Quantification of Warming Climate-Induced Changes in Terrestrial Arctic River Ice Thickness and Phenology

A land process model [the coupled hydrological and biogeochemical model (CHANGE)] is used to quantitatively assess changes in the ice phenology, thickness, and volume of terrestrial Arctic rivers from 1979 to 2009. The CHANGE model was coupled with a river routing and discharge model enabling explicit representation of river ice and water temperature dynamics. Model-simulated river ice phenological dates and thickness were generally consistent with in situ river ice data and landscape freeze–thaw (FT) satellite observations. Climate data indicated an increasing trend in winter surface air temperature (SAT) over the pan-Arctic during the study period. Nevertheless, the river ice thickness simulations exhibited a thickening regional trend independent of SAT warming, and associated with less insulation and cooling of underlying river ice by thinning snow cover. Deeper snow depth (SND) combined with SAT warming decreased simulated ice thickness, especially for Siberian rivers, where ice thickness is more strongly correlated with SND than SAT. Overall, the Arctic river ice simulations indicated regional trends toward later fall freezeup, earlier spring breakup, and consequently a longer annual ice-free period. The simulated ice phenological dates were significantly correlated with seasonal SAT warming. It is found that SND is an important factor for winter river ice growth, while ice phenological timing is dominated by seasonal SAT. The mean total Arctic river ice volume simulated from CHANGE was 54.1 km3 based on the annual maximum ice thickness in individual grid cells, while river ice volume for the pan-Arctic rivers decreased by 2.82 km3 (0.5%) over the 1979–2009 record. Arctic river ice is shrinking as a consequence of regional climate warming and coincident with other cryospheric components, including permafrost, glaciers, and sea ice

Autumn Tree Phenology in Northern Wisconsin: Humans Versus Photographs

Ecosystem primary productivity halts when plants go dormant, and so the timing of dormancy as it relates to autumn phenology has been a focus of much interdisciplinary research. While monitoring plant phenology has its roots in directly observing specimens, digital sensors along with modern methods have also become a mainstay in phenology. Results from different methods often vary, so there is still a need to better understand how digital cameras record autumn phenology, especially in comparison with ground-based observations (Keenan et al. 2014). This study compared autumn phenology derived from direct ground observations with upward-facing fisheye photography, in the context of a larger research project (C.H.E.E.S.E.H.E.A.D.19), to precisely determine autumn tree phenology across 53 field sites in a heterogeneous temperate deciduous forest with over 220 individual trees and 1,000 digital photos sampled. Less-studied trees such as aspen (Populus spp.), birch (Betula spp.), and basswood (Tilia americana) were included in the project, as well as sugar maple and red maple (Acer spp.). The results show that inflection points from sigmoid curves and change point detection are in close agreement for critical transition dates including the start of leaf coloration (bias of change points later at i= -0.47 days) and end (i= -0.6), but with slightly less agreement for the start of leaf fall (bias of change points earlier at i= 3.8) and the end of leaf fall (bias of change points later at i= -3.39). While camera-derived transition dates correlated poorly with corresponding human-derived transition dates, the best relationship detected was between green chromatic coordinate (GCC) inflection points and leaf fall (when foliage is mostly absent from tree canopies). This work is intended as a pilot study for novel methodologies in the field of ground-based phenology

SATELLITE MICROWAVE MEASUREMENT OF LAND SURFACE PHENOLOGY: CLARIFYING VEGETATION PHENOLOGY RESPONSE TO CLIMATIC DRIVERS AND EXTREME EVENTS

The seasonality of terrestrial vegetation controls feedbacks to the climate system including land-atmosphere water, energy and carbon (CO2) exchanges with cascading effects on regional-to-global weather and circulation patterns. Proper characterization of vegetation phenology is necessary to understand and quantify changes in the earthÆs ecosystems and biogeochemical cycles and is a key component in tracking ecological species response to climate change. The response of both functional and structural vegetation phenology to climatic drivers on a global scale is still poorly understood however, which has hindered the development of robust vegetation phenology models. In this dissertation I use satellite microwave vegetation optical depth (VOD) in conjunction with an array of satellite measures, Global Positioning System (GPS) reflectometry, field observations and flux tower data to 1) clarify vegetation phenology response to water, temperature and solar irradiance constraints, 2) demonstrate the asynchrony between changes in vegetation water content and biomass and changes in greenness and leaf area in relation to land cover type and climate constraints, 3) provide enhanced assessment of seasonal recovery of vegetation biomass following wildfire and 4) present a method to more accurately model tropical vegetation phenology. This research will establish VOD as a useful and informative parameter for regional-to-global vegetation phenology modeling, more accurately define the drivers of both structural and functional vegetation phenology, and help minimize errors in phenology simulations within earth system models. This dissertation also includes the development of Gross Primary Productivity (GPP) and Net Primary Productivity (NPP) vegetation health climate indicators as part of a NASA funded project entitled Development and Testing of Potential Indicators for the National Climate Assessment; Translating EOS datasets into National Ecosystem Biophysical Indicators

