scholarly journals The impact of improved satellite retrievals on estimates of biospheric carbon balance

2019 ◽  
Author(s):  
Scot M. Miller ◽  
Anna M. Michalak
Keyword(s):  
Carbon Balance ◽  
Near Infrared ◽  
Co2 Flux ◽  
Retrieval Algorithm ◽  
Co2 Uptake ◽  
Near Infrared Radiation ◽  
Sources And Sinks ◽  
Satellite Retrievals ◽  
The Impact ◽  
Insight Into

Abstract. The Orbiting Carbon Observatory 2 (OCO-2) is NASA's first satellite dedicated to monitoring CO2 from space and could provide novel insight into CO2 fluxes across the globe. However, one continuing challenge is the development of a robust retrieval algorithm: an estimate of atmospheric CO2 from satellite observations of near infrared radiation. The OCO-2 retrievals have undergone multiple updates since the satellite's launch, and the retrieval algorithm is now on its ninth version. Some of these retrieval updates, particularly version 8, led to marked changes in the CO2 observations, changes of 0.5 ppm or more. In this study, we evaluate the extent to which current OCO-2 observations can constrain monthly CO2 sources and sinks from the biosphere, and we particularly focus on how this constraint has evolved with improvements to the OCO-2 retrieval algorithm. We find that improvements in the CO2 retrieval are having a potentially transformative effect on satellite-based estimates of the global biospheric carbon balance. The version 7 OCO-2 retrievals formed the basis of early inverse modeling studies using OCO-2 data; these observations are best-equipped to constrain the biospheric carbon balance across only continental or hemispheric regions. By contrast, newer versions of the retrieval algorithm yield a far more detailed constraint, and we are able to constrain CO2 budgets for seven global biome-based regions, particularly during the Northern Hemisphere summer when biospheric CO2 uptake is greatest. Improvements to the OCO-2 observations have had the largest impact on glint mode observations, and we also find the largest improvements in the terrestrial CO2 flux constraint when we include both nadir and glint data.

scholarly journals The impact of improved satellite retrievals on estimates of biospheric carbon balance

2020 ◽  
Vol 20 (1) ◽  
pp. 323-331 ◽  
Author(s):  
Scot M. Miller ◽  
Anna M. Michalak
Keyword(s):  
Carbon Balance ◽  
Near Infrared ◽  
Co2 Flux ◽  
Retrieval Algorithm ◽  
Co2 Uptake ◽  
Near Infrared Radiation ◽  
Sources And Sinks ◽  
Satellite Retrievals ◽  
The Impact ◽  
Insight Into

Abstract. The Orbiting Carbon Observatory 2 (OCO-2) is NASA's first satellite dedicated to monitoring CO2 from space and could provide novel insight into CO2 fluxes across the globe. However, one continuing challenge is the development of a robust retrieval algorithm: an estimate of atmospheric CO2 from satellite observations of near-infrared radiation. The OCO-2 retrievals have undergone multiple updates since the satellite's launch, and the retrieval algorithm is now on its ninth version. Some of these retrieval updates, particularly version 8, led to marked changes in the CO2 observations, changes of 0.5 ppm or more. In this study, we evaluate the extent to which current OCO-2 observations can constrain monthly CO2 sources and sinks from the biosphere, and we particularly focus on how this constraint has evolved with improvements to the OCO-2 retrieval algorithm. We find that improvements in the CO2 retrieval are having a potentially transformative effect on satellite-based estimates of the global biospheric carbon balance. The version 7 OCO-2 retrievals formed the basis of early inverse modeling studies using OCO-2 data; these observations are best equipped to constrain the biospheric carbon balance across only continental or hemispheric regions. By contrast, newer versions of the retrieval algorithm yield a far more detailed constraint, and we are able to constrain CO2 budgets for seven global biome-based regions, particularly during the Northern Hemisphere summer when biospheric CO2 uptake is greatest. Improvements to the OCO-2 observations have had the largest impact on glint-mode observations, and we also find the largest improvements in the terrestrial CO2 flux constraint when we include both nadir and glint data.

scholarly journals The Impact of Improved Aerosol Priors on Near-Infrared Measurements of Carbon Dioxide

2018 ◽  
Author(s):  
Robert R. Nelson ◽  
Christopher W. O'Dell
Keyword(s):  
Carbon Dioxide ◽  
Near Infrared ◽  
Carbon Sources ◽  
Total Carbon ◽  
Primary Sources ◽  
Retrieval Algorithm ◽  
Sources And Sinks ◽  
Earth Observing System ◽  
Infrared Measurements ◽  
The Impact

