Tag: Carbon Cycle and Ecosystems

Carbon Cycle and Ecosystems

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NASA Earth Observatory images by Joshua Stevens, using NDVI data from Aqua/MODIS and mule deer habitat data courtesy of Stoner et al. (2016). Caption by Tassia Owen with Mike Carlowicz.

Raising a new fawn is no easy task. A mother mule deer needs a lot of food for herself and her growing fawn. New satellite-based research suggests that those mule deer mothers are in tune with their environment, with reproduction patterns closely matching the cycles of plant growth in their habitat.

Mule deer need a rich supply of vegetation for the late stages of pregnancy and for nursing their offspring after birth. For this reason, birth rates peak when food sources are increasing, shortly before the peak of annual plant growth. What is surprising is that mule deer in the colder, snowy northern parts of their range give birth earlier in the year than deer in the warmer southern reaches. Through a combination of satellite measurements and ground-based population counts, scientists can see the reason for the difference from space.

Mule deer, a commonly hunted species, are closely monitored and counted by biologists and land managers. A great deal of data about the size and health of the population is collected each year in order to determine the proper number of hunting permits to issue. At the same time, remote sensing scientists have a space-based way to track the health of vegetation. It is called the Normalized Difference Vegetation Index (NDVI), which is a measure of the “greenness” of the landscape. NDVI measures how plants absorb and reflect light; the more infrared light is reflected, the healthier the vegetation. So by measuring the greenness of the mule deer habitat, scientists were able to mark the beginning and peak of the plant and deer growing season.

The map above shows the range of mule deer from southern Idaho to central Arizona. The habitat is divided into a green southern zone, a purple northern zone, and a gray transition zone. The mean NDVI for the northern and southern regions is displayed in the graph, which plots the vegetation index for each day of the calendar year. NDVI was measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on NASA’s Terra and Aqua satellites.

According to lead author David Stoner of Utah State University, vegetation greenness in the northern latitudes peaks earlier than in the southern latitudes. Since nutrient-dense food sources were available earlier in the year, there was more food available for mule deer mothers and babies at the time when they needed it most. That greenness is partly a result of a consistent steady stream of snowmelt moisture feeding the deeply rooted mountain vegetation.

“We had never tracked the deer population this way, and we had never been able to predict it with such precision,” said Stoner. “We can estimate the start and peak of the season using satellite imagery, and then we can map and predict when the deer are giving birth.”

In southern latitudes, on the other hand, the plants are more dependent on rain from summer monsoonal showers. This means vegetation quality peaks later in the year, after a brief drought that comes before the summer monsoons. As a result, does give birth later in the south than in the north.

“This kind of applied research is very important for making remote sensing data relevant to wildlife management efforts,” said Jyoteshwar Nagol, a researcher at the University of Maryland. Deer have a huge economic impact in the United States, from hunting to crop damage to car accidents. As regional climates shift or droughts occur, deer could migrate farther or expand their range to find food.

Reference
Stoner, D., Sexton, S. and Nagol, J. (2016) Ungulate Reproductive Parameters Track Satellite Observations of Plant Phenology across Latitude and Climatological Regimes. PLoS One, 11 (2) e0148780.

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Global contribution of three climate variables to the vegetation sensitivity index from 2000–2013. Temperature is in red, water availability in blue and cloudiness in green. Areas with dominant barren land and permanent ice are grey. Image credit: Sensitivity of global terrestrial ecosystems to climate variability. Alistair W. R. Seddon, Marc Macias-Fauria, Peter R. Long, David Benz & Kathy J. Willis. Nature. (2016) doi:10.1038/nature16986

MODIS data from the past 14 years is being used to generate a model that assesses how different ecosystems respond to climate variability, making it possible to compare regional sensitivity and resilience. The new index is called the vegetation sensitivity index, which makes it possible to compare vulnerability of different regions, looking at why some areas are more vulnerable than others.

The new index is unique.  Most studies about ecosystem resilience typically monitor productivity or biodiversity trends over an average climate, such as the normalized difference vegetation index (NDVI) or the enhanced vegetation index (EVI), which also uses MODIS data. This new index instead looks at response to climate variation.

Read the news article from Nature.

Read the journal article from Nature.

Walong Nature Reserve

Researchers at Michigan State University’s  Center for Systems Integration and Sustainability are combining images from Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat with information taken on the ground, to increase understanding of how biodiversity is changing in China’s Wolong Nature Reserve, home to the giant panda.

