scholarly journals A long-term (2005–2019) eddy covariance data set of CO<sub>2</sub> and H<sub>2</sub>O fluxes from the Tibetan alpine steppe

2020 ◽  
Vol 12 (4) ◽  
pp. 2705-2724
Author(s):  
Felix Nieberding ◽  
Christian Wille ◽  
Gerardo Fratini ◽  
Magnus O. Asmussen ◽  
Yuyang Wang ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Field Analysis ◽  
Climate Feedback ◽  
The Tibetan Plateau ◽  
Data Set ◽  
Alpine Steppe ◽  
Turbulence Regime ◽  
Tibet Autonomous Region ◽  
Steppe Ecosystem ◽  
Carbon Dioxide Co2

Abstract. The Tibetan alpine steppe ecosystem covers an area of roughly 800 000 km2 and contains up to 3.3 % soil organic carbon in the uppermost 30 cm, summing up to 1.93 Pg C for the Tibet Autonomous Region only (472 037 km2). With temperatures rising 2 to 3 times faster than the global average, these carbon stocks are at risk of loss due to enhanced soil respiration. The remote location and the harsh environmental conditions on the Tibetan Plateau (TP) make it challenging to derive accurate data on the ecosystem–atmosphere exchange of carbon dioxide (CO2) and water vapor (H2O). Here, we provide the first multiyear data set of CO2 and H2O fluxes from the central Tibetan alpine steppe ecosystem, measured in situ using the eddy covariance technique. The calculated fluxes were rigorously quality checked and carefully corrected for a drift in concentration measurements. The gas analyzer self-heating effect during cold conditions was evaluated using the standard correction procedure and newly revised formulations (Burba et al., 2008; Frank and Massman, 2020). A wind field analysis was conducted to identify influences of adjacent buildings on the turbulence regime and to exclude the disturbed fluxes from subsequent computations. The presented CO2 fluxes were additionally gap filled using a standardized approach. The very low net carbon uptake across the 15-year data set highlights the special vulnerability of the Tibetan alpine steppe ecosystem to become a source of CO2 due to global warming. The data are freely available at https://doi.org/10.5281/zenodo.3733202 (Nieberding et al., 2020a) and https://doi.org/10.11888/Meteoro.tpdc.270333 (Nieberding et al., 2020b) and may help us to better understand the role of the Tibetan alpine steppe in the global carbon–climate feedback.

scholarly journals A Long Term (2005–2019) Eddy Covariance Data Set of CO<sub>2</sub> and H<sub>2</sub>O Fluxes from the Tibetan Alpine Steppe

2020 ◽  
Author(s):  
Felix Nieberding ◽  
Cristian Wille ◽  
Gerardo Fratini ◽  
Magnus O. Asmussen ◽  
Yuyang Wang ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Field Analysis ◽  
Climate Feedback ◽  
The Tibetan Plateau ◽  
Data Set ◽  
Alpine Steppe ◽  
Turbulence Regime ◽  
Tibet Autonomous Region ◽  
Steppe Ecosystem ◽  
Carbon Dioxide Co2

Abstract. The Tibetan alpine steppe ecosystem covers an area of roughly 800,000 km2, containing up to 3.3 % soil organic carbon in the uppermost 30 cm, summing up to 1.93 PgvC for the Tibet Autonomous Region only (472,037 km2). With temperatures rising two to three times faster than the global average, these carbon stocks are at risk of loss due to enhanced soil respiration. The remote location and the harsh environmental conditions on the Tibetan Plateau (TP) make it challenging to derive accurate data on ecosystem-atmosphere exchange of carbon dioxide (CO2) and water vapor (H2O). Here, we provide the first multi-year data set of CO2 and H2O fluxes from the central Tibetan alpine steppe ecosystem, measured in situ using the eddy covariance technique. The calculated fluxes were rigorously quality checked and carefully corrected for a drift in concentration measurements and gas analyzer self heating during cold conditions. A wind field analysis was conducted to identify influences of adjacent buildings on the turbulence regime and to exclude the disturbed fluxes from subsequent computations. The presented CO2 fluxes were additionally gap filled using a standardized approach. The very low net carbon uptake across the 15-year data set highlights the special vulnerability of the Tibetan alpine steppe ecosystem to become a source of CO2 due to global warming. The data is freely available (https://www.doi.org/10.5281/zenodo.3733203, Nieberding et al., 2020b) and may help to better understand the role of the Tibetan alpine steppe in the global carbon-climate feedback.

