Developing scientific information that is used in policy and practice has been a longstanding challenge in many sectors and disciplines, including climate change adaptation for natural resource management. One approach to address this problem encourages scientists and decision-makers to co-produce usable information collaboratively. Researchers have proposed general principles for climate science co-production, yet few studies have applied and evaluated these principles in practice. In this study, climate change researchers and natural resource managers co-produced climate-related knowledge that was directly relevant for on-going habitat management planning. We documented our methods and assessed how and to what extent the process led to the near-term use of co-produced information, while also identifying salient information needs for future research. The co-production process resulted in: 1) an updated natural resource management plan that substantially differed from the former plan in how it addressed climate change, 2) increased understanding of climate change, its impacts, and management responses among agency staff, and 3) a prioritized list of climate-related information needs that would be useful for management decision-making. We found that having a boundary spanner—an intermediary with relevant science and management expertise that enables exchange between knowledge producers and users—guide the co-production process was critical to achieving outcomes. Central to the boundary spanner’s role were a range of characteristics and skills, such as knowledge of relevant science, familiarity with management issues, comfort translating science into practice, and an ability to facilitate climate-informed planning. By describing specific co-production methods and evaluating their effectiveness, we offer recommendations for others looking to co-produce climate change information to use in natural resource management planning and implementation.

The design of this survey protocol is based on the indicator framework presented in Wall et. al (2017 https://doi.org/10.1175/WCAS-D-16-0008.1) and is intended to evaluate projects funded by Climate Adaptation Science Centers. All survey questions were optional to complete. The intended respondents are stakeholders who were engaged in the creation of scientific knowledge and tools during these projects. The questions cover three topical areas: process (engagement in the process of knowledge production), outputs/outcomes (use of information), and impacts (building of relationships and trust). Results of the survey are presented as summary tables in order to protect personal identifiable information of the respondents. Summary information is in the form of tables and word cloud graphics to communicate results of open ended questions.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Climate Reconstruction. The data include parameters of climate reconstructions|instrumental|tree ring with a geographic location of North America. The time period coverage is from 1150 to -65 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Welcome to the North American Seasonal Precipitation Atlas.  This web application provides access to cool- and warm-season reconstructions of total precipitation and the standardized precipitation index on a 0.5° latitude/longitude grid centered over North America from AD 0000-2016.  Maps and time series can be created using tools under the "Maps" and "Time Series" menus, respectively.  For details on how to create maps and time series, see the "Help" menu.  An animation that runs through each year of the reconstruction from AD 0000-2016 is available via the web application. Questions, comments, or suggestions may be sent to Dorian J. Burnette. The entire dataset can be downloaded from NOAA Paleoclimatology.

This project examines how key institutional and emotional factors shape management decisions about changing the social science of ecological resources. We use interviews and focus groups to study how the culture and policy of individual parks, and the psychological and emotional experiences of managers responding to landscape changes, influence decisions.

To cope with complex environmental impacts in a changing climate, researchers are increasingly being asked to produce science that can directly support policy and decision making. To achieve such societal impact, scientists are using climate services to engage directly with stakeholders to better understand their needs and inform knowledge production. However, the wide variety of climate-services outcomes—ranging from establishing collegial relationships with stakeholders to obtaining specific information for inclusion into a pre-existing decision process—do not directly connect to traditional methods of measuring scientific impact (e.g., publication citations, journal impact factor). In this paper, we describe how concepts from the discipline of evaluation can be used to examine the societal impacts of climate services. We also present a case study from climate impacts and adaptation research to test a scalable evaluation approach. Those who conduct research for the purposes of climate services and those who fund applied climate research would benefit from evaluation from the beginning of project development. Doing so will help ensure that the approach, data collection, and data analysis are appropriately conceived and executed. 

