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Small lakes represent an important source of atmospheric CH4 from northern wetlands. However, spatiotemporal variations in flux magnitudes and the lack of knowledge about their main environmental controls contribute large uncertainty into the global CH4 budget. In this study, we measured methane fluxes from small lakes using chambers and bubble traps. Field investigations were carried out in July–August 2014 within the West Siberian middle and southern taiga zones. The average and median of measured methane chamber fluxes were 0.32 and 0.30 mgCH4 m−2 h−1 for middle taiga lakes and 8.6 and 4.1 mgCH4 m−2 h−1 for southern taiga lakes, respectively. Pronounced flux variability was found during measurements on individual lakes, between individual lakes and between zones. To analyze these differences and the influences of environmental controls, we developed a new dynamic process-based model. It shows good performance with emission rates from the southern taiga lakes and poor performance for individual lakes in the middle taiga region. The model shows that, in addition to well-known controls such as temperature, pH and lake depth, there are significant variations in the maximal methane production potential between these climatic zones. In addition, the model shows that variations in gas-filled pore space in lake sediments are capable of controlling the total methane emissions from individual lakes. The CH4 emissions exhibited distinct zonal differences not only in absolute values but also in their probability density functions: the middle taiga lake fluxes were best described by a lognormal distribution while the southern taiga lakes followed a power-law distribution. The latter suggests applicability of self-organized criticality theory for methane emissions from the southern taiga zone, which could help to explain the strong variability within individual lakes.

International audience

© 2017, The Author(s). During the past several decades, the Earth system has changed significantly, especially across Northern Eurasia. Changes in the socio-economic conditions of the larger countries in the region have also resulted in a variety of regional environmental changes that can have global consequences. The Northern Eurasia Future Initiative (NEFI) has been designed as an essential continuation of the Northern Eurasia Earth Science Partnership Initiative (NEESPI), which was launched in 2004. NEESPI sought to elucidate all aspects of ongoing environmental change, to inform societies and, thus, to better prepare societies for future developments. A key principle of NEFI is that these developments must now be secured through science-based strategies co-designed with regional decision-makers to lead their societies to prosperity in the face of environmental and institutional challenges. NEESPI scientific research, data, and models have created a solid knowledge base to support the NEFI program. This paper presents the NEFI research vision consensus based on that knowledge. It provides the reader with samples of recent accomplishments in regional studies and formulates new NEFI science questions. To address these questions, nine research foci are identified and their selections are briefly justified. These foci include warming of the Arctic; changing frequency, pattern, and intensity of extreme and inclement environmental conditions; retreat of the cryosphere; changes in terrestrial water cycles; changes in the biosphere; pressures on land use; changes in infrastructure; societal actions in response to environmental change; and quantification of Northern Eurasia’s role in the global Earth system. Powerful feedbacks between the Earth and human systems in Northern Eurasia (e.g., mega-fires, droughts, depletion of the cryosphere essential for water supply, retreat of sea ice) result from past and current human activities (e.g., large-scale water withdrawals, land use, and governance change) and potentially restrict or provide new opportunities for future human activities. Therefore, we propose that integrated assessment models are needed as the final stage of global change assessment. The overarching goal of this NEFI modeling effort will enable evaluation of economic decisions in response to changing environmental conditions and justification of mitigation and adaptation efforts.

This study investigates the capacity of a prognostic biosphere model to simulate global variability in atmospheric CO2 concentrations and vegetation carbon dynamics under current environmental conditions. Global data sets of atmospheric CO2 concentrations, above-ground biomass (AGB), and net primary productivity (NPP) in terrestrial vegetation were assimilated into the biosphere model using an inverse modeling method combined with an atmospheric transport model. In this process, the optimal physiological parameters of the biosphere model were estimated by minimizing the misfit between observed and modeled values, and parameters were generated to characterize various biome types. Results obtained using the model with the optimized parameters correspond to the observed seasonal variations in CO2 concentration and their annual amplitudes in both the Northern and Southern Hemispheres. In simulating the mean annual AGB and NPP, the model shows improvements in estimating the mean magnitudes and probability distributions for each biome, as compared with results obtained using prior simulation parameters. However, the model is less efficient in its simulation of AGB for forest type biomes. This misfit suggests that more accurate values of input parameters, specifically, grid mean AGB values and seasonal variabilities in physiological parameters, are required to improve the performance of the simulation model.

