Articles | Volume 20, issue 10
https://doi.org/10.5194/cp-20-2267-2024
© Author(s) 2024. 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-20-2267-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Could old tide gauges help estimate past atmospheric variability?
Laboratoire d'Océanographie Physique et Spatiale, Univ. Brest, Ifremer/CNRS/IRD, 29280 Plouzané, France
Odyssey, Inria/IMT/CNRS, 29280 Plouzané, France
Pierre Ailliot
Laboratoire de Mathematiques de Bretagne Atlantique, Univ. Brest, UMR CNRS 6205, 29200 Brest, France
Bertrand Chapron
Laboratoire d'Océanographie Physique et Spatiale, Univ. Brest, Ifremer/CNRS/IRD, 29280 Plouzané, France
Odyssey, Inria/IMT/CNRS, 29280 Plouzané, France
Pierre Tandeo
Odyssey, Inria/IMT/CNRS, 29280 Plouzané, France
IMT Atlantique, Lab-STICC, UMR CNRS 6285, 29238 Brest, France
RIKEN Cluster for Pioneering Research, Kobe, 650-0047, Japan
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Pierre Le Bras, Florian Sévellec, Pierre Tandeo, Juan Ruiz, and Pierre Ailliot
Nonlin. Processes Geophys., 31, 303–317, https://doi.org/10.5194/npg-31-303-2024, https://doi.org/10.5194/npg-31-303-2024, 2024
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The goal of this paper is to weight several dynamic models in order to improve the representativeness of a system. It is illustrated using a set of versions of an idealized model describing the Atlantic Meridional Overturning Circulation. The low-cost method is based on data-driven forecasts. It enables model performance to be evaluated on their dynamics. Taking into account both model performance and codependency, the derived weights outperform benchmarks in reconstructing a model distribution.
Pierre Tandeo, Pierre Ailliot, and Florian Sévellec
Nonlin. Processes Geophys., 30, 129–137, https://doi.org/10.5194/npg-30-129-2023, https://doi.org/10.5194/npg-30-129-2023, 2023
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The goal of this paper is to obtain probabilistic predictions of a partially observed dynamical system without knowing the model equations. It is illustrated using the three-dimensional Lorenz system, where only two components are observed. The proposed data-driven procedure is low-cost, is easy to implement, uses linear and Gaussian assumptions and requires only a small amount of data. It is based on an iterative linear Kalman smoother with a state augmentation.
Said Obakrim, Pierre Ailliot, Valérie Monbet, and Nicolas Raillard
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 67–81, https://doi.org/10.5194/ascmo-9-67-2023, https://doi.org/10.5194/ascmo-9-67-2023, 2023
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Ocean wave climate has a significant impact on human activities, and its understanding is of socioeconomic and environmental importance. In this study, we propose a statistical model that predicts wave heights in a location in the Bay of Biscay. The proposed method allows us to understand the spatiotemporal relationship between wind and waves and predicts well both wind seas and swells.
Robin Marcille, Maxime Thiébaut, Pierre Tandeo, and Jean-François Filipot
Wind Energ. Sci., 8, 771–786, https://doi.org/10.5194/wes-8-771-2023, https://doi.org/10.5194/wes-8-771-2023, 2023
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A novel data-driven method is proposed to design an optimal sensor network for the reconstruction of offshore wind resources. Based on unsupervised learning of numerical weather prediction wind data, it provides a simple yet efficient method for the siting of sensors, outperforming state-of-the-art methods for this application. It is applied in the main French offshore wind energy development areas to provide guidelines for the deployment of floating lidars for wind resource assessment.
Juan Ruiz, Pierre Ailliot, Thi Tuyet Trang Chau, Pierre Le Bras, Valérie Monbet, Florian Sévellec, and Pierre Tandeo
Geosci. Model Dev., 15, 7203–7220, https://doi.org/10.5194/gmd-15-7203-2022, https://doi.org/10.5194/gmd-15-7203-2022, 2022
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We present a new approach to validate numerical simulations of the current climate. The method can take advantage of existing climate simulations produced by different centers combining an analog forecasting approach with data assimilation to quantify how well a particular model reproduces a sequence of observed values. The method can be applied with different observations types and is implemented locally in space and time significantly reducing the associated computational cost.
