Articles | Volume 4, issue 2
https://doi.org/10.5194/wcd-4-511-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-4-511-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jun 2023
Research article |
| 14 Jun 2023
| 14 Jun 2023
Future changes in the mean and variability of extreme rainfall indices over the Guinea coast and role of the Atlantic equatorial mode
Earth and Climate Research Centre (TECLIM), Earth and Life Institute (ELI), Université catholique de Louvain (UCLouvain), Louvain-la-Neuve, Belgium
Thierry Fichefet
Earth and Climate Research Centre (TECLIM), Earth and Life Institute (ELI), Université catholique de Louvain (UCLouvain), Louvain-la-Neuve, Belgium
Hugues Goosse
Earth and Climate Research Centre (TECLIM), Earth and Life Institute (ELI), Université catholique de Louvain (UCLouvain), Louvain-la-Neuve, Belgium
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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
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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.
Annelies Sticker, François Massonnet, Thierry Fichefet, Patricia DeRepentigny, Alexandra Jahn, David Docquier, Christopher Wyburn-Powell, Daphne Quint, Erica Shivers, and Makayla Ortiz
The Cryosphere, 19, 3259–3277, https://doi.org/10.5194/tc-19-3259-2025, https://doi.org/10.5194/tc-19-3259-2025, 2025
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Our study analyzes rapid ice loss events (RILEs) in the Arctic, which are significant reductions in sea ice extent. RILEs are expected throughout the year, varying in frequency and duration with the seasons. Our research gives a year-round analysis of their characteristics in climate models and suggests that summer RILEs could begin before the middle of the century. Understanding these events is crucial as they can have profound impacts on the Arctic environment.
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).
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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
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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.
Benjamin Richaud, François Massonnet, Thierry Fichefet, Dániel Topál, Antoine Barthélemy, and David Docquier
EGUsphere, https://doi.org/10.5194/egusphere-2025-886, https://doi.org/10.5194/egusphere-2025-886, 2025
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Sea ice covers in the Arctic and Antarctic experienced intense reduction during specific recent years. Using an ocean-sea ice model, we found similarities between hemispheres and years to explain the ice reduction, such as ice melt (or lack of growth) at the ice-ocean interface. Differences between years and regions are also evident, such as increased ice transport or snow precipitation. This highlights the importance of heat stored by the ocean to explain ice melt in a warming climate.
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
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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.
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
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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.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
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We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
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
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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.
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
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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.
Steve Delhaye, Rym Msadek, Thierry Fichefet, François Massonnet, and Laurent Terray
EGUsphere, https://doi.org/10.5194/egusphere-2023-1748, https://doi.org/10.5194/egusphere-2023-1748, 2023
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The climate impact of Arctic sea ice loss may depend on the region of sea ice loss and the methodology used to study this impact. This study uses two approaches across seven climate models to investigate the winter atmospheric circulation response to regional sea ice loss. Our findings indicate a consistent atmospheric circulation response to pan-Arctic sea ice loss in most models and across both approaches. In contrast, more uncertainty emerges in the responses linked to regional sea ice loss.
Mukesh Gupta, Leandro Ponsoni, Jean Sterlin, François Massonnet, and Thierry Fichefet
EGUsphere, https://doi.org/10.5194/egusphere-2023-1560, https://doi.org/10.5194/egusphere-2023-1560, 2023
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We explored the relationship of Arctic September minimum sea ice extent with mid-summer melt pond area fraction, under the present-day climate. We confirm through the advanced numerical modelling, with an explicit melt pond scheme in the global climate model, EC-EARTH3, that melt pond fraction in mid-summer (June–July, not May) shows a strong relationship with the Arctic September sea ice extent. Satellite-based inferences validated our findings of this association.
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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
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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
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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.
Xia Lin, François Massonnet, Thierry Fichefet, and Martin Vancoppenolle
The Cryosphere, 17, 1935–1965, https://doi.org/10.5194/tc-17-1935-2023, https://doi.org/10.5194/tc-17-1935-2023, 2023
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This study provides clues on how improved atmospheric reanalysis products influence sea ice simulations in ocean–sea ice models. The summer ice concentration simulation in both hemispheres can be improved with changed surface heat fluxes. The winter Antarctic ice concentration and the Arctic drift speed near the ice edge and the ice velocity direction simulations are improved with changed wind stress. The radiation fluxes and winds in atmospheric reanalyses are crucial for sea ice simulations.
