Future changes in spatially compounding hot, wet or dry events and their implications for the world’s breadbasket regions

Environmental Research Letters, Journal Year: 2024, Volume and Issue: 19(6), P. 064011 - 064011

Published: May 1, 2024

Abstract Recent years were characterized by an increase in spatially co-occurring hot, wet or dry extreme events around the globe. In this study we analyze data from multi-model climate projections to occurrence of compounding and area affected future climates under scenarios at +1.5 ∘ C, +2.0 +3.0 C higher levels global warming using Earth System Model simulations 6th Phase Coupled Intercomparison Project. Since can strongly amplify societal impacts as economic supply chains are increasingly interdependent, want highlight that world’s breadbasket regions projected be particularly events, posing risks food security. We show spatial extent top-producing agricultural being potentially threatened extremes will drastically if mean temperatures shift C. Further identify a large land concurrently with increased risk other industries sectors addition sector.

Language: Английский

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China is suffering from fewer but more severe Drought to flood abrupt alternation events

Weather and Climate Extremes, Journal Year: 2024, Volume and Issue: 46, P. 100737 - 100737

Published: Nov. 7, 2024

Language: Английский

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Storylines of Unprecedented Extremes in the Southeast United States

Bulletin of the American Meteorological Society, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 13, 2025

Abstract Disaster planning based on historical events is like driving forward while only looking in the rear-view mirror. To expand our field of view, we use a large ensemble weather simulations to characterise current risk extreme case study locations Southeastern United States. We find that temperature have become more frequent between 1981 and 2021, heavy precipitation are also wettest months. Combining analysis people’s recent experience with rate change events, define four quadrants apply groups studies: Sitting Ducks” , “Recent Rarity”, “Living Memory”, “Fading Memory” . A critical storyline “ ducks ”: where high increase most event memory (1981-2021) has low return period today’s climate. these potential for surprise. For example, Montgomery County, Alabama, since 13 years climate 2021. In places, offer unprecedented synthetic from disaster preparedness help people imagine unprecedented. Our results not document substantial changes extremes States but propose generalizable framework using ensembles changing

Language: Английский

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Doing better rather than promising more: A basic principle applicable to both climate modelling and climate policies

PLOS Climate, Journal Year: 2025, Volume and Issue: 4(1), P. e0000466 - e0000466

Published: Jan. 30, 2025

A growing number of scientists are expressing concerns about the inadequacy climate change policies. Fewer questionning dominant modelling paradigm and IPCC’s success to prevent humanity from venturing unprepared into hitherto unknown territories. However, in view an urgent need provide readily available data on constraining uncertainty local regional impacts next few years, there is a debate most suitable path inform both mitigation adaptation strategies. Examples given how common statistical methods emerging technologies can be used exploit wealth existing knowledge drive policy. Parsimonious equitable approaches promoted that combine various lines evidence, including model diversity, large ensembles, storylines, novel applied well-calibrated, global regional, Earth System simulations, deliver more reliable information. As examplified by Paris agreement desirable warming targets, it argued display unrealistic ambitions may not best way for modellers accomplish their long-term objectives, especially consensus emergency allocated short time delivered applied.

Language: Английский

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A Machine Learning Tool for Determining the Required Sample Size for GEV Fitting in Climate Applications

Geophysical Research Letters, Journal Year: 2025, Volume and Issue: 52(6)

Published: March 23, 2025

Abstract Extreme climate events (ECEs) like heavy rainfall and heatwaves significantly impact society, change is altering their magnitude frequency. Generalized Value (GEV) distributions help quantify these ECEs guide human system design. We train a machine learning (ML) model using set of arbitrary GEV to estimate the sample size required determine return value with specific uncertainty. For negative shape parameter maximum extreme temperatures are bounded fewer samples needed given uncertainty than extremes which have positive unbounded values. example, if 1‐in‐20‐year heatwave event requires 400 1% uncertainty, one would need 20 different 20‐year simulations. Achieving such quantities will require extensive downscaling simulations, potentially aided by ML‐based methods increase ensemble size.

Language: Английский

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