Eli K. Melaas

A tale of two springs: using recent climate anomalies to characterize the sensitivity of temperate forest phenology to climate change

By the end of this century, mean annual temperatures in the Northeastern United States are expected to warm by 3–5 °C, which will have significant impacts on the structure and function of temperate forests in this region. To improve understanding of these impacts, we exploited two recent climate anomalies to explore how the springtime phenology of Northeastern temperate deciduous forests will respond to future climate warming. Specifically, springtime temperatures in 2010 and 2012 were the warmest on record in the Northeastern United States, with temperatures that were roughly equivalent to the lower end of warming scenarios that are projected for this region decades from now. Climate conditions in these two years therefore provide a unique empirical basis, that complements model-based studies, for improving understanding of how northeastern temperate forest phenology will change in the future. To perform our investigation, we analyzed near surface air temperatures from the United States Historical Climatology Network, time series of satellite-derived vegetation indices from NASA’s Moderate Resolution Imaging Spectroradiometer, and in situ phenological observations. Our study region encompassed the northern third of the eastern temperate forest ecoregion, extending from Pennsylvania to Canada. Springtime temperatures in 2010 and 2012 were nearly 3 °C warmer than long-term average temperatures from 1971–2000 over the region, leading to median anomalies of more than 100 growing degree days. In response, satellite and ground observations show that leaf emergence occurred up to two weeks earlier than normal, but with significant sensitivity to the specific timing of thermal forcing. These results are important for two reasons. First, they provide an empirical demonstration of the sensitivity of springtime phenology in northeastern temperate forests to future climate change that supports and complements modelbased predictions. Second, our results show that subtle differences in the character of thermal

Blue Hill Observatory Sunshine: Assessment of Climate Signals in the Longest Continuous Meteorological Record in North America

The Blue Hill Meteorological Observatory occupies a unique place in the history of the American Meteorological Society and the development of atmospheric science. Through its 129-yr history, the observatory has been operated by founder Abbott Lawrence Rotch (1861–1912), Harvard University, and the National Weather Service, and it is presently run by the nonprofit Blue Hill Observatory Science Center. While daily temperature and precipitation records are available through the National Climatic Data Center, they do not include the full record of sunshine duration data that were measured using a Campbell–Stokes sunshine recorder. We have recently digitized the observatorys original daily sunshine archives, and now present the first full collection and analysis of sunshine records extending from 1889 to the present. This dataset is unique and salient to modern climate research because the collection represents the earliest and longest continuous measurements of insolation outside of western Europe. This reco...

Determining past leaf-out times of New England’s deciduous forests from herbarium specimens

UNLABELLED • PREMISE OF THE STUDY There is great interest in studying leaf-out times of temperate forests because of the importance of leaf-out in controlling ecosystem processes, especially in the face of a changing climate. Remote sensing and modeling, combined with weather records and field observations, are increasing our knowledge of factors affecting variation in leaf-out times. Herbarium specimens represent a potential new source of information to determine whether the variation in leaf-out times observed in recent decades is comparable to longer time frames over past centuries.• METHODS Here we introduce the use of herbarium specimens as a method for studying long-term changes in leaf-out times of deciduous trees. We collected historical leaf-out data for the years 1834-2008 from common deciduous trees in New England using 1599 dated herbarium specimens with young leaves.• KEY RESULTS We found that leaf-out dates are strongly affected by spring temperature, with trees leafing out 2.70 d earlier for each degree C increase in mean April temperature. For each degree C increase in local temperature, trees leafed out 2.06 d earlier. Additionally, the mean response of leaf-out dates across all species and sites over time was 0.4 d earlier per decade. Our results are of comparable magnitude to results from studies using remote sensing and direct field observations.• CONCLUSIONS Across New England, mean leaf-out dates varied geographically in close correspondence with those observed in studies using satellite data. This study demonstrates that herbarium specimens can be a valuable source of data on past leaf-out times of deciduous trees.

