Jared Trok

All Publications

• Increasing risk of mass human heat mortality if historical weather patterns recur NATURE CLIMATE CHANGE Callahan, C. W., Trok, J., Wilson, A. J., Gould, C. F., Heft-Neal, S., Diffenbaugh, N. S., Burke, M. 2025

  View details for DOI 10.1038/s41558-025-02480-1

  View details for Web of Science ID 001616642800001

• Machine learning-based extreme event attribution. Science advances Trok, J. T., Barnes, E. A., Davenport, F. V., Diffenbaugh, N. S. 2024; 10 (34): eadl3242

  Abstract

  The observed increase in extreme weather has prompted recent methodological advances in extreme event attribution. We propose a machine learning-based approach that uses convolutional neural networks to create dynamically consistent counterfactual versions of historical extreme events under different levels of global mean temperature (GMT). We apply this technique to one recent extreme heat event (southcentral North America 2023) and several historical events that have been previously analyzed using established attribution methods. We estimate that temperatures during the southcentral North America event were 1.18° to 1.42°C warmer because of global warming and that similar events will occur 0.14 to 0.60 times per year at 2.0°C above preindustrial levels of GMT. Additionally, we find that the learned relationships between daily temperature and GMT are influenced by the seasonality of the forced temperature response and the daily meteorological conditions. Our results broadly agree with other attribution techniques, suggesting that machine learning can be used to perform rapid, low-cost attribution of extreme events.

  View details for DOI 10.1126/sciadv.adl3242

  View details for PubMedID 39167638

• Extreme precipitation, exacerbated by anthropogenic climate change, drove Peru's record-breaking 2023 dengue outbreak. medRxiv : the preprint server for health sciences Harris, M. J., Trok, J. T., Martel, K. S., Borbor Cordova, M. J., Diffenbaugh, N. S., Munayco, C. V., Lescano, A. G., Mordecai, E. A. 2024

  Abstract

  Anthropogenic forcing is increasing the likelihood and severity of certain extreme weather events, which may catalyze outbreaks of climate-sensitive infectious diseases. Extreme precipitation events can promote the spread of mosquito-borne illnesses by creating vector habitat, destroying infrastructure, and impeding vector control. Here, we focus on Cyclone Yaku, which caused heavy rainfall in northwestern Peru from March 7th - 20th, 2023 and was followed by the worst dengue outbreak in Peru's history. We apply generalized synthetic control methods to account for baseline climate variation and unobserved confounders when estimating the causal effect of Cyclone Yaku on dengue cases across the 56 districts with the greatest precipitation anomalies. We estimate that 67 (95% CI: 30 - 87) % of cases in cyclone-affected districts were attributable to Cyclone Yaku. The cyclone significantly increased cases for over six months, causing 38,209 (95% CI: 17,454 - 49,928) out of 57,246 cases. The largest increases in dengue incidence due to Cyclone Yaku occurred in districts with a large share of low-quality roofs and walls in residences, greater flood risk, and warmer temperatures above 24°C. Analyzing an ensemble of climate model simulations, we found that extremely intense March precipitation in northwestern Peru is 42% more likely in the current era compared to a preindustrial baseline due to climate forcing. In sum, extreme precipitation like that associated with Cyclone Yaku has become more likely with climate change, and Cyclone Yaku caused the majority of dengue cases across the cyclone-affected districts.

  View details for DOI 10.1101/2024.10.23.24309838

  View details for PubMedID 39502661

  View details for PubMedCentralID PMC11537325

• Historical evaluation and future projections of compound heatwave and drought extremes over the conterminous United States in CMIP6 ENVIRONMENTAL RESEARCH LETTERS Rastogi, D., Trok, J., Depsky, N., Monier, E., Jones, A. 2024; 19 (1)

  View details for DOI 10.1088/1748-9326/ad0efe

  View details for Web of Science ID 001117696200001

• Using Machine Learning With Partial Dependence Analysis to Investigate Coupling Between Soil Moisture and Near-Surface Temperature JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES Trok, J. T. T., Davenport, F. V. V., Barnes, E. A. A., Diffenbaugh, N. S. S. 2023; 128 (12)

  View details for DOI 10.1029/2022JD038365

  View details for Web of Science ID 001022733800001