Aleya Kaushik

Atmospheric oxygen (O₂) can be used to partition components of the carbon budget at global and regional scales. These methods generally assume a constant net biospheric O₂:CO₂ exchange ratio (ER_net), although the exchange ratios of the gross fluxes that form ER_net, assimilation (ER_a) and respiration (ER_r), vary across ecosystems and time, suggesting ER_net may also vary spatially and temporally. To investigate this, we implemented O₂ fluxes in the Simple Biosphere Model (SiB4) by assigning plant functional type- and carbon pool-specific ER_a and ER_r values based on limited observations. This produces the first global O₂ biosphere flux dataset with spatially and temporally varying fluxes (0.5° × 0.5°, 3-hourly). Results show seasonal and spatial variability in ER_net, ranging from about 1.1 in winter to 0.8 in summer, with regional summer values differing by more than 0.1 across Europe. Temporal variability is primarily driven by changes in the relative contributions of photosynthesis and respiration, whereas spatial variability reflects differences in dominant plant functional types. The modeled ER_net range exceeds the currently recognized terrestrial O₂:CO₂ variability and may effect O₂-based carbon partitioning methods. Neglecting ER_net variability can bias regional estimates of fossil fuel CO₂ signals derived from atmospheric potential oxygen. At a strongly biosphere-influenced location, a modeled O₂-derived fossil fuel CO₂ signal of 1.5 ppm is overestimated by nearly 12 ppm when ER_net variability is ignored. These results highlight the importance of accounting for biospheric contributions in O₂ partitioning methods.

The Arctic has experienced the largest increase in atmospheric CO ₂ seasonal cycle amplitude (SCA) since the 1960s. Previous studies primarily attributed this to enhanced regional warming. Here, we show that at numerous locations in the Arctic, atmospheric CO ₂ SCAs have increased at a relative rate similar to atmospheric CO ₂ abundance over the past five decades, suggesting a likely strong role of CO ₂ fertilization. Using a tagged‐tracer transport experiment and long‐term atmospheric observations of CO ₂ , carbonyl sulfide (COS), and the ¹³ C: ¹² C ratio of CO ₂ (), we quantified high northern latitude (HNL, 53°–90°N) ecosystem process changes. Results suggest that HNL terrestrial ecosystems drive Arctic CO ₂ SCA increases, with gross primary productivity (GPP) exerting a more dominant impact than ecosystem respiration. Using atmospheric COS measurements and mass balance calculation, we estimated 2000–2020 growing‐season (May–August) average HNL stomatal conductance at 0.024 ± 0.004 mol m ⁻² s ⁻¹ . This exhibited a slight negative trend of −4.3 ± 3.8 (1) %, consistent with CO ₂ –induced reductions in stomata observed previously at smaller scales. However, accounting for regional leaf area expansion, leaf‐area‐integrated ecosystem conductance was estimated to be 0.044 ± 0.006 mol m ⁻² s ⁻¹ with a small positive trend of (1) %. Furthermore, observations suggest the growing‐season intercellular‐to‐ambient CO ₂ ratio in HNL ecosystems remained constant. Integrating these estimates, we estimate growing‐season GPP in HNL ecosystems averaged 33 ± 3 (1 ) PgC yr ⁻¹ from 2000 to 2020 and increased by 12 ± 2 (1) % over this period, driven primarily by CO ₂ fertilization.

Atmospheric oxygen (O₂) can be used to partition components of the carbon budget at global and regional scales. These methods generally assume a constant net biospheric O₂:CO₂ exchange ratio (ER_net), although the exchange ratios of the gross fluxes that form ER_net, assimilation (ER_a) and respiration (ER_r), vary across ecosystems and time, suggesting ER_net may also vary spatially and temporally. To investigate this, we implemented O₂ fluxes in the Simple Biosphere Model (SiB4) by assigning plant functional type- and carbon pool-specific ER_a and ER_r values based on limited observations. This produces the first global O₂ biosphere flux dataset with spatially and temporally varying fluxes (0.5° × 0.5°, 3-hourly). Results show seasonal and spatial variability in ER_net, ranging from about 1.1 in winter to 0.8 in summer, with regional summer values differing by more than 0.1 across Europe. Temporal variability is primarily driven by changes in the relative contributions of photosynthesis and respiration, whereas spatial variability reflects differences in dominant plant functional types. The modeled ER_net range exceeds the currently recognized terrestrial O₂:CO₂ variability and may effect O₂-based carbon partitioning methods. Neglecting ER_net variability can bias regional estimates of fossil fuel CO₂ signals derived from atmospheric potential oxygen. At a strongly biosphere-influenced location, a modeled O₂-derived fossil fuel CO₂ signal of 1.5 ppm is overestimated by nearly 12 ppm when ER_net variability is ignored. These results highlight the importance of accounting for biospheric contributions in O₂ partitioning methods.

