5,200 research outputs found
Implications of "peak oil" for atmospheric CO2 and climate
Unconstrained CO2 emission from fossil fuel burning has been the dominant
cause of observed anthropogenic global warming. The amounts of "proven" and
potential fossil fuel reserves are uncertain and debated. Regardless of the
true values, society has flexibility in the degree to which it chooses to
exploit these reserves, especially unconventional fossil fuels and those
located in extreme or pristine environments. If conventional oil production
peaks within the next few decades, it may have a large effect on future
atmospheric CO2 and climate change, depending upon subsequent energy choices.
Assuming that proven oil and gas reserves do not greatly exceed estimates of
the Energy Information Administration, and recent trends are toward lower
estimates, we show that it is feasible to keep atmospheric CO2 from exceeding
about 450 ppm by 2100, provided that emissions from coal, unconventional fossil
fuels, and land use are constrained. Coal-fired power plants without
sequestration must be phased out before mid-century to achieve this CO2 limit.
It is also important to "stretch" conventional oil reserves via energy
conservation and efficiency, thus averting strong pressures to extract liquid
fuels from coal or unconventional fossil fuels while clean technologies are
being developed for the era "beyond fossil fuels". We argue that a rising price
on carbon emissions is needed to discourage conversion of the vast fossil
resources into usable reserves, and to keep CO2 beneath the 450 ppm ceiling.Comment: (22 pages, 7 figures; final version accepted by Global Biogeochemical
Cycles
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Impact of rising sea levels on Australian fur seals
Global warming is leading to many unprecedented changes in the ocean-climate system. Sea levels are rising at an increasing rate and are amplifying the impact of storm surges along coastlines. As variability in the timing and strength of storm surges has been shown to affect pup mortality in the Australian fur seal (Arctocephalus pusillus doriferus), there is a need to identify the potential impacts of increased sea level and storm surges on the breeding areas of this important marine predator in Bass Strait, south-eastern Australia. Using high-resolution aerial photography and topographic data, the present study assessed the impacts of future inundation levels on both current and potential breeding habitats at each colony. Inundation from storm surges, based on a predicted rise in sea level, was modeled at each colony from 2012 to 2100. As sea level increases, progressively less severe storm surge conditions will be required to exceed current inundation levels and, thus, have the potential for greater impacts on pup mortality at Australian fur seal colonies. The results of the present study indicate that by 2100, a 1-in-10 year storm will inundate more habitat on average than a present-day 1-in-100 year storm. The study highlights the site-specific nature of storm surge impacts, and in particular the importance of local colony topography and surrounding bathymetry with small, low-lying colonies impacted the most. An increased severity of storm surges will result in either an increase in pup mortality rates associated with storm surges, or the dispersal of individuals to higher ground and/or new colonies
Could a potential Anthropocene mass extinction define a new geological period?
A key aspect of the current debate about the Anthropocene focuses on defining a new geological epoch. Features of the Anthropocene include a biodiversity crisis with the potential to reach ‘mass extinction’ status alongside increasing global CO₂ and temperature. Previous geological boundaries associated with mass extinctions, rises in atmospheric CO₂ and rises in global temperature are more usually associated with transitions between geological periods. The current rapid increase in species extinctions suggest that a new mass extinction event is most likely imminent in the near-term future. Although CO₂ levels are currently low in comparison with the rest of the Phanerozoic, they are rising rapidly along with global temperatures. This suggests that defining the Anthropocene as a new geological period, rather than a new epoch, may be more consistent with previous geological boundaries in the Phanerozoic
Predicting the distributions of under-recorded Odonata using species distribution models
