Articles | Volume 18, issue 9
https://doi.org/10.5194/cp-18-2093-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/cp-18-2093-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Sep 2022
Research article |
| 13 Sep 2022
| 13 Sep 2022
Using a process-based dendroclimatic proxy system model in a data assimilation framework: a test case in the Southern Hemisphere over the past centuries
Jeanne Rezsöhazy
CORRESPONDING AUTHOR
Earth and Life Institute (ELI), Université catholique de Louvain (UCLouvain), Louvain-la-Neuve, Belgium
Aix Marseille University, CNRS, IRD, INRA, CEREGE, Aix-en-Provence, France
Quentin Dalaiden
Earth and Life Institute (ELI), Université catholique de Louvain (UCLouvain), Louvain-la-Neuve, Belgium
François Klein
Earth and Life Institute (ELI), Université catholique de Louvain (UCLouvain), Louvain-la-Neuve, Belgium
Hugues Goosse
Earth and Life Institute (ELI), Université catholique de Louvain (UCLouvain), Louvain-la-Neuve, Belgium
Joël Guiot
Aix Marseille University, CNRS, IRD, INRA, CEREGE, Aix-en-Provence, France
Related authors
No articles found.
Florian Sauerland, Pierre-Vincent Huot, Sylvain Marchi, Thierry Fichefet, Hugues Goosse, Konstanze Haubner, François Klein, François Massonnet, Bianca Mezzina, Eduardo Moreno-Chamarro, Pablo Ortega, Frank Pattyn, Charles Pelletier, Deborah Verfaillie, Lars Zipf, and Nicole van Lipzig
EGUsphere, https://doi.org/10.5194/egusphere-2025-2889, https://doi.org/10.5194/egusphere-2025-2889, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
We simulated the Antarctic climate from 1985 to 2014. Our model is driven using the ERA-5 reanalysis for one simulation and the EC-Earth global climate model for three others. Most of the simulated trends, such as sea ice extent and precipitation over land, have opposite signs for the two drivers, but agree between the three EC-Earth driven simulations. We conclude that these opposing trends must be due to the different drivers, and that the climate over land is less predictable than over sea.
Hugues Goosse, Stephy Libera, Alberto C. Naveira Garabato, Benjamin Richaud, Alessandro Silvano, and Martin Vancoppenolle
EGUsphere, https://doi.org/10.5194/egusphere-2025-1837, https://doi.org/10.5194/egusphere-2025-1837, 2025
Short summary
Short summary
The position of the winter sea ice edge in the Southern Ocean is strongly linked to the one of the Antarctic Circumpolar Current and thus to ocean bathymetry. This is due to the influence of the Antarctic Circumpolar Current on the southward heat flux that limits sea ice expansion, directly through oceanic processes and indirectly through its influence on atmospheric heat transport.
Atsushi Okazaki, Diego Carrio, Quentin Dalaiden, Jarrah Harrison-Lofthouse, Shunji Kotsuki, and Kei Yoshimura
EGUsphere, https://doi.org/10.5194/egusphere-2025-1389, https://doi.org/10.5194/egusphere-2025-1389, 2025
Short summary
Short summary
Data assimilation (DA) has been used to reconstruct paleoclimate fields. DA integrates model simulations and climate proxies based on their error sizes. Consequently, error information is vital for DA to function optimally. This study estimated observation errors using "innovation statistics" and demonstrated DA with estimated errors outperformed previous studies.
Ting-Chen Chen, Hugues Goosse, Matthias Aengenheyster, Kristian Strommen, Christopher Roberts, Malcolm Roberts, Rohit Ghosh, Jin-Song von Storch, and Stephy Libera
EGUsphere, https://doi.org/10.5194/egusphere-2025-666, https://doi.org/10.5194/egusphere-2025-666, 2025
Short summary
Short summary
The Southern Annular Mode (SAM) is a key driver of Southern Hemisphere climate variability, but global models often overestimate its persistence in summer. Using high-resolution models, we show this bias can be reduced, along with some improvements in jet latitude and likely a better-resolved eddy-mean flow feedback. Controlled experiments reveal the potential roles of sea surface temperature biases and ocean mesoscales, underscoring the complex mechanisms shaping SAM persistence.
Sarah Wauthy and Quentin Dalaiden
EGUsphere, https://doi.org/10.5194/egusphere-2025-192, https://doi.org/10.5194/egusphere-2025-192, 2025
Short summary
Short summary
The surface mass balance (SMB) is one of the main drivers of future Antarctic mass changes. The interannual variability of the SMB is dominated by precipitation and extreme precipitation events (EPEs). In this study, we analyze the role of precipitation and EPEs in the contrasting SMB trends observed in the ice-core records of three adjacent ice rises. Our results show that precipitation and EPEs alone cannot explain the observed contrasts and suggest that other processes may be at work.
Marie Genevieve Paule Cavitte, Hugues Goosse, Quentin Dalaiden, and Nicolas Ghilain
EGUsphere, https://doi.org/10.5194/egusphere-2024-3140, https://doi.org/10.5194/egusphere-2024-3140, 2024
Short summary
Short summary
Ice cores in East Antarctica show contrasting records of past snowfall. We tested if large-scale weather patterns could explain this by combining ice core data with an atmospheric model and radar-derived errors. However, the reconstruction produced unrealistic wind patterns to fit the ice core records. We suggest that uncertainties are not fully captured and that small-scale local wind effects, not represented in the model, could significantly influence snowfall records in the ice cores.
Bianca Mezzina, Hugues Goosse, François Klein, Antoine Barthélemy, and François Massonnet
The Cryosphere, 18, 3825–3839, https://doi.org/10.5194/tc-18-3825-2024, https://doi.org/10.5194/tc-18-3825-2024, 2024
Short summary
Short summary
We analyze years with extraordinarily low sea ice extent in Antarctica during summer, until the striking record in 2022. We highlight common aspects among these events, such as the fact that the exceptional melting usually occurs in two key regions and that it is related to winds with a similar direction. We also investigate whether the summer conditions are preceded by an unusual state of the sea ice during the previous winter, as well as the physical processes involved.