We have developed an improved version of the National Institute for Environmental Studies (NIES) three-dimensional chemical transport model (TM) designed for accurate tracer transport simulations in the stratosphere, using a hybrid sigma-isentropic (σ-θ) vertical coordinate that employs both terrain-following and isentropic parts switched smoothly around the tropopause. The air-ascending rate was derived from the effective heating rate and was used to simulate vertical motion in the isentropic part of the grid (above level 350 K), which was adjusted to fit to the observed age of the air in the stratosphere. Multi-annual simulations were conducted using the NIES TM to evaluate vertical profiles and dry-air column-averaged mole fractions of CO2 and CH4. Comparisons with balloon-borne observations over Sanriku (Japan) in 2000–2007 revealed that the tracer transport simulations in the upper troposphere and lower stratosphere are performed with accuracies of ~5% for CH4 and SF6, and ~1% for CO2 compared with the observed volume-mixing ratios. The simulated column-averaged dry air mole fractions of atmospheric carbon dioxide (XCO2) and methane (XCH4) were evaluated against daily ground-based high-resolution Fourier Transform Spectrometer (FTS) observations measured at twelve sites of the Total Carbon Column Observing Network (TCCON) (Bialystok, Bremen, Darwin, Garmisch, Izaña, Lamont, Lauder, Orleans, Park Falls, Sodankylä, Tsukuba, and Wollongong) between January 2009 and January 2011. The comparison shows the model's ability to reproduce the site-dependent seasonal cycles as observed by TCCON, with correlation coefficients typically on the order 0.8–0.9 and 0.4–0.8 for XCO2 and XCH4, respectively, and mean model biases of ±0.2% and ±0.5%, excluding Sodankylä, where strong biases are found. The ability of the model to capture the tracer total column mole fractions is strongly dependent on the model's ability to reproduce seasonal variations in tracer concentrations in the planetary boundary layer (PBL). We found a marked difference in the model's ability to reproduce near-surface concentrations at sites located some distance from multiple emission sources and where high emissions play a notable role in the tracer's budget. Comparisons with aircraft observations over Surgut (West Siberia), in an area with high emissions of methane from wetlands, show contrasting model performance in the PBL and in the free troposphere. Thus, the PBL is another critical region for simulating the tracer total column mole fractions.

The presentation contains an overview of recent findings and results of assessment of carbon cycling of forest ecosystems of Northern Eurasia. From a methodological point of view, there is a clear tendency in understanding a need of a Full and Verified Carbon Account (FCA), i.e. in reliable assessment of uncertainties for all modules and all stages of FCA. FCA is considered as a fuzzy (underspecified) system that supposes a system integration of major methods of carbon cycling study (land-ecosystem approach, LEA; process-based models; eddy covariance; and inverse modelling). Landscape-ecosystem approach 1) serves for accumulation of all relevant knowledge of landscape and ecosystems; 2) for strict systems designing the account, 3) contains all relevant spatially distributed empirical and semi-empirical data and models, and 4) is presented in form of an Integrated Land Information System (ILIS). The ILIS includes a hybrid land cover in a spatially and temporarily explicit way and corresponding attributive databases. The forest mask is provided by utilizing multi-sensor remote sensing data, geographically weighed regression and validation within GEO-wiki platform. By-pixel parametrization of forest cover is based on a special optimization algorithms using all available knowledge and information sources (data of forest inventory and different surveys, observations in situ, official statistics of forest management etc.). Major carbon fluxes within the LEA (NPP, HR, disturbances etc.) are estimated based on fusion of empirical data and aggregations with process-based elements by sets of regionally distributed models. Uncertainties within LEA are assessed for each module and at each step of the account. Within method results of LEA and corresponding uncertainties are harmonized and mutually constrained with independent outputs received by other methods based on the Bayesian approach. The above methodology have been applied to carbon account of Russian forests for 2000-2012. It has been shown that the Net Ecosystem Carbon Budget (NECB) of Russian forests for this period was in range of 0.5-0.7 Pg C yr-1 with a slight negative trend during the period due to acceleration of disturbance regimes and negative impacts of weather extremes (heat waves etc.). Uncertainties of the FCA for individual years were estimated at about 25% (CI 0.9). It has been shown that some models (e.g. majority of DGVMs) do not describe some processes on permafrost satisfactory while results of applications of ensembles of inverse models on average are closed to empirical assessments. A most important conclusion from this experience is that future improvements of knowledge of carbon cycling of Northern Eurasia forests requires development of an integrated observing system as a unified information background, as well as systems methodological improvements of all methods of cognition of carbon cycling.

