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Ecosystem carbon (C) transit time is a critical diagnostic parameter to characterize land C sequestration. This parameter has different variants in the literature, including a commonly used turnover time. However, we know little about how different transit time and turnover time are in representing carbon cycling through multiple compartments under a non-steady state. In this study, we estimate both C turnover time as defined by the conventional stock over flux and mean C transit time as defined by the mean age of C mass leaving the system. We incorporate them into the Community Atmosphere Biosphere Land Exchange (CABLE) model to estimate C turnover time and transit time in response to climate warming and rising atmospheric [CO2]. Modelling analysis shows that both C turnover time and transit time increase with climate warming but decrease with rising atmospheric [CO2]. Warming increases C turnover time by 2.4 years and transit time by 11.8 years in 2100 relative to that at steady state in 1901. During the same period, rising atmospheric [CO2] decreases C turnover time by 3.8 years and transit time by 5.5 years. Our analysis shows that 65 % of the increase in global mean C transit time with climate warming results from the depletion of fast-turnover C pool. The remaining 35 % increase results from accompanied changes in compartment C age structures. Similarly, the decrease in mean C transit time with rising atmospheric [CO2] results approximately equally from replenishment of C into fast-turnover C pool and subsequent decrease in compartment C age structure. Greatly different from the transit time, the turnover time, which does not account for changes in either C age structure or composition of respired C, underestimated impacts of warming and rising atmospheric [CO2] on C diagnostic time and potentially led to deviations in estimating land C sequestration in multi-compartmental ecosystems.

Peatland ecosystems are integral to the mitigation of climate change as they represent significant terrestrial carbon sinks. In Ireland, peatlands cover ~20% of the land area but hold up to 75% of the soil organic carbon stock however many of these ecosystems (~85% of the total area) have been degraded due to anthropogenic activities such as agriculture, forestry and extraction for horticulture or energy. Furthermore, the carbon stocks that remain in these systems are vulnerable to inter-annual variation in climate, such as changes in precipitation and temperature, which can alter the hydrological status of these systems leading to changes in key biogeochemical processes and carbon and greenhouse gas exchange. During 2018 exceptional drought and heatwave conditions were reported across Northwestern Europe, where reductions in precipitation coupled with elevated temperatures were observed. Exceptional inter-annual climatic variability was also observed at Clara bog, a near natural raised bog in the Irish midlands when data from 2018 and 2019 were compared. Precipitation in 2018 was ~300 mm lower than 2019 while the average mean annual temperature was 0.5°C higher. The reduction in precipitation, particularly during the growing season in 2018, consistently lowered the water table where ~150 consecutive days where the water table was >5cm below the surface of the bog were observed at the central ecotope location. The differing hydrological conditions between years resulted in the study area, as determined by the flux footprint of the eddy covariance tower, acting as a net source of carbon of 53.5 g C m-2 in 2018 and a net sink of 125.2 g C m-2 in 2019. The differences in the carbon dynamics between years were primarily driven by enhanced ecosystem respiration (Reco) and lower rates of Gross Primary Productivity (GPP) in the drier year, where the maximum monthly ratio of GPP:Reco during the growing season was 0.96 g C m-2 month in 2018 and 1.14 g C m-2 month in 2019. This study highlights both the vulnerability and resilience of these ecosystems to exceptional inter-annual climatic variability and emphasises the need for long-term monitoring networks to enhance our understanding of the impacts of these events when they occur.

The ecosystem carbon (C) dynamics after permafrost thaw depends on more than just climate change since soil nutrient status may also impact ecosystem C balance. It has been advocated that the potential nitrogen (N) release upon permafrost thaw could promote plant growth and thus offset soil C loss. However, compared with the widely accepted C-N interactions, little is known about the potential role of soil phosphorus (P) availability. Here we combined two-year field observations along a permafrost thaw sequence (constituted by four thaw stages, i.e., non-collapse and 5, 14, and 22 years since collapse) with an in-situ fertilization experiment (included N and P additions at the level of 10 g N m-2 yr-1 and 10 g P m-2 yr-1, respectively) in a Tibetan swamp meadow to evaluate ecosystem C-nutrient interactions upon permafrost thaw. Our results showed that changes in soil P availability rather than N availability played an important role in regulating the increases in gross primary productivity and the decreases in net ecosystem exchange along the thaw sequence. The fertilization experiment further confirmed that P addition had stronger effects on plant growth than N addition in this permafrost ecosystem. These two lines of evidence highlight the crucial role of soil P availability in altering the trajectory of permafrost C cycle under climate warming.

