scholarly journals The Orbiting Carbon Observatory-2: first 18 months of science data products

2017 ◽  
Vol 10 (2) ◽  
pp. 549-563 ◽  
Author(s):  
Annmarie Eldering ◽  
Chris W. O'Dell ◽  
Paul O. Wennberg ◽  
David Crisp ◽  
Michael R. Gunson ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Atmospheric Carbon Dioxide ◽  
Eastern China ◽  
Regional Scale ◽  
Central Africa ◽  
Retrieval Algorithm ◽  
Science Data ◽  
The North ◽  
Data Products ◽  
Atmospheric Carbon

Abstract. The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO2) with the accuracy, resolution, and coverage needed to quantify CO2 fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014 and has gathered more than 2 years of observations. The v7/v7r operational data products from September 2014 to January 2016 are discussed here. On monthly timescales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO2 dry air mole fraction, XCO2, that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of XCO2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes XCO2 enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north–south XCO2 gradient is small. Enhanced XCO2 coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north–south XCO2 gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in XCO2 across the Northern Hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO2. As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart XCO2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high-resolution global dataset.

scholarly journals The Orbiting Carbon Observatory-2: First 18 months of Science Data Products

2016 ◽  
Author(s):  
Annmarie Eldering ◽  
Chris W. O'Dell ◽  
Paul O. Wennberg ◽  
David Crisp ◽  
Michael R. Gunson ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Atmospheric Carbon Dioxide ◽  
Eastern China ◽  
Regional Scale ◽  
Central Africa ◽  
Retrieval Algorithm ◽  
Science Data ◽  
Data Set ◽  
The North ◽  
Atmospheric Carbon

Abstract. The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO2) with the accuracy, resolution, and coverage needed to quantify CO2 fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014, and joined the 705 km Afternoon Constellation on 3 August 2014. On monthly time scales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO2 dry air mole fraction, XCO2, that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of XCO2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes XCO2 enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north-south XCO2 gradient is small. Enhanced XCO2 coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north-south XCO2 gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in XCO2 across the northern hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO2. As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart XCO2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high resolution, global data set.

scholarly journals Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm

2018 ◽  
Vol 11 (12) ◽  
pp. 6539-6576 ◽  
Author(s):  
Christopher W. O'Dell ◽  
Annmarie Eldering ◽  
Paul O. Wennberg ◽  
David Crisp ◽  
Michael R. Gunson ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Atmospheric Carbon Dioxide ◽  
Regional Scale ◽  
Carbon Sources ◽  
Error Variance ◽  
Poor Quality ◽  
Retrieval Algorithm ◽  
Atmospheric Carbon ◽  
Carbon Dioxide Levels ◽  
Quality Measurements

Abstract. Since September 2014, NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite has been taking measurements of reflected solar spectra and using them to infer atmospheric carbon dioxide levels. This work provides details of the OCO-2 retrieval algorithm, versions 7 and 8, used to derive the column-averaged dry air mole fraction of atmospheric CO2 (XCO2) for the roughly 100 000 cloud-free measurements recorded by OCO-2 each day. The algorithm is based on the Atmospheric Carbon Observations from Space (ACOS) algorithm which has been applied to observations from the Greenhouse Gases Observing SATellite (GOSAT) since 2009, with modifications necessary for OCO-2. Because high accuracy, better than 0.25 %, is required in order to accurately infer carbon sources and sinks from XCO2, significant errors and regional-scale biases in the measurements must be minimized. We discuss efforts to filter out poor-quality measurements, and correct the remaining good-quality measurements to minimize regional-scale biases. Updates to the radiance calibration and retrieval forward model in version 8 have improved many aspects of the retrieved data products. The version 8 data appear to have reduced regional-scale biases overall, and demonstrate a clear improvement over the version 7 data. In particular, error variance with respect to TCCON was reduced by 20 % over land and 40 % over ocean between versions 7 and 8, and nadir and glint observations over land are now more consistent. While this paper documents the significant improvements in the ACOS algorithm, it will continue to evolve and improve as the CO2 data record continues to expand.

scholarly journals Improved Retrievals of Carbon Dioxide from the Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm

2018 ◽  
Author(s):  
Christopher W. O'Dell ◽  
Annmarie Eldering ◽  
Paul O. Wennberg ◽  
David Crisp ◽  
Michael R. Gunson ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Atmospheric Carbon Dioxide ◽  
Regional Scale ◽  
Carbon Sources ◽  
Error Variance ◽  
Poor Quality ◽  
Retrieval Algorithm ◽  
Atmospheric Carbon ◽  
Carbon Dioxide Levels ◽  
Quality Measurements