Abstract. The Orbiting Carbon Observatory-2 (OCO-2) was launched in 2014 with the goal of measuring the column-averaged dry-air mole fraction of carbon dioxide (XCO2) with sufficient precision and accuracy to infer regional carbon sources and sinks. One of the primary sources of error in near-infrared measurements of XCO2 is the scattering effects of cloud and aerosol layers. In this work, we study the impact of ingesting intelligent aerosol priors from the Goddard Earth Observing System Model, Version 5 (GEOS-5) into the OCO-2 ACOS V8 retrieval algorithm with the objective of reducing the error in XCO2 from real measurements. Multiple levels of both aerosol setup complexity and uncertainty on the aerosol priors were tested, ranging from a mostly unconstrained aerosol optical depth (AOD) setup to ingesting full aerosol profiles with high confidence. We find that using co-located GEOS-5 aerosol types and AODs with low uncertainty results in a small improvement in the retrieved XCO2 against the Total Carbon Column Observing Network relative to V8. In contrast, attempting to use modeled vertical information in the aerosol prior to improve the XCO2 retrieval generally gives poor results, as aerosol models struggle with the vertical placement of aerosol layers. To assess regional differences in XCO2, we compare our results to a global CO2 model validation suite. We find that the GEOS-5 setup performs better than V8 over Northern Africa and Central Asia, with the standard deviation of the XCO2 error reduced from 2.12 ppm to 1.83 ppm, due to a combination of smaller prior AODs and lower prior uncertainty. In general, the use of more intelligent aerosol priors shows promise but is currently restricted by the accuracy of aerosol models.

scholarly journals The impact of improved aerosol priors on near-infrared measurements of carbon dioxide

2019 ◽  
Vol 12 (3) ◽  
pp. 1495-1512 ◽  
Author(s):  
Robert R. Nelson ◽  
Christopher W. O'Dell
Keyword(s):  
Carbon Dioxide ◽  
Near Infrared ◽  
Carbon Sources ◽  
Total Carbon ◽  
Primary Sources ◽  
Retrieval Algorithm ◽  
Sources And Sinks ◽  
Earth Observing System ◽  
Infrared Measurements ◽  
The Impact

Abstract. The Orbiting Carbon Observatory-2 (OCO-2) was launched in 2014 with the goal of measuring the column-averaged dry-air mole fraction of carbon dioxide (XCO2) with sufficient precision and accuracy to infer regional carbon sources and sinks. One of the primary sources of error in near-infrared measurements of XCO2 is the scattering effects of cloud and aerosol layers. In this work, we study the impact of ingesting better informed aerosol priors from the Goddard Earth Observing System Model, Version 5 (GEOS-5) into the OCO-2 ACOS V8 retrieval algorithm with the objective of reducing the error in XCO2 from real measurements. Multiple levels of both aerosol setup complexity and uncertainty on the aerosol priors were tested, ranging from a mostly unconstrained aerosol optical depth (AOD) setup to ingesting full aerosol profiles with high confidence. We find that using co-located GEOS-5 aerosol types and AODs with low uncertainty results in a small improvement in the retrieved XCO2 against the Total Carbon Column Observing Network relative to V8. In contrast, attempting to use modeled vertical information in the aerosol prior to improve the XCO2 retrieval generally gives poor results, as aerosol models struggle with the vertical placement of aerosol layers. To assess regional differences in XCO2, we compare our results to a global CO2 model validation suite. We find that the GEOS-5 setup performs better than V8 over northern Africa and central Asia, with the standard deviation of the XCO2 error reduced from 2.12 to 1.83 ppm, due to a combination of smaller prior AODs and lower prior uncertainty. In general, the use of better informed aerosol priors shows promise but may be restricted by the current accuracy of aerosol models.

scholarly journals The role of phytoplankton dynamics in the seasonal and interannual variability of carbon in the subpolar North Atlantic – a modeling study

2012 ◽  
Vol 5 (3) ◽  
pp. 683-707 ◽  
Author(s):  
S. R. Signorini ◽  
S. Häkkinen ◽  
K. Gudmundsson ◽  
A. Olsen ◽  
A. M. Omar ◽  
...  
Keyword(s):  
Functional Groups ◽  
Mixed Layer Depth ◽  
Co2 Flux ◽  
Early Spring ◽  
Biogeochemical Model ◽  
Co2 Uptake ◽  
Biological Interactions ◽  
In Situ Data ◽  
Phytoplankton Functional Groups ◽  
The Impact

