Articles | Volume 12, issue 11
https://doi.org/10.5194/gmd-12-4585-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-4585-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Developing and optimizing shrub parameters representing sagebrush (Artemisia spp.) ecosystems in the northern Great Basin using the Ecosystem Demography (EDv2.2) model
Karun Pandit
CORRESPONDING AUTHOR
Department of Geosciences, Boise State University, 1910 University Dr,
Boise, ID 83725-1535, USA
Hamid Dashti
Department of Geosciences, Boise State University, 1910 University Dr,
Boise, ID 83725-1535, USA
Nancy F. Glenn
Department of Geosciences, Boise State University, 1910 University Dr,
Boise, ID 83725-1535, USA
Alejandro N. Flores
Department of Geosciences, Boise State University, 1910 University Dr,
Boise, ID 83725-1535, USA
Kaitlin C. Maguire
United States Geological Survey, Forest and Rangeland Ecosystem
Science Center, 970 Lusk St., Boise, ID 83706, USA
Douglas J. Shinneman
United States Geological Survey, Forest and Rangeland Ecosystem
Science Center, 970 Lusk St., Boise, ID 83706, USA
Gerald N. Flerchinger
United States Department of Agriculture, Agricultural Research
Service, 800 Park Blvd., Suite 105, Boise, ID 83712, USA
Aaron W. Fellows
United States Department of Agriculture, Agricultural Research
Service, 800 Park Blvd., Suite 105, Boise, ID 83712, USA
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Karun Pandit, Hamid Dashti, Andrew T. Hudak, Nancy F. Glenn, Alejandro N. Flores, and Douglas J. Shinneman
Biogeosciences, 18, 2027–2045, https://doi.org/10.5194/bg-18-2027-2021, https://doi.org/10.5194/bg-18-2027-2021, 2021
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A dynamic global vegetation model, Ecosystem Demography (EDv2.2), is used to understand spatiotemporal dynamics of a semi-arid shrub ecosystem under alternative fire regimes. Multi-decadal point simulations suggest shrub dominance for a non-fire scenario and a contrasting phase of shrub and C3 grass growth for a fire scenario. Regional gross primary productivity (GPP) simulations indicate moderate agreement with MODIS GPP and a GPP reduction in fire-affected areas before showing some recovery.
Rainey Aberle, Ellyn Enderlin, Shad O'Neel, Caitlyn Florentine, Louis Sass, Adam Dickson, Hans-Peter Marshall, and Alejandro Flores
EGUsphere, https://doi.org/10.5194/egusphere-2024-548, https://doi.org/10.5194/egusphere-2024-548, 2024
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Tracking seasonal snow on glaciers is critical for understanding glacier health. However, current snow detection methods struggle to distinguish seasonal snow from glacier ice. To address this, we developed a new automated workflow for tracking seasonal snow on glaciers using satellite imagery and machine learning. Applying this method can help provide insights into glacier health, water resources, and the effects of climate change on snow cover over broad spatial scales.
William Rudisill, Alejandro Flores, and Rosemary Carroll
Geosci. Model Dev., 16, 6531–6552, https://doi.org/10.5194/gmd-16-6531-2023, https://doi.org/10.5194/gmd-16-6531-2023, 2023
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It is important to know how well atmospheric models do in mountains, but there are not very many weather stations. We evaluate rain and snow from a model from 1987–2020 in the Upper Colorado River basin against the available data. The model works rather well, but there are still some uncertainties in remote locations. We then use snow maps collected by aircraft, streamflow measurements, and some advanced statistics to help identify how well the model works in ways we could not do before.
Ahmad Hojatimalekshah, Zachary Uhlmann, Nancy F. Glenn, Christopher A. Hiemstra, Christopher J. Tennant, Jake D. Graham, Lucas Spaete, Arthur Gelvin, Hans-Peter Marshall, James P. McNamara, and Josh Enterkine
The Cryosphere, 15, 2187–2209, https://doi.org/10.5194/tc-15-2187-2021, https://doi.org/10.5194/tc-15-2187-2021, 2021
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We describe the relationships between snow depth, vegetation canopy, and local-scale processes during the snow accumulation period using terrestrial laser scanning (TLS). In addition to topography and wind, our findings suggest the importance of fine-scale tree structure, species type, and distributions on snow depth. Snow depth increases from the canopy edge toward the open areas, but wind and topographic controls may affect this trend. TLS data are complementary to wide-area lidar surveys.
Karun Pandit, Hamid Dashti, Andrew T. Hudak, Nancy F. Glenn, Alejandro N. Flores, and Douglas J. Shinneman
Biogeosciences, 18, 2027–2045, https://doi.org/10.5194/bg-18-2027-2021, https://doi.org/10.5194/bg-18-2027-2021, 2021
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A dynamic global vegetation model, Ecosystem Demography (EDv2.2), is used to understand spatiotemporal dynamics of a semi-arid shrub ecosystem under alternative fire regimes. Multi-decadal point simulations suggest shrub dominance for a non-fire scenario and a contrasting phase of shrub and C3 grass growth for a fire scenario. Regional gross primary productivity (GPP) simulations indicate moderate agreement with MODIS GPP and a GPP reduction in fire-affected areas before showing some recovery.