Etienne Pauthenet, Loïc Bachelot, Kevin Balem, Guillaume Maze, Anne-Marie Tréguier, Fabien Roquet, Ronan Fablet, and Pierre Tandeo
Ocean Sci., 18, 1221–1244, https://doi.org/10.5194/os-18-1221-2022, https://doi.org/10.5194/os-18-1221-2022, 2022
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Temperature and salinity profiles are essential for studying the ocean’s stratification, but there are not enough of these data. Satellites are able to measure daily maps of the surface ocean. We train a machine to learn the link between the satellite data and the profiles in the Gulf Stream region. We can then use this link to predict profiles at the high resolution of the satellite maps. Our prediction is fast to compute and allows us to get profiles at any locations only from surface data.
Marceau Michel, Said Obakrim, Nicolas Raillard, Pierre Ailliot, and Valérie Monbet
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 83–95, https://doi.org/10.5194/ascmo-8-83-2022, https://doi.org/10.5194/ascmo-8-83-2022, 2022
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In this study, we introduce a deep learning algorithm to establish the relationship between wind and waves in order to predict the latter. The performance of the proposed method has been evaluated both on the output of numerical wave models and on in situ data and compared to other statistical methods developed by our research team. The results obtained confirm the interest of deep learning methods for forecasting ocean data.
Achim Wirth and Bertrand Chapron
Nonlin. Processes Geophys., 28, 371–378, https://doi.org/10.5194/npg-28-371-2021, https://doi.org/10.5194/npg-28-371-2021, 2021
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In non-equilibrium statistical mechanics, which describes forced-dissipative systems such as air–sea interaction, there is no universal probability density function (pdf). Some such systems have recently been demonstrated to exhibit a symmetry called a fluctuation theorem (FT), which strongly constrains the shape of the pdf. Using satellite data, the mechanical power input to the ocean by air–sea interaction following or not a FT is questioned. A FT is found to apply over specific ocean regions.
R. Fablet, M. M. Amar, Q. Febvre, M. Beauchamp, and B. Chapron
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 295–302, https://doi.org/10.5194/isprs-annals-V-3-2021-295-2021, https://doi.org/10.5194/isprs-annals-V-3-2021-295-2021, 2021
Anastasiia Tarasenko, Alexandre Supply, Nikita Kusse-Tiuz, Vladimir Ivanov, Mikhail Makhotin, Jean Tournadre, Bertrand Chapron, Jacqueline Boutin, Nicolas Kolodziejczyk, and Gilles Reverdin
Ocean Sci., 17, 221–247, https://doi.org/10.5194/os-17-221-2021, https://doi.org/10.5194/os-17-221-2021, 2021
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Data from the ARKTIKA-2018 expedition and new satellite data help us to follow rapid changes in the upper layer of the Laptev and East Siberian seas (LS, ESS) in summer 2018. With satellite-derived surface temperature, an improved SMOS salinity, and wind, we study how the fresh river water is mixed with cold sea water and ice-melted water at small time and spatial scales. The wind pushes fresh water northward and northeastward, close to and under the ice, forcing it into the deep Arctic Ocean.
Louis Marié, Fabrice Collard, Frédéric Nouguier, Lucia Pineau-Guillou, Danièle Hauser, François Boy, Stéphane Méric, Peter Sutherland, Charles Peureux, Goulven Monnier, Bertrand Chapron, Adrien Martin, Pierre Dubois, Craig Donlon, Tania Casal, and Fabrice Ardhuin
Ocean Sci., 16, 1399–1429, https://doi.org/10.5194/os-16-1399-2020, https://doi.org/10.5194/os-16-1399-2020, 2020
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With present-day techniques, ocean surface currents are poorly known near the Equator and globally for spatial scales under 200 km and timescales under 30 d. Wide-swath radar Doppler measurements are an alternative technique. Such direct surface current measurements are, however, affected by platform motions and waves. These contributions are analyzed in data collected during the DRIFT4SKIM airborne and in situ experiment, demonstrating the possibility of measuring currents from space globally.
