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  1. Shifts in vegetation phenology are a key example of the biological effects of climate change. However, there is substantial uncertainty about whether these temperature-driven trends will continue, or whether other factors—for example, photoperiod—will become more important as warming exceeds the bounds of historical variability. Here we use phenological transition dates derived from digital repeat photography6 to show that experimental whole-ecosystem warming treatments of up to +9 °C linearly correlate with a delayed autumn green-down and advanced spring green-up of the dominant woody species in a boreal Picea–Sphagnum bog. Results were confirmed by direct observation of both vegetative and reproductive phenologymore » of these and other bog plant species, and by multiple years of observations. There was little evidence that the observed responses were constrained by photoperiod. Our results indicate a likely extension of the period of vegetation activity by 1–2 weeks under a ‘CO 2 stabilization’ climate scenario (+2.6 ± 0.7 °C), and 3–6 weeks under a ‘high-CO 2 emission’ scenario (+5.9 ± 1.1 °C), by the end of the twenty-first century. We also observed severe tissue mortality in the warmest enclosures after a severe spring frost event. Failure to cue to photoperiod resulted in precocious green-up and a premature loss of frost hardiness, which suggests that vulnerability to spring frost damage will increase in a warmer world. Vegetation strategies that have evolved to balance tradeoffs associated with phenological temperature tracking may be optimal under historical climates, but these strategies may not be optimized for future climate regimes. Furthermore, these in situ experimental results are of particular importance because boreal forests have both a circumpolar distribution and a key role in the global carbon cycle.« less
  2. Peatlands contain a large portion of Earth's terrestrial soil organic matter in part due to a reduction in decomposition rates. Organic matter decomposition is initially mediated by extracellular enzyme activity, which is in turn controlled by temperature, moisture, and substrate availability; and all are subject to seasonal variation. As depth increases in peatlands, temperature variability and labile carbon inputs decrease. We hypothesized that the more stable recalcitrant subsurface would contain a smaller less diverse enzyme pool, that is better adapted to a narrow temperature range. Thus temperature dependence would be diminished at depth compared to superficial peat. Potential enzyme activitymore » rates were determined across seasons and with depth in peat samples collected from the Marcell Experimental Forest in northern Minnesota, USA. The temperature dependence, assessed by activation energy, was quantified for three hydrolytic enzymes involved in nutrient cycling at up to 15 temperature points ranging from 2 °C to 65 °C. Potential enzyme activity decreased with peat depth as expected and corresponded with changes in peat composition and microbial biomass from the acrotelm to the catotelm. In an environmentally relevant temperature range (2–23 °C), activation energy decreased with depth for β-glucosidase as predicted and leucine amino peptidase activation energy was the lowest of all enzymes. Stable temperatures at depth appear to result in a microbial community containing enzymes that have lower sensitivity to temperature increases. Surprisingly, there was no significant seasonal effect on enzyme temperature dependence observed in our study. Based on these results, and without shifts in microbial community composition, warming of peat could result in increased carbon and phosphorus cycling at the surface but little change at depth. As a result, differences in enzyme temperature sensitivity suggest nitrogen cycling could remain constant with warming, potentially resulting in proteolytic nitrogen cycling being decoupled from carbon and phosphorus cycling.« less
  3. The ability to forecast ecological carbon cycling is imperative to land management in a world where past carbon fluxes are no longer a clear guide in the Anthropocene. However, carbon–flux forecasting has not been practiced routinely like numerical weather prediction. This study explored (1) the relative contributions of model forcing data and parameters to uncertainty in forecasting flux– versus pool–based carbon cycle variables and (2) the time points when temperature and CO 2 treatments may cause statistically detectable differences in those variables. We developed an online forecasting workflow (Ecological Platform for Assimilation of Data (EcoPAD)), which facilitates iterative data–model integration.more » EcoPAD automates data transfer from sensor networks, data assimilation, and ecological forecasting. We used the Spruce and Peatland Responses Under Changing Experiments data collected from 2011 to 2014 to constrain the parameters in the Terrestrial Ecosystem Model, forecast carbon cycle responses to elevated CO 2 and a gradient of warming from 2015 to 2024, and specify uncertainties in the model output. Our results showed that data assimilation substantially reduces forecasting uncertainties. Interestingly, we found that the stochasticity of future external forcing contributed more to the uncertainty of forecasting future dynamics of C flux–related variables than model parameters. However, the parameter uncertainty primarily contributes to the uncertainty in forecasting C pool–related response variables. Given the uncertainties in forecasting carbon fluxes and pools, our analysis showed that statistically different responses of fast–turnover pools to various CO 2 and warming treatments were observed sooner than slow–turnover pools. In conclusion, our study has identified the sources of uncertainties in model prediction and thus leads to improve ecological carbon cycling forecasts in the future.« less
