Climatic Change

Terrestrial biological atmospheric carbon dioxide removal (BCDR) through bioenergy with carbon capture and storage (BECS), afforestation/reforestation, and forest and soil management is a family of proposed climate change mitigation strategies. Very high sequestration potentials for these strategies have been reported, but there has been no systematic analysis of the potential ecological limits to and environmental impacts of implementation at the scale relevant to climate change mitigation. In this analysis, we identified site-specific aspects of land, water, nutrients, and habitat that will affect local project-scale carbon sequestration and ecological impacts. Using this framework, we estimated global-scale land and resource requirements for BCDR, implemented at a rate of 1 Pg C y−1. We estimate that removing 1 Pg C y−1 via tropical afforestation would require at least 7 × 106 ha y−1 of land, 0.09 Tg y−1 of nitrogen, and 0.2 Tg y−1 of phosphorous, and would increase evapotranspiration from those lands by almost 50 %. Switchgrass BECS would require at least 2 × 108 ha of land (20 times U.S. area currently under bioethanol production) and 20 Tg y−1 of nitrogen (20 % of global fertilizer nitrogen production), consuming 4 × 1012 m3 y−1 of water. While BCDR promises some direct (climate) and ancillary (restoration, habitat protection) benefits, Pg C-scale implementation may be constrained by ecological factors, and may compromise the ultimate goals of climate change mitigation.

Salinity response to sea-level rise is evaluated for a low-gradient, tidally active estuary, the lower St. Johns River, Florida. A high-resolution numerical model is forced by continuous data of water levels and freshwater inflows for the offshore and upstream boundaries, respectively. The modeling approach is configured for salinity simulation over a 10-year record, 1997–2007, and validated at four salinity-gauging stations inside the river. The initial condition of salinity field was found to be a critical factor in the numerical simulation. Adjustments in the initial salinity condition of ± 10% required 6–9 months for the model salinity solution to dynamically equilibrate with the applied boundary conditions. Model predictions of salinity response to sea-level rise of 0.05, 0.15, and 0.30 m were diagnosed in terms of salinity change. Salinity was found to increase over the entire river, regardless of the magnitude of sea-level rise. Linear rates of salinity increase were predicted as high as 6 ppt m−1 inside the river. The change in salinity was nonuniform throughout the system and exhibited a moderate-to-strong nonlinear component. The results uncover a hotspot in the river where salinity was predicted to increase as much as ~ 2.3 ppt due to the nonlinear system response to sea-level rise.

In addition to increasing plant C inputs, strategies for enhancing soil C sequestration include reducing C turnover and increasing its residence time in soils. Two major mechanisms, (bio)chemical alteration and physicochemical protection, stabilize soil organic C (SOC) and thereby control its turnover. With (bio)chemical alteration, SOC is transformed by biotic and abiotic processes to chemical forms that are more resistant to decomposition and, in some cases, more easily retained by sorption to soil solids. With physicochemical protection, biochemical attack of SOC is inhibited by organomineral interactions at molecular to millimeter scales. Stabilization of otherwise decomposable SOC can occur via sorption to mineral and organic soil surfaces, occlusion within aggregates, and deposition in pores or other locations inaccessible to decomposers and extracellular enzymes. Soil structure is a master integrating variable that both controls and indicates the SOC stabilization status of a soil. One potential option for reducing SOC turnover and enhancing sequestration, is to modify the soil physicochemical environment to favor the activities of fungi. Specific practices that could accomplish this include manipulating the quality of plant C inputs, planting perennial species, minimizing tillage and other disturbances, maintaining a near-neutral soil pH and adequate amounts of exchangeable base cations (particularly calcium), ensuring adequate drainage, and minimizing erosion. In some soils, amendment with micro- and mesoporous sorbents that have a high specific surface – such as fly ash or charcoal – can be beneficial.

Researchers have developed many computer simulation models to project ecological responses to climatic change. Three general types of models are examined: transfer functions, stand models, and physiological models. Criteria for evaluation are, first, ability to represent observed and theoretical responses to climatic change i.e., geographical migration, individualistic responses, and disequilibrium or inertia, and second, ability to provide useful information on biological diversity and impacts on society. Because of their roots in ecological interactions at the species level, stand models best meet these criteria at present, but physiological models have greater potential, given unlimited computing power.

Climate change is expected to bring potentially significant changes to Washington State’s natural, institutional, cultural, and economic landscape. Addressing climate change impacts will require a sustained commitment to integrating climate information into the day-to-day governance and management of infrastructure, programs, and services that may be affected by climate change. This paper discusses fundamental concepts for planning for climate change and identifies options for adapting to the climate impacts evaluated in the Washington Climate Change Impacts Assessment. Additionally, the paper highlights potential avenues for increasing flexibility in the policies and regulations used to govern human and natural systems in Washington.

Wildfire can impose a direct impact on human health under climate change. While the potential impacts of climate change on wildfires and resulting air pollution have been studied, it is not known who will be most affected by the growing threat of wildfires. Identifying communities that will be most affected will inform development of fire management strategies and disaster preparedness programs. We estimate levels of fine particulate matter (PM2.5) directly attributable to wildfires in 561 western US counties during fire seasons for the present-day (2004–2009) and future (2046–2051), using a fire prediction model and GEOS-Chem, a 3-D global chemical transport model. Future estimates are obtained under a scenario of moderately increasing greenhouse gases by mid-century. We create a new term “Smoke Wave,” defined as ≥2 consecutive days with high wildfire-specific PM2.5, to describe episodes of high air pollution from wildfires. We develop an interactive map to demonstrate the counties likely to suffer from future high wildfire pollution events. For 2004–2009, on days exceeding regulatory PM2.5 standards, wildfires contributed an average of 71.3 % of total PM2.5. Under future climate change, we estimate that more than 82 million individuals will experience a 57 % and 31 % increase in the frequency and intensity, respectively, of Smoke Waves. Northern California, Western Oregon and the Great Plains are likely to suffer the highest exposure to widlfire smoke in the future. Results point to the potential health impacts of increasing wildfire activity on large numbers of people in a warming climate and the need to establish or modify US wildfire management and evacuation programs in high-risk regions. The study also adds to the growing literature arguing that extreme events in a changing climate could have significant consequences for human health.

Future scenarios of the energy system under greenhouse gas emission constraints depict dramatic growth in a range of energy technologies. Technological growth dynamics observed historically provide a useful comparator for these future trajectories. We find that historical time series data reveal a consistent relationship between how much a technology’s cumulative installed capacity grows, and how long this growth takes. This relationship between extent (how much) and duration (for how long) is consistent across both energy supply and end-use technologies, and both established and emerging technologies. We then develop and test an approach for using this historical relationship to assess technological trajectories in future scenarios. Our approach for “learning from the past” contributes to the assessment and verification of integrated assessment and energy-economic models used to generate quantitative scenarios. Using data on power generation technologies from two such models, we also find a consistent extent - duration relationship across both technologies and scenarios. This relationship describes future low carbon technological growth in the power sector which appears to be conservative relative to what has been evidenced historically. Specifically, future extents of capacity growth are comparatively low given the lengthy time duration of that growth. We treat this finding with caution due to the low number of data points. Yet it remains counter-intuitive given the extremely rapid growth rates of certain low carbon technologies under stringent emission constraints. We explore possible reasons for the apparent scenario conservatism, and find parametric or structural conservatism in the underlying models to be one possible explanation.