Vineet Yadav

Progress in soil organic matter research: litter decomposition, modelling, monitoring and sequestration

Retention and sequestration of soil organic matter is extremely important for the maintenance of soil structure, agricultural productivity and carbon sequestration. Research in soil organic matter has advanced on many fronts in the last half century. During this time understanding of the factors governing plant litter decomposition has increased considerably resulting in the formulation of process and organism-based models. Remote sensing has been shown to be useful for quickly monitoring stocks of soil organic carbon in the topsoil although much remains to be done to establish its efficacy. Fluxes of soil organic matter in the changing climatic scenarios have been studied though outcomes remain debatable. In this paper an attempt is made to present these various aspects of soil organic matter cohesively. The focus is mainly on litter decomposition, models and monitoring methods, role of soil aggregates and erosion, impact of climate change on long-term dynamics of soil organic matter and impending research themes needing further attention.

Toward reliable ensemble Kalman filter estimates of CO2 fluxes

[1] The use of ensemble filters for estimating sources and sinks of carbon dioxide (CO2) is becoming increasingly common, because they provide a relatively computationally efficient framework for assimilating high-density observations of CO2. Their applicability for estimating fluxes at high-resolutions and the equivalence of their estimates to those from more traditional “batch” inversion methods have not been demonstrated, however. In this study, we introduce a Geostatistical Ensemble Square Root Filter (GEnSRF) as a prototypical filter and examine its performance using a synthetic data study over North America at a high spatial (1 � � 1 � ) and temporal (3-hourly) resolution. The ensemble performance, both in terms of estimates and associated uncertainties, is benchmarked against a batch inverse modeling setup in order to isolate and quantify the degradation in the estimates due to the numerical approximations and parameter choices in the ensemble filter. The examined case studies demonstrate that adopting state-of-the-art covariance inflation and localization schemes is a necessary but not sufficient condition for ensuring good filter performance, as defined by its ability to yield reliable flux estimates and uncertainties across a range of resolutions. Observational density is found to be another critical factor for stabilizing the ensemble performance, which is attributed to the lack of a dynamical model for evolving the ensemble between assimilation times. This and other results point to key differences between the applicability of ensemble approaches to carbon cycle science relative to its use in meteorological applications where these tools were originally developed.

Detecting fossil fuel emissions patterns from subcontinental regions using North American in situ CO2 measurements

The ability to monitor fossil fuel carbon dioxide (FFCO2) emissions from subcontinental regions using atmospheric CO2 observations remains an important but unrealized goal. Here we explore a necessary but not sufficient component of this goal, namely, the basic question of the detectability of FFCO2 emissions from subcontinental regions. Detectability is evaluated by examining the degree to which FFCO2 emissions patterns from specific regions are needed to explain the variability observed in high-frequency atmospheric CO2 observations. Analyses using a CO2 monitoring network of 35 continuous measurement towers over North America show that FFCO2 emissions are difficult to detect during nonwinter months. We find that the compounding effects of the seasonality of atmospheric transport patterns and the biospheric CO2 flux signal dramatically hamper the detectability of FFCO2 emissions. Results from several synthetic data case studies highlight the need for advancements in data coverage and transport model accuracy if the goal of atmospheric measurement-based FFCO2 emissions detection and estimation is to be achieved beyond urban scales. Key Points Poor detectability of fossil fuel CO2 emissions from subcontinental regions Detectability assessed via attribution of emissions patterns in atmospheric data Loss in detectability due to transport modeling errors and biospheric signal

Attributing the variability of eddy-covariance CO2 flux measurements across temporal scales using geostatistical regression for a mixed northern hardwood forest