Miguel A. Aguayo, Alejandro N. Flores, James P. McNamara, Hans-Peter Marshall, and Jodi Mead
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-451, https://doi.org/10.5194/hess-2020-451, 2020
Manuscript not accepted for further review
Roland Baatz, Pamela L. Sullivan, Li Li, Samantha R. Weintraub, Henry W. Loescher, Michael Mirtl, Peter M. Groffman, Diana H. Wall, Michael Young, Tim White, Hang Wen, Steffen Zacharias, Ingolf Kühn, Jianwu Tang, Jérôme Gaillardet, Isabelle Braud, Alejandro N. Flores, Praveen Kumar, Henry Lin, Teamrat Ghezzehei, Julia Jones, Henry L. Gholz, Harry Vereecken, and Kris Van Looy
Earth Syst. Dynam., 9, 593–609, https://doi.org/10.5194/esd-9-593-2018, https://doi.org/10.5194/esd-9-593-2018, 2018
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Focusing on the usage of integrated models and in situ Earth observatory networks, three challenges are identified to advance understanding of ESD, in particular to strengthen links between biotic and abiotic, and above- and below-ground processes. We propose developing a model platform for interdisciplinary usage, to formalize current network infrastructure based on complementarities and operational synergies, and to extend the reanalysis concept to the ecosystem and critical zone.
Bangshuai Han, Shawn G. Benner, and Alejandro N. Flores
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-140, https://doi.org/10.5194/hess-2018-140, 2018
Manuscript not accepted for further review
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Water scarcity is critical in semiarid regions with intensively managed water supplies. Climate change and water allocation laws, along with hydrological processes determine the spatial and temporal water scarcity patterns. We present a new efficient method that captures plausible variability of climate change, while explicitly capturing the complex irrigation activities as constrained by local water rights. It will be useful for semi-arid watersheds to project future water scarcity patterns.
Bangshuai Han, Shawn G. Benner, John P. Bolte, Kellie B. Vache, and Alejandro N. Flores
Hydrol. Earth Syst. Sci., 21, 3671–3685, https://doi.org/10.5194/hess-21-3671-2017, https://doi.org/10.5194/hess-21-3671-2017, 2017
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The western US relies heavily on irrigation water which is allocated following local water rights. We develop and test a novel model that explicitly integrates water rights into a conceptual hydrologic model to spatially allocate irrigation water. The model well captures the timing and magnitude of irrigation water allocation in our study area and is applicable to semi-arid regions with similar water right regulations. The results could inform future water policies and management decisions.
A. A. Harpold, J. A. Marshall, S. W. Lyon, T. B. Barnhart, B. A. Fisher, M. Donovan, K. M. Brubaker, C. J. Crosby, N. F. Glenn, C. L. Glennie, P. B. Kirchner, N. Lam, K. D. Mankoff, J. L. McCreight, N. P. Molotch, K. N. Musselman, J. Pelletier, T. Russo, H. Sangireddy, Y. Sjöberg, T. Swetnam, and N. West
Hydrol. Earth Syst. Sci., 19, 2881–2897, https://doi.org/10.5194/hess-19-2881-2015, https://doi.org/10.5194/hess-19-2881-2015, 2015
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This review's objective is to demonstrate the transformative potential of lidar by critically assessing both challenges and opportunities for transdisciplinary lidar applications in geomorphology, hydrology, and ecology. We find that using lidar to its full potential will require numerous advances, including more powerful open-source processing tools, new lidar acquisition technologies, and improved integration with physically based models and complementary observations.
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A generic algorithm to automatically classify urban fabric according to the local climate zone system: implementation in GeoClimate 0.0.1 and application to French cities
Modelling water isotopologues (1H2H16O, 1H217O) in the coupled numerical climate model iLOVECLIM (version 1.1.5)
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Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
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Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
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Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
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Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
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Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
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This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, François Leconte, and Elisabeth Le Saux Wiederhold
Geosci. Model Dev., 17, 2077–2116, https://doi.org/10.5194/gmd-17-2077-2024, https://doi.org/10.5194/gmd-17-2077-2024, 2024
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Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is seen as a standard approach for classifying any zone according to a set of geographic indicators. While many methods already exist to map the LCZ, only a few tools are openly and freely available. We present the algorithm implemented in GeoClimate software to identify the LCZ of any place in the world using OpenStreetMap data.
Thomas Extier, Thibaut Caley, and Didier M. Roche
Geosci. Model Dev., 17, 2117–2139, https://doi.org/10.5194/gmd-17-2117-2024, https://doi.org/10.5194/gmd-17-2117-2024, 2024
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Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
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We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
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Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
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As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
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In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
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This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
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A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
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Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
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Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
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The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
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We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
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This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
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By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Bernd Funke, Thierry Dudok de Wit, Ilaria Ermolli, Margit Haberreiter, Doug Kinnison, Daniel Marsh, Hilde Nesse, Annika Seppälä, Miriam Sinnhuber, and Ilya Usoskin
Geosci. Model Dev., 17, 1217–1227, https://doi.org/10.5194/gmd-17-1217-2024, https://doi.org/10.5194/gmd-17-1217-2024, 2024
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We outline a road map for the preparation of a solar forcing dataset for the upcoming Phase 7 of the Coupled Model Intercomparison Project (CMIP7), considering the latest scientific advances made in the reconstruction of solar forcing and in the understanding of climate response while also addressing the issues that were raised during CMIP6.