Read the whole article from Michigan State University.

 Global map of the average amount of time that live biomass carbon and dead organic carbon spend in carbon reservoirs around the world, in years. Credit: A. Anthony Bloom

Global map of the average amount of time that live biomass carbon and dead organic carbon spend in carbon reservoirs around the world, in years. Credit: A. Anthony Bloom

February 8, 2016

New, detailed maps of the world’s natural landscapes created using NASA satellite data could help scientists better predict the impacts of future climate change.

The maps of forests, grasslands and other productive ecosystems provide the most complete picture yet of how carbon from the atmosphere is reused and recycled by Earth’s natural ecosystems.

Scientists at the University of Edinburgh, Scotland, United Kingdom; NASA’s Jet Propulsion Laboratory, Pasadena, California; and Wageningen University, Netherlands, used a computer model to analyze a decade of satellite and field study data from 2001 to 2010. The existing global maps of vegetation and fire activity they studied were produced from data from NASA’s Terra, Aqua and ICESat spacecraft. The researchers then constructed maps that show where — and for how long — carbon is stored in plants, trees and soils.

The maps reveal how the biological properties of leaves, roots and wood in different natural habitats affect their ability to store carbon across the globe, and show that some ecosystems retain carbon longer than others. For example, large swaths of the dry tropics store carbon for a relatively short time due to frequent fires, while in warm, wet climates, carbon is stored longer in vegetation than in soils.

Although it is well known that Earth’s natural ecosystems absorb and process large amounts of carbon dioxide, much less is known about where the carbon is stored or how long it remains there. Improved understanding about how carbon is stored will allow researchers to more accurately predict the impacts of climate change.

Study first author Anthony Bloom, a JPL postdoctoral scientist, said: “Our findings are a major step toward using satellite imagery to decipher how carbon flows through Earth’s natural ecosystems from satellite images. These results will help us understand how Earth’s natural carbon balance will respond to human disturbances and climate change.”

Professor Mathew Williams of the University of Edinburgh’s School of GeoSciences, who led the study, said, “Recent studies have highlighted the disagreement among Earth system models in the way they represent the current global carbon cycle. “Our results constitute a useful, modern benchmark to help improve these models and the robustness of global climate projections.”

To generate values for each of the 13,000 cells on each map, a supercomputer at the Edinburgh Compute and Data Facility ran the model approximately 1.6 trillion times.

New data can be added to the maps as it becomes available. The impact of major events such as forest fires on the ability of ecosystems to store carbon can be determined within three months of their occurrence, the researchers say.

The study, published Feb. 2 in the Proceedings of the National Academy of Sciences, was funded by the Natural Environment Research Council. The California Institute of Technology in Pasadena manages JPL for NASA.

NASA uses the vantage point of space to increase our understanding of our home planet, improve lives and safeguard our future. NASA develops new ways to observe and study Earth’s interconnected natural systems with long-term data records. The agency freely shares this unique knowledge and works with institutions around the world to gain new insights into how our planet is changing.

For more information about NASA’s Earth science activities, visit:

http://www.nasa.gov/earth

 

Media Contact

Alan Buis Jet Propulsion Laboratory, Pasadena, Calif.
818-354-0474
Alan.buis@jpl.nasa.gov

Corin Campbell
University of Edinburgh
011-44-0131-650-6382
Corin.campbell@ed.ac.uk

2016-037

Measurements of Pollution in the Troposphere (MOPITT) on NASA’s Terra Satellite collects data on atmospheric carbon monoxide (CO) levels in the atmosphere.  Learn more about MOPITT’s newest science applications and contributions at the American Geophysical Union’s 2015 Fall Meeting.

On Tuesday, 15 December 2015 from 8:00 AM – 12:20 PM in Moscone South – Poster Hall.

Separating Transported and Local Atmospheric Carbon Monoxide in Australasia with Satellite and Ground-based Remote Sensing,” by R. R. Buchholz, D. P. Edwards, M. N. Deeter, H. M. Worden, L. K. Emmons, N. B. Jones, C. Paton-Walsh, N. M. Deutscher, V. Velaszco, D. W. T. Griffith, J. Robinson and D. Smale

Abstract:

A range of measurement techniques are required to understand atmospheric composition. No single instrument can measure all you need to know about the atmosphere, due to differences in temporal and spatial scales. Satellites help interpret synoptic-scale contributions to composition, but provide little fine-scale information due to sparse measurement timing and spatial averaging. In contrast, ground-based solar-tracking FTIR instruments can capture fine-scale chemistry and dynamic influence, but being point measurements, have trouble identifying transported signals. Knowing the relative contribution of transported to local sources of atmospheric pollution is important for developing realistic air quality policies and providing accurate air quality forecasts.

