@article{subedi_walls_barichivich_boyles_ross_hogan_tupy_2022, title={Future changes in habitat availability for two specialist snake species in the imperiled rocklands of South Florida, USA}, ISSN={["2578-4854"]}, DOI={10.1111/csp2.12802}, journal={CONSERVATION SCIENCE AND PRACTICE}, author={Subedi, Suresh C. and Walls, Susan C. and Barichivich, William J. and Boyles, Ryan and Ross, Michael S. and Hogan, J. Aaron and Tupy, John A.}, year={2022}, month={Aug} }
 @article{maudlin_mcneal_dinon-aldridge_davis_boyles_atkins_2020, title={Website Usability Differences between Males and Females: An Eye-Tracking Evaluation of a Climate Decision Support System}, volume={12}, ISSN={["1948-8335"]}, DOI={10.1175/WCAS-D-18-0127.1}, abstractNote={ABSTRACT Decision support systems—collections of related information located in a central place to be used for decision-making—can be used as platforms from which climate information can be shared with decision-makers. Unfortunately, these tools are not often evaluated, meaning developers do not know how useful or usable their products are. In this study, a web-based climate decision support system (DSS) for foresters in the southeastern United States was evaluated by using eye-tracking technology. The initial study design was exploratory and focused on assessing usability concerns within the website. Results showed differences between male and female forestry experts in their eye-tracking behavior and in their success with completing tasks and answering questions related to the climate information presented in the DSS. A follow-up study, using undergraduate students from a large university in the southeastern United States, aimed to determine whether similar gender differences existed and could be detected and, if so, whether the cause(s) could be determined. The second evaluation, similar to the first, showed that males and females focused their attention on different aspects of the website; males focused more on the maps depicting climate information while females focused more on other aspects of the website (e.g., text, search bars, and color bars). DSS developers should consider the possibility of gender differences when designing a web-based DSS and include website features that draw user attention to important DSS elements to effectively support various populations of users.}, number={1}, journal={WEATHER CLIMATE AND SOCIETY}, author={Maudlin, Lindsay C. and McNeal, Karen S. and Dinon-Aldridge, Heather and Davis, Corey and Boyles, Ryan and Atkins, Rachel M.}, year={2020}, month={Jan}, pages={183–192} }
 @article{bhardwaj_misra_mishra_wootten_boyles_bowden_terando_2018, title={Downscaling future climate change projections over Puerto Rico using a non-hydrostatic atmospheric model}, volume={147}, ISSN={["1573-1480"]}, url={http://dx.doi.org/10.1007/s10584-017-2130-x}, DOI={10.1007/s10584-017-2130-x}, number={1-2}, journal={CLIMATIC CHANGE}, author={Bhardwaj, Amit and Misra, Vasubandhu and Mishra, Akhilesh and Wootten, Adrienne and Boyles, Ryan and Bowden, J. H. and Terando, Adam J.}, year={2018}, month={Mar}, pages={133–147} }
 @article{wootten_terando_reich_boyles_semazzi_2017, title={Characterizing Sources of Uncertainty from Global Climate Models and Downscaling Techniques}, volume={56}, ISSN={["1558-8432"]}, DOI={10.1175/jamc-d-17-0087.1}, abstractNote={Abstract In recent years, climate model experiments have been increasingly oriented toward providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here a method is presented, on the basis of a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. The method is applied to the southeastern United States using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios is typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast for precipitation and ~30% for extreme heat days (>35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a subsample of all models is available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. This article concludes with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.}, number={12}, journal={JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY}, author={Wootten, A. and Terando, A. and Reich, B. J. and Boyles, R. P. and Semazzi, F.}, year={2017}, month={Dec}, pages={3245–3262} }
