Data assimilation framework around the LPJ-GUESS model for the optimised simulation of CH4 emission from Northern wetlands

2020 ◽  
Author(s):  
Jalisha Theanutti Kallingal ◽  
Marko Scholze ◽  
Janne Rinne ◽  
Johan Lindstrom
Keyword(s):  
Posterior Distribution ◽  
Measurement Data ◽  
Flux Measurement ◽  
The Arctic ◽  
Atmospheric Methane ◽  
Dynamic Global Vegetation Model ◽  
First Results ◽  
Methane Fluxes ◽  
Global Vegetation ◽  
Work Knowledge

<p><span>Wetlands in the boreal zone are a significant source of atmospheric methane, and hence they have been intensively studied with mechanistic models for the assessment of methane dynamics. The arctic-enabled dynamic global vegetation model LPJ-GUESS is one of the models that allow quantification and understanding of the natural methane fluxes at various scales ranging from local to regional and global, but with several uncertainties. Complexity in the underlying environmental processes, warming driven alternative paths of meteorological phenomena and changes in hydrological and vegetation conditions are exigent for a calibrated and optimised LPJ-GUESS. In this study, we used the Markov chain Monte Carlo (using Metropolis-Hastings formula) algorithm to quantify the uncertainties of LPJ-GUESS. Application of this method allows greater search of the posterior distribution, leading to a more complete characterisation of the posterior distribution with reduced risk of sample impoverishment. We will present first results from an assimilation experiment optimising LPJ-GUESS model process parameters using the flux measurement data from 2005 to 2015 from the Siikaneva wetlands in southern Finland. We<span>  </span>analyse the parameter efficiency of LPJ-GUESS by looking into the posterior parameter distributions, parameter correlations, and the interconnections of the processes they control. As a part of this work, knowledge about how the methane data can constrain the parameters and processes is derived. </span></p>

scholarly journals Methane in Zackenberg Valley, NE Greenland: multidecadal growing season fluxes of a high-Arctic tundra

2021 ◽  
Vol 18 (23) ◽  
pp. 6093-6114
Author(s):  
Johan H. Scheller ◽  
Mikhail Mastepanov ◽  
Hanne H. Christiansen ◽  
Torben R. Christensen
Keyword(s):  
Critical Role ◽  
Flux Measurement ◽  
Methane Flux ◽  
Landscape Scale ◽  
Valley Floor ◽  
The Arctic ◽  
Atmospheric Methane ◽  
Arctic Ecosystems ◽  
Atmospheric Concentration ◽  
Methane Fluxes

Abstract. The carbon balance of high-latitude terrestrial ecosystems plays an essential role in the atmospheric concentration of trace gases, including carbon dioxide (CO2) and methane (CH4). Increasing atmospheric methane levels have contributed to ∼ 20 % of the observed global warming since the pre-industrial era. Rising temperatures in the Arctic are expected to promote the release of methane from Arctic ecosystems. Still, existing methane flux measurement efforts are sparse and highly scattered, and further attempts to assess the landscape fluxes over multiple years are needed. Here we combine multi-year July–August methane flux monitoring (2006–2019) from automated flux chambers in the central fens of Zackenberg Valley, northeast Greenland, with several flux measurement campaigns on the most common vegetation types in the valley to estimate the landscape fluxes over 14 years. Methane fluxes based on manual chamber measurements are available from campaigns in 1997, 1999–2000, and in shorter periods from 2007–2013 and were summarized in several published studies. The landscape fluxes are calculated for the entire valley floor and a smaller subsection of the valley floor, containing the productive fen area, Rylekærene. When integrated for the valley floor, the estimated July–August landscape fluxes were low compared to the single previous estimate, while the landscape fluxes for Rylekærene were comparable to previous estimates. The valley floor was a net methane source during July–August, with estimated mean methane fluxes ranging from 0.18 to 0.67 mg m−2 h−1. The mean methane fluxes in the fen-rich Rylekærene were substantially higher, with fluxes ranging from 0.98 to 3.26 mg m−2 h−1. A 2017–2018 erosion event indicates that some fen and grassland areas in the center of the valley are becoming unstable following pronounced fluvial erosion and a prolonged period of permafrost warming. Although such physical disturbance in the landscape can disrupt the current ecosystem–atmosphere flux patterns, even pronounced future erosion of ice-rich areas is unlikely to impact methane fluxes on a landscape scale significantly. Instead, projected changes in future climate in the valley play a more critical role. The results show that multi-year landscape methane fluxes are highly variable on a landscape scale and stress the need for long-term spatially distributed measurements in the Arctic.

