Publications

@article{Holland15Aug.2019,

title = {Changing Seasonal Predictability of Arctic Summer Sea Ice Area in a Warming Climate},

author = {Marika M Holland and Laura Landrum and David Bailey and Steve Vavrus},

url = {https://doi.org/10.1175/JCLI-D-19-0034.1},

doi = {10.1175/JCLI-D-19-0034.1},

year  = {2019},

date = {2019-08-15},

journal = {Journal of Climate},

volume = {32},

number = {16},

pages = {4963-4979},

publisher = {American Meteorological Society},

address = {Boston MA, USA},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.1002/ecy.2856,

title = {Quantifying trends and uncertainty in prehistoric forest composition in the upper Midwestern United States},

author = {Andria Dawson and Christopher J Paciorek and Simon J Goring and Stephen T Jackson and Jason S McLachlan and John W Williams},

url = {https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/ecy.2856},

doi = {https://doi.org/10.1002/ecy.2856},

year  = {2019},

date = {2019-08-05},

journal = {Ecology},

volume = {100},

number = {12},

pages = {e02856},

abstract = {Abstract Forest ecosystems in eastern North America have been in flux for the last several thousand years, well before Euro-American land clearance and the 20th-century onset of anthropogenic climate change. However, the magnitude and uncertainty of prehistoric vegetation change have been difficult to quantify because of the multiple ecological, dispersal, and sedimentary processes that govern the relationship between forest composition and fossil pollen assemblages. Here we extend STEPPS, a Bayesian hierarchical spatiotemporal pollen–vegetation model, to estimate changes in forest composition in the upper Midwestern United States from about 2,100 to 300 yr ago. Using this approach, we find evidence for large changes in the relative abundance of some species, and significant changes in community composition. However, these changes took place against a regional background of changes that were small in magnitude or not statistically significant, suggesting complexity in the spatiotemporal patterns of forest dynamics. The single largest change is the infilling of Tsuga canadensis in northern Wisconsin over the past 2,000 yr. Despite range infilling, the range limit of T. canadensis was largely stable, with modest expansion westward. The regional ecotone between temperate hardwood forests and northern mixed hardwood/conifer forests shifted southwestward by 15–20 km in Minnesota and northwestern Wisconsin. Fraxinus, Ulmus, and other mesic hardwoods expanded in the Big Woods region of southern Minnesota. The increasing density of paleoecological data networks and advances in statistical modeling approaches now enables the confident detection of subtle but significant changes in forest composition over the last 2,000 yr.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Notaro2019,

title = {Elucidating observed land surface feedbacks across sub-Saharan Africa},

author = {Michael Notaro and Fuyao Wang and Yan Yu},

url = {https://link.springer.com/article/10.1007/s00382-019-04730-3},

doi = {10.1007/s00382-019-04730-3},

issn = {1432-0894},

year  = {2019},

date = {2019-08-01},

journal = {Climate Dynamics},

volume = {53},

number = {3},

pages = {1741-1763},

abstract = {This study examines the role of terrestrial forcings on the regional climate of sub-Saharan Africa through the application of a multivariate statistical method, stepwise generalized equilibrium feedback assessment, to an array of observational, reanalysis, and remote sensing data products. By applying multiple datasets, data uncertainty and the robustness of assessed land surface feedbacks are considered. The approach from our 2017 study is expanded to decompose the relative contribution of vegetation, soil moisture, and oceanic forcings; investigate the role of evapotranspiration (ET) partitioning in terrestrial feedbacks; and compare land surface feedbacks among four key regions, namely the Sahel, Greater Horn of Africa, West African monsoon region, and Congo. ET partitioning differs notably among sub-Saharan regions and between available observational datasets. Across sub-Saharan Africa as a whole, oceanic and terrestrial forcings impose a relatively comparable impact on year-round atmospheric conditions. The land surface feedbacks are most pronounced across the semi-arid Sahel and Greater Horn of Africa, although with unique seasonality of such feedbacks between regions. Moisture recycling is the dominant mechanism in these regions, with positive soil moisture--vegetation--rainfall feedbacks. The direct feedback of soil moisture anomalies on atmospheric conditions outweighed that of leaf area index anomalies. There is a clear need for more extensive observations of ET, its partitioning, and soil moisture across sub-Saharan Africa, as these data uncertainties propagate into the reliability of assessed soil moisture--ET feedbacks, particularly across the Sahel.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.1029/2019JG005186,

title = {Large Spatial and Temporal Variability of Carbon Dioxide and Methane in a Eutrophic Lake},

