2020
|
Helbig, Manuel; Waddington, James M; Alekseychik, Pavel; Amiro, Brian; Aurela, Mika; Barr, Alan G; Black, Andrew T; Carey, Sean K; Chen, Jiquan; Chi, Jinshu; Desai, Ankur R; Dunn, Allison; Euskirchen, Eugenie S; Flanagan, Lawrence B; Friborg, Thomas; Garneau, Michelle; Grelle, Achim; Harder, Silvie; Heliasz, Michal; Humphreys, Elyn R; Ikawa, Hiroki; Isabelle, Pierre-Erik; Iwata, Hiroki; Jassal, Rachhpal; Korkiakoski, Mika; Kurbatova, Juliya; Kutzbach, Lars; Lapshina, Elena; Lindroth, Anders; Löfvenius, Mikaell Ottosson; Lohila, Annalea; Mammarella, Ivan; Marsh, Philip; Moore, Paul A; Maximov, Trofim; Nadeau, Daniel F; Nicholls, Erin M; Nilsson, Mats B; Ohta, Takeshi; Peichl, Matthias; Petrone, Richard M; Prokushkin, Anatoly; Quinton, William L; Roulet, Nigel; Runkle, Benjamin R K; Sonnentag, Oliver; Strachan, Ian B; Taillardat, Pierre; Tuittila, Eeva-Stiina; Tuovinen, Juha-Pekka; Turner, Jessica; Ueyama, Masahito; Varlagin, Andrej; Vesala, Timo; Wilmking, Martin; Zyrianov, Vyacheslav; Schulze, Christopher: The biophysical climate mitigation potential of boreal peatlands during the growing season. In: Environmental Research Letters, vol. 15, no. 10, pp. 104004, 2020. @article{Helbig_2020,
title = {The biophysical climate mitigation potential of boreal peatlands during the growing season},
author = {Manuel Helbig and James M Waddington and Pavel Alekseychik and Brian Amiro and Mika Aurela and Alan G Barr and Andrew T Black and Sean K Carey and Jiquan Chen and Jinshu Chi and Ankur R Desai and Allison Dunn and Eugenie S Euskirchen and Lawrence B Flanagan and Thomas Friborg and Michelle Garneau and Achim Grelle and Silvie Harder and Michal Heliasz and Elyn R Humphreys and Hiroki Ikawa and Pierre-Erik Isabelle and Hiroki Iwata and Rachhpal Jassal and Mika Korkiakoski and Juliya Kurbatova and Lars Kutzbach and Elena Lapshina and Anders Lindroth and Mikaell Ottosson Löfvenius and Annalea Lohila and Ivan Mammarella and Philip Marsh and Paul A Moore and Trofim Maximov and Daniel F Nadeau and Erin M Nicholls and Mats B Nilsson and Takeshi Ohta and Matthias Peichl and Richard M Petrone and Anatoly Prokushkin and William L Quinton and Nigel Roulet and Benjamin R K Runkle and Oliver Sonnentag and Ian B Strachan and Pierre Taillardat and Eeva-Stiina Tuittila and Juha-Pekka Tuovinen and Jessica Turner and Masahito Ueyama and Andrej Varlagin and Timo Vesala and Martin Wilmking and Vyacheslav Zyrianov and Christopher Schulze},
url = {https://doi.org/10.1088/1748-9326/abab34},
doi = {10.1088/1748-9326/abab34},
year = {2020},
date = {2020-10-01},
journal = {Environmental Research Letters},
volume = {15},
number = {10},
pages = {104004},
publisher = {IOP Publishing},
abstract = {Peatlands and forests cover large areas of the boreal biome and are critical for global climate regulation. They also regulate regional climate through heat and water vapour exchange with the atmosphere. Understanding how land-atmosphere interactions in peatlands differ from forests may therefore be crucial for modelling boreal climate system dynamics and for assessing climate benefits of peatland conservation and restoration. To assess the biophysical impacts of peatlands and forests on peak growing season air temperature and humidity, we analysed surface energy fluxes and albedo from 35 peatlands and 37 evergreen needleleaf forests—the dominant boreal forest type—and simulated air temperature and vapour pressure deficit (VPD) over hypothetical homogeneous peatland and forest landscapes. We ran an evapotranspiration model using land surface parameters derived from energy flux observations and coupled an analytical solution for the surface energy balance to an atmospheric boundary layer (ABL) model. We found that peatlands, compared to forests, are characterized by higher growing season albedo, lower aerodynamic conductance, and higher surface conductance for an equivalent VPD. This combination of peatland surface properties results in a ∼20% decrease in afternoon ABL height, a cooling (from 1.7 to 2.5 °C) in afternoon air temperatures, and a decrease in afternoon VPD (from 0.4 to 0.7 kPa) for peatland landscapes compared to forest landscapes. These biophysical climate impacts of peatlands are most pronounced at lower latitudes (∼45°N) and decrease toward the northern limit of the boreal biome (∼70°N). Thus, boreal peatlands have the potential to mitigate the effect of regional climate warming during the growing season. The biophysical climate mitigation potential of peatlands needs to be accounted for when projecting the future climate of the boreal biome, when assessing the climate benefits of conserving pristine boreal peatlands, and when restoring peatlands that have experienced peatland drainage and mining.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Peatlands and forests cover large areas of the boreal biome and are critical for global climate regulation. They also regulate regional climate through heat and water vapour exchange with the atmosphere. Understanding how land-atmosphere interactions in peatlands differ from forests may therefore be crucial for modelling boreal climate system dynamics and for assessing climate benefits of peatland conservation and restoration. To assess the biophysical impacts of peatlands and forests on peak growing season air temperature and humidity, we analysed surface energy fluxes and albedo from 35 peatlands and 37 evergreen needleleaf forests—the dominant boreal forest type—and simulated air temperature and vapour pressure deficit (VPD) over hypothetical homogeneous peatland and forest landscapes. We ran an evapotranspiration model using land surface parameters derived from energy flux observations and coupled an analytical solution for the surface energy balance to an atmospheric boundary layer (ABL) model. We found that peatlands, compared to forests, are characterized by higher growing season albedo, lower aerodynamic conductance, and higher surface conductance for an equivalent VPD. This combination of peatland surface properties results in a ∼20% decrease in afternoon ABL height, a cooling (from 1.7 to 2.5 °C) in afternoon air temperatures, and a decrease in afternoon VPD (from 0.4 to 0.7 kPa) for peatland landscapes compared to forest landscapes. These biophysical climate impacts of peatlands are most pronounced at lower latitudes (∼45°N) and decrease toward the northern limit of the boreal biome (∼70°N). Thus, boreal peatlands have the potential to mitigate the effect of regional climate warming during the growing season. The biophysical climate mitigation potential of peatlands needs to be accounted for when projecting the future climate of the boreal biome, when assessing the climate benefits of conserving pristine boreal peatlands, and when restoring peatlands that have experienced peatland drainage and mining. |
Alexander, Sarah; Atsbeha, Ezana; Negatu, Selam; Kirksey, Kristen; Brossard, Dominique; Holzer, Elizabeth; Block, Paul: Development of an interdisciplinary, multi-method approach to seasonal climate forecast communication at the local scale. In: Climatic Change, vol. 162, no. 4, pp. 2021-2042, 2020, ISSN: 1573-1480. @article{Alexander2020,
title = {Development of an interdisciplinary, multi-method approach to seasonal climate forecast communication at the local scale},
author = {Sarah Alexander and Ezana Atsbeha and Selam Negatu and Kristen Kirksey and Dominique Brossard and Elizabeth Holzer and Paul Block},
url = {https://doi.org/10.1007/s10584-020-02845-9},
doi = {10.1007/s10584-020-02845-9},
issn = {1573-1480},
year = {2020},
date = {2020-10-01},
journal = {Climatic Change},
volume = {162},
number = {4},
pages = {2021-2042},
abstract = {Bridging the gap between seasonal climate forecast development and science communication best practice is a critical step towards the integration of climate information into decision-making practices for enhanced community resilience to climate variability. Recent efforts in the physical sciences have focused on the development of seasonal climate forecasts, with increased emphasis on tailoring this information to user needs at the local scale. Advances in science communication have progressed understandings of how to leverage subjective decision-making processes and trust to communicate risky, probabilistic information. Yet, seasonal climate forecasts remain underutilized in local decision-making, due to challenging divides between the physical and social sciences and the lack of an approach that combines expert knowledge across disciplines. We outline an interdisciplinary, multi-method approach to communicate local-scale predictive information by advancing a co-produced ``package'' that pairs a highly visual bulletin with public engagement sessions, both developed with direct user-developer engagement, leveraging existing networks and novel inclusion of uncertainty through locally relevant analogies to enhance the understanding of probabilistic information. Systematic observations revealed some level of understanding among the target audience, yet identified major processes of confusion that inhibit forecast utility. Probabilistic predictions communicated in reference to ``normal'' years proved to be an unintelligible comparison for individuals, given preferences for certainty in interpreting risk-related information. Our approach addresses key gaps in the literature and serves as a framework for bridging the disconnect between forecast development and science communication to advance communication and integration of climate predictions for community benefit.