Thomas Holding, Ian G. Ashton, Jamie D. Shutler, Peter E. Land, Philip D. Nightingale, Andrew P. Rees, Ian Brown, Jean-Francois Piolle, Annette Kock, Hermann W. Bange, David K. Woolf, Lonneke Goddijn-Murphy, Ryan Pereira, Frederic Paul, Fanny Girard-Ardhuin, Bertrand Chapron, Gregor Rehder, Fabrice Ardhuin, and Craig J. Donlon
Ocean Sci., 15, 1707–1728, https://doi.org/10.5194/os-15-1707-2019, https://doi.org/10.5194/os-15-1707-2019, 2019
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FluxEngine is an open-source software toolbox designed to allow for the easy and accurate calculation of air–sea gas fluxes. This article describes new functionality and capabilities, which include the ability to calculate fluxes for nitrous oxide and methane, optimisation for running FluxEngine on a stand-alone desktop computer, and extensive new features to support the in situ measurement community. Four research case studies are used to demonstrate these new features.
Fabrice Ardhuin, Yevgueny Aksenov, Alvise Benetazzo, Laurent Bertino, Peter Brandt, Eric Caubet, Bertrand Chapron, Fabrice Collard, Sophie Cravatte, Jean-Marc Delouis, Frederic Dias, Gérald Dibarboure, Lucile Gaultier, Johnny Johannessen, Anton Korosov, Georgy Manucharyan, Dimitris Menemenlis, Melisa Menendez, Goulven Monnier, Alexis Mouche, Frédéric Nouguier, George Nurser, Pierre Rampal, Ad Reniers, Ernesto Rodriguez, Justin Stopa, Céline Tison, Clément Ubelmann, Erik van Sebille, and Jiping Xie
Ocean Sci., 14, 337–354, https://doi.org/10.5194/os-14-337-2018, https://doi.org/10.5194/os-14-337-2018, 2018
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The Sea surface KInematics Multiscale (SKIM) monitoring mission is a proposal for a future satellite that is designed to measure ocean currents and waves. Using a Doppler radar, the accurate measurement of currents requires the removal of the mean velocity due to ocean wave motions. This paper describes the main processing steps needed to produce currents and wave data from the radar measurements. With this technique, SKIM can provide unprecedented coverage and resolution, over the global ocean.
Julie Bessac, Pierre Ailliot, Julien Cattiaux, and Valerie Monbet
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 1–16, https://doi.org/10.5194/ascmo-2-1-2016, https://doi.org/10.5194/ascmo-2-1-2016, 2016
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Several multi-site stochastic generators of zonal and meridional components of wind are proposed in this paper. Various questions are explored, such as the modeling of the regime in a multi-site context, the extraction of relevant clusterings from extra variables or from the local wind data, and the link between weather types extracted from wind data and large-scale weather regimes. We also discuss the relative advantages of hidden and observed regime-switching models.
Related subject area
Subject: Atmospheric Dynamics | Archive: Historical Records | Timescale: Centennial-Decadal
Technical note: An improved methodology for calculating the Southern Annular Mode index to aid consistency between climate studies
Reassessing long-standing meteorological records: an example using the national hottest day in Ireland
Extreme historical droughts and floods in the Hanjiang River Basin, China, since 1426
Influence of warming and atmospheric circulation changes on multidecadal European flood variability
Assimilating monthly precipitation data in a paleoclimate data assimilation framework
Historical droughts in the Qing dynasty (1644–1911) of China
Impact of different estimations of the background-error covariance matrix on climate reconstructions based on data assimilation
Causes of increased flood frequency in central Europe in the 19th century
A 305-year continuous monthly rainfall series for the island of Ireland (1711–2016)
Changes in the strength and width of the Hadley Circulation since 1871
Ecosystem effects of CO2 concentration: evidence from past climates
Laura Velasquez-Jimenez and Nerilie J. Abram
Clim. Past, 20, 1125–1139, https://doi.org/10.5194/cp-20-1125-2024, https://doi.org/10.5194/cp-20-1125-2024, 2024
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The Southern Annular Mode (SAM) influences climate in the Southern Hemisphere. We investigate the effects of calculation method and data used to calculate the SAM index and how it influences the relationship between the SAM and climate. We propose a method to calculate a natural SAM index that facilitates consistency between studies, including when using different data resolutions, avoiding distortion of SAM impacts and allowing for more reliable results of past and future SAM trends.
