Articles | Volume 18, issue 12
https://doi.org/10.5194/gmd-18-3857-2025
https://doi.org/10.5194/gmd-18-3857-2025
Methods for assessment of models
 | 
30 Jun 2025
Methods for assessment of models |  | 30 Jun 2025

Estimation of above- and below-ground ecosystem parameters for DVM-DOS-TEM v0.7.0 using MADS v1.7.3

Elchin E. Jafarov, Hélène Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali

Related authors

Fusing Regional and Global Datasets to Develop a Composite Land Cover Product Across High Latitudes
Valeria Briones, Hélène Genet, Elchin E. Jafarov, Brendan M. Rogers, Jennifer D. Watts, Anna-Maria Virkkala, Annett Bartsch, Benjamin C. Maglio, Joshua Rady, and Susan M. Natali
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-226,https://doi.org/10.5194/essd-2025-226, 2025
Preprint under review for ESSD
Short summary
Thermal modeling of three lakes within the continuous permafrost zone in Alaska using the LAKE 2.0 model
Jason A. Clark, Elchin E. Jafarov, Ken D. Tape, Benjamin M. Jones, and Victor Stepanenko
Geosci. Model Dev., 15, 7421–7448, https://doi.org/10.5194/gmd-15-7421-2022,https://doi.org/10.5194/gmd-15-7421-2022, 2022
Short summary
The importance of freeze–thaw cycles for lateral tracer transport in ice-wedge polygons
Elchin E. Jafarov, Daniil Svyatsky, Brent Newman, Dylan Harp, David Moulton, and Cathy Wilson
The Cryosphere, 16, 851–862, https://doi.org/10.5194/tc-16-851-2022,https://doi.org/10.5194/tc-16-851-2022, 2022
Short summary
New insights into the drainage of inundated ice-wedge polygons using fundamental hydrologic principles
Dylan R. Harp, Vitaly Zlotnik, Charles J. Abolt, Bob Busey, Sofia T. Avendaño, Brent D. Newman, Adam L. Atchley, Elchin Jafarov, Cathy J. Wilson, and Katrina E. Bennett
The Cryosphere, 15, 4005–4029, https://doi.org/10.5194/tc-15-4005-2021,https://doi.org/10.5194/tc-15-4005-2021, 2021
Short summary
New insights into the drainage of inundated Arctic polygonal tundra using fundamental hydrologic principles
Dylan R. Harp, Vitaly Zlotnik, Charles J. Abolt, Brent D. Newman, Adam L. Atchley, Elchin Jafarov, and Cathy J. Wilson
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-100,https://doi.org/10.5194/tc-2020-100, 2020
Manuscript not accepted for further review
Short summary

Related subject area

Biogeosciences
Alquimia v1.0: a generic interface to biogeochemical codes – a tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Sergi Molins, Benjamin J. Andre, Jeffrey N. Johnson, Glenn E. Hammond, Benjamin N. Sulman, Konstantin Lipnikov, Marcus S. Day, James J. Beisman, Daniil Svyatsky, Hang Deng, Peter C. Lichtner, Carl I. Steefel, and J. David Moulton
Geosci. Model Dev., 18, 3241–3263, https://doi.org/10.5194/gmd-18-3241-2025,https://doi.org/10.5194/gmd-18-3241-2025, 2025
Short summary
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev., 18, 3131–3155, https://doi.org/10.5194/gmd-18-3131-2025,https://doi.org/10.5194/gmd-18-3131-2025, 2025
Short summary
Parameterization toolbox for a physical–biogeochemical model compatible with FABM – a case study: the coupled 1D GOTM–ECOSMO E2E for the Sylt–Rømø Bight, North Sea
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025,https://doi.org/10.5194/gmd-18-2961-2025, 2025
Short summary
H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025,https://doi.org/10.5194/gmd-18-2921-2025, 2025
Short summary
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
Geosci. Model Dev., 18, 2521–2544, https://doi.org/10.5194/gmd-18-2521-2025,https://doi.org/10.5194/gmd-18-2521-2025, 2025
Short summary

Cited articles

Andresen, C. G., Lawrence, D. M., Wilson, C. J., McGuire, A. D., Koven, C., Schaefer, K., Jafarov, E., Peng, S., Chen, X., Gouttevin, I., Burke, E., Chadburn, S., Ji, D., Chen, G., Hayes, D., and Zhang, W.: Soil moisture and hydrology projections of the permafrost region – a model intercomparison, The Cryosphere, 14, 445–459, https://doi.org/10.5194/tc-14-445-2020, 2020. 
Barajas-Solano, D. A., Wohlberg, B. E., Vesselinov, V. V., and Tartakovsky, D. M.: Linear functional minimization for inverse modeling, Water Resour. Res., 51, 4516–4531, https://doi.org/10.1002/2014WR016179, 2015. 
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, J. Hydrol., 249, 11–29, https://doi.org/10.1016/S0022-1694(01)00421-8, 2001. 
Birch, L., Schwalm, C. R., Natali, S., Lombardozzi, D., Keppel-Aleks, G., Watts, J., Lin, X., Zona, D., Oechel, W., Sachs, T., Black, T. A., and Rogers, B. M.: Addressing biases in Arctic–boreal carbon cycling in the Community Land Model Version 5, Geosci. Model Dev., 14, 3361–3382, https://doi.org/10.5194/gmd-14-3361-2021, 2021. 
Bloom, A. A., Exbrayat, J.-F., Van Der Velde, I. R., Feng, L., and Williams, M.: The decadal state of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools, and residence times, P. Natl. Acad. Sci. USA, 113, 1285–1290, https://doi.org/10.1073/pnas.1515160113, 2016. 
Short summary
This study improves how we tune ecosystem models to reflect carbon and nitrogen storage in Arctic soils. By comparing model outputs with data from a black spruce forest in Alaska, we developed a clearer, more efficient method of matching observations. This is a key step towards understanding how Arctic ecosystems may respond to warming and release carbon, helping make future climate predictions more reliable.
Share