Canopy temperature (T can) is a key driver of plant function that emerges as a result of interacting biotic and abiotic processes and properties. However, understanding controls on T can and forecasting canopy responses to weather extremes and climate change is difficult due to sparse measurements of T can at appropriate spatial and temporal scales. Burgeoning observations of T can from thermal cameras enable evaluation of energy budget theory and better understanding of how environmental controls, leaf traits, and canopy structure influence temperature patterns. The canopy scale is relevant for connecting to remote sensing and testing biosphere model predictions. We anticipate that future breakthroughs in understanding of ecosystem responses to climate change will result from multi-scale observations of T can across a range of ecosystems.
Understanding and predicting the relationship between leaf temperature ( T leaf ) and air temperature ( T air ) is essential for projecting responses to a warming climate, as studies suggest that many forests are near thermal thresholds for carbon uptake. Based on leaf measurements, the limited leaf homeothermy hypothesis argues that daytime T leaf is maintained near photosynthetic temperature optima and below damaging temperature thresholds. Specifically, leaves should cool below T air at higher temperatures (i.e., > ∼25–30°C) leading to slopes <1 in T leaf / T air relationships and substantial carbon uptake when leaves are cooler than air. This hypothesis implies that climate warming will be mitigated by a compensatory leaf cooling response. A key uncertainty is understanding whether such thermoregulatory behavior occurs in natural forest canopies. We present an unprecedented set of growing season canopy-level leaf temperature ( T can ) data measured with thermal imaging at multiple well-instrumented forest sites in North and Central America. Our data do not support the limited homeothermy hypothesis: canopy leaves are warmer than air during most of the day and only cool below air in mid to late afternoon, leading to T can / T air slopes >1 and hysteretic behavior. We find that the majority of ecosystem photosynthesis occurs when canopy leaves are warmer than air. Using energy balance and physiological modeling, we show that key leaf traits influence leaf-air coupling and ultimately the T can / T air relationship. Canopy structure also plays an important role in T can dynamics. Future climate warming is likely to lead to even greater T can , with attendant impacts on forest carbon cycling and mortality risk.
Understanding the unfolding challenges of climate change relies on climate models, many of which have large summer warm and dry biases over Northern Hemisphere continental midlatitudes. This work, with the example of the model used in the updated version of the weather@home distributed climate model framework, shows the potential for improving climate model simulations through a multiphased parameter refinement approach, particularly over the northwestern United States (NWUS). Each phase consists of (1) creating a perturbed parameter ensemble with the coupled global-regional atmospheric model, (2) building statistical emulators that estimate climate metrics as functions of parameter values, (3) and using the emulators to further refine the parameter space. The refinement process includes sensitivity analyses to identify the most influential parameters for various model output metrics; results are then used to cull parameters with little influence. Three phases of this iterative process are carried out before the results are considered to be satisfactory; that is, a handful of parameter sets are identified that meet acceptable bias reduction criteria. Results not only indicate that 74 % of the NWUS regional warm biases can be reduced by refining global atmospheric parameters that control convection and hydrometeor transport, as well as land surface parameters that affect plant photosynthesis, transpiration, and evaporation, but also suggest that this iterative approach to perturbed parameters has an important role to play in the evolution of physical parameterizations.
Changing climate conditions impact ecosystem dynamics and have local to global impacts on water and carbon cycles. Many processes in dynamic vegetation models (DVMs) are parameterized, and the unknown/unknowable parameter values introduce uncertainty that has rarely been quantified in projections of forced changes. In this study, we identify processes and parameters that introduce the largest uncertainties in the vegetation state simulated by the DVM Top‐down Representation of Interactive Foliage and Flora Including Dynamics (TRIFFID) coupled to a regional climate model. We adjust parameters simultaneously in an ensemble of equilibrium vegetation simulations and use statistical emulation to explore sensitivities to, and interactions among, parameters. We find that vegetation distribution is most sensitive to parameters related to carbon allocation and competition. Using a suite of statistical emulators, we identify regions of parameter space that reduce the error in modeled forest cover by 31±9%. We then generate large initial atmospheric condition ensembles with 10 improved DVM parameterizations under preindustrial, contemporary, and future climate conditions to assess uncertainty in the forced response due to parameterization. We find that while most parameterizations agree on the direction of future vegetation transitions in the western United States, the magnitude varies considerably: for example, in the northwest coast the expansion of broadleaf trees and corresponding decline of needleleaf trees ranges from 4 to 28% across 10 DVM parameterizations under projected future climate conditions. We demonstrate that model parameterization contributes to uncertainty in vegetation transition and carbon cycle feedback under nonstationary climate conditions, which has important implications for carbon stocks, ecosystem services, and climate feedback.
Widespread increases in the burned area over the past half-century are evident across the western United States (US) despite decreases in the number of ignitions (Bowman et al., 2020;Keeley & Syphard, 2019). Several factors are suspected to have contributed to long-term increases in fire activity including the legacy of aggressive and successful fire suppression that has increased aboveground biomass (Rogers et al., 2020), increased human settlement in fire prone lands (Syphard et al., 2007), and climate change that increases fuel dryness and extends the fire season length (e.g., Abatzoglou & Williams, 2016). Extreme wildfires often occur during fire weather extremes (Stavros et al., 2014). This is particularly true in autumn in California and the Pacific Northwest US as a byproduct of chronically dry fuels prior to the onset of the rain season, which creates a flammable landscape, and strong offshore, downslope winds that drive rapid rates of fire spread (Nauslar et al., 2018;Williams et al., 2019). For example, the 2020 Labor Day fires in western Oregon spread rapidly under conditions of near record downslope winds and near record-breaking fire weather (Abatzoglou, Rupp, et al., 2021).Studies have documented increases in autumn fire weather indices and the number of high fire danger days over the past four decades in California (e.g., Goss et al., 2020;Khorshidi et al., 2020). While such changes are consistent with anthropogenic climate change (ACC), statistically rare wind-driven fire weather extremes that have been linked with recent catastrophic fires present a potentially more tenuous link to human-caused climate change given they are a function of both thermodynamic and dynamic elements (National Academy of Sciences, 2016).
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