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14 results on '"Subin, Zachary M."'

1. The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty

Author

Lawrence, David M, Fisher, Rosie A, Koven, Charles D, Oleson, Keith W, Swenson, Sean C, Bonan, Gordon, Collier, Nathan, Ghimire, Bardan, van Kampenhout, Leo, Kennedy, Daniel, Kluzek, Erik, Lawrence, Peter J, Li, Fang, Li, Hongyi, Lombardozzi, Danica, Riley, William J, Sacks, William J, Shi, Mingjie, Vertenstein, Mariana, Wieder, William R, Xu, Chonggang, Ali, Ashehad A, Badger, Andrew M, Bisht, Gautam, van den Broeke, Michiel, Brunke, Michael A, Burns, Sean P, Buzan, Jonathan, Clark, Martyn, Craig, Anthony, Dahlin, Kyla, Drewniak, Beth, Fisher, Joshua B, Flanner, Mark, Fox, Andrew M, Gentine, Pierre, Hoffman, Forrest, Keppel‐Aleks, Gretchen, Knox, Ryan, Kumar, Sanjiv, Lenaerts, Jan, Leung, L Ruby, Lipscomb, William H, Lu, Yaqiong, Pandey, Ashutosh, Pelletier, Jon D, Perket, Justin, Randerson, James T, Ricciuto, Daniel M, Sanderson, Benjamin M, Slater, Andrew, Subin, Zachary M, Tang, Jinyun, Thomas, R Quinn, Martin, Maria Val, and Zeng, Xubin

Subjects
Earth Sciences, Atmospheric Sciences, Geoinformatics, Climate Action, global land model, Earth System Modeling, carbon and nitrogen cycling, hydrology, benchmarking, Atmospheric sciences
Abstract

The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time-evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.

Published
2019

2. A Hybrid Reduced‐Order Model of Fine‐Resolution Hydrologic Simulations at a Polygonal Tundra Site

Author

Liu, Yaning, Bisht, Gautam, Subin, Zachary M, Riley, William J, and Pau, George Shu Heng

Subjects
Hydrology, Earth Sciences, Physical Geography and Environmental Geoscience, Soil Sciences, Crop and Pasture Production, Environmental Engineering, Soil sciences
Abstract

High-resolution predictions of land surface hydrological dynamics are desirable for improved investigations of regional- and watershed-scale processes. Direct deterministic simulations of fine-resolution land surface variables present many challenges, including high computational cost. We therefore propose the use of reduced-order modeling techniques to facilitate emulation of fine-resolution simulations. We use an emulator, Gaussian process regression, to approximate fine-resolution four-dimensional soil moisture fields predicted using a three-dimensional surface-subsurface hydrological simulator (PFLOTRAN). A dimension-reduction technique known as “proper orthogonal decomposition” is further used to improve the efficiency of the resulting reduced-order model (ROM). The ROM reduces simulation computational demand to negligible levels compared to the underlying fine-resolution model. In addition, the ROM that we constructed is equipped with an uncertainty estimate, allowing modelers to construct a ROM consistent with uncertainty in the measured data. The ROM is also capable of constructing statistically equivalent analogs that can be used in uncertainty and sensitivity analyses. We apply the technique to four polygonal tundra sites near Barrow, Alaska that are part of the Department of Energy’s Next-Generation Ecosystem Experiments (NGEE)-Arctic project. The ROM is trained for each site using simulated soil moisture from 1998-2000 and validated using the simulated data for 2002 and 2006. The average relative RMSEs of the ROMs are under 1%.

Published
2016

5. The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty

Author

Lawrence, David M., Fisher, Rosie A., Koven, Charles D., Oleson, Keith W., Swenson, Sean C., Bonan, Gordon B., Collier, Nathan, Ghimire, Bardan, van Kampenhout, Leo, Kennedy, Daniel, Kluzek, Erik, Lawrence, Peter J., Li, Fang, Li, Hongyi, Lombardozzi, Danica L., Riley, William J., Sacks, William J., Shi, Mingjie, Vertenstein, Mariana, Wieder, William R., Xu, Chonggang, Ali, Ashehad A., Badger, Andrew M., Bisht, Gautam, van den Broeke, Michiel, Brunke, Michael A., Burns, Sean P., Buzan, Jonathan, Clark, Martyn, Craig, Anthony, Dahlin, Kyla, Drewniak, Beth, Fisher, Joshua B., Flanner, Mark, Fox, Andrew M., Gentine, Pierre, Hoffman, Forrest, Keppel-Aleks, Gretchen, Knox, Ryan, Kumar, Sanjiv, Lenaerts, Jan, Leung, L. Ruby, Lipscomb, William H., Lu, Yaqiong, Pandey, Ashutosh, Pelletier, Jon D., Perket, Justin, Randerson, James T., Ricciuto, Daniel M., Sanderson, Benjamin M., Slater, Andrew, Subin, Zachary M., Tang, Jinyun, Thomas, R. Quinn, Martin, Maria Val, Zeng, Xubin, Lawrence, David M., Fisher, Rosie A., Koven, Charles D., Oleson, Keith W., Swenson, Sean C., Bonan, Gordon B., Collier, Nathan, Ghimire, Bardan, van Kampenhout, Leo, Kennedy, Daniel, Kluzek, Erik, Lawrence, Peter J., Li, Fang, Li, Hongyi, Lombardozzi, Danica L., Riley, William J., Sacks, William J., Shi, Mingjie, Vertenstein, Mariana, Wieder, William R., Xu, Chonggang, Ali, Ashehad A., Badger, Andrew M., Bisht, Gautam, van den Broeke, Michiel, Brunke, Michael A., Burns, Sean P., Buzan, Jonathan, Clark, Martyn, Craig, Anthony, Dahlin, Kyla, Drewniak, Beth, Fisher, Joshua B., Flanner, Mark, Fox, Andrew M., Gentine, Pierre, Hoffman, Forrest, Keppel-Aleks, Gretchen, Knox, Ryan, Kumar, Sanjiv, Lenaerts, Jan, Leung, L. Ruby, Lipscomb, William H., Lu, Yaqiong, Pandey, Ashutosh, Pelletier, Jon D., Perket, Justin, Randerson, James T., Ricciuto, Daniel M., Sanderson, Benjamin M., Slater, Andrew, Subin, Zachary M., Tang, Jinyun, Thomas, R. Quinn, Martin, Maria Val, and Zeng, Xubin