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bridging the gap between seasonal climate forecast development and science communication best practice is a critical step towards the integration of climate information into decision-making practices for enhanced community resilience to climate variability. Recent efforts in the physical sciences have focused on the development of seasonal climate forecasts, with increased emphasis on tailoring this information to user needs at the local scale. Advances in science communication have progressed understandings of how to leverage subjective decision-making processes and trust to communicate risky, probabilistic information. Yet, seasonal climate forecasts remain underutilized in local decision-making, due to challenging divides between the physical and social sciences and the lack of an approach that combines expert knowledge across disciplines. We outline an interdisciplinary, multi-method approach to communicate local-scale predictive information by advancing a co-produced ``package'' that pairs a highly visual bulletin with public engagement sessions, both developed with direct user-developer engagement, leveraging existing networks and novel inclusion of uncertainty through locally relevant analogies to enhance the understanding of probabilistic information. Systematic observations revealed some level of understanding among the target audience, yet identified major processes of confusion that inhibit forecast utility. Probabilistic predictions communicated in reference to ``normal'' years proved to be an unintelligible comparison for individuals, given preferences for certainty in interpreting risk-related information. Our approach addresses key gaps in the literature and serves as a framework for bridging the disconnect between forecast development and science communication to advance communication and integration of climate predictions for community benefit. |
Francis, Jennifer Ann; Skific, Natasa; Vavrus, Stephen J: Increased persistence of large-scale circulation regimes over Asia in the era of amplified Arctic warming, past and future. In: Scientific Reports, vol. 10, no. 1, pp. 14953, 2020, ISSN: 2045-2322. @article{Francis2020,
title = {Increased persistence of large-scale circulation regimes over Asia in the era of amplified Arctic warming, past and future},
author = {Jennifer Ann Francis and Natasa Skific and Stephen J Vavrus},
url = {https://www.nature.com/articles/s41598-020-71945-4},
doi = {10.1038/s41598-020-71945-4},
issn = {2045-2322},
year = {2020},
date = {2020-09-11},
journal = {Scientific Reports},
volume = {10},
number = {1},
pages = {14953},
abstract = {Extreme weather events in Asia have been occurring with increasing frequency as the globe warms in response to rising concentrations of greenhouse gases. Many of these events arise from weather regimes that persist over a region for days or even weeks, resulting in disruptive heatwaves, droughts, flooding, snowfalls, and cold spells. We investigate changes in the persistence of large-scale weather systems through a pattern-recognition approach based on daily 500 hPa geopotential height anomalies over the Asian continent. By tracking consecutive days that the atmosphere resides in a particular pattern, we identify long-duration events (LDEs), defined as lasting longer than three days, and measure their frequency of occurrence over time in each pattern. We find that regimes featuring positive height anomalies in high latitudes are occurring more often as the Arctic warms faster than mid-latitudes, both in the recent past and in model projections for the twenty-first century assuming unabated greenhouse gas emissions. The increased dominance of these patterns corresponds to a higher likelihood of LDEs, suggesting that persistent weather conditions will occur more frequently. By mapping observed temperature and precipitation extremes onto each atmospheric regime, we gain insight into the types of disruptive weather events that will become more prevalent as particular patterns become more common.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Extreme weather events in Asia have been occurring with increasing frequency as the globe warms in response to rising concentrations of greenhouse gases. Many of these events arise from weather regimes that persist over a region for days or even weeks, resulting in disruptive heatwaves, droughts, flooding, snowfalls, and cold spells. We investigate changes in the persistence of large-scale weather systems through a pattern-recognition approach based on daily 500 hPa geopotential height anomalies over the Asian continent. By tracking consecutive days that the atmosphere resides in a particular pattern, we identify long-duration events (LDEs), defined as lasting longer than three days, and measure their frequency of occurrence over time in each pattern. We find that regimes featuring positive height anomalies in high latitudes are occurring more often as the Arctic warms faster than mid-latitudes, both in the recent past and in model projections for the twenty-first century assuming unabated greenhouse gas emissions. The increased dominance of these patterns corresponds to a higher likelihood of LDEs, suggesting that persistent weather conditions will occur more frequently. By mapping observed temperature and precipitation extremes onto each atmospheric regime, we gain insight into the types of disruptive weather events that will become more prevalent as particular patterns become more common. |
Gari, Yared; Block, Paul; Assefa, Getachew; Mekonnen, Muluneh; Tilahun, Seifu A: Quantifying the United Nations’ Watercourse Convention Indicators to Inform Equitable Transboundary River Sharing: Application to the Nile River Basin. In: Water, vol. 12, no. 9, 2020, ISSN: 2073-4441. @article{w12092499,
title = {Quantifying the United Nations’ Watercourse Convention Indicators to Inform Equitable Transboundary River Sharing: Application to the Nile River Basin},
author = {Yared Gari and Paul Block and Getachew Assefa and Muluneh Mekonnen and Seifu A Tilahun},
url = {https://www.mdpi.com/2073-4441/12/9/2499},
doi = {10.3390/w12092499},
issn = {2073-4441},
year = {2020},
date = {2020-09-08},
journal = {Water},
volume = {12},
number = {9},
abstract = {East African riparian countries have debated sharing Nile River water for centuries. To define a reasonable allocation of water to each country, the United Nations’ Watercourse Convention could be a key legal instrument. However, its applicability has been questioned given its overly generalized guidance and non-quantifiable factors. This study identified and evaluated appropriate indicators that best describe reasonable and equitable principles and factors detailed under Article 6 of the convention in order to allocate Nile River water among the states. Potential indicators (n = 75) were defined based on multiple sources that can address conflicting interests specific to this basin context. A questionnaire based on these indicators was developed and distributed to 215 prominent experts from five professional groups on five continents. To analyze the presence of agreements or disagreements within and outside of the basin, as well as differences across expert groups, a k-mean clustering analysis and statistical tests (ANOVA and t-test) were employed. The results imply agreement on 75% of the proposed indicators by all experts across all continents. However, a significant difference in identifying the importance and relevance of many indicators between experts from Egypt and other countries was evident. This study thus demonstrates how the UN watercourse convention principles can be quantified and applied to transboundary water allocation, and ideally lead to informed discourse between basin countries in conflict.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
East African riparian countries have debated sharing Nile River water for centuries. To define a reasonable allocation of water to each country, the United Nations’ Watercourse Convention could be a key legal instrument. However, its applicability has been questioned given its overly generalized guidance and non-quantifiable factors. This study identified and evaluated appropriate indicators that best describe reasonable and equitable principles and factors detailed under Article 6 of the convention in order to allocate Nile River water among the states. Potential indicators (n = 75) were defined based on multiple sources that can address conflicting interests specific to this basin context. A questionnaire based on these indicators was developed and distributed to 215 prominent experts from five professional groups on five continents. To analyze the presence of agreements or disagreements within and outside of the basin, as well as differences across expert groups, a k-mean clustering analysis and statistical tests (ANOVA and t-test) were employed. The results imply agreement on 75% of the proposed indicators by all experts across all continents. However, a significant difference in identifying the importance and relevance of many indicators between experts from Egypt and other countries was evident. This study thus demonstrates how the UN watercourse convention principles can be quantified and applied to transboundary water allocation, and ideally lead to informed discourse between basin countries in conflict. |