Katherine Dooley, Ciaran Kelly, Natascha Seifert, Therese Myslinski, Sophie O'Kelly, Rushna Siraj, Ciara Crosby, Jack Kevin Dunne, Kate McCauley, James Donoghue, Eoin Gaddren, Daniel Conway, Jordan Cooney, Niamh McCarthy, Eoin Cullen, Simon Noone, Conor Murphy, and Peter Thorne
Clim. Past, 19, 1–22, https://doi.org/10.5194/cp-19-1-2023, https://doi.org/10.5194/cp-19-1-2023, 2023
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The highest currently recognised air temperature (33.3 °C) ever recorded in the Republic of Ireland was logged at Kilkenny Castle in 1887. This paper reassesses the plausibility of the record using various methods such as inter-station reassessment and 20CRv3 reanalysis. As a result, Boora 1976 at 32.5 °C is presented as a more reliable high-temperature record for the Republic of Ireland. The final decision however rests with the national meteorological service, Met Éireann.
Xiaodan Zhang, Guoyu Ren, Yuda Yang, He Bing, Zhixin Hao, and Panfeng Zhang
Clim. Past, 18, 1775–1796, https://doi.org/10.5194/cp-18-1775-2022, https://doi.org/10.5194/cp-18-1775-2022, 2022
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Applying yearly drought and flood records from historical documents and precipitation data in the period of instrumental measurements, this study constructs a time series of extreme droughts and floods in the Hanjiang River Basin from 1426–2017 and analyzes the temporal and spatial characteristics of the extreme drought and flood event variations.
Stefan Brönnimann, Peter Stucki, Jörg Franke, Veronika Valler, Yuri Brugnara, Ralf Hand, Laura C. Slivinski, Gilbert P. Compo, Prashant D. Sardeshmukh, Michel Lang, and Bettina Schaefli
Clim. Past, 18, 919–933, https://doi.org/10.5194/cp-18-919-2022, https://doi.org/10.5194/cp-18-919-2022, 2022
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Floods in Europe vary on time scales of several decades. Flood-rich and flood-poor periods alternate. Recently floods have again become more frequent. Long time series of peak stream flow, precipitation, and atmospheric variables reveal that until around 1980, these changes were mostly due to changes in atmospheric circulation. However, in recent decades the role of increasing atmospheric moisture due to climate warming has become more important and is now the main driver of flood changes.
Veronika Valler, Yuri Brugnara, Jörg Franke, and Stefan Brönnimann
Clim. Past, 16, 1309–1323, https://doi.org/10.5194/cp-16-1309-2020, https://doi.org/10.5194/cp-16-1309-2020, 2020
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Data assimilation is becoming more and more important for past climate reconstructions. The assimilation of monthly resolved precipitation information has not been explored much so far. In this study we analyze the impact of assimilating monthly precipitation amounts and the number of wet days within an existing paleoclimate data assimilation framework. We find increased skill in the reconstruction, suggesting that monthly precipitation can constitute valuable input for future reconstructions.
Kuan-Hui Elaine Lin, Pao K. Wang, Pi-Ling Pai, Yu-Shiuan Lin, and Chih-Wei Wang
Clim. Past, 16, 911–931, https://doi.org/10.5194/cp-16-911-2020, https://doi.org/10.5194/cp-16-911-2020, 2020
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This study reconstructs drought chronologies of the Qing dynasty (1644–1911) based on Chinese documentary records from the REACHES database. In addition to drought records, ecological and societal records are also retrieved. Tests are performed to cross-check data and time series. Six severe drought periods are identified, and spatial patterns are revealed through multivariable analysis. Drought consequence networks are built highlighting human intervention affecting famine and social turmoil.
Veronika Valler, Jörg Franke, and Stefan Brönnimann
Clim. Past, 15, 1427–1441, https://doi.org/10.5194/cp-15-1427-2019, https://doi.org/10.5194/cp-15-1427-2019, 2019
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In recent years, the data assimilation approach was adapted to the field of paleoclimatology to reconstruct past climate fields by combining model simulations and observations.
