Unseen or unrealistic? Using ensemble simulations to explore unseen weather extremes
Weather extremes cause high socio-economic impacts globally and are projected to become more frequent in the future due to climate change. Quantifying and explaining the effect climate change has already had on climatic extremes is of high importance but is restricted by the brevity and sparsity of...
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| Format: | Thesis |
| Language: | unknown |
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2023
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| Online Access: | https://doi.org/10.26174/thesis.lboro.21802488.v1 https://figshare.com/articles/thesis/Unseen_or_unrealistic_Using_ensemble_simulations_to_explore_unseen_weather_extremes/21802488 |
| Summary: | Weather extremes cause high socio-economic impacts globally and are projected to become more frequent in the future due to climate change. Quantifying and explaining the effect climate change has already had on climatic extremes is of high importance but is restricted by the brevity and sparsity of observed meteorological records. Furthermore, some policy makers are interested in the worst plausible events for decision making, and even the longest observed records (~100 years) might not capture such “unseen” events. In this work, large ensemble simulations are employed as numerous alternative realizations of the real world to quantify the likelihood of climate extremes and explain their nonstationary behaviour beyond what is possible from observed records. This research follows the UNprecedented Simulated Extreme ENsemble (UNSEEN) approach, an emerging asset that has yet to be fully exploited. The applicability of the method and its potential are explored. Statistical tests are developed to evaluate UNSEEN. Furthermore, a novel UNSEEN-trends approach is developed, facilitating detection of changes in 100-year precipitation values over short multi-decadal periods. A case study for Svalbard reveals a rise in 3-day precipitation extremes, such that the 100-year event estimated in 1981 occurs with a return period of around 40 years in 2015. The method is furthermore tested on floods in the Amazon using a hydro-climatological modelling framework. Flood magnitudes far beyond observed values are detected, but a rare bias-correction phenomenon unrealistically altering flood simulations is found. This result indicates that, besides statistical tests, performing physical credibility checks might uncover otherwise 'hidden' modelling errors that may lead to unrealistic extreme events. These findings are incorporated into an UNSEEN protocol, including an open and transferable workflow to enhance the uptake of UNSEEN. This new workflow for example demonstrates that the 2020 March-May Siberian heat wave, which led to ... |
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