Fog is a key part of the hydrological cycle in many regions of the world. Coastal fog specifically influences coastal biomes and impacts maritime activities and commerce. Due to complex interactions with landforms, the spatial distributions of coastal fog frequency defy simple characterization. This study is the first to analyze the summertime spatio-temporal heterogeneity of fog along the Oregon coast, a region which depends on coastal fog for shading and as a hydrological input during its warm, dry summers. Direct measures of fog require expensive optical instrumentation that is not broadly deployed. To increase the spatial coverage of in-situ fog records, we used publicly available coastal imagery data sets to detect fog presence at low cost. The coastal imagery consisted of six camera sites located along central and northern Oregon beaches that spanned a 150 km distance. We analyzed the spatio-temporal patterns of this dataset in conjunction with surface weather metrics from the Newport Municipal Airport, which included a visibility sensor for precise fog measurements. We found that the timing of fog occurrence and the overall fog frequency along the coast varied across the locations. Despite this heterogeneity, we found that the average monthly fog frequency at the camera sites had a moderate linear relationship to different metrics of fog and low cloud presence at the airport, suggesting that the airport fog measurements were a reasonable proxy for relative changes in spatially averaged fog frequency. We also found that when there was fog at a camera site, weather conditions at the airport (high relative humidity, low wind speed and lifting condensation level) tended to be supportive of fog formation. However, we found surface weather conditions at the airport were insufficient for real-time fog detection at any particular site along the coast, highlighting the need for more in-situ fog measurements.

The 2021 Pacific Northwest heat dome shows how acute thermal stress challenges prevailing assumptions about ecological resilience and adaptation. Extreme heat events are revealing physiological limits in forests that are not captured by conventional climate risk frameworks.

Temperature of leaves and canopies is critically important for many physiological processes, including photosynthesis, respiration, and transpiration. But the variation of canopy temperature and its relationship with air temperature across a range of ecosystems and environmental conditions is understudied, challenging our ability to predict canopy temperature responses in a rapidly warming climate. Therefore, to better understand how environmental drivers and site characteristics interact with vegetation types to influence these temperature dynamics, we analyzed canopy temperature estimates derived from upwelling longwave radiation measurements across seven years and 36 NEON (National Ecological Observatory Network) sites in the USA. Canopy temperature consistently exceeded or closely tracked air temperature, with the strength and magnitude of this relationship varying by vegetation type. Multiple linear regression analysis confirmed incoming shortwave radiation as the dominant driver of Δ T (canopy temperature—air temperature). While this driver was consistently important across all sites, sites within the same vegetation type tended to respond similarly to the full set of environmental drivers. Vegetation height was associated with variation in the magnitude of Δ T . These findings support the use of vegetation type groupings to capture generalizable patterns in canopy temperature dynamics, patterns that are critical for understanding ecosystem responses under future climate scenarios.

A declining spring snowpack is expected to have widespread effects on montane and subalpine forests in western North America and across the globe. The way that tree water demands respond to this change will have important impacts on forest health and downstream water subsidies. Here, we present data from a network of sap velocity sensors and xylem water isotope measurements from three common tree species (Picea engelmannii, Abies lasiocarpa and Populus tremuloides) across a hillslope transect in a subalpine watershed in the Upper Colorado River basin. We use these data to compare tree- and stand-level responses to the historically high spring snowpack but low summer rainfall of 2019 against the low spring snowpack but high summer rainfall amounts of 2021 and 2022. From the sap velocity data, we found that only 40 % of the trees showed an increase in cumulative transpiration in response to the large snowpack year (2019), illustrating the absence of a common response to interannual spring snowpack variability. The trees that increased water use during the year with the large spring snowpack were all found in dense canopy stands – irrespective of species – while trees in open-canopy stands were more reliant on summer rains and, thus, more active during the years with modest snow and higher summer rain amounts. Using the sap velocity data along with supporting measurements of soil moisture and snow depth, we propose three mechanisms that lead to stand density modulating the tree-level response to changing seasonality of precipitation: Topographically mediated convergence zones have consistent access to recharge from snowmelt which supports denser stands with high water demands that are more reliant and sensitive to changing snow. Interception of summer rain in dense stands reduces the throughfall of summer rain to surface soils, limiting the sensitivity of the dense stands to changes in summer rain. Shading in dense stands allows the snowpack to persist deeper into the growing season, providing high local reliance on snow during the fore-summer (early-summer) drought period. Combining data generated from natural gradients in stand density, like this experiment, with results from controlled forest-thinning experiments can be used to develop a better understanding of the responses of forested ecosystems to futures with reduced spring snowpack.