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
Geosci. Model Dev., 17, 1249–1269, https://doi.org/10.5194/gmd-17-1249-2024, https://doi.org/10.5194/gmd-17-1249-2024, 2024
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Before using climate models to study the impacts of climate change, bias adjustment is commonly applied to the models to ensure that they correspond with observations at a local scale. However, this can introduce undesirable distortions into the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods, facilitating their transparent and rigorous application.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
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We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Douglas McNeall, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 17, 1059–1089, https://doi.org/10.5194/gmd-17-1059-2024, https://doi.org/10.5194/gmd-17-1059-2024, 2024
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We can run simulations of the land surface and carbon cycle, using computer models to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024, https://doi.org/10.5194/gmd-17-975-2024, 2024
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Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, https://doi.org/10.5194/gmd-17-957-2024, 2024
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This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
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This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
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The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Deepeshkumar Jain, Suryachandra A. Rao, Ramu A. Dandi, Prasanth A. Pillai, Ankur Srivastava, Maheswar Pradhan, and Kiran V. Gangadharan
Geosci. Model Dev., 17, 709–729, https://doi.org/10.5194/gmd-17-709-2024, https://doi.org/10.5194/gmd-17-709-2024, 2024
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The present paper discusses and evaluates the new Monsoon Mission Coupled Forecast System model (MMCFS) version 2.0 which upgrades the currently operational MMCFS v1.0 at the Indian Meteorological Department, India. The individual model components have been substantially upgraded independently by their respective scientific groups. MMCFS v2.0 includes these upgrades in the operational coupled model. The new model shows significant skill improvement in simulating the Indian monsoon.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
Geosci. Model Dev., 17, 529–543, https://doi.org/10.5194/gmd-17-529-2024, https://doi.org/10.5194/gmd-17-529-2024, 2024
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Cost-reducing modeling strategies are applied to high-resolution simulations of the Southern Ocean in a changing climate. They are evaluated with respect to observations and traditional, lower-resolution modeling methods. The simulations effectively reproduce small-scale ocean flows seen in satellite data and are largely consistent with traditional model simulations after 4 °C of warming. Small-scale flows are found to intensify near bathymetric features and to become more variable.
Karl E. Taylor
Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, https://doi.org/10.5194/gmd-17-415-2024, 2024
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Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for some common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova
Geosci. Model Dev., 17, 229–259, https://doi.org/10.5194/gmd-17-229-2024, https://doi.org/10.5194/gmd-17-229-2024, 2024
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This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
Geosci. Model Dev., 17, 261–273, https://doi.org/10.5194/gmd-17-261-2024, https://doi.org/10.5194/gmd-17-261-2024, 2024
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We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
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The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
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We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
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For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
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Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev., 17, 53–69, https://doi.org/10.5194/gmd-17-53-2024, https://doi.org/10.5194/gmd-17-53-2024, 2024
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This study presents a deep learning architecture, multi-scale feature fusion (MFF), to improve the forecast skills of precipitations especially for heavy precipitations. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors so that heavy precipitations are produced.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
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Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023, https://doi.org/10.5194/gmd-16-7357-2023, 2023
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Some of our best tools to describe the state of the land system, including the intensity of heat waves, have a problem. The model currently assumes that the number of leaves in ecosystems always follows the same cycle. By using satellite observations of when leaves are present, we show that capturing the yearly changes in this cycle is important to avoid errors in estimating surface temperature. We show that this has strong implications for our capacity to describe heat waves across Europe.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
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Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
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Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Baiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
EGUsphere, https://doi.org/10.5194/egusphere-2023-1733, https://doi.org/10.5194/egusphere-2023-1733, 2023
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For the first time, we coupled a regional climate chemistry model RegCM-Chem with a dynamic vegetation model YIBs to create a regional climate-chemistry-ecology model RegCM-Chem-YIBs. We applied it to simulate climatic, chemical and ecological parameters in East Asia and fully validated it on a variety of observational data. The research results show that RegCM-Chem-YIBs model is a valuable tool for studying terrestrial carbon cycle, atmospheric chemistry, and climate change in regional scale.
Michael Meier and Christof Bigler
Geosci. Model Dev., 16, 7171–7201, https://doi.org/10.5194/gmd-16-7171-2023, https://doi.org/10.5194/gmd-16-7171-2023, 2023
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We analyzed >2.3 million calibrations and 39 million projections of leaf coloration models, considering 21 models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate scenarios. Models based on temperature, day length, and leaf unfolding performed best, especially when calibrated with generalized simulated annealing and systematically balanced or stratified samples. Projected leaf coloration shifts between −13 and +20 days by 2080–2099.
Katharina Gallmeier, J. Xavier Prochaska, Peter Cornillon, Dimitris Menemenlis, and Madolyn Kelm
Geosci. Model Dev., 16, 7143–7170, https://doi.org/10.5194/gmd-16-7143-2023, https://doi.org/10.5194/gmd-16-7143-2023, 2023
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This paper introduces an approach to evaluate numerical models of ocean circulation. We compare the structure of satellite-derived sea surface temperature anomaly (SSTa) instances determined by a machine learning algorithm at 10–80 km scales to those output by a high-resolution MITgcm run. The simulation over much of the ocean reproduces the observed distribution of SSTa patterns well. This general agreement, alongside a few notable exceptions, highlights the potential of this approach.
Angus Fotherby, Harold J. Bradbury, Jennifer L. Druhan, and Alexandra V. Turchyn
Geosci. Model Dev., 16, 7059–7074, https://doi.org/10.5194/gmd-16-7059-2023, https://doi.org/10.5194/gmd-16-7059-2023, 2023
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We demonstrate how, given a simulation of fluid and rock interacting, we can emulate the system using machine learning. This means that, for a given initial condition, we can predict the final state, avoiding the simulation step once the model has been trained. We present a workflow for applying this approach to any fluid–rock simulation and showcase two applications to different fluid–rock simulations. This approach has applications for improving model development and sensitivity analyses.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-223, https://doi.org/10.5194/gmd-2023-223, 2023
Revised manuscript accepted for GMD
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Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion by either uniform erosion processes where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea level history, material properties, and the relative influence of different erosional processes.
Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
Geosci. Model Dev., 16, 6857–6873, https://doi.org/10.5194/gmd-16-6857-2023, https://doi.org/10.5194/gmd-16-6857-2023, 2023
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In this study, to noticeably improve precipitation simulation in steep mountains, we propose a sub-grid parameterization scheme for the topographic vertical motion in CAM5-SE to revise the original vertical velocity by adding the topographic vertical motion. The dynamic lifting effect of topography is extended from the lowest layer to multiple layers, thus improving the positive deviations of precipitation simulation in high-altitude regions and negative deviations in low-altitude regions.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
EGUsphere, https://doi.org/10.5194/egusphere-2023-2720, https://doi.org/10.5194/egusphere-2023-2720, 2023
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We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth System Models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Cited articles
Ahrends, H. E., Etzold, S., Kutsch, W. L., Stockli, R., Bruegger, R.,
Jeanneret, F., Wanner, H., Buchmann, N., and Eugster, W.: Tree phenology and carbon
dioxide fluxes: use of digital photography for process-based interpretation
of the ecosystem scale, Clim. Res., 39, 261–274, 2009.
AmeriFlux: http://ameriflux.lbl.gov, last access: 7 May 2018.
Antonarakis, A. S., Munger, J. W., and Moorcroft, P. R.: Imaging
spectroscopy- and lidar-derived estimates of canopy composition and
structure to improve predictions of forest carbon fluxes and ecosystem
dynamics, Geophys. Res. Lett., 41, 2535–2542, https://doi.org/10.1002/2013GL058373,
2014.
Barnard, D. M. and Bauerle, W. L.: The implications of minimum stomatal
conductance on modeling water flux in forest canopies, J.
Geophys. Res.-Biogeo., 118, 1322–1333,
https://doi.org/10.1002/jgrg.20112, 2013.
Bonan, G. B., Williams, M., Fisher, R. A., and Oleson, K. W.: Modeling stomatal conductance in the earth system: linking leaf water-use efficiency and water transport along the soil–plant–atmosphere continuum, Geosci. Model Dev., 7, 2193–2222, https://doi.org/10.5194/gmd-7-2193-2014, 2014.
Bond-Lamberty, B., Fisk, J. P., Holm, J. A., Bailey, V., Bohrer, G., and Gough, C. M.: Moderate forest disturbance as a stringent test for gap and big-leaf models, Biogeosciences, 12, 513–526, https://doi.org/10.5194/bg-12-513-2015, 2015.
Botkin, D. B., Janak, J. F., and Wallis, J. R.: Rationale, Limitations, and
Assumptions of a Northeastern Forest Growth Simulator, IBM J.
Res. Dev., 16, 101–116, 1972.
Brabec, M. M.: Big Sagebrush (Artemisia tridentata) in a shifting climate
context: assessment of seedling responses to climate, Master's Thesis, Boise
State University, United States, 116 pp., 2014.
Bradley, B. A.: Assessing ecosystem threats from global and regional change:
hierarchical modeling of risk to sagebrush ecosystems from climate change,
land use and invasive species in Nevada, USA, Ecography, 33, 198–208, 2010.
Chambers, J. C., Miller, R. F., Board, D. I., Pyke, D. A., Roundy, B. A.,
Grace, J. B., Schupp, E. W., and Taush, R. J.: Resilience and Resistance of
Sagebrush Ecosystems: Implications for State and Transition Models and
Management Treatments, Range Ecol. Manage., 67, 440–454,
https://doi.org/10.2111/REM-D-13-00074.1, 2014.
Cleary, M. B., Pendall, E., and Ewers, B. E.: Aboveground and belowground
carbon pools after fire in mountain big sagebrush steppe, Range Ecol.
Manage., 63, 187–96, https://doi.org/10.2111/REM-D-09-00117.1, 2010.
Cleary, M. B., Naithani, K. J., Ewers, B. E., and Pendall, E.: Upscaling CO2
fluxes using leaf, soil and chamber measurements across successional growth
stages in a sagebrush steppe ecosystem, J. Arid Environ., 121, 43–51, 2015.
Combal, B., Baret, F., Weiss, M., Trubuil, A., Mace, D.,
Pragnere, A., Myneni, R., Knyazikhin, Y., and Wang, L.: Retrieval of canopy
biophysical variables from bidirectional reflectance using prior information
to solve the ill-posed inverse problem, Remote Sens. Environ., 84, 1–15, https://doi.org/10.1016/S0034-4257(02)00035-4, 2003.
Comstock, J. P. and Ehleringer, J. R.: Plant adaptation in the Great Basin and
Colorado Plateau, Great Basin Nat., 52, 195–215, 1992.
Connelly, J. W., Knick, S. T., Schroeder, M. A., and Stiver, S. J.:
Conservation Assessment of Greater Sage-grouse and Sagebrush Habitats,
Western Association of Fish and Wildlife Agencies, Unpublished Report,
Cheyenne, Wyoming, 2004.
Dietze, M. C., Serbin, S. P., Davidson, C., Desai, A. R., Fend, X., Kelly,
R., Kooper, R, LeBauer, D., Mantooth, J., McHenry, K., and Wang, D.: A
quantitative assessment of a terrestrial biospheremodel's data needs across
North American biomes, J. Geophys. Res.-Biogeo., 119, 286–300,
https://doi.org/10.1002/2013JG002392, 2014.
Dietze, M. C., Fox, A., Beck-Johnson, L. M., Betancourt, J. L., Hooten, M.
B., Jarnevich, C. S., Keitt, T. H., Kenney, M. A., Laney, C. M., Larsen, L.
G., Loescher, H. W., Lunch, C. K., Pijanowski, B. C., Randerson, J. T.,
Read, E. K., Tredennick, A. T., Vargas, R., Weathers, K. C., and White, E.