We developed a framework to estimate high-resolution spatiotemporal soil moisture (monthly, annual, and seasonal) and temperature-moisture regimes. Our approach uses the Newhall simulation model (NSM) which we fully describe in the Larger Citation. For our analyses, we developed and used open-source software (spatial_nsm) relying on Python^TM^ that was translated from jNSM software (v. 1.6.1; U.S. Department of Agriculture 2016)---a java implementation of the NSM relies on aspatial climate stations. Our software allows for spatial estimates, supports additional parameters to inform the model, and improves upon elements of the originating software. Briefly, the NSM is an accounting system of water movement in a vertical soil profile and characterizes the soil moisture and soil temperature conditions (Newhall 1972, Van Wambeke 1982, Newhall and Berdanier 1996, Van Wambeke 2000) using monthly precipitation (total), monthly air temperature (mean), and available water capacity (AWC; characteristic of soil properties defining potential to retain water) as data inputs. The model uses the Thornthwaite-Matter-Sellers PET equation (Thornthwaite 1948, Thornthwaite and Mather 1955, Sellers 1965) to reflect energy available for extracting moisture (see Larger Citation; supplemental S8), but other methods can be exchanged within spatial_nsm. The daily, enumerated simulation of soil moisture movement (NSM) is based on a two-dimensional diagram of the soil profile (Van Wambeke 2000; supplemental S9). The profile extends from the ground surface to a maximum depth of 200 cm (depth defined and characterized by the range of depth corresponding to AWC estimates). For example, clay materials can support an AWC of 200 mm with a soil depth of 80 cm, while sandy loam with an AWC of 200 mm might be as deep as 200 cm (National Soil Survey Center 1998, Van Wambeke 2000). The original NSM estimates soil temperature and moisture regimes (STMRs) to characterize the soil-climate for plant productivity. The STMR classifications described by Natural Resources Conservation Service (NRCS) in Soil Taxonomy (Soil Survey Staff 1999) and Keys to Soil Taxonomy (Soil Survey Staff 2014) were the foundation for classifying soil-climate regimes. The spatial_nsm is a spatial implementation (data inputs and outputs reflect raster surfaces) of the Newhall model. We modified several components and provided additional tools to assess trends and seasonality. These modifications broadly included the following: 1) implementing a dichotomous approach of the classification (Paetzold 1990) that can reliably produce soil temperature-moisture classifications; 2) providing methods for including air-soil monthly temperature offsets; 3) modularizing spatial_nsm that allows for integration of alternative potential evapotranspiration (PET) methods; 4) accounting for timing and redistribution of snowmelt (given data availability), which improves estimates for when and where water can infiltrate soil; and 5) calculating soil moisture, trends, and seasonality (see Larger Citation for details). For our study, we applied the model across the western United States using monthly climate averages (1981 -- 2010) to understand whether soil-climate can enhance our understanding of ecological potential and risk. We demonstrated that soil moisture or soil moisture trends correlated significantly with vegetation patterns, including sagebrush, annual herbaceous plant cover, bare ground, and fire occurrence. Because our framework has the flexibility to assess dynamic climate conditions (historical, contemporary, or projected), we can begin to improve our knowledge of changing spatiotemporal biotic patterns. These spatial resources are intended to provide tools to managers and researchers for assessing risk (invasives and fire), improving estimates of vegetation patterns, and informing prioritization of habitat management and expected restoration outcomes. Developing a spatially explicit soil-climate model involved several steps: 1) identifying and processing spatial input data, 2) processing and collecting data to enhance model, 3) executing models on a high-performance cluster, and 4) identifying key post-analyses products and evaluating soil-climate estimates (Figure 1). For step one, we acquired spatial data representing monthly precipitation and temperature (Prism Climate Group 2015), daily snow deposition (National Operational Hydrologic Remote Sensing Center 2004), soil physical properties (Polaris; Chaney et al. 2016, Chaney et al. 2019), monthly averages from climate stations (Arguez et al. 2010, Arguez et al. 2012), and daily soil conditions from the soil climate analysis network (SCAN; U.S. Department of Agriculture 2020). See the Larger Citation for descriptions of data sources in supplemental (S1; Table S1). Importantly, users of our software are not restricted to these data sources, which are generically described in Data Dependencies. For step two, we used additional data to enhance our model, including the SCAN database to define monthly ambient-temperature offsets, snow cover to account for attenuation of potential evapotranspiration (PET) and offset soil temperature, and organic matter to inform the classification of temperature regimes.

Tribal nations and Indigenous communities are key collaborators on adaptation work within the Climate Adaptation Science Center (CASC) network. The centers have partnered with numerous Tribal and Indigenous communities on projects or activities to better understand the communities’ specific knowledge of and exposure to impacts of climate change, to increase or assist with capacity to support adaptation planning, and to identify and address climate science needs. Projects and activities generated in the various CASC regions have different Tribal and Indigenous stakeholders, climate change contexts, and training needs. Consequently, these projects and activities were neither implemented nor reported consistently throughout the network. Information and materials on the various projects and activities were gathered and are presented in the Tribal and Indigenous Projects Data Sheet (hereafter, Data Sheet) with the goals of reducing inconsistencies between CASCs and benefitting other agencies who plan to implement similar activities. The Data Sheet is complementary to this report, which provides a synthesis of the CASC-led climate-related, capacity-building activities for Tribes and Indigenous communities. The results described in this report provide an analysis of the categorization of projects, activities, and individual trainings to highlight detailed information on the various ways each CASC works with and supports Native and Indigenous communities.

Soil temperature and moisture (soil-climate) affect plant growth and microbial metabolism, providing a mechanistic link between climate and growing conditions. However, spatially explicit soil-climate estimates that can inform management and research are lacking. We developed a framework to estimate spatiotemporal-varying soil moisture (monthly, annual, and seasonal) and temperature-moisture regimes as gridded surfaces by enhancing the Newhall simulation model. Importantly, our approach allows for the substitution of data and parameters, such as climate, snowmelt, soil properties, alternative potential evapotranspiration equations and air-soil temperature offsets. We applied the model across the western United States using monthly climate averages (1981–2010). The resulting data are intended to help improve conservation and habitat management, including but not limited to increasing the understanding of vegetation patterns (restoration effectiveness), the spread of invasive species and wildfire risk. The demonstrated modeled results had significant correlations with vegetation patterns—for example, soil moisture variables predicted sagebrush (R2 = 0.51), annual herbaceous plant cover (R2 = 0.687), exposed soil (R2 = 0.656) and fire occurrence (R2 = 0.343). Using our framework, we have the flexibility to assess dynamic climate conditions (historical, contemporary or projected) that could improve the knowledge of changing spatiotemporal biotic patterns and be applied to other geographic regions. 

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building.