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
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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
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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
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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
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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
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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.
Jeanne Rezsöhazy, Quentin Dalaiden, François Klein, Hugues Goosse, and Joël Guiot
Clim. Past, 18, 2093–2115, https://doi.org/10.5194/cp-18-2093-2022, https://doi.org/10.5194/cp-18-2093-2022, 2022
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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.
Steve Delhaye, Thierry Fichefet, François Massonnet, David Docquier, Rym Msadek, Svenya Chripko, Christopher Roberts, Sarah Keeley, and Retish Senan
Weather Clim. Dynam., 3, 555–573, https://doi.org/10.5194/wcd-3-555-2022, https://doi.org/10.5194/wcd-3-555-2022, 2022
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It is unclear how the atmosphere will respond to a retreat of summer Arctic sea ice. Much attention has been paid so far to weather extremes at mid-latitude and in winter. Here we focus on the changes in extremes in surface air temperature and precipitation over the Arctic regions in summer during and following abrupt sea ice retreats. We find that Arctic sea ice loss clearly shifts the extremes in surface air temperature and precipitation over terrestrial regions surrounding the Arctic Ocean.
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
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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
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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
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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.
Xia Lin, François Massonnet, Thierry Fichefet, and Martin Vancoppenolle
Geosci. Model Dev., 14, 6331–6354, https://doi.org/10.5194/gmd-14-6331-2021, https://doi.org/10.5194/gmd-14-6331-2021, 2021
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This study introduces a new Sea Ice Evaluation Tool (SITool) to evaluate the model skills on the bipolar sea ice simulations by providing performance metrics and diagnostics. SITool is applied to evaluate the CMIP6 OMIP simulations. By changing the atmospheric forcing from CORE-II to JRA55-do data, many aspects of sea ice simulations are improved. SITool will be useful for helping teams managing various versions of a sea ice model or tracking the time evolution of model performance.
Christoph Kittel, Charles Amory, Cécile Agosta, Nicolas C. Jourdain, Stefan Hofer, Alison Delhasse, Sébastien Doutreloup, Pierre-Vincent Huot, Charlotte Lang, Thierry Fichefet, and Xavier Fettweis
The Cryosphere, 15, 1215–1236, https://doi.org/10.5194/tc-15-1215-2021, https://doi.org/10.5194/tc-15-1215-2021, 2021
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The future surface mass balance (SMB) of the Antarctic ice sheet (AIS) will influence the ice dynamics and the contribution of the ice sheet to the sea level rise. We investigate the AIS sensitivity to different warmings using physical and statistical downscaling of CMIP5 and CMIP6 models. Our results highlight a contrasting effect between the grounded ice sheet (where the SMB is projected to increase) and ice shelves (where the future SMB depends on the emission scenario).
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
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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
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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.
Guillian Van Achter, Leandro Ponsoni, François Massonnet, Thierry Fichefet, and Vincent Legat
The Cryosphere, 14, 3479–3486, https://doi.org/10.5194/tc-14-3479-2020, https://doi.org/10.5194/tc-14-3479-2020, 2020
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We document the spatio-temporal internal variability of Arctic sea ice thickness and its changes under anthropogenic forcing, which is key to understanding, and eventually predicting, the evolution of sea ice in response to climate change.
The patterns of sea ice thickness variability remain more or less stable during pre-industrial, historical and future periods, despite non-stationarity on short timescales. These patterns start to change once Arctic summer ice-free events occur, after 2050.
