South Central Climate Adaptation Science Center

Ecosystem transformation involves the emergence of persistent ecological or social–ecological systems that diverge, dramatically and irreversibly, from prior ecosystem structure and function. Such transformations are occurring at increasing rates across the planet in response to changes in climate, land use, and other factors. Consequently, a dynamic view of ecosystem processes that accommodates rapid, irreversible change will be critical for effectively conserving fish, wildlife, and other natural resources, and maintaining ecosystem services. However, managing ecosystems toward states with novel structure and function is an inherently unpredictable and difficult task. Managers navigating ecosystem transformation can benefit from considering broader objectives, beyond a traditional focus on resisting ecosystem change, by also considering whether accepting inevitable change or directing it along some desirable pathway is more feasible (that is, practical and appropriate) under some circumstances (the RAD framework). By explicitly acknowledging transformation and implementing an iterative RAD approach, natural resource managers can be deliberate and strategic in addressing profound ecosystem change.

Abstract Ecosystem transformation can be defined as the emergence of a self-organizing, self-sustaining, ecological or social–ecological system that deviates from prior ecosystem structure and function. These transformations are occurring across the globe; consequently, a static view of ecosystem processes is likely no longer sufficient for managing fish, wildlife, and other species. We present a framework that encompasses three strategies for fish and wildlife managers dealing with ecosystems vulnerable to transformation. Specifically, managers can resist change and strive to maintain existing ecosystem composition, structure, and function; accept transformation when it is not feasible to resist change or when changes are deemed socially acceptable; or direct change to a future ecosystem configuration that would yield desirable outcomes. Choice of a particular option likely hinges on anticipating future change, while also acknowledging that temporal and spatial scales, recent history and current state of the system, and magnitude of change can factor into the decision. This suite of management strategies can be implemented using a structured approach of learning and adapting as ecosystems change.

Abstract Background Forest and nonforest ecosystems of the western United States are experiencing major transformations in response to land-use change, climate warming, and their interactive effects with wildland fire. Some ecosystems are transitioning to persistent alternative types, hereafter called “vegetation type conversion” (VTC). VTC is one of the most pressing management issues in the southwestern US, yet current strategies to intervene and address change often use trial-and-error approaches devised after the fact. To better understand how to manage VTC, we gathered managers, scientists, and practitioners from across the southwestern US to collect their experiences with VTC challenges, management responses, and outcomes. Results Participants in two workshops provided 11 descriptive case studies and 61 examples of VTC from their own field observations. These experiences demonstrate the extent and complexity of ecological reorganization across the region. High-severity fire was the predominant driver of VTC in semi-arid coniferous forests. By a large margin, these forests converted to shrubland, with fewer conversions to native or non-native herbaceous communities. Chaparral and sagebrush areas nearly always converted to non-native grasses through interactions among land use, climate, and fire. Management interventions in VTC areas most often attempted to reverse changes, although we found that these efforts cover only a small portion of high-severity burn areas undergoing VTC. Some areas incurred long (>10 years) observational periods prior to initiating interventions. Efforts to facilitate VTC were rare, but could cover large spatial areas. Conclusions Our findings underscore that type conversion is a common outcome of high-severity wildland fire in the southwestern US. Ecosystem managers are frontline observers of these far-reaching and potentially persistent changes, making their experiences valuable in further developing intervention strategies and research agendas. As its drivers increase with climate change, VTC appears increasingly likely in many ecological contexts and may require management paradigms to transition as well. Approaches to VTC potentially include developing new models of desired conditions, the use of experimentation by managers, and broader implementation of adaptive management strategies. Continuing to support and develop science-manager partnerships and peer learning groups will help to shape our response to ongoing rapid ecological transformations.