To improve the performance of our paleodata assimilation system, we tested various techniques that are well established in weather forecasting and evaluated their impact on assimilating instrumental data and proxy records (tree rings).
Stefan Brönnimann, Luca Frigerio, Mikhaël Schwander, Marco Rohrer, Peter Stucki, and Jörg Franke
Clim. Past, 15, 1395–1409, https://doi.org/10.5194/cp-15-1395-2019, https://doi.org/10.5194/cp-15-1395-2019, 2019
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During the 19th century flood frequency was high in central Europe, but it was low in the mid-20th century. This paper tracks these decadal changes in flood frequency for the case of Switzerland from peak discharge data back to precipitation data and daily weather reconstructions. We find an increased frequency in flood-prone weather types during large parts of the 19th century and decreased frequency in the mid-20th century. Sea-surface temperature anomalies can only explain a small part of it.
Conor Murphy, Ciaran Broderick, Timothy P. Burt, Mary Curley, Catriona Duffy, Julia Hall, Shaun Harrigan, Tom K. R. Matthews, Neil Macdonald, Gerard McCarthy, Mark P. McCarthy, Donal Mullan, Simon Noone, Timothy J. Osborn, Ciara Ryan, John Sweeney, Peter W. Thorne, Seamus Walsh, and Robert L. Wilby
Clim. Past, 14, 413–440, https://doi.org/10.5194/cp-14-413-2018, https://doi.org/10.5194/cp-14-413-2018, 2018
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This work reconstructs a continuous 305-year rainfall record for Ireland. The series reveals remarkable variability in decadal rainfall – far in excess of the typical period of digitised data. Notably, the series sheds light on exceptionally wet winters in the 1730s and wet summers in the 1750s. The derived record, one of the longest continuous series in Europe, offers a firm basis for benchmarking other long-term records and reconstructions of past climate both locally and across Europe.
J. Liu, M. Song, Y. Hu, and X. Ren
Clim. Past, 8, 1169–1175, https://doi.org/10.5194/cp-8-1169-2012, https://doi.org/10.5194/cp-8-1169-2012, 2012
I. C. Prentice and S. P. Harrison
Clim. Past, 5, 297–307, https://doi.org/10.5194/cp-5-297-2009, https://doi.org/10.5194/cp-5-297-2009, 2009
Cited articles
Alvarez-Castro, M. C., Faranda, D., and Yiou, P.: Atmospheric dynamics leading to West European summer hot temperatures since 1851, Complexity, 2018, 1–10, 2018. a
Ansell, T. J., Jones, P. D., Allan, R. J., Lister, D., Parker, D. E., Brunet, M., Moberg, A., Jacobeit, J., Brohan, P., Rayner, N. A., Aguilar, E., Alexandersson, H., Barriendos, M., Brandsma, T., Cox, N. J., Della-Marta, P. M., Drebs, A., Founda, D., Gerstengarbe, F., Hickey, K., Jónsson, T., Luterbacher, J., Nordli, Ø., Oesterle, H., Petrakis, M., Philipp, A., Rodwell, M. J., Saladie, O., Sigro, J., Slonosky, V., Srnec, L., Swail, V., García-Suárez, A. M., Tuomenvirta, H., Wang, X., Wanner, H., Werner, P., Wheeler, D., and Xoplaki, E.: Daily mean sea level pressure reconstructions for the European–North Atlantic region for the period 1850–2003, J. Climate, 19, 2717–2742, 2006. a, b
Bärring, L. and Fortuniak, K.: Multi-indices analysis of southern Scandinavian storminess 1780–2005 and links to interdecadal variations in the NW Europe–North Sea region, Int. J. Climatol., 29, 373–384, 2009. a
Brönnimann, S., Compo, G. P., Spadin, R., Allan, R., and Adam, W.: Early ship-based upper-air data and comparison with the Twentieth Century Reanalysis, Clim. Past, 7, 265–276, https://doi.org/10.5194/cp-7-265-2011, 2011. a
