North Carolina State Climate Office

Stomatal resistance (R s ) forms a pivotal component of the surface energy budget and of the terrestrial biosphere-atmosphere interactions. Using a statistical-graphical technique, the R s -related interactions between different atmospheric and physiological variables are resolved explicitly from observations made during the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE). A similar analysis was undertaken for the R s parameterization schemes, as used in the present models. Three physiological schemes (the Ball-Woodrow-Berry, Kim and Verma, and Jacobs) and one operational Jarvis-type scheme were evaluated in terms of their ability to replicate the terrestrial biosphere-atmosphere interactions.

Abstract Climate projections from 20 downscaled global climate models ( GCM s) were used with the 3‐ PG model to predict the future productivity and water use of planted loblolly pine ( Pinus taeda ) growing across the southeastern United States. Predictions were made using Representative Concentration Pathways ( RCP ) 4.5 and 8.5. These represent scenarios in which total radiative forcing stabilizes before 2100 ( RCP 4.5) or continues increasing throughout the century ( RCP 8.5). Thirty‐six sites evenly distributed across the native range of the species were used in the analysis. These sites represent a range in current mean annual temperature (14.9–21.6°C) and precipitation (1,120–1,680 mm/year). The site index of each site, which is a measure of growth potential, was varied to represent different levels of management. The 3‐ PG model predicted that aboveground biomass growth and net primary productivity will increase by 10%–40% in many parts of the region in the future. At cooler sites, the relative growth increase was greater than at warmer sites. By running the model with the baseline [ CO 2 ] or the anticipated elevated [ CO 2 ], the effect of CO 2 on growth was separated from that of other climate factors. The growth increase at warmer sites was due almost entirely to elevated [ CO 2 ]. The growth increase at cooler sites was due to a combination of elevated [ CO 2 ] and increased air temperature. Low site index stands had a greater relative increase in growth under the climate change scenarios than those with a high site index. Water use increased in proportion to increases in leaf area and productivity but precipitation was still adequate, based on the downscaled GCM climate projections. We conclude that an increase in productivity can be expected for a large majority of the planted loblolly pine stands in the southeastern United States during this century.

Abstract Extreme heat is one of the most pressing climate risks in the United States and is exacerbated by a warming climate and aging population. Much work in heat health has focused only on temperature-based metrics, which do not fully measure the physiological impact of heat stress on the human body. The U.S. Climate Reference Network (USCRN) consists of 139 sites across the United States and includes meteorological parameters that fully encompass human tolerance to heat, including relative humidity, wind, and solar radiation. Hourly and 5-min observations from USCRN are used to develop heat exposure products, including heat index (HI), apparent temperature (AT), and wet-bulb globe temperature (WBGT). Validation of this product is conducted with nearby airport and mesonet stations, with reanalysis data used to fill in data gaps. Using these derived heat products, two separate analyses are conducted. The first is based on standardized anomalies, which place current heat state in the context of a long-term climate record. In the second study, heat events are classified by time spent at various levels of severity of conditions. There is no consensus as to what defines a heat event, so a comparison of absolute thresholds (i.e., ≥30.0°, 35.0°, and 40.0°C) and relative thresholds (≥90th, 95th, and 98th percentile) will be examined. The efficacy of the product set will be studied using an extreme heat case study in the southeastern United States. While no heat exposure metric is deemed superior, each has their own advantages and caveats, especially in the context of public communication.

Web applications, also known as web apps, are increasingly common in the research communication portfolios of those working in the life sciences (e.g., [1]) and physical sciences (e.g., [2–4]). Web apps help disseminate research findings and present research outputs in ways that are accessible and meaningful to the general public—from individuals, to governments, to companies. Specifically, web apps enable exploration of scenario testing and policy analysis (i.e., to answer “what if?”) as well as coevolution of scientific and public knowledge [5,6]. However, the majority of researchers developing web apps receive little formal training or technical guidance on how to develop and evaluate the effectiveness of their web-based decision support tools. Take some of us for example. We (Saia and Nelson) are agricultural and environmental engineers with little experience in web app development, but we are interested in creating web apps to support sustainable aquaculture production in the Southeast. We had user (i.e., shellfish growers) interest, a goal in mind (i.e., develop a new forecast product and decision support tool for shellfish aquaculturalists), and received funding to support this work. Yet, we experienced several unexpected hurdles from the start of our project that ended up being fairly common hiccups to the seasoned web app developers among us (Parham). As a result, we share the following 10 simple rules, which highlight take-home messages, including lessons learned and practical tips, of our experience as burgeoning web app developers. We hope researchers interested in developing web apps draw insights from our (in)experience as they set out on their decision support tool development journey.