Burdun, Iuliia; Bechtold, Michel; Sagris, Valentina; Lohila, Annalea; Humphreys, Elyn; Desai, Ankur R; Nilsson, Mats B; Lannoy, Gabrielle De; Mander, Ülo: Satellite Determination of Peatland Water Table Temporal Dynamics by Localizing Representative Pixels of A SWIR-Based Moisture Index. In: Remote Sensing, vol. 12, no. 18, pp. 2936, 2020, ISSN: 2072-4292. @article{Burdun_2020,
title = {Satellite Determination of Peatland Water Table Temporal Dynamics by Localizing Representative Pixels of A SWIR-Based Moisture Index},
author = {Iuliia Burdun and Michel Bechtold and Valentina Sagris and Annalea Lohila and Elyn Humphreys and Ankur R Desai and Mats B Nilsson and Gabrielle De Lannoy and Ülo Mander},
url = {http://dx.doi.org/10.3390/rs12182936},
doi = {10.3390/rs12182936},
issn = {2072-4292},
year = {2020},
date = {2020-09-01},
journal = {Remote Sensing},
volume = {12},
number = {18},
pages = {2936},
publisher = {MDPI AG},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Lala, Jonathan; Tilahun, Seifu; Block, Paul: Predicting Rainy Season Onset in the Ethiopian Highlands for Agricultural Planning. In: Journal of Hydrometeorology, vol. 21, no. 7, pp. 1675-1688, 2020. @article{Lala01Jul.2020,
title = {Predicting Rainy Season Onset in the Ethiopian Highlands for Agricultural Planning},
author = {Jonathan Lala and Seifu Tilahun and Paul Block},
url = {https://doi.org/10.1175/JHM-D-20-0058.1},
doi = {10.1175/JHM-D-20-0058.1},
year = {2020},
date = {2020-07-21},
journal = {Journal of Hydrometeorology},
volume = {21},
number = {7},
pages = {1675-1688},
publisher = {American Meteorological Society},
address = {Boston MA, USA},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Pastorello, Gilberto; Trotta, Carlo; Canfora, Eleonora; Chu, Housen; Christianson, Danielle; et al,; (>200 co-authors) including,; Desai, Ankur; Reed, David; Thom, Jonathan: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. In: Scientific Data, vol. 7, no. 1, pp. 225, 2020, ISSN: 2052-4463. @article{Pastorello2020,
title = {The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data},
author = {Gilberto Pastorello and Carlo Trotta and Eleonora Canfora and Housen Chu and Danielle Christianson and et al and (>200 co-authors) including and Ankur Desai and David Reed and Jonathan Thom},
url = {https://www.nature.com/articles/s41597-020-0534-3},
doi = {10.1038/s41597-020-0534-3},
issn = {2052-4463},
year = {2020},
date = {2020-07-09},
journal = {Scientific Data},
volume = {7},
number = {1},
pages = {225},
abstract = {The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible. |
Xu, Ke; Sühring, Matthias; Metzger, Stefan; Durden, David; Desai, Ankur R: Can Data Mining Help Eddy Covariance See the Landscape? A Large-Eddy Simulation Study. In: Boundary-Layer Meteorology, vol. 176, no. 1, pp. 85-103, 2020, ISSN: 1573-1472. @article{Xu2020,
title = {Can Data Mining Help Eddy Covariance See the Landscape? A Large-Eddy Simulation Study},
author = {Ke Xu and Matthias Sühring and Stefan Metzger and David Durden and Ankur R Desai},
url = {https://link.springer.com/article/10.1007%2Fs10546-020-00513-0},
doi = {10.1007/s10546-020-00513-0},
issn = {1573-1472},
year = {2020},
date = {2020-07-01},
journal = {Boundary-Layer Meteorology},
volume = {176},
number = {1},
pages = {85-103},
abstract = {Eddy-covariance fluxes serve as an essential benchmark for Earth system models and remote sensing data. However, two challenges prevent model-data intercomparisons from fully utilizing eddy-covariance fluxes. The first challenge stems from the differing and variable spatial representativeness of the eddy-covariance measurements, or footprint bias and transience. The second originates from the phenomenon of a non-closed energy balance using eddy-covariance measurements, hypothesized to result from unaccounted mesoscale flows or under-sampling of hot spots by flux towers, among others. Previous studies have suggested that these two problems can be mitigated by either building multiple towers or by applying space--time rectification approaches, such as the environmental response function (ERF) approach. Here we ask: (1) How many eddy-flux towers do we need to sufficiently rectify location bias, close the energy budget, and sample the regional domain? (2) Can an advanced space--time rectification approach reduce the tower density, while still adequately sampling the regional flux domain? Furthermore, (3) How accurately can the ERF approach retrieve the surface-flux variation? To answer these questions, we used data from a large-eddy simulation of atmospheric flows above a heterogeneous surface as captured by an ensemble of virtual tower measurements. We calculated eddy-covariance fluxes by spatial and spatio-temporal methods. The spatial eddy-covariance method captured 89% of the prescribed total surface energy flux with about one tower per 15 km2, while the spatio-temporal method required only one tower per 40 km2 to capture 95% of surface energy. To capture 97% of energy, applying the ERF approach further reduced the required tower density to one tower per 200 km2, as a result of space--time rectification and incorporating mesoscale flows. This approach also enabled retrieving the surface spatial variation of the sensible heat flux. The results provide a reference for informing and designing future observation systems based on flux tower clusters, and scale-aware data products.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Eddy-covariance fluxes serve as an essential benchmark for Earth system models and remote sensing data. However, two challenges prevent model-data intercomparisons from fully utilizing eddy-covariance fluxes. The first challenge stems from the differing and variable spatial representativeness of the eddy-covariance measurements, or footprint bias and transience. The second originates from the phenomenon of a non-closed energy balance using eddy-covariance measurements, hypothesized to result from unaccounted mesoscale flows or under-sampling of hot spots by flux towers, among others. Previous studies have suggested that these two problems can be mitigated by either building multiple towers or by applying space--time rectification approaches, such as the environmental response function (ERF) approach. Here we ask: (1) How many eddy-flux towers do we need to sufficiently rectify location bias, close the energy budget, and sample the regional domain? (2) Can an advanced space--time rectification approach reduce the tower density, while still adequately sampling the regional flux domain? Furthermore, (3) How accurately can the ERF approach retrieve the surface-flux variation? To answer these questions, we used data from a large-eddy simulation of atmospheric flows above a heterogeneous surface as captured by an ensemble of virtual tower measurements. We calculated eddy-covariance fluxes by spatial and spatio-temporal methods. The spatial eddy-covariance method captured 89% of the prescribed total surface energy flux with about one tower per 15 km2, while the spatio-temporal method required only one tower per 40 km2 to capture 95% of surface energy. To capture 97% of energy, applying the ERF approach further reduced the required tower density to one tower per 200 km2, as a result of space--time rectification and incorporating mesoscale flows. This approach also enabled retrieving the surface spatial variation of the sensible heat flux. The results provide a reference for informing and designing future observation systems based on flux tower clusters, and scale-aware data products. |
Yu, Yan; Kalashnikova, Olga V; Garay, Michael J; Lee, Huikyo; Notaro, Michael; Campbell, James R; Marquis, Jared; Ginoux, Paul; Okin, Gregory S: Disproving the Bodélé Depression as the Primary Source of Dust Fertilizing the Amazon Rainforest. In: Geophysical Research Letters, vol. 47, no. 13, pp. e2020GL088020, 2020, (e2020GL088020 2020GL088020). @article{https://doi.org/10.1029/2020GL088020,
title = {Disproving the Bodélé Depression as the Primary Source of Dust Fertilizing the Amazon Rainforest},
author = {Yan Yu and Olga V Kalashnikova and Michael J Garay and Huikyo Lee and Michael Notaro and James R Campbell and Jared Marquis and Paul Ginoux and Gregory S Okin},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020GL088020},
doi = {https://doi.org/10.1029/2020GL088020},
year = {2020},
date = {2020-06-20},
journal = {Geophysical Research Letters},
volume = {47},
number = {13},
pages = {e2020GL088020},
abstract = {Abstract Motivated by the ongoing debates about the relative contribution of specific North African dust sources to the transatlantic dust transport to the Amazon Basin, the current study integrates a suite of satellite observations into a novel trajectory analysis framework to investigate dust transport from the leading two North African dust sources, namely, the Bodélé depression and El Djouf. In particular, this approach provides observation-constrained quantification of the dust's dry and wet deposition along its transport pathways and is validated against multiple satellite observations. The current large ensemble trajectory simulations identify favorable transport pathways from the El Djouf across the Atlantic Ocean with respect to seasonal rain belts. The limited potential for long-range transport of dust from the Bodélé depression is attributed to the currently identified extensive near-source dust removal primarily by dry and wet deposition during boreal winter and summer, respectively.},
