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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Land Surface Phenology from MODIS: Characterization of the Collection 5 Global Land Cover Dynamics Product

Information related to land surface phenology is important for a variety of applications. For example, phenology is widely used as a diagnostic of ecosystem response to global change. In addition, phenology influences seasonal scale fluxes of water, energy, and carbon between the land surface and atmosphere. Increasingly, the importance of phenology for studies of habitat and biodiversity is also being recognized. While many data sets related to plant phenology have been collected at specific sites or in networks focused on individual plants or plant species, remote sensing provides the only way to observe and monitor phenology over large scales and at regular intervals. The MODIS Global Land Cover Dynamics Product was developed to support investigations that require regional to global scale information related to spatiotemporal dynamics in land surface phenology. Here we describe the Collection 5 version of this product, which represents a substantial refinement relative to the Collection 4 product. This new version provides information related to land surface phenology at higher spatial resolution than Collection 4 (500-m vs. 1-km), and is based on 8-day instead of 16-day input data. The paper presents a brief overview of the algorithm, followed by an assessment of the product. To this end, we present (1) a comparison of results from Collection 5 versus Collection 4 for selected MODIS tiles that span a range of climate and ecological conditions, (2) a characterization of interannual variation in Collections 4 and 5 data for North America from 2001 to 2006, and (3) a comparison of Collection 5 results against ground observations for two forest sites in the northeastern United States. Results show that the Collection 5 product is qualitatively similar to Collection 4. However, Collection 5 has fewer missing values outside of regions with persistent cloud cover and atmospheric aerosols. Interannual variability in Collection 5 is consistent with expected ranges of variance suggesting that the algorithm is reliable and robust, except in the tropics where some systematic differences are observed. Finally, comparisons with ground data suggest that the algorithm is performing well, but that end of season metrics associated with vegetation senescence and dormancy have higher uncertainties than start of season metrics.

Land cover dynamics↗
NASA NTRS2013

Estimating Ground-Level PM(sub 2.5) Concentrations in the Southeastern United States Using MAIAC AOD Retrievals and a Two-Stage Model

Previous studies showed that fine particulate matter (PM(sub 2.5), particles smaller than 2.5 micrometers in aerodynamic diameter) is associated with various health outcomes. Ground in situ measurements of PM(sub 2.5) concentrations are considered to be the gold standard, but are time-consuming and costly. Satellite-retrieved aerosol optical depth (AOD) products have the potential to supplement the ground monitoring networks to provide spatiotemporally-resolved PM(sub 2.5) exposure estimates. However, the coarse resolutions (e.g., 10 km) of the satellite AOD products used in previous studies make it very difficult to estimate urban-scale PM(sub 2.5) characteristics that are crucial to population-based PM(sub 2.5) health effects research. In this paper, a new aerosol product with 1 km spatial resolution derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was examined using a two-stage spatial statistical model with meteorological fields (e.g., wind speed) and land use parameters (e.g., forest cover, road length, elevation, and point emissions) as ancillary variables to estimate daily mean PM(sub 2.5) concentrations. The study area is the southeastern U.S., and data for 2003 were collected from various sources. A cross validation approach was implemented for model validation. We obtained R(sup 2) of 0.83, mean prediction error (MPE) of 1.89 micrograms/cu m, and square root of the mean squared prediction errors (RMSPE) of 2.73 micrograms/cu m in model fitting, and R(sup 2) of 0.67, MPE of 2.54 micrograms/cu m, and RMSPE of 3.88 micrograms/cu m in cross validation. Both model fitting and cross validation indicate a good fit between the dependent variable and predictor variables. The results showed that 1 km spatial resolution MAIAC AOD can be used to estimate PM(sub 2.5) concentrations.

aerosol optical depth↗
NASA NTRS2015

Modeling Landslide Susceptibility Over Large Regions with Fuzzy Overlay

Landslide susceptibility mapping is most effective if detailed surface and subsurface information can be combined with authoritative landslide catalogs or a deep understanding of local conditions. However, these types of homogeneous input data and catalogs are frequently not available over large areas. In this study, we model landslide susceptibility in Central America and the Caribbean islands by combining three globally available datasets and one regional dataset with fuzzy overlay. This primarily heuristic model provides the flexibility to test a range of different contributing variables and the capability to compare landslide inventories within the model framework that vary greatly in their size, spatiotemporal scope, and collection methods. We create a regional susceptibility map and evaluate its performance using receiver operating characteristics for both continuous and binned susceptibility values. This susceptibility map forms the basis for a near-real-time landslide hazard assessment system that couples susceptibility with rainfall and soil moisture triggers to estimate potential landslide activity at a regional scale. The application of this susceptibility model at the regional scale provides a foundation for transferring the methodology to other geographic areas.

