Articles | Volume 18, issue 12 
            
                
                    
            
            
            https://doi.org/10.5194/gmd-18-3857-2025
                    © Author(s) 2025. 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-18-3857-2025
                    © Author(s) 2025. This work is distributed under 
the Creative Commons Attribution 4.0 License.
                the Creative Commons Attribution 4.0 License.
Estimation of above- and below-ground ecosystem parameters for DVM-DOS-TEM v0.7.0 using MADS v1.7.3
Elchin E. Jafarov
CORRESPONDING AUTHOR
                                            
                                    
                                            Woodwell Climate Research Center, Falmouth, MA, USA
                                        
                                    Hélène Genet
                                            Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, USA
                                        
                                    Velimir V. Vesselinov
                                            EnviTrace LLC, Santa Fe, NM, USA
                                        
                                    Valeria Briones
                                            Woodwell Climate Research Center, Falmouth, MA, USA
                                        
                                    Aiza Kabeer
                                            Program in Applied Mathematics, University of Arizona, Tucson, AZ, USA
                                        
                                    Andrew L. Mullen
                                            Woodwell Climate Research Center, Falmouth, MA, USA
                                        
                                    Benjamin Maglio
                                            Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, USA
                                        
                                    Tobey Carman
                                            Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, USA
                                        
                                    Ruth Rutter
                                            Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, USA
                                        
                                    Joy Clein
                                            Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, USA
                                        
                                    Chu-Chun Chang
                                            Woodwell Climate Research Center, Falmouth, MA, USA
                                        
                                    Dogukan Teber
                                            Woodwell Climate Research Center, Falmouth, MA, USA
                                        
                                    Trevor Smith
                                            Woodwell Climate Research Center, Falmouth, MA, USA
                                        
                                    Joshua M. Rady
                                            Woodwell Climate Research Center, Falmouth, MA, USA
                                        
                                    Christina Schädel
                                            Woodwell Climate Research Center, Falmouth, MA, USA
                                        
                                    Jennifer D. Watts
                                            Woodwell Climate Research Center, Falmouth, MA, USA
                                        
                                    Brendan M. Rogers
                                            Woodwell Climate Research Center, Falmouth, MA, USA
                                        
                                    Susan M. Natali
                                            Woodwell Climate Research Center, Falmouth, MA, USA
                                        
                                    Related authors
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
                                    Manuscript not accepted for further review 
                                    Short summary
                                    Short summary
                                            
                                                Arctic warming is causing permafrost to thaw, affecting ecosystems and climate. Since land cover, especially vegetation, shapes how permafrost responds, accurate maps are crucial. Using machine learning, we combined existing global and regional datasets to create a hybrid detailed 1-km map of Arctic-Boreal land cover, improving the representation of forests, shrubs, and wetlands across the circumpolar. 
                                            
                                            
                                        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
                                    Short summary
                                            
                                                Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect Arctic lakes to warm the surrounding frozen ground. We simulate water temperatures in three Arctic lakes in northern Alaska over several years. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season and that more heat storage by lakes would enhance thawing of frozen ground.
                                            
                                            
                                        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
                                    Short summary
                                            
                                                Recent research indicates the importance of lateral transport of dissolved carbon in the polygonal tundra, suggesting that the freeze-up period could further promote lateral carbon transport. We conducted subsurface tracer simulations on high-, flat-, and low-centered polygons to test the importance of the freeze–thaw cycle and freeze-up time for tracer mobility. Our findings illustrate the impact of hydraulic and thermal gradients on tracer mobility, as well as of the freeze-up time.
                                            
                                            
                                        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
                                    Short summary
                                            
                                                Polygon-shaped landforms present in relatively flat Arctic tundra result in complex landscape-scale water drainage. The drainage pathways and the time to transition from inundated conditions to drained have important implications for heat and carbon transport. Using fundamental hydrologic principles, we investigate the drainage pathways and timing of individual polygons, providing insights into the effects of polygon geometry and preferential flow direction on drainage pathways and timing.
                                            
                                            
                                        Hong Lin, Jinyang Du, John S. Kimball, Xiao Cheng, J. Patrick Donnelly, Jennifer D. Watts, and Annett Bartsch
                                        Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-503, https://doi.org/10.5194/essd-2025-503, 2025
                                    Preprint under review for ESSD 
                                    Short summary
                                    Short summary
                                            
                                                Ice cover on small water bodies is highly sensitive to climate change and influences ecosystems, water, and the carbon cycle. We produced a satellite-based ice fraction dataset for small water bodies on the Arctic Coastal Plain from 2017 to 2023. The dataset captures freeze-up and break-up timing and reveals spatial variability. It will support studies of climate–ice interactions and improve models of water and carbon processes.
                                            
