The snow-masking effect of vegetation exerts strong control on albedo in northern high latitude ecosystems. Large-scale changes in the distribution and stature of vegetation in this region will thus have important feedbacks to climate. The snow-albedo feedback is controlled largely by the contrast between snow-covered and snow-free albedo (Δα), which influences predictions of future warming in coupled climate models, despite being poorly constrained at seasonal and century time scales. Here, we compare satellite observations and coupled climate model representations of albedo and tree cover for the boreal and Arctic region. Our analyses reveal consistent declines in albedo with increasing tree cover, occurring south of latitudinal tree line, that are poorly represented in coupled climate models. Observed relationships between albedo and tree cover differ substantially between snow-covered and snow-free periods, and among plant functional type. Tree cover in models varies widely but surprisingly does not correlate well with model albedo. Furthermore, our results demonstrate a relationship between tree cover and snow-albedo feedback that may be used to accurately constrain high latitude albedo feedbacks in coupled climate models under current and future vegetation distributions.

Climate warming and drying are modifying the fire dynamics of many boreal forests, moving them towards a regime with a higher frequency of extreme fire years characterized by large burns of high severity. Plot-scale studies indicate that increased burn severity favors the recruitment of deciduous trees in the initial years following fire. Consequently, a set of biophysical effects of burn severity on postfire boreal successional trajectories at decadal timescales have been hypothesized. Prominent among these are a greater cover of deciduous tree species in intermediately aged stands after more severe burning, with associated implications for carbon and energy balances. Here we investigate whether the current vegetation composition of interior Alaska supports this hypothesis. A chronosequence of six decades of vegetation regrowth following fire was created using a database of burn scars, an existing forest biomass map, and maps of albedo and the deciduous fraction of vegetation that we derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. The deciduous fraction map depicted the proportion of aboveground biomass in deciduous vegetation, derived using a RandomForest algorithm trained with field data sets (n=69, 71% variance explained). Analysis of the difference Normalized Burn Ratio, a remotely sensed index commonly used as an indicator of burn severity, indicated that burn size and ignition date can provide a proxy of burn severity for historical fires. LIDAR remote sensing and a bioclimatic model of evergreen forest distribution were used to further refine the stratification of the current landscape by burn severity. Our results show that since the 1950s, more severely burned areas in interior Alaska have produced a vegetation cohort that is characterized by greater deciduous biomass. We discuss the importance of this shift in vegetation composition due to climate-induced changes in fire severity for carbon sequestration in forest biomass and surface reflectance (albedo), among other feedbacks to climate. © 2011 Blackwell Publishing Ltd.

Severity of burning can influence multiple aspects of forest composition, carbon cycling, and climate forcing. We quantified how burn severity affected vegetation recovery and albedo change during early succession in Canadian boreal regions by combining satellite observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Canadian Large Fire Database. We used the MODIS-derived difference Normalized Burn Ratio (dNBR) and initial changes in spring albedo as measures of burn severity. We found that the most severe burns had the greatest reduction in summer MODIS Enhanced Vegetation Index (EVI) in the first year after fire, indicating greater loss of vegetation cover. By 5–8 years after fire, summer EVI for all severity classes had recovered to within 90%–108% of prefire levels. Spring and summer albedo progressively increased during the first 7 years after fire, with more severely burned areas showing considerably larger postfire albedo increases during spring and more rapid increases during summer as compared with moderate- and low-severity burns. After 5–7 years, increases in spring albedo above prefire levels were considerably larger in high-severity burns (0.20 ± 0.06; defined by dNBR percentiles greater than 75%) as compared to changes observed in moderate- (0.16 ± 0.06; for dNBR percentiles between 45% and 75%) or low-severity burns (0.13 ± 0.06; for dNBR percentiles between 20% and 45%). The sensitivity of spring albedo to dNBR was similar in all ecozones and for all vegetation types along gradients of burn severity. These results suggest carbon losses associated with increases in burn severity observed in some areas of boreal forests may be at least partly offset, in terms of climate impacts, by increases in negative forcing associated with changes in surface albedo.

The economically and ecologically catastrophic introduction of invasive brown treesnakes to the Pacific Island of Guam has long served as a cautionary tale about the dangers of invasive species and the seeming impossibility of their management on a landscape scale. USDA Wildlife Services and federal and private partners have engineered a system for the automated manufacture and aerial delivery of toxic baits for landscape-scale suppression of brown treesnakes in large and remote forest plots. The helicopter-borne dispensing module can launch four bait cartridges per second, and a single payload of 3,600 cartridges can treat 30 ha of forest at 120 baits/ha in 15 to 30 minutes depending on flightpath efficiency. In this paper we recap the research, development, testing, and implementation of the system, including the procedures for monitoring biological responses to bait applications during an experimental suppression within a 55-ha forest plot surrounded by a snake-proof barrier.