Landslide↗
NASA NTRS2014

Open Source GIS Connectors to NASA GES DISC Satellite Data

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) houses a suite of high spatiotemporal resolution GIS data including satellite-derived and modeled precipitation, air quality, and land surface parameter data. The data are valuable to various GIS research and applications at regional, continental, and global scales. On the other hand, many GIS users, especially those from the ArcGIS community, have difficulties in obtaining, importing, and using our data due to factors such as the variety of data products, the complexity of satellite remote sensing data, and the data encoding formats. We introduce a simple open source ArcGIS data connector that significantly simplifies the access and use of GES DISC data in ArcGIS.

user↗
NASA NTRS2014

Assessing the Ability of Instantaneous Aircraft and Sonde Measurements to Characterize Climatological Means and Long-Term Trends in Tropospheric Composition

Over four decades of measurements exist that sample the 3-D composition of reactive trace gases in the troposphere from approximately weekly ozone sondes, instrumentation on civil aircraft, and individual comprehensive aircraft field campaigns. An obstacle to using these data to evaluate coupled chemistry-climate models (CCMs)the models used to project future changes in atmospheric composition and climateis that exact space-time matching between model fields and observations cannot be done, as CCMs generate their own meteorology. Evaluation typically involves averaging over large spatiotemporal regions, which may not reflect a true average due to limited or biased sampling. This averaging approach generally loses information regarding specific processes. Here we aim to identify where discrete sampling may be indicative of long-term mean conditions, using the GEOS-Chem global chemical-transport model (CTM) driven by the MERRA reanalysis to reflect historical meteorology from 2003 to 2012 at 2o by 2.5o resolution. The model has been sampled at the time and location of every ozone sonde profile available from the Would Ozone and Ultraviolet Radiation Data Centre (WOUDC), along the flight tracks of the IAGOSMOZAICCARABIC civil aircraft campaigns, as well as those from over 20 individual field campaigns performed by NASA, NOAA, DOE, NSF, NERC (UK), and DLR (Germany) during the simulation period. Focusing on ozone, carbon monoxide and reactive nitrogen species, we assess where aggregates of the in situ data are representative of the decadal mean vertical, spatial and temporal distributions that would be appropriate for evaluating CCMs. Next, we identically sample a series of parallel sensitivity simulations in which individual emission sources (e.g., lightning, biogenic VOCs, wildfires, US anthropogenic) have been removed one by one, to assess where and when the aggregated observations may offer constraints on these processes within CCMs. Lastly, we show results of an additional 31-year simulation from 1980-2010 of GEOS-Chem driven by the MACCity emissions inventory and MERRA reanalysis at 4o by 5o. We sample the model at every WOUDC sonde and flight track from MOZAIC and NASA field campaigns to evaluate which aggregate observations are statistically reflective of long-term trends over the period.

Atmospheric composition↗
NASA NTRS2015

An Inversion Analysis of Recent Variability in Natural CO2 Fluxes Using GOSAT and In Situ Observations

About one-half of the global CO2 emissions from fossil fuel combustion and deforestation accumulates in the atmosphere, where it contributes to global warming. The rest is taken up by vegetation and the ocean. The precise contribution of the two sinks, and their location and year-to-year variability are, however, not well understood. We use two different approaches, batch Bayesian synthesis inversion and variational data assimilation, to deduce the global spatiotemporal distributions of CO2 fluxes during 2009-2010. One of our objectives is to assess different sources of uncertainties in inferred fluxes, including uncertainties in prior flux estimates and observations, and differences in inversion techniques. For prior constraints, we utilize fluxes and uncertainties from the CASA-GFED model of the terrestrial biosphere and biomass burning driven by satellite observations and interannually varying meteorology. We also use measurement-based ocean flux estimates and two sets of fixed fossil CO2 emissions. Here, our inversions incorporate column CO2 measurements from the GOSAT satellite (ACOS retrieval, filtered and bias-corrected) and in situ observations (individual flask and afternoon-average continuous observations) to estimate fluxes in 108 regions over 8-day intervals for the batch inversion and at 3 x 3.75 weekly for the variational system. Relationships between fluxes and atmospheric concentrations are derived consistently for the two inversion systems using the PCTM atmospheric transport model driven by meteorology from the MERRA reanalysis. We compare the posterior fluxes and uncertainties derived using different data sets and the two inversion approaches, and evaluate the posterior atmospheric concentrations against independent data including aircraft measurements. The optimized fluxes generally resemble those from other studies. For example, the results indicate that the terrestrial biosphere is a net CO2 sink, and a GOSAT-only inversion suggests a shift in the global sink from the tropics south to the north relative to the prior and to an in-situ-only inversion. We also find a smaller terrestrial sink in higher-latitude northern regions in boreal summer of 2010 relative to 2009.

