Net Ecosystem Exchange Research Articles

Multi-year observations reveal a larger than expected autumn respiration signal across northeast Eurasia

Abstract. Site-level observations have shown pervasive cold season CO2 release across Arctic and boreal ecosystems, impacting annual carbon budgets. Still, the seasonality of CO2 emissions are poorly quantified across much of the high latitudes due to the sparse coverage of site-level observations. Space-based observations provide the opportunity to fill some observational gaps for studying these high-latitude ecosystems, particularly across poorly sampled regions of Eurasia. Here, we show that data-driven net ecosystem exchange (NEE) from atmospheric CO2 observations implies strong summer uptake followed by strong autumn release of CO2 over the entire cold northeastern region of Eurasia during the 2015–2019 study period. Combining data-driven NEE with satellite-based estimates of gross primary production (GPP), we show that this seasonality implies less summer heterotrophic respiration (Rh) and greater autumn Rh than would be expected given an exponential relationship between respiration and surface temperature. Furthermore, we show that this seasonality of NEE and Rh over northeastern Eurasia is not captured by the TRENDY v8 ensemble of dynamic global vegetation models (DGVMs), which estimate that 47 %–57 % (interquartile range) of annual Rh occurs during August–April, while the data-driven estimates suggest 59 %–76 % of annual Rh occurs over this period. We explain this seasonal shift in Rh by respiration from soils at depth during the zero-curtain period, when sub-surface soils remain unfrozen up to several months after the surface has frozen. Additional impacts of physical processes related to freeze–thaw dynamics may contribute to the seasonality of Rh. This study confirms a significant and spatially extensive early cold season CO2 efflux in the permafrost-rich region of northeast Eurasia and suggests that autumn Rh from subsurface soils in the northern high latitudes is not well captured by current DGVMs.

Interannual Variations of Water and Carbon Dioxide Fluxes over a Semiarid Alpine Steppe on the Tibetan Plateau

Water and carbon exchanges between grassland and the atmosphere are important processes for water balance and carbon balance. Based on eddy covariance observations over a semiarid alpine steppe ecosystem in Bange on the Tibetan Plateau during the growing season from 2014 to 2017, the variations in evapotranspiration (ET), net ecosystem exchange (NEE), and their components and the associated driving factors were analyzed. Linear and nonlinear models were applied to investigate the relationships between fluxes and their controlling factors over different timescales. The results show that the average ET for the growing season ranged from 1.1 to 2.4 mm/d with an average of 2.0 mm/d for the four consecutive years. Drought conditions reduced the surface conductance and hence the Priestley–Taylor coefficient. Mean T/ET was low (0.34) due to low vegetation cover. Plant growth increased the T/ET ratio during the growing season, whereas soil water content (SWC) explained most of the variation of ET and E on daily and monthly scales. The Enhanced Vegetation Index (EVI) was the most important controlling factor for temperature. Transpiration increased with SWC in dry conditions. For the growing season in 2014, 2016, and 2017, Bange was a carbon sink, while it was a carbon source in 2015. The largest CO2 flux was higher and the temperature sensitivity coefficient (Q10) was lower for 2015 than for the other three years. SWC affected these photosynthesis and respiration parameters. The ratio of respiration (Re) to gross primary production (GPP) was the highest during the 2015 growing season. Both on daily and monthly scales, Re was positively and linearly correlated with GPP. The most important controlling factor for the CO2 flux was EVI on daily and monthly scales.

How does irrigation alter the water, carbon, and nitrogen budgets in a large endorheic river basin?

