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Abstract The general public and scientific community alike are abuzz over the release of ChatGPT and GPT-4. Among many concerns being raised about the emergence and widespread use of tools based on large language models (LLMs) is the potential for them to propagate biases and inequities. We hope to open a conversation within the environmental data science community to encourage the circumspect and responsible use of LLMs. Here, we pose a series of questions aimed at fostering discussion and initiating a larger dialogue. To improve literacy on these tools, we provide background information on the LLMs that underpin tools like ChatGPT. We identify key areas in research and teaching in environmental data science where these tools may be applied, and discuss limitations to their use and points of concern. We also discuss ethical considerations surrounding the use of LLMs to ensure that as environmental data scientists, researchers, and instructors, we can make well-considered and informed choices about engagement with these tools. Our goal is to spark forward-looking discussion and research on how as a community we can responsibly integrate generative AI technologies into our work. 

During the 1930s Dust Bowl drought in the central United States, species with the C₃photosynthetic pathway expanded throughout C₄-dominated grasslands. This widespread increase in C₃grasses during a decade of low rainfall and high temperatures is inconsistent with well-known traits of C₃vs. C₄pathways. Indeed, water use efficiency is generally lower, and photosynthesis is more sensitive to high temperatures in C₃than C₄species, consistent with the predominant distribution of C₃grasslands in cooler environments and at higher latitudes globally. We experimentally imposed extreme drought for 4 y in mixed C₃/C₄grasslands in Kansas and Wyoming and, similar to Dust Bowl observations, also documented three- to fivefold increases in C₃/C₄biomass ratios. To explain these paradoxical responses, we first analyzed long-term climate records to show that under nominal conditions in the central United States, C₄grasses dominate where precipitation and air temperature are strongly related (warmest months are wettest months). In contrast, C₃grasses flourish where precipitation inputs are less strongly coupled to warm temperatures. We then show that during extreme drought years, precipitation–temperature relationships weaken, and the proportion of precipitation falling during cooler months increases. This shift in precipitation seasonality provides a mechanism for C₃grasses to respond positively to multiyear drought, resolving the Dust Bowl paradox. Grasslands are globally important biomes and increasingly vulnerable to direct effects of climate extremes. Our findings highlight how extreme drought can indirectly alter precipitation seasonality and shift ecosystem phenology, affecting function in ways not predictable from key traits of C₃and C₄species.