Accurately representing the response of ecosystems to environmental change in land surface models (LSM) is crucial to making accurate predictions of future climate. Many LSMs do not correctly capture plant respiration and growth fluxes, particularly in response to extreme climatic events. This is in part due to the unrealistic assumption that total plant carbon expenditure (PCE) is always equal to gross carbon accumulation by photosynthesis. We present and evaluate a simple model of labile carbon storage and utilisation (SUGAR), designed to be integrated into an LSM, that allows simulated plant respiration and growth to vary independently of photosynthesis. SUGAR buffers simulated PCE against seasonal variation in photosynthesis, producing more constant (less variable) predictions of plant growth and respiration relative to an LSM that does not represent labile carbon storage. This allows the model to more accurately capture observed carbon fluxes at a large-scale drought experiment in a tropical moist forest in the Amazon, relative to the Joint UK Land Environment Simulator LSM (JULES). SUGAR is designed to improve the representation of carbon storage in LSMs and provides a simple framework that allows new processes to be integrated as the empirical understanding of carbon storage in plants improves. The study highlights the need for future research into carbon storage and allocation in plants, particularly in response to extreme climate events such as drought.

The recent research and policy efforts on climate change mitigation highlight the need for proper understanding of the effects of many types of land management interventions on greenhouse gas exchange processes. The complexity of carbon and nitrogen cycles, which is the case also for agricultural ecosystems, call for model-based research approaches. These can make the decision-making applications easier as well. The agricultural use of sewage sludge is widespread in many countries. There are a number of case studies about its possible effects on greenhouse gas fluxes under different climatic conditions, but there are not many experiences in relevant model-based assessments. In our contribution, the Biome-BGC MuSo (v.6.) model was used for the investigation of the main characteristics of ecosystem exchange of carbon in arable land of warm dry temperate climate in the Great Plain of Hungary. The Biome-BGC is one of the most widely used biogeochemical models, it is capable of handling different land management activities, have a multilayer soil module and enable a quite detailed ecophysiological parameterization, which make it suitable for the targeted study. The results of laboratory analyses of soil profiles of the study area were used for the parameterization (element contents, organic matter, etc.). The poster presents the first results of the integrated measurement and modelling research work.

An algorithm of forest cover mapping based on combined GIS-based analysis of multi-band satellite imagery, digital elevation model, and ground truth data was developed. Using the classification principles and an approach of Russian forest scientist Kolesnikov, maps of forest types and forest growing conditions (FGC) were build. The first map is based on RS-composite classification, while the second map is constructed on the basis of DEM-composite classification. The spatial combination of this two layers were also used for extrapolation and mapping of ecosystem carbon stock values (kgC/m2). The proposed approach was applied for the test site area (~3600 km2), located in the Northern Siberia boreal forests of Evenkia near Tura settlement.

Forests of the Western Himalaya, India, are impacted by the summer monsoon and winter seasonal rainfall events and associated changes in the meteorology. Here, we assess the scale interactions between observed forest ecosystem fluxes and meteorological parameters, particularly rainfall seasonality and extremes. The scale interactions were investigated using daily observed fluxes and meteorological parameters of 1080 days of 2014–2016 from a Pinus roxburghii dominated forest and using wavelet spectral analysis method. The mixed forest of this study was a sink of CO2 having the average NEE −3.21 gC m−2 day−1 for the period of observations. Result of the wavelet coherence analysis from observed data indicated a statistically significant correlation (> 0.7 at 95 % confidence level) between daily average NEE and daily total rainfall having band periods of 70–120 days, 35–64 days and 60–90 days of monsoon periods of 2014–16, respectively, where rainfall leading to NEE. Impact of heavy rainfall events of monsoon periods over NEE of the forest patch was found to have average band periods of 4 days; whereas, the winter time heavy rainfall events were having average band periods of 15 days with very high local correlation (> 0.7 at 95 % confidence level) inferring that ecosystem exchange rate was mirroring rainfall events. Although CASA-GFDE3 model simulated daily NEE values of 2014–15 were found have a low fraction of explained variance (= 0.08) with respect to observations, modeled NEE-rainfall relationship of 2014 was found to corroborate well with the observed pattern; however for 2015, the phase relationship between modeled NEE and rainfall around band period of 100 days was opposite to the flux tower observations. Subsequently, heavy rainfall events and daily average air temperatures were also found to be coherent during monsoon period having wavelet coherences > 0.8 indicating a cause and effect relationship between both parameters, and rainfall events were mirroring temperature variations. Therefore, it is anticipated that the Pinus roxburghii dominated forest productivity of Western Himalaya, India, are expected to increase even with the increment of heavy rainfall events in the near 20 future.