Supplementary material to "A Long Term (2005–2019) Eddy Covariance Data Set of CO<sub>2</sub> and H<sub>2</sub>O Fluxes from the Tibetan Alpine Steppe"

2020 ◽  
Author(s):  
Felix Nieberding ◽  
Cristian Wille ◽  
Gerardo Fratini ◽  
Magnus O. Asmussen ◽  
Yuyang Wang ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Data Set ◽  
Alpine Steppe ◽  
Supplementary Material

Relationship Between Gas Emissions and Dry Matter Intake in Mature Angus Cows

2021 ◽  
Vol 99 (Supplement_2) ◽  
pp. 32-33
Author(s):  
Amanda Holder ◽  
Megan A Gross ◽  
Alexi Moehlenpah ◽  
Paul Beck
Keyword(s):  
Dry Matter ◽  
Significant Positive Correlation ◽  
Crossover Design ◽  
Dry Matter Intake ◽  
Data Set ◽  
Gas Emissions ◽  
Positive Correlation ◽  
Wide Range ◽  
Carbon Dioxide Co2 ◽  
Second Period

Abstract The objective of this study was to examine the effects of diet quality on greenhouse gas emissions and dry matter intake (DMI). We used 42 mature, gestating Angus cows (600±69 kg; and BSC 5.3±1.1) with a wide range in DMI EPD (-1.36 to 2.29). Cows were randomly assigned to 2 diet sequences forage-concentrate (FC) or concentrate-forage(CF) determined by the diet they consumed in each period (forage or concentrate). The cows were adapted to the diet and the SmartFeed individual intake units for 14 d followed by 45 d of intake data collection for each period. Body weight was recorded on consecutive weigh days at the beginning and end of each period and then once every two wk for the duration of a period. Cows were exposed to the GreenFeed Emission Monitoring (GEM) system for no less than 9 d during each period. The GEM system was used to measure emissions of carbon dioxide (CO2) and methane (CH4). Only cows with a minimum of 20 total &gt;3-m visits to the GEM were included in the data set. Data were analyzed in a crossover design using GLIMMIX in SASv.9.4. Within the CF sequence there was a significant, positive correlation between TMR DMI and CH4 (r=0.81) and TMR DMI and CO2 (r=0.69), however, gas emissions during the second period on the hay diet were not correlated with hay intake. There was a significant, positive correlation between hay DMI and CO2 (r=0.76) and hay DMI and CH4 (r=0.74) when cows first consumed forage (FC). In comparison to the CF sequence, cows on the FC sequence showed a positive correlation between CO2 and TMR DMI during the second period. There was also a significant positive correlation between hay and TMR DMI when assessed across (r=0.43) or within sequence (FC r=0.41, CF r=0.47).

scholarly journals Update on GOSAT TANSO-FTS performance, operations, and data products after more than 6 years in space

2016 ◽  
Vol 9 (6) ◽  
pp. 2445-2461 ◽  
Author(s):  
Akihiko Kuze ◽  
Hiroshi Suto ◽  
Kei Shiomi ◽  
Shuji Kawakami ◽  
Makoto Tanaka ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Near Infrared ◽  
Satellite System ◽  
Fourier Transform Spectrometer ◽  
Infrared Sensor ◽  
Data Set ◽  
Dry Air ◽  
Retrieval Accuracy ◽  
Carbon Dioxide Co2