Soils as sources and sinks for atmospheric methane

1997 ◽  
Vol 77 (2) ◽  
pp. 167-177 ◽  
Author(s):  
Edward Topp ◽  
Elizabeth Pattey
Keyword(s):  
Organic Matter ◽  
Methane Oxidation ◽  
Cultural Practices ◽  
Agricultural Soils ◽  
Flux Measurement ◽  
Methane Emissions ◽  
Spatial And Temporal Variability ◽  
Atmospheric Methane ◽  
Bacterial Populations ◽  
Methane Fluxes

Methane is considered to be a significant greenhouse gas. Methane is produced in soils as the end product of the anaerobic decomposition of organic matter. In the absence of oxygen, methane is very stable, but under aerobic conditions it is mineralized to carbon dioxide by methanotrophic bacteria. Soil methane emissions, primarily from natural wetlands, landfills and rice paddies, are estimated to represent about half of the annual global methane production. Oxidation of atmospheric methane by well-drained soils accounts for about 10% of the global methane sink. Whether a soil is a net source or sink for methane depends on the relative rates of methanogenic and methanotrophic activity. A number of factors including pH, Eh, temperature and moisture content influence methane transforming bacterial populations and soil fluxes. Several techniques are available for measuring methane fluxes. Flux estimation is complicated by spatial and temporal variability. Soil management can impact methane transformations. For example, landfilling of organic matter can result in significant methane emissions, whereas some cultural practices such as nitrogen fertilization inhibit methane oxidation by agricultural soils. Key words: Methane, methanogenesis, methane oxidation, soil, flux measurement

scholarly journals Methane in Zackenberg Valley, NE Greenland: Multidecadal growing season fluxes of a high Arctic tundra

2021 ◽  
Author(s):  
Johan H. Scheller ◽  
Mikhail Mastepanov ◽  
Hanne H. Christiansen ◽  
Torben R. Christensen
Keyword(s):  
Critical Role ◽  
Terrestrial Ecosystems ◽  
Methane Flux ◽  
High Arctic ◽  
Landscape Scale ◽  
The Arctic ◽  
Atmospheric Methane ◽  
Arctic Ecosystems ◽  
Atmospheric Concentration ◽  
Methane Fluxes

Abstract. The carbon balance of high-latitude terrestrial ecosystems plays an essential role in the atmospheric concentration of trace gases, including carbon dioxide (CO2) and methane (CH4). Increasing levels of atmospheric methane have contributed to ~20 % of the observed global warming since the pre-industrial era. Rising temperatures in the Arctic are expected to promote the release of methane from Arctic ecosystems. Still, existing methane flux data collection efforts are sparse and highly scattered, and further attempts to assess the landscape fluxes over multiple years are needed.Here we use multiyear monitoring from automated flux chambers located on the fringe of a fen area in the center of Zackenberg Valley, northeast Greenland, from July and August (2006–2019). Direct measurements of methane fluxes showed high variability, with mean July–August fluxes ranging from 0.26 to 3.41 mg CH4 m−2 h−1. Methane fluxes based on manual chamber measurements are available from campaigns in 1997, 1999–2000, and in shorter periods from 2007–2013 and have been summarized in several published studies. Fluxes from the multiyear monitoring were combined with fluxes from the most common vegetation types, measured in 2007, and a detailed vegetation cover map to assess the methane flux on a landscape-scale and its variability over time.July–August landscape fluxes, estimated in the current study for the 2006–2019 period, were low compared to previous estimations. For the full study area covering the valley floor, the net methane source during these months was estimated as 0.06 to 0.83 mg CH4 m−2 h−1 and as 0.26 to 3.45 mg CH4 m−2 h−1 for the central fen-rich areas.A 2017–2018 erosion event indicates that some fen and grassland areas along the river in the center of the valley are becoming unstable following pronounced fluvial erosion and a prolonged period of permafrost warming. Although such physical disturbance in the landscape can disrupt the current ecosystem–atmosphere flux patterns, even pronounced future erosion along the river is unlikely to impact methane fluxes at a landscape-scale significantly. Instead, projected changes in future climate in the valley play a more critical role. The results show that multiyear landscape methane fluxes are highly variable at a landscape-scale and stress the need for long-term spatially distributed measurements in the Arctic.