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
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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
Ageet, S., Fink, A. H., Maranan, M., Diem, J. E., Hartter, J., Ssali, A. L.,
and Ayabagabo, P.: Validation of Satellite Rainfall Estimates over Equatorial
East Africa, J. Hydrometeorol., 23, 129–151, https://doi.org/10.1175/jhm-d-21-0145.1, 2022. a
Akinsanola, A. A. and Zhou, W.: Projections of West African summer monsoon
rainfall extremes from two CORDEX models, Clim. Dynam., 52, 2017–2028,
https://doi.org/10.1007/s00382-018-4238-8, 2019. a, b, c, d
Akinsanola, A. A., Ongoma, V., and Kooperman, G. J.: Evaluation of CMIP6
models in simulating the statistics of extreme precipitation over Eastern
Africa, Atmos. Res., 254, 105509, https://doi.org/10.1016/j.atmosres.2021.105509, 2021. a
Atiah, W. A., Tsidu, G. M., Amekudzi, L. K., and Yorke, C.: Trends and
interannual variability of extreme rainfall indices over Ghana, West Africa,
Theor. Appl. Climatol., 140, 1393–1407, https://doi.org/10.1007/s00704-020-03114-6, 2020. a, b
Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., Schamm, K.,
Schneider, U., and Ziese, M.: A description of the global land-surface
precipitation data products of the Global Precipitation Climatology Centre
with sample applications including centennial (trend) analysis from
1901–present, Earth Syst. Sci. Data, 5, 71–99, https://doi.org/10.5194/essd-5-71-2013, 2013. a
Beucher, F., Lafore, J.-P., and Chapelon, N.: Simulation and analysis of the
moist vortex associated with the extreme rain event of Ouagadougou in 2009,
Q. J. Roy. Meteorol. Soc., 146, 86–104, https://doi.org/10.1002/qj.3645, 2019. a
Bichet, A. and Diedhiou, A.: Less frequent and more intense rainfall along the coast of the Gulf of Guinea in West and Central Africa (1981–2014), Clim. Res., 76, 191–201, https://doi.org/10.3354/cr01537, 2018. a
Bjerknes, J.: Atmospheric teleconnections from the equatorial Pacific, Mon. Weather Rev., 97, 163–172, https://doi.org/10.1175/1520-0493(1969)097<0163:atftep>2.3.co;2, 1969. a
Contractor, S., Donat, M. G., Alexander, L. V., Ziese, M., Meyer-Christoffer,
A., Schneider, U., Rustemeier, E., Becker, A., Durre, I., and Vose, R. S.:
Rainfall Estimates on a Gridded Network (REGEN) – a global land-based gridded dataset of daily precipitation from 1950 to 2016, Hydrol. Earth Syst. Sci., 24, 919–943, https://doi.org/10.5194/hess-24-919-2020, 2020. a
Delhaye, S., Fichefet, T., Massonnet, F., Docquier, D., Msadek, R., Chripko,
S., Roberts, C., Keeley, S., and Senan, R.: Summertime changes in climate
extremes over the peripheral Arctic regions after a sudden sea ice retreat,
Weather Clim. Dynam., 3, 555–573, https://doi.org/10.5194/wcd-3-555-2022, 2022. a
Diatta, S. and Fink, A. H.: Statistical relationship between remote climate
indices and West African monsoon variability, Int. J. Climatol., 34, 3348–3367, https://doi.org/10.1002/joc.3912, 2014. a
Diatta, S., Diedhiou, C. W., Dione, D. M., and Sambou, S.: Spatial Variation
and Trend of Extreme Precipitation in West Africa and Teleconnections with
Remote Indices, Atmosphere, 11, 999, https://doi.org/10.3390/atmos11090999, 2020. a, b
Diedhiou, A., Bichet, A., Wartenburger, R., Seneviratne, S. I., Rowell, D. P., Sylla, M. B., Diallo, I., Todzo, S., Touré, N. E., Camara, M.,
Ngatchah, B. N., Kane, N. A., Tall, L., and Affholder, F.: Changes in climate
extremes over West and Central Africa at 1.5 ∘C and 2 ∘C global warming, Environ. Res. Lett., 13, 065020, https://doi.org/10.1088/1748-9326/aac3e5, 2018. a
Dike, V. N., Lin, Z.-H., and Ibe, C. C.: Intensification of Summer Rainfall
Extremes over Nigeria during Recent Decades, Atmosphere, 11, 1084,
https://doi.org/10.3390/atmos11101084, 2020. a
Dosio, A., Jury, M. W., Almazroui, M., Ashfaq, M., Diallo, I., Engelbrecht,
F. A., Klutse, N. A. B., Lennard, C., Pinto, I., Sylla, M. B., and Tamoffo,
A. T.: Projected future daily characteristics of African precipitation based
on global (CMIP5, CMIP6) and regional (CORDEX, CORDEX-CORE) climate
models, Clim. Dynam., 57, 3135–3158, https://doi.org/10.1007/s00382-021-05859-w, 2021. a, b, c
Elagib, N. A., Zayed, I. S. A., Saad, S. A., Mahmood, M. I., Basheer, M., and
Fink, A. H.: Debilitating floods in the Sahel are becoming frequent, J. Hydrol., 599, 126362, https://doi.org/10.1016/j.jhydrol.2021.126362, 2021. a
Engel, T., Fink, A. H., Knippertz, P., Pante, G., and Bliefernicht, J.: Extreme Precipitation in the West African Cities of Dakar and Ouagadougou:
Atmospheric Dynamics and Implications for Flood Risk Assessments, J.