                                            
                                        Anna-Maria Virkkala, Isabel Wargowsky, Judith Vogt, McKenzie A. Kuhn, Simran Madaan, Richard O'Keefe, Tiffany Windholz, Kyle A. Arndt, Brendan M. Rogers, Jennifer D. Watts, Kelcy Kent, Mathias Göckede, David Olefeldt, Gerard Rocher-Ros, Edward A. G. Schuur, David Bastviken, Kristoffer Aalstad, Kelly Aho, Joonatan Ala-Könni, Haley Alcock, Inge Althuizen, Christopher D. Arp, Jun Asanuma, Katrin Attermeyer, Mika Aurela, Sivakiruthika Balathandayuthabani, Alan Barr, Maialen Barret, Ochirbat Batkhishig, Christina Biasi, Mats P. Björkman, Andrew Black, Elena Blanc-Betes, Pascal Bodmer, Julia Boike, Abdullah Bolek, Frédéric Bouchard, Ingeborg Bussmann, Lea Cabrol, Eleonora Canfora, Sean Carey, Karel Castro-Morales, Namyi Chae, Andres Christen, Torben R. Christensen, Casper T. Christiansen, Housen Chu, Graham Clark, Francois Clayer, Patrick Crill, Christopher Cunada, Scott J. Davidson, Joshua F. Dean, Sigrid Dengel, Matteo Detto, Catherine Dieleman, Florent Domine, Egor Dyukarev, Colin Edgar, Bo Elberling, Craig A. Emmerton, Eugenie Euskirchen, Grant Falvo, Thomas Friborg, Michelle Garneau, Mariasilvia Giamberini, Mikhail V. Glagolev, Miquel A. Gonzalez-Meler, Gustaf Granath, Jón Guðmundsson, Konsta Happonen, Yoshinobu Harazono, Lorna Harris, Josh Hashemi, Nicholas Hasson, Janna Heerah, Liam Heffernan, Manuel Helbig, Warren Helgason, Michal Heliasz, Greg Henry, Geert Hensgens, Tetsuya Hiyama, Macall Hock, David Holl, Beth Holmes, Jutta Holst, Thomas Holst, Gabriel Hould-Gosselin, Elyn Humphreys, Jacqueline Hung, Jussi Huotari, Hiroki Ikawa, Danil V. Ilyasov, Mamoru Ishikawa, Go Iwahana, Hiroki Iwata, Marcin Antoni Jackowicz-Korczynski, Joachim Jansen, Järvi Järveoja, Vincent E. J. Jassey, Rasmus Jensen, Katharina Jentzsch, Robert G. Jespersen, Carl-Fredrik Johannesson, Chersity P. Jones, Anders Jonsson, Ji Young Jung, Sari Juutinen, Evan Kane, Jan Karlsson, Sergey Karsanaev, Kuno Kasak, Julia Kelly, Kasha Kempton, Marcus Klaus, George W. Kling, Natacha Kljun, Jacqueline Knutson, Hideki Kobayashi, John Kochendorfer, Kukka-Maaria Kohonen, Pasi Kolari, Mika Korkiakoski, Aino Korrensalo, Pirkko Kortelainen, Egle Koster, Kajar Koster, Ayumi Kotani, Praveena Krishnan, Juliya Kurbatova, Lars Kutzbach, Min Jung Kwon, Ethan D. Kyzivat, Jessica Lagroix, Theodore Langhorst, Elena Lapshina, Tuula Larmola, Klaus S. Larsen, Isabelle Laurion, Justin Ledman, Hanna Lee, A. Joshua Leffler, Lance Lesack, Anders Lindroth, David Lipson, Annalea Lohila, Efrén López-Blanco, Vincent L. St. Louis, Erik Lundin, Misha Luoto, Takashi Machimura, Marta Magnani, Avni Malhotra, Marja Maljanen, Ivan Mammarella, Elisa Männistö, Luca Belelli Marchesini, Phil Marsh, Pertti J. Martkainen, Maija E. Marushchak, Mikhail Mastepanov, Alex Mavrovic, Trofim Maximov, Christina Minions, Marco Montemayor, Tomoaki Morishita, Patrick Murphy, Daniel F. Nadeau, Erin Nicholls, Mats B. Nilsson, Anastasia Niyazova, Jenni Nordén, Koffi Dodji Noumonvi, Hannu Nykanen, Walter Oechel, Anne Ojala, Tomohiro Okadera, Sujan Pal, Alexey V. Panov, Tim Papakyriakou, Dario Papale, Sang-Jong Park, Frans-Jan W. Parmentier, Gilberto Pastorello, Mike Peacock, Matthias Peichl, Roman Petrov, Kyra St. Pierre, Norbert Pirk, Jessica Plein, Vilmantas Preskienis, Anatoly Prokushkin, Jukka Pumpanen, Hilary A. Rains, Niklas Rakos, Aleski Räsänen, Helena Rautakoski, Riika Rinnan, Janne Rinne, Adrian Rocha, Nigel Roulet, Alexandre Roy, Anna Rutgersson, Aleksandr F. Sabrekov, Torsten Sachs, Erik Sahlée, Alejandro Salazar, Henrique Oliveira Sawakuchi, Christopher Schulze, Roger Seco, Armando Sepulveda-Jauregui, Svetlana Serikova, Abbey Serrone, Hanna M. Silvennoinen, Sofie Sjogersten, June Skeeter, Jo Snöälv, Sebastian Sobek, Oliver Sonnentag, Emily H. Stanley, Maria Strack, Lena Strom, Patrick Sullivan, Ryan Sullivan, Anna Sytiuk, Torbern Tagesson, Pierre Taillardat, Julie Talbot, Suzanne E. Tank, Mario Tenuta, Irina Terenteva, Frederic Thalasso, Antoine Thiboult, Halldor Thorgeirsson, Fenix Garcia Tigreros, Margaret Torn, Amy Townsend-Small, Claire Treat, Alain Tremblay, Carlo Trotta, Eeva-Stiina Tuittila, Merritt Turetsky, Masahito Ueyama, Muhammad Umair, Aki Vähä, Lona van Delden, Maarten van Hardenbroek, Andrej Varlagin, Ruth K. Varner, Elena Veretennikova, Timo Vesala, Tarmo Virtanen, Carolina Voigt, Jorien E. Vonk, Robert Wagner, Katey Walter Anthony, Qinxue Wang, Masataka Watanabe, Hailey Webb, Jeffrey M. Welker, Andreas Westergaard-Nielsen, Sebastian Westermann, Jeffrey R. White, Christian Wille, Scott N. Williamson, Scott Zolkos, Donatella Zona, and Susan M. Natali
                                        Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-585, https://doi.org/10.5194/essd-2025-585, 2025
                                    Preprint under review for ESSD 
                                    Short summary
                                    Short summary
                                            
                                                This dataset includes monthly measurements of carbon dioxide and methane exchange between land, water, and the atmosphere from over 1,000 sites in Arctic and boreal regions. It combines measurements from a variety of ecosystems, including wetlands, forests, tundra, lakes, and rivers, gathered by over 260 researchers from 1984–2024. This dataset can be used to improve and reduce uncertainty in carbon budgets in order to strengthen our understanding of climate feedbacks in a warming world.
                                            
                                            
                                        Anna C. Talucci, Michael M. Loranty, Jean E. Holloway, Brendan M. Rogers, Heather D. Alexander, Natalie Baillargeon, Jennifer L. Baltzer, Logan T. Berner, Amy Breen, Leya Brodt, Brian Buma, Jacqueline Dean, Clement J. F. Delcourt, Lucas R. Diaz, Catherine M. Dieleman, Thomas A. Douglas, Gerald V. Frost, Benjamin V. Gaglioti, Rebecca E. Hewitt, Teresa Hollingsworth, M. Torre Jorgenson, Mark J. Lara, Rachel A. Loehman, Michelle C. Mack, Kristen L. Manies, Christina Minions, Susan M. Natali, Jonathan A. O'Donnell, David Olefeldt, Alison K. Paulson, Adrian V. Rocha, Lisa B. Saperstein, Tatiana A. Shestakova, Seeta Sistla, Oleg Sizov, Andrey Soromotin, Merritt R. Turetsky, Sander Veraverbeke, and Michelle A. Walvoord
                                    Earth Syst. Sci. Data, 17, 2887–2909, https://doi.org/10.5194/essd-17-2887-2025, https://doi.org/10.5194/essd-17-2887-2025, 2025
                                    Short summary
                                    Short summary
                                            
                                                Wildfires have the potential to accelerate permafrost thaw and the associated feedbacks to climate change. We assembled a dataset of permafrost thaw depth measurements from burned and unburned sites contributed by researchers from across the northern high-latitude region. We estimated maximum thaw depth for each measurement, which addresses a key challenge: the ability to assess impacts of wildfire on maximum thaw depth when measurement timing varies.
                                            
                                            
                                        Ricarda Winkelmann, Donovan P. Dennis, Jonathan F. Donges, Sina Loriani, Ann Kristin Klose, Jesse F. Abrams, Jorge Alvarez-Solas, Torsten Albrecht, David Armstrong McKay, Sebastian Bathiany, Javier Blasco Navarro, Victor Brovkin, Eleanor Burke, Gokhan Danabasoglu, Reik V. Donner, Markus Drüke, Goran Georgievski, Heiko Goelzer, Anna B. Harper, Gabriele Hegerl, Marina Hirota, Aixue Hu, Laura C. Jackson, Colin Jones, Hyungjun Kim, Torben Koenigk, Peter Lawrence, Timothy M. Lenton, Hannah Liddy, José Licón-Saláiz, Maxence Menthon, Marisa Montoya, Jan Nitzbon, Sophie Nowicki, Bette Otto-Bliesner, Francesco Pausata, Stefan Rahmstorf, Karoline Ramin, Alexander Robinson, Johan Rockström, Anastasia Romanou, Boris Sakschewski, Christina Schädel, Steven Sherwood, Robin S. Smith, Norman J. Steinert, Didier Swingedouw, Matteo Willeit, Wilbert Weijer, Richard Wood, Klaus Wyser, and Shuting Yang
                                        EGUsphere, https://doi.org/10.5194/egusphere-2025-1899, https://doi.org/10.5194/egusphere-2025-1899, 2025
                                    Short summary
                                    Short summary
                                            
                                                The Tipping Points Modelling Intercomparison Project (TIPMIP) is an international collaborative effort to systematically assess tipping point risks in the Earth system using state-of-the-art coupled and stand-alone domain models. TIPMIP will provide a first global atlas of potential tipping dynamics, respective critical thresholds and key uncertainties, generating an important building block towards a comprehensive scientific basis for policy- and decision-making.
                                            