P.: Iterative near-term ecological forecasting: Needs, opportunities, and
challenges, P. Natl. Acad. Sci. USA, 115, 1424–1432,
https://doi.org/10.1073/pnas.1710231115, 2018.
Duursma, R. A., Blackman, C. J., Lopez, R., Martin-StPaul, N. K., Cochard,
H., and Medlyn, B. E.: On the minimum leaf conductance: its role in models of
plant water use, and ecological and environmental controls, New Phytol., online version, 221, 693–705,
https://doi.org/10.1111/nph.15395, 2019.
ED-2 model development team: Ecosystem Demography
model (ED-2) code repository, available at: https://github.com/EDmodel/ED2 (last access: 20 September 2019), 2014.
Farquhar, G. D. and Sharkey T. D.: Stomatal conductance and photosynthesis,
Ann. Rev. Plant Physiol., 33, 317–345, 1982.
Fellows, A. W., Flerchinger, G. N., Seyfried, M. S., and Lohse, K.: Data for
Partitioned Carbon and Energy Fluxes Within the Reynolds Creek Critical Zone
Observatory, Data set, https://doi.org/10.18122/B2TD7V,
2017.
Fer, I., Kelly, R., Moorcroft, P. R., Richardson, A. D., Cowdery, E. M., and Dietze, M. C.: Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation, Biogeosciences, 15, 5801–5830, https://doi.org/10.5194/bg-15-5801-2018, 2018.
Fisher, R., McDowell, N., Purves, D., Moorcroft, P., Sitch, S., Cox, P.,
Huntingford, C., Meir, P., and Woodward, F. I.: Assessing uncertainties in a
second-generation dynamic vegetation model caused by ecological scale
limitations, New Phytol., 187, 666–681, https://doi.org/10.1111/j.1469-8137.2010.03340.x, 2010.
Fisher, R. A., Koven, C. D., Anderegg, W. R. L, Christoffersen, B. O.,
Dietze, M. C., Farrior, C. E. Holm, J. A., Hurtt, G. C., Knox, R. G.,
Lawrence, P. J., Lichstein, J. W., Longo, M., Matheny, A. M., Medvigy, D.,
Muller-Landau, H. C., Powell, T. L., Serbin, S. P., Sato, H., Shuman, J. K.,
Smith, B., Trugman, A. T., Viskari, T., Verbeeck, H., Weng, E., Xu, C., Xu,
X., Zhang, T., and Moorcroft, P. R.: Vegetation demographics in Earth System
Models: A review of progress and priorities, Glob. Change Biol., 24, 35–24,
https://doi.org/10.1111/gcb.13910, 2018.
Flores, A., Masarik, M., and Watson, K.: A 30-Year, Multi-Domain
High-Resolution Climate Simulation Dataset for the Interior Pacific
Northwest and Southern Idaho, data set, https://doi.org/10.18122/B2LEAFD001, 2016.
Gill, R. A. and Jackson, R. B.: Global patterns of root turnover for
terrestrial ecosystems, New Phytol., 147, 13–31,
doi.org/10.1046/j.1469-8137.2000.00681.x, 2000.
Glenn, N.F., Spaete, L. P., Shrestha, R., Li, A., Ilangakoon, N., Mitchell,
J., Ustin, S. L., Qi, Y., Dashti, H., and Finan, K.: Shrubland Species
Cover, Biometric, Carbon and Nitrogen Data, Southern Idaho, 2014, ORNL DAAC,
Oak Ridge, Tennessee, USA, data set, https://doi.org/10.3334/ORNLDAAC/1503, 2017.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition
of the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Hoffman, E. O. and Gardner, R. H.: Evaluation of Uncertainties in
Environmental Radiological Assessment Models, in: Radiological Assessments: a Textbook on Environmental Dose
Analysis, edited by: Till, J. E. and Meyer, H.
R., U.S. Nuclear Regulatory Commission Washington, DC, Report No.
NUREG/CR-3332, 1983.
Howard, J. L.: Artemisia tridentata subsp. Wyomingensis, in: Fire
Effects Information System, U.S. Department of Agriculture, Forest
Service, Rocky Mountain Research Station, Fire Sciences Laboratory
(Producer), available at:
https://www.fs.fed.us/database/feis/plants/shrub/arttriw/all.html (last
access: 20 August 2018), 1999.
Keating, E. H., Doherty, J., Vrugt, J. A., and Kang, Q. J.: Optimization
and uncertainty assessment of strongly nonlinear groundwater models with
high parameter dimensionality, Water Resour. Res., 46, W10517,
https://doi.org/10.1029/2009WR008584, 2010.
Kim, Y., Knox, R. G., Longo, M., Medvigy, D., Hutyra, L. R., Pyle, E. H.,
Wofsy, S. C., Bras, R. L., and Moorcroft, P. R.: Seasonal carbon dynamics
and water fluxes in an Amazon rainforest, Glob. Change Biol., 18,
1322–1334, https://doi.org/10.1111/j.1365-2486.2011.02629.x, 2012.
Knick, S. T. and Dobkin, D. S.: Teetering on the Edge or Too Late?
Conservation and Research Issues for Avifauna of Sagebrush Habitats, The
Condor, 105, 611–634, https://doi.org/10.1650/7329, 2003.
Knox, R. G., Longo, M., Swann, A. L. S., Zhang, K., Levine, N. M., Moorcroft, P. R., and Bras, R. L.: Hydrometeorological effects of historical land-conversion in an ecosystem-atmosphere model of Northern South America, Hydrol. Earth Syst. Sci., 19, 241–273, https://doi.org/10.5194/hess-19-241-2015, 2015.