Soil depth affects simulated carbon and water in the MC2 dynamic global vegetation model

2014 ◽  
Vol 294 ◽  
pp. 84-93 ◽  
Author(s):  
Wendy Peterman ◽  
Dominique Bachelet ◽  
Ken Ferschweiler ◽  
Timothy Sheehan
Keyword(s):  
Soil Depth ◽  
Vegetation Model ◽  
Dynamic Global Vegetation Model ◽  
Global Vegetation

Assessment of land use and land cover datasets for Brazil and impact on C emissions

2021 ◽  
Author(s):  
Thais M. Rosan ◽  
Kees Klein Goldewijk ◽  
Raphael Ganzenmüller ◽  
Michael O'Sullivan ◽  
Julia Pongratz ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Carbon Dioxide Emissions ◽  
Dynamic Global Vegetation Model ◽  
Global Carbon Budget ◽  
National Estimates ◽  
National Trends ◽  
Global Vegetation ◽  
Land Cover Maps ◽  
The Impact

<p>Brazil is responsible for about one third of the global land use and land cover change (LULCC) carbon dioxide emissions. However, there is a disagreement among different methodologies on the magnitude and trends in emissions and their geographic distribution. One of the main uncertainties is associated with different LULCC datatasets used as input in the different approaches. In this work we perform an evaluation of LULCC datasets for Brazil, including the global dataset (HYDE 3.2) used in the annual Global Carbon Budget (GCB), and national Brazilian dataset (MapBiomas) over the period 2000-2018. We also analyze the latest global HYDE 3.3 dataset based on new FAO inventory estimates and multi-annual ESA CCI satellite-based land cover maps. Results show that the new HYDE 3.3 can represent well the observed spatial variation in cropland and pastures areas over the last decades compared to national data (MapBiomas) and shows an improvement compared to HYDE 3.2 used in GCB. However, the magnitude of LULCC assessed with HYDE 3.3 is lower than national estimates from MapBiomas. Finally, we used HYDE 3.3 as input to two different approaches included in GCB, a global bookkeeping model (BLUE) and a process-based Dynamic Global Vegetation Model (JULES-ES) to determine the impact of the new version of HYDE dataset on Brazil’s land-use emissions trends over the period 2000-2017. Both JULES-ES and BLUE now simulate a negative land-use emissions trend for the last two decades. This negative trend is in agreement with Brazilian INPE-EM, global H&N bookkeeping models, FAO and as reported in National GHG inventories (NGHGI), although magnitudes differ among approaches. Overall, the inclusion of the multi-annual ESA CCI Land Cover dataset to allocate spatially the FAO statistical data has improved spatial representation of agricultural area change in Brazil in the last two decades, contributing to improve global model capability to simulate Brazil’s LULCC emissions in agreement with national trends estimates and spatial distribution.</p>

Exploring the variation in ecosystem scale methane fluxes and its drivers within a boreal mire complex

2021 ◽  
Author(s):  
Koffi Dodji Noumonvi ◽  
Joshua L. Ratcliffe ◽  
Mats Öquist ◽  
Mats B. Nilsson ◽  
Matthias Peichl
Keyword(s):  
Eddy Covariance ◽  
Land Surface ◽  
Chemical Properties ◽  
Growing Season ◽  
Small Scale ◽  
Atmospheric Methane ◽  
Flux Footprint ◽  
Growing Season Length ◽  
Methane Fluxes ◽  
Northern Peatlands

<p>Northern peatlands cover a small fraction of the earth’s land surface, and yet they are one of the most important natural sources of atmospheric methane. With climate change causing rising temperatures, changes in water balance and increased growing season length, peatland contribution to atmospheric methane concentration is likely to increase, justifying the increased attention given to northern peatland methane dynamics. Northern peatlands often occur as heterogeneous complexes characterized by hydromorphologically distinct features from < 1 m² to tens of km², with differing physical, hydrological and chemical properties. The more commonly understood small-scale variation between hummocks, lawns and hollows has been well explored using chamber measurements. Single tower eddy covariance measurements, with a typical 95% flux footprint of < 0.5 km², have been used to assess the ecosystem scale methane exchange. However, how representative single tower flux measurements are of an entire mire complex is not well understood. To address this knowledge gap, the present study takes advantage of a network of four eddy covariance towers located less than 3 km apart at four mires within a typical boreal mire complex in northern Sweden. The variation of methane fluxes and its drivers between the four sites will be explored at different temporal scales, i.e. half-hourly, daily and at a growing-season scale.</p>

scholarly journals A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions

2018 ◽  
Vol 15 (9) ◽  
pp. 2909-2930 ◽  
Author(s):  
Sebastian Lienert ◽  
Fortunat Joos
Keyword(s):  
Land Use ◽  
Carbon Emissions ◽  
Latin Hypercube Sampling ◽  
Historical Period ◽  
Model Parameters ◽  
Dynamic Global Vegetation Model ◽  
Order Of Magnitude ◽  
The Difference ◽  
Wood Harvest ◽  
Global Vegetation