The climate benefits of some Voluntary Carbon Market projects may be overestimated due to a lack of accounting for albedo change. Here we analyze 172 Afforestation, Reforestation, and Revegetation projects within the market and find more than 10% occur in places where albedo may entirely negate the climate mitigation benefit, and a quarter occur in places where albedo may halve the mitigation benefit. Yet, the majority are concentrated where albedo changes are expected to be minimal, and 9% of projects occur where albedo would augment the mitigation benefit. Recent data are making albedo accounting possible, and we outline an iterative approach for incorporating albedo considerations into carbon crediting protocols to prioritize projects with greater climate benefit and more accurately quantify credits that may be used to address unabated emissions. We also call on the scientific community to create tools to enable accounting for other important biophysical changes, such as evapotranspiration, which is not yet quantifiable within the Voluntary Carbon Market.

Heatwaves threaten ecosystem carbon balances, yet the mechanisms driving short-term carbon flux responses remain poorly understood. Here, integrating high-frequency eddy covariance (EC) data from 140 global flux tower sites (872 site-years) with detailed process-based modeling, we examine ecosystem responses during and immediately after heatwaves. We show that heatwaves caused a −40% (range [−29%, −128%]) reduction in net ecosystem productivity (NEP) compared to pre-heatwave values, with this reduction persisting over the following two weeks (−38% range [+3%, −154%]). We attributed NEP decreases to photosynthesis decreases more than to ecosystem respiration (RE) increases. Forest sites had greater NEP decreases during heatwaves than non-forest sites, but remained carbon sinks afterwards, indicating resilience. Our modeling analysis of extreme heatwaves at selected EC sites shows that decreased photosynthesis, increased maintenance respiration, and decreased plant non-structural carbon reserves during heatwaves drive carbon cycle changes that persist for weeks. Consistent with phenocam observations, we modeled a reduction in leaf area index caused by reduced non-structural carbon reserves, leading to early leaf senescence and longer-term impacts. Ongoing increases in heatwaves are therefore likely to reduce NEP across a range of ecosystems, exacerbating carbon cycle feedback.

To predict ecological responses at broad environmental scales, grass species are commonly grouped into two broad functional types based on photosynthetic pathway. However, closely related species may have distinctive anatomical and physiological attributes that influence ecological responses, beyond those related to photosynthetic pathway alone. Hyperspectral leaf reflectance can provide an integrated measure of covarying leaf traits that may result from phylogenetic trait conservatism and/or environmental conditions. Understanding whether spectra‐trait relationships are lineage specific or reflect environmental variation across sites is necessary for using hyperspectral reflectance to predict plant responses to environmental changes across spatial scales. We measured hyperspectral leaf reflectance (400–2400 nm) and 12 structural, biochemical, and physiological leaf traits from five grass‐dominated sites spanning the Great Plains of North America. We assessed if variation in leaf reflectance spectra among grass species is explained more by evolutionary lineage (as captured by tribes or subfamilies), photosynthetic pathway (C 3 or C 4 ), or site differences. We then determined whether leaf spectra can be used to predict leaf traits within and across lineages. Our results using redundancy analysis ordination (RDA) show that grass tribe identity explained more variation in leaf spectra (adjusted R 2 = 0.12) than photosynthetic pathway, which explained little variation in leaf spectra (adjusted R 2 = 0.00). Furthermore, leaf reflectance from the same tribe across multiple sites was more similar than leaf reflectance from the same site across tribes (adjusted R 2 = 0.12 and 0.08, respectively). Across all sites and species, trait predictions based on spectra ranged considerably in predictive accuracies ( R 2 = 0.65 to <0.01), but R 2 was >0.80 for certain lineages and sites. The relationship between Vc max , a measure of photosynthetic capacity, and spectra was particularly promising. Chloridoideae, a lineage more common at drier sites, appears to have distinct spectra‐trait relationships compared with other lineages. Overall, our results show that evolutionary relatedness explains more variation in grass leaf spectra than photosynthetic pathway or site, but consideration of lineage‐ and site‐specific trait relationships is needed to interpret spectral variation across large environmental gradients.