Ryan L. Fogt, Quentin Dalaiden, and Gemma K. O'Connor
Clim. Past, 20, 53–76, https://doi.org/10.5194/cp-20-53-2024, https://doi.org/10.5194/cp-20-53-2024, 2024
Short summary
Short summary
Antarctic sea ice is rapidly changing, with record lows set in 2017, 2022, and 2023 following decades of increase. To place these changes in a longer historical context, reconstructions have been created; however, they are quite different prior to observations. Here we find that the differences are more strongly tied to the implied connection of each reconstruction with the atmospheric circulation rather than differences in seasonality or geographic representation.
Marie G. P. Cavitte, Hugues Goosse, Kenichi Matsuoka, Sarah Wauthy, Vikram Goel, Rahul Dey, Bhanu Pratap, Brice Van Liefferinge, Thamban Meloth, and Jean-Louis Tison
The Cryosphere, 17, 4779–4795, https://doi.org/10.5194/tc-17-4779-2023, https://doi.org/10.5194/tc-17-4779-2023, 2023
Short summary
Short summary
The net accumulation of snow over Antarctica is key for assessing current and future sea-level rise. Ice cores record a noisy snowfall signal to verify model simulations. We find that ice core net snowfall is biased to lower values for ice rises and the Dome Fuji site (Antarctica), while the relative uncertainty in measuring snowfall increases rapidly with distance away from the ice core sites at the ice rises but not at Dome Fuji. Spatial variation in snowfall must therefore be considered.
Elizabeth R. Thomas, Diana O. Vladimirova, Dieter R. Tetzner, B. Daniel Emanuelsson, Nathan Chellman, Daniel A. Dixon, Hugues Goosse, Mackenzie M. Grieman, Amy C. F. King, Michael Sigl, Danielle G. Udy, Tessa R. Vance, Dominic A. Winski, V. Holly L. Winton, Nancy A. N. Bertler, Akira Hori, Chavarukonam M. Laluraj, Joseph R. McConnell, Yuko Motizuki, Kazuya Takahashi, Hideaki Motoyama, Yoichi Nakai, Franciéle Schwanck, Jefferson Cardia Simões, Filipe Gaudie Ley Lindau, Mirko Severi, Rita Traversi, Sarah Wauthy, Cunde Xiao, Jiao Yang, Ellen Mosely-Thompson, Tamara V. Khodzher, Ludmila P. Golobokova, and Alexey A. Ekaykin
Earth Syst. Sci. Data, 15, 2517–2532, https://doi.org/10.5194/essd-15-2517-2023, https://doi.org/10.5194/essd-15-2517-2023, 2023
Short summary
Short summary
The concentration of sodium and sulfate measured in Antarctic ice cores is related to changes in both sea ice and winds. Here we have compiled a database of sodium and sulfate records from 105 ice core sites in Antarctica. The records span all, or part, of the past 2000 years. The records will improve our understanding of how winds and sea ice have changed in the past and how they have influenced the climate of Antarctica over the past 2000 years.
Joel Guiot, Nicolas Bernigaud, Alberte Bondeau, Laurent Bouby, and Wolfgang Cramer
Clim. Past, 19, 1219–1244, https://doi.org/10.5194/cp-19-1219-2023, https://doi.org/10.5194/cp-19-1219-2023, 2023
Short summary
Short summary
In the Mediterranean the vine has been an important part of the economy since Roman times. Viticulture expanded within Gaul during warmer climate phases and regressed during cold periods. Now it is spreading strongly to northern Europe and suffering from drought in North Africa, Spain, and southern Italy. This will worsen if global warming exceeds 2 °C above the preindustrial period. While the driver of this is increased greenhouse gases, we show that the main past forcing was volcanic activity.
Koffi Worou, Thierry Fichefet, and Hugues Goosse
Weather Clim. Dynam., 4, 511–530, https://doi.org/10.5194/wcd-4-511-2023, https://doi.org/10.5194/wcd-4-511-2023, 2023
Short summary
Short summary
The Atlantic equatorial mode (AEM) of variability is partly responsible for the year-to-year rainfall variability over the Guinea coast. We used the current climate models to explore the present-day and future links between the AEM and the extreme rainfall indices over the Guinea coast. Under future global warming, the total variability of the extreme rainfall indices increases over the Guinea coast. However, the future impact of the AEM on extreme rainfall events decreases over the region.
Nathaelle Bouttes, Fanny Lhardy, Aurélien Quiquet, Didier Paillard, Hugues Goosse, and Didier M. Roche
Clim. Past, 19, 1027–1042, https://doi.org/10.5194/cp-19-1027-2023, https://doi.org/10.5194/cp-19-1027-2023, 2023
Short summary
Short summary
The last deglaciation is a period of large warming from 21 000 to 9000 years ago, concomitant with ice sheet melting. Here, we evaluate the impact of different ice sheet reconstructions and different processes linked to their changes. Changes in bathymetry and coastlines, although not often accounted for, cannot be neglected. Ice sheet melt results in freshwater into the ocean with large effects on ocean circulation, but the timing cannot explain the observed abrupt climate changes.
Andrew P. Schurer, Gabriele C. Hegerl, Hugues Goosse, Massimo A. Bollasina, Matthew H. England, Michael J. Mineter, Doug M. Smith, and Simon F. B. Tett
Clim. Past, 19, 943–957, https://doi.org/10.5194/cp-19-943-2023, https://doi.org/10.5194/cp-19-943-2023, 2023
Short summary
Short summary
We adopt an existing data assimilation technique to constrain a model simulation to follow three important modes of variability, the North Atlantic Oscillation, El Niño–Southern Oscillation and the Southern Annular Mode. How it compares to the observed climate is evaluated, with improvements over simulations without data assimilation found over many regions, particularly the tropics, the North Atlantic and Europe, and discrepancies with global cooling following volcanic eruptions are reconciled.
