Second State of the Carbon Cycle Report

Shrestha, Gyami; Cavallaro, N.; Lorenzoni, Laura; Seadler, A.; Zhu, Zhiliang; Gurwick, N. P.; Larson, Elaine; Birdsey, Richard A.; Mayes, Melanie A.; Najjar, Raymond G.; Reed, Sasha C.; Romero-Lankao, Paty

doi:10.7930/soccr2.2018

Cited by 38 publications

(11 citation statements)

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“…The consequences of these impacts, particularly those driven by land use [25], soil warming [26], and soil moisture [27,28] can be framed through fundamental principles of soil science: physics, chemistry, and biology. While future scenarios include local conditions that could increase soil C stocks, these increases are largely due to increased productivity; globally, however, increased C destabilization is likely to offset these limited accruals [29]. By focusing on these processes and mechanisms of C destabilization, we provide a context for empirical and computational scientists to focus on questions that directly inform the potential for soils to affect climate through the carbon cycle.…”

Section: Introductionmentioning

confidence: 99%

What do we know about soil carbon destabilization?

Bailey

Pries

Lajtha

2019

Environ. Res. Lett.

130

106

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Most empirical and modeling research on soil carbon (C) dynamics has focused on those processes that control and promote C stabilization. However, we lack a strong, generalizable understanding of the mechanisms through which soil organic carbon (SOC) is destabilized in soils. Yet a clear understanding of C destabilization processes in soil is needed to quantify the feedbacks of the soil C cycle to the Earth system. Destabilization includes processes that occur along a spectrum through which SOC shifts from a ‘protected’ state to an ‘available’ state to microbial cells where it can be mineralized to gaseous forms or to soluble forms that are then lost from the soil system. These processes fall into three general categories: (1) release from physical occlusion through processes such as tillage, bioturbation, or freeze-thaw and wetting-drying cycles; (2) C desorption from soil solids and colloids; and (3) increased C metabolism. Many processes that stabilize soil C can also destabilize C, and C gain or loss depends on the balance between competing reactions. For example, earthworms may both destabilize C through aggregate destruction, but may also create new aggregates and redistribute C into mineral horizon. Similarly, mycorrhizae and roots form new soil C but may also destabilize old soil C through priming and promoting microbial mining; labile C inputs cause C stabilization through increased carbon use efficiency or may fuel priming. Changes to the soil environment that affect the solubility of minerals or change the relative surfaces charges of minerals can destabilize SOC, including increased pH or in the reductive dissolution of Fe-bearing minerals. By considering these different physical, chemical, and biological controls as processes that contribute to soil C destabilization, we can develop thoughtful new hypotheses about the persistence and vulnerability of C in soils and make more accurate and robust predictions of soil C cycling in a changing environment.

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Section: Introductionmentioning

confidence: 99%

What do we know about soil carbon destabilization?

Bailey

Pries

Lajtha

2019

Environ. Res. Lett.

130

106

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“…The capability to monitor atmospheric carbon dioxide (CO 2 ) using in situ and remote sensing observations combined with numerical models has rapidly evolved to improve our understanding of biogenic CO 2 sources and sinks and to provide independent estimates of anthropogenic CO 2 emissions (Cavallaro et al, 2018;National Research Council, 2010). Changes in atmospheric CO 2 can be used to infer uptake and release of CO 2 from terrestrial ecosystems and the ocean through inverse methods (Enting, 2002), which in turn can help us understand how the natural carbon cycle responds to both natural and human-induced environmental changes including climate disturbances and human land-use management (e.g., Bousquet et al, 2000;Patra et al, 2005;Rdenbeck et al, 2003;Schimel et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Regional CO₂ Mole Fractions in the ECMWF CAMS Real‐Time Atmospheric Analysis and NOAA CarbonTracker Near‐Real‐Time Reanalysis With Airborne Observations From ACT‐America Field Campaigns

