Search for: All records

Widespread shifts in land cover and land management (LCLM) are being incentivized as tools to mitigate climate change, creating an urgent need for prognostic assessments of how LCLM impacts surface energy balance and temperature. Historically, observational studies have tended to focus on how LCLM impacts surface temperature (T_surf), usually at annual timescales. However, understanding the potential for LCLM change to confer climate adaptation benefits, or to produce unintended adverse consequences, requires careful consideration of impacts on bothT_surfand the near-surface air temperature (T_a,local) when they are most consequential for ecosystem and societal well-being (e.g. on hot summer days). Here, long-term data from 130 AmeriFlux towers distributed between 19–71 °N are used to systematically explore LCLM impacts on bothT_surfandT_a,local, with an explicit focus on midday summer periods when adaptive cooling is arguably most needed. We observe profound impacts of LCLM onT_surfat midday, frequently amounting to differences of 10 K or more from one site to the next. LCLM impacts onT_a,localare smaller but still significant, driving variation of 5–10 K across sites. The magnitude of LCLM impacts on bothT_surfandT_a,localis not well explained by plant functional type, climate regime, or albedo; however, we show that LCLM shifts that enhance ET or increase canopy height are likely to confer a local mid-day cooling benefit for bothT_surfandT_a,localmost of the time. At night, LCLM impacts on temperature are much smaller, such that averaging across the diurnal cycle will underestimate the potential for land cover to mediate microclimate when the consequences for plant and human well-being are most stark. Finally, during especially hot periods, land cover impacts onT_a,localandT_surfare less coordinated, and ecosystems that tend to cool the air during normal conditions may have a diminished capacity to do so when it is very hot. We end with a set of practical recommendations for future work evaluating the biophysical impacts and adaptation potential of LCLM shifts.

 

Nature-based Climate Solutions (NbCS) are managed alterations to ecosystems designed to increase carbon sequestration or reduce greenhouse gas emissions. While they have growing public and private support, the realizable benefits and unintended consequences of NbCS are not well understood. At regional scales where policy decisions are often made, NbCS benefits are estimated from soil and tree survey data that can miss important carbon sources and sinks within an ecosystem, and do not reveal the biophysical impacts of NbCS for local water and energy cycles. The only direct observations of ecosystem-scale carbon fluxes, e.g., by eddy covariance flux towers, have not yet been systematically assessed for what they can tell us about NbCS potentials, and state-of-the-art remote sensing products and land-surface models are not yet being widely used to inform NbCS policy making or implementation. As a result, there is a critical mismatch between the point- and tree- scale data most often used to assess NbCS benefits and impacts, the ecosystem and landscape scales where NbCS projects are implemented, and the regional to continental scales most relevant to policy making. Here, we propose a research agenda to confront these gaps using data and tools that have long been used to understand the mechanisms driving ecosystem carbon and energy cycling, but have not yet been widely applied to NbCS. We outline steps for creating robust NbCS assessments at both local to regional scales that are informed by ecosystem-scale observations, and which consider concurrent biophysical impacts, future climate feedbacks, and the need for equitable and inclusive NbCS implementation strategies. We contend that these research goals can largely be accomplished by shifting the scales at which pre-existing tools are applied and blended together, although we also highlight some opportunities for more radical shifts in approach. 

The oak (Quercus) species of eastern North America are declining in abundance, threatening the many socioecological benefits they provide. We discuss the mechanisms responsible for their loss, many of which are rooted in the prevailing view that oaks are drought tolerant. We then synthesize previously published data to comprehensively review the drought response strategies of eastern US oaks, concluding that whether or not eastern oaks are drought tolerant depends firmly on the metric of success. Although the anisohydric strategy of oaks sometimes confers a gas exchange and growth advantage, it exposes oaks to damaging hydraulic failure, such that oaks are just as or more likely to perish during drought than neighboring species. Consequently, drought frequency is not a strong predictor of historic patterns of oak abundance, although long-term climate and fire frequency are strongly correlated with declines in oak dominance. The oaks’ ability to survive drought may become increasingly difficult in a drier future.

 

Recent advances in remote sensing of solar‐induced chlorophyll fluorescence (SIF) have garnered wide interest from the biogeoscience and Earth system science communities, due to the observed linearity between SIF and gross primary productivity (GPP) at increasing spatiotemporal scales. Three recent studies, Maguire et al., (2020,https://doi.org/10.1029/2020GL087858), He et al. (2020,https://doi.org/10.1029/2020GL087474), and Marrs et al. (2020,https://doi.org/10.1029/2020GL087956) highlight a nonlinear relationship between fluorescence and photochemical yields and show empirical evidence for the decoupling of SIF, stomata, and the carbon reactions of photosynthesis. Such mechanistic studies help advance our understanding of what SIF is and what it is not. We argue that these findings are not necessarily contradictory to the linear SIF‐GPP relationship observed at the satellite scale and provide context for where, when, and why fluorescence and photosynthesis diverge at smaller spatiotemporal scales. Understanding scale dependencies of remote sensing data is crucial for interpreting SIF as a proxy for GPP.