Brönnimann, S., Allan, R., Ashcroft, L., Baer, S., Barriendos, M., Brázdil, R., Brugnara, Y., Brunet, M., Brunetti, M., Chimani, B., Cornes, R., Domínguez-Castro, F., Filipiak, J., Founda, D., García Herrera, R., Gergis, J., Grab, S., Hannak, L., Huhtamaa, H., Jacobsen, K. S., Jones, P., Jourdain, S., Kiss, A., Lin, K. E., Lorrey, A., Lundstad, E., Luterbacher, J., Mauelshagen, F., Maugeri, M., Maughan, N., Moberg, A., Neukom, R., Nicholson, S., Noone, S., Nordli, Ø., Björg Ólafsdóttir, K., Pearce, P. R., Pfister, L., Pribyl, K., Przybylak, R., Pudmenzky, C., Rasol, D., Reichenbach, D., Řezníčková, L., Rodrigo, F. S., Rohr, C., Skrynyk, O., Slonosky, V., Thorne, P., Valente, M. A., Vaquero, J. M., Westcottt, N. E., Williamson, F., and Wyszyński, P.: Unlocking pre-1850 instrumental meteorological records: A global inventory, B. Am. Meteorol. Soc., 100, ES389–ES413, 2019. a
Bryant, K. M. and Akbar, M.: An exploration of wind stress calculation techniques in hurricane storm surge modeling, J. Mar. Sci. Eng., 4, 58, https://doi.org/10.3390/jmse4030058, 2016. a
Cazenave, A. and Llovel, W.: Contemporary sea level rise, Annu. Rev. Mar. Sci., 2, 145–173, 2010. a
Codiga, D. L.: Unified tidal analysis and prediction using the UTide Matlab functions, GSO Tech. Rep., Graduate School of Oceanography, Univ. of Rhode Island Narragansett, RI, 59 pp., 2011. a
Compo, G. P., Slivinski, L. C., Whitaker, J. S., Sardeshmukh, P. D., McColl, C., Brohan, P., Allan, R., Yin, X., Vose, R., Spencer, L. J., Ashcroft, L., Bronnimann, S.,Brunet, M., Camuffo, D., Cornes, R., Cra, T. A., Crouthamel, R., Dominguez-Castro, F., Freeman, J. E., Gergis, J., Giese, B. S., Hawkins, E., Jones, P. D., Jourdain, S., Kaplan, A., Kennedy, J., Kubota, H., Blancq, F. L., Lee, T., Lorrey, A., Luterbacher, J., Maugeri, M., Mock, C. J., Moore, K., Przybylak, R., Pudmenzky, C., Reason, C., Slonosky, V. C., Tinz, B., Titchner, H., Trewin, B., Valente, M. A., Wang, X. L., Wilkinson, C., Wood, K., and Wyszynski, P.: The international surface pressure databank version 4, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, CO, https://doi.org/10.5065/9EYR-TY90, 2019. a, b
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P., Brönnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman, P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri, M., Mok, H. Y., Nordli, Ø., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D., and Worley, S. J.: The twentieth century reanalysis project, Q. J. Roy. Meteorol. Soc., 137, 1–28, 2011. a, b
Evensen, G.: The ensemble Kalman filter: Theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, 2003. a
Fan, J.: Local linear regression smoothers and their minimax efficiencies, Ann. Stat., 21, 196–216, 1993. a
Gregory, J. M., Griffies, S. M., Hughes, C. W., Lowe, J. A., Church, J. A., Fukimori, I., Gomez, N., Kopp, R. E., Landerer, F., Le Cozannet, G., Ponte, R. M., Stammer, D., Tamisiea, M. E., and van de Wal, R. S. W.: Concepts and terminology for sea level: Mean, variability and change, both local and global, Surv. Geophys., 40, 1251–1289, 2019. a, b
Haigh, I. and Marcos, M.: GESLA (Global Extreme Sea Level Analysis), https://gesla787883612.wordpress.com/downloads/ (last access: 7 October 2024), 2024. a
Haigh, I. D., Marcos, M., Talke, S. A., Woodworth, P. L., Hunter, J. R., Hague, B. S., Arns, A., Bradshaw, E., and Thompson, P.: GESLA version 3: A major update to the global higher-frequency sea-level dataset, Geosci. Data J., 10, 293–314, 2023. a