[1] The relationships between terrestrial carbon dioxide flux and its primary environmental drivers are uncertain because the processes controlling CO2 cycling, especially at ecosystem scales, are not well understood. This uncertainty is compounded by the fact that the importance of controlling processes, and therefore environmental drivers, may differ across temporal scales. This paper presents and applies a geostatistical regression (GR) approach that can be used with eddy‐covariance data to investigate the relationships between carbon flux and environmental variables at multiple time scales, ranging from monthly to daily. The approach uses an adaptation of the Bayes Information Criterion to identify an optimal set of environmental variables that are able to explain the observed variability in carbon flux. In addition, GR quantifies the temporal correlation in the portion of the flux signal that cannot be explained by the selected variables and directly accounts for this correlation in the analysis. This GR approach was applied to data from the University of Michigan Biological Station (UMBS) AmeriFlux site to (i) identify the dominant explanatory variables for Net Ecosystem Exchange (NEE), Gross Ecosystem Exchange (GEE), and heterotrophic and autotrophic respiration (Rh+a )a t different temporal scales, (ii) evaluate whether environmental variables can be used to isolate the GEE and Rh+a signals from the NEE measurements, and (iii) determine the impact of temporal scale on the inferred relationships between environmental variables and CO2 flux. The results confirm the strong correlation between respiration and temperature and the influence of solar radiation on carbon uptake during the growing season. In addition, results highlight the influence of variables such as precipitation, vapor pressure deficit, and the fraction of photosynthetically active radiation (fPAR) in carbon cycling at UMBS. Many relationships between flux and auxiliary variables are found to be scale‐dependent. Site‐specific and remote‐sensing leaf area index and fPAR data are not found to be interchangeable at finer temporal scales. Results also show that a linear GR model is able to capture what may initially appear to be nonlinear relationships between flux and environmental variables, because this apparent nonlinearity is found to be explained by the covariability among key auxiliary variables. Finally, results indicate that GR can be used to identify variables that partially isolate GEE and Rh+a from the NEE signal at finer temporal scales at UMBS.

Adding ecosystem function to agent-based land use models

The objective of this paper is to examine issues in the inclusion of simulations of ecosystem functions in agent-based models of land use decision-making. The reasons for incorporating these simulations include local interests in land fertility and global interests in carbon sequestration. Biogeochemical models are needed in order to calculate such fluxes. The Century model is described with particular attention to the land use choices that it can encompass. When Century is applied to a land use problem the combinatorial choices lead to a potentially unmanageable number of simulation runs. Century is also parameter-intensive. Three ways of including Century output in agent-based models, ranging from separately calculated look-up tables to agents running Century within the simulation, are presented. The latter may be most efficient, but it moves the computing costs to where they are most problematic. Concern for computing costs should not be a roadblock.

Carbon dioxide and methane measurements from the Los Angeles Megacity Carbon Project – Part 1: calibration, urban enhancements, and uncertainty estimates

We report continuous surface observations of carbon dioxide (CO2) and methane (CH4) from the Los Angeles (LA) Megacity Carbon Project during 2015. We devised a calibration strategy, methods for selection of background air masses, calculation of urban enhancements, and a detailed algorithm for estimating uncertainties in urban-scale CO2 and CH4 measurements. These methods are essential for understanding carbon fluxes from the LA megacity and other complex urban environments globally. We estimate background mole fractions entering LA using observations from four “extra-urban” sites including two “marine” sites located south of LA in La Jolla (LJO) and offshore on San Clemente Island (SCI), one “continental” site located in Victorville (VIC), in the high desert northeast of LA, and one “continental/mid-troposphere” site located on Mount Wilson (MWO) in the San Gabriel Mountains. We find that a local marine background can be established to within ~1 ppm CO2 and ~10 ppb CH4 using these local measurement sites. Overall, atmospheric carbon dioxide and methane levels are highly variable across Los Angeles. “Urban” and “suburban” sites show moderate to large CO2 and CH4 enhancements relative to a marine background estimate. The USC (University of Southern California) site near downtown LA exhibits median hourly enhancements of ~20 ppm CO2 and ~150 ppb CH4 during 2015 as well as ~15 ppm CO2 and ~80 ppb CH4 during mid-afternoon hours (12:00–16:00 LT, local time), which is the typical period of focus for flux inversions. The estimated measurement uncertainty is typically better than 0.1 ppm CO2 and 1 ppb CH4 based on the repeated standard gas measurements from the LA sites during the last 2 years, similar to Andrews et al. (2014). The largest component of the measurement uncertainty is due to the single-point calibration method; however, the uncertainty in the background mole fraction is much larger than the measurement uncertainty. The background uncertainty for the marine background estimate is ~10 and ~15 % of the median mid-afternoon enhancement near downtown LA for CO2 and CH4, respectively. Overall, analytical and background uncertainties are small relative to the local CO2 and CH4 enhancements; however, our results suggest that reducing the uncertainty to less than 5 % of the median mid-afternoon enhancement will require detailed assessment of the impact of meteorology on background conditions.