Carbon dioxide↗
NASA NTRS2016

Cloud Regimes as a Tool for Systematic Study of Various Aerosol-Cloud-Precipitation Interactions

Systematic changes of clouds and precipitation are notoriously difficult to ascribe to aerosols. This presentation will showcase yet one more attempt to at least credibly detect the signal of aerosol-cloud-precipitation interactions. We surmise that the concept of cloud regimes (CRs) is appropriate to conduct such an investigation. Previous studies focused on what we call here dynamical CRs, and while we continue to adopt those too for our analysis, we have found that a different way of organizing cloud systems, namely via microphysical regimes is also promising. Our analysis relies on MODIS Collection 6 Level-3 data for clouds and aerosols, and TRMM-TMPA data for precipitation. The regimes are derived by applying clustering analysis on MODIS joint histograms, and once each grid cell is assigned a regime, aerosol and precipitation data can be spatiotemporally matched and composited by regime. The composites of various cloud and precipitation variables for high (upper quartile of distribution) and low (lower quartile) aerosol loadings can then be contrasted. We seek evidence of aerosol effects both in regimes with large fractions of deep ice-rich clouds, as well as regimes where low liquid phase clouds dominate. Signals can be seen, especially when the analysis is broken by land-ocean and when additional filters are applied, but there are of course caveats which will be discussed.

cloud regimes↗
NASA NTRS2015

Antarctic Firn Compaction Rates from Repeat-Track Airborne Radar Data: I. Methods

While measurements of ice-sheet surface elevation change are increasingly used to assess mass change, the processes that control the elevation fluctuations not related to ice-flow dynamics (e.g. firn compaction and accumulation) remain difficult to measure. Here we use radar data from the Thwaites Glacier (West Antarctica) catchment to measure the rate of thickness change between horizons of constant age over different time intervals: 2009-10, 2010-11 and 2009-11. The average compaction rate to approximately 25m depth is 0.33ma(exp -1), with largest compaction rates near the surface. Our measurements indicate that the accumulation rate controls much of the spatio-temporal variations in the compaction rate while the role of temperature is unclear due to a lack of measurements. Based on a semi-empirical, steady-state densification model, we find that surveying older firn horizons minimizes the potential bias resulting from the variable depth of the constant age horizon. Our results suggest that the spatiotemporal variations in the firn compaction rate are an important consideration when converting surface elevation change to ice mass change. Compaction rates varied by up to 0.12ma(exp -1) over distances less than 6km and were on average greater than 20% larger during the 2010-11 interval than during 2009-10.