note = {e2020GL088020 2020GL088020},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract Motivated by the ongoing debates about the relative contribution of specific North African dust sources to the transatlantic dust transport to the Amazon Basin, the current study integrates a suite of satellite observations into a novel trajectory analysis framework to investigate dust transport from the leading two North African dust sources, namely, the Bodélé depression and El Djouf. In particular, this approach provides observation-constrained quantification of the dust's dry and wet deposition along its transport pathways and is validated against multiple satellite observations. The current large ensemble trajectory simulations identify favorable transport pathways from the El Djouf across the Atlantic Ocean with respect to seasonal rain belts. The limited potential for long-range transport of dust from the Bodélé depression is attributed to the currently identified extensive near-source dust removal primarily by dry and wet deposition during boreal winter and summer, respectively. |
Vavrus, Stephen J; He, Feng; Kutzbach, John E; Ruddiman, William F: Rapid neoglaciation on Ellesmere Island promoted by enhanced summer snowfall in a transient climate model simulation of the middle-late-Holocene. In: The Holocene, vol. 30, no. 10, pp. 1474-1480, 2020. @article{doi:10.1177/0959683620932967,
title = {Rapid neoglaciation on Ellesmere Island promoted by enhanced summer snowfall in a transient climate model simulation of the middle-late-Holocene},
author = {Stephen J Vavrus and Feng He and John E Kutzbach and William F Ruddiman},
url = {https://doi.org/10.1177/0959683620932967},
doi = {10.1177/0959683620932967},
year = {2020},
date = {2020-06-12},
journal = {The Holocene},
volume = {30},
number = {10},
pages = {1474-1480},
abstract = {Arctic neoglaciation following the Holocene Thermal Maximum is an important feature of late-Holocene climate. We investigated this phenomenon using a transient 6000-year simulation with the CESM-CAM5 climate model driven by orbital forcing, greenhouse gas concentrations, and a land use reconstruction. During the first three millennia analyzed here (6–3 ka), mean Arctic snow depth increases, despite enhanced greenhouse forcing. Superimposed on this secular trend is a very abrupt increase in snow depth between 5 and 4.9 ka on Ellesmere Island and the Greenland coasts, in rough agreement with the timing of observed neoglaciation in the region. This transition is especially extreme on Ellesmere Island, where end-of-summer snow coverage jumps from nearly 0 to virtually 100% in 1 year, and snow depth increases to the model’s imposed maximum within 15 years. This climatic shift involves more than the Milankovitch-based expectation of cooler summers causing less snow melt. Coincident with the onset of the cold regime are two consecutive summers with heavy snowfall on Ellesmere Island that help to short-circuit the normal seasonal melt cycle. These heavy snow seasons are caused by synoptic-scale, cyclonic circulation anomalies over the Arctic Ocean and Canadian Archipelago, including an extremely positive phase of the Arctic Oscillation. Our study reveals that a climate model can produce sudden climatic transitions in this region prone to glacial inception and exceptional variability, due to a dynamic mechanism (more summer snowfall induced by an extreme circulation anomaly) that augments the traditional Milankovitch thermodynamic explanation of orbitally induced glacier development.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Arctic neoglaciation following the Holocene Thermal Maximum is an important feature of late-Holocene climate. We investigated this phenomenon using a transient 6000-year simulation with the CESM-CAM5 climate model driven by orbital forcing, greenhouse gas concentrations, and a land use reconstruction. During the first three millennia analyzed here (6–3 ka), mean Arctic snow depth increases, despite enhanced greenhouse forcing. Superimposed on this secular trend is a very abrupt increase in snow depth between 5 and 4.9 ka on Ellesmere Island and the Greenland coasts, in rough agreement with the timing of observed neoglaciation in the region. This transition is especially extreme on Ellesmere Island, where end-of-summer snow coverage jumps from nearly 0 to virtually 100% in 1 year, and snow depth increases to the model’s imposed maximum within 15 years. This climatic shift involves more than the Milankovitch-based expectation of cooler summers causing less snow melt. Coincident with the onset of the cold regime are two consecutive summers with heavy snowfall on Ellesmere Island that help to short-circuit the normal seasonal melt cycle. These heavy snow seasons are caused by synoptic-scale, cyclonic circulation anomalies over the Arctic Ocean and Canadian Archipelago, including an extremely positive phase of the Arctic Oscillation. Our study reveals that a climate model can produce sudden climatic transitions in this region prone to glacial inception and exceptional variability, due to a dynamic mechanism (more summer snowfall induced by an extreme circulation anomaly) that augments the traditional Milankovitch thermodynamic explanation of orbitally induced glacier development. |
Yu, Yan; Mao, Jiafu; Thornton, Peter E; Notaro, Michael; Wullschleger, Stan D; Shi, Xiaoying; Hoffman, Forrest M; Wang, Yaoping: Quantifying the drivers and predictability of seasonal changes in African fire. In: Nature Communications, vol. 11, no. 1, pp. 2893, 2020, ISSN: 2041-1723. @article{Yu2020,
title = {Quantifying the drivers and predictability of seasonal changes in African fire},
author = {Yan Yu and Jiafu Mao and Peter E Thornton and Michael Notaro and Stan D Wullschleger and Xiaoying Shi and Forrest M Hoffman and Yaoping Wang},
url = {https://doi.org/10.1038/s41467-020-16692-w},
doi = {10.1038/s41467-020-16692-w},
issn = {2041-1723},
year = {2020},
date = {2020-06-09},
journal = {Nature Communications},
volume = {11},
number = {1},
pages = {2893},
abstract = {Africa contains some of the most vulnerable ecosystems to fires. Successful seasonal prediction of fire activity over these fire-prone regions remains a challenge and relies heavily on in-depth understanding of various driving mechanisms underlying fire evolution. Here, we assess the seasonal environmental drivers and predictability of African fire using the analytical framework of Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) and machine learning techniques (MLTs). The impacts of sea-surface temperature, soil moisture, and leaf area index are quantified and found to dominate the fire seasonal variability by regulating regional burning condition and fuel supply. Compared with previously-identified atmospheric and socioeconomic predictors, these slowly evolving oceanic and terrestrial predictors are further identified to determine the seasonal predictability of fire activity in Africa. Our combined SGEFA-MLT approach achieves skillful prediction of African fire one month in advance and can be generalized to provide seasonal estimates of regional and global fire risk.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Africa contains some of the most vulnerable ecosystems to fires. Successful seasonal prediction of fire activity over these fire-prone regions remains a challenge and relies heavily on in-depth understanding of various driving mechanisms underlying fire evolution. Here, we assess the seasonal environmental drivers and predictability of African fire using the analytical framework of Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) and machine learning techniques (MLTs). The impacts of sea-surface temperature, soil moisture, and leaf area index are quantified and found to dominate the fire seasonal variability by regulating regional burning condition and fuel supply. Compared with previously-identified atmospheric and socioeconomic predictors, these slowly evolving oceanic and terrestrial predictors are further identified to determine the seasonal predictability of fire activity in Africa. Our combined SGEFA-MLT approach achieves skillful prediction of African fire one month in advance and can be generalized to provide seasonal estimates of regional and global fire risk. |
Notaro, Michael; Wang, Fuyao; Yu, Yan; Mao, Jiafu: Projected changes in the terrestrial and oceanic regulators of climate variability across sub-Saharan Africa. In: Climate Dynamics, vol. 55, no. 5, pp. 1031-1057, 2020, ISSN: 1432-0894. @article{Notaro2020,
title = {Projected changes in the terrestrial and oceanic regulators of climate variability across sub-Saharan Africa},
author = {Michael Notaro and Fuyao Wang and Yan Yu and Jiafu Mao},
url = {https://doi.org/10.1007/s00382-020-05308-0},
doi = {10.1007/s00382-020-05308-0},
issn = {1432-0894},
year = {2020},
date = {2020-06-03},
journal = {Climate Dynamics},