In this study, we exploit the complementary limitations and sensitivities of two instruments to gain information about carbon monoxide (CO) sources at three stations in Australasia: Darwin and Wollongong in Australia and Lauder in New Zealand. Total column amounts of CO are compared between the satellite-borne Measurements of Pollution in the Troposphere (MOPITT) and ground-based solar FTIR instruments in the TCCON and NDACC networks. Several CO timeseries anomalies are highlighted as representative of pollution delivery pathways in relation to local, regional and long-distance contributions. Large-scale pollution events are captured by both instruments, but only the satellite instrument can provide regional and global context. MOPITT identifies long-range transport of pollution from biomass burning in South America and southern Africa, while the FTIR can additionally capture local urban and biomass burning influences. Unusually low CO, sourced from southern latitudes, is also measured by both instruments. Interannual variability is significantly different at each site and is diagnosed with chemical transport modeling (CAM-chem) to quantify the role of emissions versus meteorology.

On Wednesday, 16 December 2015 from 8:00 AM – 12:20 PM in Moscone South – Poster Hall.

Carbon Monoxide Data Assimilation for Atmospheric Composition and Climate Science: Evaluating Performance with Current and Future Observations,” by Jérôme Barré, David Edwards, Helen Worden, Avelino Arellano, Benjamin Gaubert, Arlindo Da Silva, and Jeffrey Anderson

Abstract:

Current satellite observations of tropospheric composition made from low Earth orbit provide at best one or two measurements each day at any given location. Comparisons of Terra/MOPITT carbon monoxide (CO) and IASI/Metop CO observation assimilations will be presented. We use the DART Ensemble Adjustment Kalman Filter to assimilate observations in the CAM-Chem global chemistry-climate model. Data assimilation impacts due to both different instrument capabilities (i.e. vertical sensitivity and global coverage) will be discussed. Coverage is global but sparse, often with large uncertainties in individual measurements that limit examination of local and regional atmospheric composition over short time periods. This has hindered the operational uptake of these data for monitoring air quality and population exposure, and for initializing and evaluating chemical weather forecasts. By the end of the current decade there are planned geostationary Earth orbit (GEO) satellite missions for atmospheric composition over North America, East Asia and Europe with additional missions proposed. Together, these present the possibility of a constellation of geostationary platforms to achieve continuous time-resolved high-density observations of continental domains for mapping pollutant sources and variability on diurnal and local scales. We describe Observing System Simulation Experiments (OSSEs) to evaluate the contributions of these GEO missions to improve knowledge of near-surface air pollution due to intercontinental long-range transport and quantify chemical precursor emissions. Our approach uses an efficient computational method to sample a high-resolution global GEOS-5 chemistry Nature Run over each geographical region of the GEO constellation. The demonstration carbon monoxide (CO) observation simulator, which will be expanded to other chemical pollutants, currently produces multispectral retrievals (MOPITT-like) and captures realistic scene-dependent variation in measurement vertical sensitivity and cloud cover. The impact of observing over each region is evaluated independently. Winter and summer cases studies are investigated i.e. where emissions, cloud cover and CO lifetime significantly change.

On Tuesday, 15 December 2015 from 1:40 PM – 6:00 PM in Moscone South – Poster Hall.

Limiting Factors for Satellite-Based Retrievals of Surface-Level Carbon Monoxide,” by Sara Martinez-Alonso, Merritt N Deeter, Helen Marie Worden, Jerome Barré, and The MOPITT Team

Abstract:

CO is mostly produced in the lower troposphere by incomplete combustion of biomass and fuels. CO oxidation consumes ~75% of the tropospheric OH, which then is not available to remove CH4 and other greenhouse gases. CO oxidation also leads to the production of tropospheric O3. These critical impacts of CO on air quality and climate require accurate determination of the abundance and evolution of CO near the surface.

Satellite retrievals would be well-suited to monitor surface CO globally. However, how do they compare to actual surface abundances? Some aspects to be considered include: the vertical sensitivity of retrievals (given by the averaging kernels), or how thick are the atmospheric layers that can be resolved; the vertical correlation length of CO with respect to the thickness of those layers; and the horizontal variability of CO with respect to the instrument’s footprint.