Hugues Goosse, Sofia Allende Contador, Cecilia M. Bitz, Edward Blanchard-Wrigglesworth, Clare Eayrs, Thierry Fichefet, Kenza Himmich, Pierre-Vincent Huot, François Klein, Sylvain Marchi, François Massonnet, Bianca Mezzina, Charles Pelletier, Lettie Roach, Martin Vancoppenolle, and Nicole P. M. van Lipzig
The Cryosphere, 17, 407–425, https://doi.org/10.5194/tc-17-407-2023, https://doi.org/10.5194/tc-17-407-2023, 2023
Short summary
Short summary
Using idealized sensitivity experiments with a regional atmosphere–ocean–sea ice model, we show that sea ice advance is constrained by initial conditions in March and the retreat season is influenced by the magnitude of several physical processes, in particular by the ice–albedo feedback and ice transport. Atmospheric feedbacks amplify the response of the winter ice extent to perturbations, while some negative feedbacks related to heat conduction fluxes act on the ice volume.
Pepijn Bakker, Hugues Goosse, and Didier M. Roche
Clim. Past, 18, 2523–2544, https://doi.org/10.5194/cp-18-2523-2022, https://doi.org/10.5194/cp-18-2523-2022, 2022
Short summary
Short summary
Natural climate variability plays an important role in the discussion of past and future climate change. Here we study centennial temperature variability and the role of large-scale ocean circulation variability using different climate models, geological reconstructions and temperature observations. Unfortunately, uncertainties in models and geological reconstructions are such that more research is needed before we can describe the characteristics of natural centennial temperature variability.
Guillian Van Achter, Thierry Fichefet, Hugues Goosse, and Eduardo Moreno-Chamarro
The Cryosphere, 16, 4745–4761, https://doi.org/10.5194/tc-16-4745-2022, https://doi.org/10.5194/tc-16-4745-2022, 2022
Short summary
Short summary
We investigate the changes in ocean–ice interactions in the Totten Glacier area between the last decades (1995–2014) and the end of the 21st century (2081–2100) under warmer climate conditions. By the end of the 21st century, the sea ice is strongly reduced, and the ocean circulation close to the coast is accelerated. Our research highlights the importance of including representations of fast ice to simulate realistic ice shelf melt rate increase in East Antarctica under warming conditions.
Nidheesh Gangadharan, Hugues Goosse, David Parkes, Heiko Goelzer, Fabien Maussion, and Ben Marzeion
Earth Syst. Dynam., 13, 1417–1435, https://doi.org/10.5194/esd-13-1417-2022, https://doi.org/10.5194/esd-13-1417-2022, 2022
Short summary
Short summary
We describe the contributions of ocean thermal expansion and land-ice melting (ice sheets and glaciers) to global-mean sea-level (GMSL) changes in the Common Era. The mass contributions are the major sources of GMSL changes in the pre-industrial Common Era and glaciers are the largest contributor. The paper also describes the current state of climate modelling, uncertainties and knowledge gaps along with the potential implications of the past variabilities in the contemporary sea-level rise.
Nicolas Ghilain, Stéphane Vannitsem, Quentin Dalaiden, Hugues Goosse, Lesley De Cruz, and Wenguang Wei
Earth Syst. Sci. Data, 14, 1901–1916, https://doi.org/10.5194/essd-14-1901-2022, https://doi.org/10.5194/essd-14-1901-2022, 2022
Short summary
Short summary
Modeling the climate at high resolution is crucial to represent the snowfall accumulation over the complex orography of the Antarctic coast. While ice cores provide a view constrained spatially but over centuries, climate models can give insight into its spatial distribution, either at high resolution over a short period or vice versa. We downscaled snowfall accumulation from climate model historical simulations (1850–present day) over Dronning Maud Land at 5.5 km using a statistical method.
Koffi Worou, Hugues Goosse, Thierry Fichefet, and Fred Kucharski
Earth Syst. Dynam., 13, 231–249, https://doi.org/10.5194/esd-13-231-2022, https://doi.org/10.5194/esd-13-231-2022, 2022
Short summary
Short summary
Over the Guinea Coast, the increased rainfall associated with warm phases of the Atlantic Niño is reasonably well simulated by 24 climate models out of 31, for the present-day conditions. In a warmer climate, general circulation models project a gradual decrease with time of the rainfall magnitude associated with the Atlantic Niño for the 2015–2039, 2040–2069 and 2070–2099 periods. There is a higher confidence in these changes over the equatorial Atlantic than over the Guinea Coast.
Charles Pelletier, Thierry Fichefet, Hugues Goosse, Konstanze Haubner, Samuel Helsen, Pierre-Vincent Huot, Christoph Kittel, François Klein, Sébastien Le clec'h, Nicole P. M. van Lipzig, Sylvain Marchi, François Massonnet, Pierre Mathiot, Ehsan Moravveji, Eduardo Moreno-Chamarro, Pablo Ortega, Frank Pattyn, Niels Souverijns, Guillian Van Achter, Sam Vanden Broucke, Alexander Vanhulle, Deborah Verfaillie, and Lars Zipf
Geosci. Model Dev., 15, 553–594, https://doi.org/10.5194/gmd-15-553-2022, https://doi.org/10.5194/gmd-15-553-2022, 2022
Short summary
Short summary
We present PARASO, a circumpolar model for simulating the Antarctic climate. PARASO features five distinct models, each covering different Earth system subcomponents (ice sheet, atmosphere, land, sea ice, ocean). In this technical article, we describe how this tool has been developed, with a focus on the
coupling interfacesrepresenting the feedbacks between the distinct models used for contribution. PARASO is stable and ready to use but is still characterized by significant biases.