Global warming increases ecosystem respiration (ER), creating a positive carbon-climate feedback. Thermal acclimation, the direct responses of biological communities to reduce the effects of temperature changes on respiration rates, is a critical mechanism that compensates for warming-induced ER increases and dampens this positive feedback. However, the extent and effects of this mechanism across diverse ecosystems remain unclear. By analyzing CO2 flux data from 93 eddy covariance sites worldwide, we observed thermal acclimation at 84 % of the sites. If sustained, thermal acclimation could reduce projected warming-induced nighttime ER increases by at least 25 % across most climate zones by 2041-2060. Strong thermal acclimation is particularly evident in ecosystems at high elevation, with low-carbon-content soils, and within tundra, semi-arid, and warm-summer Mediterranean climates, supporting the hypothesis that extreme environments favor the evolution of greater acclimation potential. Moreover, ecosystems with dense vegetation and high productivity such as humid tropical and subtropical forests generally exhibit strong thermal acclimation, suggesting that regions with substantial CO2 uptake may continue to serve as strong carbon sinks. Conversely, some ecosystems in cold continental climates show signs of enhancing thermal responses, the opposite of thermal acclimation, which could exacerbate carbon losses as climate warms. Our study underscores the widespread yet climate-specific patterns of thermal acclimation in global terrestrial ER, emphasizing the need to incorporate these patterns into Earth System Models for more accurate carbon-climate feedback projections.

Our understanding of how photosynthetic capacity varies among C4 species and across growth and measurement conditions remains limited. We collated 1696 CO2 response curves of net CO2 assimilation rate (A/Ci curves) from C4 species grown and measured at various environmental conditions and used these data to estimate the apparent maximum carboxylation activity of phosphoenolpyruvate carboxylase (VpmaxA) and CO2-saturated net photosynthetic rate (Amax), two key parameters describing photosynthetic capacity. We examined how VpmaxA and Amax vary with species-specific traits, growth and measurement conditions. We found little systematic variation of VpmaxA and Amax across the classical C4 biochemical subtypes or growth forms, but showed that growth temperature and measurement conditions are major factors determining C4 photosynthetic capacity. We found no evidence that common C4 model species (e.g. maize, sorghum and Setaria viridis) differ in photosynthetic capacity from other C4 species when grown in controlled environments. However, C4 model species showed up to twice the photosynthetic capacity of other C4 species when grown in the field. Our multivariate model accounts for 47-51% of the variation reported in VpmaxA and Amax, and we argue that environmental conditions have a greater influence on C4 photosynthetic capacity than biochemical subtypes or growth forms.

The limited homeothermy hypothesis states that leaves maintain their temperature within an optimal range for photosynthesis by increasing transpiration during warm conditions. Under limited homeothermy, plants may offset thermal stress caused by climate change. If this hypothesis is true, we should observe: 1) leaf temperature increasing slower than air temperature and 2) leaves cooler than air during warm conditions. We tested these predictions with an energy balance model for evergreen needleleaf forest sites in the National Ecological Observatory Network. A key feature of our model was its vertical stratification of the canopy, which allowed us to analyze vertical gradients in canopy temperature. This feature is especially important given that prior work has focused on the tops of forest canopies. Our results do not support limited homeothermy at any canopy position. In all canopy strata, leaf temperature increased faster than air and periods with leaves cooler than air were rare. In such cases, cooling was due to emitted radiation, not transpiration. But, when water was abundant, transpiration could produce mildly homeothermic behavior. We attribute these results to the needle-like shape of leaves in our study sites. This leaf shape increases boundary layer conductance and causes heat gain from surrounding air to overpower heat loss from transpiration when leaves are cooler than air. Our results indicate that needleleaf forests cannot avert thermal stress in a warming world. Thermal limits on photosynthesis and non-linear increases in respiration with temperature may weaken the role of evergreen forests as a global carbon sink.

An issue of global concern is how climate change forcing is transmitted to ecosystems. Forest ecosystems in mountain landscapes may demonstrate buffering and perhaps decoupling of long‐term rates of temperature change, because vegetation, topography, and local winds (e.g., cold air pooling) influence temperature and potentially create microclimate refugia (areas which are relatively protected from climate change). We tested these ideas by comparing 45‐year regional rates of air temperature change to unique temporal and spatial air temperature records in the understory of regionally representative stable old forest at the H.J. Andrews Experimental Forest, Oregon, USA. The 45‐year seasonal patterns and rates of warming were similar throughout the forested landscape and matched regional rates observed at 88 standard meteorological stations in Oregon and Washington, indicating buffering, but not decoupling of long‐term climate change rates. Consideration of the energy balance explains these results: while shading and airflows produce spatial patterns of temperature, these processes do not counteract global increases in air temperature driven by increased downward, longwave radiation forced by increased anthropogenic greenhouse gases in the atmosphere. In some months, the 45‐year warming in the forest understory equaled or exceeded spatial differences of air temperature between the understory and the canopy or canopy openings and was comparable to temperature change over 1,000 m elevation, while in other months there has been little change. These findings have global implications because they indicate that microclimate refugia are transient, even in this forested mountain landscape.