To investigate these questions we analyze MOPITT retrievals, DISCOVER-AQ and NOAA profiles, as well as WDCGG surface measurements. MOPITT, on board NASA’s Terra satellite, has been measuring tropospheric CO since 2000, providing the longest global CO record to date. Its unique multispectral CO product offers enhanced sensitivity to CO near the surface. Vertical profiles of the lower troposphere were acquired during the DISCOVER-AQ airborne campaigns over selected regions of the USA. NOAA’s airborne flask sampling program results in a multi-year, multi-seasonal record of vertical profiles from near the surface up to the mid troposphere, acquired over a number of stations, mostly in North America. Long-term, cross-calibrated surface CO data from ground stations worldwide are available through the WDCGG.

On Wednesday, 16 December 2015 from 8:00 AM – 12:20 PM in Moscone South – Poster Hall.

Chemical Response of CESM/CAM-Chem to MOPITT CO Ensemble-based Chemical Data Assimilation,” by Benjamin Gaubert, Avelino F. Arellano, Jerome Barre, Helen M. Worden, Louisa K. Emmons, Simone Tilmes, Rebecca Buchholz, Christine Wiedinmyer, Francis Vitt, Jeffrey L. Anderson, Merritt N. Deeter, Jean-Francois Lamarque and David P. Edwards

Abstract:

Carbon Monoxide is a key component in tropospheric chemistry. It plays an important role by affecting the oxidative capacity through its reaction with OH and being a precursor of tropospheric ozone. One year of multispectral retrievals of CO partial columns obtained from the MOPITT instrument have been assimilated into the Community Atmosphere Model with Chemistry (CAM-Chem). The assimilation is carried out using an Ensemble Adjustment Kalman Filter algorithm within the Data Assimilation Research Testbed (DART) package. Two assimilation experiments have been performed: 1) assimilation of meteorological observations and 2) joint assimilation of meteorological observations and MOPITT CO. We first evaluate the assimilation performance by investigating skill scores and other statistics for the two experiments, and comparing to independent CO datasets such as surface (WDCGG), aircraft (MOZAIC-IAGOS), and FTS (NDACC). Our results clearly demonstrate an overall improvement for spatio-temporal magnitude and variability in representing CO abundance in CAM-Chem. We then investigate the response of CAM-Chem to changes in CO fields (via CO assimilation) focusing mainly on the oxidative capacity (i.e., OH distribution, methane lifetime) and CO chemical production and loss (i.e., regional to global budget). This is carried out by analyzing the mean 6-hourly forecast adjustments as reflected between the two experiments. We show that changes in CO directly impact OH abundance, with subsequent non-linear responses in CO chemical production (CO from methane and VOCs) and CO loss. This is clearly evident in NOx-limited regions (e.g., Southern Hemisphere, remote sites). Such analysis has direct implications on the consistencies in inverse modeling estimates of CO sources through improved representation of chemical response (including full chemistry) in atmospheric chemistry models and through multi-species constraints.

On Wednesday, 16 December 2015 from 11:35 AM – 11:50 AM in Moscone West – 3012

Evaluation of Meteorology Data for MOPITT Operational Processing,” by Daniel Ziskin, Merritt Deeter, Helen Worden, Debbie Mao, and Vincil Dean

Abstract:

Measurements Of Pollution In The Troposphere[1] (MOPITT) is an instrument flying aboard NASA’s Terra satellite[2]. It measures CO using correlated spectroscopy[3]. As part of its processing it uses surface temperature, an atmospheric temperature profile and a water vapor profile from analysis. Since there are many analysis products on the market (e.g. GMAO, NCEP, ECMWF etc.) that meet MOPITT’s operational requirements, the question arises as to which product is most apt? There is a collection of “validation data” that MOPITT compares its CO retrievals against[4]. The validation dataset has been acquired by in situ air samples taken by aircraft at a series of altitudes. We can run our processing system in “validation mode” which processes the satellite data for only the days that validation data exists and for a spatial subset that corresponds to the region where the validation data has been collected. We will run the MOPITT retrievals in validation mode separately using each variety of analysis data. We will create a cost function that will provide a scalar estimate of the retrieved CO profile error relative to the validation dataset which is assumed to be “the truth”. The retrieval errors of each of the input datasets will be compared to each other to provide insight into the best choice for use in operational MOPITT processing.