Atmospheric oxygen (O2) can be used to partition components of the carbon budget at global and regional scales. These methods generally assume a constant net biospheric O2:CO2 exchange ratio (ERnet), although the exchange ratios of the gross fluxes that form ERnet, assimilation (ERa) and respiration (ERr), vary across ecosystems and time, suggesting ERnet may also vary spatially and temporally. To investigate this, we implemented O2 fluxes in the Simple Biosphere Model (SiB4) by assigning plant functional type- and carbon pool-specific ERa and ERr values based on limited observations. This produces the first global O2 biosphere flux dataset with spatially and temporally varying fluxes (0.5° × 0.5°, 3-hourly). Results show seasonal and spatial variability in ERnet, ranging from about 1.1 in winter to 0.8 in summer, with regional summer values differing by more than 0.1 across Europe. Temporal variability is primarily driven by changes in the relative contributions of photosynthesis and respiration, whereas spatial variability reflects differences in dominant plant functional types. The modeled ERnet range exceeds the currently recognized terrestrial O2:CO2 variability and may effect O2-based carbon partitioning methods. Neglecting ERnet variability can bias regional estimates of fossil fuel CO2 signals derived from atmospheric potential oxygen. At a strongly biosphere-influenced location, a modeled O2-derived fossil fuel CO2 signal of 1.5 ppm is overestimated by nearly 12 ppm when ERnet variability is ignored. These results highlight the importance of accounting for biospheric contributions in O2 partitioning methods.

Enhanced photosynthetic CO₂ uptake (gross primary productivity; GPP) by terrestrial plants in response to rising atmospheric CO₂ concentrations constitutes the largest and most uncertain ecosystem feedback on climate. Direct measurement of GPP at scales above the leaf is not possible due to co-occurring respiratory fluxes. Carbonyl sulfide (COS) has emerged as a promising tracer of GPP across scales because of its predominantly one-way flux into leaves and the use of leaf relative uptake (LRU) to convert COS uptake into GPP. However, differences in the relative uptake of COS to CO₂ between C3 and C4 vegetation across scales must be accounted for in the application of COS as a tracer and could potentially be applied to detect climate-driven shifts in C3/C4 vegetation distributions. These aspects have largely been ignored, with most COS measurements focusing on C3 vegetation, and only limited C4 COS measurements available.Here, we develop an atmospheric-based approach to identify and quantify C4 vegetation contributions to large-scale photosynthetic uptake using existing COS and CO₂ concentration measurements from multiple NOAA Global Monitoring Laboratory (GML) network sites. We analyze the seasonal cycle amplitudes of COS as a function of CO₂, defined as atmospheric relative uptake (ARU), and identify sites that deviate from the regression line describing the majority of sites. We derive an analytical framework linking leaf-, ecosystem-, and atmospheric-scale relative uptake, explaining how shifts in physiological traits characteristic of C3 and C4 vegetation influence atmospheric COS and CO₂ signals. Using this framework together with gridded fluxes from the land surface model: Simple Biosphere Model (version 4; SiB4), we show that sites influenced by C4 vegetation exhibit systematic deviations in ARU relative to predominantly C3-influenced sites. These results demonstrate that ARU provides a viable means of detecting and quantifying C4 vegetation contributions to GPP at regional scales (102 – 103 km2), and can be used to detect climate-driven shifts in C3/C4 distributions. Our study advances the use of COS as a tracer of GPP and of C3 and C4 photosynthesis across scales.

Increasing atmospheric CO ₂ seasonal cycle amplitudes in boreal regions have been attributed to climate-driven changes in land ecosystems, but terrestrial biosphere models (TBMs) are unable to replicate observations, leading to large uncertainties in future predictions of carbon cycle changes. Accurately partitioning net ecosystem exchange into its component fluxes—gross primary production (GPP) and respiration—is essential for understanding impacts of changing climate on the Arctic and boreal carbon balance, yet these component fluxes cannot be measured directly. Carbonyl sulfide (OCS) has been used to infer GPP at site to global scales, because its one-way uptake by plants is an analog for photosynthesis. However, expanding site-level process understanding to regional scales remains challenging. Here, we use atmospheric OCS mole fraction observations representative of Alaskan boreal forests to evaluate simulations of OCS fluxes in a state-of-the-science TBM. We use TBM-estimated OCS fluxes and surface influence functions on the order of 100–1000 km to simulate OCS mole fractions at the NOAA Global Monitoring Laboratory’s CRV tower site in central Alaska. By comparing with atmospheric observations, we can evaluate the TBM over much larger scales than is possible using eddy covariance data while still providing valuable information about underlying mechanisms. Comparisons reveal a missing ecosystem sink corresponding to a concentration difference of 22.3 ± 9.1 ppt OCS for July–November relative to observed concentrations of 433 ± 26 ppt at CRV. Solely improving the temperature sensitivity of apparent mesophyll conductance reduces the July–November mismatch between modeled and observed OCS concentration data by ∼5.3 ± 2.7 ppt. Consideration of alternate land cover maps provides an additional ∼5.3 ± 3.0 ppt towards the mismatch. These results demonstrate a strong decoupling of OCS and GPP, especially after the end of the growing season. Our analyzes demonstrate the limitations of using OCS as a proxy for GPP and highlight potential missing processes that need to be incorporated into future OCS modeling efforts to maximize its potential as a photosynthetic tracer.