Hydrometeorol., 18, 2937–2957, https://doi.org/10.1175/jhm-d-16-0218.1, 2017. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Faye, A. and Akinsanola, A. A.: Evaluation of extreme precipitation indices
over West Africa in CMIP6 models, Clim. Dynam., 58, 925–939, https://doi.org/10.1007/s00382-021-05942-2, 2022. a, b, c, d
Fofana, M., Adounkpe, J., Larbi, I., Hounkpe, J., Koubodana, H. D., Toure, A., Bokar, H., Dotse, S.-Q., and Limantol, A. M.: Urban flash flood and extreme rainfall events trend analysis in Bamako, Mali, Environ. Challeng., 6, 100449, https://doi.org/10.1016/j.envc.2022.100449, 2022. a
Funk, C. C., Peterson, P. J., Landsfeld, M. F., Pedreros, D. H., Verdin, J. P., Rowland, J. D., Romero, B. E., Husak, G. J., Michaelsen, J. C., and Verdin, A. P.: A quasi-global precipitation time series for drought monitoring, USGS, https://doi.org/10.3133/ds832, 2014. a
Gutiérrez, J., Jones, R., Narisma, G., Alves, L., Amjad, M., Gorodetskaya, I., Grose, M., Klutse, N., Krakovska, S., Li, J., Martínez-Castro, D., Mearns, L., Mernild, S., Ngo-Duc, T., van den Hurk, B., and Yoon, J.-H.: Climate Change 2021: The Physical Science Basis, in: Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, chap. Atlas, Cambridge University Press, Cambridge, UK and New York, NY, USA, 1927–2058, https://doi.org/10.1017/9781009157896.021, 2021. a
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K.-L., Joyce, R. J., Kidd, C., Nelkin, E. J., Sorooshian, S., Stocker, E. F., Tan, J., Wolff, D. B., and Xie, P.: Satellite Precipitation Measurement, in: Volume 1, chap. Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG), Springer International Publishing, Cham, 343–353, https://doi.org/10.1007/978-3-030-24568-9_19, 2020. a
IPCC: Summary for Policymakers, Cambridge University Press, Cambridge, UK and New York, NY, USA, 3–32, https://doi.org/10.1017/9781009157896.001, 2021. a
Jia, F., Cai, W., Wu, L., Gan, B., Wang, G., Kucharski, F., Chang, P., and
Keenlyside, N.: Weakening Atlantic Niño–Pacific connection under greenhouse warming, Sci. Adv., 5, eaax4111, https://doi.org/10.1126/sciadv.aax4111, 2019. a
Klutse, N. A. B., Ajayi, V. O., Gbobaniyi, E. O., Egbebiyi, T. S., Kouadio, K., Nkrumah, F., Quagraine, K. A., Olusegun, C., Diasso, U., Abiodun, B. J., Lawal, K., Nikulin, G., Lennard, C., and Dosio, A.: Potential impact of 1.5 ∘C and 2 ∘C global warming on consecutive dry and wet days over West Africa, Environ. Res. Lett., 13, 055013, https://doi.org/10.1088/1748-9326/aab37b, 2018. a
Klutse, N. A. B., Quagraine, K. A., Nkrumah, F., Quagraine, K. T.,
Berkoh-Oforiwaa, R., Dzrobi, J. F., and Sylla, M. B.: The Climatic Analysis
of Summer Monsoon Extreme Precipitation Events over West Africa in CMIP6
Simulations, Earth Syst. Environ., 5, 25–41,
https://doi.org/10.1007/s41748-021-00203-y, 2021. a
Kpanou, M., Laux, P., Brou, T., Vissin, E., Camberlin, P., and Roucou, P.:
Spatial patterns and trends of extreme rainfall over the southern coastal
belt of West Africa, Theor. Appl. Climatol., 143, 473–487,
https://doi.org/10.1007/s00704-020-03441-8, 2020. a, b
Kubota, T., Shige, S., Hashizume, H., Aonashi, K., Takahashi, N., Seto, S.,
Hirose, M., Takayabu, Y. N., Ushio, T., Nakagawa, K., Iwanami, K., Kachi, M.,
and Okamoto, K.: Global Precipitation Map Using Satellite-Borne Microwave
Radiometers by the GSMaP Project: Production and Validation, IEEE T. Geosci. Remote, 45, 2259–2275, https://doi.org/10.1109/tgrs.2007.895337, 2007. a
Kucharski, F. and Joshi, M. K.: Influence of tropical South Atlantic