Interactions between urban vegetation and surface urban heat islands: a case study in the Boston metropolitan region

Many studies have used thermal data from remote sensing to characterize how land use and surface properties modify the climate of cities. However, relatively few studies have examined the impact of elevated temperature on ecophysiological processes in urban areas. In this paper, we use time series of Landsat data to characterize and quantify how geographic variation in Bostons surface urban heat island (SUHI) affects the growing season of vegetation in and around the city, and explore how the quality and character of vegetation patches in Boston affect local heat island intensity. Results from this analysis show strong coupling between Bostons SUHI and vegetation phenology at the scale of both individual landscape units and for the region as a whole, with significant detectable signatures in both surface temperature and growing season length extending 15 km from Bostons urban core. On average, land surface temperatures were about 7 °C warmer and the growing season was 18–22 days longer in Boston relative to adjacent rural areas. Within Bostons urban core, patterns of temperature and timing of phenology in areas with higher vegetation amounts (e.g., parks) were similar to those in adjacent rural areas, suggesting that vegetation patches provide an important ecosystem service that offsets the urban heat island at local scales. Local relationships between phenology and temperature were affected by the intensity of urban land use surrounding vegetation patches and possibly by the presence of exotic tree species that are common in urban areas. Results from this analysis show how species composition, land cover configuration, and vegetation patch sizes jointly influence the nature and magnitude of coupling between vegetation phenology and SUHIs, and demonstrate that urban vegetation provides a significant ecosystem service in cities by decreasing the local intensity of SUHIs.

Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery

Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 min) imagery, we derived time series characterizing vegetation colour, including “canopy greenness”, processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the “greenness rising” and end of the “greenness falling” stages. The database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems.

Using VIIRS Day/Night Band to Measure Electricity Supply Reliability: Preliminary Results from Maharashtra, India

Unreliable electricity supplies are common in developing countries and impose large socio-economic costs, yet precise information on electricity reliability is typically unavailable. This paper presents preliminary results from a machine-learning approach for using satellite imagery of nighttime lights to develop estimates of electricity reliability for western India at a finer spatial scale. We use data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar Partnership (SNPP) satellite together with newly-available data from networked household voltage meters. Our results point to the possibilities of this approach as well as areas for refinement. With currently available training data, we find a limited ability to detect individual outages identified by household-level measurements of electricity voltage. This is likely due to the relatively small number of individual outages observed in our preliminary data. However, we find that the approach can estimate electricity reliability rates for individual locations fairly well, with the predicted versus actual regression yielding an R2 > 0.5. We also find that, despite the after midnight overpass time of the SNPP satellite, the reliability estimates derived are representative of daytime reliability.

A Method for Robust Estimation of Vegetation Seasonality from Landsat and Sentinel-2 Time Series Data

Time series from Landsat and Sentinel-2 satellites have great potential for modeling vegetation seasonality. However, irregular time sampling and frequent data loss due to clouds, snow, and short growing seasons, makes this modeling a challenge. We describe a new method for modeling seasonal vegetation index dynamics from satellite time series data. The method is based on box constrained separable least squares fits to logistic model functions combined with seasonal shape priors. To enable robust estimates, we extract a base level (i.e., the minimum dormant season value) from the frequency distribution of clear-sky vegetation index values. A seasonal shape prior is computed from several years of data, and in the final fits local parameters are box constrained. More specifically, if enough data values exist in a certain time period, the corresponding local parameters determining the shape of the model function over this period are relaxed and allowed to vary freely. If there are no observations in a period, the corresponding local parameters are locked to the parameters of the shape prior. The method is flexible enough to model interannual variations, yet robust enough when data are sparse. We test the method with Landsat, Sentinel-2, and MODIS data over a forested site in Sweden, demonstrating the feasibility and potential of the method for operational modeling of growing seasons.

An integrated phenology modelling framework in r

1INRA, UMR ISPA, Villenave d’Ornon, France; 2Department of Applied Ecology and Environmental Biology, Ghent University, Aquitaine, Belgium; 3Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA; 4Earth Systems Research Center, University of New Hampshire, Durham, NH, USA; 5Department of Earth & Environment, Boston University, Boston, MA, USA; 6School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA and 7Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA