Jonas Schwaab, Mathias Hauser, Robin D Lamboll, Lea Beusch, Lukas Gudmundsson, Yann Quilcaille, Quentin Lejeune, Sarah Schöngart, Carl-Friedrich Schleussner, Shruti Nath, Joeri Rogelj, Zebedee Nicholls, Sonia I Seneviratne
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引用次数: 0
Abstract
Due to insufficient climate action over the past decade, it is increasingly likely that 1.5 °C of global warming will be exceeded - at least temporarily - in the 21st century. Such a temporary temperature overshoot carries additional climate risks which are poorly understood. Earth System Model climate projections are only available for a very limited number of overshoot pathways, thereby preventing comprehensive analysis of their impacts. Here, we address this issue by presenting a novel dataset of spatially resolved emulated annual temperature projections for different overshoot pathways. The dataset was created using the FaIR and MESMER emulators. First, FaIR was employed to translate ten different emission scenarios, including seven that are characterised by overshoot, into a large ensemble of forced global mean temperatures. These global mean temperatures were then converted into stochastic ensembles of local annual temperature fields using MESMER. To ensure an optimal tradeoff between accurate characterization of the ensemble spread and storage requirements for large ensembles, this procedure was accompanied by testing the sensitivity of sample quantiles to different ensemble sizes. The resulting dataset offers the unique opportunity to study local and regional climate change impacts of a range of overshoot scenarios, including the timing and magnitude of temperature thresholds exceedance.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.