Abstract A field experiment was performed in Oak Ridge, Tennessee, with four instrumented towers placed over grass at increasing distances (4, 30, 50, 124, and 300 m) from a built-up area. Stations were aligned in such a way to simulate the impact of small-scale encroachment on temperature observations. As expected, temperature observations were warmest for the site closest to the built environment with an average temperature difference of 0.31° and 0.24°C for aspirated and unaspirated sensors, respectively. Mean aspirated temperature differences were greater during the evening (0.47°C) than during the day (0.16°C). This was particularly true for evenings following greater daytime solar insolation (20+ MJ day −1 ) with surface winds from the direction of the built environment where mean differences exceeded 0.80°C. The impact of the built environment on air temperature diminished with distance with a warm bias only detectable out to tower B′ located 50 m away. The experimental findings were comparable to a known case of urban encroachment at a U.S. Climate Reference Network station in Kingston, Rhode Island. The experimental and operational results both lead to reductions in the diurnal temperature range of ~0.39°C for fan-aspirated sensors. Interestingly, the unaspirated sensor had a larger reduction in diurnal temperature range (DTR) of 0.48°C. These results suggest that small-scale urban encroachment within 50 m of a station can have important impacts on daily temperature extrema (maximum and minimum) with the magnitude of these differences dependent upon prevailing environmental conditions and sensing technology.

Abstract The Community Collaborative Rain, Hail and Snow (CoCoRaHS) network is a well-regarded, trusted source of precipitation data. The network’s volunteers also provide weather and climate observations through daily comments, significant weather reports, and condition monitoring reports. Designed to meet a need for local information about drought events and their impacts, “condition monitoring” was initiated as a pilot project in North Carolina and South Carolina in 2013 and launched nationally in October 2016. Volunteers regularly report on how precipitation, or a lack thereof, affects their local environment and community by ranking current conditions on a seven-point scale ranging from severely dry to severely wet and sharing observations through written narratives. This study assesses the usefulness of these reports for drought monitoring and decision-making, drawing from the >7,100 reports submitted in the Carolinas between October 2016 and June 2020. This period encompasses the Carolinas’ climate patterns and extreme events such as droughts, wildfires, and hurricanes (“drought busters”). Three aspects of usefulness were evaluated in the reports: the extent to which volunteers’ assessments of dry-to-wet conditions correspond to objective drought indicators (EDDI, SPI, SPEI) typically employed for monitoring drought; how volunteers’ qualitative observations depict changing conditions, focusing on two flash droughts in 2019; and actual use of the reports by National Weather Service offices, State Climate Offices, U.S. Drought Monitor authors, and drought response committees. Although ­report content can vary widely, findings show that volunteers’ assessments reflect meteorological conditions and provide on-the-ground details that are being incorporated into existing drought monitoring processes.

Tropical cyclones (TCs) can produce large rainfall totals which lead to devastating flooding, loss of life, and significant damage to infrastructure. Many studies have examined future changes in TC precipitation; however, few have considered changes owing to differences in the synoptic environment during landfall. Here, we focus on three North Atlantic TCs that impacted the southeastern United States: Hurricanes Floyd (1999), Matthew (2016), and Florence (2018). While these storms were impactful when they occurred, how might the impacts of similar systems change in a future climate? We address these questions using a pseudo–global warming (PGW) approach and ensembles of convection-allowing numerical model simulations. With this method, we compare future changes in precipitation characteristics such as accumulated rainfall and rain-rate frequency and distribution to assess how these changes differ as a function of synoptic environment. Hurricanes Matthew and Floyd, which have more synoptic-scale forcing for ascent while over our study region than Hurricane Florence, exhibit higher average rain rates in the present and future, but Hurricane Florence exhibits the largest increases in rain rates with warming (34% ± 12% vs 23% ± 9% and 21% ± 6% for Hurricanes Matthew and Floyd, respectively). When we consider accumulated precipitation, Hurricanes Matthew and Floyd have larger areal increases in precipitation totals greater than 250 mm than Hurricane Florence (17 600 ± 800 km2 and 22 400 ± 400 km2 vs 9800 ± 500 km2, respectively). These results point to the potential for future TCs in synoptically forced environments to have larger spatial footprints of heavy precipitation but smaller increases in rain rate than storms with less synoptic forcing, especially when considering overland precipitation.