We review a current state of a forest type mapping with Landsat data in Siberia. Target algorithm should be based on dynamic vegetation approach to be applicable to the analysis of the forest type distribution for Siberia, aiming at capability of mapping Siberian forest landscapes for applications such as predicting response of forest composition to climate change. We present data for several areas in West Siberian middle taiga, Central Siberia and East Siberia near Yakutsk. Analysis of the field survey, forest inventory data was made to produce forest type classification accounting for several stages for forest succession and variations in habitats and landforms. Supervised classification was applied to the areas were the ground truth and inventory data are available, including several limited area maps and vegetation survey transects. In Laryegan basin in West Siberia the upland forest areas are dominated by mix of Scots pine on sandy soils and Siberian pine with presence of fir and spruce on the others. Abundance of Scots pine decreases to the west due to change in soils. Those types are separable using Landsat spectral data. In the permafrost area around Yakutsk the most widespread succession type is birch to larch. Three stages of the birch to larch succession are detectable from Landsat image. When Landsat data is used in both West and East Siberia, distinction between deciduous broad-leaved species (birch, aspen, and willow) is generally difficult. Similar problem exist for distinguishing between dark coniferous species (Siberian pine, fir and spruce). Image classification can be improved by applying landform type analysis, such as separation into floodplain, terrace, sloping hills. Additional layers of information can be a promising way to complement Landsat data.

We designed a method to simulate atmospheric CO2 concentrations at several continuous observation sites around the globe using surface fluxes at a very high spatial resolution. The simulations presented in this study were performed using the Global Eulerian-Lagrangian Coupled Atmospheric model (GELCA), comprising a Lagrangian particle dispersion model coupled to a global atmospheric tracer transport model with prescribed global surface CO2 flux maps at a 1 × 1 km resolution. The surface fluxes used in the simulations were prepared by assembling the individual components of terrestrial, oceanic and fossil fuel CO2 fluxes. This experimental setup (i.e. a transport model running at a medium resolution, coupled to a high-resolution Lagrangian particle dispersion model together with global surface fluxes at a very high resolution), which was designed to represent high-frequency variations in atmospheric CO2 concentration, has not been reported at a global scale previously. Two sensitivity experiments were performed: (a) using the global transport model without coupling to the Lagrangian dispersion model, and (b) using the coupled model with a reduced resolution of surface fluxes, in order to evaluate the performance of Eulerian-Lagrangian coupling and the role of high-resolution fluxes in simulating high-frequency variations in atmospheric CO2 concentrations. A correlation analysis between observed and simulated atmospheric CO2 concentrations at selected locations revealed that the inclusion of both Eulerian-Lagrangian coupling and high-resolution fluxes improves the high-frequency simulations of the model. The results highlight the potential of a coupled Eulerian-Lagrangian model in simulating high-frequency atmospheric CO2 concentrations at many locations worldwide. The model performs well in representing observations of atmospheric CO2 concentrations at high spatial and temporal resolutions, especially for coastal sites and sites located close to sources of large anthropogenic emissions. While this study focused on simulations of CO2 concentrations, the model could be used for other atmospheric compounds with known estimated emissions.