 @article{cumbie-ward_boyles_2016, title={Evaluation of a High-Resolution SPI for Monitoring Local Drought Severity}, volume={55}, ISSN={["1558-8432"]}, url={http://dx.doi.org/10.1175/jamc-d-16-0106.1}, DOI={10.1175/jamc-d-16-0106.1}, abstractNote={Abstract A standardized precipitation index (SPI) that uses high-resolution, daily estimates of precipitation from the National Weather Service over the contiguous United States has been developed and is referred to as HRD SPI. There are two different historical distributions computed in the HRD SPI dataset, each with a different combination of normals period (1971–2000 or 1981–2010) and clustering solution of gauge stations. For each historical distribution, the SPI is computed using the NCEP Stage IV and Advanced Hydrologic Prediction Service (AHPS) gridded precipitation datasets for a total of four different HRD SPI products. HRD SPIs are found to correlate strongly with independently produced SPIs over the 10-yr period from 2005 to 2015. The drought-monitoring utility of the HRD SPIs is assessed with case studies of drought in the central and southern United States during 2012 and over the Carolinas during 2007–08. A monthly comparison between HRD SPIs and independently produced SPIs reveals generally strong agreement during both events but weak agreement in areas where radar coverage is poor. For both study regions, HRD SPI is compared with the U.S. Drought Monitor (USDM) to assess the best combination of precipitation input, normals period, and station clustering solution. SPI generated with AHPS precipitation and the 1981–2010 PRISM normals and associated cluster solution is found to best capture the spatial extent and severity of drought conditions indicated by the USDM. This SPI is also able to resolve local variations in drought conditions that are not shown by either the USDM or comparison SPI datasets.}, number={10}, journal={JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY}, publisher={American Meteorological Society}, author={Cumbie-Ward, Rebecca V. and Boyles, Ryan P.}, year={2016}, month={Oct}, pages={2247–2262} }
 @article{wootten_bowden_boyles_terando_2016, title={The Sensitivity of WRF Downscaled Precipitation in Puerto Rico to Cumulus Parameterization and Interior Grid Nudging}, volume={55}, ISSN={["1558-8432"]}, url={http://dx.doi.org/10.1175/jamc-d-16-0121.1}, DOI={10.1175/jamc-d-16-0121.1}, abstractNote={Abstract The sensitivity of the precipitation over Puerto Rico that is simulated by the Weather Research and Forecasting (WRF) Model is evaluated using multiple combinations of cumulus parameterization (CP) schemes and interior grid nudging. The NCEP–DOE AMIP-II reanalysis (R-2) is downscaled to 2-km horizontal grid spacing both with convective-permitting simulations (CP active only in the middle and outer domains) and with CP schemes active in all domains. The results generally show lower simulated precipitation amounts than are observed, regardless of WRF configuration, but activating the CP schemes in the inner domain improves the annual cycle, intensity, and placement of rainfall relative to the convective-permitting simulations. Furthermore, the use of interior-grid-nudging techniques in the outer domains improves the placement and intensity of rainfall in the inner domain. Incorporating a CP scheme at convective-permitting scales (<4 km) and grid nudging at non-convective-permitting scales (>4 km) improves the island average correlation of precipitation by 0.05–0.2 and reduces the island average RMSE by up to 40 mm on average over relying on the explicit microphysics at convective-permitting scales with grid nudging. Projected changes in summer precipitation between 2040–42 and 1985–87 using WRF to downscale CCSM4 range from a 2.6-mm average increase to an 81.9-mm average decrease, depending on the choice of CP scheme. The differences are only associated with differences between WRF configurations, which indicates the importance of CP scheme for projected precipitation change as well as historical accuracy.}, number={10}, journal={JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY}, author={Wootten, A. and Bowden, J. H. and Boyles, R. and Terando, A.}, year={2016}, month={Oct}, pages={2263–2281} }
 @article{coopersmith_cosh_bell_boyles_2016, title={Using machine learning to produce near surface soil moisture estimates from deeper in situ records at US Climate Reference Network (USCRN) locations: Analysis and applications to AMSR-E satellite validation}, volume={98}, ISSN={["1872-9657"]}, DOI={10.1016/j.advwatres.2016.10.007}, abstractNote={Surface soil moisture is a critical parameter for understanding the energy flux at the land atmosphere boundary. Weather modeling, climate prediction, and remote sensing validation are some of the applications for surface soil moisture information. The most common in situ measurement for these purposes are sensors that are installed at depths of approximately 5 cm. There are however, sensor technologies and network designs that do not provide an estimate at this depth. If soil moisture estimates at deeper depths could be extrapolated to the near surface, in situ networks providing estimates