Krause, P., Boyle, D. P., and Bäse, F.: Comparison of different efficiency criteria for hydrological model assessment, Adv. Geosci., 5, 89–97, https://doi.org/10.5194/adgeo-5-89-2005, 2005.
Kwon, H., Pendall, E., Ewers, B. E., Cleary, M., and Naithani, K.: Spring
drought regulates summer net ecosystem CO2 exchange in a sagebrush-steppe
ecosystem, Agr. Forest Meteorol., 148, 381–391,
https://doi.org/10.1016/j.agrformet.2007.09.010 , 2008.
Kwon, B., Kim, H. S., Jeon, J., and Yi, M. J.: Effects of Temporal and
Interspecific Variation of Specific Leaf Area on Leaf Area Index Estimation
of Temperate Broadleaved Forests in Korea, Forests, 7, 215,
https://doi.org/10.3390/f7100215, 2016.
Lambrecht, S. C., Shattuckb, A. K., and Loik, M. E.: Combined drought and
episodic freezing effects on seedlings of low- and high-elevation subspecies
of sagebrush (Artemisia tridentata), Physiol. Plantarum, 130, 207–217, 2007.
LCC: Landscape Conservation Cooperatives: 2015 LCC Network Areas OGC
Webservices, available at:
https://www.sciencebase.gov/catalog/item/55c52e08e4b033ef5212bd75, last
access: 21 April 2018.
LeBauer, D. S., Wang, D., Richter, K. T., Davidson, C. C., and Dietze, M.
C.: Facilitating feedbacks between field measurements and ecosystem models,
Ecol. Monogr., 83, 133–154, https://doi.org/10.1890/12-0137.1, 2013.
Leuning, R.: A critical appraisal of a combined stomatal-photosynthesis
model for C3 plants, Plant Cell Environ., 18, 339–355, 1995.
Li, C., Sun, O. J., Xiao, C., and Han, X.: Differences in Net Primary
Productivity Among Contrasting Habitats in Artemisia ordosica Rangeland of
Northern China, Range Ecol. Manage., 62, 345–350,
https://doi.org/10.2111/07-084.1, 2009.
McArthur, E. D. and Plummer, A. P.: Biogeography and management of native
western shrubs: a case study, Section Tridentatae of Artemisia, Great Basin
Naturalist Memoirs, 2 , 15,
https://scholarsarchive.byu.edu/gbnm/vol2/iss1/15, 1978.
McIver, J. and Brunson, M.: Multidisciplinary, Multisite Evaluation of
Alternative Sagebrush Steppe Restoration Treatments: The SageSTEP Project,
Range Ecol. Manage., 67, 435–439, https://doi.org/10.2111/REM-D-14-00085.1,
2014.
Medvigy, D. M.: The state of the regional carbon cycle: Results from a
coupled constrained ecosystem-atmosphere model, PhD Thesis, Harvard Univ.,
Cambridge, MA, 2006.
Medvigy, D. M. and Moorcroft, P. R.: Predicting ecosystem dynamics at
regional scales: an evaluation of a terrestrial biosphere model for the
forests of northeastern North America, Phil. Trans. R. Soc. B, 367,
222–235, https://doi.org/10.1098/rstb.2011.0253, 2012.
Medvigy, D., Wofsy, S. C., Munger, J. W., Hollinger, D. Y., and Moorcroft,
P. R.: Mechanistic scaling of ecosystemfunction and dynamics in space and
time: Ecosystem Demography model version 2, J. Geophys. Res., 114, G01002,
https://doi.org/10.1029/2008JG000812, 2009.
Medvigy, D., Jeong, S. J., Clark, K. L., Skowronski, N. S., and Schäfer,
K. V. R.: Effects of seasonal variatio n of photosynthetic capacity onthe
carbon fluxes of a temperate deciduous forest, J. Geophys.
Res.-Biogeo., 118, 1703–1714, https://doi.org/10.1002/2013JG002421,
2013.
Miller, P. A. and Smith, B.: Modelling Tundra vegetation response to recent
Arctic warming, Ambio, 41, 281–291, 2012.
Miller R. F., Knick, S. T., Pyke, D. A., Meinke, C. W., Hanser, S. E., Wisdom, M. J., and Hild, A. L.: Characteristics of sagebrush habitats and limitations to long-term conservation, in: Greater
sage-grouse–ecology and conservation of a landscape species and its
habitats, edited by: Knick S. T. and Connelly, J. W., Studies in avian biology no. 38, University of California Press,
Berkeley, CA, USA, 145–185, 2011.
Mitchell, J. J., Glenn, N. F., Sankey, T. T., Derryberry D. R., Anderson, M.
O., and Hrusk, R. C.: Small-footprint Lidar Estimations of Sagebrush Canopy
Characteristics, Photogramm. Eng. Rem. S., 77,
521–530, 2011.
Mo, X., Chen, J. M., Ju, W., and Black, T. A.: Optimization of ecosystem
model parameters through assimilating eddy covariance flux data with an
ensemble Kalman filter, Ecol. Model., 217, 157–173,
https://doi.org/10.1016/j.ecolmodel.2008.06.021, 2008.
Moorcroft, P. R.: Recent advances in ecosystem-atmosphere interactions: an
ecological perspective, Phil. Trans. R. Soc. B, 270, 1215–1227, https://doi.org/10.1098/rspb.2002.2251, 2003.
Moorcroft, P. R., Hurtt, G. C., and Pacala, S. W.: A method for scaling
vegetation dynamics: the Ecosystem Demography model (ED), Ecol. Monogr., 71,
557–586, https://doi.org/10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2,
2001.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models, Part I – A discussion of principles, J. Hydrol., 10, 282–290, 1970.