Abstract This study assesses projections from 24 CMIP5 models of number, duration, and severity of pluvial and drought events utilizing 6‐month standardized precipitation index. Increased variability of standardized precipitation index is projected globally. More frequent, longer lasting, and stronger pluvials are projected in wet regions, and the same for droughts in dry regions. Worsening pluvials and droughts are most apparent in the Northern Hemisphere midlatitudes and the Americas, respectively. Uniquely, this study investigates pluvials and droughts in locations where the precipitation trend is of the opposite sign. In drying regions, 40% of grid points project an increase in number and 65% project an increase in duration of severe pluvials. Projections for severe drought events in wetting regions show similar projections. As precipitation trends alone do not provide information about pluvial and drought characteristics this study has important implications for planning and resilience.

Abstract Natural and cultural resource managers are increasingly working with the scientific community to create information on how best to adapt to the current and projected impacts of climate change. Engaging with these managers is a strategy that researchers can use to ensure that scientific outputs and findings are actionable (or useful and usable). In this article, the authors adapt Davidson’s wheel of participation to characterize and describe common stakeholder engagement strategies across the spectrum of Inform, Consult, Participate, and Empower. This adapted framework provides researchers with a standardized vocabulary for describing their engagement approach, guidance on how to select an approach, methods for implementing engagement, and potential barriers to overcome. While there is often no one “best” approach to engaging with stakeholders, researchers can use the objectives of their project and the decision context in which their stakeholders operate to guide their selection. Researchers can also revisit this framework over time as their project objectives shift and their stakeholder relationships evolve.

At present, the National Oceanic and Atmospheric Administration is developing new technologies that could offer explicit estimates of the probability that a thunderstorm could produce a tornado up to an hour ahead of the event. Such technologies could radically alter how risk spaces are represented and understood by those who must decide whether or not to take protective action. In addition, there are relatively few studies that examine mapped representations of uncertainty in weather information or the influence of this uncertainty information in weather hazard decision making. To address these gaps, this study presents research subjects with a variety of representations of uncertainty based on the precepts of cartography and information visualization. We propose and test for the existence of three geospatial framing effects that potentially influence subjective estimates of risk: distance from a hazard, warning boundary inclusion or exclusion, and symbolic color coding of uncertainty information. Using a series of computer-aided geographic experiments with a large sample (N = 5,564) of the U.S. population, we find evidence for the existence of each of the three proposed geospatial framing effects. Two of these framings are controlled by the mapmaker—in this case, the weather forecaster—and thus should be considered during the development stages of new products. We discuss the practical implications of the experimental study for current and future tornado warning practices. Key Words: cartography, risk, tornado, uncertainty, visualization.

The green-up of vegetation in spring brings a pulse of food resources that many animals track during migration. However, green-up phenology is changing with climate change, posing an immense challenge for species that time their migrations to coincide with these resource pulses. We evaluated changes in green-up phenology from 2002 to 2021 in relation to the migrations of 150 Western-Hemisphere bird species using eBird citizen science data. We found that green-up phenology has changed within bird migration routes, and yet the migrations of most species align more closely with long-term averages of green-up than with current conditions. Changing green-up strongly influenced phenological mismatches, especially for longer-distance migrants. These results reveal that bird migration may have limited flexibility to adjust to changing vegetation phenology and emphasize the mounting challenge migratory animals face in following en route resources in a changing climate.

The Weather Research and Forecasting (WRF) model and a combination of the Regional Spectral Model (RSM) and the Japanese Meteorological Agency Non-Hydrostatic Model (NHM) were used to dynamically downscale selected CMIP5 global climate models to provide 2-km projections with hourly model output for Puerto Rico and the U.S. Virgin Islands. Two 20-year time slices were downscaled for historical (1986-2005) and future (2041-2060) periods following RCP8.5. Projected changes to mean and extreme temperature and precipitation were quantified for Holdridge life zones within Puerto Rico and for the U.S. Virgin Islands. The evaluation reveals a persistent cold bias for all islands in the U.S. Caribbean, a dry bias across Puerto Rico, and a wet bias on the windward side of mountains within the U.S. Virgin Islands. Despite these biases, model simulations show a robust drying pattern for all islands that is generally larger for Puerto Rico (25% annual rainfall reduction for some life zones) than the U.S. Virgin Islands (12% island average). The largest precipitation reductions are found during the more convectively active afternoon and evening hours. Within Puerto Rico, the model uncertainty increases for the wetter life zones, especially for precipitation. Across the life zones, both models project unprecedented maximum and minimum temperatures that may exceed 200 days annually above the historical baseline with only small changes to the frequency of extreme rainfall. By contrast, in the U.S. Virgin Islands, there is no consensus on the location of the largest drying relative to the windward and leeward side of the islands. However, the models project the largest increases in maximum temperature on the southern side of St. Croix and in higher elevations of St. Thomas and St. John.