Hugues Goosse, Quentin Dalaiden, Marie G. P. Cavitte, and Liping Zhang
Clim. Past, 17, 111–131, https://doi.org/10.5194/cp-17-111-2021, https://doi.org/10.5194/cp-17-111-2021, 2021
Short summary
Short summary
Polynyas are ice-free oceanic areas within the sea ice pack. Small polynyas are regularly observed in the Southern Ocean, but large open-ocean polynyas have been rare over the past decades. Using records from available ice cores in Antarctica, we reconstruct past polynya activity and confirm that those events have also been rare over the past centuries, but the information provided by existing data is not sufficient to precisely characterize the timing of past polynya opening.
Marie G. P. Cavitte, Quentin Dalaiden, Hugues Goosse, Jan T. M. Lenaerts, and Elizabeth R. Thomas
The Cryosphere, 14, 4083–4102, https://doi.org/10.5194/tc-14-4083-2020, https://doi.org/10.5194/tc-14-4083-2020, 2020
Short summary
Short summary
Surface mass balance (SMB) and surface air temperature (SAT) are correlated at the regional scale for most of Antarctica, SMB and δ18O. Areas with low/no correlation are where wind processes (foehn, katabatic wind warming, and erosion) are sufficiently active to overwhelm the synoptic-scale snow accumulation. Measured in ice cores, the link between SMB, SAT, and δ18O is much weaker. Random noise can be removed by core record averaging but local processes perturb the correlation systematically.
David Parkes and Hugues Goosse
The Cryosphere, 14, 3135–3153, https://doi.org/10.5194/tc-14-3135-2020, https://doi.org/10.5194/tc-14-3135-2020, 2020
Short summary
Short summary
Direct records of glacier changes rarely go back more than the last 100 years and are few and far between. We used a sophisticated glacier model to simulate glacier length changes over the last 1000 years for those glaciers that we do have long-term records of, to determine whether the model can run in a stable, realistic way over a long timescale, reproducing recent observed trends. We find that post-industrial changes are larger than other changes in this time period driven by recent warming.
Cited articles
Acevedo, W., Fallah, B., Reich, S., and Cubasch, U.: Assimilation of pseudo-tree-ring-width observations into an atmospheric general circulation model, Clim. Past, 13, 545–557, https://doi.org/10.5194/cp-13-545-2017, 2017. a
Agosta, C., Amory, C., Kittel, C., Orsi, A., Favier, V., Gallée, H., van den Broeke, M. R., Lenaerts, J. T. M., van Wessem, J. M., van de Berg, W. J., and Fettweis, X.: Estimation of the Antarctic surface mass balance using the regional climate model MAR (1979–2015) and identification of dominant processes, The Cryosphere, 13, 281–296, https://doi.org/10.5194/tc-13-281-2019, 2019. a
Anchukaitis, K. J., Wilson, R., Briffa, K. R., Büntgen, U., Cook, E. R., D'Arrigo, R., Davi, N., Esper, J., Frank, D., Gunnarson, B. E., Hegerl, G., Helama, S., Klesse, S., Krusic, P. J., Linderholm, H. W., Myglan, V., Osborn, T. J., Zhang, P., Rydval, M., Schneider, L., Schurer, A., Wiles, G., and Zorita, E.: Last millennium Northern Hemisphere summer temperatures from tree rings: Part II, spatially resolved reconstructions, Quaternary Sci. Rev., 163, 1–22, https://doi.org/10.1016/j.quascirev.2017.02.020, 2017. a, b
Babst, F., Bodesheim, P., Charney, N., Friend, A. D., Girardin, M. P., Klesse, S., Moore, D. J., Seftigen, K., Björklund, J., Bouriaud, O., Dawson, A., DeRose, R. J., Dietze, M. C., Eckes, A. H., Enquist, B., Frank, D. C., Mahecha, M. D., Poulter, B., Record, S., Trouet, V., Turton, R. H., Zhang, Z., and Evans, M. E.: When tree rings go global: Challenges and
opportunities for retro- and prospective insight, Quaternary Sci.
Rev., 197, 1–20, https://doi.org/10.1016/j.quascirev.2018.07.009, 2018. a
Badgeley, J. A., Steig, E. J., Hakim, G. J., and Fudge, T. J.: Greenland temperature and precipitation over the last 20 000 years using data assimilation, Clim. Past, 16, 1325–1346, https://doi.org/10.5194/cp-16-1325-2020, 2020. a
Boucher, É., Guiot, J., Hatté, C., Daux, V., Danis, P.-A., and Dussouillez, P.: An inverse modeling approach for tree-ring-based climate reconstructions under changing atmospheric CO2 concentrations, Biogeosciences, 11, 3245–3258, https://doi.org/10.5194/bg-11-3245-2014, 2014. a
Brady, E., Stevenson, S., Bailey, D., Liu, Z., Noone, D., Nusbaumer, J., Otto-Bliesner, B. L., Tabor, C., Tomas, R., Wong, T., Zhang, J., and Zhu, J.: The Connected Isotopic Water Cycle in the Community Earth System Model Version 1, J. Adv. Model. Earth Syst., 11, 2547–2566, https://doi.org/10.1029/2019MS001663, 2019 (data available at: https://www.earthsystemgrid.org/dataset/ucar.cgd.ccsm4.CESM_CAM5_LME.html, last access: 12 May 2021). a, b, c
Briffa, K. R., Schweingruber, F. H., Jones, P. D., Osborn, T. J., Harris,
I. C., Shiyatov, S. G., Vaganov, E. A., and Grudd, H.: Trees tell of past
climates: but are they speaking less clearly today?, Philos. T. Roy. Soc. Lond. B, 353, 65–73, https://doi.org/10.1098/rstb.1998.0191, 1998. a
