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.
Development and technical paper
| 
05 Nov 2019
Development and technical paper |
| 05 Nov 2019
| 05 Nov 2019
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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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.
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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.
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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
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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
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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.
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-451, https://doi.org/10.5194/hess-2020-451, 2020
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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
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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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Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
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Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
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This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
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
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
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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.
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, https://doi.org/10.5194/gmd-17-3949-2024, 2024
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Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814, https://doi.org/10.5194/gmd-17-3801-2024, https://doi.org/10.5194/gmd-17-3801-2024, 2024
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All accurate climate models use equations with poorly defined parameters, where knobs for the parameters are turned to fit the observations. This process is called tuning. In this article, we use another paradigm. We use a thermodynamic hypothesis, the maximum entropy production, to compute temperatures, energy fluxes, and precipitation, where tuning is impossible. For now, the 1D vertical model is used for a tropical atmosphere. The correct order of magnitude of precipitation is computed.
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731, https://doi.org/10.5194/gmd-17-3687-2024, https://doi.org/10.5194/gmd-17-3687-2024, 2024
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We developed a regionally refined climate model that allows resolved convection and performed a 20-year projection to the end of the century. The model has a resolution of 3.25 km in California, which allows us to predict climate with unprecedented accuracy, and a resolution of 100 km for the rest of the globe to achieve efficient, self-consistent simulations. The model produces superior results in reproducing climate patterns over California that typical modern climate models cannot resolve.
Xiaohui Zhong, Xing Yu, and Hao Li
Geosci. Model Dev., 17, 3667–3685, https://doi.org/10.5194/gmd-17-3667-2024, https://doi.org/10.5194/gmd-17-3667-2024, 2024
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In order to forecast localized warm-sector rainfall in the south China region, numerical weather prediction models are being run with finer grid spacing. The conventional convection parameterization (CP) performs poorly in the gray zone, necessitating the development of a scale-aware scheme. We propose a machine learning (ML) model to replace the scale-aware CP scheme. Evaluation against the original CP scheme has shown that the ML-based CP scheme can provide accurate and reliable predictions.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
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Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431, https://doi.org/10.5194/gmd-17-3409-2024, https://doi.org/10.5194/gmd-17-3409-2024, 2024
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Climate field reconstruction encompasses methods that estimate the evolution of climate in space and time based on natural archives. It is useful to investigate climate variations and validate climate models, but its implementation and use can be difficult for non-experts. This paper introduces a user-friendly Python package called cfr to make these methods more accessible, thanks to the computational and visualization tools that facilitate efficient and reproducible research on past climates.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev., 17, 3433–3445, https://doi.org/10.5194/gmd-17-3433-2024, https://doi.org/10.5194/gmd-17-3433-2024, 2024
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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 driven by either uniform erosion 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.
Safae Oumami, Joaquim Arteta, Vincent Guidard, Pierre Tulet, and Paul David Hamer
Geosci. Model Dev., 17, 3385–3408, https://doi.org/10.5194/gmd-17-3385-2024, https://doi.org/10.5194/gmd-17-3385-2024, 2024
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In this paper, we coupled the SURFEX and MEGAN models. The aim of this coupling is to improve the estimation of biogenic fluxes by using the SURFEX canopy environment model. The coupled model results were validated and several sensitivity tests were performed. The coupled-model total annual isoprene flux is 442 Tg; this value is within the range of other isoprene estimates reported. The ultimate aim of this coupling is to predict the impact of climate change on biogenic emissions.
Lars Ackermann, Thomas Rackow, Kai Himstedt, Paul Gierz, Gregor Knorr, and Gerrit Lohmann
Geosci. Model Dev., 17, 3279–3301, https://doi.org/10.5194/gmd-17-3279-2024, https://doi.org/10.5194/gmd-17-3279-2024, 2024
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We present long-term simulations with interactive icebergs in the Southern Ocean. By melting, icebergs reduce the temperature and salinity of the surrounding ocean. In our simulations, we find that this cooling effect of iceberg melting is not limited to the surface ocean but also reaches the deep ocean and propagates northward into all ocean basins. Additionally, the formation of deep-water masses in the Southern Ocean is enhanced.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Beiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
Geosci. Model Dev., 17, 3259–3277, https://doi.org/10.5194/gmd-17-3259-2024, https://doi.org/10.5194/gmd-17-3259-2024, 2024
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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. Results show that RegCM-Chem–YIBs model is a valuable tool for studying the terrestrial carbon cycle, atmospheric chemistry, and climate change on a regional scale.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
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Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
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We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
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Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
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Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
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Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
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We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
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This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
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
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
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The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
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This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
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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
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
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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.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-38, https://doi.org/10.5194/gmd-2024-38, 2024
Revised manuscript accepted for GMD
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Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
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.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
EGUsphere, https://doi.org/10.5194/egusphere-2024-1, https://doi.org/10.5194/egusphere-2024-1, 2024
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This article gives an overview introduction of the IAP-CAS S2S (sub-seasonal to seasonal) ensemble forecasting system and MJO forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its advantages but also exhibits some biases, including underdispersive ensemble, overestimated amplitude and faster propagation speed when forecasting MJO. We also provide the explanation towards these biases and prospects for further improvement of this system in the future.
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.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
EGUsphere, https://doi.org/10.5194/egusphere-2024-335, https://doi.org/10.5194/egusphere-2024-335, 2024
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Large volcanic eruptions deposit material into the upper-atmosphere, which is capable of altering temperature and wind patterns of the Earth's atmosphere for years following. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a testbed for climate attribution studies.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Peters, Bjørn Hallvard Samset, Benjamin M. Sanderson, and Ragnhild Bieltvedt Skeie
EGUsphere, https://doi.org/10.5194/egusphere-2024-196, https://doi.org/10.5194/egusphere-2024-196, 2024
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The CICERO-SCM has existed as a FORTRAN model since 1999 and consists of a part that calculates radiative forcing and concentrations from emissions, and an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper we describe an updated version ported to python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
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.
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...