Hansen, B.: Econometrics, Princeton University Press, ISBN 9780691235899, 2022. a
Harter, L., Pineau-Guillou, L., and Chapron, B.: Underestimation of extremes in sea level surge reconstruction, Sci. Rep., 14, 14875, https://doi.org/10.1038/s41598-024-65718-6, 2024. a
Hawkins, E.: Weather Rescue Data, GitHub [data set], https://github.com/ed-hawkins/weather-rescue-data/tree/main/ (last access: 7 October 2024). a
Hawkins, E., Brohan, P., Burgess, S. N., Burt, S., Compo, G. P., Gray, S. L., Haigh, I. D., Hersbach, H., Kuijjer, K., Martínez-Alvarado, O., McColl, C., Schurer, A. P., Slivinski, L., and Williams, J.: Rescuing historical weather observations improves quantification of severe windstorm risks, Nat. Hazards Earth Syst. Sci., 23, 1465–1482, https://doi.org/10.5194/nhess-23-1465-2023, 2023. a, b, c
Horsburgh, K. and Wilson, C.: Tide-surge interaction and its role in the distribution of surge residuals in the North Sea, J. Geophys. Res.-Oceans, 112, C08003, https://doi.org/10.1029/2006JC004033, 2007. a, b
Jones, P. D., Folland, C. K., Jacobeit, J., Yiou, P., Brunet M., Luterbacher, J., Moberg, A., Chen, D., and Casale, R.: EMULATE (European and North Atlantic daily to MULtidecadal climATE variability), UEA [data set], https://crudata.uea.ac.uk/projects/emulate/LANDSTATION_MSLP/ (last access: 7 October 2024), 2024. a
Krueger, O., Schenk, F., Feser, F., and Weisse, R.: Inconsistencies between long-term trends in storminess derived from the 20CR reanalysis and observations, J. Climate, 26, 868–874, 2013. a
Lazure, P. and Dumas, F.: An external–internal mode coupling for a 3D hydrodynamical model for applications at regional scale (MARS), Adv. Water Resour., 31, 233–250, 2008. a
Liu, J. S.: Nonparametric hierarchical Bayes via sequential imputations, Ann. Stat., 24, 911–930, 1996. a
Marcos, M., Puyol, B., Amores, A., Pérez Gómez, B., Fraile, M. Á., and Talke, S. A.: Historical tide gauge sea-level observations in Alicante and Santander (Spain) since the 19th century, Geosci. Data J., 8, 144–153, 2021. a
Melchior, P.: The tides of the planet Earth, Oxford, https://ui.adsabs.harvard.edu/abs/1983opp..book.....M (last access: 2 October 2024), 1983. a
Météo France: Données climatologiques de base – horaires, https://www.data.gouv.fr/fr/datasets/donnees-climatologiques-de-base-horaires/ (last access: 7 October 2024), 2024. a
NOAA-CIRES-DOE: 20th Century Reanalysis, https://psl.noaa.gov/data/20thC_Rean/ (last access: 7 October 2024), 2024. a
Pineau-Guillou, L., Ardhuin, F., Bouin, M.-N., Redelsperger, J.-L., Chapron, B., Bidlot, J.-R., and Quilfen, Y.: Strong winds in a coupled wave–atmosphere model during a North Atlantic storm event: Evaluation against observations, Q. J. Roy. Meteorol. Soc., 144, 317–332, 2018. a
Pineau-Guillou, L., Delouis, J.-M., and Chapron, B.: Characteristics of Storm Surge Events Along the North-East Atlantic Coasts, J. Geophys. Res.-Oceans, 128, e2022JC019493, https://doi.org/10.1029/2022JC019493, 2023. a, b
Quintana, G. I., Tandeo, P., Drumetz, L., Leballeur, L., and Pavec, M.: Statistical forecast of the marine surge, Nat. Hazards, 108, 2905–2917, 2021. a
Rabiner, L.: A tutorial on hidden Markov models and selected applications in speech recognition, Proc. IEEE, 77, 257–286, https://doi.org/10.1109/5.18626, 1989. a