author = {Luke C Loken and John T Crawford and Paul J Schramm and Philipp Stadler and Ankur R Desai and Emily H Stanley},

url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019JG005186},

doi = {https://doi.org/10.1029/2019JG005186},

year  = {2019},

date = {2019-07-02},

journal = {Journal of Geophysical Research: Biogeosciences},

volume = {124},

number = {7},

pages = {2248-2266},

abstract = {Abstract Lakes are conduits of greenhouse gases to the atmosphere; however, most efflux estimates for individual lakes are based on extrapolations from a limited number of locations. Within-lake variability in carbon dioxide (CO2) and methane (CH4) arises from differences in water sources, mixing, atmospheric exchange, and biogeochemical transformations, all of which vary across multiple temporal and spatial scales. We asked, how variable are CO2 and CH4 across the surface of a single lake, how do spatial patterns change seasonally, and how well does the typical sampling location represent the entire lake surface? During the 2016 ice-free period, we mapped surface water concentrations of CO2 and CH4 approximately weekly in Lake Mendota (USA) and modeled diffusive gas exchange. During stratification, CO2 was generally lower than atmospheric saturation (mean 19.81 μM) and relatively homogenous (mean coefficient of variation 0.12), whereas CH4 was routinely extremely supersaturated (mean 0.29 μM) with greater spatial heterogeneity (mean coefficient of variation 0.65). During fall mixis, concentrations of both gases increased and became more spatially variable, but their spatial arrangements differed. In this system, samples collected from the lake center reasonably well represented the spatially weighted mean CO2 concentration but overestimated annual CO2 efflux by 21%. For CH4, the lake center underestimated annual diffusive efflux by only 8.6% but poorly represented lakewide concentrations and fluxes on any given day. Upscaling from a single site to the whole lake requires consideration of spatial variation to assess lakewide carbon dynamics due to heterogeneity in within-lake processing, transport to the lake surface, and exchange with the atmosphere.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.3389/frwa.2019.00003,

title = {A Grand Prediction: Communicating and Evaluating 2018 Summertime Upper Blue Nile Rainfall and Streamflow Forecasts in Preparation for Ethiopia's New Dam},

author = {Annalise G Blum and Ben Zaitchik and Sarah Alexander and Shu Wu and Ying Zhang and Shraddhanand Shukla and Temesgen Alemneh and Paul Block},

url = {https://www.frontiersin.org/article/10.3389/frwa.2019.00003},

doi = {10.3389/frwa.2019.00003},

issn = {2624-9375},

year  = {2019},

date = {2019-07-02},

journal = {Frontiers in Water},

volume = {1},

pages = {3},

abstract = {When complete, the Grand Ethiopian Renaissance Dam (GERD) will be the largest hydropower dam in Africa. The GERD has become a focal point of geopolitical tensions because it will allow Ethiopia greater control over the Blue Nile River, Egypt's main source of freshwater. To inform discussions of filling plans and responses, we created a probabilistic seasonal forecast for Upper Blue Nile rainfall and streamflow in the GERD basin. Eight statistical models and eight dynamical models were used to forecast the rainy season (June–September), which were then converted into river flow for June–December 2018. Both statistical and dynamical models predicted a high probability of average to above average rainfall as well as Upper Blue Nile flow in the GERD basin. Actual summer precipitation in 2018 was slightly below the long-term mean but well within the range considered to be “near normal.” Leveraging the increasingly online media landscape for science communication, we made the forecast publicly available through a blog and shared with regional decision-makers in advance of the 2018 rainy season. The blog attracted news coverage in the region focusing primarily on the relatively low likelihood of below-average Nile flow across the forecast ensemble. When asked for feedback on the blog, Ethiopian decision-makers and forecasters reported that flow predictions included in our blog were useful and not part of existing products. Access and comprehension were noted barriers to the use of these types of forecasts, consistent with prior research in forecast communication and dissemination. Forecasts available on such blogs can inform a shared understanding among decision-makers in the management of transboundary waters, yet effective communication and dissemination remain a challenge.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.1111/geb.12937,

title = {Covariations between plant functional traits emerge from constraining parameterization of a terrestrial biosphere model},

author = {Marc Peaucelle and Cédric Bacour and Philippe Ciais and Nicolas Vuichard and Sylvain Kuppel and Josep Peñuelas and Luca Belelli Marchesini and Peter D Blanken and Nina Buchmann and Jiquan Chen and Nicolas Delpierre and Ankur R Desai and Eric Dufrene and Damiano Gianelle and Cristina Gimeno-Colera and Carsten Gruening and Carole Helfter and Lukas Hörtnagl and Andreas Ibrom and Richard Joffre and Tomomichi Kato and Thomas E Kolb and Beverly Law and Anders Lindroth and Ivan Mammarella and Lutz Merbold and Stefano Minerbi and Leonardo Montagnani and Ladislav Šigut and Mark Sutton and Andrej Varlagin and Timo Vesala and Georg Wohlfahrt and Sebastian Wolf and Dan Yakir and Nicolas Viovy},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/geb.12937},

