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Plants with the C 4 photosynthesis pathway typically respond to climate change differently from more common C 3 -type plants, due to their distinct anatomical and biochemical characteristics. These different responses are expected to drive changes in global C 4 and C 3 vegetation distributions. However, current C 4 vegetation distribution models may not predict this response as they do not capture multiple interacting factors and often lack observational constraints. Here, we used global observations of plant photosynthetic pathways, satellite remote sensing, and photosynthetic optimality theory to produce an observation-constrained global map of C 4 vegetation. We find that global C 4 vegetation coverage decreased from 17.7% to 17.1% of the land surface during 2001 to 2019. This was the net result of a reduction in C 4 natural grass cover due to elevated CO 2 favoring C 3 -type photosynthesis, and an increase in C 4 crop cover, mainly from corn (maize) expansion. Using an emergent constraint approach, we estimated that C 4 vegetation contributed 19.5% of global photosynthetic carbon assimilation, a value within the range of previous estimates (18–23%) but higher than the ensemble mean of dynamic global vegetation models (14 ± 13%; mean ± one standard deviation). Our study sheds insight on the critical and underappreciated role of C 4 plants in the contemporary global carbon cycle. Due to fundamental anatomical and biochemical differences, C 3 and C 4 plant species tend to differ in their biogeography and response to climate change. Here, the authors use global observations and optimality theory to map patterns and temporal trends in C 4 species distribution and the contribution of C 4 plants to global photosynthesis.

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. The paper analyzes the impact of omitting biophysical effects from carbon credits on climate mitigation. It shows that some Voluntary Carbon Market projects may result in net warming due to albedo while others enhance cooling.

Tropical forests are hyper‐diverse and perform critical functions that regulate global climate, yet they are also threatened by rising temperatures. Canopy temperatures depart considerably from air temperatures, sometimes by as much as air temperatures are projected to increase by the end of this century; however, canopy temperatures are rarely measured or considered in climate change analyses. Our results from near‐continuous thermal imaging of a well‐studied tropical forest show that canopy temperatures reached a maximum of ~34°C, and exceeded maximum air temperatures by as much as 7°C. Comparing different canopy surfaces reveals that bark was the warmest, followed by a deciduous canopy, flowers, and coolest was an evergreen canopy. Differences among canopy surfaces were largest during afternoon hours, when the evergreen canopy cooled more rapidly than other canopy surfaces, presumably due to transpiration. Gross primary productivity (GPP), estimated from eddy covariance measurements, was more strongly associated with canopy temperatures than air temperatures or vapor pressure deficit. The rate of GPP increase with canopy temperatures slowed above ~28–29°C, but GPP continued to increase until ~31–32°C. Although future warming is projected to be greater in high‐latitude regions, we show that tropical forest productivity is highly sensitive to small changes in temperature. Important biophysical and physiological characteristics captured by canopy temperatures allow more accurate predictions of GPP compared to commonly used air temperatures. Results suggest that as air temperatures continue to warm with climate change, canopy temperatures will increase at a ~40% higher rate, with uncertain but potentially large impacts on tropical forest productivity.

In recent decades, intense droughts, insect outbreaks, and wildfires have led to decreasing tree growth and increasing mortality in many temperate forests. We compared annual tree-ring width data from 1,097 populations in the coterminous United States to climate data and evaluated site-specific tree responses to climate variations throughout the 20th century. For each population, we developed a climate-driven growth equation by using climate records to predict annual ring widths. Forests within the southwestern United States appear particularly sensitive to drought and warmth. We input 21st century climate projections to the equations to predict growth responses. Our results suggest that if temperature and aridity rise as they are projected to, southwestern trees will experience substantially reduced growth during this century. As tree growth declines, mortality rates may increase at many sites. Increases in wildfires and bark-beetle outbreaks in the most recent decade are likely related to extreme drought and high temperatures during this period. Using satellite imagery and aerial survey data, we conservatively calculate that ≈2.7% of southwestern forest and woodland area experienced substantial mortality due to wildfires from 1984 to 2006, and ≈7.6% experienced mortality associated with bark beetles from 1997 to 2008. We estimate that up to ≈18% of southwestern forest area (excluding woodlands) experienced mortality due to bark beetles or wildfire during this period. Expected climatic changes will alter future forest productivity, disturbance regimes, and species ranges throughout the Southwest. Emerging knowledge of these impending transitions informs efforts to adaptively manage southwestern forests.

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.

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.