polar firn↗
NASA NTRS2015

Monitoring Atmospheric CO2 From Space: Challenge & Approach

Atmospheric CO2 is the key radiative forcing for the Earth's climate and may contribute a major part of the Earth's warming during the past 150 years. Advanced knowledge on the CO2 distributions and changes can lead considerable model improvements in predictions of the Earth's future climate. Large uncertainties in the predictions have been found for decades owing to limited CO2 observations. To obtain precise measurements of atmospheric CO2, certain challenges have to be overcome. For an example, global annual means of the CO2 are rather stable, but, have a very small increasing trend that is significant for multi-decadal long-term climate. At short time scales (a second to a few hours), regional and subcontinental gradients in the CO2 concentration are very small and only in an order of a few parts per million (ppm) compared to the mean atmospheric CO2 concentration of about 400 ppm, which requires atmospheric CO2 space monitoring systems with extremely high accuracy and precision (about 0.5 ppm or 0.125%) in spatiotemporal scales around 75 km and 10-s. It also requires a decadal-scale system stability. Furthermore, rapid changes in high latitude environments such as melting ice, snow and frozen soil, persistent thin cirrus clouds in Amazon and other tropical areas, and harsh weather conditions over Southern Ocean all increase difficulties in satellite atmospheric CO2 observations. Space lidar approaches using Integrated Path Differential Absorption (IPDA) technique are considered to be capable of obtaining precise CO2 measurements and, thus, have been proposed by various studies including the 2007 Decadal Survey (DS) of the U.S. National Research Council. This study considers to use the Intensity-Modulated Continuous-Wave (IM-CW) lidar to monitor global atmospheric CO2 distribution and variability from space. Development and demonstration of space lidar for atmospheric CO2 measurements have been made through joint adventure of NASA Langley Research Center and Exelis, Inc. As prototype space IPDA lidars, airborne laser absorption lidar systems operating in 1.57 CO2 absorption band have been developed and tested through lab, ground-based range, and flight campaigns. Very encouraging results have been obtained. The signal-to-noise ratio (SNR) for clear sky IPDA measurements of CO2 differential absorption optical depth (DAOD) for a 10-s integration over vegetated areas with about 10 km range was found to be as high as 1300, resulting in an error 0.077% or equivalent CO2 mixing ratio (XCO2) column precision of ~0.3 ppm. Precise range measurements using the IM-CW lidar approach were also achieved, and the uncertainties have been shown to be at sub meter level. Based on the airborne lidar development, space lidar and atmospheric CO2 observations are simulated. It shows that with the IM-CW approach, accurate atmospheric CO2 measurements can be achieved from space, and a space mission such as that proposed by the DS will meet science goals of atmospheric CO2 monitoring.

Lin, Bing↗
NASA NTRS2015

Regional and Global Atmospheric CO2 Measurements Using 1.57 Micron IM-CW Lidar

Atmospheric CO2 is a critical forcing for the Earth's climate, and knowledge of its distribution and variations influences predictions of the Earth's future climate. Accurate observations of atmospheric CO2 are also crucial to improving our understanding of CO2 sources, sinks and transports. To meet these science needs, NASA is developing technologies for the Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) space mission, which is aimed at global CO2 observations. Meanwhile an airborne investigation of atmospheric CO2 distributions as part of the NASA Suborbital Atmospheric Carbon and Transport â€" America (ACT-America) mission will be conducted with lidar and in situ instrumentation over the central and eastern United States during all four seasons and under a wide range of meteorological conditions. In preparing for the ASCENDS mission, NASA Langley Research Center and Exelis Inc./Harris Corp. have jointly developed and demonstrated the capability of atmospheric CO2 column measurements with an intensity-modulated continuous-wave (IM-CW) lidar. Since 2005, a total of 14 flight campaigns have been conducted. A measurement precision of approx.0.3 ppmv for a 10-s average over desert and vegetated surfaces has been achieved, and the lidar CO2 measurements also agree well with in-situ observations. Significant atmospheric CO2 variations on various spatiotemporal scales have been observed during these campaigns. For example, around 10-ppm CO2 changes were found within free troposphere in a region of about 200A-300 sq km over Iowa during a summer 2014 flight. Results from recent flight campaigns are presented in this paper. The ability to achieve the science objectives of the ASCENDS mission with an IM-CW lidar is also discussed in this paper, along with the plans for the ACT-America aircraft investigation that begins in the winter of 2016.

Lin, Bing↗
NASA NTRS2015

Atmospheric CO2 Variability Observed From ASCENDS Flight Campaigns

Significant atmospheric CO2 variations on various spatiotemporal scales were observed during ASCENDS flight campaigns. For example, around 10-ppm CO2 changes were found within free troposphere in a region of about 200x300 sq km over Iowa during a summer 2014 flight. Even over extended forests, about 2-ppm CO2 column variability was measured within about 500-km distance. For winter times, especially over snow covered ground, relatively less horizontal CO2 variability was observed, likely owing to minimal interactions between the atmosphere and land surface. Inter-annual variations of CO2 drawdown over cornfields in the Mid-West were found to be larger than 5 ppm due to slight differences in the corn growing phase and meteorological conditions even in the same time period of a year. Furthermore, considerable differences in atmospheric CO2 profiles were found during winter and summer campaigns. In the winter CO2 was found to decrease from about 400 ppm in the atmospheric boundary layer (ABL) to about 392 ppm above 10 km, while in the summer CO2 increased from 386 ppm in the ABL to about 396 ppm in free troposphere. These and other CO2 observations are discussed in this presentation.