                                            
                                        Qing Ying, Benjamin Poulter, Jennifer D. Watts, Kyle A. Arndt, Anna-Maria Virkkala, Lori Bruhwiler, Youmi Oh, Brendan M. Rogers, Susan M. Natali, Hilary Sullivan, Amanda Armstrong, Eric J. Ward, Luke D. Schiferl, Clayton D. Elder, Olli Peltola, Annett Bartsch, Ankur R. Desai, Eugénie Euskirchen, Mathias Göckede, Bernhard Lehner, Mats B. Nilsson, Matthias Peichl, Oliver Sonnentag, Eeva-Stiina Tuittila, Torsten Sachs, Aram Kalhori, Masahito Ueyama, and Zhen Zhang
                                    Earth Syst. Sci. Data, 17, 2507–2534, https://doi.org/10.5194/essd-17-2507-2025, https://doi.org/10.5194/essd-17-2507-2025, 2025
                                    Short summary
                                    Short summary
                                            
                                                We present daily methane (CH4) fluxes of northern wetlands at 10 km resolution during 2016–2022 (WetCH4) derived from a novel machine learning framework. We estimated an average annual CH4 emission of 22.8 ± 2.4 Tg CH4 yr−1 (15.7–51.6 Tg CH4 yr−1). Emissions were intensified in 2016, 2020, and 2022, with the largest interannual variation coming from Western Siberia. Continued, all-season tower observations and improved soil moisture products are needed for future improvement of CH4 upscaling.
                                            
                                            
                                        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
                                    Manuscript not accepted for further review 
                                    Short summary
                                    Short summary
                                            
                                                Arctic warming is causing permafrost to thaw, affecting ecosystems and climate. Since land cover, especially vegetation, shapes how permafrost responds, accurate maps are crucial. Using machine learning, we combined existing global and regional datasets to create a hybrid detailed 1-km map of Arctic-Boreal land cover, improving the representation of forests, shrubs, and wetlands across the circumpolar. 
                                            
                                            
                                        Adam M. Young, Thomas Milliman, Koen Hufkens, Keith Ballou, Christopher Coffey, Kai Begay, Michael Fell, Mostafa Javadian, Alison K. Post, Christina Schädel, Zakary Vladich, Oscar Zimmerman, Dawn M. Browning, Christopher R. Florian, Minkyu Moon, Michael D. SanClements, Bijan Seyednasrollah, Mark A. Friedl, and Andrew D. Richardson
                                        Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-120, https://doi.org/10.5194/essd-2025-120, 2025
                                    Revised manuscript accepted for ESSD 
                                    Short summary
                                    Short summary
                                            
                                                Here, we describe the PhenoCam V3.0 public data release. The PhenoCam Network characterizes vegetation phenology in ecosystems across the US and around the world using repeat digital photography. This V3.0 release includes new additions to the data records (e.g., camera NDVI and simplified data sets) and provides >4800 site years of phenological time series and transition dates, a 170% increase relative to the previous data release (V2.0). Over 450 of the time series are 5 y or longer in length.
                                            
                                            
                                        Lucas R. Diaz, Clement J. F. Delcourt, Moritz Langer, Michael M. Loranty, Brendan M. Rogers, Rebecca C. Scholten, Tatiana A. Shestakova, Anna C. Talucci, Jorien E. Vonk, Sonam Wangchuk, and Sander Veraverbeke
                                    Earth Syst. Dynam., 15, 1459–1482, https://doi.org/10.5194/esd-15-1459-2024, https://doi.org/10.5194/esd-15-1459-2024, 2024
                                    Short summary
                                    Short summary
                                            
                                                Our study in eastern Siberia investigated how fires affect permafrost thaw depth in larch forests. We found that fire induces deeper thaw, yet this process was mediated by topography and vegetation. By combining field and satellite data, we estimated summer thaw depth across an entire fire scar. This research provides insights into post-fire permafrost dynamics and the use of satellite data for mapping fire-induced permafrost thaw.
                                            
                                            
                                        Xiaoran Zhu, Dong Chen, Maruko Kogure, Elizabeth Hoy, Logan T. Berner, Amy L. Breen, Abhishek Chatterjee, Scott J. Davidson, Gerald V. Frost, Teresa N. Hollingsworth, Go Iwahana, Randi R. Jandt, Anja N. Kade, Tatiana V. Loboda, Matt J. Macander, Michelle Mack, Charles E. Miller, Eric A. Miller, Susan M. Natali, Martha K. Raynolds, Adrian V. Rocha, Shiro Tsuyuzaki, Craig E. Tweedie, Donald A. Walker, Mathew Williams, Xin Xu, Yingtong Zhang, Nancy French, and Scott Goetz
                                    Earth Syst. Sci. Data, 16, 3687–3703, https://doi.org/10.5194/essd-16-3687-2024, https://doi.org/10.5194/essd-16-3687-2024, 2024
                                    Short summary
                                    Short summary
                                            
                                                The Arctic tundra is experiencing widespread physical and biological changes, largely in response to warming, yet scientific understanding of tundra ecology and change remains limited due to relatively limited accessibility and studies compared to other terrestrial biomes. To support synthesis research and inform future studies, we created the Synthesized Alaskan Tundra Field Dataset (SATFiD), which brings together field datasets and includes vegetation, active-layer, and fire properties.
                                            
                                            
                                        Surendra Shrestha, Christopher A. Williams, Brendan M. Rogers, John Rogan, and Dominik Kulakowski
                                    Biogeosciences, 21, 2207–2226, https://doi.org/10.5194/bg-21-2207-2024, https://doi.org/10.5194/bg-21-2207-2024, 2024
                                    Short summary
                                    Short summary
                                            
                                                Here, we generated chronosequences of leaf area index (LAI) and surface albedo as a function of time since fire to demonstrate the differences in the characteristic trajectories of post-fire biophysical changes among seven forest types and 21 level III ecoregions of the western United States (US) using satellite data from different sources. We also demonstrated how climate played the dominant role in the recovery of LAI and albedo 10 and 20 years after wildfire events in the western US.
                                            
                                            
                                        Sarah M. Ludwig, Luke Schiferl, Jacqueline Hung, Susan M. Natali, and Roisin Commane
                                    Biogeosciences, 21, 1301–1321, https://doi.org/10.5194/bg-21-1301-2024, https://doi.org/10.5194/bg-21-1301-2024, 2024
                                    Short summary
                                    Short summary
                                            
                                                Landscapes are often assumed to be homogeneous when using eddy covariance fluxes, which can lead to biases when calculating carbon budgets. In this study we report eddy covariance carbon fluxes from heterogeneous tundra. We used the footprints of each flux observation to unmix the fluxes coming from components of the landscape. We identified and quantified hot spots of carbon emissions in the landscape. Accurately scaling with landscape heterogeneity yielded half as much regional carbon uptake.
                                            
                                            
                                        Thomas D. Hessilt, Brendan M. Rogers, Rebecca C. Scholten, Stefano Potter, Thomas A. J. Janssen, and Sander Veraverbeke
                                    Biogeosciences, 21, 109–129, https://doi.org/10.5194/bg-21-109-2024, https://doi.org/10.5194/bg-21-109-2024, 2024
                                    Short summary
                                    Short summary
                                            
                                                In boreal North America, snow and frozen ground prevail in winter, while fires occur in summer. Over the last 20 years, the northwestern parts have experienced earlier snow disappearance and more ignitions. This is opposite to the southeastern parts. However, earlier ignitions following earlier snow disappearance timing led to larger fires across the region. Snow disappearance timing may be a good proxy for ignition timing and may also influence important atmospheric conditions related to fires.
                                            