doi = {https://doi.org/10.1111/geb.12937},

year  = {2019},

date = {2019-06-30},

journal = {Global Ecology and Biogeography},

volume = {28},

number = {9},

pages = {1351-1365},

abstract = {Abstract Aim The mechanisms of plant trait adaptation and acclimation are still poorly understood and, consequently, lack a consistent representation in terrestrial biosphere models (TBMs). Despite the increasing availability of geo-referenced trait observations, current databases are still insufficient to cover all vegetation types and environmental conditions. In parallel, the growing number of continuous eddy-covariance observations of energy and CO2 fluxes has enabled modellers to optimize TBMs with these data. Past attempts to optimize TBM parameters mostly focused on model performance, overlooking the ecological properties of ecosystems. The aim of this study was to assess the ecological consistency of optimized trait-related parameters while improving the model performances for gross primary productivity (GPP) at sites. Location Worldwide. Time period 1992–2012. Major taxa studied Trees and C3 grasses. Methods We optimized parameters of the ORCHIDEE model against 371 site-years of GPP estimates from the FLUXNET network, and we looked at global covariation among parameters and with climate. Results The optimized parameter values were shown to be consistent with leaf-scale traits, in particular, with well-known trade-offs observed at the leaf level, echoing the leaf economic spectrum theory. Results showed a marked sensitivity of trait-related parameters to local bioclimatic variables and reproduced the observed relationships between traits and climate. Main conclusions Our approach validates some biological processes implemented in the model and enables us to study ecological properties of vegetation at the canopy level, in addition to some traits that are difficult to observe experimentally. This study stresses the need for: (a) implementing explicit trade-offs and acclimation processes in TBMs; (b) improving the representation of processes to avoid model-specific parameterization; and (c) performing systematic measurements of traits at FLUXNET sites in order to gather information on plant ecophysiology and plant diversity, together with micro-meteorological conditions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.1002/eap.1955,

title = {Land-use and climatic causes of environmental novelty in Wisconsin since 1890},

author = {John W Williams and Kevin D Burke and Michael S Crossley and Daniel A Grant and Volker C Radeloff},

url = {https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/eap.1955},

doi = {https://doi.org/10.1002/eap.1955},

year  = {2019},

date = {2019-06-14},

journal = {Ecological Applications},

volume = {29},

number = {7},

pages = {e01955},

abstract = {Abstract Multiple global change drivers are increasing the present and future novelty of environments and ecological communities. However, most assessments of environmental novelty have focused only on future climate and were conducted at scales too broad to be useful for land management or conservation. Here, using historical county-level data sets of agricultural land use, forest composition, and climate, we conduct a regional-scale assessment of environmental novelty for Wisconsin landscapes from ca. 1890 to 2012. Agricultural land-use data include six cropland types, livestock densities for four livestock species, and human populations. Forestry data comprise biomass-weighted relative abundances for 15 tree genera. Climate data comprise seasonal means for temperature and precipitation. We found that forestry and land use are the strongest cause of environmental novelty (NoveltyForest = 3.66, NoveltyAg = 2.83, NoveltyClimate = 1.60, with Wisconsin's forests transformed by early 20th-century logging and its legacies and multiple waves of agricultural innovation and obsolescence. Climate change is the smallest contributor to contemporary novelty, with precipitation signals stronger than temperature. Magnitudes and causes of environmental novelty are strongly spatially patterned, with novelty in southern Wisconsin roughly twice that in northern Wisconsin. Forestry is the most important cause of novelty in the north, land use and climate change are jointly important in the southwestern Wisconsin, and land use and forest composition are most important in central and eastern Wisconsin. Areas of high regional novelty tend also to be areas of high local change, but local change has not pushed all counties beyond regional baselines. Seven counties serve as the best historical analogues for over one-half of contemporary Wisconsin counties (40/72), and so can offer useful historical counterparts for contemporary systems and help managers coordinate to tackle similar environmental challenges. Multi-dimensional environmental novelty analyses, like those presented here, can help identify the best historical analogues for contemporary ecosystems, places where new management rules and practices may be needed because novelty is already high, and the main causes of novelty. Separating regional novelty clearly from local change and measuring both across many dimensions and at multiple scales thus helps advance ecology and sustainability science alike.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.1111/nph.15835,

