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Atmospheric methane (CH4) is a potent greenhouse gas, and its mole fraction has more than doubled since the preindustrial era. Fossil fuel extraction and use are among the largest anthropogenic sources of CH4 emissions, but the precise magnitude of these contributions is a subject of debate. Carbon-14 in CH4 (14CH4) can be used to distinguish between fossil (14C-free) CH4 emissions and contemporaneous biogenic sources; however, poorly constrained direct 14CH4 emissions from nuclear reactors have complicated this approach since the middle of the 20th century. Moreover, the partitioning of total fossil CH4 emissions (presently 172 to 195 teragrams CH4 per year) between anthropogenic and natural geological sources (such as seeps and mud volcanoes) is under debate; emission inventories suggest that the latter account for about 40 to 60 teragrams CH4 per year. Geological emissions were less than 15.4 teragrams CH4 per year at the end of the Pleistocene, about 11,600 years ago, but that period is an imperfect analogue for present-day emissions owing to the large terrestrial ice sheet cover, lower sea level and extensive permafrost. Here we use preindustrial-era ice core 14CH4 measurements to show that natural geological CH4 emissions to the atmosphere were about 1.6 teragrams CH4 per year, with a maximum of 5.4 teragrams CH4 per year (95 per cent confidence limit)—an order of magnitude lower than the currently used estimates. This result indicates that anthropogenic fossil CH4 emissions are underestimated by about 38 to 58 teragrams CH4 per year, or about 25 to 40 per cent of recent estimates. Our record highlights the human impact on the atmosphere and climate, provides a firm target for inventories of the global CH4 budget, and will help to inform strategies for targeted emission reductions.

Abstract. This paper aims to assess the spatial variability in the response of CO2 exchange to irradiance across the Arctic tundra during peak season using light response curve (LRC) parameters. This investigation allows us to better understand the future response of Arctic tundra under climatic change. Peak season data were collected during different years (between 1998 and 2010) using the micrometeorological eddy covariance technique from 12 circumpolar Arctic tundra sites, in the range of 64–74° N. The LRCs were generated for 14 days with peak net ecosystem exchange (NEE) using an NEE–irradiance model. Parameters from LRCs represent site-specific traits and characteristics describing the following: (a) NEE at light saturation (Fcsat), (b) dark respiration (Rd), (c) light use efficiency (α), (d) NEE when light is at 1000 μmol m−2 s−1 (Fc1000), (e) potential photosynthesis at light saturation (Psat) and (f) the light compensation point (LCP). Parameterization of LRCs was successful in predicting CO2 flux dynamics across the Arctic tundra. We did not find any trends in LRC parameters across the whole Arctic tundra but there were indications for temperature and latitudinal differences within sub-regions like Russia and Greenland. Together, leaf area index (LAI) and July temperature had a high explanatory power of the variance in assimilation parameters (Fcsat, Fc1000 and Psat, thus illustrating the potential for upscaling CO2 exchange for the whole Arctic tundra. Dark respiration was more variable and less correlated to environmental drivers than were assimilation parameters. This indicates the inherent need to include other parameters such as nutrient availability, substrate quantity and quality in flux monitoring activities.

For fast timescales or long prediction horizons, the AC optimal power flow (OPF) problem becomes a computational challenge for large-scale, realistic AC networks. To overcome this challenge, this paper presents a novel network reduction methodology that leverages an efficient mixed-integer linear programming (MILP) formulation of a Kron-based reduction that is optimal in the sense that it balances the degree of the reduction with resulting modeling errors in the reduced network. The method takes as inputs the full AC network and a pre-computed library of AC load flow data and uses the graph Laplacian to constraint nodal reductions to only be feasible for neighbors of non-reduced nodes. This results in a highly effective MILP formulation which is embedded within an iterative scheme to successively improve the Kron-based network reduction until convergence. The resulting optimal network reduction is, thus, grounded in the physics of the full network. The accuracy of the network reduction methodology is then explored for a 100+ node medium-voltage radial distribution feeder example across a wide range of operating conditions. It is finally shown that a network reduction of 25-85% can be achieved within seconds and with worst-case voltage magnitude deviation errors within any super node cluster of less than 0.01pu. These results illustrate that the proposed optimization-based approach to Kron reduction of networks is viable for larger networks and suitable for use within various power system applications.

Recent shifts in the geographic distribution of marine species have been linked to shifts in preferred thermal habitats. These shifts in distribution have already posed challenges for living marine resource management, and there is a strong need for projections of how species might be impacted by future changes in ocean temperatures during the 21st century. We modeled thermal habitat for 686 marine species in the Atlantic and Pacific oceans using long-term ecological survey data from the North American continental shelves. These habitat models were coupled to output from sixteen general circulation models that were run under high (RCP 8.5) and low (RCP 2.6) future greenhouse gas emission scenarios over the 21st century to produce 32 possible future outcomes for each species. The models generally agreed on the magnitude and direction of future shifts for some species (448 or 429 under RCP 8.5 and RCP 2.6, respectively), but strongly disagreed for other species (116 or 120 respectively). This allowed us to identify species with more or less robust predictions. Future shifts in species distributions were generally poleward and followed the coastline, but also varied among regions and species. Species from the U.S. and Canadian west coast including the Gulf of Alaska had the highest projected magnitude shifts in distribution, and many species shifted more than 1000 km under the high greenhouse gas emissions scenario. Following a strong mitigation scenario consistent with the Paris Agreement would likely produce substantially smaller shifts and less disruption to marine management efforts. Our projections offer an important tool for identifying species, fisheries, and management efforts that are particularly vulnerable to climate change impacts.

AbstractIndirect climate effects on tree fecundity that come through variation in size and growth (climate-condition interactions) are not currently part of models used to predict future forests. Trends in species abundances predicted from meta-analyses and species distribution models will be misleading if they depend on the conditions of individuals. Here we find from a synthesis of tree species in North America that climate-condition interactions dominate responses through two pathways, i) effects of growth that depend on climate, and ii) effects of climate that depend on tree size. Because tree fecundity first increases and then declines with size, climate change that stimulates growth promotes a shift of small trees to more fecund sizes, but the opposite can be true for large sizes. Change the depresses growth also affects fecundity. We find a biogeographic divide, with these interactions reducing fecundity in the West and increasing it in the East. Continental-scale responses of these forests are thus driven largely by indirect effects, recommending management for climate change that considers multiple demographic rates.