                                            
                                        Stefano Potter, Sol Cooperdock, Sander Veraverbeke, Xanthe Walker, Michelle C. Mack, Scott J. Goetz, Jennifer Baltzer, Laura Bourgeau-Chavez, Arden Burrell, Catherine Dieleman, Nancy French, Stijn Hantson, Elizabeth E. Hoy, Liza Jenkins, Jill F. Johnstone, Evan S. Kane, Susan M. Natali, James T. Randerson, Merritt R. Turetsky, Ellen Whitman, Elizabeth Wiggins, and Brendan M. Rogers
                                    Biogeosciences, 20, 2785–2804, https://doi.org/10.5194/bg-20-2785-2023, https://doi.org/10.5194/bg-20-2785-2023, 2023
                                    Short summary
                                    Short summary
                                            
                                                Here we developed a new burned-area detection algorithm between 2001–2019 across Alaska and Canada at 500 m resolution. We estimate 2.37 Mha burned annually between 2001–2019 over the domain, emitting 79.3 Tg C per year, with a mean combustion rate of 3.13 kg C m−2. We found larger-fire years were generally associated with greater mean combustion. The burned-area and combustion datasets described here can be used for local- to continental-scale applications of boreal fire science.
                                            
                                            
                                        Michael Moubarak, Seeta Sistla, Stefano Potter, Susan M. Natali, and Brendan M. Rogers
                                    Biogeosciences, 20, 1537–1557, https://doi.org/10.5194/bg-20-1537-2023, https://doi.org/10.5194/bg-20-1537-2023, 2023
                                    Short summary
                                    Short summary
                                            
                                                Tundra wildfires are increasing in frequency and severity with climate change.  We show using a combination of field measurements and computational modeling that tundra wildfires result in a positive feedback to climate change by emitting significant amounts of long-lived greenhouse gasses. With these effects, attention to tundra fires is necessary for mitigating climate change.
                                            
                                            
                                        Peter Stimmler, Mathias Goeckede, Bo Elberling, Susan Natali, Peter Kuhry, Nia Perron, Fabrice Lacroix, Gustaf Hugelius, Oliver Sonnentag, Jens Strauss, Christina Minions, Michael Sommer, and Jörg Schaller
                                    Earth Syst. Sci. Data, 15, 1059–1075, https://doi.org/10.5194/essd-15-1059-2023, https://doi.org/10.5194/essd-15-1059-2023, 2023
                                    Short summary
                                    Short summary
                                            
                                                Arctic soils store large amounts of carbon and nutrients. The availability of nutrients, such as silicon, calcium, iron, aluminum, phosphorus, and amorphous silica, is crucial to understand future carbon fluxes in the Arctic. Here, we provide, for the first time, a unique dataset of the availability of the abovementioned nutrients for the different soil layers, including the currently frozen permafrost layer. We relate these data to several geographical and geological parameters.
                                            
                                            
                                        Luke D. Schiferl, Jennifer D. Watts, Erik J. L. Larson, Kyle A. Arndt, Sébastien C. Biraud, Eugénie S. Euskirchen, Jordan P. Goodrich, John M. Henderson, Aram Kalhori, Kathryn McKain, Marikate E. Mountain, J. William Munger, Walter C. Oechel, Colm Sweeney, Yonghong Yi, Donatella Zona, and Róisín Commane
                                    Biogeosciences, 19, 5953–5972, https://doi.org/10.5194/bg-19-5953-2022, https://doi.org/10.5194/bg-19-5953-2022, 2022
                                    Short summary
                                    Short summary
                                            
                                                As the Arctic rapidly warms, vast stores of thawing permafrost could release carbon dioxide (CO2) into the atmosphere. We combined observations of atmospheric CO2 concentrations from aircraft and a tower with observed CO2 fluxes from tundra ecosystems and found that the Alaskan North Slope in not a consistent source nor sink of CO2. Our study shows the importance of using both site-level and atmospheric measurements to constrain regional net CO2 fluxes and improve biogenic processes in models.
                                            
                                            
                                        Dave van Wees, Guido R. van der Werf, James T. Randerson, Brendan M. Rogers, Yang Chen, Sander Veraverbeke, Louis Giglio, and Douglas C. Morton
                                    Geosci. Model Dev., 15, 8411–8437, https://doi.org/10.5194/gmd-15-8411-2022, https://doi.org/10.5194/gmd-15-8411-2022, 2022
                                    Short summary
                                    Short summary
                                            
                                                We present a global fire emission model based on the GFED model framework with a spatial resolution of 500 m. The higher resolution allowed for a more detailed representation of spatial heterogeneity in fuels and emissions. Specific modules were developed to model, for example, emissions from fire-related forest loss and belowground burning. Results from the 500 m model were compared to GFED4s, showing that global emissions were relatively similar but that spatial differences were substantial.
                                            
                                            
                                        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
                                    Short summary
                                            
                                                Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect Arctic lakes to warm the surrounding frozen ground. We simulate water temperatures in three Arctic lakes in northern Alaska over several years. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season and that more heat storage by lakes would enhance thawing of frozen ground.
                                            
                                            
                                        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
                                    Short summary
                                            
                                                Recent research indicates the importance of lateral transport of dissolved carbon in the polygonal tundra, suggesting that the freeze-up period could further promote lateral carbon transport. We conducted subsurface tracer simulations on high-, flat-, and low-centered polygons to test the importance of the freeze–thaw cycle and freeze-up time for tracer mobility. Our findings illustrate the impact of hydraulic and thermal gradients on tracer mobility, as well as of the freeze-up time.
                                            
                                            
                                        Anna-Maria Virkkala, Susan M. Natali, Brendan M. Rogers, Jennifer D. Watts, Kathleen Savage, Sara June Connon, Marguerite Mauritz, Edward A. G. Schuur, Darcy Peter, Christina Minions, Julia Nojeim, Roisin Commane, Craig A. Emmerton, Mathias Goeckede, Manuel Helbig, David Holl, Hiroki Iwata, Hideki Kobayashi, Pasi Kolari, Efrén López-Blanco, Maija E. Marushchak, Mikhail Mastepanov, Lutz Merbold, Frans-Jan W. Parmentier, Matthias Peichl, Torsten Sachs, Oliver Sonnentag, Masahito Ueyama, Carolina Voigt, Mika Aurela, Julia Boike, Gerardo Celis, Namyi Chae, Torben R. Christensen, M. Syndonia Bret-Harte, Sigrid Dengel, Han Dolman, Colin W. Edgar, Bo Elberling, Eugenie Euskirchen, Achim Grelle, Juha Hatakka, Elyn Humphreys, Järvi Järveoja, Ayumi Kotani, Lars Kutzbach, Tuomas Laurila, Annalea Lohila, Ivan Mammarella, Yojiro Matsuura, Gesa Meyer, Mats B. Nilsson, Steven F. Oberbauer, Sang-Jong Park, Roman Petrov, Anatoly S. Prokushkin, Christopher Schulze, Vincent L. St. Louis, Eeva-Stiina Tuittila, Juha-Pekka Tuovinen, William Quinton, Andrej Varlagin, Donatella Zona, and Viacheslav I. Zyryanov
                                    Earth Syst. Sci. Data, 14, 179–208, https://doi.org/10.5194/essd-14-179-2022, https://doi.org/10.5194/essd-14-179-2022, 2022
                                    Short summary
                                    Short summary
                                            
                                                The effects of climate warming on carbon cycling across the Arctic–boreal zone (ABZ) remain poorly understood due to the relatively limited distribution of ABZ flux sites. Fortunately, this flux network is constantly increasing, but new measurements are published in various platforms, making it challenging to understand the ABZ carbon cycle as a whole. Here, we compiled a new database of Arctic–boreal CO2 fluxes to help facilitate large-scale assessments of the ABZ carbon cycle.
                                            