1. Absences in distributional data may result either from the true absence of a species or from a false absence due to lack of recording effort. I use general linear models (GLMs) and species distribution models (SDMs) to investigate this problem in North American Odonata and present a potential solution. 2. I use multi-model selection methods based on Akaike's information criterion to evaluate the ability of water-energy variables, human population density, and recording effort to explain patterns of odonate diversity in the USA and Canada using GLMs. Water-energy variables explain a large proportion of the variance in odonate diversity, but the residuals of these models are significantly related to recorder effort. 3. I then create SDMs for 176species that are found solely in the USA and Canada using model averaging of eight different methods. These give predictions of hypothetical true distributions of each of the 176species based on climate variables, which I compare with observed distributions to identify areas where potential under-recording may occur. 4. Under-recording appears to be highest in northern Canada, Alaska, and Quebec, as well as the interior of the USA. The proportion of predicted species that have been observed is related to recorder effort and population density. Maps for individual species have been made available online () to facilitate recording in the future. 5. This analysis has illustrated a problem with current odonate recording in the form of unbalanced recorder effort. However, the SDM approach also provides the solution, targeting recorder effort in such a way as to maximise returns from limited resources
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Counteracting the climate effects of volcanic eruptions using short-lived greenhouse gases
A large volcanic eruption might constitute a climate emergency, significantly altering global temperature and precipitation for several years. Major future eruptions will occur, but their size or timing cannot be predicted. We show, for the first time, that it may be possible to counteract these climate effects through deliberate emissions of short-lived greenhouse gases, dampening the abrupt impact of an eruption. We estimate an emission pathway countering a hypothetical eruption 3 times the size of Mount Pinatubo in 1991. We use a global climate model to evaluate global and regional responses to the eruption, with and without counteremissions. We then raise practical, financial, and ethical questions related to such a strategy. Unlike the more commonly discussed geoengineering to mitigate warming from long-lived greenhouse gases, designed emissions to counter temporary cooling would not have the disadvantage of needing to be sustained over long periods. Nevertheless, implementation would still face significant challenges
Impact of changes in barometric pressure on landfill methane emission
Citation: Xu, Liukang, Xiaomao Lin, Jim Amen, Karla Welding, and Dayle McDermitt. “Impact of Changes in Barometric Pressure on Landfill Methane Emission.” Global Biogeochemical Cycles 28, no. 7 (2014): 679–95. https://doi.org/10.1002/2013GB004571.Landfill methane emissions were measured continuously using the eddy covariance method from June to December 2010. The study site was located at the Bluff Road Landfill in Lincoln, Nebraska, USA. Our results show that landfill methane emissions strongly depended on changes in barometric pressure; rising barometric pressure suppressed the emission, while falling barometric pressure enhanced the emission, a phenomenon called barometric pumping. There was up to a 35-fold variation in day-to-day methane emissions due to changes in barometric pressure. Wavelet coherence analysis revealed a strong spectral coherency between variations of barometric pressure and methane emission at periodicities ranging from 1 day to 8 days. Power spectrum and ogive analysis showed that at least 10 days of continuous measurements was needed in order to capture 90% of the total variance in the methane emission time series at our landfill site. From our results, it is clear that point-in-time measurements taken at monthly or longer time intervals using techniques such as the trace plume method, the mass balance method, or the closed-chamber method will be subject to large variations in measured emission rates because of the barometric pumping phenomenon. Estimates of long-term integrated methane emissions from landfills based on such measurements could yield uncertainties, ranging from 28.8% underestimation to 32.3% overestimation. Our results demonstrate a need for continuous measurements to quantify annual total landfill emissions. This conclusion may apply to the study of methane emissions from wetlands, peatlands, lakes, and other environmental contexts where emissions are from porous media or ebullition. Other implications from the present study for hazard gas monitoring programs are also discussed
BusTr: Predicting Bus Travel Times from Real-Time Traffic
We present BusTr, a machine-learned model for translating road traffic
forecasts into predictions of bus delays, used by Google Maps to serve the
majority of the world's public transit systems where no official real-time bus
tracking is provided. We demonstrate that our neural sequence model improves
over DeepTTE, the state-of-the-art baseline, both in performance (-30% MAPE)
and training stability. We also demonstrate significant generalization gains
over simpler models, evaluated on longitudinal data to cope with a constantly
evolving world.Comment: 14 pages, 2 figures, 5 tables. Citation: "Richard Barnes, Senaka
Buthpitiya, James Cook, Alex Fabrikant, Andrew Tomkins, Fangzhou Xu (2020).
BusTr: Predicting Bus Travel Times from Real-Time Traffic. 26th ACM SIGKDD
Conference on Knowledge Discovery and Data Mining. doi:
10.1145/3394486.3403376
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