• Irrigation substantially alters the water, carbon, and nitrogen budgets in the cropland area. • Evapotranspiration as well as surface runoff and baseflow is sharply increased after irrigation. • Water use efficiency of each crop species is significantly enhanced because of irrigation. • The cropland area of the Heihe River Basin switches from being a carbon source to a carbon sink as a result of irrigation. Irrigation can substantially alter the hydrologic and biogeochemical cycles in river basins. However, little attention has been given to the quantitative evaluation of the effects of irrigation on both of these cycles. In this study, we utilized the latest version of the Community Land Model (CLM5) to assess the effects of irrigation on the water, carbon, and nitrogen budgets in a cropland area of a large endorheic river basin (Heihe River Basin, HRB) in northwestern China and further identified the contributions of each type of crop to the abovementioned budgets. The model has been validated against local flux towers, remote sensing products, and reanalysis datasets, demonstrating the capability of CLM5 in capturing the temporal and spatial characteristics of water, carbon, and nitrogen at the basin scale. Two numerical experiments, one with irrigation and one without it, were conducted to explore the relationships between water and carbon/nitrogen budgets for the period from 2000 to 2018. The simulation results showed that the greatest amount of irrigation was applied in the summer (>50 mm month −1 ) and then declined to a low level during the other seasons. Evapotranspiration (ET) in the cropland area sharply increased from 106 mm year −1 to 508 mm year −1 , with an increase in surface runoff and baseflow after irrigation. Both irrigated gross primary production (GPP), net ecosystem exchange (NEE) and water use efficiency (WUE) peaked in the summer, which corresponded to the crop growing season. A shift in NEE from 6 gC m −2 year −1 to –229 gC m −2 year −1 indicated that the cropland area in the HRB switched from being a carbon source to a carbon sink because of irrigation. The corn area presented the largest increase/decrease in GPP/NEE after irrigation, whereas the lowest increase/decrease in GPP/NEE was observed in the cotton area. Total ecosystem carbon (TEC) and total ecosystem nitrogen (TEN) markedly increased as a result of irrigation, with increases of 3.29 × 10 13 gC and 2.96 × 10 12 gN, respectively, throughout the year in the cropland area. Overall, our study demonstrates that irrigation has substantial impacts on water, carbon, and nitrogen budgets in the cropland area of the HRB and provides important insights into the application of CLM5 in an arid basin with multiple crop types.

Matching land surface phenology with the phenology of net ecosystem exchange (NEE) in the Kruger National Park, South Africa

Vegetation phenology plays an important role in the inter-annual variability of Net Ecosystem Exchange (NEE). However, the relationship between vegetation phenology and NEE is still largely unknown in the southern African region. The relatively recent availability of Eddy-Covariance (EC) flux towers in the region, which allow for measurements of the interactions between the terrestrial biosphere and the atmosphere, have expanded our understanding of Carbon Flux Phenology (CFP), but the towers' spatial coverage and representation is still limited. By contrast, satellite-based measurements of terrestrial environments provide repeat global spatial coverage that offers a complimentary approach to flux tower estimates of phenology and NEE. This study therefore aimed to investigate the relationship between Land Surface Phenology (LSP), calculated from FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) and from MISR (Multi-angle Imaging Spectroradiometer), and CFP using 18 years of EC flux data at the Skukuza flux tower site, Kruger National Park, South Africa. Cross-correlation results showed that there is a moderate association between NEE and FAPAR from both sensors (MISR-HR, R2 = 0.54; MODIS, R2 = 0.53) where FAPAR leads NEE by 8-days. Specifically, only the start of carbon uptake (SOSNEE) has a strong correlation with FAPAR derived start of season (SOSFAPAR). In this instance, MISR-HR FAPAR outperformed MODIS at estimating the SOSNEE dates (i.e., R2 = 0.61 and R2 = 0.28, respectively) and overall showed the least difference to CFP (Mean signed difference ∼ 7–12 days). The findings show that MISR-HR FAPAR can be successfully used to approximate the SOSNEE as well as NEE. In summary, this study presents insight into the relationship between LSP and CFP that contributes to our understanding of how LSP can be used to assist monitoring of carbon dynamics in African savannas. Further investigation into the complicated nature of the interactions between environmental variables (e.g. rainfall, soil moisture, temperature, etc.) that influence both vegetation and carbon dynamics will provide important insight into the potential of upscaling NEE estimates.