It has been 60 years since the discovery of C4 photosynthesis, an event that rewrote our understanding of plant adaptation, ecosystem responses to global change, and global food security. Despite six decades of research, one aspect of C4 photosynthesis that remains poorly understood is how the pathway fits into the broader context of adaptive trait spectra, which form our modern view of functional trait ecology. The C4 CO2-concentrating mechanism supports a general C4 plant phenotype capable of fast growth and high resource-use efficiencies. The fast-efficient C4 phenotype has the potential to operate at high productivity rates, while allowing for less biomass allocation to root production and nutrient acquisition, thereby providing opportunities for the evolution of novel trait covariances and the exploitation of new ecological niches. We propose the placement of the C4 fast-efficient phenotype near the acquisitive pole of the world-wide leaf economic spectrum, but with a pathway-specific span of trait space, wherein selection shapes both acquisitive and conservative adaptive strategies. A trait-based perspective of C4 photosynthesis will open new paths to crop improvement, global biogeochemical modeling, the management of invasive species, and the restoration of disturbed ecosystems, particularly in grasslands.

Grasses are cosmopolitan, existing in many biome and climate types from xeric to tropical. Traits that control physiological responses to drought vary strongly among grass lineages, suggesting that tolerance strategies may differ with evolutionary history. Here, we withheld water from 12 species representing 6 tribes of grasses to compare how tolerant and intolerant species respond to drought in different grass lineages. We measured physiological, morphological, and microanatomical traits. Dominant lineages from tropical savannas, like Andropogoneae, tolerated drought due to above and belowground morphological traits, while temperate grasses utilized conservative leaf physiology (gas exchange) and microanatomy. Increased intrinsic water-use efficiency (iWUE) coincided with a larger number of stomata, resulting in greater water loss (with inherently greater carbon gain) and increased drought sensitivity. Inherent leaf and root economic strategies impacting drought response were observed in all species, resulting in either high SLA or SRL, but not both. Our results indicate that grasses subjected to severe drought were influenced by microanatomical traits (e.g., number of stomata and xylem area) which were shared within lineages. In addition, grasses recovered at least 50% of physiological functioning across all lineages and 92% within Andropogoneae species, illustrating how drought can influence functional responses across diverse grass lineages.

Hyperspectral remote sensing has the potential to map numerous attributes of the Earth's surface, including spatial patterns of biological diversity. Grasslands are one of the largest biomes on Earth. Accurate mapping of grassland biodiversity relies on spectral discrimination of endmembers of species or plant functional types. We focused on spectral separation of grass lineages that dominate global grassy biomes: Andropogoneae (C-4), Chloridoideae (C-4), and Pooideae (C-3). We examined leaf reflectance spectra (350-2,500 nm) from 43 grass species representing these grass lineages from four representative grassland sites in the Great Plains region of North America. We assessed the utility of leaf reflectance data for classification of grass species into three major lineages and by collection site. Classifications had very high accuracy (94%) that were robust to site differences in species and environment. We also show an information loss using multispectral sensors, that is, classification accuracy of grass lineages using spectral bands provided by current multispectral satellites is much lower (accuracy of 85.2% and 61.3% using Sentinel 2 and Landsat 8 bands, respectively). Our results suggest that hyperspectral data have an exciting potential for mapping grass functional types as informed by phylogeny. Leaf-level hyperspectral separability of grass lineages is consistent with the potential increase in biodiversity and functional information content from the next generation of satellite-based spectrometers.