at other depths would see their values enhanced. Soil moisture sensors from the U.S. Climate Reference Network (USCRN) were used to generate models of 5 cm soil moisture, with 10 cm soil moisture measurements and antecedent precipitation as inputs, via machine learning techniques. Validation was conducted with the available, in situ, 5 cm resources. It was shown that a 5 cm estimate, which was extrapolated from a 10 cm sensor and antecedent local precipitation, produced a root-mean-squared-error (RMSE) of 0.0215 m3/m3. Next, these machine-learning-generated 5 cm estimates were also compared to AMSR-E estimates at these locations. These results were then compared with the performance of the actual in situ readings against the AMSR-E data. The machine learning estimates at 5 cm produced an RMSE of approximately 0.03 m3/m3 when an optimized gain and offset were applied. This is necessary considering the performance of AMSR-E in locations characterized by high vegetation water contents, which are present across North Carolina. Lastly, the application of this extrapolation technique is applied to the ECONet in North Carolina, which provides a 10 cm depth measurement as its shallowest soil moisture estimate. A raw RMSE of 0.028 m3/m3 was achieved, and with a linear gain and offset applied at each ECONet site, an RMSE of 0.013 m3/m3 was possible.}, journal={ADVANCES IN WATER RESOURCES}, author={Coopersmith, Evan J. and Cosh, Michael H. and Bell, Jesse E. and Boyles, Ryan}, year={2016}, month={Dec}, pages={122–131} }
 @article{wootten_boyles_2014, title={Comparison of NCEP Multisensor Precipitation Estimates with Independent Gauge Data over the Eastern United States}, volume={53}, ISSN={["1558-8432"]}, DOI={10.1175/jamc-d-14-0034.1}, abstractNote={Abstract Gauge-calibrated radar estimates of daily precipitation are compared with daily observed values of precipitation from National Weather Service (NWS) Cooperative Observer Network (COOP) stations to evaluate the multisensor precipitation estimate (MPE) product that is gridded by the National Centers for Environmental Prediction (NCEP) for the eastern United States (defined as locations east of the Mississippi River). This study focuses on a broad evaluation of MPE across the study domain by season and intensity. In addition, the aspect of precipitation type is considered through case studies of winter and summer precipitation events across the domain. Results of this study indicate a north–south gradient in the error of MPE and a seasonal pattern with the highest error in summer and autumn and the lowest error in winter. Two case studies of precipitation are also considered in this study. These case studies include instances of intense precipitation and frozen precipitation. These results suggest that MPE is less able to estimate convective-scale precipitation as compared with precipitation variations at larger spatial scales. In addition, the results suggest that MPE is subject to errors related both to the measurement gauges and to the radar estimates used. Two case studies are also included to discuss the differences with regard to precipitation type. The results from these case studies suggest that MPE may have higher error associated with estimating the liquid equivalent of frozen precipitation when compared with NWS COOP network data. The results also suggest the need for more analysis of MPE error for frozen precipitation in diverse topographic regimes.}, number={12}, journal={JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY}, author={Wootten, Adrienne and Boyles, Ryan P.}, year={2014}, month={Dec}, pages={2848–2862} }
 @article{pan_boyles_white_heitman_2012, title={Characterizing Soil Physical Properties for Soil Moisture Monitoring with the North Carolina Environment and Climate Observing Network}, volume={29}, ISSN={["0739-0572"]}, DOI={10.1175/jtech-d-11-00104.1}, abstractNote={Abstract Soil moisture has important implications for meteorology, climatology, hydrology, and agriculture. This has led to growing interest in development of in situ soil moisture monitoring networks. Measurement interpretation is severely limited without soil property data. In North Carolina, soil moisture has been monitored since 1999 as a routine parameter in the statewide Environment and Climate Observing Network (ECONet), but with little soils information available for ECONet sites. The objective of this paper is to provide soils data for ECONet development. The authors studied soil physical properties at 27 ECONet sites and generated a database with 13 soil physical parameters, including sand, silt, and clay contents; bulk density; total porosity; saturated hydraulic conductivity; air-dried water content; and