title = {Growth and opportunities in networked synthesis through AmeriFlux},

author = {Trevor F Keenan and David J P Moore and Ankur Desai},

url = {https://nph.onlinelibrary.wiley.com/doi/abs/10.1111/nph.15835},

doi = {https://doi.org/10.1111/nph.15835},

year  = {2019},

date = {2019-05-08},

journal = {New Phytologist},

volume = {222},

number = {4},

pages = {1685-1687},

abstract = {Since its inception in 1996 with just 15 sites, the AmeriFlux network has grown to include over 300 sites, representing every major ecosystem type across the Americas. This grassroots coalition of the willing (Novick et al., 2018) has for the past two decades continuously measured the exchange of carbon, water and energy between ecosystems and the atmosphere. Recent years in particular have seen remarkable growth in both the degree of coordination of activities within the AmeriFlux network, and the number of researchers involved. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bechtold2019,

title = {PEAT-CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model},

author = {M Bechtold and G J M De Lannoy and R D Koster and R H Reichle and S P Mahanama and W Bleuten and M A Bourgault and C Brümmer and I Burdun and A R Desai and K Devito and T Grünwald and M Grygoruk and E R Humphreys and J Klatt and J Kurbatova and A Lohila and T M Munir and M B Nilsson and J S Price and M Röhl and A Schneider and B Tiemeyer},

url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018MS001574},

doi = {https://doi.org/10.1029/2018MS001574},

year  = {2019},

date = {2019-05-07},

journal = {Journal of Advances in Modeling Earth Systems},

volume = {11},

number = {7},

pages = {2130-2162},

abstract = {Peatlands are poorly represented in global Earth system modeling frameworks. Here we add a peatland-specific land surface hydrology module, PEAT-CLSM, to the Catchment Land Surface Model, CLSM, of the NASA Goddard Earth Observing System, GEOS, framework. The amended TOPMODEL approach of the original CLSM that uses topography characteristics to model catchment processes is discarded, and a peatland-specific model concept is realized in its place. To facilitate its utilization in operational GEOS efforts, PEAT-CLSM uses the basic structure of CLSM and the same global input data. Parameters used in PEAT-CLSM are based on literature data. A suite of CLSM and PEAT-CLSM simulations for peatland areas between 40 degree N and 75 degrees N is presented and evaluated against a newly compiled data set of groundwater table depth and eddy covariance observations of latent and sensible heat fluxes in natural and seminatural peatlands. CLSM's simulated groundwater tables are too deep and variable, whereas PEAT-CLSM simulates a mean groundwater table depth of -0.20 m (snow-free unfrozen period) with moderate temporal fluctuations (standard deviation of 0.10 m), in significantly better agreement with in situ observations. Relative to an operational CLSM version that simply includes peat as a soil class, the temporal correlation coefficient is increased on average by 0.16 and reaches 0.64 for bogs and 0.66 for fens when driven with global atmospheric forcing data. In PEAT-CLSM, runoff is increased on average by 38 percent and evapotranspiration is reduced by 19 percent. The evapotranspiration reduction constitutes a significant improvement relative to eddy covariance measurements.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{rs11070798,

title = {Spatio-Temporal Variability in Remotely Sensed Vegetation Greenness Across Yellowstone National Park},

author = {Michael Notaro and Kristen Emmett and Donal O’Leary},

url = {https://www.mdpi.com/2072-4292/11/7/798},

doi = {10.3390/rs11070798},

issn = {2072-4292},

year  = {2019},

date = {2019-04-03},

journal = {Remote Sensing},

volume = {11},

number = {7},

abstract = {The study’s objective was to quantify the responses of vegetation greenness and productivity to climate variability and change across complex topographic, climatic, and ecological gradients in Yellowstone National Park through the use of remotely sensed data. The climate change signal in Yellowstone was pronounced, including substantial warming, an abrupt decline in snowpack, and more frequent droughts. While phenological studies are increasing in Yellowstone, the near absence of long-term and continuous ground-based phenological measurements motivated the study’s application of remotely sensed data to aid in identifying ecological vulnerabilities and guide resource management in light of on ongoing environmental change. Correlation, time-series, and empirical orthogonal function analyses for 1982-2015 focused on Daymet data and vegetation indices (VIs) from the Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS). The study’s key questions address unique time scales. First, what are the dominant meteorological drivers of variability in vegetation greenness on seasonal to interannual time scales? Key results include: (1) Green-up is the most elevation- and climate-sensitive phenological stage, with La Niña-induced cool, wet conditions or an anomalously deep snowpack delaying the green-up wave. (2) Drought measures were the dominant contributors towards phenological variability, as winter-spring drought corresponded to enhanced April-June greening and spring-summer drought corresponded to reduced August-September greening. Second, how have patterns of productivity changed in response to climate change and disturbances? Key results include: (1) The park predominantly exhibited positive productivity trends, associated with lodgepole pine re-establishment and growth following the 1988 fires. (2) Landscapes which were undisturbed by the 1988 fires showed no apparent sign of warming-induced greening. This study motivates a systematic investigation of remote-sensing data across western parks to identify ecological vulnerabilities and support the development of climate change vulnerability assessments and adaptation strategies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yan_2019,

