Extreme precipitation, exacerbated by anthropogenic climate change, drove Peru's record-breaking 2023 dengue outbreak DOI Creative Commons
Mallory Harris, Jared T. Trok, Kevin S. Martel

и другие.

Опубликована: Окт. 23, 2024

Anthropogenic forcing is increasing the likelihood and severity of certain extreme weather events, which may catalyze outbreaks climate-sensitive infectious diseases. Extreme precipitation events can promote spread mosquito-borne illnesses by creating vector habitat, destroying infrastructure, impeding control. Here, we focus on Cyclone Yaku, caused heavy rainfall in northwestern Peru from March 7th - 20th, 2023 was followed worst dengue outbreak Peru's history. We apply generalized synthetic control methods to account for baseline climate variation unobserved confounders when estimating causal effect Yaku cases across 56 districts with greatest anomalies. estimate that 67 (95% CI: 30 87) % cyclone-affected were attributable Yaku. The cyclone significantly increased over six months, causing 38,209 17,454 49,928) out 57,246 cases. largest increases incidence due occurred a large share low-quality roofs walls residences, greater flood risk, warmer temperatures above 24°

Язык: Английский

Artificial intelligence for modeling and understanding extreme weather and climate events DOI Creative Commons
Gustau Camps‐Valls, Miguel‐Ángel Fernández‐Torres, Kai-Hendrik Cohrs

и другие.

Nature Communications, Год журнала: 2025, Номер 16(1)

Опубликована: Фев. 24, 2025

In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences, by improving weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. The latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous, small sample sizes data limited annotations. This paper reviews how AI is being used to analyze climate events (like floods, droughts, wildfires, heatwaves), highlighting importance creating accurate, transparent, reliable models. We discuss hurdles dealing data, integrating real-time information, deploying understandable models, all crucial steps for gaining stakeholder trust meeting regulatory needs. provide an overview can help identify explain more effectively, disaster response communication. emphasize need collaboration across different fields create solutions that are practical, understandable, trustworthy enhance readiness risk reduction. Artificial Intelligence transforming study like helping overcome challenges integration. review article highlights models improve response, communication trust.

Язык: Английский

Процитировано

3
A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models DOI Open Access
Qingchun Guo,

Zhenfang He,

Zhaosheng Wang

и другие.

Water, Год журнала: 2024, Номер 16(19), С. 2870 - 2870

Опубликована: Окт. 9, 2024

Climate change affects the water cycle, resource management, and sustainable socio-economic development. In order to accurately predict climate in Weifang City, China, this study utilizes multiple data-driven deep learning models. The data for 73 years include monthly average air temperature (MAAT), minimum (MAMINAT), maximum (MAMAXAT), total precipitation (MP). different models artificial neural network (ANN), recurrent NN (RNN), gate unit (GRU), long short-term memory (LSTM), convolutional (CNN), hybrid CNN-GRU, CNN-LSTM, CNN-LSTM-GRU. CNN-LSTM-GRU MAAT prediction is best-performing model compared other with highest correlation coefficient (R = 0.9879) lowest root mean square error (RMSE 1.5347) absolute (MAE 1.1830). These results indicate that method a suitable model. This can also be used surface modeling. will help flood control management.

Язык: Английский

Процитировано

16
Predicting Atlantic and Benguela Niño events with deep learning DOI Creative Commons
Marie‐Lou Bachèlery, Julien Brajard, Massimiliano Patacchiola

и другие.

Science Advances, Год журнала: 2025, Номер 11(14)

Опубликована: Апрель 2, 2025

Atlantic and Benguela Niño events substantially affect the tropical region, with far-reaching consequences on local marine ecosystems, African climates, El Southern Oscillation. While accurate forecasts of these are invaluable, state-of-the-art dynamic forecasting systems have shown limited predictive capabilities. Thus, extent to which variability is predictable remains an open question. This study explores potential deep learning in this context. Using a simple convolutional neural network architecture, we show that Atlantic/Benguela Niños can be predicted up 3 4 months ahead. Our model excels peak-season remarkable accuracy extending lead time 5 months. Detailed analysis reveals our model’s ability exploit known physical precursors, such as long-wave ocean dynamics, for predictions events. challenges perception unpredictable highlights learning’s advance understanding critical climate

Язык: Английский

Процитировано

2
Contributions of Atmospheric Ridging and Low Soil Moisture to the Record‐Breaking June 2023 Mexico‐Texas Heatwave DOI Creative Commons
Dmitri Kalashnikov, Deepti Singh, Mingfang Ting

и другие.