Abstract

The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time-evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.

Published
2019
Full Text
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6. The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty

Author

Sub Dynamics Meteorology, Afd Taalwetenschap, Marine and Atmospheric Research, Lawrence, David M., Fisher, Rosie A., Koven, Charles D., Oleson, Keith W., Swenson, Sean C., Bonan, Gordon, Collier, Nathan, Ghimire, Bardan, van Kampenhout, Leo, Kennedy, Daniel, Kluzek, Erik, Lawrence, Peter J., Li, Fang, Li, Hongyi, Lombardozzi, Danica, Riley, William J., Sacks, William J., Shi, Mingjie, Vertenstein, Mariana, Wieder, William R., Xu, Chonggang, Ali, Ashehad A., Badger, Andrew M., Bisht, Gautam, van den Broeke, Michiel, Brunke, Michael A., Burns, Sean P., Buzan, Jonathan, Clark, Martyn, Craig, Anthony, Dahlin, Kyla, Drewniak, Beth, Fisher, Joshua B., Flanner, Mark, Fox, Andrew M., Gentine, Pierre, Hoffman, Forrest, Keppel-Aleks, Gretchen, Knox, Ryan, Kumar, Sanjiv, Lenaerts, Jan, Leung, L. Ruby, Lipscomb, William H., Lu, Yaqiong, Pandey, Ashutosh, Pelletier, Jon D., Perket, Justin, Randerson, James T., Ricciuto, Daniel M., Sanderson, Benjamin M., Slater, Andrew, Subin, Zachary M., Tang, Jinyun, Thomas, R. Quinn, Martin, Maria Val, Zeng, Xubin, Sub Dynamics Meteorology, Afd Taalwetenschap, Marine and Atmospheric Research, Lawrence, David M., Fisher, Rosie A., Koven, Charles D., Oleson, Keith W., Swenson, Sean C., Bonan, Gordon, Collier, Nathan, Ghimire, Bardan, van Kampenhout, Leo, Kennedy, Daniel, Kluzek, Erik, Lawrence, Peter J., Li, Fang, Li, Hongyi, Lombardozzi, Danica, Riley, William J., Sacks, William J., Shi, Mingjie, Vertenstein, Mariana, Wieder, William R., Xu, Chonggang, Ali, Ashehad A., Badger, Andrew M., Bisht, Gautam, van den Broeke, Michiel, Brunke, Michael A., Burns, Sean P., Buzan, Jonathan, Clark, Martyn, Craig, Anthony, Dahlin, Kyla, Drewniak, Beth, Fisher, Joshua B., Flanner, Mark, Fox, Andrew M., Gentine, Pierre, Hoffman, Forrest, Keppel-Aleks, Gretchen, Knox, Ryan, Kumar, Sanjiv, Lenaerts, Jan, Leung, L. Ruby, Lipscomb, William H., Lu, Yaqiong, Pandey, Ashutosh, Pelletier, Jon D., Perket, Justin, Randerson, James T., Ricciuto, Daniel M., Sanderson, Benjamin M., Slater, Andrew, Subin, Zachary M., Tang, Jinyun, Thomas, R. Quinn, Martin, Maria Val, and Zeng, Xubin