                                            
                                        David Olefeldt, Mikael Hovemyr, McKenzie A. Kuhn, David Bastviken, Theodore J. Bohn, John Connolly, Patrick Crill, Eugénie S. Euskirchen, Sarah A. Finkelstein, Hélène Genet, Guido Grosse, Lorna I. Harris, Liam Heffernan, Manuel Helbig, Gustaf Hugelius, Ryan Hutchins, Sari Juutinen, Mark J. Lara, Avni Malhotra, Kristen Manies, A. David McGuire, Susan M. Natali, Jonathan A. O'Donnell, Frans-Jan W. Parmentier, Aleksi Räsänen, Christina Schädel, Oliver Sonnentag, Maria Strack, Suzanne E. Tank, Claire Treat, Ruth K. Varner, Tarmo Virtanen, Rebecca K. Warren, and Jennifer D. Watts
                                    Earth Syst. Sci. Data, 13, 5127–5149, https://doi.org/10.5194/essd-13-5127-2021, https://doi.org/10.5194/essd-13-5127-2021, 2021
                                    Short summary
                                    Short summary
                                            
                                                Wetlands, lakes, and rivers are important sources of the greenhouse gas methane to the atmosphere. To understand current and future methane emissions from northern regions, we need maps that show the extent and distribution of specific types of wetlands, lakes, and rivers. The Boreal–Arctic Wetland and Lake Dataset (BAWLD) provides maps of five wetland types, seven lake types, and three river types for northern regions and will improve our ability to predict future methane emissions.
                                            
                                            
                                        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
                                    Short summary
                                            
                                                Polygon-shaped landforms present in relatively flat Arctic tundra result in complex landscape-scale water drainage. The drainage pathways and the time to transition from inundated conditions to drained have important implications for heat and carbon transport. Using fundamental hydrologic principles, we investigate the drainage pathways and timing of individual polygons, providing insights into the effects of polygon geometry and preferential flow direction on drainage pathways and timing.
                                            
                                            
                                        Leah Birch, Christopher R. Schwalm, Sue Natali, Danica Lombardozzi, Gretchen Keppel-Aleks, Jennifer Watts, Xin Lin, Donatella Zona, Walter Oechel, Torsten Sachs, Thomas Andrew Black, and Brendan M. Rogers
                                    Geosci. Model Dev., 14, 3361–3382, https://doi.org/10.5194/gmd-14-3361-2021, https://doi.org/10.5194/gmd-14-3361-2021, 2021
                                    Short summary
                                    Short summary
                                            
                                                The high-latitude landscape or Arctic–boreal zone has been warming rapidly, impacting the carbon balance both regionally and globally. Given the possible global effects of climate change, it is important to have accurate climate model simulations. We assess the simulation of the Arctic–boreal carbon cycle in the Community Land Model (CLM 5.0). We find biases in both the timing and magnitude photosynthesis. We then use observational data to improve the simulation of the carbon cycle.
                                            
                                            
                                        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. 
                    
                
                        
                        Briones, V., Jafarov, E. E., Genet, H., Rogers, B. M., Rutter, R. M., Carman, T. B., Clein, J., Euschkirchen, E. S., Schuur, E. A., Watts, J. D., and Natali, S. M.: Exploring the interplay between soil thermal and hydrological changes and their impact on carbon fluxes in permafrost ecosystems, Environ. Res. Lett., 19, 074003, https://doi.org/10.1088/1748-9326/ad50ed, 2024. 
                    
                
                        
                        Brunetti, G., Šimunek, J., Wöhling, T., and Stumpp, C.: An in-depth analysis of Markov-Chain Monte Carlo ensemble samplers for inverse vadose zone modeling, J. Hydrol., 624, 129822, https://doi.org/10.1016/j.jhydrol.2023.129822, 2023. 
                    
                
                        
                        Calef, M. P., David McGuire, A., Epstein, H. E., Scott Rupp, T., and Shugart, H. H.: Analysis of vegetation distribution in Interior Alaska and sensitivity to climate change using a logistic regression approach, J. Biogeogr., 32, 863–878, https://doi.org/10.1111/j.1365-2699.2004.01185.x, 2005. 
                    
                
                        
                        Castelletti, A., Galelli, S., Ratto, M., Soncini-Sessa, R., and Young, P. C.: A general framework for Dynamic Emulation Modelling in environmental problems, Environ. Modell. Softw., 34, 5–18, https://doi.org/10.1016/j.envsoft.2012.01.002, 2012. 
                    
                
                        
                        Chapin, F. S. and Kedrowski, R. A.: Seasonal Changes in Nitrogen and Phosphorus Fractions and Autumn Retranslocation in Evergreen and Deciduous Taiga Trees, Ecology, 64, 376–391, https://doi.org/10.2307/1937083, 1983. 
                    
                
                        
                        Dagon, K., Sanderson, B. M., Fisher, R. A., and Lawrence, D. M.: A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5, Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020, 2020. 
                    
                
                        
                        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. 
                    
                
                        
                        Efstratiadis, A. and Koutsoyiannis, D.: One decade of multi-objective calibration approaches in hydrological modelling: a review, Hydrolog. Sci. J., 55, 58–78, https://doi.org/10.1080/02626660903526292, 2010. 
                    
                
                        
                        Euskirchen, E. S., McGUIRE, A. D., Kicklighter, D. W., Zhuang, Q., Clein, J. S., Dargaville, R. J., Dye, D. G., Kimball, J. S., McDONALD, K. C., Melillo, J. M., Romanovsky, V. E., and Smith, N. V.: Importance of recent shifts in soil thermal dynamics on growing season length, productivity, and carbon sequestration in terrestrial high-latitude ecosystems: HIGH-LATITUDE CLIMATE CHANGE INDICATORS, Glob. Change Biol., 12, 731–750, https://doi.org/10.1111/j.1365-2486.2006.01113.x, 2006. 
                    
                
                        
                        Euskirchen, E. S., McGuire, A. D., Chapin, F. S., Yi, S., and Thompson, C. C.: Changes in vegetation in northern Alaska under scenarios of climate change, 2003–2100: implications for climate feedbacks, Ecol. Appl., 19, 1022–1043, https://doi.org/10.1890/08-0806.1, 2009. 
                    
                
                        
                        Euskirchen, E. S., Edgar, C. W., Turetsky, M. R., Waldrop, M. P., and Harden, J. W.: Differential response of carbon fluxes to climate in three peatland ecosystems that vary in the presence and stability of permafrost: Carbon fluxes and permafrost thaw, J. Geophys. Res.-Biogeo., 119, 1576–1595, https://doi.org/10.1002/2014JG002683, 2014. 
                    
                
                        
                        Euskirchen, E. S., Serbin, S. P., Carman, T. B., Fraterrigo, J. M., Genet, H., Iversen, C. M., Salmon, V., and McGuire, A. D.: Assessing dynamic vegetation model parameter uncertainty across Alaskan arctic tundra plant communities, Ecol. Appl., 32, e02499, https://doi.org/10.1002/eap.2499, 2022. 
                    
                
                        
                        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. A. and Koven, C. D.: Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems, J. Adv. Model. Earth Sy., 12, e2018MS001453, https://doi.org/10.1029/2018MS001453, 2020. 
                    
                
                        
                        Forrester, A. I. J., Bressloff, N. W., and Keane, A. J.: Optimization using surrogate models and partially converged computational fluid dynamics simulations, P. R. Soc. A, 462, 2177–2204, https://doi.org/10.1098/rspa.2006.1679, 2006. 
                    
                
                        
                        Fox, A. M., Hoar, T. J., Anderson, J. L., Arellano, A. F., Smith, W. K., Litvak, M. E., MacBean, N., Schimel, D. S., and Moore, D. J. P.: Evaluation of a Data Assimilation System for Land Surface Models Using CLM4.5, J. Adv. Model. Earth Sy., 10, 2471–2494, https://doi.org/10.1029/2018MS001362, 2018. 
                    