title = {Predictability of tropical vegetation greenness using sea surface temperatures},

author = {Binyan Yan and Jiafu Mao and Xiaoying Shi and Forrest M Hoffman and Michael Notaro and Tianjun Zhou and Nate Mcdowell and Robert E Dickinson and Min Xu and Lianhong Gu and Daniel M Ricciuto},

url = {https://doi.org/10.1088/2515-7620/ab178a},

doi = {10.1088/2515-7620/ab178a},

year  = {2019},

date = {2019-04-01},

journal = {Environmental Research Communications},

volume = {1},

number = {3},

pages = {031003},

publisher = {IOP Publishing},

abstract = {Much research has examined the sensitivity of tropical terrestrial ecosystems to various environmental drivers. The predictability of tropical vegetation greenness based on sea surface temperatures (SSTs), however, has not been well explored. This study employed fine spatial resolution remotely-sensed Enhanced Vegetation Index (EVI) and SST indices from tropical ocean basins to investigate the predictability of tropical vegetation greenness in response to SSTs and established empirical models with optimal parameters for hindcast predictions. Three evaluation metrics were used to assess the model performance, i.e., correlations between historical observed and predicted values, percentage of correctly predicted signs of EVI anomalies, and percentage of correct signs for extreme EVI anomalies. Our findings reveal that the pan-tropical EVI was tightly connected to the SSTs over tropical ocean basins. The strongest impacts of SSTs on EVI were identified mainly over the arid or semi-arid tropical regions. The spatially-averaged correlation between historical observed and predicted EVI time series was 0.30 with its maximum value reaching up to 0.84. Vegetated areas across South America (25.76%), Africa (33.13%), and Southeast Asia (39.94%) were diagnosed to be associated with significant SST-EVI correlations (p < 0.01). In general, statistical models correctly predicted the sign of EVI anomalies, with their predictability increasing from ∼60% to nearly 100% when EVI was abnormal (anomalies exceeding one standard deviation). These results provide a basis for the prediction of changes in greenness of tropical terrestrial ecosystems at seasonal to intra-seasonal scales. Moreover, the statistics-based observational relationships have the potential to facilitate the benchmarking of Earth System Models regarding their ability to capture the responses of tropical vegetation growth to long-term signals of oceanic forcings.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Reed01Apr.2019,

title = {Evaluation of Low-Cost, Automated Lake Ice Thickness Measurements},

author = {David E Reed and Ankur R Desai and Emily C Whitaker and Henry Nuckles},

url = {https://doi.org/10.1175/JTECH-D-18-0214.1},

doi = {10.1175/JTECH-D-18-0214.1},

year  = {2019},

date = {2019-04-01},

journal = {Journal of Atmospheric and Oceanic Technology},

volume = {36},

number = {4},

pages = {527-534},

publisher = {American Meteorological Society},

address = {Boston MA, USA},

abstract = {Climate change is expected to decrease ice coverage and thickness globally while increasing the variability of ice coverage and thickness on midlatitude lakes. Ice thickness affects physical, biological, and chemical processes as well as safety conditions for scientists and the general public. Measurements of ice thickness that are both temporally frequent and spatially extensive remain a technical challenge. Here new observational methods using repurposed soil moisture sensors that facilitate high spatial–temporal sampling of ice thickness are field tested on Lake Mendota in Wisconsin during the winter 2015/16 season. Spatial variability in ice thickness was high, with differences of 10 cm of ice column thickness over 1.05 km of horizontal distance. When observational data were compared with manual measurements and model output from both the Freshwater Lake (FLake) model and General Lake Model (GLM), ice thickness from sensors matches manual measurements, whereas GLM and FLake results showed a thinner and thicker ice layer, respectively. The FLake-modeled ice column temperature effectively remained at 0°C, not matching observations. We also show that daily ice dynamics follows the expected linear function of ice thickness growth/melt, improving confidence in sensor accuracy under field conditions. We have demonstrated a new method that allows low-cost and high-frequency measurements of ice thickness, which will be needed both to advance winter limnology and to improve on-ice safety.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{KEATINGBITONTI2019134,