water retention at six pressures. Soil properties were highly variable among individual ECONet sites [coefficients of variation (CVs) ranging from 12% to 80%]. This wide range of properties suggests very different behavior among sites with respect to soil moisture. A principal component analysis indicated parameter groupings associated primarily with soil texture, bulk density, and air-dried water content accounted for 80% of the total variance in the dataset. These results suggested that a few specific soil properties could be measured to provide an understanding of differences in sites with respect to major soil properties. The authors also illustrate how the measured soil properties have been used to develop new soil moisture products and data screening for the North Carolina ECONet. The methods, analysis, and results presented here have applications to North Carolina and for other regions with heterogeneous soils where soil moisture monitoring is valuable.}, number={7}, journal={JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY}, publisher={American Meteorological Society}, author={Pan, Weinan and Boyles, R. P. and White, J. G. and Heitman, J. L.}, year={2012}, month={Jul}, pages={933–943} }
 @article{misra_michael_boyles_chassignet_griffin_o'brien_2012, title={Reconciling the Spatial Distribution of the Surface Temperature Trends in the Southeastern United States}, volume={25}, ISSN={["1520-0442"]}, DOI={10.1175/jcli-d-11-00170.1}, abstractNote={Abstract This study attempts to explain the considerable spatial heterogeneity in the observed linear trends of monthly mean maximum and minimum temperatures (Tmax and Tmin) from station observations in the southeastern (SE) United States (specifically Florida, Alabama, Georgia, South Carolina, and North Carolina). In a majority of these station sites, the warming trends in Tmin are stronger in urban areas relative to rural areas. The linear trends of Tmin in urban areas of the SE United States are approximately 7°F century−1 compared to about 5.5°F century−1 in rural areas. The trends in Tmax show weaker warming (or stronger cooling) trends with irrigation, while trends in Tmin show stronger warming trends. This functionality of the temperature trends with land features also shows seasonality, with the boreal summer season showing the most consistent relationship in the trends of both Tmax and Tmin. This study reveals that linear trends in Tmax in the boreal summer season show a cooling trend of about 0.5°F century−1 with irrigation, while the same observing stations on an average display warming trends in Tmin of about 3.5°F century−1. The seasonality and the physical consistency of these relationships with existing theories may suggest that urbanization and irrigation have a nonnegligible influence on the spatial heterogeneity of the surface temperature trends over the SE United States. The study also delineates the caveats and limitations of the conclusions reached herein due to the potential influence of perceived nonclimatic discontinuities (which incidentally could also have a seasonal cycle) that have not been taken into account.}, number={10}, journal={JOURNAL OF CLIMATE}, author={Misra, V. and Michael, J. -P. and Boyles, R. and Chassignet, E. P. and Griffin, M. and O'Brien, J. J.}, year={2012}, month={May}, pages={3610–3618} }
 @article{kehoe_raman_boyles_2010, title={Characteristics of Landfalling Tropical Cyclones in North Carolina}, volume={33}, ISSN={["1521-060X"]}, DOI={10.1080/01490419.2010.518059}, abstractNote={Trends in the Atlantic tropical cyclones and the cyclones that had tracks through North Carolina were analyzed for more than 100 years. From about 1970, there appears to be an increase in the mean number of storms developing. The number of storms affecting North Carolina each decade has been increasing since the 1960s. In the 1980s, 1990s, and into the 2000s, there was an increase in the number of landfalling storms in North Carolina. Although August and September are the most active months of the Atlantic hurricane season, the hurricane season for North Carolina peaks in September. Wind distribution and frictional convergence associated with landfalling hurricanes in North Carolina are discussed. Convection and precipitation patterns of landfalling hurricanes are presented. Two examples of the effect of spatial surface moisture distribution on intensification of tropical cyclones over land after landfall are discussed.}, number={4}, journal={MARINE GEODESY}, author={Kehoe, Jennifer and Raman, Sethu and Boyles, Ryan}, year={2010}, pages={394–411} }