Abstract. We developed an ecosystem/biogeochemical model system, which includes multiple phytoplankton functional groups and carbon cycle dynamics, and applied it to investigate physical-biological interactions in Icelandic waters. Satellite and in situ data were used to evaluate the model. Surface seasonal cycle amplitudes and biases of key parameters (DIC, TA, pCO2, air-sea CO2 flux, and nutrients) are significantly improved when compared to surface observations by prescribing deep water values and trends, based on available data. The seasonality of the coccolithophore and "other phytoplankton" (diatoms and dinoflagellates) blooms is in general agreement with satellite ocean color products. Nutrient supply, biomass and calcite concentrations are modulated by light and mixed layer depth seasonal cycles. Diatoms are the most abundant phytoplankton, with a large bloom in early spring and a secondary bloom in fall. The diatom bloom is followed by blooms of dinoflagellates and coccolithophores. The effect of biological changes on the seasonal variability of the surface ocean pCO2 is nearly twice the temperature effect, in agreement with previous studies. The inclusion of multiple phytoplankton functional groups in the model played a major role in the accurate representation of CO2 uptake by biology. For instance, at the peak of the bloom, the exclusion of coccolithophores causes an increase in alkalinity of up to 4 μmol kg−1 with a corresponding increase in DIC of up to 16 μmol kg−1. During the peak of the bloom in summer, the net effect of the absence of the coccolithophores bloom is an increase in pCO2 of more than 20 μatm and a reduction of atmospheric CO2 uptake of more than 6 mmol m−2 d−1. On average, the impact of coccolithophores is an increase of air-sea CO2 flux of about 27%. Considering the areal extent of the bloom from satellite images within the Irminger and Icelandic Basins, this reduction translates into an annual mean of nearly 1500 tonnes C yr−1.

Impact of albedo and cloud retrievals on the NO2 tropospheric column derived from Sentinel-5P TROPOMI observations

2020 ◽  
Author(s):  
Henk Eskes ◽  
Maarten Sneep ◽  
Jos van Geffen ◽  
Folkert Boersma ◽  
Ping Wang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cloud Fraction ◽  
Trace Gas ◽  
Retrieval Algorithm ◽  
Absorption Feature ◽  
Total Uncertainty ◽  
Retrieval Algorithms ◽  
Global Coverage ◽  
Consistent Treatment ◽  
The Impact

<p>Sentinel-5P, with the TROPOMI instrument, was launched in October 2017 and is providing unique high-quality and high-resolution (5 km) observations of trace gas pollutants with a daily global coverage. In our contribution we will discuss the retrieval of nitrogen dioxide (NO2). A major contributions to the total uncertainty of these measurements are the TROPOMI retrievals of cloud fraction and effective cloud pressure (or altitude). Several cloud retrieval algorithms have been implemented, deriving cloud height information from the near-infrared O2-A band, O2-B band or the O2-O2 absorption feature near 477nm. In our presentation we will show the importance of a consistent treatment of clouds and albedo as input for the retrieval radiative transfer calculations. The impact of the different cloud products on the retrieved NO2 is demonstrated for a new implementation of the FRESCO O2-A band cloud retrieval algorithm and an implementation of the O2-O2 retrievals for TROPOMI. A recipe to make optimal use of the available cloud information is presented.</p>

scholarly journals Water level variation at a beaver pond significantly impacts net CO<sub>2</sub> uptake of a continental bog

2021 ◽  
Author(s):  
Hongxing He ◽  
Tim Moore ◽  
Elyn R. Humphreys ◽  
Peter M. Lafleur ◽  
Nigel T. Roulet
Keyword(s):  
Water Level ◽  
Co2 Flux ◽  
Water Levels ◽  
Co2 Uptake ◽  
Beaver Pond ◽  
Human Disturbances ◽  
Flux Dynamics ◽  
Net Co2 Uptake ◽  
Northern Peatlands ◽  
The Impact

Abstract. The carbon (C) dynamics of northern peatlands are sensitive to hydrological changes owing to ecohydrological feedback. We quantified and evaluated the impact of water level variations in a beaver pond (BP) on the CO2 flux dynamics of an adjacent, raised Sphagnum – shrub-dominated bog in southern Canada. We applied the CoupModel to the Mer Bleue bog, where the hydrological, energy and CO2 fluxes have been measured continuously for over 20 years. The lateral flow from the bog to the BP was estimated by the hydraulic gradient between the peatland and the BP's water level and the vertical profile of peat hydraulic conductivity. The model outputs were compared with the measured hydrological components, CO2 flux and energy flux data (1998–2019). CoupModel was able to reproduce the measured data well. The simulation shows that variation in the BP water level (naturally occurring or due to management) influenced the bog net ecosystem exchange of CO2 (NEE). Over 1998–2004, the BP water level was 0.75 to 1.0 m lower than during 2017–2019. Simulated net CO2 uptake was 55 g C m−2 yr−1 lower during 1998–2004 compared to 2017–2019 when there was no BP disturbance, which was similar to the differences in measured NEE between those periods. Peatland annual NEE was well correlated with water table depth within the bog, and NEE also shows a linear relation with the water level at the BP, with a slope of −120 g CO2-C m−2 yr−1 m−1. The current modelling predicts the bog may switch from CO2 sink to source when the BP water levels drop lower than ~ 1.7 m below the peat surface at the eddy covariance tower, 250 m from the BP. This study highlights the importance of natural and human disturbances to adjacent water bodies in regulating net CO2 uptake function of northern peatlands.