Abstract Global “hot spots” for land–atmosphere coupling have been identified through various modeling studies—both local and global in scope. One hot spot that is common to many of these analyses is the U.S. southern Great Plains (SGP). In this study, we perform a mesoscale analysis, enabled by the Oklahoma Mesonet, that bridges the spatial and temporal gaps between preceding local and global analyses of coupling. We focus primarily on east–west variations in seasonal coupling in the context of interannual variability over the period spanning 2000–15. Using North American Regional Reanalysis (NARR)-derived standardized anomalies of convective triggering potential (CTP) and the low-level humidity index (HI), we investigate changes in the covariance of soil moisture and the atmospheric low-level thermodynamic profile during seasonal hydrometeorological extremes. Daily CTP and HI z scores, dependent upon climatology at individual NARR grid points, were computed and compared to in situ soil moisture observations at the nearest mesonet station to provide nearly collocated annual composites over dry and wet soils. Extreme dry and wet year CTP and HI z -score distributions are shown to deviate significantly from climatology and therefore may constitute atmospheric precursors to extreme events. The most extreme rainfall years differ from climatology but also from one another, indicating variability in the strength of land–atmosphere coupling during these years. Overall, the covariance between soil moisture and CTP/HI is much greater during drought years, and coupling appears more consistent. For example, propagation of drought during 2011 occurred under antecedent CTP and HI conditions that were identified by this study as being conducive to positive dry feedbacks demonstrating potential utility of this framework in forecasting regional drought propagation.

Abstract Although significant improvements have been made to the prediction and understanding of extreme precipitation events in recent decades, there is still much to learn about these impactful events on the subseasonal time scale. This study focuses on identifying synoptic patterns and precursors ahead of an extreme precipitation event over the contiguous United States (CONUS). First, we provide a robust definition for 14-day “extreme precipitation events” and partition the CONUS into six different geographic regions to compare and contrast the synoptic patterns associated with events in those regions. Then, several atmospheric variables from ERA-Interim (e.g., geopotential height and zonal winds) are composited to understand the evolution of the atmospheric state before and during a 14-day extreme precipitation event. Common synoptic signals seen during events include significant zonally oriented trough–ridge patterns, an energized subtropical jet stream, and enhanced moisture transport into the affected area. Also, atmospheric-river activity increases in the specific region during these events. Modes of climate variability and lagged composites are then investigated for their potential use in lead-time prediction. Key findings include synoptic-scale anomalies in the North Pacific Ocean and regional connections to modes such as the Pacific–North American pattern and the North Pacific Oscillation. Taken together, our results represent a significant step forward in understanding the evolution of 14-day extreme precipitation events for potential damage and casualty mitigation.

Abstract Societies worldwide make large investments in the sustainability of integrated human-freshwater systems, but uncertainty about water supplies under climate change poses a major challenge. Investments in infrastructure, water regulation, or payments for ecosystem services may boost water availability, but may also yield poor returns on investment if directed to locations where water supply unexpectedly fluctuates due to shifting climate. How should investments in water sustainability be allocated across space and among different types of projects? Given the high costs of investments in water sustainability, decision-makers are typically risk-intolerant, and considerable uncertainty about future climate conditions can lead to decision paralysis. Here, we use mathematical optimization models to find Pareto-optimal satisfaction of human and environmental water needs across a large drought-prone river basin for a range of downscaled climate projections. We show how water scarcity and future uncertainty vary independently by location, and that joint consideration of both factors can provide guidance on how to allocate water sustainability investments. Locations with high water scarcity and low uncertainty are good candidates for high-cost, high-reward investments; locations with high scarcity but also high uncertainty may benefit most from low regret investments that minimize the potential for stranded assets if water supply increases. Given uncertainty in climate projections in many regions worldwide, our analysis illustrates how explicit consideration of uncertainty may help to identify the most effective strategies for investments in the long-term sustainability of integrated human-freshwater systems.

