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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 are 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 multiscale observations of T can across a range of ecosystems., (© 2021 The Authors New Phytologist © 2021 New Phytologist Foundation.)

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]