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Duality in climate science
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Delivery of palatable 2 °C mitigation scenarios depends on speculative negative emissions or changing the past. Scientists must make their assumptions transparent and defensible, however politically uncomfortable the conclusions.
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Climate Science Documents
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Uncertainty in the response of transpiration to CO2 and implications for climate change
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While terrestrial precipitation is a societally highly relevant climate variable, there is little consensus among climate models about its projected 21st century changes. An important source of precipitable water over land is plant transpiration. Plants control transpiration by opening and closing their stomata. The sensitivity of this process to increasing CO2 concentrations is uncertain. To assess the impact of this uncertainty on future climate, we perform experiments with an intermediate complexity Earth System Climate Model (UVic ESCM) for a range of model-imposed transpiration- sensitivities to CO2. Changing the sensitivity of transpiration to CO2 causes simulated terrestrial precipitation to change by −10% to +27% by 2100 under a high emission scenario. This study emphasises the importance of an improved assessment of the dynamics of environmental impact on vegetation to better predict future changes of the terrestrial hydrological and carbon cycles.
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Climate Science Documents
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Novel climates, no-analog communities, and ecological surprises
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No-analog communities (communities that are compositionally unlike any found today) occurred frequently in the past and will develop in the greenhouse world of the future. The well documented no-analog plant communities of late-glacial North America are closely linked to “novel” climates also lacking modern analogs, characterized by high seasonality of temperature. In climate simulations for the Intergovernmental Panel on Climate Change A2 and B1 emission scenarios, novel climates arise by 2100 AD, primarily in tropical and subtropical regions. These future novel climates are warmer than any present climates globally, with spatially variable shifts in precipitation, and increase the risk of species reshuffling into future no-analog communities and other ecological surprises. Most ecological models are at least partially parameterized from modern observations and so may fail to accurately predict ecological responses to these novel climates. There is an urgent need to test the robustness of ecological models to climate conditions outside modern experience.
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Climate Science Documents
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Global non-linear effect of temperature on economic production
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Growing evidence demonstrates that climatic conditions can have a profound impact on the functioning of modern human societies (1,2), but effects on economic activity appear inconsistent. Fundamental productive elements of modern economies, such as workers and crops, exhibit highly non-linear responses to local temperature even in wealthy countries (3,4). In contrast, aggregate macroeconomic productivity of entire wealthy countries is reported not to respond to temperature (5), while poor countries respond only linearly (5,6). Resolving this conflict between micro and macro observations is critical to understanding the role of wealth in coupled human–natural systems (7,8) and to anticipating the global impact of climate change (9,10). Here we unify these seemingly contradictory results by accounting for non-linearity at the macro scale. We show that overall economic productivity is non- linear in temperature for all countries, with productivity peaking at an annual average temperature of 13 6C and declining strongly at higher temperatures. The relationship is globally generalizable, unchanged since 1960, and apparent for agricultural and non-agricultural activity in both rich and poor countries. These results provide the first evidence that economic activity in all regions is coupled to the global climate and establish a new empirical foundation for modelling economic loss in response to climate change (11,12), with important implications. If future adaptation mimics past adaptation, unmitigated warming is expected to reshape the global economy by reducing average global incomes roughly 23% by 2100 and widening global income inequality, relative to scenarios without climate change. In contrast to prior estimates, expected global losses are approximately linear in global mean temperature, with median losses many times larger than leading models indicate.
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Climate Science Documents
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Palaeodata-informed modelling of large carbon losses from recent burning of boreal forests
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Wildfires play a key role in the boreal forest carbon cycle(1,2), and models suggest that accelerated burning will increase boreal C emissions in the coming century (3). However, these predictions may be compromised because brief observational records provide limited constraints to model initial conditions (4). We confronted this limitation by using palaeoenvironmental data to drive simulations of long-term C dynamics in the Alaskan bo- real forest. Results show that fire was the dominant control on C cycling over the past millennium, with changes in fire frequency accounting for 84% of C stock variability. A recent rise in fire frequency inferred from the palaeorecord5 led to simulated C losses of 1.4 kg C m?2(12% of ecosystem C stocks) from 1950 to 2006. In stark contrast, a small net C sink of 0.3 kg C m?2 occurred if the past fire regime was assumed to be similar to the modern regime, as is common in models of C dynamics. Although boreal fire regimes are heterogeneous, recent trends6 and future projections (7) point to increasing fire activity in response to climate warming throughout the biome. Thus, predictions (8) that terrestrial C sinks of northern high latitudes will mitigate rising atmospheric CO2 may be over-optimistic.
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Climate Science Documents
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Development of a Spatially Explicit Surface Coal Mining Predictive Model
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The goal of this project was to create a spatially explicit 1km2 grid cell model for the Appalachian Landscape Conservation Cooperative (Figure 1) predicting where surface coal mining is likely to occur in in a projected future time period, under two different scenarios. To accomplish this goal we combined GIS spatial analysis, a Random Forests predictive model, and future mining buildout scenarios. This report provides a detailed methodology of our approach and discussion of our results.
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Tools & Resources
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Assessing Future Energy Development
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Breaking Traditional Barriers to Model Climate Change and Land Use Impacts on Freshwater Mussels
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Thomas Kwak, Leader of the U.S. Geological Survey North Carolina Cooperative Fish and Wildlife Research Unit and Professor of Biology, North Carolina State University, will give a presentation on global declines in the abundance and diversity of freshwater mussels that have been attributed to a wide array of human activities that cause pollution, water-quality degradation, and habitat destruction.
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Events
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USFS Landscape Science Webinar
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When the past cannot predict the future: Using process-based landscape models to predict forest dynamics in a changing world.
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Events
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Can Landscape-scale Management Influence Insect Outbreak Dynamics
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We hypothesized that landscape connectivity of host tree species increases forest susceptibility to insect pest damage. We evaluated this hypothesis for spruce budworm within a 6 million hectare “experimental” landscape at the international border between the Midwestern US and Canada, containing wilderness plus two contrasting harvest patterns (coarse vs. fine).
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Events
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Appalachian LCC Science Delivery Workshop
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The Appalachian LCC is setting up a science delivery workshop to share recently develop products and train partners in decision support planning tools.
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Events