Atmospheric oxygen (O2) allows to separate the natural and anthropogenic components in the atmospheric CO2 signal, thereby providing additional constraints on these processes in the global carbon cycle. This is enabled through the ratio of O2 and CO2 in carbon cycle processes: the Exchange Ratio (ER). This ER signal has distinct values for combustion of different fossil fuel types, as well as between photosynthesis and respiration processes. Using these ER signals, we aim to further explore the potential of using atmospheric O2 observations in CO2 emission verification. For that, we are developing a global scale data assimilation system that can, next to CO2, assimilate O2 observations. This is our new multi-tracer implementation, specifically aimed at decadal and annual timescales: the CarbonTracker Europe Long Window system. Additionally, we implemented O2 and the O2/CO2 exchange ratios into the Simple Biosphere model (SiB4) to further understand the influence of biosphere exchange on using Atmospheric Potential Oxygen (APO) as a tracer for fossil fuel emissions. We will present the results from this biosphere O2 and CO2 modelling to get a first theoretical assessment of the variability of the biosphere O2 and CO2 ER signals, both over space (related to the plant functional types) and time (related to seasonal patterns). These biosphere model results, are subsequently used in our first attempt of atmospheric inverse estimates of CO2 fluxes using O2 as a tracer. Finally, we will show our progress towards understanding the implications of the variability in the ERs for photosynthesis and respiration on APO calculations, as well as their influence on fossil fuel estimates using atmospheric O2.

To predict the effect of climate and CO2 on vegetation and soils, process-based models require accurate numerical representations of photosynthesis, respiration, turnover rates of below-ground carbon, and plant community change. We add 13C and 14C to the Lund-Potsdam-Jena (LPJ) dynamic global vegetation model (DGVM), which also includes representations of fire, land-use change, agriculture, and vegetation dynamics. Six sets of model simulations CO2 and 13CO2 fluxes are transported using the TM5 atmospheric transport model and evaluated by comparison to observations of CO2 and 13CO2 in measurements obtained from NOAA global monitoring sites. Updating the respiration and photosynthesis formulations in LPJ improves the seasonal cycle of simulated CO2 fluxes but does not improve simulated 13CO2 fluxes. Global annual land 13C photosynthetic fractionation and disequilibrium are similar to previous estimates by other DGVMs. To evaluate model performance for 14C, simulated soil and heterotrophic respiration 14C:C ratios are compared to observations from the International Soil Radiocarbon Database. Simulated soil Δ14C values are significantly higher than measurements as found in previous studies. Soil flux Δ14C values have less of a positive bias than soil Δ14C values indicating that, while soil residence times could be improved, some old soil carbon is very recalcitrant and may not need to be modeled to reproduce fluxes. This study emphasizes the power of using 13C and 14C to test whether model changes that improve bulk C are improving underlying processes such as stomatal conductance or soil turnover rates.

Quantification of the long-term carbon uptake by the land biosphere is of key importance for climate action. Traditional methods of estimating the carbon sink include atmospheric inversions, which use CO₂ measurements to reduce inherent biases in simulations of the land biosphere. The atmospheric CO₂ measurements used are informative on different time scales from days to decades, which are often difficult to separate from the data. Additional data sources can be used to separate the decadal change in sink magnitude from the shorter-term impacts of e.g. droughts. An example is the use of remotely-sensed above-ground biomass changes that have recently gained traction to estimate the stock change of carbon at the surface (Δbiomass), caused by months and years of integrated Net Ecosystem Exchange (NEE). We therefore built a Bayesian framework in which we constrain decades of daily NEE with both atmospheric CO2 observations as well as satellite-based Δbiomass products. With this integration we aim to better constrain the magnitude, inter-annual variability and location of land carbon sinks and sources. We focus the initial tests of the system on European carbon fluxes and find that Europe is a small long-term sink of CO₂, albeit with large regional differences. Most notably, vegetation of central European comes out as a net source of CO₂ into the atmosphere in our system, a finding that is supported by both by the Δbiomass product and the atmospheric CO₂ data. In this presentation we further explore the limits of the attempted integration, aiming to pave the way for future syntheses of atmospheric inversions with novel data products.