Multi-year eddy-covariance measurements at a pre-alpine humid grassland site: Dataset overview, drought responses, and effects of land management

Impacts of drought events on terrestrial ecosystems have been studied extensively in the last decades in light of the long-term human-induced global warming trend and the recent occurrence of severe heat waves and droughts across the globe. The 2003 European summer heat wave sparked high attention towards such events in Switzerland, where ecosystems were largely affected again by summer dry periods in 2015 and 2018. With a newly applied processing scheme of calculating fluxes of net ecosystem exchange of CO2, latent and sensible heat from eddy-covariance measurements, the impact of droughts on GPP and exchange of energy between land and atmosphere are investigated at a managed grassland site in Switzerland. The rate of primary production is considerably reduced under the influence of dry conditions. This effect is further intensified by grassland management. Evapotranspiration, on the other hand, seems to be unaffected by the lack of precipitation at the site.

Interactive effects of warming and managements on carbon fluxes in grasslands: A global meta-analysis

The prediction of future carbon(C) dynamics of grassland ecosystems requires knowledge of how warming and management practices (e.g., fertilization, grazing, and mowing) interact to affect C fluxes. In this study, we synthesized experimental data extracted from 350 observations from 64 publications to quantify the direction and magnitude of individual and interactive effects between climatic warming and grassland management practices on seven variables related to C fluxes. Warming significantly enhanced gross primary productivity (GPP) and ecosystem respiration (ER) by 9.81% and 16.43%, respectively. Similarly, fertilization significantly increased GPP, net primary productivity (NPP), aboveground net primary productivity (ANPP), ER, and net ecosystem exchange (NEE) by 29.51%, 24.79%, 33.52%, 21.81%, and 33.89%, respectively. In contrast, grazing and mowing reduced ANPP by 19.95% and 22.83%, respectively. Furthermore, the combination of warming and fertilization significantly increased GPP and ANPP by 38.09% and 22.89%, respectively. More importantly, the results showed that additive interactions dominated the response of most of the studied variables, while synergistic interactions were found only for ANPP and ER to warming and fertilization. The responses of C fluxes to warming and management were strongly dependent on the environmental factors, experimental duration, and grassland types. Our meta-analysis highlights the importance of the interactions between warming and management on ecosystem C exchange. Therefore, incorporating these key interactions into future prediction models should enhance the accuracy of prediction of future climate change impacts on grassland ecosystems and inform the development of sustainable grassland management strategies.

Evaluating Global Atmospheric Inversions of Terrestrial Net Ecosystem Exchange CO2 Over North America on Seasonal and Sub‐Continental Scales

AbstractAtmospheric inversion estimates of net ecosystem exchange (NEE) of CO2 are increasingly relevant to climate policy. We evaluated sub‐continental, seasonal estimates of CO2 NEE from nine global inversion systems that participated in the Orbiting Carbon Observatory‐2 model intercomparison project (OCO‐2 v9 MIP), using 98 research flights conducted over the central and eastern United States from 2016 to 2018 as part of the Atmospheric Carbon and Transport ‐ America mission. We found that the seasonal amplitude of NEE in the central and eastern United States is underestimated in these models and model‐data biases are largest for those inversions with the smallest seasonal flux amplitudes. These results were independent of whether the inversions used satellite or in situ data. The largest NEE biases were observed in the Midwest croplands and eastern forests. Future experiments are needed to determine the causes of the persistent biases and if they are associated with biases in annual flux estimates.

Carbon flux variation and associated biomass energy storage economic value implications in the Dinghushan Biosphere Reserve

Subtropical forests are a major carbon (C) storage pool and are therefore rich in biomass energy reservoirs. However, there have been no studies that quantify the C storage economic value, especially in the region of the Dinghushan Biosphere Reserve. This study is the first attempt to apply the eddy covariance technique to measure biomass energy storage and the associated economic value and demonstrates that CO2 flux measurement is a viable method for quantifying the ecosystem biomass energy storage. CO2 flux and micro-meteorological data were collected and analyzed to calculate the daily C sequestration and establish their inter-relationship. In addition, we used the C price estimates of Guangdong Province in China to determine the capital value of the C sequestered. The measured average annual precipitation was 1459.4 mm, the average temperature was 22.9 °C and the highest photosynthetic photon flux density (PPFD) was 334.92 μmol m−2s−1. CO2 flux and biomass energy storage varied seasonally. Photosynthetic photon flux density (PPFD) was highly correlated with gross primary production (GPP) (r2 = 0.99, p < 0.0001). Soil temperature was correlated with net ecosystem exchange (NEE) (r2 = 0.84, p < 0.0001). There was a positive correlation (r2 = 0.49, p < 0.0001) between soil moisture and NEE. The Dinghushan Biosphere Reserve sequestered an average of 5300 tCy−1 equivalent to RMB 3.3 million (USD 530,000). We conclude that economically, the Dinghushan Biosphere Reserve is highly significant to China's Guangdong Province because of its rich biomass energy reserves via C sequestration and our findings will stimulate the formulation of more aggressive conservation policies that will inevitably lead to better management of the reserve.