Rodrigues, D., Alvarez-Castro, M. C., Messori, G., Yiou, P., Robin, Y., and Faranda, D.: Dynamical properties of the North Atlantic atmospheric circulation in the past 150 years in CMIP5 models and the 20CRv2c reanalysis, J. Climate, 31, 6097–6111, 2018. a
Slivinski, L. C., Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Giese, B. S., McColl, C., Allan, R., Yin, X., Vose, R., Titchner, H., Kennedy, J., Spencer, L. J., Ashcroft, L., Brönnimann, S., Brunet, M., Camuffo, D., Cornes, R., Cram, T. A., Crouthamel, R., Domínguez-Castro, F., Freeman, J. E., Gergis, J., Hawkins, E., Jones, P. D., Jourdain, S., Kaplan, A., Kubota, H., Le Blancq, F., Lee, T.-C., Lorrey, A., Luterbacher, J., Maugeri, M., Mock, C. J., Moore, G. W. K., Przybylak, R., Pudmenzky, C., Reason, C., Slonosky, V. C., Smith, C. A., Tinz, B., Trewin, B., Valente, M. A., Wang, X. L., Wilkinson, C., Wood, K., and Wyszyński, P.: Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system, Q. J. Roy. Meteorol. Soc., 145, 2876–2908, 2019. a, b
Snyder, C., Bengtsson, T., Bickel, P., and Anderson, J.: Obstacles to high-dimensional particle filtering, Mon. Weather Rev., 136, 4629–4640, 2008. a
Tadesse, M. G. and Wahl, T.: A database of global storm surge reconstructions, Sci. Data, 8, 125, https://doi.org/10.1038/s41597-021-00906-x, 2021. a
Takeda, H., Farsiu, S., and Milanfar, P.: Kernel regression for image processing and reconstruction, IEEE T. Image Process., 16, 349–366, 2007. a
UCO/CIRES|DOC/NOAA/OAR/ESRL/PSL: The International Surface Pressure Databank version 4, UCO/CIRES|DOC/NOAA/OAR/ESRL/PSL [data set], https://doi.org/10.5065/9EYR-TY90, 2024a. a
UCO/CIRES|DOC/NOAA/OAR/ESRL/PSL: Monthly Maps: Number of Observations per Day for International Surface Pressure Databank Version 4.7, https://web.archive.org/web/20230527064622/https://psl.noaa.gov/data/20CRv3_ISPD_obscounts_bymonth/ (last access: 7 October 2024), 2024b. a
Wohland, J., Omrani, N.-E., Witthaut, D., and Keenlyside, N. S.: Inconsistent wind speed trends in current twentieth century reanalyses, J. Geophys. Res.-Atmos., 124, 1931–1940, 2019. a
Woodworth, P. L., Melet, A., Marcos, M., Ray, R. D., Wöppelmann, G., Sasaki, Y. N., Cirano, M., Hibbert, A., Huthnance, J. M., Monserrat, S., and Merrifield, M. A.: Forcing factors affecting sea level changes at the coast, Surv. Geophys., 40, 1351–1397, 2019. a
Wöppelmann, G., Pouvreau, N., and Simon, B.: Brest sea level record: a time series construction back to the early eighteenth century, Ocean Dynam., 56, 487–497, 2006. a
Yiou, P., Boichu, M., Vautard, R., Vrac, M., Jourdain, S., Garnier, E., Fluteau, F., and Menut, L.: Ensemble meteorological reconstruction using circulation analogues of 1781–1785, Clim. Past, 10, 797–809, https://doi.org/10.5194/cp-10-797-2014, 2014. a
Zucchini, W., MacDonald, I. L., and Langrock, R.: Hidden Markov models for time series: an introduction using R, CRC Press, https://doi.org/10.1201/9781420010893, 2017. a
Short summary
Old observations are necessary to understand the atmosphere. When direct observations are not available, one can use indirect observations, such as tide gauges, which measure the sea level in port cities. The sea level rises when local air pressure decreases and when wind pushes water towards the coast. Several centuries-long tide gauge records are available. We show that these can be complementary to direct pressure observations for studying storms and anticyclones in the 19th century.
Old observations are necessary to understand the atmosphere. When direct observations are not...