Carbon (C) sequestration in the soil is considered as a potential important mechanism to mitigate greenhouse gas (GHG) emissions of the agricultural sector. It can be quantified by the net ecosystem carbon budget (NECB) describing the change of soil C as the sum of all relevant import and export fluxes. NECB was investigated here in detail for an intensively grazed dairy pasture in Switzerland. Two budget approaches with different system boundaries were applied: NECBtot for system boundaries including the grazing cows and NECBpast for system boundaries excluding the cows. CO2 and CH4 exchange induced by soil/vegetation processes as well as direct emissions by the animals were derived from eddy covariance measurements. Other C fluxes were either measured (milk yield, concentrate feeding) or derived based on animal performance data (intake, excreta). For the investigated year, both approaches resulted in a small near-neutral C budget: NECBtot −27 ± 62 and NECBpast 23 ± 76 g C m−2 yr−1. The considerable uncertainties, depending on the approach, were mainly due to errors in the CO2 exchange or in the animal-related fluxes. The comparison of the NECB results with the annual exchange of other GHG revealed CH4 emissions from the cows to be the major contributor in terms of CO2 equivalents, but with much lower uncertainty compared to NECB. Although only 1 year of data limit the representativeness of the carbon budget results, they demonstrate the important contribution of the non-CO2 fluxes depending on the chosen system boundaries and the effect of their propagated uncertainty in an exemplary way. The simultaneous application and comparison of both NECB approaches provides a useful consistency check for the carbon budget determination and can help to identify and eliminate systematic errors.

Inverse modeling techniques used to quantify surface carbon fluxes commonly assume that the uncertainty of fossil fuel CO2 (FFCO2) emissions is negligible and that intra-annual variations can be neglected. To investigate these assumptions, we analyzed the differences between four fossil fuel emission inventories with spatial and temporal differences over Europe and their impact on the model simulated CO2 concentration. Large temporal flux variations characterize the hourly fields (~40 % and ~80 % for the seasonal and diurnal cycles, peak-to-peak) and annual country totals differ by 10 % on average and up to 40 % for some countries (i.e., the Netherlands). These emissions have been prescribed to seven different transport models, resulting in 28 different FFCO2 concentrations fields. The modeled FFCO2 concentration time series at surface sites using time-varying emissions show larger seasonal cycles (+2 ppm at the Hungarian tall tower (HUN)) and smaller diurnal cycles in summer (−1 ppm at HUN) than when using constant emissions. The concentration range spanned by all simulations varies between stations, and is generally larger in winter (up to ~10 ppm peak-to-peak at HUN) than in summer (~5 ppm). The contribution of transport model differences to the simulated concentration std-dev is 2–3 times larger than the contribution of emission differences only, at typical European sites used in global inversions. These contributions to the hourly (monthly) std-dev's amount to ~1.2 (0.8) ppm and ~0.4 (0.3) ppm for transport and emissions, respectively. First comparisons of the modeled concentrations with 14C-based fossil fuel CO2 observations show that the large transport differences still hamper a quantitative evaluation/validation of the emission inventories. Changes in the estimated monthly biosphere flux (Fbio) over Europe, using two inverse modeling approaches, are relatively small (less that 5 %) while changes in annual Fbio (up to ~0.15 % GtC yr−1) are only slightly smaller than the differences in annual emission totals and around 30 % of the mean European ecosystem carbon sink. These results point to an urgent need to improve not only the transport models but also the assumed spatial and temporal distribution of fossil fuel emission inventories.