Vegetation composition shifts, and in particular, shrub expansion across the Arctic tundra are some of the most important and widely observed responses of high-latitude ecosystems to rapid climate warming. These changes in vegetation potentially alter ecosystem carbon balances by affecting a complex set of soil-plant-atmosphere interactions. In this review, we synthesize the literature on (a) observed shrub expansion, (b) key climatic and environmental controls and mechanisms that affect shrub expansion, (c) impacts of shrub expansion on ecosystem carbon balance, and (d) research gaps and future directions to improve process representations in land models. A broad range of evidence, including in-situ observations, warming experiments, and remotely sensed vegetation indices have shown increases in growth and abundance of woody plants, particularly tall deciduous shrubs, and advancing shrublines across the circumpolar Arctic. This recent shrub expansion is affected by several interacting factors including climate warming, accelerated nutrient cycling, changing disturbance regimes, and local variation in topography and hydrology. Under warmer conditions, tall deciduous shrubs can be more competitive than other plant functional types in tundra ecosystems because of their taller maximum canopy heights and often dense canopy structure. Competitive abilities of tall deciduous shrubs vs herbaceous plants are also controlled by variation in traits that affect carbon and nutrient investments and retention strategies in leaves, stems, and roots. Overall, shrub expansion may affect tundra carbon balances by enhancing ecosystem carbon uptake and altering ecosystem respiration, and through complex feedback mechanisms that affect snowpack dynamics, permafrost degradation, surface energy balance, and litter inputs. Observed and projected tall deciduous shrub expansion and the subsequent effects on surface energy and carbon balances may alter feedbacks to the climate system. Land models, including those integrated in Earth System Models, need to account for differences in plant traits that control competitive interactions to accurately predict decadal- to centennial-scale tundra vegetation and carbon dynamics.

Wildland fire management has reached a crossroads. Current perspectives are not capable of answering interdisciplinary adaptation and mitigation challenges posed by increases in wildfire risk to human populations and the need to reintegrate fire as a vital landscape process. Fire science has been, and continues to be, performed in isolated "silos," including institutions (e.g., agencies versus universities), organizational structures (e.g., federal agency mandates versus local and state procedures for responding to fire), and research foci (e.g., physical science, natural science, and social science). These silos tend to promote research, management, and policy that focus only on targeted aspects of the "wicked" wildfire problem. In this article, we provide guiding principles to bridge diverse fire science efforts to advance an integrated agenda of wildfire research that can help overcome disciplinary silos and provide insight on how to build fire-resilient communities.

NASA has launched the decade-long Arctic-Boreal Vulnerability Experiment (ABoVE). While the initial phases focus on field and airborne data collection, early integration with modeling activities is important to benefit future modeling syntheses. We compiled feedback from ecosystem modeling teams on key data needs, which encompass carbon biogeochemistry, vegetation, permafrost, hydrology, and disturbance dynamics. A suite of variables was identified as part of this activity with a critical requirement that they are collected concurrently and representatively over space and time. Individual projects in ABoVE may not capture all these needs, and thus there is both demand and opportunity for the augmentation of field observations, and synthesis of the observations that are collected, to ensure that science questions and integrated modeling activities are successfully implemented.

Research activities focused on estimating the direct emissions of carbon from wildland fires across North America are reviewed as part of the North American Carbon Program disturbance synthesis. A comparison of methods to estimate the loss of carbon from the terrestrial biosphere to the atmosphere from wildland fires is presented. Published studies on emissions from recent and historic time periods and five specific cases are summarized, and new emissions estimates are made using contemporary methods for a set of specific fire events. Results from as many as six terrestrial models are compared. We find that methods generally produce similar results within each case, but estimates vary based on site location, vegetation (fuel) type, and fire weather. Area normalized emissions range from 0.23 kg C m⁻² for shrubland sites in southern California/NW Mexico to as high as 6.0 kg C m⁻² in northern conifer forests. Total emissions range from 0.23 to 1.6 Tg C for a set of 2003 fires in chaparral-dominated landscapes of California to 3.9 to 6.2 Tg C in the dense conifer forests of western Oregon. While the results from models do not always agree, variations can be attributed to differences in model assumptions and methods, including the treatment of canopy consumption and methods to account for changes in fuel moisture, one of the main drivers of variability in fire emissions. From our review and synthesis, we identify key uncertainties and areas of improvement for understanding the magnitude and spatial-temporal patterns of pyrogenic carbon emissions across North America.

The tundra is warming more rapidly than any other biome on Earth, and the potential ramifications are far-reaching because of global feedback effects between vegetation and climate. A better understanding of how environmental factors shape plant structure and function is crucial for predicting the consequences of environmental change for ecosystem functioning. Here we explore the biome-wide relationships between temperature, moisture and seven key plant functional traits both across space and over three decades of warming at 117 tundra locations. Spatial temperature-trait relationships were generally strong but soil moisture had a marked influence on the strength and direction of these relationships, highlighting the potentially important influence of changes in water availability on future trait shifts in tundra plant communities. Community height increased with warming across all sites over the past three decades, but other traits lagged far behind predicted rates of change. Our findings highlight the challenge of using space-for-time substitution to predict the functional consequences of future warming and suggest that functions that are tied closely to plant height will experience the most rapid change. They also reveal the strength with which environmental factors shape biotic communities at the coldest extremes of the planet and will help to improve projections of functional changes in tundra ecosystems with climate warming.

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.