Abstract. A dynamic global vegetation model (DGVM) is applied in a probabilistic framework and benchmarking system to constrain uncertain model parameters by observations and to quantify carbon emissions from land-use and land-cover change (LULCC). Processes featured in DGVMs include parameters which are prone to substantial uncertainty. To cope with these uncertainties Latin hypercube sampling (LHS) is used to create a 1000-member perturbed parameter ensemble, which is then evaluated with a diverse set of global and spatiotemporally resolved observational constraints. We discuss the performance of the constrained ensemble and use it to formulate a new best-guess version of the model (LPX-Bern v1.4). The observationally constrained ensemble is used to investigate historical emissions due to LULCC (ELUC) and their sensitivity to model parametrization. We find a global ELUC estimate of 158 (108, 211) PgC (median and 90 % confidence interval) between 1800 and 2016. We compare ELUC to other estimates both globally and regionally. Spatial patterns are investigated and estimates of ELUC of the 10 countries with the largest contribution to the flux over the historical period are reported. We consider model versions with and without additional land-use processes (shifting cultivation and wood harvest) and find that the difference in global ELUC is on the same order of magnitude as parameter-induced uncertainty and in some cases could potentially even be offset with appropriate parameter choice.

scholarly journals The Jena Diversity-Dynamic Global Vegetation Model (JeDi-DGVM): a diverse approach to representing terrestrial biogeography and biogeochemistry based on plant functional trade-offs

2013 ◽  
Vol 10 (6) ◽  
pp. 4137-4177 ◽  
Author(s):  
R. Pavlick ◽  
D. T. Drewry ◽  
K. Bohn ◽  
B. Reu ◽  
A. Kleidon
Keyword(s):  
Functional Diversity ◽  
Land Surface ◽  
Grid Cell ◽  
Surface Fluxes ◽  
Growth Strategies ◽  
Vegetation Model ◽  
Terrestrial Biosphere ◽  
Dynamic Global Vegetation Model ◽  
Trade Offs ◽  
Global Vegetation

Abstract. Terrestrial biosphere models typically abstract the immense diversity of vegetation forms and functioning into a relatively small set of predefined semi-empirical plant functional types (PFTs). There is growing evidence, however, from the field ecology community as well as from modelling studies that current PFT schemes may not adequately represent the observed variations in plant functional traits and their effect on ecosystem functioning. In this paper, we introduce the Jena Diversity-Dynamic Global Vegetation Model (JeDi-DGVM) as a new approach to terrestrial biosphere modelling with a richer representation of functional diversity than traditional modelling approaches based on a small number of fixed PFTs. JeDi-DGVM simulates the performance of a large number of randomly generated plant growth strategies, each defined by a set of 15 trait parameters which characterize various aspects of plant functioning including carbon allocation, ecophysiology and phenology. Each trait parameter is involved in one or more functional trade-offs. These trade-offs ultimately determine whether a strategy is able to survive under the climatic conditions in a given model grid cell and its performance relative to the other strategies. The biogeochemical fluxes and land surface properties of the individual strategies are aggregated to the grid-cell scale using a mass-based weighting scheme. We evaluate the simulated global biogeochemical patterns against a variety of field and satellite-based observations following a protocol established by the Carbon-Land Model Intercomparison Project. The land surface fluxes and vegetation structural properties are reasonably well simulated by JeDi-DGVM, and compare favourably with other state-of-the-art global vegetation models. We also evaluate the simulated patterns of functional diversity and the sensitivity of the JeDi-DGVM modelling approach to the number of sampled strategies. Altogether, the results demonstrate the parsimonious and flexible nature of a functional trade-off approach to global vegetation modelling, i.e. it can provide more types of testable outputs than standard PFT-based approaches and with fewer inputs. The approach implemented here in JeDi-DGVM sets the foundation for future applications that will explore the impacts of explicitly resolving diverse plant communities, allowing for a more flexible temporal and spatial representation of the structure and function of the terrestrial biosphere.

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