In interior Alaska’s 115 million acres of boreal forest, white and black spruce are the dominant tree species. Climate models suggest that the region is becoming warmer and drier, resulting in declining growth of black and white spruce, according to some researchers. These drier conditions also may lead to greater risk of stand-replacing wildfires, resulting in forests dominated by birch and aspen, which are early-successional tree species.
To compare long-term growth trends of the dominant coniferous and deciduous tree species, a team of researchers with the USDA Forest Service Pacific Northwest Research Station and the University of Alaska Anchorage analyzed tree cores collected from the Tanana Valley and measured tree-ring widths of these four tree species over the past 150 years. They also compared growth against monthly temperature and precipitation data to determine if there is a correlation between climate and growth.
The team found that white and black spruce have not experienced as rapid a growth decline as earlier studies suggested; instead, their annual growth remains near the long-term mean. Of the four species examined, aspen showed the greatest recent growth decline, likely reflecting a widespread insect outbreak. Among the climate variables that will affect the future growth of these species, summer rainfall was identified as a significant factor.
Sagebrush ecosystems are a major component of western U.S. landscapes and they provide vital habitat to a wide array of wildlife species, including greater sage-grouse and pygmy rabbits. However, in recent decades, sagebrush ecosystems have been reduced or degraded by a wide range of disturbances, including human development, overgrazing, severe fires, and encroachment by cheatgrass and pinyon-juniper woodlands. These factors are expected to continue or worsen with anticipated climate change.
A comprehensive numerical modeling framework was developed to estimate the effects of collective global changes upon ozone pollution in the US in 2050. The framework consists of the global climate and chemistry models, PCM (Parallel Climate Model) and MOZART-2 (Model for Ozone and Related Chemical Tracers v.2), coupled with regional meteorology and chemistry models, MM5 (Mesoscale Meteorological model) and CMAQ (Community Multi-scale Air Quality model). The modeling system was applied for two 10-year simulations: 1990–1999 as a present-day base case and 2045–2054 as a future case. For the current decade, the daily maximum 8-h moving average (DM8H) ozone mixing ratio distributions for spring, summer and fall showed good agreement with observations. The future case simulation followed the Intergovernmental Panel on Climate Change (IPCC) A2 scenario together with business-as-usual US emission projections and projected alterations in land use, land cover (LULC) due to urban expansion and changes in vegetation. For these projections, US anthropogenic NOx (NO+NO2) and VOC (volatile organic carbon) emissions increased by approximately 6% and 50%, respectively, while biogenic VOC emissions decreased, in spite of warmer temperatures, due to decreases in forested lands and expansion of croplands, grasslands and urban areas. A stochastic model for wildfire emissions was applied that projected 25% higher VOC emissions in the future. For the global and US emission projection used here, regional ozone pollution becomes worse in the 2045–2054 period for all months. Annually, the mean DM8H ozone was projected to increase by 9.6 ppbv (22%). The changes were higher in the spring and winter (25%) and smaller in the summer (17%). The area affected by elevated ozone within the US continent was projected to increase; areas with levels exceeding the 75 ppbv ozone standard at least once a year increased by 38%. In addition, the length of the ozone season was projected to increase with more pollution episodes in the spring and fall. For selected urban areas, the system projected a higher number of pollution events per year and these events had more consecutive days when DM8H ozone exceed 75 ppbv.
Forests are considered a natural solution for mitigating climate change because they absorb and store atmospheric carbon. With Alaska boasting 129 million acres of forest, this state can play a crucial role as a carbon sink for the United States. Until recently, the volume of carbon stored in Alaska’s forests was unknown, as was their future carbon sequestration capacity.
In 2007, Congress passed the Energy Independence and Security Act that directed the Department of the Interior to assess the stock and flow of carbon in all the lands and waters of the United States. In 2012, a team composed of researchers with the U.S. Geological Survey, U.S. Forest Service, and the University of Alaska assessed how much carbon Alaska’s forests can sequester.