Büntgen, U., Tegel, W., Nicolussi, K., McCormick, M., Frank, D., Trouet, V., Kaplan, J., Herzig, F., Heussner, K.-U., Wanner, H., Luterbacher, J., and Esper, J.: 2500 Years of European Climate Variability and Human
Susceptibility, Science, 331, 578–582, 2011. a
Cook, E. R., Meko, D. M., Stahle, D. W., and Cleaveland, M. K.: Drought
reconstructions for the continental United States, J. Climate, 12,
1145–1163, https://doi.org/10.1175/1520-0442(1999)012<1145:drftcu>2.0.co;2, 1999. a
Cook, E. R., Anchukaitis, K. J., Buckley, B. M., D'Arrigo, R. D., Jacoby,
G. C., and Wright, W. E.: Asian monsoon failure and megadrought during the
last millennium, Science, 328, 486–489, https://doi.org/10.1126/science.1185188,
2010. a
Cook, E. R., Krusic, P. J., Anchukaitis, K. J., Buckley, B. M., Nakatsuka, T., and Sano, M.: Tree-ring reconstructed summer temperature anomalies for
temperate East Asia since 800 C.E, Clim. Dynam., 41, 2957–2972,
https://doi.org/10.1007/s00382-012-1611-x, 2013. a
Crespin, E., Goosse, H., Fichefet, T., and Mann, M. E.: The 15th century Arctic warming in coupled model simulations with data assimilation, Clim. Past, 5, 389–401, https://doi.org/10.5194/cp-5-389-2009, 2009. a
D'Arrigo, R., Wilson, R., Liepert, B., and Cherubini, P.: On the 'Divergence Problem' in Northern Forests: A review of the tree-ring evidence and possible
causes, Global Planet. Change, 60, 289–305,
https://doi.org/10.1016/j.gloplacha.2007.03.004, 2008. a
Dee, S. G., Steiger, N. J., Emile-Geay, J., and Hakim, G. J.: On the utility
of proxy system models for estimating climate states over common era,
J. Adv. Model. Earth Syst., 8, 1164–1179,
https://doi.org/10.1002/2016MS000677, 2016. a, b
Drew, D. M., Downes, G. M., and Battaglia, M.: CAMBIUM, a process-based model of daily xylem development in Eucalyptus, J. Theor. Biol., 264, 395–406, https://doi.org/10.1016/j.jtbi.2010.02.013, 2010. a
Dubinkina, S., Goosse, H., Sallaz-Damaz, Y., Crespin, E., and Crucifix, M.:
Testing a Particle Filter To Reconstruct Climate Changes Over the Past
Centuries, Int. J. Bifurcat. Chaos, 21, 3611–3618,
https://doi.org/10.1142/S0218127411030763, 2011. a
Dufrêne, E., Davi, H., François, C., Le Maire, G., Le Dantec,
V., and Granier, A.: Modelling carbon and water cycles in a beech forest.
Part I: Model description and uncertainty analysis on modelled NEE,
Ecol. Model., 185, 407–436, https://doi.org/10.1016/j.ecolmodel.2005.01.004, 2005. a
Esper, J., Cook, E. R., and Schweingruber, F. H.: Low-frequency signals in long tree-ring chronologies for reconstructing past temperature variability,
Science, 295, 2250–2253, https://doi.org/10.1126/science.1066208, 2002. a
Esper, J., George, S. S., Anchukaitis, K., D'Arrigo, R., Ljungqvist, F.,
Luterbacher, J., Schneider, L., Stoffel, M., Wilson, R., and Büntgen,
U.: Large-scale, millennial-length temperature reconstructions from
tree-rings, Dendrochronologia, 50, 81–90,
https://doi.org/10.1016/j.dendro.2018.06.001, 2018. a
Evans, M. N., Tolwinski-Ward, S. E., Thompson, D. M., and Anchukaitis, K. J.: Applications of proxy system modeling in high resolution paleoclimatology, Quaternary Sci. Rev., 76, 16–28, https://doi.org/10.1016/j.quascirev.2013.05.024, 2013. a
Franke, J., Brönnimann, S., Bhend, J., and Brugnara, Y.: A monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations, Scientific Data, 4, 1–19, https://doi.org/10.1038/sdata.2017.76,
2017. a, b, c
Franke, J., Valler, V., Brönnimann, S., Neukom, R., and Jaume-Santero, F.: The importance of input data quality and quantity in climate field reconstructions – results from the assimilation of various tree-ring collections, Clim. Past, 16, 1061–1074, https://doi.org/10.5194/cp-16-1061-2020, 2020. a
Fritts, H. C.: Tree rings and climate, Academic Press, London, https://doi.org/10.1016/B978-0-12-268450-0.X5001-0, 1976. a
Gea-Izquierdo, G., Guibal, F., Joffre, R., Ourcival, J. M., Simioni, G., and Guiot, J.: Modelling the climatic drivers determining photosynthesis and carbon allocation in evergreen Mediterranean forests using multiproxy long time series, Biogeosciences, 12, 3695–3712, https://doi.org/10.5194/bg-12-3695-2015, 2015. a, b, c
Gennaretti, F., Gea-Izquierdo, G., Boucher, E., Berninger, F., Arseneault, D., and Guiot, J.: Ecophysiological modeling of photosynthesis and carbon allocation to the tree stem in the boreal forest, Biogeosciences, 14, 4851–4866, https://doi.org/10.5194/bg-14-4851-2017, 2017. a, b, c
Gennaretti, F., Boucher, E., Nicault, A., Gea-Izquierdo, G., Arseneault, D., Berninger, F., Savard, M. M., Bégin, C., and Guiot, J.: Underestimation of the Tambora effects in North American taiga ecosystems, Environ. Res. Lett., 13, 034017, https://doi.org/10.1088/1748-9326/aaac0c, 2018. a, b
Gong, D. and Wang, S.: Definition of Antarctic oscillation index, Geophys. Res. Lett., 26, 459–462, https://doi.org/10.1029/1999GL900003, 1999. a
Goosse, H., Crespin, E., de Montety, A., Mann, M. E., Renssen, H., and Timmermann, A.: Reconstructing surface temperature changes over the past 600 years using climate model simulations with data assimilation, J.
