HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands

Wetlands are one of the most significant natural sources of methane (CH4) to the atmosphere. They emit CH4 because decomposition of soil organic matter in waterlogged anoxic conditions produces CH4, in addition to carbon dioxide (CO2). Production of CH4 and how much of it escapes to the atmosphere depend on a multitude of environmental drivers. Models simulating the processes leading to CH4 emissions are thus needed for upscaling observations to estimate present CH4 emissions and for producing scenarios of future atmospheric CH4 concentrations. Aiming at a CH4 model that can be added to models describing peatland carbon cycling, we developed a model called HIMMELI that describes CH4 build-up in and emissions from peatland soils. It is not a full peatland carbon cycle model but it requires the rate of anoxic soil respiration as input. Driven by soil temperature, leaf area index (LAI) of aerenchymatous peatland vegetation and water table depth (WTD), it simulates the concentrations and transport of CH4, CO2 and oxygen (O2) in a layered one-dimensional peat column. Here, we present the HIMMELI model structure, results of tests on the model sensitivity to the input data and to the description of the peat column (peat depth and layer thickness), and an intercomparison of the modelled and measured CH4 fluxes at Siikaneva, a peatland flux measurement site in Southern Finland. As HIMMELI describes only the CH4-related processes, not the full carbon cycle, our analysis revealed mechanisms and dependencies that may remain hidden when testing CH4 models connected to complete peatland carbon models, which is usually the case. Our results indicated that 1) the model is flexible and robust and thus suitable for different environments; 2) the simulated CH4 emissions largely depend on the prescribed rate of anoxic respiration; 3) the sensitivity of the total CH4 emission to other input variables, LAI and WTD, is mainly mediated via the O2 concentrations that affect the CH4 production and oxidation rates; 4) with given input respiration, the peat column description does not affect significantly the simulated CH4 emissions

Seasonal Dynamics of Mobile Carbon Supply in Quercus aquifolioides at the Upper Elevational Limit

Many studies have tried to explain the physiological mechanisms of the alpine treeline phenomenon, but the debate on the alpine treeline formation remains controversial due to opposite results from different studies. The present study explored the carbon-physiology of an alpine shrub species (Quercus aquifolioides) grown at its upper elevational limit compared to lower elevations, to test whether the elevational limit of alpine shrubs (<3 m in height) are determined by carbon limitation or growth limitation. We studied the seasonal variations in non-structural carbohydrate (NSC) and its pool size in Q. aquifolioides grown at 3000 m, 3500 m, and at its elevational limit of 3950 m above sea level (a.s.l.) on Zheduo Mt., SW China. The tissue NSC concentrations along the elevational gradient varied significantly with season, reflecting the season-dependent carbon balance. The NSC levels in tissues were lowest at the beginning of the growing season, indicating that plants used the winter reserve storage for re-growth in the early spring. During the growing season, plants grown at the elevational limit did not show lower NSC concentrations compared to plants at lower elevations, but during the winter season, storage tissues, especially roots, had significantly lower NSC concentrations in plants at the elevational limit compared to lower elevations. The present results suggest the significance of winter reserve in storage tissues, which may determine the winter survival and early-spring re-growth of Q. aquifolioides shrubs at high elevation, leading to the formation of the uppermost distribution limit. This result is consistent with a recent hypothesis for the alpine treeline formation

Efficient Bayesian inference for large chaotic dynamical systems

Estimating parameters of chaotic geophysical models is challenging due to their inherent unpredictability. These models cannot be calibrated with standard least squares or filtering methods if observations are temporally sparse. Obvious remedies, such as averaging over temporal and spatial data to characterize the mean behavior, do not capture the subtleties of the underlying dynamics. We perform Bayesian inference of parameters in high-dimensional and computationally demanding chaotic dynamical systems by combining two approaches: (i) measuring model-data mismatch by comparing chaotic attractors and (ii) mitigating the computational cost of inference by using surrogate models. Specifically, we construct a likelihood function suited to chaotic models by evaluating a distribution over distances between points in the phase space; this distribution defines a summary statistic that depends on the geometry of the attractor, rather than on pointwise matching of trajectories. This statistic is computationally expensive to simulate, compounding the usual challenges of Bayesian computation with physical models. Thus, we develop an inexpensive surrogate for the log likelihood with the local approximation Markov chain Monte Carlo method, which in our simulations reduces the time required for accurate inference by orders of magnitude. We investigate the behavior of the resulting algorithm with two smaller-scale problems and then use a quasi-geostrophic model to demonstrate its large-scale application

Efficient Bayesian inference for large chaotic dynamical systems

Abstract Estimating parameters of chaotic geophysical models is challenging due to their inherent unpredictability. These models cannot be calibrated with standard least squares or filtering methods if observations are temporally sparse. Obvious remedies, such as averaging over temporal and spatial data to characterize the mean behavior, do not capture the subtleties of the underlying dynamics. We perform Bayesian inference of parameters in high-dimensional and computationally demanding chaotic dynamical systems by combining two approaches: (i) measuring model–data mismatch by comparing chaotic attractors and (ii) mitigating the computational cost of inference by using surrogate models. Specifically, we construct a likelihood function suited to chaotic models by evaluating a distribution over distances between points in the phase space; this distribution defines a summary statistic that depends on the geometry of the attractor, rather than on pointwise matching of trajectories. This statistic is computationally expensive to simulate, compounding the usual challenges of Bayesian computation with physical models. Thus, we develop an inexpensive surrogate for the log likelihood with the local approximation Markov chain Monte Carlo method, which in our simulations reduces the time required for accurate inference by orders of magnitude. We investigate the behavior of the resulting algorithm with two smaller-scale problems and then use a quasi-geostrophic model to demonstrate its large-scale application

Early snowmelt significantly enhances boreal springtime carbon uptake

We determine the annual timing of spring recovery from space-borne microwave radiometer observations across northern hemisphere boreal evergreen forests for 1979–2014. We find a trend of advanced spring recovery of carbon uptake for this period, with a total average shift of 8.1 d (2.3 d/decade). We use this trend to estimate the corresponding changes in gross primary production (GPP) by applying in situ carbon flux observations. Micrometeorological CO2 measurements at four sites in northern Europe and North America indicate that such an advance in spring recovery would have increased the January–June GPP sum by 29 g⋅C⋅m−2 [8.4 g⋅C⋅m−2 (3.7%)/decade]. We find this sensitivity of the measured springtime GPP to the spring recovery to be in accordance with the corresponding sensitivity derived from simulations with a land ecosystem model coupled to a global circulation model. The model-predicted increase in springtime cumulative GPP was 0.035 Pg/decade [15.5 g⋅C⋅m−2 (6.8%)/decade] for Eurasian forests and 0.017 Pg/decade for forests in North America [9.8 g⋅C⋅m−2 (4.4%)/decade]. This change in the springtime sum of GPP related to the timing of spring snowmelt is quantified here for boreal evergreen forests