Moisture inputs drive soil respiration (SR) dynamics in semi-arid and arid ecosystems. However, determining the contributions of root and microbial respiration to SR, and their separate temporal responses to periodic drought and water pulses, remains poorly understood. This study was conducted in a pine forest ecosystem with a Mediterranean-type climate that receives seasonally varying precipitation inputs from both rainfall (in the winter) and fog-drip (primarily in the summer). We used automated SR measurements, radiocarbon SR source partitioning, and a water addition experiment to understand how SR, and its separate root and microbial sources, respond to seasonal and episodic changes in moisture. Seasonal changes in SR were driven by surface soil water content and large changes in root respiration contributions. Superimposed on these seasonal patterns were episodic pulses of precipitation that determined the short-term SR patterns. Warm season precipitation pulses derived from fog-drip, and rainfall following extended dry periods, stimulated the largest SR responses. Microbial respiration dominated these SR responses, increasing within hours, whereas root respiration responded more slowly over days. We conclude that root and microbial respiration sources respond differently in timing and magnitude to both seasonal and episodic moisture inputs. These findings have important implications for the mechanistic representation of SR in models and the response of dry ecosystems to changes in precipitation patterns.

At the ecosystem scale, water-use efficiency (WUE) is defined broadly as the ratio of carbon assimilated to water evaporated by an ecosystem. WUE is an important aspect of carbon and water cycling and has been used to assess forest ecosystem responses to climate change and rising atmospheric CO2 concentrations. This study investigates the influence of meteorological and radiation variables on forest WUE by analyzing an 18 year (1998-2015) half-hourly time series of carbon and water fluxes measured with the eddy covariance technique in an old-growth conifer forest in the Pacific Northwest, USA. Three different metrics of WUE exhibit an overall increase over the period 1998-2007 mainly due to an increase in gross primary productivity (GPP) and a decrease in evapotranspiration (ET). However, the WUE metrics did not exhibit an increase across the period from 2008 to 2015 due to a greater reduction in GPP relative to ET. The strength of associations among particular meteorological variables and WUE varied with the scale of temporal aggregation used. In general, vapor pressure deficit and air temperature appear to control WUE at half-hourly and daily time scales, whereas atmospheric CO2 concentration was identified as the most important factor controlling monthly WUE. Carbon and water fluxes and the consequent WUE showed a weak correlation to the Standard Precipitation Index, while carbon fluxes were strongly dependent on the combined effect of multiple climate factors. The inferred patterns and controls on forest WUE highlighted have implications for improved understanding and prediction of possible adaptive adjustments of forest physiology in response to climate change and rising atmospheric CO2 concentrations.

Long-term soil CO2 emission measurements are necessary for detecting trends and interannual variability in the terrestrial carbon cycle. Such records are becoming increasingly valuable as ecosystems experience altered environmental conditions associated with climate change. From 2013 to 2021, we continuously measured soil CO2 concentrations in the two dominant high elevation forest types, mixed conifer and aspen, in the upper Colorado River basin. We quantified the soil CO2 flux during the summer months, and found that the mean and total CO2 flux in both forests was related to the prior winter’s snowfall and current summer’s rainfall, with greater sensitivity to rainfall. We observed a decline in surface soil CO2 production, which we attributed to warming and a decrease in amount and frequency of summer rains. Our results demonstrate strong precipitation control on the soil CO2 flux in mountainous regions, a finding which has important implications for carbon cycling under future environmental change.

Stable isotope ratios of H ( δ 2 H ), O ( δ 18 O), and C ( δ 13 C) are linked to key biogeochemical processes of the water and carbon cycles; however, the degree to which isotope-associated processes are reflected in macroscale ecosystem flux observations remains unquantified. Here through formal information assessment, new measurements of δ 13 C of net ecosystem exchange ( NEE ) as well as δ 2 H and δ 18 O of latent heat ( LH ) fluxes across the United States National Ecological Observation Network (NEON) are used to determine conditions under which isotope measurements are informative of environmental exchanges. We find all three isotopic datasets individually contain comparable amounts of information about NEE and LH fluxes as wind speed observations. Such information from isotope measurements, however, is largely unique. Generally, δ 13 C provides more information about LH as aridity increases or mean annual precipitation decreases. δ 2 H provides more information about LH as temperatures or mean annual precipitation decreases, and also provides more information about NEE as temperatures decrease. Overall, we show that the stable isotope datasets collected by NEON contribute non-trivial amounts of new information about bulk environmental fluxes useful for interpreting biogeochemical and ecohydrological processes at landscape scales. However, the utility of this new information varies with environmental conditions at continental scales. This study provides an approach for quantifying the value adding non-traditional sensing approaches to environmental monitoring sites and the patterns identified here are expected to aid in modeling and data interpretation efforts focused on constraining carbon and water cycles’ mechanisms.