A chemistry-transport model (CTM) intercomparison experiment (TransCom-CH4) has been designed to investigate the roles of surface emissions, transport and chemical loss in simulating the global methane distribution. Model simulations were conducted using twelve models and four model variants and results were archived for the period of 1990–2007. All but one model transports were driven by reanalysis products from 3 different meteorological agencies. The transport and removal of CH4 in six different emission scenarios were simulated, with net global emissions of 513 ± 9 and 514 ± 14 Tg CH4 yr−1 for the 1990s and 2000s, respectively. Additionally, sulfur hexafluoride (SF6) was simulated to check the interhemispheric transport, radon (222Rn) to check the subgrid scale transport, and methyl chloroform (CH3CCl3) to check the chemical removal by the tropospheric hydroxyl radical (OH). The results are compared to monthly or annual mean time series of CH4, SF6 and CH3CCl3 measurements from 8 selected background sites, and to satellite observations of CH4 in the upper troposphere and stratosphere. Most models adequately capture the vertical gradients in the stratosphere, the average long-term trends, seasonal cycles, interannual variations (IAVs) and interhemispheric (IH) gradients at the surface sites for SF6, CH3CCl3 and CH4. The vertical gradients of all tracers between the surface and the upper troposphere are consistent within the models, revealing vertical transport differences between models. An average IH exchange time of 1.39 ± 0.18 yr is derived from SF6 time series. Sensitivity simulations suggest that the estimated trends in exchange time, over the period of 1996–2007, are caused by a change of SF6 emissions towards the tropics. Using six sets of emission scenarios, we show that the decadal average CH4 growth rate likely reached equilibrium in the early 2000s due to the flattening of anthropogenic emission growth since the late 1990s. Up to 60% of the IAVs in the observed CH4 concentrations can be explained by accounting for the IAVs in emissions, from biomass burning and wetlands, as well as meteorology in the forward models. The modeled CH4 budget is shown to depend strongly on the troposphere-stratosphere exchange rate and thus on the model's vertical grid structure and circulation in the lower stratosphere. The 15-model median CH4 and CH3CCl3 atmospheric lifetimes are estimated to be 9.99 ± 0.08 and 4.61 ± 0.13 yr, respectively, with little IAV due to transport and temperature.

Fourteen global atmospheric transport models were evaluated by comparing the simulation of 222Rn against measurements at three continental stations in Germany: Heidelberg, Freiburg and Schauinsland. Hourly concentrations simulated by the models using a common 222Rn-flux without temporal variations were investigated for 2002 and 2003. We found that the mean simulated concentrations in Heidelberg are related to the diurnal amplitude of boundary layer height in each model. Summer mean concentrations simulated by individual models were negatively correlated with the seasonal mean of diurnal amplitude of boundary layer height, while in winter the correlation was positive. We also found that the correlations between simulated and measured concentrations at Schauinsland were higher when the simulated concentrations were interpolated to the station altitude in most models. Temporal variations of the mismatch between simulated and measured concentrations suggest that there are significant interannual variations in the 222Rn exhalation rate in this region. We found that the local inversion layer during daytime in summer in Freiburg has a significant effect on 222Rn concentrations. We recommend Freiburg concentrations for validation of models that resolve local stable layers and those at Heidelberg for models without this capability.

ロシア連邦のシベリア地域において航空機とタワーを用いた観測ネットワークを展開して温室効果ガスの長期モニタリングを行った. シベリア地域の二酸化炭素濃度は季節変動の振幅が同じ緯度帯の沿岸域に比べて明らかに大きく, シベリアにおける陸上生態系の活動が盛んであることがうかがえた. シベリアのメタン濃度は同じ緯度帯の沿岸域で観測された濃度より明らかに高い上にその季節変動には冬季と夏季と年に２回の極大値が見られ, 湿地や天然ガス起源のメタン放出が大気中濃度の変動に大きく影響していることが示唆された., Long-term monitoring for atmospheric greenhouse gases has been conducted by observational network using aircraft and towers in Siberia, Russia. The amplitudes in seasonal variation of carbon dioxide (CO2) observed by towers are 28-33ppb, which are more than 10 ppm larger than those observed at background sites, implying higher biospheric activities in Siberia compared to coastal region. Small vertical difference in annual mean CO2 mixing ratio indicates that emission and absorption fluxes are identical in Siberia. Daytime methane (CH4) mixing ratios exhibit rise in summer and winter at almost all tower sites, which implies that large amount of CH4 is released in summer, especially from wetland. Extremely higher annual mean CH4 in lower altitude compared to upper troposphere suggests that land surface in Siberia acts as a substantial source for CH4.