Oleson, K., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven,
C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S. C.,
Thornton, P. E., Bozbiyik, A., Fisher, R., Heald, C. L., Kluzek, E.,
Lamarque, J.-F., Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S.,
Ricciuto, D. M., Sacks, W., Sun, Y., Tang, J., and Yang, Z.-L.: Technical
Description of version 4.5 of the Community Land Model (CLM), NCAR Technical
Note NCAR/TN-503+STR, Boulder, Colorado, 420 pp., 2013.
Olsoy, P. J., Mitchell, J. J., Levia, D. F., Clark, P. E., and Glenn, N. F.:
Estimation of big sagebrush leaf area index with terrestrial laser scanning,
Ecol. Indic., 61, 815–821,
https://doi.org/10.1016/j.ecolind.2015.10.034, 2016.
Pacala, S. W., Canham, C. D., and Silander Jr., J. A.: Forest models defined by field measurements. I. The design of a northeastern forest simulator, Can.
J. For. Res., 23, 1980–1988, 1993.
Pandit, K: Modified source codes for ED2 with shrub parameters, Zenodo, https://doi.org/10.5281/zenodo.3461233, 2019.
Pereira, F. F., Farinosi, F., Arias, M. E., Lee, E., Briscoe, J., and Moorcroft, P. R.: Technical note: A hydrological routing scheme for the Ecosystem Demography model (ED2+R) tested in the Tapajós River basin in the Brazilian Amazon, Hydrol. Earth Syst. Sci., 21, 4629–4648, https://doi.org/10.5194/hess-21-4629-2017, 2017.
Qi, Y., Dennison, P. E., Jolly, W. M, Kropp, R. C., and Brewer, S. C.:
Spectroscopic analysis of seasonal changes in live fuel moisture content and
leaf dry mass, Remote Sens. Environ., 150, 198–206,
https://doi.org/10.1016/j.rse.2014.05.004, 2014.
Qi, Y., Ustin, S. L., and Glenn, N. F.: Imaging Spectroscopic Analysis of
Biochemical Traits for Shrub Species in Great Basin, USA, Remote Sens., 10,
1621, https://doi.org/10.3390/rs10101621, 2018.
Quan, X., He, B., and Li, X.: A Bayesian Network-Based Method to Alleviate
the Ill-Posed Inverse Problem: A Case Study on Leaf Area Index and Canopy
Water Content Retrieval, IEEE T. Geosci. Remote,
33, 6507–6517, https://doi.org/10.1109/TGRS.2015.2442999, 2015
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet M., Berbigier
P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grünwald,
T., Havránková, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila,
T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F.,
Ourcival, J. M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M.,
Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and
Valentini, R.: On the separation of net ecosystem exchange into assimilation
and ecosystem respiration: review and improved algorithm, Glob. Change
Biol., 11, 1424–39, https://doi.org/10.1111/j.1365-2486.2005.001002.x,
2005.
Richardson, A. D., Williams, M., Hollinger, D. Y., Moore, D. J. P., Dail, D.
B., Davidson, E. A., Scott, N. A., Evans, R. E., Hughes, H., Lee, J. T.,
Rodrigues, C., and Savage, K.: Estimating parameters of a forest ecosystem C
model with measurements of stocks and fluxes as joint constraints,
Oecologia, 164, 25–40, https://doi.org/10.1007/s00442-010-1628-y, 2010.
Schlaepfer, D. R., Lauenrotha, W. K., and Bradford, J. B.: Modeling
regeneration responses of big sagebrush (Artemisia tridentata) to abiotic
conditions, Ecol. Model., 286, 66–77,
https://doi.org/10.1016/j.ecolmodel.2014.04.021, 2014.
Schmidtlein, S., Tichy, L., Feilhauer, H., and Faude, U.: A brute-force
approach to vegetation classification, J. Veg. Sci., 21,
1162–1171, https://doi.org/10.1111/j.1654-1103.2010.01221.x, 2010.
Schroeder, M. A., Aldridge, C. L., Apa, A. D., Bohne, J. R., Braun, C. E.,
Bunnell, S. D., Connelly, J. W., Deibert, P. A., Gardner, S. C., Hilliard,
M. A., Kobriger, G. D., McAdam, S. M., McCarthy, C. W., McCarthy, J. J.,
Mitchell, D. L., Rickerson, E. V., and Stiver, S. J.: Distribution of
Sage-Grouse in North America, The Condor, 106, 363–376,
https://doi.org/10.1650/7425, 2004.
Schwantes, A. M., Swenson, J. J., and Jackson, R. B.: Quantifying
drought-induced tree mortality in the open canopy woodlands of central
Texas, Remote Sens.Environ., 181, 54–64,
https://doi.org/10.1016/j.rse.2016.03.027, 2016.
Sellers, P. J., Berry, J. A., Collatz, G. J., Field, C. B., and Hall, F. G.:
Canopy reflectance, photosynthesis, and transpiration, III. A reanalysis
using improved leaf models and a new canopy integration scheme, Remote
Sens. Environ., 42, 187–216, https://doi.org/10.1016/0034-4257(92)90102-P,
1992.
Seyfried, M. S., Harris, R., Marks, D., and Jacob, B.: A geographic database
for watershed research: Reynolds Creek Experimental Watershed, Idaho, USA,
Tech. Bull. NWRC 2000-3, 26 pp., Northwest Watershed Res. Cent., Agric. Res.
Serv., U.S. Dep. of Agric., Boise, Idaho, 2000
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W.,
Kaplans, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and
Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and
terrestrial carbon cycling in the LPJ dynamic vegetation model, Glob.