Abstract Hydrologic extremes of drought and flooding stress water resources and damage communities in the Red River basin, located in the south-central United States. For example, the summer of 2011 was the third driest summer in Oklahoma state history and the driest in Texas state history. When the long-term drought conditions ended in the spring of 2015 as El Niño brought record precipitation to the region, there were also catastrophic floods that caused loss of life and property. Hydrologic extremes such as these have occurred throughout the historical record, but decision-makers need to know how the frequency of these events is expected to vary in a changing climate so that they can mitigate these impacts and losses. Therefore, the goals of this study focus on how these hydrologic extremes impact water resources in the Red River basin, how the frequency of such events is expected to change in the future, and how this study can aid local water-resource managers and decision-makers. Heavy-precipitation events were defined at the historical 90th and 99th percentiles, and severe-drought events were identified at a threshold of the standardized precipitation evapotranspiration index’s value of less than or equal to −1. The results show an increase in the frequency of severe-drought events in the western Red River basin and a rise in heavy-rainfall events in the east by the end of the century, especially under RCP 8.5. Therefore, decision-makers and water-resource managers will likely need to prepare for both hydrologic extremes depending on their location within the basin.

Abstract Quantitative understanding of vegetation dynamics over timespans beyond a century remains limited. In this regard, the pollen‐based reconstruction of past vegetation enables unique research opportunities by quantifying changes in plant community compositions during hundreds to thousands of years. Critically, the methodological basis for most reconstruction approaches rests upon estimates of pollen productivity and dispersal. Previous studies, however, have reached contrasting conclusions concerning these estimates, which may be perceived to challenge the applicability and reliability of pollen‐based reconstruction. Here we show that conflicting estimates of pollen production and dispersal are, at least in part, artifacts of fixed assumptions of pollen dispersal and insufficient spatial resolution of vegetation data surrounding the pollen‐collecting lake. We implemented a Bayesian statistical model that related pollen assemblages in surface sediments of 33 small lakes (<2 ha) in the northeastern United States, with surrounding vegetation ranging from 10 1 to >10 5 m from the lake margin. Our analysis revealed three key insights. First, pollen productivity is largely conserved within taxa and across forest types. Second, when local (within a 1‐km radius) vegetation abundances are not considered, pollen‐source areas may be overestimated for some common taxa (Cupressaceae, Pinus , Quercus , and Tsuga ). Third, pollen dispersal mechanisms may differ between local and regional scales; this is missed by pollen‐dispersal models used in previous studies. These findings highlight the complex interactions between vegetation heterogeneity on the landscape and pollen dispersal. We suggest that, when estimating pollen productivity and dispersal, both detailed local and extended regional vegetation must be taken into account. Also, both deductive (mechanistic models) and inductive (statistical models) approaches are needed to better understand the emergent properties of pollen dispersal in heterogeneous landscapes.

Abstract The term “tornado outbreak” appeared in the meteorological literature in the 1950s and was used to highlight severe weather events with multiple tornadoes. The exact meaning of “tornado outbreak,” however, evolved over the years. Depending on the availability of scientific data, technological advancements, and the intended purpose of these definitions, authors offered a diverse set of approaches to shape the perception and applications of the term “tornado outbreak.” This paper reviews over 200 peer-reviewed publications—by decade—to outline the evolving nature of the “tornado outbreak” definition and to examine the changes in the “tornado outbreak” definition or its perception. A final discussion highlights the importance, limitations, and potential future evolution of what defines a “tornado outbreak.”

