Biomass offsets little or none of permafrost carbon release from soils, streams, and wildfire: an expert assessment

As the permafrost region warms, its large organic carbon pool will be increasingly vulnerable to decomposition, combustion, and hydrologic export. Models predict that some portion of this release will be offset by increased production of Arctic and boreal biomass; however, the lack of robust estimates of net carbon balance increases the risk of further overshooting international emissions targets. Precise empirical or model-based assessments of the critical factors driving carbon balance are unlikely in the near future, so to address this gap, we present estimates from 98 permafrost-region experts of the response of biomass, wildfire, and hydrologic carbon flux to climate change. Results suggest that contrary to model projections, total permafrost-region biomass could decrease due to water stress and disturbance, factors that are not adequately incorporated in current models. Assessments indicate that end-of-the-century organic carbon release from Arctic rivers and collapsing coastlines could increase by 75% while carbon loss via burning could increase four-fold. Experts identified water balance, shifts in vegetation community, and permafrost degradation as the key sources of uncertainty in predicting future system response. In combination with previous findings, results suggest the permafrost region will become a carbon source to the atmosphere by 2100 regardless of warming scenario but that 65%–85% of permafrost carbon release can still be avoided if human emissions are actively reduced

Functional convergence in regulation of net CO2 flux in heterogeneous tundra landscapes in Alaska and Sweden

1. Arctic landscapes are characterized by extreme vegetation patchiness, often with sharply defined borders between very different vegetation types. This patchiness makes it difficult to predict landscape-level C balance and its change in response to environment. 2. Here we develop a model of net CO2 flux by arctic landscapes that is independent of vegetation composition, using instead a measure of leaf area derived from NDVI (normalized-difference vegetation index). 3. Using the light response of CO2 flux (net ecosystem exchange, NEE) measured in a wide range of vegetation in arctic Alaska and Sweden, we exercise the model using various data subsets for parameter estimation and tests of predictions. 4. Overall, the model consistently explains similar to 80% of the variance in NEE knowing only the estimated leaf area index (LAI), photosynthetically active photon flux density (PPFD) and air temperature. 5. Model parameters derived from measurements made in one site or vegetation type can be used to predict NEE in other sites or vegetation types with acceptable accuracy and precision. Further improvements in model prediction may come from incorporating an estimate of moss area in addition to LAI, and from using vegetation-specific estimates of LAI. 6. The success of this model at predicting NEE independent of any information on species composition indicates a high level of convergence in canopy structure and function in the arctic landscape

Linking Hydrology and Biogeochemistry to assess the impact of Lateral Nutrient Fluxes

Until recently, it has been challenging to couple hydrological and biogeochemical processes at the watershed scale. We have coupled two models, WTB and MEL, to simulate lateral water and nutrient fluxes and their influence on ecosystem functioning. WTB is a spatially explicit water balance model. Vertical flow was simulated using a capacitance model with lateral flow dependent on head development and the local slope of the confining layer. The Multiple Element Limitation (MEL) model is an ecosystem model, developed to examine limitation in vegetation acclimating to changes in the availability of two resources (carbon and nitrogen). MEL also incorporates the recycling of resources through the soil. In our coupled model, nutrients are treated as inert solutes and are transported vertically as well as laterally using a mixing model. Nutrients moving down the slope are repeatedly taken up, cycled through vegetation and soils, and released back into the soil solution. We are currently identifying the possibilities for incorporating flood dynamics into the model. We evaluated the impact of adding lateral nutrient fluxes to the original MEL model using a virtual experiment. The model (coupled and MEL only) was applied to a small, well defined catchment. After a simulation period of three years, we detect a redistribution of the stock of inorganic N. A larger amount of N is present near the river than at the top of the slopes of the catchment, largely due to lateral fluxes. Comparing the coupled model to the MEL model, we also find large losses of inorganic N in the coupled model due to large vertical fluxes out of the root zone. These vertical out-fluxes cause a smaller N uptake by plants. To detect if Carbon (C) uptake by plants is affected due to the changes in N distribution, the simulation period has to be increased due to a lag time in the optimization of the C:N ratio in plant biomass

Predicting gross primary productivity in terrestrial ecosystems

Our goal was to construct a simple, highly aggregated model, driven by easily available data sets, that accurately predicted terrestrial gross primary productivity (GPP; carboxylation plus oxygenation) in diverse environments and ecosystems. Our starting point was a fine-scale, multilayer model of half-hourly canopy processes that has been parametrized for Harvard Forest, Massachusetts. Over varied growing season conditions, this fine-scale model predicted hourly carbon and latent energy fluxes that were in good agreement with data from eddy covariance studies. Using an heuristic process, we derived a simple aggregated set of equations operating on cumulative or average values of the most sensitive driving variables (leaf area index, mean foliar N concentration, canopy height, average daily temperature and temperature range, atmospheric transmittance, latitude, day of year, atmospheric CO2 concentration, and an index of soil moisture). We calibrated the aggregated model to provide estimates of GPP similar to those of the fine-scale model across a wide range of these driving variables. Our calibration across this broad range of conditions captured 96% of fine-scale model behavior, but was computationally many orders of magnitude faster. We then tested the assumptions we had made in generating the aggregated model by applying it in different ecosystems. Using the same parameter values derived for Harvard Forest, the aggregated model made sound predictions of GPP for wet-sedge tundra in the Arctic under a variety of experimental manipulations, and also for a range of forest types across the OTTER (Oregon Transect Ecosystem Research) transect in Oregon, running from coastal Sitka spruce to high-plateau mountain juniper

Pan-Arctic modelling of net ecosystem exchange of CO2

Net ecosystem exchange (NEE) of C varies greatly among Arctic ecosystems. Here, we show that approximately 75 per cent of this variation can be accounted for in a single regression model that predicts NEE as a function of leaf area index (LAI), air temperature and photosynthetically active radiation (PAR). The model was developed in concert with a survey of the light response of NEE in Arctic and subarctic tundras in Alaska, Greenland, Svalbard and Sweden. Model parametrizations based on data collected in one part of the Arctic can be used to predict NEE in other parts of the Arctic with accuracy similar to that of predictions based on data collected in the same site where NEE is predicted. The principal requirement for the dataset is that it should contain a sufficiently wide range of measurements of NEE at both high and low values of LAI, air temperature and PAR, to properly constrain the estimates of model parameters. Canopy N content can also be substituted for leaf area in predicting NEE, with equal or greater accuracy, but substitution of soil temperature for air temperature does not improve predictions. Overall, the results suggest a remarkable convergence in regulation of NEE in diverse ecosystem types throughout the Arctic

Scaling an Instantaneous Model of Tundra NEE to the Arctic Landscape

We scale a model of net ecosystem CO 2 exchange (NEE) for tundra ecosystems and assess model performance using eddy covariance measurements at three tundra sites. The model, initially developed using instantaneous (seconds-minutes) chamber flux (~m 2) observations, independently represents ecosystem respiration (ER) and gross primary production (GPP), and requires only temperature (T), photosynthetic photon flux density (I 0), and leaf area index (L) as inputs. We used a synthetic data set to parameterize the model so that available in situ observations could be used to assess the model. The model was then scaled temporally to daily resolution and spatially to about 1 km 2 resolution, and predicted values of NEE, and associated input variables, were compared to observations obtained from eddy covariance measurements at three flux tower sites over several growing seasons. We compared observations to modeled NEE calculated using T and I 0 measured at the towers, and L derived from MODIS data. Cumulative NEE estimates were within 17 and 11% of instrumentation period and growing season observations, respectively. Predictions improved when one site-year experiencing anomalously dry conditions was excluded