                
                        
                        Genet, H., McGuire, A. D., Barrett, K., Breen, A., Euskirchen, E. S., Johnstone, J. F., Kasischke, E. S., Melvin, A. M., Bennett, A., Mack, M. C., Rupp, T. S., Schuur, A. E. G., Turetsky, M. R., and Yuan, F.: Modeling the effects of fire severity and climate warming on active layer thickness and soil carbon storage of black spruce forests across the landscape in interior Alaska, Environ. Res. Lett., 8, 045016, https://doi.org/10.1088/1748-9326/8/4/045016, 2013. 
                    
                
                        
                        Genet, H., He, Y., Lyu, Z., McGuire, A. D., Zhuang, Q., Clein, J., D'Amore, D., Bennett, A., Breen, A., Biles, F., Euskirchen, E. S., Johnson, K., Kurkowski, T., (Kushch) Schroder, S., Pastick, N., Rupp, T. S., Wylie, B., Zhang, Y., Zhou, X., and Zhu, Z.: The role of driving factors in historical and projected carbon dynamics of upland ecosystems in Alaska, Ecol. Appl., 28, 5–27, https://doi.org/10.1002/eap.1641, 2018. 
                    
                
                        
                        Hansen, P. C.: Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion, Society for Industrial and Applied Mathematics, https://doi.org/10.1137/1.9780898719697, 1998. 
                    
                
                        
                        Harden, J. W., Koven, C. D., Ping, C., Hugelius, G., David McGuire, A., Camill, P., Jorgenson, T., Kuhry, P., Michaelson, G. J., O'Donnell, J. A., Schuur, E. A. G., Tarnocai, C., Johnson, K., and Grosse, G.: Field information links permafrost carbon to physical vulnerabilities of thawing, Geophys. Res. Lett., 39, 2012GL051958, https://doi.org/10.1029/2012GL051958, 2012. 
                    
                
                        
                        Harp, D. R., Atchley, A. L., Painter, S. L., Coon, E. T., Wilson, C. J., Romanovsky, V. E., and Rowland, J. C.: Effect of soil property uncertainties on permafrost thaw projections: a calibration-constrained analysis, The Cryosphere, 10, 341–358, https://doi.org/10.5194/tc-10-341-2016, 2016. 
                    
                
                        
                        Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated high-resolution grids of monthly climatic observations – the CRU TS3.10 Dataset: UPDATED HIGH-RESOLUTION GRIDS OF MONTHLY CLIMATIC OBSERVATIONS, Int. J. Climatol., 34, 623–642, https://doi.org/10.1002/joc.3711, 2014. 
                    
                
                        
                        Hugelius, G., Strauss, J., Zubrzycki, S., Harden, J. W., Schuur, E. A. G., Ping, C.-L., Schirrmeister, L., Grosse, G., Michaelson, G. J., Koven, C. D., O'Donnell, J. A., Elberling, B., Mishra, U., Camill, P., Yu, Z., Palmtag, J., and Kuhry, P.: Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps, Biogeosciences, 11, 6573–6593, https://doi.org/10.5194/bg-11-6573-2014, 2014. 
                    
                
                        
                        Jafarov, E.: Estimation of above- and below-ground ecosystem parameters for the DVM-DOS-TEM v0.7.0 model using MADS v1.7.3 (model archive), Zenodo [code and data set], https://doi.org/10.5281/zenodo.14940535, 2025. 
                    
                
                        
                        Jafarov, E. E., Romanovsky, V. E., Genet, H., McGuire, A. D., and Marchenko, S. S.: The effects of fire on the thermal stability of permafrost in lowland and upland black spruce forests of interior Alaska in a changing climate, Environ. Res. Lett., 8, 035030, https://doi.org/10.1088/1748-9326/8/3/035030, 2013. 
                    
                
                        
                        Jafarov, E. E., Nicolsky, D. J., Romanovsky, V. E., Walsh, J. E., Panda, S. K., and Serreze, M. C.: The effect of snow: How to better model ground surface temperatures, Cold Reg. Sci. Technol., 102, 63–77, https://doi.org/10.1016/j.coldregions.2014.02.007, 2014. 
                    
                
                        
                        Jafarov, E. E., Harp, D. R., Coon, E. T., Dafflon, B., Tran, A. P., Atchley, A. L., Lin, Y., and Wilson, C. J.: Estimation of subsurface porosities and thermal conductivities of polygonal tundra by coupled inversion of electrical resistivity, temperature, and moisture content data, The Cryosphere, 14, 77–91, https://doi.org/10.5194/tc-14-77-2020, 2020. 
                    
                
                        
                        Jean, M., Melvin, A. M., Mack, M. C., and Johnstone, J. F.: Broadleaf Litter Controls Feather Moss Growth in Black Spruce and Birch Forests of Interior Alaska, Ecosystems, 23, 18–33, https://doi.org/10.1007/s10021-019-00384-8, 2020. 
                    
                
                        
                        Kelly, R., Chipman, M. L., Higuera, P. E., Stefanova, I., Brubaker, L. B., and Hu, F. S.: Recent burning of boreal forests exceeds fire regime limits of the past 10,000 years, P. Natl. Acad. Sci. USA, 110, 13055–13060, https://doi.org/10.1073/pnas.1305069110, 2013. 
                    
                
                        
                        Koven, C. D., Lawrence, D. M., and Riley, W. J.: Permafrost carbon-climate feedback is sensitive to deep soil carbon decomposability but not deep soil nitrogen dynamics, P. Natl. Acad. Sci. USA, 112, 3752–3757, https://doi.org/10.1073/pnas.1415123112, 2015. 
                    
                
                        
                        Koziel, S., Ciaurri, D. E., and Leifsson, L.: Surrogate-Based Methods, in: Computational Optimization, Methods and Algorithms, vol. 356, edited by: Koziel, S. and Yang, X.-S., Springer Berlin Heidelberg, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-20859-1_3, 33–59, 2011. 
                    
                
                        
                        Lara, M. J., Genet, H., McGuire, A. D., Euskirchen, E. S., Zhang, Y., Brown, D. R. N., Jorgenson, M. T., Romanovsky, V., Breen, A., and Bolton, W. R.: Thermokarst rates intensify due to climate change and forest fragmentation in an Alaskan boreal forest lowland, Glob. Change Biol., 22, 816–829, https://doi.org/10.1111/gcb.13124, 2016. 
                    
                
                        
                        Lawrence, D. M., Koven, C. D., Swenson, S. C., Riley, W. J., and Slater, A. G.: Permafrost thaw and resulting soil moisture changes regulate projected high-latitude CO2 and CH4 emissions, Environ. Res. Lett., 10, 094011, https://doi.org/10.1088/1748-9326/10/9/094011, 2015. 
                    
                
                        
                        Levenberg, K.: A method for the solution of certain non-linear problems in least squares, Q. Appl. Math., 2, 164–168, 1944. 
                    
                
                        
                        Lin, Y., O'Malley, D., and Vesselinov, V. V.: A computationally efficient parallel Levenberg-Marquardt algorithm for highly parameterized inverse model analyses, Water Resour. Res., 52, 6948–6977, https://doi.org/10.1002/2016WR019028, 2016. 
                    
                
                        
                        Linde, N., Renard, P., Mukerji, T., and Caers, J.: Geological realism in hydrogeological and geophysical inverse modeling: A review, Adv. Water Resour., 86, 86–101, https://doi.org/10.1016/j.advwatres.2015.09.019, 2015. 
                    
                
                        
                        Ling, X.-L., Fu, C.-B., Yang, Z.-L., and Guo, W.-D.: Comparison of different sequential assimilation algorithms for satellite-derived leaf area index using the Data Assimilation Research Testbed (version Lanai), Geosci. Model Dev., 12, 3119–3133, https://doi.org/10.5194/gmd-12-3119-2019, 2019. 
                    