 @article{carbone_rhee_mizzell_boyles_2008, title={A regional-scale drought monitoring tool for the Carolinas}, volume={89}, ISSN={["1520-0477"]}, DOI={10.1175/BAMS-89-1-20}, number={1}, journal={BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY}, author={Carbone, Gregory J. and Rhee, Jinyoung and Mizzell, Hope P. and Boyles, Ryan}, year={2008}, month={Jan}, pages={20–28} }
 @article{childs_raman_boyles_2007, title={High-resolution numerical simulations of hurricane Isabel (2003) over North Carolina}, volume={41}, ISSN={["0921-030X"]}, DOI={10.1007/s11069-006-9050-9}, number={3}, journal={NATURAL HAZARDS}, author={Childs, Peter and Raman, Sethu and Boyles, Ryan}, year={2007}, month={Jun}, pages={401–411} }
 @article{boyles_raman_sims_2007, title={Sensitivity of mesoscale surface dynamics to surface soil and vegetation contrasts over the carolina sandhills}, volume={164}, ISSN={["0033-4553"]}, DOI={10.1007/s00024-007-0227-2}, number={8-9}, journal={PURE AND APPLIED GEOPHYSICS}, author={Boyles, Ryan and Raman, Sethu and Sims, Aaron}, year={2007}, month={Sep}, pages={1547–1576} }
 @article{holder_boyles_robinson_raman_fishel_2006, title={Calculating a daily normal temperature range that reflects daily temperature variability}, volume={87}, ISSN={["1520-0477"]}, DOI={10.1175/BAMS-87-6-769}, abstractNote={Normal temperatures, which are calculated by the National Climatic Data Center for locations across the country, are quality-controlled, smoothed 30-yr-average temperatures. They are used in many facets of media, industry, and meteorology, and a given day's normal maximum and minimum temperatures are often used synonymously with what the observed temperature extremes “should be.” However, allowing some leeway to account for natural daily and seasonal variations can more accurately reflect the ranges of temperature that we can expect on a particular day—a “normal range.” Providing such a range, especially to the public, presents a more accurate perspective on what the temperature “usually” is on any particular day of the year. One way of doing this is presented in this study for several locations across North Carolina. The results yield expected higher variances in the cooler months and seem to well represent the varied weather that locations in North Carolina tend to experience. Day-to-day variations in the normal range are larger than expected, but are retained rather than smoothed. The method is simple and applicable to any location with a complete 30-yr record and with a temperature variance time series that follows a bell curve. The normal-range product has many potential applications.}, number={6}, journal={BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY}, author={Holder, Christopher and Boyles, Ryan and Robinson, Peter and Raman, Sethli and Fishel, Greg}, year={2006}, month={Jun}, pages={769-+} }
 @article{holder_boyles_syed_niyogi_raman_2006, title={Comparison of collocated automated (NCECONet) and manual (COOP) climate observations in North Carolina}, volume={23}, ISSN={["0739-0572"]}, DOI={10.1175/jtech1873.1}, abstractNote={Abstract The National Weather Service's Cooperative Observer Program (COOP) is a valuable climate data resource that provides manually observed information on temperature and precipitation across the nation. These data are part of the climate dataset and continue to be used in evaluating weather and climate models. Increasingly, weather and climate information is also available from automated weather stations. A comparison between these two observing methods is performed in North Carolina, where 13 of these stations are collocated. Results indicate that, without correcting the data for differing observation times, daily temperature observations are generally in good agreement (0.96 Pearson product–moment correlation for minimum temperature, 0.89 for maximum temperature). Daily rainfall values recorded by the two different systems correlate poorly (0.44), but the correlations are improved (to 0.91) when corrections are made for the differences in observation times between the COOP and automated stations. Daily rainfall correlations especially improve with rainfall amounts less than 50 mm day−1. Temperature and rainfall have high correlation (nearly 1.00 for maximum and minimum temperatures, 0.97 for rainfall) when monthly averages are used. Differences of the data between the two platforms consistently indicate that COOP instruments may be recording warmer maximum temperatures, cooler minimum temperatures, and larger amounts of rainfall, especially with higher rainfall rates. Root-mean-square errors are reduced by up to 71% with the day-shift and hourly corrections. This study shows that COOP and automated data [such as from the North Carolina Environment and Climate Observing Network (NCECONet)] can, with simple corrections, be used in conjunction for various climate analysis applications such as climate change and site-to-site comparisons. This allows a higher spatial density of data and a larger density of environmental parameters, thus potentially improving the accuracy of the data that are relayed to the public and used in climate studies.}, number={5}, journal={JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY}, author={Holder, C and Boyles, R and Syed, A and Niyogi, D and Raman, S}, year={2006}, month={May}, pages={671–682} }
