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Mechanistic representations of biogeochemical processes in ecosystem models are rapidly advancing, requiring advancements in model evaluation approaches. Here we quantify multiple aspects of model functional performance to evaluate improved process representations in ecosystem models. We compare semi‐empirical stomatal models with hydraulic constraints against more mechanistic representations of stomatal and hydraulic functioning at a semi‐arid pine site using a suite of metrics and analytical tools. We find that models generally perform similarly under unstressed conditions, but performance diverges under atmospheric and soil drought. The more empirical models better capture synergistic information flows between soil water potential and vapor pressure deficit to transpiration, while the more mechanistic models are overly deterministic. Although models can be parameterized to yield similar functional performance, alternate parameterizations could not overcome structural model constraints that underestimate the unique information contained in soil water potential about transpiration. Additionally, both multilayer canopy and big‐leaf models were unable to capture the magnitude of canopy temperature divergence from air temperature, and we demonstrate that errors in leaf temperature can propagate to considerable error in simulated transpiration. This study demonstrates the value of merging underutilized observational data streams with emerging analytical tools to characterize ecosystem function and discriminate among model process representations. Plain Language Summary: Earth system models are an essential tool for understanding the consequences of changing climate conditions on forest ecosystems. Models are rapidly incorporating more realistic representations of how drought impacts ecosystem carbon and water cycling. These advancements need to be thoroughly evaluated to ensure that the models adequately capture the plant functional response to drought stress. Here we merge underutilized measurements with new analytical tools to evaluate several model representations of plant response to drought. These tools allow us to both better understand relationships among drought stress and ecosystem response, as well as quantify model accuracy. We find that models generally perform similarly under unstressed conditions, but performance diverges under drought. Key Points: We evaluate several model formulations for coupling plant hydraulic and stomatal response using functional performance metricsInformation flows from soil water potential and vapor pressure deficit to transpiration reflect structural differences among modelsConsiderable biases in modeled canopy temperature propagate to a 5% offset in cumulative growing season transpiration [ABSTRACT FROM AUTHOR]

Extreme precipitation events are expected to increase in magnitude in response to global warming, but the magnitude of the forced response may vary considerably across distances of ~ 100 km or less. To examine the spatial variability of extreme precipitation and its sensitivity to global warming with high statistical certainty, we use a large (16,980 years), initial-condition ensemble of dynamically downscaled global climate model simulations. Under approximately 2 °C of global warming above a recent baseline period, we find large variability in the change (0 to > 60%) of the magnitude of very rare events (from 10 to 1000-year return period values of annual maxima of daily precipitation) across the western United States. Western (and predominantly windward) slopes of coastal ranges, the Cascades, and the Sierra Nevada typically show smaller increases in extreme precipitation than eastern slopes and bordering valleys and plateaus, but this pattern is less evident in the continental interior. Using the generalized extreme value shape parameter to characterize the tail of the precipitation distribution (light to heavy tail), we find that heavy tails dominate across the study region, but light tails are common on the western slopes of mountain ranges. The majority of the region shows a tendency toward heavier tails under warming, though some regions, such as plateaus of eastern Oregon and Washington, and the crest of the Sierra Nevada, show a lightening of tails. Spatially, changes in long return-period precipitation amounts appear to partially result from changes in the shape of the tail of the distribution. [ABSTRACT FROM AUTHOR]