scholarly journals The impact of the optical radiation spectrum of artificial lighting on the milk producing ability of cows

2021 ◽  
Vol 36 ◽  
pp. 05016
Author(s):  
N.L. Olin ◽  
E.N. Efremova ◽  
A.M. Niyazov ◽  
P.L. Lekomtsev
Keyword(s):  
Infrared Radiation ◽  
Optical Radiation ◽  
Near Infrared ◽  
Spectral Composition ◽  
Light Flux ◽  
Near Infrared Radiation ◽  
Visible Radiation ◽  
Substantial Impact ◽  
Artificial Lighting ◽  
The Impact

Artificial lighting is of considerable importance in livestock industry. If there is sufficient light flux and optimal spectral composition, it can have a substantial impact on the comfortable condition of cows, and, as a result, on milk producing ability. The sensitivity of the cow’s eye to optical radiation is varied in the range of the visible spectrum, and its visual apparatus is adapted to recognize natural different types of feed. The leaves of plants take up visible radiation in the red and blue regions of the spectrum, while they reflect intensely in the near-infra-red region. Consequently, cows, having the ability to perceive near-infrared radiation, can give favor to higher-quality feed, the consumption of which, among other things, will increase milk producing ability. The findings of the studies demonstrate that the cow’s eye reacts to near-infrared radiation, and the combined lighting contributes to a more adequate behavior of the animals, which in turn has a positive effect on milk producing ability.

scholarly journals CO<sub>2</sub> flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

2007 ◽  
Vol 4 (6) ◽  
pp. 1005-1025 ◽  
Author(s):  
L. Kutzbach ◽  
J. Schneider ◽  
T. Sachs ◽  
M. Giebels ◽  
H. Nykänen ◽  
...  
Keyword(s):  
Linear Regression ◽  
Nonlinear Regression ◽  
Co2 Flux ◽  
Exponential Model ◽  
Co2 Uptake ◽  
Co2 Fluxes ◽  
Closed Chamber ◽  
Exponential Regression ◽  
Closure Time ◽  
Over Time

Abstract. Closed (non-steady state) chambers are widely used for quantifying carbon dioxide (CO2) fluxes between soils or low-stature canopies and the atmosphere. It is well recognised that covering a soil or vegetation by a closed chamber inherently disturbs the natural CO2 fluxes by altering the concentration gradients between the soil, the vegetation and the overlying air. Thus, the driving factors of CO2 fluxes are not constant during the closed chamber experiment, and no linear increase or decrease of CO2 concentration over time within the chamber headspace can be expected. Nevertheless, linear regression has been applied for calculating CO2 fluxes in many recent, partly influential, studies. This approach has been justified by keeping the closure time short and assuming the concentration change over time to be in the linear range. Here, we test if the application of linear regression is really appropriate for estimating CO2 fluxes using closed chambers over short closure times and if the application of nonlinear regression is necessary. We developed a nonlinear exponential regression model from diffusion and photosynthesis theory. This exponential model was tested with four different datasets of CO2 flux measurements (total number: 1764) conducted at three peatlands sites in Finland and a tundra site in Siberia. Thorough analyses of residuals demonstrated that linear regression was frequently not appropriate for the determination of CO2 fluxes by closed-chamber methods, even if closure times were kept short. The developed exponential model was well suited for nonlinear regression of the concentration over time c(t) evolution in the chamber headspace and estimation of the initial CO2 fluxes at closure time for the majority of experiments. However, a rather large percentage of the exponential regression functions showed curvatures not consistent with the theoretical model which is considered to be caused by violations of the underlying model assumptions. Especially the effects of turbulence and pressure disturbances by the chamber deployment are suspected to have caused unexplainable curvatures. CO2 flux estimates by linear regression can be as low as 40% of the flux estimates of exponential regression for closure times of only two minutes. The degree of underestimation increased with increasing CO2 flux strength and was dependent on soil and vegetation conditions which can disturb not only the quantitative but also the qualitative evaluation of CO2 flux dynamics. The underestimation effect by linear regression was observed to be different for CO2 uptake and release situations which can lead to stronger bias in the daily, seasonal and annual CO2 balances than in the individual fluxes. To avoid serious bias of CO2 flux estimates based on closed chamber experiments, we suggest further tests using published datasets and recommend the use of nonlinear regression models for future closed chamber studies.

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