In this review, we discuss current research on forest carbon risk from natural disturbance under climate change for the United States, with emphasis on advancements in analytical mapping and modeling tools that have potential to drive research for managing future long-term stability of forest carbon. As a natural mechanism for carbon storage, forests are a critical component of meeting climate mitigation strategies designed to combat anthropogenic emissions. Forests consist of long-lived organisms (trees) that can store carbon for centuries or more. However, trees have finite lifespans, and disturbances such as wildfire, insect and disease outbreaks, and drought can hasten tree mortality or reduce tree growth, thereby slowing carbon sequestration, driving carbon emissions, and reducing forest carbon storage in stable pools, particularly the live and standing dead portions that are counted in many carbon offset programs. Many forests have natural disturbance regimes, but climate change and human activities disrupt the frequency and severity of disturbances in ways that are likely to have consequences for the long-term stability of forest carbon. To minimize negative effects and maximize resilience of forest carbon, disturbance risks must be accounted for in carbon offset protocols, carbon management practices, and carbon mapping and modeling techniques. This requires detailed mapping and modeling of the quantities and distribution of forest carbon across the United States and hopefully one day globally; the frequency, severity, and timing of disturbances; the mechanisms by which disturbances affect carbon storage; and how climate change may alter each of these elements. Several tools (e.g. fire spread models, imputed forest inventory models, and forest growth simulators) exist to address one or more of the aforementioned items and can help inform management strategies that reduce forest carbon risk, maintain long-term stability of forest carbon, and further explore challenges, uncertainties, and opportunities for evaluating the continued potential of, and threats to, forests as viable mechanisms for forest carbon storage, including carbon offsets. A growing collective body of research and technological improvements have advanced the science, but we highlight and discuss key limitations, uncertainties, and gaps that remain.

Land surface models (LSMs) play a crucial role in elucidating water and carbon cycles by simulating processes such as plant transpiration and evaporation from bare soil, yet calibration often relies on comparing LSM outputs of landscape total evapotranspiration (ET) and discharge with measured bulk fluxes. Discrepancies in partitioning into component fluxes predicted by various LSMs have been noted, prompting the need for improved evaluation methods. Stable water isotopes serve as effective tracers of component hydrologic fluxes, but data and model integration challenges have hindered their widespread application. Leveraging National Ecological Observation Network measurements of water isotope ratios at 16 US sites over 3 years combined with LSM-modeled fluxes, we employed an isotope-enabled mass balance framework to simulate ET isotope values (delta ET) within three operational LSMs (Mosaic, Noah, and VIC) to evaluate their partitioning. Models simulating delta ET values consistent with observations were deemed more reflective of water cycling in these ecosystems. Mosaic exhibited the best overall performance (Kling-Gupta Efficiency of 0.28). For both Mosaic and Noah there were robust correlations between bare soil evaporation fraction and error (negative) as well as transpiration fraction and error (positive). We found the point at which errors are smallest (x-intercept of the multi-site regression) is at a higher transpiration fraction than is currently specified in the models. Which means that transpiration fraction is underestimated on average. Stable isotope tracers offer an additional tool for model evaluation and identifying areas for improvement, potentially enhancing LSM simulations and our understanding of land-surface hydrologic processes.

Coastal fog occurs along many of the world’s west coast continental environments. It is particularly consequential during summer when an increased frequency of fog co-occurs with the seasonal dryness characteristic of most west coast climate systems, for example, in the Pacific coast of North and South America, the southwestern African coast, and southern coastal Europe. Understanding coastal fog formation and effects has consequences for many disciplines, including the physical (e.g., atmospheric science, oceanography), biological (e.g., biogeography, ecophysiology), and socio-ecological realms (e.g., Indigenous cultural knowledge, public safety, economics). Although research practices differ across disciplines, they share many of the challenges needed to advance fog science. For example, coastal fog remains difficult to reliably monitor when, where, and why it occurs, which adds difficulty to understanding fog’s effects on all facets of the integrated coastal system. These shared challenges provide ripe opportunities for interdisciplinary collaboration, a template with past success in advancing fog-related science that can continue to have success in the future. In this perspectives review, we summarize the current status and frontiers of fog-related science from multiple disciplines, leveraging examples primarily drawn from the Pacific Northwest coastal region of the United States to show how interdisciplinary collaboration is needed to continue to advance our collective understanding of coastal fog formation and effects on west coast environments.

Although natural climate solutions (NCS) are increasingly deployed within carbon markets and beyond, their climate benefits may be over-estimated due to a lack of accounting for changes in albedo. Here we analyze 172 Afforestation, Reforestation, and Revegetation projects within the voluntary carbon market and find more than 10% occur in places where albedo entirely negates the climate mitigation benefit and a quarter occur in places where albedo halves the mitigation benefit. Yet, the majority of projects are concentrated where albedo changes are expected to be minimal, and 9% of projects occur where albedo would augment the mitigation benefit. New data make albedo accounting possible for the first time, and we outline an iterative approach for incorporating albedo considerations into carbon crediting protocols. Doing so will enable prioritization of projects with greater climate benefit and more accurately quantify credits that may be used to address unabated emissions.