Geophys. Res., 115, 1–17, https://doi.org/10.1029/2009jd012737, 2010. a, b, c
Goosse, H., Crespin, E., Dubinkina, S., Loutre, M. F., Mann, M. E., Renssen, H., Sallaz-Damaz, Y., and Shindell, D.: The role of forcing and internal dynamics in explaining the “Medieval Climate Anomaly”, Clim. Dynam., 39,
2847–2866, https://doi.org/10.1007/s00382-012-1297-0, 2012. a
Guiot, J., Boucher, E., and Gea-Izquierdo, G.: Process models and model-data
fusion in dendroecology, Frontiers in Ecology and Evolution, 2, 1–12,
https://doi.org/10.3389/fevo.2014.00052, 2014. a, b
Hakim, G. J., Emile-Geay, J., Steig, E. J., Noone, D., Anderson, D. M., Tardif, R., Steiger, N., and Perkins, W. A.: The last millennium climate reanalysis project: Framework and first results, J. Geophys. Res., 121,
6745–6764, https://doi.org/10.1002/2016JD024751, 2016. a, b
Hartig, F., Minunno, F., and Paul, S.: BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics. R package version 0.1.6., GitHub [code], https://github.com/florianhartig/BayesianTools (last access: 20 February 2021), 2019. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci. Eng., 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007. a
Jones, P. D., Briffa, K. R., Osborn, T. J., Lough, J. M., Van Ommen, T. D., Vinther, B. M., Luterbacher, J., Wahl, E. R., Zwiers, F. W., Mann, M. E., Schmidt, G. A., Ammann, C. M., Buckley, B. M., Cobb, K. M., Esper, J., Goosse, H., Graham, N., Jansen, E., Kiefer, T., Kull, C., Küttel, M., Mosley-Thompson, E., Overpeck, J. T., Riedwyl, N., Schulz, M., Tudhope, A. W., Villalba, R., Wanner, H., Wolff, E., and Xoplaki, E.: High-resolution palaeoclimatology of the last millennium: A review of current status and future prospects, Holocene, 19, 3–49, https://doi.org/10.1177/0959683608098952, 2009. a, b
Kalnay, E.: Atmospheric Modeling, Data Assimilation and Predictability, Cambridge University Press, https://doi.org/10.1017/CBO9780511802270, 2003. a
Kilbourne, H., Yu, Z., Neukom, R., et al.: A global multiproxy database for temperature reconstructions of the Common Era, figshare, Data Collection [data set], https://doi.org/10.6084/m9.figshare.c.3285353.v2, 2017. a
Klein, F.: Particle filter in offline data assimilation, ELIC Gogs, https://gogs.elic.ucl.ac.be/TECLIM/DA_Offline.git, last access: 1 December 2021. a
Luterbacher, J., Dietrich, D., Xoplaki, E., Grosjean, M., and Wanner, H.:
European Seasonal and Annual Temperature Variability, Trends, and Extremes
since 1500, Science, 303, 1499–1503, https://doi.org/10.1126/science.1093877, 2004. a
Mann, M. E., Bradley, R. S., and Hughes, M. K.: Global-scale temperature
patterns and climate forcing over the past six centuries, Nature, 392,
779–787, https://doi.org/10.1038/nature02478, 1998. a
Mann, M. E., Bradley, R. S., and Hughes, M. K.: Northern Hemisphere temperatures during the past millennium, Geophys. Res. Lett., 26, 759–762, 1999. a
Mann, M. E., Rutherford, S., Wahl, E., and Ammann, C.: Robustness of
proxy-based climate field reconstruction methods, J. Geophys. Res.-Atmos., 112, 1–18, https://doi.org/10.1029/2006JD008272, 2007. a
Mann, M. E., Zhang, Z., Hughes, M. K., Bradley, R. S., Miller, S. K., Rutherford, S., and Ni, F.: Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia, P. Natl. Acad. Sci. USA, 105, 13252–13257, https://doi.org/10.1073/pnas.0805721105, 2008. a
Matsikaris, A., Widmann, M., and Jungclaus, J.: On-line and off-line data assimilation in palaeoclimatology: a case study, Clim. Past, 11, 81–93, https://doi.org/10.5194/cp-11-81-2015, 2015. a
Medley, B. and Thomas, E. R.: Increased snowfall over the Antarctic Ice Sheet mitigated twentieth-century sea-level rise, Nat. Clim. Change, 9,
34–39, https://doi.org/10.1038/s41558-018-0356-x, 2019. a
Medley, B., McConnell, J. R., Neumann, T. A., Reijmer, C. H., Chellman, N., Sigl, M., and Kipfstuhl, S.: Temperature and Snowfall in Western Queen Maud Land Increasing Faster Than Climate Model Projections, Geophys. Res. Lett., 45, 1472–1480, https://doi.org/10.1002/2017GL075992, 2018. a, b
Misson, L.: MAIDEN: a model for analyzing ecosystem processes in dendroecology, Can. J. Forest Res., 34, 874–887, https://doi.org/10.1139/x03-252, 2004. a, b, c
Myhre, G., Shindell, D., Bréon, F.-M., Collins, W., Fuglestvedt, J., Huang, J., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T., Robock, A., Stephens, G., Takemura, T., and Zhang, H.: Anthropogenic and Natural Radiative Forcing, in: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, USA, https://doi.org/10.1017/CBO9781107415324.018, 2013. a
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models part I – A discussion of principles, J. Hydrol., 10,
282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970. a