Change Biol., 9, 161–185, 2003.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M.,
Duda, M. G., Huang, X., Wang, W., and Powers, J. G.: A Description of the
Advanced Research WRF Version 3. NCAR Technical Note NCAR/TN-475+STR,
https://doi.org/10.5065/D68S4MVH, 2008.
Smith, B., Trentice, C. I., and Sykes, M. T.: Representation of vegetation
dynamics in the modelling of terrestrial ecosystems: comparing two
contrasting approaches within European climate space, Global Ecol.
Biogeogr., 10, 621–637, 2001.
Steinberg, P. D.: Artemisia arbuscula, iFire Effects Information System, U.S. Department of Agriculture, Forest Service, Rocky Mountain
Research Station, Fire Sciences Laboratory (Producer), available at:
https://www.fs.fed.us/database/feis/plants/shrub/artarb/all.html (last
access: 27 July 2019), 2002.
Stephenson, G. R. (Ed.): CZO Dataset: Reynolds Creek – Geology, Soil Survey,
Vegetation, GIS/Map Data (1960–1970), available at:
http://criticalzone.org/reynolds/data/dataset/3722/, last access: 18 May
2018.
Sturges, D. L.: Soil water withdrawl and root characteristics of Big
Sagebrush, Am. Midl. Nat., 98, 257–274, 1977.
Tabler, R. D.: The root system of Artemisia tridentata at 9,500 feet in
Wyoming, Ecology, 45, 633–636, 1964.
Trugman, A. T., Fenton, N. J., Bergeron, Y., Xu, X., Welp, L. R., and
Medvigy, D.: Climate, soil organic layer, and nitrogen jointly drive forest
development after fire in the North American boreal zone, J.
Adv. Model. Earth Syst., 8, 1180–1209,
https://doi.org/10.1002/2015MS000576, 2016.
USDA: Plant materials Technical Note No. MT-114, Natural Resources
Conservation Service, United States Department of Agriculture, August 2016,
2016.
USDA: Soil Survey Staff, Natural Resources Conservation Service, United
States Department of Agriculture, Web Soil Survey, available at:
https://websoilsurvey.sc.egov.usda.gov/, last access: 28 March, 2018a.
USDA: The PLANTS Database, National
Plant Data Team, Greensboro, NC 27401-4901 USA, available at: http://plants.usda.gov, last access: 20 August, 2018b.
Wambura, F. J., Ndombab, P. M., Kongob, V., and Tumboc, S. D.: Uncertainty of
runoff projections under changing climate in Wami River sub-basin, J. Hydrol.-Reg. Studies, 4, 333–348, 2015.
Wang, D., LeBauer, D., and Dietze, M.: Predicting yields of short-rotation
hybrid poplar (Populus spp.) for the United States through model–data
synthesis, Ecol. Appl., 23, 944–958, 2013.
Waring, B. G., Averill, C., and Hawkes, C. V.: Differences in fungal and
bacterial physiology alter soil carbon and nitrogen cycling: insights from
meta-analysis and theoretical models, Ecol. Lett., 16, 887–894, https://doi.org/10.1111/ele.12125, 2016
Weiss, A. and Norman, J. M.: Partitioning solar radiation into direct and
diffuse, visible and near-infrared components, Agr. Forest Meteorol., 34,
205–213, 1985.
Wolf, A., Callaghan, T. V., and Larson, K.: Future changes in vegetation and
ecosystem function of the Barents Region, Clim. Change, 87, 51–73, 2008.
Zaehle, S., Sitch, S., Smith, B., and Hatterman, F.: Effects of parameter
uncertainties on the modeling of terrestrialbiosphere dynamics, Global
Biogeochem. Cy., 19, GB3020, https://doi.org/10.1029/2004GB002395, 2005.
Zhang, K., de Almeida Castanho, A. D., Galbraith, D. R., Moghim, S., Levine,
N. M., Bras, R. L., Coe, M. T., Costa, M. H., Malhi, Y., Longo, M., Knox, R.
G., McKnight, S., Wang, J., and Moorcroft, P. R.: The fate of Amazonian
ecosystems over the coming century arising from changes in climate,
atmospheric CO2, and land use, Glob. Change Biol., 21, 2569–2587,
https://doi.org/10.1111/gcb.12903, 2015.
Zhang, K., Ma. J., Zhu, G., Ma, T., Han, T., and Feng, L. L.: Parameter
sensitivity analysis and optimization for a satellite-based
evapotranspiration model across multiple sites using Moderate Resolution
Imaging Spectroradiometer and flux data, J. Geophys. Res.-Atmos., 122,
230–245, https://doi.org/10.1002/2016JD025768, 2017.
Zhang, Y, Guanter, L., Berry, J. A., Joiner, J., Tol, C. V. D., Huete, A.,
Gitelson, A., Voigt, M., and Kohler, P.: Estimation of vegetation
photosynthetic capacity from space-based measurements of chlorophyll
fluorescence for terrestrial biosphere models, Glob. Change Biol., 20,
3727–3742, https://doi.org/10.1111/gcb.12664, 2014.
Short summary
We explored shrub parameters representing sagebrush ecosystems within a dynamic vegetation model and estimated gross primary production (GPP) for two sagebrush sites in the northern Great Basin. Comparison with observations from eddy covariance (EC) tower data showed our modeled results were encouraging, although some seasonal underestimates were apparent. We believe our findings on preliminary parameterization of shrub PFT is an important step towards subsequent studies on shrubland ecosystems.
We explored shrub parameters representing sagebrush ecosystems within a dynamic vegetation model...