Chen

Zhang

et al. 2019

JGR Atmospheres

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This study systematically examines the regional uncertainties and biases in carbon dioxide (CO2) mole fractions from two of the state‐of‐the‐art global CO2 analysis products, namely, the Copernicus Atmosphere Monitoring Service (CAMS) real‐time atmospheric analysis from the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the CarbonTracker Near‐Real‐Time (CT‐NRT) reanalysis from the National Oceanic and Atmospheric Administration (NOAA), by evaluation against hundreds of hours of airborne in situ measurements from the summer 2016 and winter 2017 Atmospheric Carbon and Transport (ACT)‐America field campaigns. Both the CAMS and CT‐NRT analyses agree reasonably well with the independent ACT‐America airborne CO2 measurements in the free troposphere, with root‐mean‐square deviations (RMSDs) between analyses and observations generally between 1 and 2 ppm but show considerably larger uncertainties in the atmospheric boundary layer where the RMSDs exceed 8 ppm in the lowermost 1 km of the troposphere in summer. There are strong variations in accuracy and bias between seasons, and across three different subregions in the United States (Mid‐Atlantic, Midwest, and South), with the largest uncertainties in the Mid‐Atlantic region in summer. Overall, the RMSDs of the CAMS and CT‐NRT analyses against airborne data are comparable to each other and largely consistent with the differences between the two analyses. The current study provides uncertainty estimates for both analysis products over North America and suggests that these two independent estimates can be used to approximate regional CO2 analysis uncertainties. Both statistics are important in future studies in quantifying the uncertainties in regional CO2 mole fraction and flux estimates.

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“…This idea has been widely used with the space-borne OMI instrument that 2 preceded TROPOMI (see Sun et al, 2018a, and references therein for a discussion of oversampling with OMI observations). 3 However, the spatial resolution of TROPOMI is a factor of 15 finer than OMI (3.5×7 km 2 for TROPOMI and 14×26 km 2 for 4 OMI, both at nadir). Oversampling with OMI often required years of observations (e.g., Zhu et al, 2014).…”

mentioning

confidence: 99%

A double peak in the seasonality of California's photosynthesis as observed from space

Turner¹,

Köhler²,

Magney³

et al. 2019

Preprint

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<p><strong>Abstract.</strong> Solar-Induced chlorophyll Fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and gross primary productivity (GPP). The recently launched TROPOspheric Monitoring Instrument (TROPOMI) features the required spectral resolution and signal-to-noise ratio to retrieve SIF from space. Here we present an oversampling and downscaling method to obtain 500-m spatial resolution SIF over California. We report daily values based on a 14-day window. TROPOMI SIF data show a strong correspondence with daily GPP estimates at AmeriFlux sites across multiple ecosystems in California. We find a linear relationship between SIF and GPP that is largely invariant across ecosystems with an intercept that is not significantly different from zero. Measurements of SIF from TROPOMI agree with MODIS vegetation indices (NDVI, EVI, and NIRv) at annual timescales but indicate different temporal dynamics at monthly and daily timescales. TROPOMI SIF data show a double peak in the seasonality of photosynthesis, a feature that is not present in the MODIS vegetation indices. The different seasonality in the vegetation indices may be due to a clear-sky bias in the vegetation indices, whereas SIF has a low sensitivity to clouds and can detect the downregulation of photosynthesis even when plants appear green. We further decompose the spatio-temporal patterns in the SIF data based on land cover. The double peak in the seasonality of California's photosynthesis is due to two processes that are out of phase: grasses, chaparral, and oak savanna ecosystems show an April maximum while evergreen forests peak in June. An empirical orthogonal function (EOF) analysis corroborates the phase offset and spatial patterns driving the double peak. The EOF analysis further indicates that two spatio-temporal patterns explain 84&#8201;% of the variability in the SIF data. Results shown here are promising for obtaining global GPP at sub-kilometer spatial scales and identifying the processes driving carbon uptake.</p>

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Second State of the Carbon Cycle Report

Cited by 38 publications

References 0 publications

What do we know about soil carbon destabilization?

What do we know about soil carbon destabilization?

Evaluation of Regional CO₂ Mole Fractions in the ECMWF CAMS Real‐Time Atmospheric Analysis and NOAA CarbonTracker Near‐Real‐Time Reanalysis With Airborne Observations From ACT‐America Field Campaigns

A double peak in the seasonality of California's photosynthesis as observed from space

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Second State of the Carbon Cycle Report

Cited by 38 publications

References 0 publications

What do we know about soil carbon destabilization?

What do we know about soil carbon destabilization?

Evaluation of Regional CO2 Mole Fractions in the ECMWF CAMS Real‐Time Atmospheric Analysis and NOAA CarbonTracker Near‐Real‐Time Reanalysis With Airborne Observations From ACT‐America Field Campaigns

A double peak in the seasonality of California's photosynthesis as observed from space

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Evaluation of Regional CO₂ Mole Fractions in the ECMWF CAMS Real‐Time Atmospheric Analysis and NOAA CarbonTracker Near‐Real‐Time Reanalysis With Airborne Observations From ACT‐America Field Campaigns