Peter M. Lafleur

This study extends our previous work showing that a process-based (PB) model, the DIMONA PB model, could accurately simulate peatland soil water dynamics when driven by water table depth, d_WT, or by near surface soil water contents, θ. Here, we explore the model’s ability to simulate the peatland canopy photosynthesis, growth, biomass, height, and gross primary productivity (GPP) of vascular plants and bryophytes—thus ecosystem GPP—using either of these drivers. The DIMONA PB model is embedded into the DIMONA online modelling platform, a web application capable of ingesting data from the Internet and performing machine learning (ML) modelling and Internet of Things (IoT) modelling complementary to PB modelling. We test whether the DIMONA PB model, driven by d_WT (Hypothesis 1) and by near-surface θ (Hypothesis 2), can successfully simulate peatland ecosystem GPP at the Mer Bleue bog (Ontario, Canada). Two model runs were generated, one driven by d_WT and another by near-surface θ. Both model runs performed with similar accuracy. Data fit for simulated on observed GPP reached 0.72 for R², 1.7 umol CO₂ m⁻² s⁻¹ for RMSE, and 0.88 for Willmott’s index of agreement at an hourly time step and 0.91, 0.8 g C m⁻² d⁻¹, and 0.92, respectively, at a daily time step. We use the output from the two model runs to examine whether the model’s modifiers (i.e., equations) for water control can capture the specifics of contrasting hydrological conditions on peatland GPP (Hypothesis 3). Both model runs closely simulated the observed GPP to contrasting peatland hydrological conditions under similar meteorological forcing. We illustrate the ability of the DIMONA platform to facilitate the parameterization of DIMONA models for any geographic location, as well as its ability to perform IoT modelling of real-time photosynthesis at two site locations and ML modelling for ecosystem GPP as a complementary tool to PB modelling.

Long‐term atmospheric CO₂ concentration records have suggested a reduction in the positive effect of warming on high‐latitude carbon uptake since the 1990s. A variety of mechanisms have been proposed to explain the reduced net carbon sink of northern ecosystems with increased air temperature, including water stress on vegetation and increased respiration over recent decades. However, the lack of consistent long‐term carbon flux and in situ soil moisture data has severely limited our ability to identify the mechanisms responsible for the recent reduced carbon sink strength. In this study, we used a record of nearly 100 site‐years of eddy covariance data from 11 continuous permafrost tundra sites distributed across the circumpolar Arctic to test the temperature (expressed as growing degree days, GDD) responses of gross primary production (GPP), net ecosystem exchange (NEE), and ecosystem respiration (ER) at different periods of the summer (early, peak, and late summer) including dominant tundra vegetation classes (graminoids and mosses, and shrubs). We further tested GPP, NEE, and ER relationships with soil moisture and vapor pressure deficit to identify potential moisture limitations on plant productivity and net carbon exchange. Our results show a decrease in GPP with rising GDD during the peak summer (July) for both vegetation classes, and a significant relationship between the peak summer GPP and soil moisture after statistically controlling for GDD in a partial correlation analysis. These results suggest that tundra ecosystems might not benefit from increased temperature as much as suggested by several terrestrial biosphere models, if decreased soil moisture limits the peak summer plant productivity, reducing the ability of these ecosystems to sequester carbon during the summer.

Foundation species have disproportionately large impacts on ecosystem structure and function. As a result, future changes to their distribution may be important determinants of ecosystem carbon (C) cycling in a warmer world. We assessed the role of a foundation tussock sedge (Eriophorum vaginatum) as a climatically vulnerable C stock using field data, a machine learning ecological niche model, and an ensemble of terrestrial biosphere models (TBMs). Field data indicated that tussock density has decreased by ∼0.97 tussocks per m² over the past ∼38 years on Alaska’s North Slope from ∼1981 to 2019. This declining trend is concerning because tussocks are a large Arctic C stock, which enhances soil organic layer C stocks by 6.9% on average and represents 745 Tg C across our study area. By 2100, we project that changes in tussock density may decrease the tussock C stock by 41% in regions where tussocks are currently abundant (e.g. −0.8 tussocks per m² and −85 Tg C on the North Slope) and may increase the tussock C stock by 46% in regions where tussocks are currently scarce (e.g. +0.9 tussocks per m² and +81 Tg C on Victoria Island). These climate-induced changes to the tussock C stock were comparable to, but sometimes opposite in sign, to vegetation C stock changes predicted by an ensemble of TBMs. Our results illustrate the important role of tussocks as a foundation species in determining future Arctic C stocks and highlight the need for better representation of this species in TBMs.