volume = {55},
number = {5},
pages = {1031-1057},
abstract = {Future changes in the sign and intensity of ocean--land--atmosphere interactions have been insufficiently studied, despite implications for regional climate change projections, extreme event statistics, and seasonal climate predictability. In response to this deficiency, the present study focuses on projected responses to the enhanced greenhouse effect in: (1) the mean state of the atmosphere and land surface; (2) oceanic and terrestrial drivers of sub-Saharan climate variability; and (3) total seasonal climate predictability of sub-Saharan Africa, a region known for its pronounced land--atmosphere coupling. Analysis focuses on output from 23 Earth System Models in the Coupled Model Intercomparison Project Phase Five for the late twentieth and twenty-first centuries. It is projected that the greatest warming across sub-Saharan Africa will occur over the Sahel, the monsoon season will become more persistent into late summer and autumn, short rains in the Horn of Africa (HOA) will intensify, and leaf area index will increase across the HOA. Stepwise Generalized Equilibrium Feedback Assessment, i.e. a multivariate statistical approach, is applied to the model output over sub-Saharan Africa in order to explore the oceanic and terrestrial drivers of regional climate. The models indicate that the study region's climate variability is dominated by oceanic drivers, with secondary contributions from soil moisture and very modest impacts from vegetation. Overall, the general model consensus of future projections indicates a concerning diminished seasonal predictability of sub-Saharan African regional climate based on key oceanic and terrestrial predictors and an elevated role of the land surface (associated with soil moisture anomalies) compared to oceanic drivers in regulating regional climate variability.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Future changes in the sign and intensity of ocean--land--atmosphere interactions have been insufficiently studied, despite implications for regional climate change projections, extreme event statistics, and seasonal climate predictability. In response to this deficiency, the present study focuses on projected responses to the enhanced greenhouse effect in: (1) the mean state of the atmosphere and land surface; (2) oceanic and terrestrial drivers of sub-Saharan climate variability; and (3) total seasonal climate predictability of sub-Saharan Africa, a region known for its pronounced land--atmosphere coupling. Analysis focuses on output from 23 Earth System Models in the Coupled Model Intercomparison Project Phase Five for the late twentieth and twenty-first centuries. It is projected that the greatest warming across sub-Saharan Africa will occur over the Sahel, the monsoon season will become more persistent into late summer and autumn, short rains in the Horn of Africa (HOA) will intensify, and leaf area index will increase across the HOA. Stepwise Generalized Equilibrium Feedback Assessment, i.e. a multivariate statistical approach, is applied to the model output over sub-Saharan Africa in order to explore the oceanic and terrestrial drivers of regional climate. The models indicate that the study region's climate variability is dominated by oceanic drivers, with secondary contributions from soil moisture and very modest impacts from vegetation. Overall, the general model consensus of future projections indicates a concerning diminished seasonal predictability of sub-Saharan African regional climate based on key oceanic and terrestrial predictors and an elevated role of the land surface (associated with soil moisture anomalies) compared to oceanic drivers in regulating regional climate variability. |
Helbig, Manuel; Waddington, James Michael; Alekseychik, Pavel; Amiro, Brian D; Aurela, Mika; Barr, Alan G; Black, Andrew T; Blanken, Peter D; Carey, Sean K; Chen, Jiquan; Chi, Jinshu; Desai, Ankur R; Dunn, Allison; Euskirchen, Eugenie S; Flanagan, Lawrence B; Forbrich, Inke; Friborg, Thomas; Grelle, Achim; Harder, Silvie; Heliasz, Michal; Humphreys, Elyn R; Ikawa, Hiroki; Isabelle, Pierre-Erik; Iwata, Hiroki; Jassal, Rachhpal; Korkiakoski, Mika; Kurbatova, Juliya; Kutzbach, Lars; Lindroth, Anders; Löfvenius, Mikaell Ottosson; Lohila, Annalea; Mammarella, Ivan; Marsh, Philip; Maximov, Trofim; Melton, Joe R; Moore, Paul A; Nadeau, Daniel F; Nicholls, Erin M; Nilsson, Mats B; Ohta, Takeshi; Peichl, Matthias; Petrone, Richard M; Petrov, Roman; Prokushkin, Anatoly; Quinton, William L; Reed, David E; Roulet, Nigel T; Runkle, Benjamin R K; Sonnentag, Oliver; Strachan, Ian B; Taillardat, Pierre; Tuittila, Eeva-Stiina; Tuovinen, Juha-Pekka; Turner, Jessica; Ueyama, Masahito; Varlagin, Andrej; Wilmking, Martin; Wofsy, Steven C; Zyrianov, Vyacheslav: Increasing contribution of peatlands to boreal evapotranspiration in a warming climate. In: Nature Climate Change, vol. 10, no. 6, pp. 555-560, 2020, ISSN: 1758-6798. @article{Helbig2020,
title = {Increasing contribution of peatlands to boreal evapotranspiration in a warming climate},
author = {Manuel Helbig and James Michael Waddington and Pavel Alekseychik and Brian D Amiro and Mika Aurela and Alan G Barr and Andrew T Black and Peter D Blanken and Sean K Carey and Jiquan Chen and Jinshu Chi and Ankur R Desai and Allison Dunn and Eugenie S Euskirchen and Lawrence B Flanagan and Inke Forbrich and Thomas Friborg and Achim Grelle and Silvie Harder and Michal Heliasz and Elyn R Humphreys and Hiroki Ikawa and Pierre-Erik Isabelle and Hiroki Iwata and Rachhpal Jassal and Mika Korkiakoski and Juliya Kurbatova and Lars Kutzbach and Anders Lindroth and Mikaell Ottosson Löfvenius and Annalea Lohila and Ivan Mammarella and Philip Marsh and Trofim Maximov and Joe R Melton and Paul A Moore and Daniel F Nadeau and Erin M Nicholls and Mats B Nilsson and Takeshi Ohta and Matthias Peichl and Richard M Petrone and Roman Petrov and Anatoly Prokushkin and William L Quinton and David E Reed and Nigel T Roulet and Benjamin R K Runkle and Oliver Sonnentag and Ian B Strachan and Pierre Taillardat and Eeva-Stiina Tuittila and Juha-Pekka Tuovinen and Jessica Turner and Masahito Ueyama and Andrej Varlagin and Martin Wilmking and Steven C Wofsy and Vyacheslav Zyrianov},
url = {https://www.nature.com/articles/s41558-020-0763-7},
doi = {10.1038/s41558-020-0763-7},
issn = {1758-6798},
year = {2020},
date = {2020-06-01},
journal = {Nature Climate Change},
volume = {10},
number = {6},
pages = {555-560},
abstract = {The response of evapotranspiration (ET) to warming is of critical importance to the water and carbon cycle of the boreal biome, a mosaic of land cover types dominated by forests and peatlands. The effect of warming-induced vapour pressure deficit (VPD) increases on boreal ET remains poorly understood because peatlands are not specifically represented as plant functional types in Earth system models. Here we show that peatland ET increases more than forest ET with increasing VPD using observations from 95 eddy covariance tower sites. At high VPD of more than 2kPa, peatland ET exceeds forest ET by up to 30%. Future (2091--2100) mid-growing season peatland ET is estimated to exceed forest ET by over 20% in about one-third of the boreal biome for RCP4.5 and about two-thirds for RCP8.5. Peatland-specific ET responses to VPD should therefore be included in Earth system models to avoid biases in water and carbon cycle projections.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The response of evapotranspiration (ET) to warming is of critical importance to the water and carbon cycle of the boreal biome, a mosaic of land cover types dominated by forests and peatlands. The effect of warming-induced vapour pressure deficit (VPD) increases on boreal ET remains poorly understood because peatlands are not specifically represented as plant functional types in Earth system models. Here we show that peatland ET increases more than forest ET with increasing VPD using observations from 95 eddy covariance tower sites. At high VPD of more than 2kPa, peatland ET exceeds forest ET by up to 30%. Future (2091--2100) mid-growing season peatland ET is estimated to exceed forest ET by over 20% in about one-third of the boreal biome for RCP4.5 and about two-thirds for RCP8.5. Peatland-specific ET responses to VPD should therefore be included in Earth system models to avoid biases in water and carbon cycle projections. |
Liu, Ying; Wu, Chaoyang; Sonnentag, Oliver; Desai, Ankur R; Wang, Jian: Using the red chromatic coordinate to characterize the phenology of forest canopy photosynthesis. In: Agricultural and Forest Meteorology, vol. 285-286, pp. 107910, 2020, ISSN: 0168-1923. @article{LIU2020107910,
title = {Using the red chromatic coordinate to characterize the phenology of forest canopy photosynthesis},
author = {Ying Liu and Chaoyang Wu and Oliver Sonnentag and Ankur R Desai and Jian Wang},
url = {https://www.sciencedirect.com/science/article/pii/S0168192320300125},
doi = {https://doi.org/10.1016/j.agrformet.2020.107910},
issn = {0168-1923},
year = {2020},
date = {2020-05-15},
journal = {Agricultural and Forest Meteorology},
volume = {285-286},
pages = {107910},
