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As climate change continues to accelerate, there is a growing need for collaboration between geoscientists and community partners to cocreate equitable science-based climate solutions that align with community priorities. However, the curriculum of many geoscience Ph.D. programs focuses almost exclusively on physical science knowledge production confined to the academic sphere with little attention to usability or applications of climate information to decision-making. A consequence is that there is often little educational support to train future generations of geoscientists with the skills needed to serve as knowledge brokers and interpret climate information into the context needed for decision-making. To address this, a group of students and faculty at the University of Michigan developed a pilot course to provide hands-on experience engaging with local communities to develop climate solutions. This project involved a collaboration with the Washtenaw County Water Resources Commissioner's Office to discuss their high-priority objectives. The inaugural project involved identifying high- priority locations in Washtenaw County for rain garden development to be used for internal planning purposes. The process of recurring communications with partners played a critical role in tailoring the project to have ongoing use for local government after the course. This project provided a student-driven bridge between climate knowledge production, climate science usability, and community engagement. This type of formal engagement with community partners provides opportunities to train the next generation of geoscientists to have the skills to engage with practitioners and decision-makers and make climate science more accessible. KEYWORDS: Climate change; Community; Communications/ decision making; Education; Local effects; Societal impacts, 1. Introduction Climate change represents one of the most complex issues of our time, requiring an approach that goes beyond traditional scientific boundaries and requires the integration of knowledge across [...]

Large numbers of Americans of all ages are interested in learning more about climate change education (CCE) (Perkins et al. 2017) and high schools have the opportunity to contribute to that learning. The "Next Generation Science Standards" (NGSS Lead States 2013) attempt to address climate change for high school students across disciplines. Despite the presence of climate change in NGSS, most high schools do not dedicate a class specifically to climate change or even to Earth Science. This leaves the burden of CCE to physics, chemistry, and biology classes, which often make space for it by mentioning the topic in ancillary form. To leave students with applicable knowledge, CCE must be interdisciplinary. This allows students to address the most important questions--ones left out when CCE is just taught from an Earth Science perspective--such as "How might farmers adapt to changing climatic conditions? How might such adaptations affect the price of staple foods, such as corn or wheat?" (Monroe, Oxarart, and Plate 2013). Many articles reference what CCE should do (Chang and Pascua 2017; Monroe, Oxarart, and Plate 2013; Monroe et al. 2017; Reid 2019; Schreiner, Henriksen, and Kirkeby Hansen 2005; Stapleton 2019). None of these lists can be accomplished in passing. A stand-alone climate change course provides time for students to develop needed synthesis and skills and allows teachers in other classes to address climate change as it naturally comes up. In this case study, the authors provide evidence of that demand and propose a path for schools to deliver on it.

To differentiate and understand drivers behind coastal ice cover trends and variability, we advance development of a model combining satellite, in situ, and teleconnection data along the shoreline of Earth's largest freshwater lake (Lake Superior). Previous studies suggest a regime shift in Lake Superior's ice cover starting in 1998. Our study includes seven years of new data and subsequent model analysis that provide new insight into characteristics of the post‐1998 regime. In addition to providing a valuable extension to the historical ice cover record for this domain, we find the regime shift in coastal ice cover starting in 1998 is characterized by pronounced variability, and not simply a shift in pre‐1998 trends. Our findings represent an important stepping stone for future ice and climate modeling not only on Lake Superior but across the entire Great Lakes region and in other global high‐latitude coastal regions as well. Plain Language Summary: Ice cover on the Laurentian Great Lakes has become more variable over the past 25 years. To better understand this variability, we re‐develop a model from a previous study focused on a portion of the shoreline of Lake Superior. The previous study, which culminated in 2015, suggested a change in both interannual variability and long‐term trends in ice cover after 1998. At the time of the previous study, however, the extent to which those changes might continue to propagate into the future was unclear. Here, we extend the historical ice cover record through 2022 while also exploring a broad range of potential explanatory variables in a simulation model. Our analysis indicates that the post‐1998 regime is characterized by more pronounced variability than previous studies indicated, with near‐record‐high years of ice cover followed by years of very little or even no appreciable ice cover. These interannual ice cover dynamics were not evident in the historical record prior to 1998, and their persistence from 1998 through 2022 underscores the importance of not only differentiating regime shifts from trends in climate change studies, but also of the value in correctly reflecting those regime shifts in simulation and forecasting models. Key Points: For the past 25 years, ice cover along Lake Superior's shoreline has been characterized by pronounced interannual variabilityWe reproduce these dynamics in two probabilistic models (Cox survival and beta) combining satellite, in situ, and teleconnection dataCoastal ice conditions along the shoreline of Earth's largest freshwater lake reflect a regime shift that began in the late 1990s [ABSTRACT FROM AUTHOR]

Scenario planning is a tool used to explore a set of plausible futures shaped by specific trajectories. When applied in participatory contexts, it is known as participatory scenario planning (PSP), which has grown in its usage for planning, policy, and decision-making within the context of climate change. There has been no high-level synthesis of systematic reviews covering the overall state and direction of PSP for climate adaptation and management. We draw from four systematic reviews on PSP published between 2015 and 2021 to substantiate the credibility of the process and identify a set of standard practices to make PSP a more accessible and usable tool for not only researchers, but policymakers, practitioners, and other end users who may benefit from PSP. We summarize and synthesize the range of PSP processes and characteristics, highlighting four common trends that provoke additional inquiry: PSP's contribution to social learning and bias, the varying use of quantitative information in scenario development, issues related to carrying out monitoring and evaluation, and the varying completion of practices recommended by established PSP literature. We propose four processes as integral to maximizing PSP's usability for end users and recommend these areas for further study: identifying social imbalances throughout the PSP process, recognizing bias as inherent to PSP, explicitly addressing, and incorporating uncertainty, and allocating resources for monitoring and evaluation. [ABSTRACT FROM AUTHOR]

Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be downscaled to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically downscaled GCM simulations for 1971-2000--a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) downscaling methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically downscaled data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of downscaled climate data and the potential for misinterpretation of future estimates of Salmonella infections.