Ecosystem-atmosphere CO2 exchange from semiarid mangroves in the Gulf of California

Mangrove wetlands play an essential role as blue carbon ecosystems. However, worldwide they also face imminent threats caused by anthropogenic activities and climate change. Mangroves in the semiarid region of the Gulf of California are highly vulnerable to both threats. In this study, we present eddy covariance measurements of net ecosystem exchange of CO2 (NEE), gross primary productivity (GPP), and ecosystem respiration (Reco) at a preserved mangrove (Estero El Sargento) and a site influenced by anthropogenic activities (Bahia del Tobari) located in northwest Mexico. Both sites were net annual carbon sinks but had different dynamics. The net annual carbon gain for Estero El Sargento was −717 g C m−2 y−1 while for Bahia deTobari was −247 g C m−2 y−1. Over the studied year, Estero El Sargento had lower Reco(329 g C m−2) during the winter-spring months and an overall lower annual Reco/GPP (0.42) than Bahia de Tobari. The Reco was notably high during the winter-spring period at Bahia de Tobari when agriculture drainage from the intensive food production activities at the Yaqui Valley reaches the bay. The contrasting NEE patterns and magnitude between both mangrove sites suggest that anthropogenic activities influencing coastal ecosystems exert an important control on the CO2 sink potential.

Complementing XCO2 imagery with ground-based CO2 and 14CO2 measurements to monitor CO2 emissions from fossil fuels on a regional to local scale

Abstract. Various satellite imagers of the vertically integrated column of carbon dioxide (XCO2) are under development to enhance the capabilities for the monitoring of fossil fuel (FF) CO2 emissions. XCO2 images can be used to detect plumes from cities and large industrial plants and to quantify the corresponding emission using atmospheric inversions techniques. However, this potential and the ability to catch the signal from more diffuse FF CO2 sources can be hampered by the mix between these FF signals and a background signal from other types of CO2 surface fluxes, and in particular of biogenic CO2 fluxes. The deployment of dense ground-based air-sampling networks for CO2 and radiocarbon (14CO2) could complement the spaceborne imagery by supporting the separation between the fossil fuel and biogenic or biofuel (BF) CO2 signals. We evaluate this potential complementarity with a high-resolution analytical inversion system focused on northern France, western Germany, Belgium, Luxembourg, and a part of the Netherlands and with pseudo-data experiments. The inversion system controls the FF and BF emissions from the large urban areas and plants, in addition to regional budgets of more diffuse emissions or of biogenic fluxes (NEE, net ecosystem exchange), at an hourly scale over a whole day. The system provides results corresponding to the assimilation of pseudo-data from a single track of a 300 km swath XCO2 imager at 2 km resolution and from surface ground-based CO2 and/or 14CO2 networks. It represents the diversity of 14CO2 sources and sinks and not just the dilution of radiocarbon-free FF CO2 emissions. The uncertainty in the resulting FF CO2 emissions at local (urban area/plant) to regional scales is directly derived and used to assess the potential of the different combinations of observation systems. The assimilation of satellite observations yields estimates of the morning regional emissions with an uncertainty down to 10 % (1σ) in the satellite field of view, from an assumed uncertainty of 15 % in the prior estimates. However, it does not provide direct information about emissions outside the satellite field of view or about afternoon or nighttime emissions. The co-assimilation of 14CO2 and CO2 surface observations leads to a further reduction of the uncertainty in the estimates of FF emissions. However, this further reduction is significant only in administrative regions with three or more 14CO2 and CO2 sampling sites. The uncertainty in the estimates of 1 d emission in North Rhine-Westphalia, a region with three sampling sites, decreases from 8 % to 6.6 % when assimilating the in situ 14CO2 and CO2 data in addition to the satellite data. Furthermore, this additional decrease appears to be larger when the ground stations are close to large FF emission areas, providing an additional direct constraint for the estimate of these sources rather than supporting the characterization of the background signal from the NEE and its separation from that of the FF emissions. More generally, the results indicate no amplification of the potential of each observation subsystem when they are combined into a large observation system with satellite and surface data.

Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data

Abstract. Inaccurate parameter estimation is a significant source of uncertainty in complex terrestrial biosphere models. Model parameters may have large spatial variability, even within a vegetation type. Model uncertainty from parameters can be significantly reduced by model–data fusion (MDF), which, however, is difficult to implement over a large region with traditional methods due to the high computational cost. This study proposed a hybrid modeling approach that couples a terrestrial biosphere model with a data-driven machine learning method, which is able to consider both satellite information and the physical mechanisms. We developed a two-step framework to estimate the essential parameters of the revised Integrated Biosphere Simulator (IBIS) pixel by pixel using the satellite-derived leaf area index (LAI) and gross primary productivity (GPP) products as “true values.” The first step was to estimate the optimal parameters for each sample using a modified adaptive surrogate modeling algorithm (MASM). We applied the Gaussian process regression algorithm (GPR) as a surrogate model to learn the relationship between model parameters and errors. In our second step, we built an extreme gradient boosting (XGBoost) model between the optimized parameters and local environmental variables. The trained XGBoost model was then used to predict optimal parameters spatially across the deciduous forests in the eastern United States. The results showed that the parameters were highly variable spatially and quite different from the default values over forests, and the simulation errors of the GPP and LAI could be markedly reduced with the optimized parameters. The effectiveness of the optimized model in estimating GPP, ecosystem respiration (ER), and net ecosystem exchange (NEE) were also tested through site validation. The optimized model reduced the root mean square error (RMSE) from 7.03 to 6.22 gC m−2 d−1 for GPP, 2.65 to 2.11 gC m−2 d−1 for ER, and 4.45 to 4.38 gC m−2 d−1 for NEE. The mean annual GPP, ER, and NEE of the region from 2000 to 2019 were 5.79, 4.60, and −1.19 Pg yr−1, respectively. The strategy used in this study requires only a few hundred model runs to calibrate regional parameters and is readily applicable to other complex terrestrial biosphere models with different spatial resolutions. Our study also emphasizes the necessity of pixel-level parameter calibration and the value of remote sensing products for per-pixel parameter optimization.

How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies.

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler’s experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in “trial-and-error” calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler’s assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.

Annual carbon sequestration and loss rates under altered hydrology and fire regimes in southeastern USA pocosin peatlands.

Peatlands drained for agriculture or forestry are susceptible to the rapid release of greenhouse gases (GHGs) through enhanced microbial decomposition and increased frequency of deep peat fires. We present evidence that rewetting drained subtropical wooded peatlands (STWPs) along the southeastern USA coast, primarily pocosin bogs, could prevent significant carbon (C) losses. To quantify GHG emissions and storage from drained and rewetted pocosin we used eddy covariance techniques, the first such estimates that have been applied to this major bog type, on a private drained (PD) site supplemented by static chamber measurements at PD and Pocosin Lakes National Wildlife Refuge. Net ecosystem exchange measurements showed that the loss was 21.2Mg CO2 ha-1 year-1 (1Mg=106 g) in the drained pocosin. Under a rewetted scenario, where the annual mean water table depth (WTD) decreased from 60 to 30 cm, the C loss was projected to fall to 2Mg CO2 ha-1 year-1 , a 94% reduction. If the WTD was 20 cm, the peatlands became a net carbon sink (-3.3Mg CO2 ha-1 year-1 ). Hence, net C reductions could reach 24.5Mg CO2 ha-1 year-1 , and when scaled up to the 4000 ha PD site nearly 100,000 Mg CO2 year-1 of creditable C could be amassed. We conservatively estimate among the 0.75million ha of southeastern STWPs, between 450 and 770 km2 could be rewet, reducing annual GHG emissions by 0.96-1.6Tg (1Tg=1012 g) of CO2 , through suppressed microbial decomposition and 1.7-2.8Tg via fire prevention, respectively. Despite covering <0.01% of US land area, rewetting drained pocosin can potentially provide 2.4% of the annual CO2 nationwide reduction target of 0.18 Pg (1Pg=1015 g). Suggesting pocosin restoration can contribute disproportionately to the US goal of achieving net-zero emission by 2050.