Abstract Agricultural decision-making that adapts to climate variability is essential to global food security. Crop production can be severely impacted by drought, flood, and heat, as seen in recent years in parts of the United States. Seasonal climate forecasts can help producers reduce crop losses, but many nationwide, publicly available seasonal forecasts currently lack relevance for agricultural producers, in part because they do not reflect their decision needs. This study examines the seasonal forecast needs of winter wheat producers in the southern Great Plains to understand what climate information is most useful and what lead times are most relevant for decision-making. An online survey of 119 agricultural advisers, cooperative extension agents in Oklahoma, Kansas, Texas, and Colorado, was conducted and gave insights into producers’ preferences for forecast elements, what weather and climate extremes have the most impact on decision-making, and the decision timing of major farm practices. The survey participants indicated that winter wheat growers were interested not only in directly modeled variables, such as total monthly rainfall, but also in derived elements, such as consecutive number of dry days. Moreover, these agricultural advisers perceived that winter wheat producers needed seasonal climate forecasts to have a lead time of 0–2.5 months—the planning lead time for major farm practices, like planting or harvesting. A forecast calendar and monthly rankings for forecast elements were created that can guide forecasters and advisers as they develop decision tools for winter wheat producers and that can serve as a template for other time-sensitive decision tools developed for stakeholder communities.

Climate models provide information that resource managers, policy makers, and researchers can use when planning for the future. While this information is valuable in the broad sense, the low spatial-resolution often lacks local details that resource managers and decision makers need to plan for their communities. Therefore, statistically downscaled climate projections provide a high-resolution output and offer local information that is more beneficial than coarse-resolution global climate model output. In the Red River Basin, located in the south-central U.S., this detailed information is used to develop long-term water plans. This area is prone to drought conditions and heavy precipitation events, and studies have consistently estimated that this will continue in the future. This paper introduces a dataset of statistically downscaled climate projections of daily minimum and maximum temperature and daily precipitation that is a useful tool for studies regarding climatological and hydrological aspects in the region. The dataset was created using two quantile mapping techniques to downscale the CCSM4, MPI-ESM-LR, and MIROC5 model outputs to a 0.1-degree spatial resolution. Furthermore, we describe the added value of coproduction of knowledge between climate scientists and end users, or in this case impacts modelers and decision makers, for creating climate projections that can be used for climate risk assessments. A case study of the data’s development and application is provided, detecting the mean daily changes in temperature and precipitation through the end of the century in the Red River Basin for two representative concentration pathways. After applying the users’ inputs to develop the datasets, results for this example estimate an increase in mean daily precipitation in the eastern portion of the basin and as much as a 15% decline in the west by the end of the century. Furthermore, mean daily temperature is expected to rise across the entire basin in all scenarios by up to 6–7°C.

Abstract Heavy precipitation events and their associated flooding can have major impacts on communities and stakeholders. There is a lack of knowledge, however, about how stakeholders make decisions at the subseasonal-to-seasonal (S2S) time scales (i.e., 2 weeks to 3 months). To understand how decisions are made and S2S predictions are or can be used, the project team for “Prediction of Rainfall Extremes at Subseasonal to Seasonal Periods” (PRES 2 iP) conducted a 2-day workshop in Norman, Oklahoma, during July 2018. The workshop engaged 21 professionals from environmental management and public safety communities across the contiguous United States in activities to understand their needs for S2S predictions of potential extended heavy precipitation events. Discussions and role-playing activities aimed to identify how workshop participants manage uncertainty and define extreme precipitation, the time scales over which they make key decisions, and the types of products they use currently. This collaboration with stakeholders has been an integral part of PRES 2 iP research and has aimed to foster actionable science. The PRES 2 iP team is using the information produced from this workshop to inform the development of predictive models for extended heavy precipitation events and to collaboratively design new forecast products with our stakeholders, empowering them to make more-informed decisions about potential extreme precipitation events.