Published
2019

7. The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty

Author

Forest Resources and Environmental Conservation, Lawrence, David M., Fisher, Rosie A., Koven, Charles D., Oleson, Keith W., Swenson, Sean C., Bonan, Gordon B., Collier, Nathan, Ghimire, Bardan, van Kampenhout, Leo, Kennedy, Daniel, Kluzek, Erik, Lawrence, Peter J., Li, Fang, Li, Hongyi, Lombardozzi, Danica L., Riley, William J., Sacks, William J., Shi, Mingjie, Vertenstein, Mariana, Wieder, William R., Xu, Chonggang, Ali, Ashehad A., Badger, Andrew M., Bisht, Gautam, van den Broeke, Michiel, Brunke, Michael A., Burns, Sean P., Buzan, Jonathan, Clark, Martyn, Craig, Anthony, Dahlin, Kyla, Drewniak, Beth, Fisher, Joshua B., Flanner, Mark, Fox, Andrew M., Gentine, Pierre, Hoffman, Forrest, Keppel-Aleks, Gretchen, Knox, Ryan, Kumar, Sanjiv, Lenaerts, Jan, Leung, L. Ruby, Lipscomb, William H., Lu, Yaqiong, Pandey, Ashutosh, Pelletier, Jon D., Perket, Justin, Randerson, James T., Ricciuto, Daniel M., Sanderson, Benjamin M., Slater, Andrew, Subin, Zachary M., Tang, Jinyun, Thomas, R. Quinn, Martin, Maria Val, Zeng, Xubin, Forest Resources and Environmental Conservation, Lawrence, David M., Fisher, Rosie A., Koven, Charles D., Oleson, Keith W., Swenson, Sean C., Bonan, Gordon B., Collier, Nathan, Ghimire, Bardan, van Kampenhout, Leo, Kennedy, Daniel, Kluzek, Erik, Lawrence, Peter J., Li, Fang, Li, Hongyi, Lombardozzi, Danica L., Riley, William J., Sacks, William J., Shi, Mingjie, Vertenstein, Mariana, Wieder, William R., Xu, Chonggang, Ali, Ashehad A., Badger, Andrew M., Bisht, Gautam, van den Broeke, Michiel, Brunke, Michael A., Burns, Sean P., Buzan, Jonathan, Clark, Martyn, Craig, Anthony, Dahlin, Kyla, Drewniak, Beth, Fisher, Joshua B., Flanner, Mark, Fox, Andrew M., Gentine, Pierre, Hoffman, Forrest, Keppel-Aleks, Gretchen, Knox, Ryan, Kumar, Sanjiv, Lenaerts, Jan, Leung, L. Ruby, Lipscomb, William H., Lu, Yaqiong, Pandey, Ashutosh, Pelletier, Jon D., Perket, Justin, Randerson, James T., Ricciuto, Daniel M., Sanderson, Benjamin M., Slater, Andrew, Subin, Zachary M., Tang, Jinyun, Thomas, R. Quinn, Martin, Maria Val, and Zeng, Xubin

Abstract

The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time-evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.

Published
2019

9. LakeMIP Kivu: evaluating the representation of a large, deep tropical lake by a set of one-dimensional lake models

Author

Thiery, Wim, primary, Stepanenko, Victor M., additional, Fang, Xing, additional, Jöhnk, Klaus D., additional, Li, Zhongshun, additional, Martynov, Andrey, additional, Perroud, Marjorie, additional, Subin, Zachary M., additional, Darchambeau, François, additional, Mironov, Dmitrii, additional, and Van Lipzig, Nicole P. M., additional

Published
2014
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14. A Hybrid Reduced-Order Model of Fine-Resolution Hydrologic Simulations at a Polygonal Tundra Site.

Author

Yaning Liu, Bisht, Gautam, Subin, Zachary M., Riley, William J., and George Shu Heng Pau

Subjects
REDUCED-order models, ECOSYSTEM dynamics, TUNDRA ecology
Abstract

We propose a hybrid reduced-order model that efficiently predicts fine-resolution responses to forcings by first training the model with solutions from a computationally intensive model. The method was applied to Next-Generation Ecosystem Experiments–Arctic study sites to predict fine-resolution soil moisture fields based on precipitation and evapotranspiration rates. An average accuracy of 99% is achieved. High-resolution predictions of land surface hydrological dynamics are desirable for improved investigations of regional- and watershed-scale processes. Direct deterministic simulations of fine-resolution land surface variables present many challenges, including high computational cost. We therefore propose the use of reduced-order modeling techniques to facilitate emulation of fine-resolution simulations. We use an emulator, Gaussian process regression, to approximate fine-resolution four-dimensional soil moisture fields predicted using a three-dimensional surface-subsurface hydrological simulator (PFLOTRAN). A dimension-reduction technique known as “proper orthogonal decomposition” is further used to improve the efficiency of the resulting reduced-order model (ROM). The ROM reduces simulation computational demand to negligible levels compared to the underlying fine-resolution model. In addition, the ROM that we constructed is equipped with an uncertainty estimate, allowing modelers to construct a ROM consistent with uncertainty in the measured data. The ROM is also capable of constructing statistically equivalent analogs that can be used in uncertainty and sensitivity analyses. We apply the technique to four polygonal tundra sites near Barrow, Alaska that are part of the Department of Energy's Next-Generation Ecosystem Experiments (NGEE)–Arctic project. The ROM is trained for each site using simulated soil moisture from 1998–2000 and validated using the simulated data for 2002 and 2006. The average relative RMSEs of the ROMs are under 1%. [ABSTRACT FROM AUTHOR]

Published
2016
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