Coupled modeling of peatlands carbon cycle and carbon dioxide emission from their peat deposits

COmplex Model of BOg LAndscapes (COMBOLA) is a set of dynamic models of carbon and nitrogen turnover, net ecosystem exchange, water balance, heat and water transport, generation and transfer of CO2 and CH4 in a peat deposit on a number of time scales. Combined modeling of carbon turnover and carbon dioxide emission from peat deposits on an annual time scale is carried out as part of COMBOLA for several bog landscapes in Western-Siberian south taiga. One-dimensional vertical reaction-diffusion model for carbon dioxide emission from peat deposit is combined with compartment carbon cycle model allowing estimate both bog type vegetation changes and greenhouse gases balance under probable climate change or perturbations initiated by human activities. Water table level (WTL) separating aerobic peat layer from the anaerobic one is calculated by means of the whole peat layer depth and its water balance relations. Minimal statistical information is used for calibrating temperature dependencies together with coefficients of heat and water transfer equations. The stationary states of carbon cycle are accompanied by steady average profiles of carbon dioxide concentration and can loose stability under human or climatic perturbations. The model dynamics is simulated with several climatic projections of the IPSL atmospheric circulation model for 21st century with anthropogenic scenarios RCP-2.6 and RCP-8.5.

Permafrost Landscape History Shapes Fluvial Chemistry, Ecosystem Carbon Balance, and Potential Trajectories of Future Change

AbstractIntensifying permafrost thaw alters carbon cycling by mobilizing large amounts of terrestrial substrate into aquatic ecosystems. Yet, few studies have measured aquatic carbon fluxes and constrained drivers of ecosystem carbon balance across heterogeneous Arctic landscapes. Here, we characterized hydrochemical and landscape controls on fluvial carbon cycling, quantified fluvial carbon fluxes, and estimated fluvial contributions to ecosystem carbon balance across 33 watersheds in four ecoregions in the continuous permafrost zone of the western Canadian Arctic: unglaciated uplands, ice‐rich moraine, and organic‐rich lowlands and till plains. Major ions, stable isotopes, and carbon speciation and fluxes revealed patterns in carbon cycling across ecoregions defined by terrain relief and accumulation of organics. In previously unglaciated mountainous watersheds, bicarbonate dominated carbon export (70% of total) due to chemical weathering of bedrock. In lowland watersheds, where soil organic carbon stores were largest, lateral transport of dissolved organic carbon (50%) and efflux of biotic CO2 (25%) dominated. In watersheds affected by thaw‐induced mass wasting, erosion of ice‐rich tills enhanced chemical weathering and increased particulate carbon fluxes by two orders of magnitude. From an ecosystem carbon balance perspective, fluvial carbon export in watersheds not affected by thaw‐induced wasting was, on average, equivalent to 6%–16% of estimated net ecosystem exchange (NEE). In watersheds affected by thaw‐induced wasting, fluvial carbon export approached 60% of NEE. Because future intensification of thermokarst activity will amplify fluvial carbon export, determining the fate of carbon across diverse northern landscapes is a priority for constraining trajectories of permafrost region ecosystem carbon balance.

Quantifying Northern High Latitude Gross Primary Productivity (GPP) Using Carbonyl Sulfide (OCS).