Using eddy covariance, remote sensing, and in situ observations to improve models of springtime phenology in temperate deciduous forests

Phenological events in temperate forests, such as bud burst and senescence, exert strong control over seasonal fluxes of water, energy and carbon. The timing of these transitions is influenced primarily by air temperature and photoperiod, although the exact nature and magnitude of these controls is poorly understood. In this dissertation, I use in situ and remotely sensed observations of phenology in combination with surface meteorological data and measurements of biosphere-atmosphere carbon exchanges to improve understanding and develop models of canopy phenology in temperate forest ecosystems. In the first element of this research I use surface air temperatures and eddy covariance measurements of carbon dioxide fluxes to evaluate and refine widely used approaches for predicting the onset of photosynthesis in spring that account for geographic variation in thermal and photoperiod constraints on phenology. Results from this analysis show that the refined models predict the onset of spring photosynthetic activity with significantly higher accuracy than existing models. A key challenge in developing and testing these models, however, is lack of adequate data sets that characterize phenology over large areas at multi-decadal time scales. To address this need, I develop a new method for estimating long-term average and interannual dynamics in the phenology of temperate forests using time series of Landsat TM/ETM+ images. Results show that estimated spring and autumn transition dates agree closely with in-situ measurements and that Landsat-derived estimates for the start and end of the growing season in Southern New England varied by as much as 4 weeks over the 30-year record of Landsat images. In the final element of this dissertation, I use meteorological data, species composition maps, satellite remote sensing, and ground observations to develop models of springtime leaf onset in temperate deciduous forests that account for geographic differences in how forest communities respond to springtime climate forcing. Results demonstrate important differences in cumulative heating requirements and photoperiod cues among forest types and that regional differences in species composition explain substantial geographic variation in springtime phenology of temperate forests. Together, the results from this dissertation provide an improved basis for observing and modeling springtime phenology in temperate forests

Refining terrestrial biosphere feedbacks to climate change through precise characterization of terrestrial vegetation

Climate change is primarily driven by the human activities of fossil fuel combustion and land use change, which together result in the emissions of greenhouse gases such as carbon dioxide (CO₂). The terrestrial biosphere currently absorbs about a third of total anthropogenic CO₂ emissions, mostly through primary production by vegetation. The continued function of vegetation as a CO₂ sink is uncertain, as climate change has the potential to enhance or restrict the carbon uptake capacity of vegetation. Uncertainty in terrestrial vegetation function in the context of climate change, due in part to a lack of precise observations of leaf biochemistry and function with which to develop models, therefore limits the confidence of climate change projections. In its entirety, this thesis examines the potential for more precise observations of leaf function and their integration across a variety of models and observational scales. The first chapter provides an introductory overview of the subsequent four chapters and how each compliments the other. The second chapter demonstrates the role of the terrestrial biosphere in influencing the relationship between temperature change and cumulative CO₂ emissions. The third chapter provides adaptations to current radiative transfer modelling approaches to improve estimations of leaf biochemical constituents. The fourth chapter applies high spatiotemporal resolution observations of leaf phenology, the timing of leaf emergence and senescence, across North America to predict species-specific leaf phenology patterns under various emissions scenarios throughout the 21st century. The fifth chapter provides an approach to detect declines in ecosystem processes such as carbon uptake using observational leaf phenology networks. These chapter results indicate that 1) uncertainty in the land-borne fraction of carbon emissions contributes largely to uncertainty in the relationship between temperature change and emissions, 2) spectral subdomains and prior estimation of leaf structure improves leaf biochemistry estimations, 3) leaf senescence timing may diverge between boreal and temperate species under a high emissions scenario, and 4) declines in vegetational carbon uptake can be accurately detected using quantitative phenocam-based indicators. The fundamental and technical insights provided through this thesis will facilitate more reliable and functionally resolved projections of terrestrial biosphere feedbacks to climate change

Monitoring the vegetation start of season (SOS) across the island of Ireland using the MERIS global vegetation index