Abstract. A data set containing more than 6 years (February 2009 to present) of radiance spectra for carbon dioxide (CO2) and methane (CH4) observations has been acquired by the Greenhouse gases Observing SATellite (GOSAT, available at http://data.gosat.nies.go.jp/GosatUserInterfaceGateway/guig/GuigPage/open.do), nicknamed “Ibuki”, Thermal And Near infrared Sensor for carbon Observation Fourier Transform Spectrometer (TANSO-FTS). This paper provides updates on the performance of the satellite and TANSO-FTS sensor and describes important changes to the data product, which has recently been made available to users. With these changes the typical accuracy of retrieved column-averaged dry air mole fractions of CO2 and CH4 (XCO2 and XCH4, respectively) are 2 ppm or 0.5 % and 13 ppb or 0.7 %, respectively. Three major anomalies of the satellite system affecting TANSO-FTS are reported: a failure of one of the two solar paddles in May 2014, a switch to the secondary pointing system in January 2015, and most recently a cryocooler shutdown and restart in August 2015. The Level 1A (L1A) (raw interferogram) and the Level 1B (L1B) (radiance spectra) of version V201 described here have long-term uniform quality and provide consistent retrieval accuracy even after the satellite system anomalies. In addition, we discuss the unique observation abilities of GOSAT made possible by an agile pointing mechanism, which allows for optimization of global sampling patterns.

scholarly journals Monitoring compliance with sulfur content regulations of shipping fuel by in situ measurements of ship emissions

2015 ◽  
Vol 15 (17) ◽  
pp. 10087-10092 ◽  
Author(s):  
L. Kattner ◽  
B. Mathieu-Üffing ◽  
J. P. Burrows ◽  
A. Richter ◽  
S. Schmolke ◽  
...  
Keyword(s):  
Sulfur Content ◽  
Air Pollutant ◽  
Monitoring Site ◽  
Data Set ◽  
The North ◽  
Position Data ◽  
Gas Measurements ◽  
Set Up ◽  
Carbon Dioxide Co2

Abstract. In 1997 the International Maritime Organisation (IMO) adopted MARPOL Annex VI to prevent air pollution by shipping emissions. It regulates, among other issues, the sulfur content in shipping fuels, which is transformed into the air pollutant sulfur dioxide (SO2) during combustion. Within designated Sulfur Emission Control Areas (SECA), the sulfur content was limited to 1 %, and on 1 January 2015, this limit was further reduced to 0.1 %. Here we present the set-up and measurement results of a permanent ship emission monitoring site near Hamburg harbour in the North Sea SECA. Trace gas measurements are conducted with in situ instruments and a data set from September 2014 to January 2015 is presented. By combining measurements of carbon dioxide (CO2) and SO2 with ship position data, it is possible to deduce the sulfur fuel content of individual ships passing the measurement station, thus facilitating the monitoring of compliance of ships with the IMO regulations. While compliance is almost 100 % for the 2014 data, it decreases only very little in 2015 to 95.4 % despite the much stricter limit. We analysed more than 1400 ship plumes in total and for months with favourable conditions, up to 40 % of all ships entering and leaving Hamburg harbour could be checked for their sulfur fuel content.

scholarly journals Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms; validation against collocated MODIS and CALIOP data

2015 ◽  
Vol 8 (12) ◽  
pp. 12663-12707 ◽  
Author(s):  
T. E. Taylor ◽  
C. W. O'Dell ◽  
C. Frankenberg ◽  
P. Partain ◽  
H. Q. Cronk ◽  
...  
Keyword(s):  
Surface Pressure ◽  
Near Infrared ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Orthogonal Polarization ◽  
Screening Methods ◽  
Data Set ◽  
Atmospheric Scattering ◽  
Carbon Dioxide Co2 ◽  
Cloud Screening