We outline a methodology of full and verified carbon account of terrestrial ecosystems (FCA) that supposes unbiased assessment of relevant proxy values (here: Net Ecosystem Carbon Budget) and reliable estimation of uncertainties. The FCA is a fuzzy (underspecified) system, of which membership function is inherently stochastic. Thus, any individually used method of FCA is not able to estimate structural uncertainties, that is why usually reported “within method” uncertainties are inevitably partial. Attempting at estimation of “full uncertainties” of the studied system we combine the major methods of terrestrial ecosystems carbon account (landscape-ecosystem method, LEA; process-based models; eddy covariance; and inverse modeling). Assessment of the uncertainties of FCA is provided within each method. Landscape-ecosystem approach (LEA) presents the empirical basis of the FCA in form of an Integrated Land Information System; serves for strict systems designing the account; contains all relevant empirical and semi-empirical data and models. By-pixel parametrization of land cover is provided by utilizing multi-sensor remote sensing data within Geo-Wiki platform and other relevant information based on special optimization algorithms. Major carbon fluxes within the LEA (NPP, HR, disturbances, etc.) are estimated based on fusion of empirical data with process-based elements by sets of regionally distributed models. “Within method” results and uncertainties of the methods examined are harmonized and mutually constrained based on the Bayesian approach. The above methodology have been applied to carbon account of Russian forests for 2000-2010; uncertainties of the FCA for individual years were estimated in limits of ±25%, CI 0.9. We discussed strengths and weaknesses of the approach; system requirements to different methods of the FCA, information and research needs; unresolved problems of cognition of fuzzy system; and obtained and potential levels of uncertainties.

International audience; This study presents the outcome of an inverse modeling intercomparison experiment on the use of total column CO 2 retrievals from Greenhouse Gas Observing Satellite (GOSAT) for quantifying global sources and sinks of CO 2. Eight research groups submitted inverse modeling results for the first year of GOSAT measurements. Inversions were carried out using only GOSAT data, a combination of GOSAT and surface measurements, and using only surface measurements. As expected, the most robust flux estimates are obtained at large scales (e.g., within 20% of the annual flux at the global scale), and they quickly diverge toward the scale of the subcontinental TRANSCOM regions and beyond (to >100% of the annual flux). We focus our analysis on a shift in the CO 2 uptake over land from the Tropics toward the Northern Hemisphere Extra tropics of ∼1 PgC/yr when GOSAT data are used in the inversions. This shift is largely driven by TRANSCOM regions Europe and Northern Africa, showing, respectively, an increased uptake and release of 0.7 and 0.9 PgC/yr. Inversions using GOSAT data show a reduced gradient between midlatitudes of the Northern Hemisphere and the Tropics, consistent with the latitudinal shift in carbon uptake. However, the reduced gradients degrade the agreement with background aircraft and surface measurements. To narrow the range of inversion-derived flux, estimates will require further efforts to understand the differences not only between the retrieval schemes but also between inverse models, as their contributions to the overall uncertainty are estimated to be of similar magnitude.

This study presents the outcome of an inverse modeling intercomparison experiment on the use of total column CO2 retrievals from Greenhouse Gas Observing Satellite (GOSAT) for quantifying global sources and sinks of CO2. Eight research groups submitted inverse modeling results for the first year of GOSAT measurements. Inversions were carried out using only GOSAT data, a combination of GOSAT and surface measurements, and using only surface measurements. As expected, the most robust flux estimates are obtained at large scales (e.g., within 20% of the annual flux at the global scale), and they quickly diverge toward the scale of the subcontinental TRANSCOM regions and beyond (to >100% of the annual flux). We focus our analysis on a shift in the CO2 uptake over land from the Tropics toward the Northern Hemisphere Extra tropics of approximate to 1 PgC/yr when GOSAT data are used in the inversions. This shift is largely driven by TRANSCOM regions Europe and Northern Africa, showing, respectively, an increased uptake and release of 0.7 and 0.9 PgC/yr. Inversions using GOSAT data show a reduced gradient between midlatitudes of the Northern Hemisphere and the Tropics, consistent with the latitudinal shift in carbon uptake. However, the reduced gradients degrade the agreement with background aircraft and surface measurements. To narrow the range of inversion-derived flux, estimates will require further efforts to understand the differences not only between the retrieval schemes but also between inverse models, as their contributions to the overall uncertainty are estimated to be of similar magnitude.