Lin, Bing↗
NASA NTRS2016

Analyzing and Visualizing Precipitation and Soil Moisture in ArcGIS

Precipitation and soil moisture are among the most important parameters in many land GIS (Geographic Information System) research and applications. These data are available globally from NASA GES DISC (Goddard Earth Science Data and Information Services Center) in GIS-ready format at 10-kilometer spatial resolution and 24-hour or less temporal resolutions. In this presentation, well demonstrate how rainfall and soil moisture data are used in ArcGIS to analyze and visualize spatiotemporal patterns of droughts and their impacts on natural vegetation and agriculture in different parts of the world.

precipitation↗
NASA NTRS2015

Ammonia and Methane Dairy Emission Plumes in the San Joaquin Valley of California from Individual Feedlot to Regional Scales

Agricultural ammonia (NH3) emissions are highly uncertain, with high spatiotemporal variability and a lack of widespread in situ measurements. Regional NH3 emission estimates using mass balance or emission ratio approaches are uncertain due to variable NH3 sources and sinks as well as unknown plume correlations with other dairy source tracers. We characterize the spatial distributions of NH3 and methane (CH4) dairy plumes using in situ surface and airborne measurements in the Tulare dairy feedlot region of the San Joaquin Valley, California, during the NASA Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality 2013 field campaign. Surface NH3 and CH4 mixing ratios exhibit large variability with maxima localized downwind of individual dairy feedlots. The geometric mean NH3:CH4 enhancement ratio derived from surface measurements is 0.15 +/- 0.03 ppmv ppmv−1. Individual dairy feedlots with spatially distinct NH3 and CH4 source pathways led to statistically significant correlations between NH3 and CH4 in 68% of the 69 downwind plumes sampled. At longer sampling distances, the NH3:CH4 enhancement ratio decreases 20-30%, suggesting the potential for NH3 deposition as a loss term for plumes within a few kilometers downwind of feedlots. Aircraft boundary layer transect measurements directly above surface mobile measurements in the dairy region show comparable gradients and geometric mean enhancement ratios within measurement uncertainties, even when including NH3 partitioning to submicron particles. Individual NH3 and CH4 plumes sampled at close proximity where losses are minimal are not necessarily correlated due to lack of mixing and distinct source pathways. Our analyses have important implications for constraining NH3 sink and plume variability influences on regional NH3 emission estimates and for improving NH3 emission inventory spatial allocations.

Miller, David J.↗
NASA NTRS2015

Contribution of Phenological and Physiological Variations on Northern Vegetation Productivity Changes over Last Three Decades

Plant phenology and maximum photosynthetic state determine spatiotemporal variability of gross primary productivity (GPP) of vegetation. Recent warming induced impacts accelerate shifts of phenology and physiological status over Northern vegetated land. Thus, understanding and quantifying these changes are very important. Here, we investigate 1) how vegetation phenology and physiological status (maximum photosynthesis) are evolved over last three decades and 2) how such components (phenology and physiological status) contribute on inter-annual variation of the GPP during the last three decades. We utilized both long-term remotely sensed (GIMMS (Global Inventory Modeling and Mapping Studies), NDVI3g (Normalized Difference Vegetation Index 3rd generation) and MODIS (Moderate Resolution Imaging Spectroradiometer)) to extract larger scale phenology metrics (growing season start, end and duration); and productivity (i.e., growing season integrated vegetation index, GSIVI) to answer these questions. For evaluation purpose, we also introduced field-measured phenology and productivity datasets (e.g., FLUXNET) and possible remotely-sensed and modeled metrics at continental and regional scales. From this investigation, we found that onset of the growing season has advanced by 1.61 days per decade and the growing season end has delayed by 0.67 days per decade over the circumpolar region. This asymmetric extension of growing season results in a longer growing-season trend (2.96 days per decade) and widespread increasing vegetation-productivity trend (2.96 GSIVI per decade) over Northern land. However, the regionally-diverged phenology shift and maximum photosynthetic state contribute differently characterized productivity, inter-annual variability and trend. We quantified that about 50 percent, 13 percent and 6.5 percent of Northern land's inter-annual variability are dominantly controlled by the onset of the growing season, the end of the growing season and the maximum photosynthetic state, respectively. Productivity characterization over the other approximately 30 percent region has been driven by these co-dominant drivers. Our study clearly shows that regionally different contribution of phenological and physiological components on characterizing vegetation production over the last three decades.

productivity↗
NASA NTRS2016

Identifying and Analyzing Uncertainty Structures in the TRMM Microwave Imager Precipitation Product over Tropical Ocean Basins