sea-surface temperatures on the Indian summer monsoon in CMIP5 models, Q. J. Roy. Meteorol. Soc., 143, 1351–1363, https://doi.org/10.1002/qj.3009, 2017. a, b
Lafore, J.-P., Beucher, F., Peyrillé, P., Diongue-Niang, A., Chapelon,
N., Bouniol, D., Caniaux, G., Favot, F., Ferry, F., Guichard, F., Poan, E.,
Roehrig, R., and Vischel, T.: A multi-scale analysis of the extreme rain
event of Ouagadougou in 2009, Q. J. Roy. Meteorol. Soc., 143, 3094–3109, https://doi.org/10.1002/qj.3165, 2017. a
Li, T., Jiang, Z., Treut, H. L., Li, L., Zhao, L., and Ge, L.: Machine learning to optimize climate projection over China with multi-model ensemble
simulations, Environ. Res. Lett., 16, 094028, https://doi.org/10.1088/1748-9326/ac1d0c, 2021. a
Losada, T., Rodríguez-Fonseca, B., Janicot, S., Gervois, S., Chauvin, F.,
and Ruti, P.: A multi-model approach to the Atlantic Equatorial mode: impact
on the West African monsoon, Clim. Dynam., 35, 29–43,
https://doi.org/10.1007/s00382-009-0625-5, 2010a. a
Losada, T., Rodríguez-Fonseca, B., Polo, I., Janicot, S., Gervois, S.,
Chauvin, F., and Ruti, P.: Tropical response to the Atlantic Equatorial mode:
AGCM multimodel approach, Clim. Dynam., 35, 45–52,
https://doi.org/10.1007/s00382-009-0624-6, 2010b. a
Losada, T., Rodriguez-Fonseca, B., Mohino, E., Bader, J., Janicot, S., and
Mechoso, C. R.: Tropical SST and Sahel rainfall: A non-stationary
relationship, Geophys. Res. Lett., 39, L12705, https://doi.org/10.1029/2012GL052423, 2012. a
Lübbecke, J. F., Rodríguez-Fonseca, B., Richter, I., Martín-Rey,
M., Losada, T., Polo, I., and Keenlyside, N. S.: Equatorial Atlantic
variability-Modes, mechanisms, and global teleconnections, Wiley Interdisciplin. Rev.: Clim. Change, 9, e527, https://doi.org/10.1002/wcc.527, 2018. a
Maidment, R. I., Grimes, D., Black, E., Tarnavsky, E., Young, M., Greatrex, H., Allan, R. P., Stein, T., Nkonde, E., Senkunda, S., and Alcántara, E. M. U.: A new, long-term daily satellite-based rainfall dataset for
operational monitoring in Africa, Sci. Data, 4, 170063, https://doi.org/10.1038/sdata.2017.63, 2017. a
Maranan, M., Fink, A. H., and Knippertz, P.: Rainfall types over southern West Africa: Objective identification, climatology and synoptic environment,
Q. J. Roy. Meteorol. Soc., 144, 1628–1648, https://doi.org/10.1002/qj.3345, 2018. a
Markus, Z., Rauthe-Schöch, A., Hänsel, S., Finger, P., Rustemeier, E., and
Schneider, U.: GPCC Full Data Daily Version 2022 at 1.0°: Daily Land-Surface
Precipitation from Rain-Gauges built on GTS-based and Historic Data,
DWD, https://doi.org/10.5676/DWD_GPCC/FD_D_V2022_100, 2022. a
Monerie, P.-A., Sanchez-Gomez, E., Pohl, B., Robson, J., and Dong, B.: Impact
of internal variability on projections of Sahel precipitation change,
Environ. Res. Lett., 12, 114003, https://doi.org/10.1088/1748-9326/aa8cda, 2017. a, b, c, d
Mouhamed, L., Traore, S. B., Alhassane, A., and Sarr, B.: Evolution of some
observed climate extremes in the West African Sahel, Weather Clim. Extrem., 1, 19–25, https://doi.org/10.1016/j.wace.2013.07.005, 2013. a
New, M., Hewitson, B., Stephenson, D. B., Tsiga, A., Kruger, A., Manhique, A., Gomez, B., Coelho, C. A. S., Masisi, D. N., Kululanga, E., Mbambalala, E., Adesina, F., Saleh, H., Kanyanga, J., Adosi, J., Bulane, L., Fortunata, L., Mdoka, M. L., and Lajoie, R.: Evidence of trends in daily climate extremes over southern and west Africa, J. Geophys. Res., 111, D14102, https://doi.org/10.1029/2005jd006289, 2006. a, b, c