title = {Influence of increasing carbonate saturation in Atlantic bottom water during the late Miocene},

author = {Caitlin R Keating-Bitonti and Shanan E Peters},

url = {https://www.sciencedirect.com/science/article/pii/S003101821830628X},

doi = {https://doi.org/10.1016/j.palaeo.2019.01.006},

issn = {0031-0182},

year  = {2019},

date = {2019-03-15},

journal = {Palaeogeography, Palaeoclimatology, Palaeoecology},

volume = {518},

pages = {134-142},

abstract = {The late Miocene witnessed the tectonic uplift of the Isthmus of Panama, the onset of modern-like thermohaline circulation, changes in global patterns of deep-sea sedimentation, and a negative shift of ~1‰ in the carbon isotopic composition (δ13C) of marine carbonate sediments. Although previous work has attributed the late Miocene carbon isotopic shift (LMCS) to biological and environmental factors, the reasons for this apparent shift in the global carbon cycle remain incompletely understood. Here we combine both core-based sedimentological and isotopic data from three Walvis Ridge sites in the southeastern Atlantic Ocean with macrostratigraphic data from the entire Atlantic basin to show that the LMCS marks the establishment of modern, glacial/interglacial seawater carbonate saturation levels in the Atlantic. Between 10 and 7 million years ago (Ma) the Atlantic Ocean shows a trend of increasing seafloor area preserving deep-sea carbonate sediments. Neogene carbonate sedimentation in the Atlantic Ocean peaked at 7 Ma, coinciding with a δ13C shift of approximately −0.8‰ in Walvis Ridge benthic foraminifera, similar to the magnitude of LMCS. Northern-sourced waters in the late Miocene likely shifted seawater carbonate chemistry throughout the Atlantic basin by introducing bottom waters with higher carbonate ion concentrations. LMCS reflects the introduction of a carbonate ion effect on North Atlantic Deep Water (NADW) by increasing Northern Hemisphere glacial carbonate weathering. A carbonate ion flux to the Labrador Seawater contribution of NADW raises the possibility of a carbonate burial-mediated feedback with the global climate system that led to additional cooling during the Miocene-Pliocene transition.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Delorit2019,

title = {Using Seasonal Forecasts to Inform Water Market-Scale Option Contracts},

author = {Justin D Delorit and Paul J Block},

url = {https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29WR.1943-5452.0001068},

year  = {2019},

date = {2019-03-15},

journal = {Journal of Water Resources Planning and Management},

volume = {145},

number = {5},

pages = {04019018},

abstract = {Option contracts have been successfully established in water-scare regions to facilitate temporary transfers of water between users. The benefits are clear when the contract water quantity can be guaranteed by the seller. Where water right allocations are subject to interannual variability, guaranteed delivery may not be feasible. Skillful season-ahead forecasts of water right allocations and crop market prices have the potential to inform option contracts in such circumstances. Here, two theoretical growers’ cooperatives, representing high-value and low-value cropping within a basin, are modeled as exclusive water-trading partners. A two-firm demand equilibrium model is developed from a coupled crop yield and economic model, conditioned on each cooperative’s production inputs. Subsequently, several combinations of single-stage and multistage season-ahead forecasts of allocations and crop prices inform both the demand and an option contract model. Evaluating combinations of forecast and persistence-informed models against a perfect foresight-informed model, both the efficacy of the forecast-informed option contract framework and the value of forecast information are determined. Results indicate combinations including multistage season-ahead forecasts of allocations produce results that most closely represent expected trade volume, direction of trading, and market-scale joint profitability across a range of intercooperative water rights ownership scenarios. The multistage allocation forecasts exert more influence over option contract utility than forecasts of product market prices. This work highlights the value of forecast-informed decision making as it pertains to promoting market-scale economic and water use efficiency.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alexander2019,

title = {Model Selection Based on Sectoral Application Scale for Increased Value of Hydroclimate-Prediction Information},

author = {Sarah Alexander and Shu Wu and Paul Block},

url = {https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29WR.1943-5452.0001044},