The researchers concluded that ecosystems of Alaska could be a substantial carbon sink. Carbon sequestration is estimated at 22.5 to 70.0 teragrams (Tg) of carbon per year over the remainder of this century. In particular, Alaska’s dense coastal temperate forests and soils are estimated to sequester 3.4 to 7.8 Tg of carbon per year. Forest management activities were found to have long-term effects on the maximum amount of carbon a site can sequester. These findings helped inform the carbon assessment sections of Chugach and Tongass National Forests’ land management plans.
From 2007 through 2017, Oregon implemented an incentive program for biomass collection and production. This research evaluates renewable biomass production and deliveries during a 3-year period (2012 to 2014) in which this tax credit was in place. We evaluated total delivered tons, average payments per load, delivered location, and average transportation distance of woody biomass. We found that total delivered tons of biomass decreased each year between 2012 and 2014, as did the number of users participating in the tax credit program. The average delivered tons, by participant, was more than double in 2014 its level in earlier years, suggesting that fewer, larger entities were participating. We also evaluated differences in biomass delivery, based on receipts, transportation distances, and tons delivered, for each land ownership class. There were statistically significant differences between private and public land ownership for 2012 and 2013 but not for 2014, which included fewer applicants. Our study showed that effective biomass utilization policies need to provide sufficient economic incentives to encourage adoption by both participants and biomass energy producers, and, to be effective, to consider the complete supply chain and type of energy produced. Future economic conditions in Oregon will most likely include rapid changes in renewable energy technologies and fluctuations in fossil fuel prices, and any truly effective renewable energy policies must be sufficiently nimble to account for these and other uncertainties.
A recent expansion in wood energy use at schools in Alaska has resulted in more than a dozen wood energy systems in operation. However, few have been evaluated for fuel efficiency and pollution impacts, both of which can be examined via combustion gas analysis. In this research, we monitored the wood energy system at a public school during winter heating conditions. Wood energy parameters were sampled on three occasions during early, mid, and late winter in northern Alaska. Combustion gas was sampled for a range of parameters that indicated boiler performance, including gas emmissions of oxygen (O2), carbon dioxide (CO2), carbon monoxide (CO), excess air, combustion efficiency, and stack temperature, which were monitored over 6 days. We observed differences in combustion gas composition between seasons as well as the response of combustion efficiency to gas concentrations. Combustion efficiency most strongly correlated with excess air (R2 = 0.693), but poorly correlated with stack temperature (R2 = 0.005). The primary combustion gases (O2, CO2, and CO) were moderately correlated with combustion efficiency (with R2 values of 0.40, 0.56, and 0.55, respectively). Seasonal differences were found between early, mid, and late winter, with generally less variation in combustion gas contents occurring during late winter. Mean combustion gas concentrations also varied with heating season. In all cases, mid-winter means were significantly different than early and late winter values. This research found that more efficient combustion of wood fuels should lead to cost savings, especially during early and late heating seasons. The findings should also be relevant to those of other wood-energy-using schools (in Alaska and elsewhere) that experience severe mid-winter conditions coupled with milder shoulder seasons.
Monitoring vegetation phenology is important for managers at several scales. Across decades, changes in the timing, pattern, and duration of significant life cycle events for plant groups can foreshadow shifts in species assemblages that can affect ecosystem services. In the shorter term, managers need phenological information to time activities such as grazing, ecological restoration plantings, biocontrol of pests, seed collection, and wildlife monitoring. However, tools to deliver timely seasonal development have been limited either spatially (data from a single tower or weather station, or on a single species, or both) or temporally (annually, quarterly, or monthly summaries). We developed another option called PhenoMap. This is a weekly assessment of land surface “greenness” across the continental United States that employs the Normalized Differential Vegetation Index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. Here we present the PhenoMap Web map and its validation by using 54 in situ PhenoCam camera sites representing six vegetation structure types and 31 different ecoregions. We found that PhenoMap effectively tracks phenology on grasslands, shrublands, deciduous broadleaf and mixed forests. Results for evergreen needleleaf sites were poor owing to the low green-up signal relative to the total amount of foliage detected by NDVI. Issues of extent and field of view were critical when assessing remotely sensed data with in situ oblique camera imagery.