Novella, N. S. and Thiaw, W. M.: African Rainfall Climatology Version 2 for
Famine Early Warning Systems, J. Appl. Meteorol. Clim., 52, 588–606, https://doi.org/10.1175/jamc-d-11-0238.1, 2013. a
Odoulami, R. C. and Akinsanola, A. A.: Recent assessment of West African summer monsoon daily rainfall trends, Weather, 73, 283–287, https://doi.org/10.1002/wea.2965, 2017. a, b, c
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016. a
Polo, I., Rodríguez-Fonseca, B., Losada, T., and García-Serrano, J.: Tropical
Atlantic Variability Modes (1979–2002). Part I: Time-Evolving SST Modes
Related to West African Rainfall, J. Climate, 21, 6457–6475,
https://doi.org/10.1175/2008JCLI2607.1, 2008. a, b
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late
nineteenth century, J. Geophys. Res.-Atmos., 108, 4407, https://doi.org/10.1029/2002JD002670, 2003. a
Rehfeld, K., Hébert, R., Lora, J. M., Lofverstrom, M., and Brierley, C. M.: Variability of surface climate in simulations of past and future, Earth Syst. Dynam., 11, 447–468, https://doi.org/10.5194/esd-11-447-2020, 2020. a
Richter, I. and Tokinaga, H.: An overview of the performance of CMIP6 models in the tropical Atlantic: mean state, variability, and remote impacts,
Clim. Dynam., 55, 2579–2601, https://doi.org/10.1007/s00382-020-05409-w, 2020. a, b
Rodríguez-Fonseca, B., Mohino, E., Mechoso, C. R., Caminade, C., Biasutti, M., Gaetani, M., Garcia-Serrano, J., Vizy, E. K., Cook, K., Xue, Y., Polo, I., Losada, T., Druyan, L., Fontaine, B., Bader, J., Doblas-Reyes, F. J., Goddard, L., Janicot, S., Arribas, A., Lau, W., Colman, A., Vellinga, M., Rowell, D. P., Kucharski, F., and Voldoire, A.: Variability and
Predictability of West African Droughts: A Review on the Role of Sea Surface
Temperature Anomalies, J. Climate, 28, 4034–4060, https://doi.org/10.1175/JCLI-D-14-00130.1, 2015. a
Sadeghi, M., Nguyen, P., Naeini, M. R., Hsu, K., Braithwaite, D., and
Sorooshian, S.: PERSIANN-CCS-CDR, a 3-hourly 0.04∘ global
precipitation climate data record for heavy precipitation studies, Sci.
Data, 8, 157, https://doi.org/10.1038/s41597-021-00940-9, 2021. a
Sanogo, S., Peyrillé, P., Roehrig, R., Guichard, F., and Ouedraogo, O.:
Extreme Precipitating Events in Satellite and Rain Gauge Products over the
Sahel, J. Climate, 35, 1915–1938, https://doi.org/10.1175/jcli-d-21-0390.1, 2022. a, b
Schubert, S. D., Stewart, R. E., Wang, H., Barlow, M., Berbery, E. H., Cai, W., Hoerling, M. P., Kanikicharla, K. K., Koster, R. D., Lyon, B., Mariotti, A., Mechoso, C. R., Müller, O. V., Rodriguez-Fonseca, B., Seager, R.,
Seneviratne, S. I., Zhang, L., and Zhou, T.: Global Meteorological Drought: A Synthesis of Current Understanding with a Focus on SST Drivers of
Precipitation Deficits, J. Climate, 29, 3989–4019, https://doi.org/10.1175/JCLI-D-15-0452.1, 2016. a
Seneviratne, S. I., Zhang, X., Adnan, M., Badi,W., Dereczynski, C., Luca, A. D., Ghosh, S., Iskandar, I., Kossin, J., Lewis, S., Otto, F., Pinto,
I., Satoh, M., Vicente-Serrano, S. M., Wehner, M., and Zhou, B.: Climate Change 2021: The Physical Science Basis, in: Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, chap. Weather and climate extreme events in a changing climate, Cambridge University Press, 1513–1766, https://doi.org/10.1017/9781009157896.013, 2021. a
Sillmann, J., Kharin, V. V., Zhang, X., Zwiers, F. W., and Bronaugh, D.:
Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model
evaluation in the present climate, J. Geophys. Res.-Atmos., 118, 1716–1733, https://doi.org/10.1002/jgrd.50203, 2013a.