  4. Accurate simulations of soil respiration and carbon dioxide (CO 2) efflux are critical to project global biogeochemical cycles and the magnitude of carbon (C) feedbacks to climate change in Earth system models (ESMs). Currently, soil respiration is not represented well in ESMs, and few studies have attempted to address this deficiency. In this study, we evaluated the simulation of soil respiration in the Energy Exascale Earth System Model (E3SM) using long-term observations from the Missouri Ozark AmeriFlux (MOFLUX) forest site in the central U.S. Simulations using the default model parameters significantly underestimated annual soil respiration and gross primary production, whilemore » underestimating soil water potential during growing seasons and overestimating it during non-growing seasons. A site-specific soil water retention curve significantly improved modelled soil water potential, gross primary production and soil respiration. However, the model continued to underestimate soil respiration during peak growing seasons, and overestimate soil respiration during non-peak growing seasons. One potential reason may be that the current model does not adequately represent the seasonal cycle of microbial organisms and soil macroinvertebrates, which have high biomass and activity during peak growing seasons and tend to be dormant during non-growing seasons. In conclusion, our results confirm that modelling soil respiration can be significantly improved by better model representations of the soil water retention curve.« less
  5. Here, we are conducting a large-scale, long-term climate change response experiment in an ombrotrophic peat bog in Minnesota to evaluate the effects of warming and elevated CO 2 on ecosystem processes using empirical and modeling approaches. To better frame future assessments of peatland responses to climate change, we characterized and compared spatial vs. temporal variation in measured C cycle processes and their environmental drivers. We also conducted a sensitivity analysis of a peatland C model to identify how variation in ecosystem parameters contributes to model prediction uncertainty. High spatial variability in C cycle processes resulted in the inability to determinemore » if the bog was a C source or sink, as the 95% confidence interval ranged from a source of 50 g C m –2 yr –1 to a sink of 67 g C m –2 yr –1. Model sensitivity analysis also identified that spatial variation in tree and shrub photosynthesis, allocation characteristics, and maintenance respiration all contributed to large variations in the pretreatment estimates of net C balance. Variation in ecosystem processes can be more thoroughly characterized if more measurements are collected for parameters that are highly variable over space and time, and especially if those measurements encompass environmental gradients that may be driving the spatial and temporal variation (e.g., hummock vs. hollow microtopographies, and wet vs. dry years). Together, the coupled modeling and empirical approaches indicate that variability in C cycle processes and their drivers must be taken into account when interpreting the significance of experimental warming and elevated CO 2 treatments.« less
  6. Large uncertainties exist in predicting responses of wetland methane (CH 4) fluxes to future climate change. However, sources of the uncertainty have not been clearly identified despite the fact that methane production and emission processes have been extensively explored. In this study, we took advantage of manual CH 4 flux measurements under ambient environment from 2011 to 2014 at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site and developed a data-informed process-based methane module. The module was incorporated into the Terrestrial ECOsystem (TECO) model before its parameters were constrained with multiple years of methane flux data formore » forecasting CH 4 emission under five warming and two elevated CO 2 treatments at SPRUCE. We found that 9°C warming treatments significantly increased methane emission by approximately 400%, and elevated CO 2 treatments stimulated methane emission by 10.4%–23.6% in comparison with ambient conditions. The relative contribution of plant-mediated transport to methane emission decreased from 96% at the control to 92% at the 9°C warming, largely to compensate for an increase in ebullition. The uncertainty in plant-mediated transportation and ebullition increased with warming and contributed to the overall changes of emissions uncertainties. At the same time, our modeling results indicated a significant increase in the emitted CH 4:CO 2 ratio. This result, together with the larger warming potential of CH 4, will lead to a strong positive feedback from terrestrial ecosystems to climate warming. In conclusion, the model-data fusion approach used in this study enabled parameter estimation and uncertainty quantification for forecasting methane fluxes.« less
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  7. We report that accurate simulation of soil thermal dynamics is essential for realistic prediction of soil biogeochemical responses to climate change. To facilitate ecological forecasting at the Spruce and Peatland Responses Under Climatic and Environmental change site, we incorporated a soil temperature module into a Terrestrial ECOsystem (TECO) model by accounting for surface energy budget, snow dynamics, and heat transfer among soil layers and during freeze-thaw events. We conditioned TECO with detailed soil temperature and snow depth observations through data assimilation before the model was used for forecasting. The constrained model reproduced variations in observed temperature from different soil layers,more » the magnitude of snow depth, the timing of snowfall and snowmelt, and the range of frozen depth. The conditioned TECO forecasted probabilistic distributions of soil temperature dynamics in six soil layers, snow, and frozen depths under temperature treatments of +0.0, +2.25, +4.5, +6.75, and +9.0°C. Air warming caused stronger elevation in soil temperature during summer than winter due to winter snow and ice. And soil temperature increased more in shallow soil layers in summer in response to air warming. Whole ecosystem warming (peat + air warmings) generally reduced snow and frozen depths. The accuracy of forecasted snow and frozen depths relied on the precision of weather forcing. Uncertainty is smaller for forecasting soil temperature but large for snow and frozen depths. Lastly, timely and effective soil thermal forecast, constrained through data assimilation that combines process-based understanding and detailed observations, provides boundary conditions for better predictions of future biogeochemical cycles.« less

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