The aim of this study was to develop a methodology, based on satellite remote sensing, to estimate the vegetation Start of Season (SOS) across the whole island of Ireland on an annual basis. This growing body of research is known as Land Surface Phenology (LSP) monitoring. The SOS was estimated for each year from a 7-year time series of 10-day composited, 1.2 km reduced resolution MERIS Global Vegetation Index (MGVI) data from 2003 to 2009, using the time series analysis software, TIMESAT. The selection of a 10-day composite period was guided by in-situ observations of leaf unfolding and cloud cover at representative point locations on the island. The MGVI time series was smoothed and the SOS metric extracted at a point corresponding to 20% of the seasonal MGVI amplitude. The SOS metric was extracted on a per pixel basis and gridded for national scale coverage. There were consistent spatial patterns in the SOS grids which were replicated on an annual basis and were qualitatively linked to variation in landcover. Analysis revealed that three statistically separable groups of CORINE Land Cover (CLC) classes could be derived from differences in the SOS, namely agricultural and forest land cover types, peat bogs, and natural and semi-natural vegetation types. These groups demonstrated that managed vegetation, e.g. pastures has a significantly earlier SOS than in unmanaged vegetation e.g. natural grasslands. There was also interannual spatio-temporal variability in the SOS. Such variability was highlighted in a series of anomaly grids showing variation from the 7-year mean SOS. An initial climate analysis indicated that an anomalously cold winter and spring in 2005/2006, linked to a negative North Atlantic Oscillation index value, delayed the 2006 SOS countrywide, while in other years the SOS anomalies showed more complex variation. A correlation study using air temperature as a climate variable revealed the spatial complexity of the air temperature-SOS relationship across the Republic of Ireland as the timing of maximum correlation varied from November to April depending on location. The SOS was found to occur earlier due to warmer winters in the Southeast while it was later with warmer winters in the Northwest. The inverse pattern emerged in the spatial patterns of the spring correlates. This contrasting pattern would appear to be linked to vegetation management as arable cropping is typically practiced in the southeast while there is mixed agriculture and mostly pastures to the west. Therefore, land use as well as air temperature appears to be an important determinant of national scale patterns in the SOS. The TIMESAT tool formed a crucial component of the estimation of SOS across the country in all seven years as it minimised the negative impact of noise and data dropouts in the MGVI time series by applying a smoothing algorithm. The extracted SOS metric was sensitive to temporal and spatial variation in land surface vegetation seasonality while the spatial patterns in the gridded SOS estimates aligned with those in landcover type. The methodology can be extended for a longer time series of FAPAR as MERIS will be replaced by the ESA Sentinel mission in 2013, while the availability of full resolution (300m) MERIS FAPAR and equivalent sensor products holds the possibility of monitoring finer scale seasonality variation. This study has shown the utility of the SOS metric as an indicator of spatiotemporal variability in vegetation phenology, as well as a correlate of other environmental variables such as air temperature. However, the satellite-based method is not seen as a replacement of ground-based observations, but rather as a complementary approach to studying vegetation phenology at the national scale. In future, the method can be extended to extract other metrics of the seasonal cycle in order to gain a more comprehensive view of seasonal vegetation development

Study on Regional Responses of Pan-Arctic Terrestrial Ecosystems to Recent Climate Variability Using Satellite Remote Sensing

I applied a satellite remote sensing based production efficiency model (PEM) using an integrated AVHRR and MODIS FPAR/LAI time series with a regionally corrected NCEP/NCAR reanalysis surface meteorology and NASA/GEWEX shortwave solar radiation inputs to assess annual terrestrial net primary productivity (NPP) for the pan-Arctic basin and Alaska from 1983 to 2005. I developed a satellite remote sensing based evapotranspiration (ET) algorithm using GIMMS NDVI with the above meteorology inputs to assess spatial patterns and temporal trends in ET over the pan-Arctic region. I then analyzed associated changes in the regional water balance defined as the difference between precipitation (P) and ET. I finally analyzed the effects of regional climate oscillations on vegetation productivity and the regional water balance. The results show that low temperature constraints on Boreal-Arctic NPP are decreasing by 0.43% per year ( P \u3c 0.001), whereas a positive trend in vegetation moisture constraints of 0.49% per year ( P = 0.04) are offsetting the potential benefits of longer growing seasons and contributing to recent drought related disturbances in NPP. The PEM simulations of NPP seasonality, annual anomalies and trends are similar to stand inventory network measurements of boreal aspen stem growth ( r = 0.56; P = 0.007) and atmospheric CO2 measurement based estimates of the timing of growing season onset (r = 0.78; P \u3c 0.001). The simulated monthly ET results agree well (RMSE = 8.3 mm month-1; R2 = 0.89) with tower measurements for regionally dominant land cover types. Generally positive trends in ET, precipitation and available river discharge measurements imply that the pan-Arctic terrestrial water cycle is intensifying. Increasing water deficits occurred in some boreal and temperate grassland regions, which agree with regional drought records and recent satellite observations of vegetation browning and productivity decreases. Climate oscillations including Arctic Oscillation and Pacific Decadal Oscillation influence NPP by regulating seasonal patterns of low temperature and moisture constraints to photosynthesis. The pan-Arctic water balance is changing in complex ways in response to climate change and variability, with direct linkages to terrestrial carbon and energy cycles. Consequently, drought induced NPP decreases may become more frequent and widespread, though the occurrence and severity of drought events will depend on future water cycle patterns