                
                        
                        Luo, Y., Ahlström, A., Allison, S. D., Batjes, N. H., Brovkin, V., Carvalhais, N., Chappell, A., Ciais, P., Davidson, E. A., Finzi, A., Georgiou, K., Guenet, B., Hararuk, O., Harden, J. W., He, Y., Hopkins, F., Jiang, L., Koven, C., Jackson, R. B., Jones, C. D., Lara, M. J., Liang, J., McGuire, A. D., Parton, W., Peng, C., Randerson, J. T., Salazar, A., Sierra, C. A., Smith, M. J., Tian, H., Todd-Brown, K. E. O., Torn, M., Van Groenigen, K. J., Wang, Y. P., West, T. O., Wei, Y., Wieder, W. R., Xia, J., Xu, X., Xu, X., and Zhou, T.: Toward more realistic projections of soil carbon dynamics by Earth system models, Global Biogeochem. Cy., 30, 40–56, https://doi.org/10.1002/2015GB005239, 2016. 
                    
                
                        
                        MacBean, N., Peylin, P., Chevallier, F., Scholze, M., and Schürmann, G.: Consistent assimilation of multiple data streams in a carbon cycle data assimilation system, Geosci. Model Dev., 9, 3569–3588, https://doi.org/10.5194/gmd-9-3569-2016, 2016. 
                    
                
                        
                        Marquardt, D. W.: An Algorithm for Least-Squares Estimation of Nonlinear Parameters, J. Soc. Ind. Appl. Math., 11, 431–441, 1963. 
                    
                
                        
                        Matthes, H., Damseaux, A., Westermann, S., Beer, C., Boone, A., Burke, E., Decharme, B., Genet, H., Jafarov, E., Langer, M., Parmentier, F.-J., Porada, P., Gagne-Landmann, A., Huntzinger, D., Rogers, B., Schädel, C., Stacke, T., Wells, J., and Wieder, W.: Advances in Permafrost Representation: Biophysical Processes in Earth System Models and the Role of Offline Models, Permafrost Periglac., 36, 302–318, https://doi.org/10.1002/ppp.2269, 2025. 
                    
                
                        
                        McGuire, A. D., Koven, C., Lawrence, D. M., Clein, J. S., Xia, J., Beer, C., Burke, E., Chen, G., Chen, X., Delire, C., Jafarov, E., MacDougall, A. H., Marchenko, S., Nicolsky, D., Peng, S., Rinke, A., Saito, K., Zhang, W., Alkama, R., Bohn, T. J., Ciais, P., Decharme, B., Ekici, A., Gouttevin, I., Hajima, T., Hayes, D. J., Ji, D., Krinner, G., Lettenmaier, D. P., Luo, Y., Miller, P. A., Moore, J. C., Romanovsky, V., Schädel, C., Schaefer, K., Schuur, E. A. G., Smith, B., Sueyoshi, T., and Zhuang, Q.: Variability in the sensitivity among model simulations of permafrost and carbon dynamics in the permafrost region between 1960 and 2009: MODELING PERMAFROST CARBON DYNAMICS, Global Biogeochem. Cy., 30, 1015–1037, https://doi.org/10.1002/2016GB005405, 2016. 
                    
                
                        
                        McGuire, A. D., Lawrence, D. M., Koven, C., Clein, J. S., Burke, E., Chen, G., Jafarov, E., MacDougall, A. H., Marchenko, S., Nicolsky, D., Peng, S., Rinke, A., Ciais, P., Gouttevin, I., Hayes, D. J., Ji, D., Krinner, G., Moore, J. C., Romanovsky, V., Schädel, C., Schaefer, K., Schuur, E. A. G., and Zhuang, Q.: Dependence of the evolution of carbon dynamics in the northern permafrost region on the trajectory of climate change, P. Natl. Acad. Sci. USA, 115, 3882–3887, https://doi.org/10.1073/pnas.1719903115, 2018. 
                    
                
                        
                        Melvin, A. M., Mack, M. C., Johnstone, J. F., David McGuire, A., Genet, H., and Schuur, E. A. G.: Differences in Ecosystem Carbon Distribution and Nutrient Cycling Linked to Forest Tree Species Composition in a Mid-Successional Boreal Forest, Ecosystems, 18, 1472–1488, https://doi.org/10.1007/s10021-015-9912-7, 2015. 
                    
                
                        
                        Mishra, U., Hugelius, G., Shelef, E., Yang, Y., Strauss, J., Lupachev, A., Harden, J. W., Jastrow, J. D., Ping, C.-L., Riley, W. J., Schuur, E. A. G., Matamala, R., Siewert, M., Nave, L. E., Koven, C. D., Fuchs, M., Palmtag, J., Kuhry, P., Treat, C. C., Zubrzycki, S., Hoffman, F. M., Elberling, B., Camill, P., Veremeeva, A., and Orr, A.: Spatial heterogeneity and environmental predictors of permafrost region soil organic carbon stocks, Sci. Adv., 7, eaaz5236, https://doi.org/10.1126/sciadv.aaz5236, 2021. 
                    
                
                        
                        Natali, S. M., Holdren, J. P., Rogers, B. M., Treharne, R., Duffy, P. B., Pomerance, R., and MacDonald, E.: Permafrost carbon feedbacks threaten global climate goals, P. Natl. Acad. Sci. USA, 118, e2100163118, https://doi.org/10.1073/pnas.2100163118, 2021. 
                    
                
                        
                        Nicolsky, D. J., Romanovsky, V. E., and Tipenko, G. S.: Using in-situ temperature measurements to estimate saturated soil thermal properties by solving a sequence of optimization problems, The Cryosphere, 1, 41–58, https://doi.org/10.5194/tc-1-41-2007, 2007. 
                    
                
                        
                        Nicolsky, D. J., Romanovsky, V. E., and Panteleev, G. G.: Estimation of soil thermal properties using in-situ temperature measurements in the active layer and permafrost, Cold Reg. Sci. Technol., 55, 120–129, https://doi.org/10.1016/j.coldregions.2008.03.003, 2009. 
                    
                
                        
                        Nocedal, J. and Wright, S.: Numerical Optimization, Springer New York, https://doi.org/10.1007/978-0-387-40065-5, 2006. 
                    
                
                        
                        O'Malley, D. and Vesselinov, V. V.: Bayesian-information-gap decision theory with an application to CO2 sequestration: BAYESIAN-INFORMATION-GAP DECISION THEORY, Water Resour. Res., 51, 7080–7089, https://doi.org/10.1002/2015WR017413, 2015. 
                    
                
                        
                        Pastick, N. J., Duffy, P., Genet, H., Rupp, T. S., Wylie, B. K., Johnson, K. D., Jorgenson, M. T., Bliss, N., McGuire, A. D., Jafarov, E. E., and Knight, J. F.: Historical and projected trends in landscape drivers affecting carbon dynamics in Alaska, Ecol. Appl., 27, 1383–1402, https://doi.org/10.1002/eap.1538, 2017. 
                    
                
                        
                        Peylin, P., Bacour, C., MacBean, N., Leonard, S., Rayner, P., Kuppel, S., Koffi, E., Kane, A., Maignan, F., Chevallier, F., Ciais, P., and Prunet, P.: A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle, Geosci. Model Dev., 9, 3321–3346, https://doi.org/10.5194/gmd-9-3321-2016, 2016. 
                    
                
                        
                        Pujol, J.: The solution of nonlinear inverse problems and the Levenberg-Marquardt method, Geophysics, 72, W1–W16, https://doi.org/10.1190/1.2732552, 2007. 
                    
                
                        
                        Queipo, N. V., Haftka, R. T., Shyy, W., Goel, T., Vaidyanathan, R., and Kevin Tucker, P.: Surrogate-based analysis and optimization, Prog. Aerosp. Sci., 41, 1–28, https://doi.org/10.1016/j.paerosci.2005.02.001, 2005. 
                    