A statistical approach for isolating fossil fuel emissions in atmospheric inverse problems

Independent verification and quantification of fossil fuel (FF) emissions constitutes a considerable scientific challenge. By coupling atmospheric observations of CO2 with models of atmospheric transport, inverse models offer the possibility of overcoming this challenge. However, disaggregating the biospheric and FF flux components of terrestrial fluxes from CO2 concentration measurements has proven to be difficult, due to observational and modeling limitations. In this study, we propose a statistical inverse modeling scheme for disaggregating winter-time fluxes on the basis of their unique error covariances and covariates, where these covariances and covariates are representative of the underlying processes affecting FF and biospheric fluxes. The application of the method is demonstrated with one synthetic and two real data prototypical inversions by using in-situ CO2 measurements over North America. Inversions are performed only for the month of January, as predominance of biospheric CO2 signal relative to FF CO2 signal and observational limitations, preclude disaggregation of the fluxes in other months. The quality of disaggregation is assessed primarily through examination of a posteriori covariance between disaggregated FF and biospheric fluxes at regional scales. Findings indicate that the proposed method is able to robustly disaggregate fluxes regionally at monthly temporal resolution with a posteriori cross-covariance lower than 0.15 µmol m-2 sec-1 between FF and biospheric fluxes. Error covariance models and covariates based on temporally varying FF inventory data provide a more robust disaggregation over static proxies (e.g., nightlight intensity, population density). However, the synthetic data case study shows that disaggregation is possible even in absence of detailed temporally varying FF inventory data.

Atmospheric CO2 Observations Reveal Strong Correlation Between Regional Net Biospheric Carbon Uptake and Solar‐Induced Chlorophyll Fluorescence

Recent studies have shown the promise of remotely sensed solar-induced chlorophyll fluorescence (SIF) in informing terrestrial carbon exchange, but analyses have been limited to either plot level (~1 km) or hemispheric/global (~10 km) scales due to the lack of a direct measure of carbon exchange at intermediate scales. Here we use a network of atmospheric CO2 observations over North America to explore the value of SIF for informing net ecosystem exchange (NEE) at regional scales. We find that SIF explains space-time NEE patterns at regional (~100 km) scales better than a variety of other vegetation and climate indicators. We further show that incorporating SIF into an atmospheric inversion leads to a spatial redistribution of NEE estimates over North America, with more uptake attributed to agricultural regions and less to needleleaf forests. Our results highlight the synergy of ground-based and spaceborne carbon cycle observations.

Regional CO2 fluxes estimated over North America for 2004 using a geostatistical inverse model

Regional CO2 fluxes estimated over North America for 2004 using a geostatistical inverse model Sharon Gourdji(1) , K Mueller(1), D Huntzinger(1), V Yadav(1), A Hirsch(2,3), A Andrews(3), A Michalak(1,4) (1) Department of Civil & Environmental Engineering, University of Michigan, Ann Arbor, MI, USA (2) Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA (3) Global Monitoring Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USA (4) Department of Atmospheric, Oceanic & Space Sciences, University of Michigan, Ann Arbor, MI, USA