Despite continuous improvements in microwave sensors and retrieval algorithms, our understanding of precipitation uncertainty is quite limited, due primarily to inconsistent findings in studies that compare satellite estimates to in situ observations over different parts of the world. This study seeks to characterize the temporal and spatial properties of uncertainty in the Tropical Rainfall Measuring Mission Microwave Imager surface rainfall product over tropical ocean basins. Two uncertainty analysis frameworks are introduced to qualitatively evaluate the properties of uncertainty under a hierarchy of spatiotemporal data resolutions. The first framework (i.e. 'climate method') demonstrates that, apart from random errors and regionally dependent biases, a large component of the overall precipitation uncertainty is manifested in cyclical patterns that are closely related to large-scale atmospheric modes of variability. By estimating the magnitudes of major uncertainty sources independently, the climate method is able to explain 45-88% of the monthly uncertainty variability. The percentage is largely resolution dependent (with the lowest percentage explained associated with a 1 deg x 1 deg spatial/1 month temporal resolution, and highest associated with a 3 deg x 3 deg spatial/3 month temporal resolution). The second framework (i.e. 'weather method') explains regional mean precipitation uncertainty as a summation of uncertainties associated with individual precipitation systems. By further assuming that self-similar recurring precipitation systems yield qualitatively comparable precipitation uncertainties, the weather method can consistently resolve about 50 % of the daily uncertainty variability, with only limited dependence on the regions of interest.

Climate↗
NASA NTRS2016

Homogeneity of a Global Multisatellite Soil Moisture Climate Data Record

Climate Data Records (CDR) that blend multiple satellite products are invaluable for climate studies, trend analysis and risk assessments. Knowledge of any inhomogeneities in the CDR is therefore critical for making correct inferences. This work proposes a methodology to identify the spatiotemporal extent of the inhomogeneities in a 36-year, global multisatellite soil moisture CDR as the result of changing observing systems. Inhomogeneities are detected at up to 24 percent of the tested pixels with spatial extent varying with satellite changeover times. Nevertheless, the contiguous periods without inhomogeneities at changeover times are generally longer than 10 years. Although the inhomogeneities have measurable impact on the derived trends, these trends are similar to those observed in ground data and land surface reanalysis, with an average error less than 0.003 cubic meters per cubic meter per year. These results strengthen the basis of using the product for long-term studies and demonstrate the necessity of homogeneity testing of multisatellite CDRs in general.

CDR↗
NASA NTRS2013

MERRA/AS: The MERRA Analytic Services Project Interim Report

MERRA AS is a cyberinfrastructure resource that will combine iRODS-based Climate Data Server (CDS) capabilities with Coudera MapReduce to serve MERRA analytic products, store the MERRA reanalysis data collection in an HDFS to enable parallel, high-performance, storage-side data reductions, manage storage-side driver, mapper, reducer code sets and realized objects for users, and provide a library of commonly used spatiotemporal operations that can be composed to enable higher-order analyses.

MapReduce↗
NASA NTRS2016

A51F-0123: Model Analysis of Tropospheric Aerosol Variability and Sources over the North Atlantic During NAAMES 2015-2016

The North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) is a five-year Earth-Venture Suborbital-2 Mission to characterize the plankton ecosystems and their influences on remote marine aerosols, boundary layer clouds, and their implications for climate in the North Atlantic. While marine-sourced aerosols have been shown to make important contributions to surface aerosol loading, cloud condensation nuclei and ice nuclei concentrations over remote marine and coastal regions, it is still a challenge to differentiate the marine biogenic aerosol signal from the strong influence of continental pollution outflow. We examine here the spatiotemporal variability and quantify the sources of tropospheric aerosols over the North Atlantic during the first two phases (November 2015 and May-June 2016) of NAAMES using a state-of-the-art chemical transport model (GEOS-Chem). The model is driven by the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) from the NASA Global Modeling and Assimilation Office (GMAO). It includes sulfate-nitrate-ammonium aerosol thermodynamics coupled to ozone-NOx-hydrocarbon-aerosol chemistry, mineral dust, sea salt, elemental and organic carbon aerosols, and especially a recently implemented parameterization for the marine primary organic aerosol emission. The simulated aerosols over the North Atlantic are evaluated with available satellite (e.g., MODIS) observations of aerosol optical depths (AOD), and aircraft and ship aerosol measurements. We diagnose transport pathways for continental pollution outflow over the North Atlantic using carbon monoxide, an excellent tracer for anthropogenic pollution transport. We also conduct model perturbation experiments to quantify the relative contributions of terrestrial and oceanic sources to the aerosol loading, AOD, and their variability over the North Atlantic.

aerosols↗