 @article{childs_qureshi_raman_alapaty_ellis_boyles_niyogi_2006, title={Simulation of convective initiation during IHOP_2002 using the flux-adjusting surface data assimilation system (FASDAS)}, volume={134}, ISSN={["1520-0493"]}, DOI={10.1175/MWR3064.1}, abstractNote={Abstract The Flux-Adjusting Surface Data Assimilation System (FASDAS) uses the surface observational analysis to directly assimilate surface layer temperature and water vapor mixing ratio and to indirectly assimilate soil moisture and soil temperature in numerical model predictions. Both soil moisture and soil temperature are important variables in the development of deep convection. In this study, FASDAS coupled within the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) was used to study convective initiation over the International H2O Project (IHOP_2002) region, utilizing the analyzed surface observations collected during IHOP_2002. Two 72-h numerical simulations were performed. A control simulation was run that assimilated all available IHOP_2002 measurements into the standard MM5 four-dimensional data assimilation. An experimental simulation was also performed that assimilated all available IHOP_2002 measurements into the FASDAS version of the MM5, where surface observations were used for the FASDAS coupling. Results from this case study suggest that the use of FASDAS in the experimental simulation led to the generation of greater amounts of precipitation over a more widespread area as compared to the standard MM5 FDDA used in the control simulation. This improved performance is attributed to better simulation of surface heat fluxes and their gradients.}, number={1}, journal={MONTHLY WEATHER REVIEW}, author={Childs, PP and Qureshi, AL and Raman, S and Alapaty, K and Ellis, R and Boyles, R and Niyogi, D}, year={2006}, month={Jan}, pages={134–148} }
 @article{raman_sims_ellis_boyles_2005, title={Numerical simulation of mesoscale circulations in a region of contrasting soil types}, volume={162}, ISSN={["1420-9136"]}, DOI={10.1007/s00024-005-2689-4}, number={8-9}, journal={PURE AND APPLIED GEOPHYSICS}, author={Raman, S and Sims, A and Ellis, R and Boyles, R}, year={2005}, month={Aug}, pages={1689–1714} }
 @article{boyles_raman_2003, title={Analysis of climate trends in North Carolina (1949-1998)}, volume={29}, ISSN={["0160-4120"]}, DOI={10.1016/S0160-4120(02)00185-X}, abstractNote={North Carolina has one of the most complex climates in the United States (U.S.). Analysis of the climate in this state is critical for agricultural and planning purposes. Climate patterns and trends in North Carolina are analyzed for the period 1949-1998. Precipitation, minimum temperature, and maximum temperature are analyzed on seasonal and annual time scales using data collected from the National Weather Service Cooperative Observer Network. Additionally, changes in patterns of occurrence of the last spring freeze and first fall freeze are investigated. Linear time series slopes are analyzed to investigate the spatial and temporal trends of climate variability in North Carolina. Spatial analysis of climate variability across North Carolina is performed using a geographic information system. While most trends are local in nature, there are general statewide patterns. Precipitation in North Carolina has increased over the past 50 years during the fall and winter seasons, but decreased during the summer. Temperatures during the last 10 years are warmer than average, but are not warmer than those experienced during the 1950s. The warm season has become longer, as measured by the dates of the last spring freeze and first fall freeze. Generally, the last 10 years were the wettest of the study period. These conclusions are consistent with earlier studies that show that the difference between the maximum and minimum temperatures is decreasing, possibly due to increased cloud cover and precipitation. Similarly, these results show that temperature patterns are in phase with the North Atlantic Oscillation and precipitation patterns appear to be correlated with the Pacific Decadal Oscillation.}, number={2-3}, journal={ENVIRONMENT INTERNATIONAL}, author={Boyles, RP and Raman, S}, year={2003}, month={Jun}, pages={263–275} }