Abstract. The objective of the National Aeronautics and Space Administration's (NASA) Orbiting Carbon Observatory-2 (OCO-2) mission is to retrieve the column-averaged carbon dioxide (CO2) dry air mole fraction (XCO2) from satellite measurements of reflected sunlight in the near-infrared. These estimates can be biased by clouds and aerosols within the instrument's field of view (FOV). Screening of the most contaminated soundings minimizes unnecessary calls to the computationally expensive Level 2 (L2) XCO2 retrieval algorithm. Hence, robust cloud screening methods have been an important focus of the OCO-2 algorithm development team. Two distinct, computationally inexpensive cloud screening algorithms have been developed for this application. The A-Band Preprocessor (ABP) retrieves the surface pressure using measurements in the 0.76 μm O2 A-band, neglecting scattering by clouds and aerosols, which introduce photon path-length (PPL) differences that can cause large deviations between the expected and retrieved surface pressure. The Iterative Maximum A-Posteriori (IMAP) Differential Optical Absorption Spectroscopy (DOAS) Preprocessor (IDP) retrieves independent estimates of the CO2 and H2O column abundances using observations taken at 1.61 μm (weak CO2 band) and 2.06 μm (strong CO2 band), while neglecting atmospheric scattering. The CO2 and H2O column abundances retrieved in these two spectral regions differ significantly in the presence of cloud and scattering aerosols. The combination of these two algorithms, which key off of different features in the spectra, provides the basis for cloud screening of the OCO-2 data set. To validate the OCO-2 cloud screening approach, collocated measurements from NASA's Moderate Resolution Imaging Spectrometer (MODIS), aboard the Aqua platform, were compared to results from the two OCO-2 cloud screening algorithms. With tuning to allow throughputs of &amp;simeq; 30 %, agreement between the OCO-2 and MODIS cloud screening methods is found to be &amp;simeq; 85 % over four 16-day orbit repeat cycles in both the winter (December) and spring (April–May) for OCO-2 nadir-land, glint-land and glint-water observations. No major, systematic, spatial or temporal dependencies were found, although slight differences in the seasonal data sets do exist and validation is more problematic with increasing solar zenith angle and when surfaces are covered in snow and ice and have complex topography. To further analyze the performance of the cloud screening algorithms, an initial comparison of OCO-2 observations was made to collocated measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). These comparisons highlight the strength of the OCO-2 cloud screening algorithms in identifying high, thin clouds but suggest some difficulty in identifying some clouds near the surface, even when the optical thicknesses are greater than 1.

scholarly journals Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model

2009 ◽  
Vol 6 (3) ◽  
pp. 5271-5304 ◽  
Author(s):  
M. Jung ◽  
M. Reichstein ◽  
A. Bondeau
Keyword(s):  
Eddy Covariance ◽  
Learning Algorithm ◽  
Gross Primary Production ◽  
Global Network ◽  
Data Set ◽  
Model Trees ◽  
Ensemble Approach ◽  
Model Tree ◽  
Biosphere Model ◽  
Variance Explained

Abstract. Global, spatially and temporally explicit estimates of carbon and water fluxes derived from empirical up-scaling eddy covariance measurements would constitute a new and possibly powerful data stream to study the variability of the global terrestrial carbon and water cycle. This paper introduces and validates a machine learning approach dedicated to the upscaling of observations from the current global network of eddy covariance towers (FLUXNET). We present a new model TRee Induction ALgorithm (TRIAL) that performs hierarchical stratification of the data set into units where particular multiple regressions for a target variable hold. We propose an ensemble approach (Evolving tRees with RandOm gRowth, ERROR) where the base learning algorithm is perturbed in order to gain a diverse sequence of different model trees which evolves over time. We evaluate the efficiency of the model tree ensemble approach using an artificial data set derived from the the Lund-Potsdam-Jena managed Land (LPJmL) biosphere model. We aim at reproducing global monthly gross primary production as simulated by LPJmL from 1998–2005 using only locations and months where high quality FLUXNET data exist for the training of the model trees. The model trees are trained with the LPJmL land cover and meteorological input data, climate data, and the fraction of absorbed photosynthetic active radiation simulated by LPJmL. Given that we know the "true result" in the form of global LPJmL simulations we can effectively study the performance of the model tree ensemble upscaling and associated problems of extrapolation capacity. We show that the model tree ensemble is able to explain 92% of the variability of the global LPJmL GPP simulations. The mean spatial pattern and the seasonal variability of GPP that constitute the largest sources of variance are very well reproduced (96% and 94% of variance explained respectively) while the monthly interannual anomalies which occupy much less variance are less well matched (41% of variance explained). We demonstrate the substantially improved accuracy of the model tree ensemble over individual model trees in particular for the monthly anomalies and for situations of extrapolation. We estimate that roughly one fifth of the domain is subject to extrapolation while the model tree ensemble is still able to reproduce 73% of the LPJmL GPP variability here. This paper presents for the first time a benchmark for a global FLUXNET upscaling approach that will be employed in future studies. Although the real world FLUXNET upscaling is more complicated than for a noise free and reduced complexity biosphere model as presented here, our results show that an empirical upscaling from the current FLUXNET network with a model tree ensemble is feasible and able to extract global patterns of carbon flux variability.

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