The northern high latitude (NHL, 40°N to 90°N) is where the second peak region of gross primary productivity (GPP) other than the tropics. The summer NHL GPP is about 80% of the tropical peak, but both regions are still highly uncertain (Norton etal. 2019, https://doi.org/10.5194/bg-16-3069-2019). Carbonyl sulfide (OCS) provides an important proxy for photosynthetic carbon uptake. Here we optimize the OCS plant uptake fluxes across the NHL by fitting atmospheric concentration simulation with the GEOS-CHEM global transport model to the aircraft profiles acquired over Alaska during NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (2012-2015). We use the empirical biome-specific linear relationship between OCS plant uptake flux and GPP to derive the six plant uptake OCS fluxes from different GPP data. Such GPP-based fluxes are used to drive the concentration simulations. We evaluate the simulations against the independent observations at two ground sites of Alaska. The optimized OCS fluxes suggest the NHL plant uptake OCS flux of -247GgS year-1, about 25% stronger than the ensemble mean of the six GPP-based OCS fluxes. GPP-based OCS fluxes systematically underestimate the peak growing season across the NHL, while a subset of models predict early start of season in Alaska, consistent with previous studies of net ecosystem exchange. The OCS optimized GPP of 34 PgCyr-1 for NHL is also about 25% more than the ensembles mean from six GPP data. Further work is needed to fully understand the environmental and biotic drivers and quantify their rate of photosynthetic carbon uptake in Arctic ecosystems.

Uncertainty in land carbon budget simulated by terrestrial biosphere models: the role of atmospheric forcing

Global estimates of the land carbon sink are often based on simulations by terrestrial biosphere models (TBMs). The use of a large number of models that differ in their underlying hypotheses, structure and parameters is one way to assess the uncertainty in the historical land carbon sink. Here we show that the atmospheric forcing datasets used to drive these TBMs represent a significant source of uncertainty that is currently not systematically accounted for in land carbon cycle evaluations. We present results from three TBMs each forced with three different historical atmospheric forcing reconstructions over the period 1850–2015. We perform an analysis of variance to quantify the relative uncertainty in carbon fluxes arising from the models themselves, atmospheric forcing, and model-forcing interactions. We find that atmospheric forcing in this set of simulations plays a dominant role on uncertainties in global gross primary productivity (GPP) (75% of variability) and autotrophic respiration (90%), and a significant but reduced role on net primary productivity and heterotrophic respiration (30%). Atmospheric forcing is the dominant driver (52%) of variability for the net ecosystem exchange flux, defined as the difference between GPP and respiration (both autotrophic and heterotrophic respiration). In contrast, for wildfire-driven carbon emissions model uncertainties dominate and, as a result, model uncertainties dominate for net ecosystem productivity. At regional scales, the contribution of atmospheric forcing to uncertainty shows a very heterogeneous pattern and is smaller on average than at the global scale. We find that this difference in the relative importance of forcing uncertainty between global and regional scales is related to large differences in regional model flux estimates, which partially offset each other when integrated globally, while the flux differences driven by forcing are mainly consistent across the world and therefore add up to a larger fractional contribution to global uncertainty.

Combining Eddy Covariance and Chamber Methods to Better Constrain CO2 and CH4 Fluxes Across a Heterogeneous Restored Tidal Wetland

AbstractTidal wetlands play an important role in global carbon cycling by storing carbon in sediment at millennial time scales, transporting dissolved carbon into coastal waters, and contributing significantly to global CH4 budgets. However, these ecosystems' greenhouse gas monitoring and predictions are challenging due to spatial heterogeneity and tidal flooding. We utilized eddy covariance and chamber measurements to quantify fluxes of CO2 and CH4 at a restored tidal saltmarsh across spatial and temporal scales. Eddy covariance data revealed that the site was a strong net sink for CO2 (−387 g C‐CO2 m−2 yr−1, SD = 46) and a small net source of CH4 (0.7 g C‐CH4 m−2 yr−1, SD = 0.4). After partitioning net ecosystem exchange of CO2 into gross primary production and ecosystem respiration, we found that high net uptake of CO2 was due to low respiration emissions rather than high photosynthetic rates. We also found that respiration rates varied between land covers with increased respiration in mudflats compared to vegetated areas. Daytime soil chamber measurements revealed that the greatest CO2 emission was from higher elevation mudflat soils (0.5 μmol m−2s−1, SE = 1.3) and CH4 emission was greatest from lower elevation Spartina foliosa soils (1.6 nmol m−2s−1, SD = 8.2). Overall, these results highlight the importance of the relationships between wetland plant community and elevation, and inundation for CO2 and CH4 fluxes. Future research should include the use of high‐resolution imagery, automated chambers, and a focus on quantifying carbon exported in tidal waters.

