A probabilistic analysis of cumulative carbon emissions and long-term planetary warming

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Video in TIB AV-Portal: A probabilistic analysis of cumulative carbon emissions and long-term planetary warming

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A probabilistic analysis of cumulative carbon emissions and long-term planetary warming
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CC Attribution 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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2015
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English

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Abstract
Efforts to mitigate and adapt to long-term climate change could benefit greatly from probabilistic estimates of cumulative carbon emissions due to fossil fuel burning and resulting CO2-induced planetary warming. Here we demonstrate the use of a reduced-form model to project these variables. We performed simulations using a large-ensemble framework with parametric uncertainty sampled to produce distributions of future cumulative emissions and consequent planetary warming. A hind-cast ensemble of simulations captured 1980–2012 historical CO2 emissions trends and an ensemble of future projection simulations generated a distribution of emission scenarios that qualitatively resembled the suite of Representative and Extended Concentration Pathways. The resulting cumulative carbon emission and temperature change distributions are characterized by 5–95th percentile ranges of 0.96–4.9 teratonnes C (Tt C) and 1.4 °C–8.5 °C, respectively, with 50th percentiles at 3.1 Tt C and 4.7 °C. Within the wide range of policy-related parameter combinations that produced these distributions, we found that low-emission simulations were characterized by both high carbon prices and low costs of non-fossil fuel energy sources, suggesting the importance of these two policy levers in particular for avoiding dangerous levels of climate warming. With this analysis we demonstrate a probabilistic approach to the challenge of identifying strategies for limiting cumulative carbon emissions and assessing likelihoods of surpassing dangerous temperature thresholds.

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identified that shows 2 degrees of warming for temperature associated with loss of the green and a sheet meanwhile other modeling groups have developed integrated assessment models for I am so the projected future carbon emissions based on economic social technological and environmental trends in our study we link these efforts by generating
a probabilistic said of carbon emission pathways non-associated cumulative emissions and global warming projections using a simple and I am like model run tens of thousands of times where probabilistic projections needed because climate adaptation
and mitigation requires likelihood estimates in order to develop robust planning for example we find that without rapidly
based in carbon taxes in renewable
technology adoption there's a very high chance that the temperature threshold associated with green and she lost will be crossed the resulting in a need to adapt to around 7 years of long
term sea-level rise Of course the results of
our study are a model-dependent and could change as a model evolves nevertheless based on our demonstration we're I am and climate modeling communities to focus on probabilistic future projections that provide actionable information for climate adaptation and mitigation planning in a rapidly warming world you were over there I had a mental
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