The regional variability in tundra and boreal carbon dioxide (CO₂) fluxes can be high, complicating efforts to quantify sink‐source patterns across the entire region. Statistical models are increasingly used to predict (i.e., upscale) CO₂ fluxes across large spatial domains, but the reliability of different modeling techniques, each with different specifications and assumptions, has not been assessed in detail. Here, we compile eddy covariance and chamber measurements of annual and growing season CO₂ fluxes of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) during 1990–2015 from 148 terrestrial high‐latitude (i.e., tundra and boreal) sites to analyze the spatial patterns and drivers of CO₂ fluxes and test the accuracy and uncertainty of different statistical models. CO₂ fluxes were upscaled at relatively high spatial resolution (1 km²) across the high‐latitude region using five commonly used statistical models and their ensemble, that is, the median of all five models, using climatic, vegetation, and soil predictors. We found the performance of machine learning and ensemble predictions to outperform traditional regression methods. We also found the predictive performance of NEE‐focused models to be low, relative to models predicting GPP and ER. Our data compilation and ensemble predictions showed that CO₂ sink strength was larger in the boreal biome (observed and predicted average annual NEE −46 and −29 g C m⁻² yr⁻¹, respectively) compared to tundra (average annual NEE +10 and −2 g C m⁻² yr⁻¹). This pattern was associated with large spatial variability, reflecting local heterogeneity in soil organic carbon stocks, climate, and vegetation productivity. The terrestrial ecosystem CO₂ budget, estimated using the annual NEE ensemble prediction, suggests the high‐latitude region was on average an annual CO₂ sink during 1990–2015, although uncertainty remains high.

Soils are warming as air temperatures rise across the Arctic and Boreal region concurrent with the expansion of tall-statured shrubs and trees in the tundra. Changes in vegetation structure and function are expected to alter soil thermal regimes, thereby modifying climate feedbacks related to permafrost thaw and carbon cycling. However, current understanding of vegetation impacts on soil temperature is limited to local or regional scales and lacks the generality necessary to predict soil warming and permafrost stability on a pan-Arctic scale. Here we synthesize shallow soil and air temperature observations with broad spatial and temporal coverage collected across 106 sites representing nine different vegetation types in the permafrost region. We showed ecosystems with tall-statured shrubs and trees (>40 cm) have warmer shallow soils than those with short-statured tundra vegetation when normalized to a constant air temperature. In tree and tall shrub vegetation types, cooler temperatures in the warm season do not lead to cooler mean annual soil temperature indicating that ground thermal regimes in the cold-season rather than the warm-season are most critical for predicting soil warming in ecosystems underlain by permafrost. Our results suggest that the expansion of tall shrubs and trees into tundra regions can amplify shallow soil warming, and could increase the potential for increased seasonal thaw depth and increase soil carbon cycling rates and lead to increased carbon dioxide loss and further permafrost thaw.

Atmospheric warming is expected to cause shifts in arctic tundra vegetation composition, especially in the abundance and distribution of shrub species. Greater shrub abundance will impact the carbon exchanges between tundra ecosystems and the atmosphere, including ecosystem respiration. Here, total respiration under the shrub canopy (RT) and its components soil respiration (RS) and respiration from the ground cover vegetation (RG) were investigated at three tundra sites in the Canadian Low Arctic with varying shrub coverage. Seasonal RT and RS mean values were significantly greater (P < 0.05) at the site with greatest shrub abundance; mean values were 3.70 and 3.22 μmol m-2 s-1, respectively. Mean RG did not differ among sites; mean values ranged from 0.45 to 0.52 μmol m-2 s-1. Soil temperature exerted a stronger control on RT and RS compared to soil moisture. Differences in RT and RS among sites were attributed to differences in soil properties, such as soil total N content and bulk density. The...

This paper aims to assess the spatial variability in the response of CO₂ exchange to irradiance across the Arctic tundra during peak season using light response curve (LRC) parameters. This investigation allows us to better understand the future response of Arctic tundra under climatic change. Peak season data were collected during different years (between 1998 and 2010) using the micrometeorological eddy covariance technique from 12 circumpolar Arctic tundra sites, in the range of 64–74° N. The LRCs were generated for 14 days with peak net ecosystem exchange (NEE) using an NEE–irradiance model. Parameters from LRCs represent site-specific traits and characteristics describing the following: (a) NEE at light saturation (F_csat), (b) dark respiration (R_d), (c) light use efficiency (α), (d) NEE when light is at 1000 μmol m⁻² s⁻¹ (F_c1000), (e) potential photosynthesis at light saturation (P_sat) and (f) the light compensation point (LCP). Parameterization of LRCs was successful in predicting CO₂ flux dynamics across the Arctic tundra. We did not find any trends in LRC parameters across the whole Arctic tundra but there were indications for temperature and latitudinal differences within sub-regions like Russia and Greenland. Together, leaf area index (LAI) and July temperature had a high explanatory power of the variance in assimilation parameters (F_csat, F_c1000 and P_sat, thus illustrating the potential for upscaling CO₂ exchange for the whole Arctic tundra. Dark respiration was more variable and less correlated to environmental drivers than were assimilation parameters. This indicates the inherent need to include other parameters such as nutrient availability, substrate quantity and quality in flux monitoring activities.