Matteo Mastropierro, Katsumasa Tanaka, Irina Melnikova, Philippe Ciais
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引用次数: 0
Abstract
The Global Warming Potential-star (GWP*) approach is a way to convert the emissions of short-lived climate forcers to CO2-equivalent emissions while maintaining consistency with temperature outcomes. Here we evaluate the performance of GWP* when it is used to account for non-CO2 gases within the carbon budget framework. We convert methane (CH4) emissions to CO2-equivalent emissions via GWP* and calculate the temperature through simple climate models. We show that GWP* can accurately convert CH4 emissions to reproduce the temperature until 2100 under a variety of scenarios, including overshoot scenarios, except those with a rapid decline in CH4 emissions. Beyond 2100, however, the use of GWP* can lead to temperature overestimation since it extends beyond its calibration range. Furthermore, we find that under scenarios designed to achieve identical temperature targets but with varying overshoot profiles, cumulative CO2-eq budgets (GWP*-basis) generally increase with overshoot length and magnitude. This is driven by the internal dynamics of our model, as characterized by its negative zero-emission commitment. While the use of GWP* enhances such effects with increasing overshoot length, it exerts opposite effects with increasing overshoot magnitude.
期刊介绍:
npj Climate and Atmospheric Science is an open-access journal encompassing the relevant physical, chemical, and biological aspects of atmospheric and climate science. The journal places particular emphasis on regional studies that unveil new insights into specific localities, including examinations of local atmospheric composition, such as aerosols.
The range of topics covered by the journal includes climate dynamics, climate variability, weather and climate prediction, climate change, ocean dynamics, weather extremes, air pollution, atmospheric chemistry (including aerosols), the hydrological cycle, and atmosphere–ocean and atmosphere–land interactions. The journal welcomes studies employing a diverse array of methods, including numerical and statistical modeling, the development and application of in situ observational techniques, remote sensing, and the development or evaluation of new reanalyses.