year  = {2019},

date = {2019-02-18},

journal = {Journal of Water Resources Planning and Management},

volume = {145},

number = {5},

pages = {04019006},

abstract = {Advance predictions of seasonal precipitation may provide information to aid water resource management decisions in various sectors. Yet, a disconnect between the spatial scale upon which skillful predictions are issued and the sectoral decision-making scale renders current predictive information inadequate in many cases. This study explores season-ahead precipitation prediction skill for a local region in the Blue Nile basin, Ethiopia, to better understand how model structure may serve to provide more skillful and valuable predictive information to the end user. Statistical downscaling of global dynamic and regional empirical models and development of a high-resolution, locally tailored statistical model indicate that model structure and prediction skill are inextricably linked. Statistical approaches specific to the local region show higher prediction skill at the sectoral decision-making scale compared with dynamic approaches, offering the potential to aid local communities in many regions that are currently vulnerable to highly variable spatial precipitation patterns. Linking the local-scale precipitation predictions with a simple reservoir model in Ethiopia illustrates application of local-scale predictive information for enhanced value in planning and management decisions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Keller1136,

title = {Neoproterozoic glacial origin of the Great Unconformity},

author = {Brenhin C Keller and Jon M Husson and Ross N Mitchell and William F Bottke and Thomas M Gernon and Patrick Boehnke and Elizabeth A Bell and Nicholas L Swanson-Hysell and Shanan E Peters},

url = {https://www.pnas.org/content/116/4/1136},

doi = {10.1073/pnas.1804350116},

issn = {0027-8424},

year  = {2019},

date = {2019-01-22},

journal = {Proceedings of the National Academy of Sciences},

volume = {116},

number = {4},

pages = {1136-1145},

publisher = {National Academy of Sciences},

abstract = {It has long been observed that the sequence of sedimentary rocks deposited in the past half-billion years often sharply overlies older igneous or metamorphic basement at an erosional surface known as the Great Unconformity. We provide evidence that this unconformity may record rapid erosion during Neoproterozoic textquotedblleftsnowball Earthtextquotedblright glaciations. We show that the extent of Phanerozoic sedimentation in shallow continental seas can be accurately reproduced by modeling the accommodation space produced by the proposed glacial erosion, underlining the importance of glaciation as a means for lowering erosional base level. These results provide constraints on the sedimentary and geochemical environment in which the first multicellular animals evolved and diversified in the textquotedblleftCambrian explosiontextquotedblright following the unconformity.The Great Unconformity, a profound gap in Earthtextquoterights stratigraphic record often evident below the base of the Cambrian system, has remained among the most enigmatic field observations in Earth science for over a century. While long associated directly or indirectly with the occurrence of the earliest complex animal fossils, a conclusive explanation for the formation and global extent of the Great Unconformity has remained elusive. Here we show that the Great Unconformity is associated with a set of large global oxygen and hafnium isotope excursions in magmatic zircon that suggest a late Neoproterozoic crustal erosion and sediment subduction event of unprecedented scale. These excursions, the Great Unconformity, preservational irregularities in the terrestrial bolide impact record, and the first-order pattern of Phanerozoic sedimentation can together be explained by spatially heterogeneous Neoproterozoic glacial erosion totaling a global average of 3–5 vertical kilometers, along with the subsequent thermal and isostatic consequences of this erosion for global continental freeboard.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.1111/jbi.13491,

title = {Assessing the environmental and dispersal controls on Fagus grandifolia distributions in the Great Lakes region},

author = {Megan Seeley and Simon Goring and John W Williams},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/jbi.13491},

doi = {https://doi.org/10.1111/jbi.13491},

year  = {2019},

date = {2019-01-15},

journal = {Journal of Biogeography},

volume = {46},

number = {2},

pages = {405-419},

abstract = {Abstract Aim This paper assesses the relative importance of environmental filtering and dispersal limitations as controls on the western range limit of Fagus grandifolia, a common mesic late-successional tree species in the eastern United States. We also test for differences in species–environment relationships between range-edge populations of F. grandifolia in eastern Wisconsin and core populations in Michigan. Because environmental conditions between the states differ moderately, while in Michigan dispersal presumably no longer limits F. grandifolia distributions, F. grandifolia offers a classic case study for biogeographers, foresters, and palaeoecologists interested in understanding processes governing species range limits. Location Wisconsin and Michigan, USA. Taxon Fagus grandifolia. Methods This study combines historical datasets of F. grandifolia from the Public Land Survey, environmental covariates from soil maps and historical climate data, three spatial scenarios of dispersal limitation, and five species distribution models (SDMs). We test dispersal limitation and environmental filtering hypotheses by assessing SDM transferability between core and edge populations, measuring the importance of dispersal and environmental predictors, and using a residual autocovariate model to test for spatial processes not represented by these predictors. Results Fagus grandifolia presence was best predicted by total snowfall in Michigan and by dispersal, summer precipitation, and potential evapotranspiration (PET) in Wisconsin. Following the addition of dispersal as a predictor, most Wisconsin models improved and spatial autocorrelation effects largely disappeared. Transferability between core and edge populations was moderate to low. Main conclusions Both environmental and dispersal limitations appear to govern the western range limit of F. grandifolia. Species–environment relationships differ between range-edge and core populations, suggesting either stronger environmental filtering at the range edge or fine-scale, spatially varying interactions between environmental factors governing moisture availability in core populations. Although lakes, like Lake Michigan, both moderate regional climates and act as dispersal barriers, these effects can be disentangled through the joint analysis of SDMs and historic observational datasets.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sühring2019,

