Projecting Extreme Temperatures and Precipitation for the Gulf Coast Region of the United States

Abstract Submission: In this study, we develop an open-source online computing infrastructure that utilizes the cloud-based platform Google Earth Engine (GEE) and the NEX-GDDP-CMIP6, a downscaled global dataset with a spatial resolution of 25 km. This infrastructure is designed to assess future projections of climate change and variability in relation to historical data across various locations, regions, administrative boundaries, and watershed levels. First, we create a model from a selection of climate models that categorizes conditions into four groups based on user-defined percentile thresholds (for example, the 10th and 90th percentiles for annual precipitation and average temperature): i) dry and hot, ii) dry and cold, iii) wet and hot, and iv) wet and cold. We specifically calculate projected deviations for several key climate indices within these four model groups, including the maximum precipitation over a 5-day period (RX5day), the maximum one-day precipitation of the year (RX1day), total annual precipitation (PRCPTOT), the hottest day of the year (TXx), the coldest day of the year (TNn), the number of frost days (FD), and the number of summer days (SU), among others.
We focus on the Gulf Coast region of the United States—comprising Texas, Louisiana, Mississippi, Alabama, and Florida—to project extreme temperatures and precipitation through the end of 2100. We compare climate models with ground-based observations from the historical period. Using these climate models, we then evaluate likely future projections. We assess the likelihood of extreme temperature and precipitation events exceeding the 95th and 99th percentiles, as well as specific absolute thresholds. Additionally, we analyze the intensity of consecutive precipitation days and consecutive heatwave days. This study identifies hotspot locations for precipitation extremes, temperature extremes, and areas where both occur. It is essential that these findings contribute to the development of effective climate change adaptation and mitigation strategies for the future.

Learning Objectives/Expected Outcome (Optional) : The primary objective of this study is to create an easily expandable training program for undergraduate students, focusing on the analysis of climate change effects. We incorporate both fundamental and advanced courses using GEE and Google Colaboratory. Although the case study is centered on the Gulf Coast region of the United States, the techniques and training materials can be adapted for use in other regions within the USA and internationally. This resulting tool provides opportunities for computational research. By equipping students with hands-on skills and insights derived from detailed climate projections, the project aims to enhance their understanding of climate change impacts and empower them to tackle climate-related challenges.