Geophysical Research Letters, Год журнала: 2025, Номер 52(5)

Опубликована: Фев. 27, 2025

Abstract June 2023 witnessed the hottest, largest, and longest‐lasting heatwave across Mexico Texas between 1940 2023. We apply constructed analogs with multiple linear regression models to quantify contribution of different drivers daily temperature anomalies during this heatwave. On hottest day (20 June), circulation, soil moisture, their interaction explained 3.82°C (90% CI: 2.72–4.91°C) 5.42°C observed anomaly most residual attributed thermodynamic effects long‐term warming. Using CESM2‐LENS2, we find that 2023‐like patterns are not projected increase in frequency but will become 1.9°C hotter by mid‐21st century under SSP3‐7.0. The simulated these could produce temperatures >50°C (122°F) south Texas, representing a low‐likelihood yet physically plausible worst‐case scenario inform disaster preparedness adaptation planning.

Язык: Английский

Процитировано

1
Carbon price prediction research based on CEEMDAN-VMD secondary decomposition and BiLSTM DOI
Ming Fang, Yuanliang Zhang, Wei Liang

и другие.

Environmental Science and Pollution Research, Год журнала: 2025, Номер unknown

Опубликована: Март 17, 2025

Язык: Английский

Процитировано

0
Attributing climate and weather extremes to Northern Hemisphere sea ice and terrestrial snow: progress, challenges and ways forward DOI Creative Commons
Kunhui Ye,

Judah Cohen,

Hans W. Chen

и другие.

npj Climate and Atmospheric Science, Год журнала: 2025, Номер 8(1)

Опубликована: Май 3, 2025

Abstract Sea ice and snow are crucial components of the cryosphere climate system. Both sea spring in Northern Hemisphere (NH) have been decreasing at an alarming rate a changing climate. Changes NH linked with variety weather extremes including cold spells, heatwaves, droughts wildfires. Understanding these linkages will benefit predictions extremes. However, existing work on this has largely fragmented is subject to large uncertainties physical pathways methodologies. This prevented further substantial progress attributing change, potentially risk loss critical window for effective change mitigation. In review, we synthesize current by evaluating observed linkages, their pathways, suggesting ways forward future research efforts. By adopting same framework both snow, highlight combined influence cryospheric feedback We suggest that from improving observational networks, addressing causality complexity using multiple lines evidence, large-ensemble approaches artificial intelligence, achieving synergy between different methodologies/disciplines, widening context, coordinated international collaboration.

Язык: Английский

Процитировано

0
Linking European Temperature Variations to Atmospheric Circulation With a Neural Network: A Pilot Study in a Climate Model DOI Creative Commons

Enora Cariou,

Julien Cattiaux, Saïd Qasmi

и другие.

Geophysical Research Letters, Год журнала: 2025, Номер 52(9)

Опубликована: Май 8, 2025

Abstract In Europe, temperature variations are mainly driven by the North Atlantic atmospheric circulation. Here, with data from MIROC6 large ensemble, we investigate a convolutional neural network (a UNET) for reconstructing daily anomalies in Europe Sea Level Pressure (SLP) as proxy of circulation, and compare results traditional analogs approach. We show an excellent ability UNET to estimate given information SLP only. This novel method outperforms method, at both inter‐annual time scales. Our study also shows that during training, learns such seasonal cycle relationship between sea‐level pressure anomalies, which could explain part its scores. exploratory work opens up promising prospects estimating contribution variability observed variations.

Язык: Английский

Процитировано

0
Extreme precipitation, exacerbated by anthropogenic climate change, drove Peru's record-breaking 2023 dengue outbreak DOI Creative Commons
Mallory Harris, Jared T. Trok, Kevin S. Martel

и другие.

Опубликована: Окт. 23, 2024

Anthropogenic forcing is increasing the likelihood and severity of certain extreme weather events, which may catalyze outbreaks climate-sensitive infectious diseases. Extreme precipitation events can promote spread mosquito-borne illnesses by creating vector habitat, destroying infrastructure, impeding control. Here, we focus on Cyclone Yaku, caused heavy rainfall in northwestern Peru from March 7th - 20th, 2023 was followed worst dengue outbreak Peru's history. We apply generalized synthetic control methods to account for baseline climate variation unobserved confounders when estimating causal effect Yaku cases across 56 districts with greatest anomalies. estimate that 67 (95% CI: 30 87) % cyclone-affected were attributable Yaku. The cyclone significantly increased over six months, causing 38,209 17,454 49,928) out 57,246 cases. largest increases incidence due occurred a large share low-quality roofs walls residences, greater flood risk, warmer temperatures above 24°

Язык: Английский

Процитировано

1