                
                        
                        Razavi, S., Tolson, B. A., and Burn, D. H.: Review of surrogate modeling in water resources, Water Resour. Res., 48, 2011WR011527, https://doi.org/10.1029/2011WR011527, 2012. 
                    
                
                        
                        Regis, R. G. and Shoemaker, C. A.: A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions, INFORMS J. Comput., 19, 497–509, https://doi.org/10.1287/ijoc.1060.0182, 2007. 
                    
                
                        
                        Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019. 
                    
                
                        
                        Ruess, R. W., Cleve, K. V., Yarie, J., and Viereck, L. A.: Contributions of fine root production and turnover to the carbon and nitrogen cycling in taiga forests of the Alaskan interior, Can. J. Forest Res., 26, 1326–1336, https://doi.org/10.1139/x26-148, 1996. 
                    
                
                        
                        Rykiel, E. J.: Testing ecological models: the meaning of validation, Ecol. Model., 90, 229–244, https://doi.org/10.1016/0304-3800(95)00152-2, 1996. 
                    
                
                        
                        Schädel, C., Rogers, B. M., Lawrence, D. M., Koven, C. D., Brovkin, V., Burke, E. J., Genet, H., Huntzinger, D. N., Jafarov, E., McGuire, A. D., Riley, W. J., and Natali, S. M.: Earth system models must include permafrost carbon processes, Nat. Clim. Change, 14, 114–116, https://doi.org/10.1038/s41558-023-01909-9, 2024. 
                    
                
                        
                        Schaefer, K. and Jafarov, E.: A parameterization of respiration in frozen soils based on substrate availability, Biogeosciences, 13, 1991–2001, https://doi.org/10.5194/bg-13-1991-2016, 2016. 
                    
                
                        
                        Scholze, M., Kaminski, T., Knorr, W., Blessing, S., Vossbeck, M., Grant, J. P., and Scipal, K.: Simultaneous assimilation of SMOS soil moisture and atmospheric CO2 in-situ observations to constrain the global terrestrial carbon cycle, Remote Sens. Environ., 180, 334–345, https://doi.org/10.1016/j.rse.2016.02.058, 2016. 
                    
                
                        
                        Schürmann, G. J., Kaminski, T., Köstler, C., Carvalhais, N., Voßbeck, M., Kattge, J., Giering, R., Rödenbeck, C., Heimann, M., and Zaehle, S.: Constraining a land-surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System V1.0, Geosci. Model Dev., 9, 2999–3026, https://doi.org/10.5194/gmd-9-2999-2016, 2016. 
                    
                
                        
                        Schuur, E. A. G., Abbott, B. W., Commane, R., Ernakovich, J., Euskirchen, E., Hugelius, G., Grosse, G., Jones, M., Koven, C., Leshyk, V., Lawrence, D., Loranty, M. M., Mauritz, M., Olefeldt, D., Natali, S., Rodenhizer, H., Salmon, V., Schädel, C., Strauss, J., Treat, C., and Turetsky, M.: Permafrost and Climate Change: Carbon Cycle Feedbacks From the Warming Arctic, Annu. Rev. Env. Resour., 47, 343–371, https://doi.org/10.1146/annurev-environ-012220-011847, 2022. 
                    
                
                        
                        Shaver, G. R. and Chapin, F. S.: Long-term responses to factorial, NPK fertilizer treatment by Alaskan wet and moist tundra sedge species, Ecography, 18, 259–275, https://doi.org/10.1111/j.1600-0587.1995.tb00129.x, 1995. 
                    
                
                        
                        Tian, H., Melillo, J. M., Kicklighter, D. W., McGuire, A. D., and Helfrich, J.: The sensitivity of terrestrial carbon storage to historical climate variability and atmospheric CO2 in the United States, Tellus B, 51, 414, https://doi.org/10.3402/tellusb.v51i2.16318, 1999. 
                    
                
                        
                        Tran, A. P., Dafflon, B., and Hubbard, S. S.: Coupled land surface–subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra, The Cryosphere, 11, 2089–2109, https://doi.org/10.5194/tc-11-2089-2017, 2017. 
                    
                
                        
                        Transtrum, M. K. and Sethna, J. P.: Improvements to the Levenberg-Marquardt algorithm for nonlinear least-squares minimization, arXiv [preprint], https://doi.org/10.48550/ARXIV.1201.5885, 2012. 
                    
                
                        
                        Treharne, R., Rogers, B. M., Gasser, T., MacDonald, E., and Natali, S.: Identifying Barriers to Estimating Carbon Release From Interacting Feedbacks in a Warming Arctic, Front. Clim., 3, 716464, https://doi.org/10.3389/fclim.2021.716464, 2022. 
                    
                
                        
                        Turetsky, M. R., Abbott, B. W., Jones, M. C., Anthony, K. W., Olefeldt, D., Schuur, E. A. G., Grosse, G., Kuhry, P., Hugelius, G., Koven, C., Lawrence, D. M., Gibson, C., Sannel, A. B. K., and McGuire, A. D.: Carbon release through abrupt permafrost thaw, Nat. Geosci., 13, 138–143, https://doi.org/10.1038/s41561-019-0526-0, 2020. 
                    
                
                        
                        Vesselinov, V. V.: MADS: Model Analysis and Decision Support in Julia, GitHub [code], https://github.com/madsjulia/Mads.jl (last access: 25 June 2025), 2022. 
                    
                
                        
                        Virkkala, A.-M., Abdi, A. M., Luoto, M., and Metcalfe, D. B.: Identifying multidisciplinary research gaps across Arctic terrestrial gradients, Environ. Res. Lett., 14, 124061, https://doi.org/10.1088/1748-9326/ab4291, 2019. 
                    
                
                        
                        Wutzler, T. and Carvalhais, N.: Balancing multiple constraints in model-data integration: Weights and the parameter block approach, J. Geophys. Res.-Biogeo., 119, 2112–2129, https://doi.org/10.1002/2014JG002650, 2014. 
                    
                
                        
                        Xu, T., Valocchi, A. J., Ye, M., Liang, F., and Lin, Y.: Bayesian calibration of groundwater models with input data uncertainty, Water Resour. Res., 53, 3224–3245, https://doi.org/10.1002/2016WR019512, 2017. 
                    
                
                        
                        Yi, S., McGuire, A. D., Harden, J., Kasischke, E., Manies, K., Hinzman, L., Liljedahl, A., Randerson, J., Liu, H., Romanovsky, V., Marchenko, S., and Kim, Y.: Interactions between soil thermal and hydrological dynamics in the response of Alaska ecosystems to fire disturbance: soil thermal and hydrological dynamics, J. Geophys. Res., 114, G02015, https://doi.org/10.1029/2008JG000841, 2009. 
                    
                
                        
                        Yi, S., McGuire, A. D., Kasischke, E., Harden, J., Manies, K., Mack, M., and Turetsky, M.: A dynamic organic soil biogeochemical model for simulating the effects of wildfire on soil environmental conditions and carbon dynamics of black spruce forests, J. Geophys. Res., 115, G04015, https://doi.org/10.1029/2010JG001302, 2010.  
                    
                
                        
                        Zhuang, Q., McGuire, A. D., O'Neill, K. P., Harden, J. W., Romanovsky, V. E., and Yarie, J.: Modeling soil thermal and carbon dynamics of a fire chronosequence in interior Alaska, J. Geophys. Res., 108, 8147, https://doi.org/10.1029/2001JD001244, 2002. 
                    
                Download
        - Article
                            (2324 KB) 
- Full-text XML
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.
            This study improves how we tune ecosystem models to reflect carbon and nitrogen storage in...
            
         
 
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
             
             
            