abstract = {Vegetation phenology has received increasing attention in climate change research. Near-surface sensing using digital repeat photography has proven to be useful for ecosystem-scale monitoring of vegetation phenology. However, our understanding of the link between phenological metrics derived from digital repeat photography and the phenology of forest canopy photosynthesis is still incomplete, especially for evergreen plant species. Using 49 site-years of digital images from the PhenoCam Network from eight evergreen needleleaf forest (ENF) and six deciduous broadleaf forest (DBF) sites in North America, we explored the potential of the green chromatic (GCC) and red chromatic coordinates (RCC) in tracking forest canopy photosynthesis by comparing camera-derived start- and end-of-growing season (SOS and EOS, respectively) with corresponding estimates derived from eddy covariance-derived daily gross primary productivity (GPP). We found that for DBF sites, both GCC and RCC performed comparable in capturing SOS and EOS. However, similar to earlier studies, GCC had limited potential in capturing GPP-based SOS or EOS for ENF sites. In contrast, we found RCC was a better predictor of both GPP-based SOS and EOS for ENF sites. Environmental and ecological explanations were both provided that phenological transitions derived from RCC were highly correlated with spring and autumn meteorological conditions, as well as having overall higher correlations with phenology based on LAI, a critical variable for describing canopy development. Our results demonstrate that RCC is an underappreciated metric for tracking vegetation phenology, especially for ENF sites where GCC failed to provide reliable estimates for GPP-based SOS or EOS. Our results improve confidence in using digital repeat photography to characterize the phenology of canopy photosynthesis across forest types.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Vegetation phenology has received increasing attention in climate change research. Near-surface sensing using digital repeat photography has proven to be useful for ecosystem-scale monitoring of vegetation phenology. However, our understanding of the link between phenological metrics derived from digital repeat photography and the phenology of forest canopy photosynthesis is still incomplete, especially for evergreen plant species. Using 49 site-years of digital images from the PhenoCam Network from eight evergreen needleleaf forest (ENF) and six deciduous broadleaf forest (DBF) sites in North America, we explored the potential of the green chromatic (GCC) and red chromatic coordinates (RCC) in tracking forest canopy photosynthesis by comparing camera-derived start- and end-of-growing season (SOS and EOS, respectively) with corresponding estimates derived from eddy covariance-derived daily gross primary productivity (GPP). We found that for DBF sites, both GCC and RCC performed comparable in capturing SOS and EOS. However, similar to earlier studies, GCC had limited potential in capturing GPP-based SOS or EOS for ENF sites. In contrast, we found RCC was a better predictor of both GPP-based SOS and EOS for ENF sites. Environmental and ecological explanations were both provided that phenological transitions derived from RCC were highly correlated with spring and autumn meteorological conditions, as well as having overall higher correlations with phenology based on LAI, a critical variable for describing canopy development. Our results demonstrate that RCC is an underappreciated metric for tracking vegetation phenology, especially for ENF sites where GCC failed to provide reliable estimates for GPP-based SOS or EOS. Our results improve confidence in using digital repeat photography to characterize the phenology of canopy photosynthesis across forest types. |
Trachsel, Mathias; Dawson, Andria; Paciorek, Christopher J; Williams, John W; McLachlan, Jason S; Cogbill, Charles V; Foster, David R; Goring, Simon J; Jackson, Stephen T; and, Wyatt W Oswald: Comparison of settlement-era vegetation reconstructions for STEPPS and REVEALS pollen–vegetation models in the northeastern United States. In: Quaternary Research, vol. 95, pp. 23-42, 2020. @article{Trachsel2020,
title = {Comparison of settlement-era vegetation reconstructions for STEPPS and REVEALS pollen–vegetation models in the northeastern United States},
author = {Mathias Trachsel and Andria Dawson and Christopher J Paciorek and John W Williams and Jason S McLachlan and Charles V Cogbill and David R Foster and Simon J Goring and Stephen T Jackson and Wyatt W Oswald and et al.},
url = {https://www.cambridge.org/core/journals/quaternary-research/article/comparison-of-settlementera-vegetation-reconstructions-for-stepps-and-reveals-pollenvegetation-models-in-the-northeastern-united-states/FC6AD4D4A50AE5B00ABE4F4B0CA8FC71},
doi = {10.1017/qua.2019.81},
year = {2020},
date = {2020-04-07},
journal = {Quaternary Research},
volume = {95},
pages = {23-42},
abstract = {Reconstructions of prehistoric vegetation composition help establish natural baselines, variability, and trajectories of forest dynamics before and during the emergence of intensive anthropogenic land use. Pollen–vegetation models (PVMs) enable such reconstructions from fossil pollen assemblages using process-based representations of taxon-specific pollen production and dispersal. However, several PVMs and variants now exist, and the sensitivity of vegetation inferences to PVM selection, variant, and calibration domain is poorly understood. Here, we compare the reconstructions, parameter estimates, and structure of a Bayesian hierarchical PVM, STEPPS, both to observations and to REVEALS, a widely used PVM, for the pre–Euro-American settlement-era vegetation in the northeastern United States (NEUS). We also compare NEUS-based STEPPS parameter estimates to those for the upper midwestern United States (UMW). Both PVMs predict the observed macroscale patterns of vegetation composition in the NEUS; however, reconstructions of minor taxa are less accurate and predictions for some taxa differ between PVMs. These differences can be attributed to intermodel differences in structure and parameter estimates. Estimates of pollen productivity from STEPPS broadly agree with estimates produced for use in REVEALS, while comparison between pollen dispersal parameter estimates shows no significant relationship. STEPPS parameter estimates are similar between the UMW and NEUS, suggesting that STEPPS parameter estimates are transferable between floristically similar regions and scales.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Reconstructions of prehistoric vegetation composition help establish natural baselines, variability, and trajectories of forest dynamics before and during the emergence of intensive anthropogenic land use. Pollen–vegetation models (PVMs) enable such reconstructions from fossil pollen assemblages using process-based representations of taxon-specific pollen production and dispersal. However, several PVMs and variants now exist, and the sensitivity of vegetation inferences to PVM selection, variant, and calibration domain is poorly understood. Here, we compare the reconstructions, parameter estimates, and structure of a Bayesian hierarchical PVM, STEPPS, both to observations and to REVEALS, a widely used PVM, for the pre–Euro-American settlement-era vegetation in the northeastern United States (NEUS). We also compare NEUS-based STEPPS parameter estimates to those for the upper midwestern United States (UMW). Both PVMs predict the observed macroscale patterns of vegetation composition in the NEUS; however, reconstructions of minor taxa are less accurate and predictions for some taxa differ between PVMs. These differences can be attributed to intermodel differences in structure and parameter estimates. Estimates of pollen productivity from STEPPS broadly agree with estimates produced for use in REVEALS, while comparison between pollen dispersal parameter estimates shows no significant relationship. STEPPS parameter estimates are similar between the UMW and NEUS, suggesting that STEPPS parameter estimates are transferable between floristically similar regions and scales. |
Fisher, Joshua B; Lee, Brian; Purdy, Adam J; Halverson, Gregory H; Dohlen, Matthew B; Cawse-Nicholson, Kerry; Wang, Audrey; Anderson, Ray G; Aragon, Bruno; Arain, Altaf M; Baldocchi, Dennis D; Baker, John M; Barral, Hélène; Bernacchi, Carl J; Bernhofer, Christian; Biraud, Sébastien C; Bohrer, Gil; Brunsell, Nathaniel; Cappelaere, Bernard; Castro-Contreras, Saulo; Chun, Junghwa; Conrad, Bryan J; Cremonese, Edoardo; Demarty, Jérôme; Desai, Ankur R; Ligne, Anne De; Foltýnová, Lenka; Goulden, Michael L; Griffis, Timothy J; Grünwald, Thomas; Johnson, Mark S; Kang, Minseok; Kelbe, Dave; Kowalska, Natalia; Lim, Jong-Hwan; Maïnassara, Ibrahim; McCabe, Matthew F; Missik, Justine E C; Mohanty, Binayak P; Moore, Caitlin E; Morillas, Laura; Morrison, Ross; Munger, William J; Posse, Gabriela; Richardson, Andrew D; Russell, Eric S; Ryu, Youngryel; Sanchez-Azofeifa, Arturo; Schmidt, Marius; Schwartz, Efrat; Sharp, Iain; Šigut, Ladislav; Tang, Yao; Hulley, Glynn; Anderson, Martha; Hain, Christopher; French, Andrew; Wood, Eric; Hook, Simon: ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station. In: Water Resources Research, vol. 56, no. 4, pp. e2019WR026058, 2020, (e2019WR026058 2019WR026058). @article{https://doi.org/10.1029/2019WR026058,
