, was derived. Recently these methods were further improved to yield aerosol optical thickness and single scattering albedo over land and ocean for 19 years of TOMS data (1979-1992,1997-2002), making it one of the longest and most valuable time series for aerosols presently available. Such long time series are essential to quantify the effect of aerosols on the Earth& climate. The OM1 instrument is better suited to measure aerosols than the TOMS instruments because of the smaller footprint, and better spectral coverage. The better capabilities of OMI will enable us to provide an improved aerosol product, but the knowledge will also be used for further analysis of the aerosol record from TOMS. The OM1 aerosol product that is currently being developed for OM1 combines the TOMS experience and the multi-spectral techniques that are used in the visible and near infrared. The challenge for this new product is to provide aerosol optical thickness and single scattering albedo from the near ultraviolet to the visible (330-500 nm) over land and ocean. In this presentation the methods for deriving the OM1 aerosol product will be presented. Part of these methods developed for OM1 can already be applied to TOMS data and results of such analysis will be shown.

Veefkind, J. P.↗
NASA NTRSDate not supplied

From the Upper Troposphere Through the Stratosphere: How Satellite Measurements Help Us Decode the Past to Better Project the Future

Decades of observations of key chemical species are allowing us to more fully understand processes important to transport and composition from the upper troposphere through the stratosphere. This talk will focus on a few examples and how they are informing our understanding of variability, trends, and future projections. The growth of the Antarctic ozone hole (late 1970s – mid 1990s) caused a dynamical perturbation to the Southern Hemisphere stratospheric circulation visible in ozone, one of our longest and best observed chemical constituents, and illustrates the connection between chemical change and the coupled radiative and dynamical response that challenges chemistry-climate models (CCMs). The quasi-biennial oscillation (QBO) is the dominant mode of interannual variability in the tropical stratosphere, however its impacts on stratospheric circulation and composition can be traced globally. The QBOs timing with respect to the seasonal cycle in each hemisphere is significant in understanding its impact on up to decadal scale variability. The El Nino Southern Oscillation (ENSO), which dominates tropical tropospheric interannual variability, also affects the stratosphere and its influence is visible in our growing record of tropospheric and stratospheric composition measurements. I will discuss how our knowledge of response has grown and how representation of these observed responses in CCMs is key both to understanding recent trends and to reducing uncertainty in future changes of composition.

Luke David Oman↗
NASA NTRS2022

Northeast US Ecological Forecasting: Modeling Invasive Plant Habitat Suitability to Support Management Efforts in the American Northeast

Invasive plant species threaten environmental and economic interests when they spread into new areas, outcompete native species, and disrupt ecosystem services. If the spread is not controlled early, species can become well-established and increasingly difficult to manage. The National Park Service (NPS) Invasive Plant Management Teams (IPMTs) strive for an “early detection, rapid response” approach to reducing invasive species spread. Management teams can better prioritize their work with the help of species distribution models (SDMs), which map habitat suitability by combining species occurrences with environmental predictor variables. Scarce invaded range data for newly arrived invasive species presents a particular challenge for producing accurate models. To improve future modeling efforts, this project compared SDM methods using different spatial scales to model two plant species invasive to the Northeast US: the well-established Japanese stiltgrass (Microstegium vimineum) and newer invasive species wavyleaf basketgrass (Oplismenus undulatifolius). The team used NASA Earth observations and climate datasets to model occurrence data and predictor layers at a US-specific extent (90m2 spatial resolution) and global extent (1 km2 spatial resolution). Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) provided data for US Normalized Difference Moisture Indices (NDMI), while global NDMI and topographic predictor layers were derived from Shuttle Radar Topography Mission (SRTM) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting models indicated important predictor variables for each species and explored the benefits and tradeoffs of using global data to model habitat suitability for new-arrival invasive species.

Rebecca Ohman↗
NASA NTRS2023

Three-Wavelength Approach for Aerosol-Cloud Discrimination in the Sage III/ISS Aerosol Extinction Dataset

The tropical upper troposphere and lower stratosphere (UTLS) region is dominated by aerosols and clouds affecting Earth’s radiation budget and climate. Thus, satellites’ continuous monitoring and identification of these layers is crucial for quantifying their radiative impact. However, distinguishing between aerosols and clouds is challenging, especially under the perturbed UTLS conditions during post-volcanic eruptions and wildfire events. Aerosol-cloud discrimination is primarily based on their disparate wavelength-dependent scattering and absorption properties. In this study, we use aerosol extinction observations in the tropical (15°N-15°S) UTLS from June 2017 to February 2021, available from the latest generation of the Stratospheric Aerosol and Gas Experiment (SAGE) instrument-SAGE III onboard the International Space Station (ISS) to study aerosols and clouds. During this period, the SAGE III/ISS provided better coverage over the tropics at additional wavelength channels (relative to previous SAGE missions) and witnessed several volcanic and wildfire events that perturbed the tropical UTLS. We explore the advantage of having an extinction coefficient at an additional wavelength channel (1550 nm) from the SAGE III/ISS in aerosol-cloud discrimination using a method based on thresholds of two extinction coefficient ratios, R1R1 (520 nm/1020 nm) and R2R2 (1020 nm/1550 nm). This method was proposed earlier by Kent et al. [Appl. Opt. 36, 8639 (1997) [CrossRef] ] for the SAGE III-Meteor-3M but was never tested for the tropical region under volcanically perturbed conditions. We call this method the Extinction Color Ratio (ECR) method. The ECR method is applied to the SAGE III/ISS aerosol extinction data to obtain cloud-filtered aerosol extinction coefficients, cloud-top altitude, and seasonal cloud occurrence frequency during the entire study period. Cloud-filtered aerosol extinction coefficient obtained using the ECR method revealed the presence of enhanced aerosols in the UTLS following volcanic eruptions and wildfire events consistent with the Ozone Mapping and Profiler Suite (OMPS) and space-borne lidar-Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The cloud-top altitude obtained from the SAGE III/ISS is within 1 km of the nearly co-located observations from OMPS and CALIOP. In general, the seasonal mean cloud-top altitude from the SAGE III/ISS events peaks during the December, January, and February months, with sunset events showing higher cloud tops than the sunrise events, indicating the seasonal and diurnal variation of the tropical convection. The seasonal altitude distribution of cloud occurrence frequency obtained from the SAGE III/ISS also agrees well with CALIOP observations within 10%. We show that the ECR method is a simple approach that relies on thresholds independent of the sampling period, providing cloud-filtered aerosol extinction coefficients uniformly for climate studies irrespective of the UTLS conditions. However, since the predecessor of SAGE III did not include a 1550 nm channel, the usefulness of this approach is limited to short-term climate studies after 2017.

Clouds Aerosols↗