Abstract. Since September 2014, NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite has been taking measurements of reflected solar spectra and using them to infer atmospheric carbon dioxide levels. This work provides details of the OCO-2 retrieval algorithm, versions 7 and 8, used to derive the column-averaged dry air mole fraction of atmospheric CO2,/sub> (XCO2) for the roughly 100,000 cloud-free measurements recorded by OCO-2 each day. The algorithm is based on the Atmospheric Carbon Observations from Space (ACOS) algorithm which has been applied to observations from the Greenhouse Gases Observing SATellite (GOSAT) since 2009, with modifications necessary for OCO-2. Because high accuracy, better than 0.25 %, is required in order to accurately infer carbon sources and sinks from XCO2, significant errors and regional-scale biases in the measurements must be minimized. We discuss efforts to filter out poor quality measurements, and correct the remaining good-quality measurements to minimize regional-scale biases. Updates to the radiance calibration and retrieval forward model in version 8 have improved many aspects of the retrieved data products. The version 8 data appear to have reduced regional-scale biases overall, and demonstrate a clear improvement over the version 7 data. In particular, error variance with respect to TCCON was reduced by 20 % over land and 40 % over ocean between versions 7 and 8, and nadir and glint observations over land are now more consistent. While this paper documents the significant improvements in the ACOS algorithm, it will continue to evolve and improve as the CO2 data record continues to expand.

scholarly journals MODIS observed increase in duration and spatial extent of sediment plumes in Greenland fjords

2014 ◽  
Vol 8 (4) ◽  
pp. 1161-1176 ◽  
Author(s):  
B. Hudson ◽  
I. Overeem ◽  
D. McGrath ◽  
J. P. M. Syvitski ◽  
A. Mikkelsen ◽  
...  
Keyword(s):  
North Atlantic Ocean ◽  
Regional Scale ◽  
Greenland Ice Sheet ◽  
Sediment Concentration ◽  
Retrieval Algorithm ◽  
Freshwater Flux ◽  
River Plumes ◽  
Data Set ◽  
In Situ Data ◽  
The North

Abstract. The freshwater flux from the Greenland Ice Sheet (GrIS) to the North Atlantic Ocean carries extensive but poorly documented volumes of sediment. We develop a suspended sediment concentration (SSC) retrieval algorithm using a large Greenland specific in situ data set. This algorithm is applied to all cloud-free NASA Moderate Resolution Imaging Spectrometer (MODIS) Terra images from 2000 to 2012 to monitor SSC dynamics at six river plumes in three fjords in southwest Greenland. Melt-season mean plume SSC increased at all but one site, although these trends were primarily not statistically significant. Zones of sediment concentration > 50 mg L−1 expanded in three river plumes, with potential consequences for biological productivity. The high SSC cores of sediment plumes ( > 250 mg L−1 expanded in one-third of study locations. At a regional scale, higher volumes of runoff were associated with higher melt-season mean plume SSC values, but this relationship did not hold for individual rivers. High spatial variability between proximal plumes highlights the complex processes operating in Greenland's glacio–fluvial–fjord systems.

Patterns in data of extreme droughts/floods and harvest grades derived from historical documents in eastern China during 801–1910

2020 ◽  
Author(s):  
Zhixin Hao
Keyword(s):  
Crop Production ◽  
Eastern China ◽  
Regional Scale ◽  
Extreme Drought ◽  
Historical Documents ◽  
The North ◽  
Historical Factors ◽  
Extreme Flood ◽  
The Impact

<p>In China, historical documents record a large quantity of information related to climate change and grain harvest. This information can help to explore the impacts of extreme drought or flood on crop production, which can provide implications for the adaptation of agriculture to higher-probability extreme climate in the context of global warming. In this paper, reported extreme drought/flood chronologies and reconstructed grain harvest series derived from historical documents were adopted in order to investigate the association between the reported frequency of extreme drought/flood in eastern China and reconstructed poor harvests during 801–1910. The results show that extreme droughts were reported more often in 801–870, 1031–1230, 1481–1530, and 1581–1650 over the whole of eastern China. On a regional scale, extreme droughts were reported more often in 1031–1100, 1441–1490, 1601–1650, and 1831–1880 in the North China Plain, 801–870, 1031–1120, 1161–1220, and 1471–1530 in Jianghuai, and 991–1040, 1091–1150, 1171–1230, 1411–1470, and 1481–1530 in Jiangnan. The grain harvest was reconstructed to be generally poor in 801–940, 1251–1650, and 1841–1910, but the reconstructed harvests were bumper in 951–1250 and 1651–1840, approximately. During the entire period from 801 to 1910, the frequency of reporting of extreme droughts in any subregion of eastern China was significantly associated over the long term with lower reconstructed harvests. The association between reported frequency of extreme floods and reconstructed low harvests appeared to be much weaker, while reconstructed harvest was much worse when extreme drought and extreme flood in different subregions were reported in the same year. The association between reconstructed poor harvests and reported frequency of regional extreme droughts was weak during the warm epoch of 920–1300 but strong during the cold epoch of 1310–1880, which could imply that a warm climate could weaken the impact of extreme drought on poor harvests; yet other historical factors may also contribute to these different patterns extracted from the two datasets.</p>

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