title = {ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station},
author = {Joshua B Fisher and Brian Lee and Adam J Purdy and Gregory H Halverson and Matthew B Dohlen and Kerry Cawse-Nicholson and Audrey Wang and Ray G Anderson and Bruno Aragon and Altaf M Arain and Dennis D Baldocchi and John M Baker and Hélène Barral and Carl J Bernacchi and Christian Bernhofer and Sébastien C Biraud and Gil Bohrer and Nathaniel Brunsell and Bernard Cappelaere and Saulo Castro-Contreras and Junghwa Chun and Bryan J Conrad and Edoardo Cremonese and Jérôme Demarty and Ankur R Desai and Anne De Ligne and Lenka Foltýnová and Michael L Goulden and Timothy J Griffis and Thomas Grünwald and Mark S Johnson and Minseok Kang and Dave Kelbe and Natalia Kowalska and Jong-Hwan Lim and Ibrahim Maïnassara and Matthew F McCabe and Justine E C Missik and Binayak P Mohanty and Caitlin E Moore and Laura Morillas and Ross Morrison and William J Munger and Gabriela Posse and Andrew D Richardson and Eric S Russell and Youngryel Ryu and Arturo Sanchez-Azofeifa and Marius Schmidt and Efrat Schwartz and Iain Sharp and Ladislav Šigut and Yao Tang and Glynn Hulley and Martha Anderson and Christopher Hain and Andrew French and Eric Wood and Simon Hook},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019WR026058},
doi = {https://doi.org/10.1029/2019WR026058},
year = {2020},
date = {2020-04-06},
journal = {Water Resources Research},
volume = {56},
number = {4},
pages = {e2019WR026058},
abstract = {Abstract The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station on 29 June 2018 by the National Aeronautics and Space Administration (NASA). The primary science focus of ECOSTRESS is centered on evapotranspiration (ET), which is produced as Level-3 (L3) latent heat flux (LE) data products. These data are generated from the Level-2 land surface temperature and emissivity product (L2_LSTE), in conjunction with ancillary surface and atmospheric data. Here, we provide the first validation (Stage 1, preliminary) of the global ECOSTRESS clear-sky ET product (L3_ET_PT-JPL, Version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against the site measurements (clear-sky instantaneous/time of overpass: r2 = 0.88; overall bias = 8%; normalized root-mean-square error, RMSE = 6%). ET uncertainty was generally consistent across climate zones, biome types, and times of day (ECOSTRESS samples the diurnal cycle), though temperate sites are overrepresented. The 70-m-high spatial resolution of ECOSTRESS improved correlations by 85%, and RMSE by 62%, relative to 1-km pixels. This paper serves as a reference for the ECOSTRESS L3 ET accuracy and Stage 1 validation status for subsequent science that follows using these data.},
note = {e2019WR026058 2019WR026058},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station on 29 June 2018 by the National Aeronautics and Space Administration (NASA). The primary science focus of ECOSTRESS is centered on evapotranspiration (ET), which is produced as Level-3 (L3) latent heat flux (LE) data products. These data are generated from the Level-2 land surface temperature and emissivity product (L2_LSTE), in conjunction with ancillary surface and atmospheric data. Here, we provide the first validation (Stage 1, preliminary) of the global ECOSTRESS clear-sky ET product (L3_ET_PT-JPL, Version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against the site measurements (clear-sky instantaneous/time of overpass: r2 = 0.88; overall bias = 8%; normalized root-mean-square error, RMSE = 6%). ET uncertainty was generally consistent across climate zones, biome types, and times of day (ECOSTRESS samples the diurnal cycle), though temperate sites are overrepresented. The 70-m-high spatial resolution of ECOSTRESS improved correlations by 85%, and RMSE by 62%, relative to 1-km pixels. This paper serves as a reference for the ECOSTRESS L3 ET accuracy and Stage 1 validation status for subsequent science that follows using these data. |
Yang, Guang; Guo, Shenglian; Liu, Pan; Block, Paul: Integration and Evaluation of Forecast-Informed Multiobjective Reservoir Operations. In: Journal of Water Resources Planning and Management, vol. 146, no. 6, pp. 04020038, 2020. @article{Yang2020,
title = {Integration and Evaluation of Forecast-Informed Multiobjective Reservoir Operations},
author = {Guang Yang and Shenglian Guo and Pan Liu and Paul Block},
url = {https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29WR.1943-5452.0001229},
year = {2020},
date = {2020-03-30},
journal = {Journal of Water Resources Planning and Management},
volume = {146},
number = {6},
pages = {04020038},
abstract = {Incorporating streamflow forecasts into reservoir operations can improve water resources management efficiency, yet the forecast value in multipurpose reservoir systems is rarely investigated, let alone the relationship between forecast accuracy and value in multiobjective reservoir operation. Here, we propose a forecast-informed framework to derive multiobjective operating rules based on radial basis functions and the Pareto archived dynamically dimensioned search optimization algorithm and subsequently develop indicators reflective of Pareto fronts with and without forecast information to characterize forecast value. Based on a case study of the Hanjiang cascade of reservoirs in the Yangtze River Basin, China, the optimal inclusion of streamflow forecasts notably improves the performance of multiobjective reservoir operations, mainly by significantly increasing the hydropower generation. The relationship between forecast accuracy and value is explored by comparing four accuracy indicators (Nash–Sutcliffe efficiency, mutual information, correlation coefficient, and Kullback–Leibler distance) and forecast value. The correlation coefficient is found to be the most suitable forecast indicator given its high correlation with forecast value and stability in the regression. For multiobjective forecast-informed reservoir systems, it is critical to understand and define the relationship between forecast accuracy and forecast value; if improvements in accuracy lead to steep gains in value, investing in further forecast model development may be warranted.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Incorporating streamflow forecasts into reservoir operations can improve water resources management efficiency, yet the forecast value in multipurpose reservoir systems is rarely investigated, let alone the relationship between forecast accuracy and value in multiobjective reservoir operation. Here, we propose a forecast-informed framework to derive multiobjective operating rules based on radial basis functions and the Pareto archived dynamically dimensioned search optimization algorithm and subsequently develop indicators reflective of Pareto fronts with and without forecast information to characterize forecast value. Based on a case study of the Hanjiang cascade of reservoirs in the Yangtze River Basin, China, the optimal inclusion of streamflow forecasts notably improves the performance of multiobjective reservoir operations, mainly by significantly increasing the hydropower generation. The relationship between forecast accuracy and value is explored by comparing four accuracy indicators (Nash–Sutcliffe efficiency, mutual information, correlation coefficient, and Kullback–Leibler distance) and forecast value. The correlation coefficient is found to be the most suitable forecast indicator given its high correlation with forecast value and stability in the regression. For multiobjective forecast-informed reservoir systems, it is critical to understand and define the relationship between forecast accuracy and forecast value; if improvements in accuracy lead to steep gains in value, investing in further forecast model development may be warranted. |
Husson, Jon M; Linzmeier, Benjamin J; Kitajima, Kouki; Ishida, Akizumi; Maloof, Adam C; Schoene, Blair; Peters, Shanan E; Valley, John W: Large isotopic variability at the micron-scale in ‘Shuram’ excursion carbonates from South Australia. In: Earth and Planetary Science Letters, vol. 538, pp. 116211, 2020, ISSN: 0012-821X. @article{HUSSON2020116211,
title = {Large isotopic variability at the micron-scale in ‘Shuram’ excursion carbonates from South Australia},
author = {Jon M Husson and Benjamin J Linzmeier and Kouki Kitajima and Akizumi Ishida and Adam C Maloof and Blair Schoene and Shanan E Peters and John W Valley},
url = {https://www.sciencedirect.com/science/article/pii/S0012821X20301540},
doi = {https://doi.org/10.1016/j.epsl.2020.116211},
issn = {0012-821X},
year = {2020},
date = {2020-03-17},
journal = {Earth and Planetary Science Letters},
volume = {538},
pages = {116211},