Neukom, R., Luterbacher, J., Villalba, R., Küttel, M., Frank, D., Jones, P. D., Grosjean, M., Wanner, H., Aravena, J. C., Black, D. E., Christie, D. A., D'Arrigo, R., Lara, A., Morales, M., Soliz-Gamboa, C., Srur, A., Urrutia, R., and von Gunten, L.: Multiproxy summer and winter surface air temperature field reconstructions for southern South America covering the
past centuries, Clim. Dynam., 37, 35–51,
https://doi.org/10.1007/s00382-010-0793-3, 2011. a, b, c
Neukom, R., Gergis, J., Karoly, D. J., Wanner, H., Curran, M., Elbert, J., González-Rouco, F., Linsley, B. K., Moy, A. D., Mundo, I., Raible, C. C., Steig, E. J., Van Ommen, T., Vance, T., Villalba, R., Zinke, J., and
Frank, D.: Inter-hemispheric temperature variability over the past
millennium, Nat. Clim. Change, 4, 362–367, https://doi.org/10.1038/nclimate2174,
2014. a, b
Okazaki, A., Miyoshi, T., Yoshimura, K., Greybush, S. J., and Zhang, F.: Revisiting Online and Offline Data Assimilation Comparison for Paleoclimate
Reconstruction: An Idealized OSSE Study, J. Geophys. Res.-Atmos., 126, 1–17, https://doi.org/10.1029/2020JD034214, 2021. a, b
Oke, P. R. and Sakov, P.: Representation error of oceanic observations for
data assimilation, J. Atmos. Ocean. Tech., 25, 1004–1017, https://doi.org/10.1175/2007JTECHO558.1, 2008. a
PAGES 2k Consortium: Continental-scale temperature variability during the past two millennia, Nat. Geosci., 6, 339–346, https://doi.org/10.1038/ngeo1797, 2013. a, b
Rezsöhazy, J., Goosse, H., Guiot, J., Gennaretti, F., Boucher, E., André, F., and Jonard, M.: Application and evaluation of the dendroclimatic process-based model MAIDEN during the last century in Canada and Europe, Clim. Past, 16, 1043–1059, https://doi.org/10.5194/cp-16-1043-2020, 2020. a, b, c, d
Rubino, M., Etheridge, D. M., Thornton, D. P., Howden, R., Allison, C. E., Francey, R. J., Langenfelds, R. L., Steele, L. P., Trudinger, C. M., Spencer, D. A., Curran, M. A. J., van Ommen, T. D., and Smith, A. M.: Revised records of atmospheric trace gases CO2, CH4, N2O and δ13C-CO2 over the last 2000 years from Law Dome, Antarctica, Earth Syst. Sci. Data, 11, 473–492, https://doi.org/10.5194/essd-11-473-2019, 2019a. a, b
Rubino, M., Etheridge, D., Thornton, D., Allison, C., Francey, R., Langenfelds, R., Steele, P., Trudinger, C., Spencer, D., Curran, M., Van Ommen, T., and Smith, A.: Law Dome Ice Core 2000-Year CO2, CH4, N2O and δ13C-CO2. v1, CSIRO, Data Collection [data set], https://doi.org/10.25919/5bfe29ff807fb, 2019b. a, b
Sato, M. and Schmidt, G.: Forcings in GISS Climate Model, NASA Goddard Institute for Space Studies (GISS) [data set],
https://data.giss.nasa.gov/modelforce/ghgases/ (last access: 30 November 2021. a
Schwarz, G.: Estimating the Dimension of a Model, Ann. Stat., 6, 461–464, https://doi.org/10.1214/aos/1176344136, 1978. a
Sheffield, J., Goteti, G., and Wood, E. F.: Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling, J. Climate, 19, 3088–3111, https://doi.org/10.1175/JCLI3790.1, 2006 (data available at: http://hydrology.princeton.edu/data.php, last access: 5 May 2021). a, b
Souverijns, N., Gossart, A., Gorodetskaya, I. V., Lhermitte, S., Mangold, A., Laffineur, Q., Delcloo, A., and van Lipzig, N. P. M.: How does the ice sheet surface mass balance relate to snowfall? Insights from a ground-based precipitation radar in East Antarctica, The Cryosphere, 12, 1987–2003, https://doi.org/10.5194/tc-12-1987-2018, 2018. a
Steiger, N. J. and Smerdon, J. E.: A pseudoproxy assessment of data assimilation for reconstructing the atmosphere–ocean dynamics of hydroclimate extremes, Clim. Past, 13, 1435–1449, https://doi.org/10.5194/cp-13-1435-2017, 2017. a
Steiger, N. J., Hakim, G. J., Steig, E. J., Battisti, D. S., and Roe, G. H.:
Assimilation of time-averaged pseudoproxies for climate reconstruction,
J. Climate, 27, 426–441, https://doi.org/10.1175/JCLI-D-12-00693.1, 2014. a, b
Steiger, N. J., Smerdon, J. E., Cook, E. R., and Cook, B. I.: A reconstruction of global hydroclimate and dynamical variables over the Common Era, Scientific Data, 5, 1–15, https://doi.org/10.1038/sdata.2018.86, 2018. a, b, c
Stenni, B., Curran, M. A. J., Abram, N. J., Orsi, A., Goursaud, S., Masson-Delmotte, V., Neukom, R., Goosse, H., Divine, D., van Ommen, T., Steig, E. J., Dixon, D. A., Thomas, E. R., Bertler, N. A. N., Isaksson, E., Ekaykin, A., Werner, M., and Frezzotti, M.: Antarctic climate variability on regional and continental scales over the last 2000 years, Clim. Past, 13, 1609–1634, https://doi.org/10.5194/cp-13-1609-2017, 2017. a, b
Stevenson, S., Otto-Bliesner, B. L., Brady, E. C., Nusbaumer, J., Tabor, C., Tomas, R., Noone, D. C., and Liu, Z.: Volcanic Eruption Signatures in the Isotope-Enabled Last Millennium Ensemble, Paleoceanography and Paleoclimatology, 34, 1534–1552, https://doi.org/10.1029/2019PA003625, 2019 (data available at: https://www.earthsystemgrid.org/dataset/ucar.cgd.ccsm4.CESM_CAM5_LME.html, last access: 12 May 2021). a, b, c