Extreme wind‐driven autumn wildfires are hazardous to life and property, due to their rapid rate of spread. Recent catastrophic autumn wildfires in the western United States co‐occurred with record‐ or near‐record autumn fire weather indices that are a byproduct of extreme fuel dryness and strong offshore dry winds. Here, we use a formal, probabilistic, extreme event attribution analysis to investigate the anthropogenic influence on extreme autumn fire weather in 2017 and 2018. We show that while present‐day anthropogenic climate change has slightly decreased the prevalence of strong offshore downslope winds, it has increased the likelihood of extreme fire weather indices by 40% in areas where recent autumn wind‐driven fires have occurred in northern California and Oregon. The increase was primarily through increased autumn fuel aridity and warmer temperatures during dry wind events. These findings illustrate that anthropogenic climate change is exacerbating autumn fire weather extremes that contribute to high‐impact catastrophic fires in populated regions of the western US. Plain Language Summary: Over the last several years, California and western Oregon have seen their largest and most destructive wildfires on record. The rapid and extensive growth of many of these fires that invaded populated areas was driven by strong, dry, offshore, downslope autumn winds over fuels that had become exceedingly dry over the summer and remained dry into autumn. We used simulations of both the modern‐era climate and a climate that could have been, absent human influence, to investigate the effect of anthropogenic climate change on the likelihood of extreme fire weather conditions (warm, very dry, and very windy) that were present during recent catastrophic wildfires. Despite a small decrease in the frequency of strong offshore winds, anthropogenic climate change has already increased the likelihood of extreme autumn fire weather across most of the west coast of the US through higher temperature and drier fuels, heightening the risk to life and property. Key Points: Anthropogenic climate change has already increased the likelihood of autumn wind‐driven extreme fire weather conditions in the western USIncreased autumn fuel aridity and warmer temperatures during dry wind events increased the likelihood of extreme fire weather in 2017 and 2018 indices by 40%Present‐day anthropogenic climate change has slightly decreased the prevalence of strong offshore downslope winds [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Changing climate conditions can impact the composition of an ecosystem. As plant types better suited for the new climate conditions begin to thrive, they can out‐compete the historical vegetation. This transformation can have impacts on the local water cycle and, if changes are large enough, on the global carbon cycle. Dynamic vegetation models (DVMs) are used to analyze the impacts of climate on vegetation and project future changes. DVMs are used across scales ranging from watersheds, informing management decisions, to the globe as an important component of the global carbon cycle. The mathematical representations of processes in DVMs are imperfect and introduce uncertainty in model projections. In this study we identify processes that introduce the largest uncertainties in the vegetation distribution modeled by a DVM. We then identify settings of model parameters that improve the modeled historical vegetation when compared to observations. While unique model parameterizations simulated the historical vegetation well, each parameterization simulated different future vegetation transitions under future climate conditions. Our study shows that model parameterization introduces uncertainty in the projected future changes in vegetation. Quantifying and appreciating this uncertainty are important when considering management decisions based on projected vegetation changes at the local to global scale. Key Points: Multiple unique parameterizations of a dynamic vegetation model met evaluation criteriaParameterizations simulated varying amounts of vegetation transitions under nonstationary climate contributing to uncertainty in the simulated carbon cycleSimulated vegetation distributions were most sensitive to parameters related to carbon allocation and competition [ABSTRACT FROM AUTHOR]

• Douglas-fir water stress was driven by elevated vapor pressure deficit. • Elevated VPD reduced GPP more than decreased rainfall during the growing season. • Decreasing spring rainfall did not significantly alter deep soil water content. Understanding how trees respond to drought is critical to understanding forest sensitivity to global climate change, which can help inform forest policy and management decisions. However, mechanisms governing carbon fixation and water fluxes in response to increased temperatures and water limitation in regions with Mediterranean climates, with wet winters and dry summers, remain only partially understood. We tested the effect of increased vapor pressure deficit (VPD) and decreased rainfall on water and carbon fluxes of Douglas-fir (Pseudotsuga menziesii) trees using the Soil-Plant-Atmosphere model (SPA). We simulated a 50-year-old Douglas-fir stand on the western slopes of the Cascade Mountains in Oregon, USA. Simulation results showed that increasing the daily maximum VPD by 0.25–2.5 kPa during the summer increased cumulative transpiration by 1–3% and decreased cumulative gross primary production by 3–25%. In contrast, decreasing rainfall by 10–100% during the spring and summer decreased cumulative transpiration by 2–16% and decreased cumulative gross primary production by 0.5–4%. Transpiration was highly sensitive to decreases in rainfall, especially in late spring and early summer but much less sensitive to increases in maximum daily VPD. In contrast, gross primary productivity was much more sensitive to VPD, with summertime increases in VPD having a 5- to 6-fold greater effect on gross primary productivity than did decreasing the rainfall. These results suggested that temperature increases expected from climate change in combination with increases in VPD are likely to reduce forest productivity regardless of soil moisture availability. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 mid-latitudes. This work, using 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 multi-phased parameter refinement approach, particularly over northwestern United States(NWUS). Each phase consists of 1) creating a perturbed physics 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, and land surface parameters that affect plant photosynthesis, transpiration and evaporation, but also suggest that this iterative approach to perturbed physics has an important role to play in the evolution of physical parameterizations. [ABSTRACT FROM AUTHOR]

Understanding and predicting the relationship between leaf temperature (Tleaf) and air temperature (Tair) 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 Tleaf is maintained near photosynthetic temperature optima and below damaging temperature thresholds. Specifically, leaves should cool below Tair at higher temperatures (i.e., > ~25-30°C) leading to slopes <1 in Tleaf/Tair 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 (Tcan) 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 Tcan/Tair 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 Tcan/Tair relationship. Canopy structure also plays an important role in Tcan dynamics. Future climate warming is likely to lead to even greater Tcan, with attendant impacts on forest carbon cycling and mortality risk. [ABSTRACT FROM AUTHOR]

Summary: Canopy temperature Tcan is a key driver of plant function that emerges as a result of interacting biotic and abiotic processes and properties. However, understanding controls on Tcan and forecasting canopy responses to weather extremes and climate change are difficult due to sparse measurements of Tcan at appropriate spatial and temporal scales. Burgeoning observations of Tcan 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 multiscale observations of Tcan across a range of ecosystems. [ABSTRACT FROM AUTHOR]