a
Sillmann, J., Kharin, V. V., Zwiers, F. W., Zhang, X., and Bronaugh, D.:
Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future
climate projections, J. Geophys. Res.-Atmos., 118, 2473–2493, https://doi.org/10.1002/jgrd.50188, 2013b. a
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, https://doi.org/10.1029/2000jd900719, 2001. a, b
Thiery, W., Lange, S., Rogelj, J., Schleussner, C.-F., Gudmundsson, L.,
Seneviratne, S. I., Andrijevic, M., Frieler, K., Emanuel, K., Geiger, T.,
Bresch, D. N., Zhao, F., Willner, S. N., Büchner, M., Volkholz, J., Bauer,
N., Chang, J., Ciais, P., Dury, M., François, L., Grillakis, M.,
Gosling, S. N., Hanasaki, N., Hickler, T., Huber, V., Ito, A., Jägermeyr,
J., Khabarov, N., Koutroulis, A., Liu, W., Lutz, W., Mengel, M., Müller, C.,
Ostberg, S., Reyer, C. P. O., Stacke, T., and Wada, Y.: Intergenerational
inequities in exposure to climate extremes, Science, 374, 158–160,
https://doi.org/10.1126/science.abi7339, 2021. a
United Nations Office for the Coordination of Humanitarian Affairs (OCHA):
Note de Synthèse: Impact des inondations Afrique de l'Ouest et du Centre, United Nations, https://reliefweb.int/attachments/33cf9237-f1a9-398a-b6e5-c8c3cfde3264/Synth%C3%A8se%20sur%20les%20inondations%20Afrique%20de%20l%20Ouest%20et%20du%20Centre.pdf
(last access: 20 April 2022), 2012. a
United Nations Office for the Coordination of Humanitarian Affairs (OCHA):
ANNUAL REPORT 2020, United Nations,
https://www.unocha.org/sites/unocha/files/2020 OCHA annual report.pdf
(last access: 20 April 2022), 2021. a
Wainwright, C. M., Black, E., and Allan, R. P.: Future Changes in Wet and Dry
Season Characteristics in CMIP5 and CMIP6 Simulations, J. Hydrometeorol., 22, 2339–2357, https://doi.org/10.1175/JHM-D-21-0017.1, 2021. a, b
WCRP – World Climate Research Programme: Coupled Model Intercomparison Project (Phase 6), https://esgf-node.llnl.gov/search/cmip6/ (last access: 15 June 2022), 2022. a
Worou, K., Goosse, H., Fichefet, T., Guichard, F., and Diakhate, M.:
Interannual variability of rainfall in the Guinean Coast region and its links
with sea surface temperature changes over the twentieth century for the
different seasons, Clim. Dynam., 55, 449–470, https://doi.org/10.1007/s00382-020-05276-5, 2020. a, b
Yang, Y., Wu, L., Cai, W., Jia, F., Ng, B., Wang, G., and Geng, T.: Suppressed Atlantic Niño/Niña variability under greenhouse warming, Nat. Clim. Change, 12, 814–821, https://doi.org/10.1038/s41558-022-01444-z, 2022. a, b, c, d
Zebiak, S. E.: Air–Sea Interaction in the Equatorial Atlantic Region, J. Climate, 6, 1567–1586, https://doi.org/10.1175/1520-0442(1993)006<1567:AIITEA>2.0.CO;2, 1993. a, b, c
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.
The Atlantic equatorial mode (AEM) of variability is partly responsible for the year-to-year...