Decision‐makers using climate projection information are often faced with the problem of data breadth, complexity, and uncertainty, which complicates the translation of climate science products in addressing management challenges. Recently, the concept of climate scenario planning attempts to simplify climate information by developing a series of plausible future “storylines.” In some cases, however, these storylines lack quantitative detail on extremes that may be useful to decision‐makers. Here, we analyse a large suite of statistically downscaled climate projections from two methods to develop quantitative projections for hydrologic extremes (heavy precipitation and drought) across Oklahoma and Texas in the United States. Downscaled projections are grouped into four specific temperature/precipitation scenarios, including “Warm/Wet,” “Hot/Dry,” “Central Tendency,” and the full multi‐model ensemble average. The region is split into three sub‐domains spanning the region's west–east precipitation gradient, and projections are examined throughout the mid‐ and late‐21st century, using two emissions scenarios (“mid‐range” and “high”). Most scenarios project increased frequency and duration of moderate or greater drought across the whole domain, with the high‐emissions Hot/Dry projections showing the most severe examples. The Warm/Wet scenario also increases the frequency of dry months, particularly in the Southern High Plains, but does not discernably alter duration, and retains a similar frequency of pluvial (wet) periods. The mid‐range projections generally retain similar evolutions among scenarios, but they reduce drought intensity and project no change in drought/pluvial frequency with the Warm/Wet scenario. Notably, the occurrence of intense precipitation increases across all scenarios and emissions categories and does not significantly differ between any of the scenarios, including Hot/Dry versus Warm/Wet. Some observed differences in extreme precipitation magnitudes between the two downscaled data sets are briefly discussed.

As effects of climate change intensify, there is a growing need to understand the thermal properties of landscapes and their influence on wildlife. A key thermal property of landscapes is vegetation structure and composition. Management approaches can alter vegetation and consequently the thermal landscape, potentially resulting in underappreciated consequences for wildlife thermoregulation. Consideration of spatial scale can clarify how management overlaid onto existing vegetation patterns affects thermal properties of landscapes relevant to wildlife. We examined effects of temperature, fire management, and vegetation structure on multi-scale habitat selection of an ectothermic vertebrate (the turtle Terrapene carolina triunguis) in the Great Plains of the central United States by linking time-since-fire data from 18 experimental burn plots to turtle telemetry locations and thermal and vegetation height data. Within three 60-ha experimental landscapes, each containing six 10-ha sub-blocks that are periodically burned, we found that turtles select time-since-fire gradients differently depending on maximum daily ambient temperature. At moderate temperatures, turtles selected sub-blocks with recent (<1 year) time-since-fire, but during relatively hot and cool conditions, they selected sub-blocks with later (2-3 year) time-since-fire that provided thermal buffering compared with recently burned sub-blocks. Within 10-ha sub-blocks, turtles selected locations with taller vegetation during warmer conditions that provided thermal buffering. Thermal performance curves revealed that turtle activity declined as temperatures exceeded ~24-29°C, and on "heat days" (≥29°C) 73% of turtles were inactive compared with 37% on non-heat days, emphasizing that thermal extremes may lead to opportunity costs (i.e., foregone benefits turtles could otherwise accrue if active). Our results indicate that management approaches that promote a mosaic of vegetation heights, like spatiotemporally dynamic fire, can provide thermal refuges at multiple spatial scales and thus be an actionable way to provide wildlife with multiple thermal options in the context of ongoing and future climate change.

To help stakeholders such as planners, resource managers, policymakers, and decision makers address environmental challenges in the Anthropocene, scientists are increasingly creating actionable science—science that is useful, usable, and used. Critical physical geography encourages the engagement of stakeholders in the creation of scientific knowledge to conduct actionable science and produce outputs that are directly relevant to stakeholder plans, decisions, or actions. Many scientists, however, lack formal training in how to partner with stakeholders using effective and ethical practices. In this article, we use the core principles for ethical research of respect for persons, beneficence, and justice from the Belmont Report (1979) as a suggested framework to examine the perspectives of stakeholders engaged in climate adaptation science projects. We argue that this framework aligns with the principles of critical physical geography and provides guidance for scientists to make their research more actionable while placing necessary emphasis on ethical considerations. We also challenge scientists to consider the broader ethical implications of engaging with these partners.