Abstract Species status assessments (SSAs) are required for endangered species by the U.S. Fish and Wildlife Service and focus on the resiliency, redundancy, and representation of endangered species. SSAs must include climate information, because climate is a factor that will impact species in the future. To aid in the inclusion of climate information, a decision support system (DSS) entitled Climate Analysis and Visualization for the Assessment of Species Status (CAnVAS) was developed by the State Climate Office of North Carolina using a coproduction approach. In this study, users viewed a mock-up version of the CAnVAS interface displaying a sample layout of future projections for three key climate variables (average precipitation, average maximum temperature, and occurrence of maximum temperature) at a location of interest. This assessment of the pilot version of the CAnVAS DSS was the first step in refining CAnVAS for species-manager use. This research analyzed the differences in usability between two pilot versions of the CAnVAS DSS through eye tracking and subsequent interviews with novice users. The two pilot versions of CAnVAS differed in the way data were displayed on graphs and the color ramps used on regional maps. We found that graphically displaying temporal climate information through box-and-whisker plots and spatially through a sequential color ramp from white to purple were more effective than alternative displays at communicating climate information on endangered species. The results of this research will be used to further develop the CAnVAS DSS tool for future implementation. Significance Statement A decision support system was developed for U.S. Fish and Wildlife Service biologists to incorporate more climate information in species status assessments for endangered species. This tool was tested through eye tracking and interviews with a novice undergraduate student sample to best refine the tool for stakeholder use. This work was able to discover that graphically displaying data in box-and-whisker format and spatially displaying data with a sequential color scheme of white to purple was best for usability purposes. The authors provide these recommendations for those who are producing usable products.

Flooding is the costliest natural hazard, globally accounting for more than 40% (2.8 billion USD) of direct damages from 1900 to 2015. While global flood risk is predicted to continue to increase overall, flood risk is highly variable at local scales and dependent on both the social and physical processes that affect the natural and built environment. Projections of flood risk at smaller scales are crucial for efforts to improve insurance markets, disaster preparedness, environmental justice, and city and regional planning. Flood risk can be quantified by integrating the dynamics of expected land use/land cover change (LULCC) and climate variability predicted under Representative Concentration Pathway forecasts at fine spatiotemporal resolutions. In this study, we present a forecast analysis of watershed-scale hydrology in the Neuse River watershed, NC from 2006 to 2100 to identify how patterns of LULCC and climate variability will influence the return period, flood peaks and volumes predicted from the 1% Annual Exceedance Probability (AEP) storms. Using the EPA’s LULCC model Integrated Climate and Land Use Scenarios (ICLUS), the CMIP5’s precipitation model of 20 regionally-downscaled Global Climate Models (GCMs), and the physically-based, distributed hydrologic model Vflo, we predict the hydrologic response of probabilistic storms through the end of the 21st century.

The impacts of extreme heat are broad and systemic, affecting not only human health and well-being, but also infrastructure, energy systems, and public services. Many of the best strategies to address these impacts are implemented at the local level, by local governments and their partners. This paper covers the approaches taken by the North Carolina State Resilience Office (SRO), the North Carolina State Climate Office (SCO) and their partners to support the development of heat action plans by local governments in their state. These agencies collaborated to develop planning support resources and a programme to guide local governments through the process of developing heat action plans. The paper examines the rationale for this approach, the composition of the resources developed, the details of the planning programme, the results of this work, and how these agencies are adapting their programmes based on what they learned. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.

Abstract Tropical cyclones (TCs) can produce large rainfall totals which lead to devastating flooding, loss of life, and significant damage to infrastructure. Many studies have examined future changes in TC precipitation; however, few have considered changes owing to differences in the synoptic environment during landfall. Here, we focus on three North Atlantic TCs that impacted the southeastern United States: Hurricanes Floyd (1999), Matthew (2016), and Florence (2018). While these storms were impactful when they occurred, how might the impacts of similar systems change in a future climate? We address these questions using a pseudo–global warming (PGW) approach and ensembles of convection-allowing numerical model simulations. With this method, we compare future changes in precipitation characteristics such as accumulated rainfall and rain-rate frequency and distribution to assess how these changes differ as a function of synoptic environment. Hurricanes Matthew and Floyd, which have more synoptic-scale forcing for ascent while over our study region than Hurricane Florence, exhibit higher average rain rates in the present and future, but Hurricane Florence exhibits the largest increases in rain rates with warming (34% ± 12% vs 23% ± 9% and 21% ± 6% for Hurricanes Matthew and Floyd, respectively). When we consider accumulated precipitation, Hurricanes Matthew and Floyd have larger areal increases in precipitation totals greater than 250 mm than Hurricane Florence (17 600 ± 800 km 2 and 22 400 ± 400 km 2 vs 9800 ± 500 km 2 , respectively). These results point to the potential for future TCs in synoptically forced environments to have larger spatial footprints of heavy precipitation but smaller increases in rain rate than storms with less synoptic forcing, especially when considering overland precipitation. Significance Statement Many previous studies demonstrate that tropical cyclone (TC) precipitation will increase in a warmer climate, but few studies consider how TC precipitation responds to climate change as a function of the accompanying weather pattern. Here, we examine future changes in precipitation for TCs in three distinct weather patterns. By analyzing the response of TC rainfall to warming for a diverse set of patterns, we can increase readiness for a variety of future scenarios, with the ultimate goal of maximizing the resilience of future transportation infrastructure.