Tardif, R., Hakim, G. J., Perkins, W. A., Horlick, K. A., Erb, M. P., Emile-Geay, J., Anderson, D. M., Steig, E. J., and Noone, D.: Last Millennium Reanalysis with an expanded proxy database and seasonal proxy modeling, Clim. Past, 15, 1251–1273, https://doi.org/10.5194/cp-15-1251-2019, 2019. a, b, c, d, e, f, g, h
Thomas, E. R., van Wessem, J. M., Roberts, J., Isaksson, E., Schlosser, E., Fudge, T. J., Vallelonga, P., Medley, B., Lenaerts, J., Bertler, N., van den Broeke, M. R., Dixon, D. A., Frezzotti, M., Stenni, B., Curran, M., and Ekaykin, A. A.: Regional Antarctic snow accumulation over the past 1000 years, Clim. Past, 13, 1491–1513, https://doi.org/10.5194/cp-13-1491-2017, 2017. a, b, c
Tingley, M. P. and Huybers, P.: Recent temperature extremes at high northern
latitudes unprecedented in the past 600 years, Nature, 496, 201–205,
https://doi.org/10.1038/nature11969, 2013. a
Tolwinski-Ward, S. E., Evans, M. N., Hughes, M. K., and Anchukaitis, K. J.: An efficient forward model of the climate controls on interannual variation in tree-ring width, Clim. Dynam., 36, 2419–2439, https://doi.org/10.1007/s00382-010-0945-5, 2011. a, b, c, d
Vaganov, E. A., Hughes, M. K., and Shashkin, A.: Growth dynamics of conifer tree rings, New York, Springer edition, ISBN: 978-3-540-26086-8, 2006. a
Valler, V., Brugnara, Y., Franke, J., and Brönnimann, S.: Assimilating monthly precipitation data in a paleoclimate data assimilation framework, Clim. Past, 16, 1309–1323, https://doi.org/10.5194/cp-16-1309-2020, 2020. a
van Leeuwen, P. J.: Particle Filtering in Geophysical Systems, Mon. Weather Rev., 137, 4089–4114, https://doi.org/10.1175/2009MWR2835.1, 2009. a, b
van Wessem, J. M., van de Berg, W. J., Noël, B. P. Y., van Meijgaard, E., Amory, C., Birnbaum, G., Jakobs, C. L., Krüger, K., Lenaerts, J. T. M., Lhermitte, S., Ligtenberg, S. R. M., Medley, B., Reijmer, C. H., van Tricht, K., Trusel, L. D., van Ulft, L. H., Wouters, B., Wuite, J., and van den Broeke, M. R.: Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 2: Antarctica (1979–2016), The Cryosphere, 12, 1479–1498, https://doi.org/10.5194/tc-12-1479-2018, 2018 (data available at: https://www.projects.science.uu.nl/iceclimate/models/racmo-data.php, last access: 22 February 2021). a, b, c
Wang, J., Yang, B., and Ljungqvist, F. C.: A millennial summer temperature reconstruction for the eastern tibetan plateau from tree-ring width, J. Climate, 28, 5289–5304, https://doi.org/10.1175/JCLI-D-14-00738.1, 2015. a
Widmann, M., Goosse, H., van der Schrier, G., Schnur, R., and Barkmeijer, J.: Using data assimilation to study extratropical Northern Hemisphere climate over the last millennium, Clim. Past, 6, 627–644, https://doi.org/10.5194/cp-6-627-2010, 2010. a, b
Wilmking, M., van der Maaten-Theunissen, M., van der Maaten, E., Scharnweber, T., Buras, A., Biermann, C., Gurskaya, M., Hallinger, M., Lange, J., Shetti,
R., Smiljanic, M., and Trouillier, M.: Global assessment of relationships
between climate and tree growth, Glob. Change Biol., 26, 3212–3220,
https://doi.org/10.1111/gcb.15057, 2020. a
Wilson, R. and Elling, W.: Temporal instability in tree-growth/climate response in the Lower Bavarian Forest region: Implications for dendroclimatic
reconstruction, Trees-Struct. Funct., 18, 19–28,
https://doi.org/10.1007/s00468-003-0273-z, 2004. a
Wilson, R., D'Arrigo, R., Buckley, B., Büntgen, U., Esper, J., Frank, D., Luckman, B., Payette, S., Vose, R., and Youngblut, D.: A matter of divergence: Tracking recent warming at hemispheric scales using tree ring data, J. Geophys. Res.-Atmos., 112, 1–17, https://doi.org/10.1029/2006JD008318, 2007. a
Wilson, R., Anchukaitis, K., Briffa, K. R., Büntgen, U., Cook, E., D'Arrigo, R., Davi, N., Esper, J., Frank, D., Gunnarson, B., Hegerl, G., Helama, S., Klesse, S., Krusic, P. J., Linderholm, H. W., Myglan, V., Osborn, T. J., Rydval, M., Schneider, L., Schurer, A., Wiles, G., Zhang, P., and Zorita, E.: Last millennium northern hemisphere summer temperatures from tree rings: Part I: The long term context, Quaternary Sci. Rev., 134,
1–18, https://doi.org/10.1016/j.quascirev.2015.12.005, 2016. a, b
Short summary
Using statistical tree-growth proxy system models in the data assimilation framework may have limitations. In this study, we successfully incorporate the process-based dendroclimatic model MAIDEN into a data assimilation procedure to robustly compare the outputs of an Earth system model with tree-ring width observations. Important steps are made to demonstrate that using MAIDEN as a proxy system model is a promising way to improve large-scale climate reconstructions with data assimilation.
Using statistical tree-growth proxy system models in the data assimilation framework may have...