CO2 and CH4 exchanges between moist moss tundra and atmosphere on Kapp Linné, Svalbard

Abstract. We measured CO2 and CH4 fluxes using chambers and eddy covariance (only CO2) from a moist moss tundra in Svalbard. The average net ecosystem exchange (NEE) during the summer (9 June–31 August) was negative (sink), with −0.139 ± 0.032 µmol m−2 s−1 corresponding to −11.8 g C m−2 for the whole summer. The cumulated NEE over the whole growing season (day no. 160 to 284) was −2.5 g C m−2. The CH4 flux during the summer period showed a large spatial and temporal variability. The mean value of all 214 samples was 0.000511 ± 0.000315 µmol m−2 s−1, which corresponds to a growing season estimate of 0.04 to 0.16 g CH4 m−2. Thus, we find that this moss tundra ecosystem is closely in balance with the atmosphere during the growing season when regarding exchanges of CO2 and CH4. The sink of CO2 and the source of CH4 are small in comparison with other tundra ecosystems in the high Arctic. Air temperature, soil moisture and the greenness index contributed significantly to explaining the variation in ecosystem respiration (Reco), while active layer depth, soil moisture and the greenness index were the variables that best explained CH4 emissions. An estimate of temperature sensitivity of Reco and gross primary productivity (GPP) showed that the sensitivity is slightly higher for GPP than for Reco in the interval 0–4.5 ∘C; thereafter, the difference is small up to about 6 ∘C and then begins to rise rapidly for Reco. The consequence of this, for a small increase in air temperature of 1∘ (all other variables assumed unchanged), was that the respiration increased more than photosynthesis turning the small sink into a small source (4.5 g C m−2) during the growing season. Thus, we cannot rule out that the reason why the moss tundra is close to balance today is an effect of the warming that has already taken place in Svalbard.

Lateral carbon export has low impact on the net ecosystem carbon balance of a polygonal tundra catchment

Abstract. Permafrost-affected soils contain large quantities of soil organic carbon (SOC). Changes in the SOC pool of a particular ecosystem can be related to its net ecosystem carbon balance (NECB) in which the balance of carbon (C) influxes and effluxes is expressed. For polygonal tundra landscapes, accounts of ecosystem carbon balances in the literature are often solely based on estimates of vertical carbon fluxes. To fill this gap, we present data regarding the lateral export rates of dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) from a polygonal tundra site in the north Siberian Lena River delta, Russia. We use water discharge observations in combination with concentration measurements of waterborne carbon to derive the lateral carbon fluxes from one growing season (2 June–8 September 2014 for DOC, 8 June–8 September 2014 for DIC). To put the lateral C fluxes into context, we furthermore present the surface–atmosphere eddy covariance fluxes of carbon dioxide (CO2) and methane (CH4) from this study site. The results show cumulative lateral DIC and DOC fluxes of 0.31–0.38 and 0.06–0.08 g m−2, respectively, during the 93 d observation period (8 June–8 September 2014). Vertical turbulent fluxes of CO2-C and CH4-C accumulated to −19.0 ± 1.2 and 1.0 ± 0.02 g m−2 in the same period. Thus, the lateral C export represented about 2 % of the net ecosystem exchange of (NEE) CO2. However, the relationship between lateral and surface–atmosphere fluxes changed over the observation period. At the beginning of the growing season (early June), the lateral C flux outpaced the surface-directed net vertical turbulent CO2 flux, causing the polygonal tundra landscape to be a net carbon source during this time of the year. Later in the growing season, the vertical turbulent CO2 flux dominated the NECB.