title = {Trade-Offs in Flux Disaggregation: A Large-Eddy Simulation Study},

author = {Matthias Sühring and Stefan Metzger and Ke Xu and Dave Durden and Ankur Desai},

url = {https://doi.org/10.1007/s10546-018-0387-x},

doi = {10.1007/s10546-018-0387-x},

issn = {1573-1472},

year  = {2019},

date = {2019-01-15},

journal = {Boundary-Layer Meteorology},

volume = {170},

number = {1},

pages = {69-93},

abstract = {Airborne flux measurements allow us to quantify the surface--atmosphere exchange over heterogeneous land surfaces. While often applied to regional-scale fluxes, it is also possible to infer component fluxes emanating from different surface patches from the measurement via disaggregation strategies. Here, we emulate flux disaggregation strategies by conducting an ensemble of virtual flight measurements within a set of large-eddy simulations over idealized surface heterogeneities and under different flow regimes. The resulting patch surface fluxes are compared with the prescribed patch surface fluxes in the simulation. To calculate fluxes along the flight legs, we apply traditional eddy-covariance and space--frequency (wavelet) methods. We show that the patch fluxes are captured best with the space--frequency method, where the disaggregation error is almost invariant of the segment length. For the eddy-covariance method, however, the error strongly depends on the segment length, with largest random and systematic errors for shorter segments. Furthermore, we determine a trade-off between a permissible disaggregation error and a sufficient resolution of the heterogeneous surface signals. Among our set-ups, an optimal segment length is determined to be 3--4 km for the eddy-covariance method, while with the space--frequency method even shorter segment lengths of a few hundreds of metres can be chosen, which enables sufficient isolation of signals from surface patches and the resolution of small-scale surface heterogeneity.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sühring2019b,

title = {Trade-Offs in Flux Disaggregation: A Large-Eddy Simulation Study},

author = {Matthias Sühring and Stefan Metzger and Ke Xu and Dave Durden and Ankur Desai},

url = {https://doi.org/10.1007/s10546-018-0387-x},

doi = {10.1007/s10546-018-0387-x},

issn = {1573-1472},

year  = {2019},

date = {2019-01-15},

journal = {Boundary-Layer Meteorology},

volume = {170},

number = {1},

pages = {69-93},

abstract = {Airborne flux measurements allow us to quantify the surface--atmosphere exchange over heterogeneous land surfaces. While often applied to regional-scale fluxes, it is also possible to infer component fluxes emanating from different surface patches from the measurement via disaggregation strategies. Here, we emulate flux disaggregation strategies by conducting an ensemble of virtual flight measurements within a set of large-eddy simulations over idealized surface heterogeneities and under different flow regimes. The resulting patch surface fluxes are compared with the prescribed patch surface fluxes in the simulation. To calculate fluxes along the flight legs, we apply traditional eddy-covariance and space--frequency (wavelet) methods. We show that the patch fluxes are captured best with the space--frequency method, where the disaggregation error is almost invariant of the segment length. For the eddy-covariance method, however, the error strongly depends on the segment length, with largest random and systematic errors for shorter segments. Furthermore, we determine a trade-off between a permissible disaggregation error and a sufficient resolution of the heterogeneous surface signals. Among our set-ups, an optimal segment length is determined to be 3--4 km for the eddy-covariance method, while with the space--frequency method even shorter segment lengths of a few hundreds of metres can be chosen, which enables sufficient isolation of signals from surface patches and the resolution of small-scale surface heterogeneity.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{tc-13-113-2019,

title = {Past and future interannual variability in Arctic sea ice in coupled climate models},

author = {J R Mioduszewski and S Vavrus and M Wang and M Holland and L Landrum},

url = {https://tc.copernicus.org/articles/13/113/2019/},

doi = {10.5194/tc-13-113-2019},

year  = {2019},

date = {2019-01-14},

journal = {The Cryosphere},

volume = {13},

number = {1},

pages = {113-124},

keywords = {},

pubstate = {published},

tppubtype = {article}

}