abstract = {Ediacaran-aged (635–541 million years ago) marine sediments contain a large negative carbon isotope (δ13C) excursion, in which carbonate δ13C values reach −12‰ (VPDB). Known as the ‘Shuram’ excursion, many workers have interpreted this δ13C record as an unprecedented perturbation to the global carbon cycle, leading to speculation about a causal connection to the broadly contemporaneous rise of animal life. Others have interpreted the δ13C signal as a product of diagenesis, thereby minimizing its relevance for understanding the evolution of metazoans. Here, we present SEM imaging and in-situ δ13C and δ18O values measured by secondary ion mass spectrometry (SIMS) to assess these competing hypotheses in the Wonoka Formation of South Australia. Our results from the minimum of the excursion show that rounded sedimentary grains of calcite have δ13C values between −12.8 to −10.6‰ and δ18O values between −17.8 to −15.5‰ (VPDB). Euhedral dolomite that appears to have grown unimpeded in open sedimentary pore spaces also is present. These early-stage dolomites are interpreted as early authigenic in origin and have δ13C values that reach +5‰, requiring a formation fluid with a substantially different δ13C composition from basin waters or bulk sediment. Together, these results provide little evidence for the hypothesis that a late diagenetic overprint has generated the ‘Shuram’ excursion in the Wonoka. Instead, they suggest the presence of a large carbon isotopic gradient in the surface environment, with shallow waters capable of precipitating carbonates with very low δ13C (down to −12‰) and deeper shelf and/or marine pore waters generating carbonates with positive carbon isotope values (up to +5‰). Because negative isotope excursions of similar magnitude are found in widely dispersed Ediacaran basins, it is likely that this gradient was characteristic of shelf environments of this period and that a still-unknown global process led to the ‘Shuram’ excursion in shallow water carbonates.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ediacaran-aged (635–541 million years ago) marine sediments contain a large negative carbon isotope (δ13C) excursion, in which carbonate δ13C values reach −12‰ (VPDB). Known as the ‘Shuram’ excursion, many workers have interpreted this δ13C record as an unprecedented perturbation to the global carbon cycle, leading to speculation about a causal connection to the broadly contemporaneous rise of animal life. Others have interpreted the δ13C signal as a product of diagenesis, thereby minimizing its relevance for understanding the evolution of metazoans. Here, we present SEM imaging and in-situ δ13C and δ18O values measured by secondary ion mass spectrometry (SIMS) to assess these competing hypotheses in the Wonoka Formation of South Australia. Our results from the minimum of the excursion show that rounded sedimentary grains of calcite have δ13C values between −12.8 to −10.6‰ and δ18O values between −17.8 to −15.5‰ (VPDB). Euhedral dolomite that appears to have grown unimpeded in open sedimentary pore spaces also is present. These early-stage dolomites are interpreted as early authigenic in origin and have δ13C values that reach +5‰, requiring a formation fluid with a substantially different δ13C composition from basin waters or bulk sediment. Together, these results provide little evidence for the hypothesis that a late diagenetic overprint has generated the ‘Shuram’ excursion in the Wonoka. Instead, they suggest the presence of a large carbon isotopic gradient in the surface environment, with shallow waters capable of precipitating carbonates with very low δ13C (down to −12‰) and deeper shelf and/or marine pore waters generating carbonates with positive carbon isotope values (up to +5‰). Because negative isotope excursions of similar magnitude are found in widely dispersed Ediacaran basins, it is likely that this gradient was characteristic of shelf environments of this period and that a still-unknown global process led to the ‘Shuram’ excursion in shallow water carbonates. |
Delaney, Frances; Ng, Peter; Dokoska, Kristina; Milner, Glenn; Potter, Kate; Notaro, Michael: Guide to Conducting a Climate Change Analysis at the Local Scale: Lessons Learned from Durham Region. Ontario Climate Consortium, Toronto, Ontario, 2020. @misc{Delaney2020,
title = {Guide to Conducting a Climate Change Analysis at the Local Scale: Lessons Learned from Durham Region},
author = {Frances Delaney and Peter Ng and Kristina Dokoska and Glenn Milner and Kate Potter and Michael Notaro},
url = {https://climateconnections.ca/app/uploads/2021/03/Final-Guide-to-Conducting-a-Climate-Change-Analysis-OCC_Nov.pdf},
year = {2020},
date = {2020-02-20},
abstract = {In 2020, the Ontario Climate Consortium (OCC), in partnership with Durham Region, developed updated climate projections for the Region, along with a guidance document known as the Guide to Conducting a Climate Change Analysis at the Local Scale: Lessons Learned from Durham Region (2020). Using the latest climate information, the OCC developed climate projections for 52 climate parameters under the RCP8.5 (business-as-usual or high emissions scenario) and RCP4.5 (moderate emissions scenario) for the short (2011-2040), medium (2041-2070) and long-term (2071-2100) future, using an ensemble of climate models.},
howpublished = {Ontario Climate Consortium, Toronto, Ontario},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
In 2020, the Ontario Climate Consortium (OCC), in partnership with Durham Region, developed updated climate projections for the Region, along with a guidance document known as the Guide to Conducting a Climate Change Analysis at the Local Scale: Lessons Learned from Durham Region (2020). Using the latest climate information, the OCC developed climate projections for 52 climate parameters under the RCP8.5 (business-as-usual or high emissions scenario) and RCP4.5 (moderate emissions scenario) for the short (2011-2040), medium (2041-2070) and long-term (2071-2100) future, using an ensemble of climate models. |
Moradi, Amir M; Dariane, Alireza B; Yang, Guang; Block, Paul: Long-range reservoir inflow forecasts using large-scale climate predictors. In: International Journal of Climatology, vol. 40, no. 13, pp. 5429-5450, 2020. @article{https://doi.org/10.1002/joc.6526,
title = {Long-range reservoir inflow forecasts using large-scale climate predictors},
author = {Amir M Moradi and Alireza B Dariane and Guang Yang and Paul Block},
url = {https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.6526},
doi = {https://doi.org/10.1002/joc.6526},
year = {2020},
date = {2020-02-17},
journal = {International Journal of Climatology},
volume = {40},
number = {13},
pages = {5429-5450},
abstract = {Abstract Identifying significant large-scale climate indicators has the potential to improve long-range streamflow forecasts. In this research, we develop streamflow forecasts for Lake Urmia basin, Iran, specifically for inflow into the Boukan and Mahabad reservoirs. In doing so, two types of inflow forecast models are considered: a single site univariate model ignoring the cross correlation between streamflow at different stations, and a multi-site multivariate forecast model which takes into consideration the cross correlations among stations. Predictor selection is performed through a principal component analysis and an adaptive-network-based fuzzy inference system is used to forecast streamflow. Forecast performance is investigated by employing different combinations of large-scale climatic information and hydrologic data. We found that gridded ocean-atmospheric circulation variables, including surface precipitation rate and omega (pressure vertical velocity), have the highest correlations (about 0.7) with annual streamflow. In general, multivariate models are able to better preserve the annual cross-correlations between streamflow at different stations, as expected, without sacrificing forecast skill as compared to the univariate forecast model approach. Additionally, as compared with the baseline feed-forward artificial neural network- and traditional multiple linear regression-forecast models, the results were approximately the same. This similarity in the forecast performance between the linear and nonlinear models is likely due to the short of data (44-sample record).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract Identifying significant large-scale climate indicators has the potential to improve long-range streamflow forecasts. In this research, we develop streamflow forecasts for Lake Urmia basin, Iran, specifically for inflow into the Boukan and Mahabad reservoirs. In doing so, two types of inflow forecast models are considered: a single site univariate model ignoring the cross correlation between streamflow at different stations, and a multi-site multivariate forecast model which takes into consideration the cross correlations among stations. Predictor selection is performed through a principal component analysis and an adaptive-network-based fuzzy inference system is used to forecast streamflow. Forecast performance is investigated by employing different combinations of large-scale climatic information and hydrologic data. We found that gridded ocean-atmospheric circulation variables, including surface precipitation rate and omega (pressure vertical velocity), have the highest correlations (about 0.7) with annual streamflow. In general, multivariate models are able to better preserve the annual cross-correlations between streamflow at different stations, as expected, without sacrificing forecast skill as compared to the univariate forecast model approach. Additionally, as compared with the baseline feed-forward artificial neural network- and traditional multiple linear regression-forecast models, the results were approximately the same. This similarity in the forecast performance between the linear and nonlinear models is likely due to the short of data (44-sample record). |