Articles | Volume 12, issue 7
https://doi.org/10.5194/cp-12-1555-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/cp-12-1555-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
21 Jul 2016
Research article |
| 21 Jul 2016
Influence of proxy data uncertainty on data assimilation for the past climate
Anastasios Matsikaris
CORRESPONDING AUTHOR
University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
Max Planck Institute for Meteorology, Hamburg, Germany
Martin Widmann
University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
Johann Jungclaus
Max Planck Institute for Meteorology, Hamburg, Germany
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Johann H. Jungclaus, Edouard Bard, Mélanie Baroni, Pascale Braconnot, Jian Cao, Louise P. Chini, Tania Egorova, Michael Evans, J. Fidel González-Rouco, Hugues Goosse, George C. Hurtt, Fortunat Joos, Jed O. Kaplan, Myriam Khodri, Kees Klein Goldewijk, Natalie Krivova, Allegra N. LeGrande, Stephan J. Lorenz, Jürg Luterbacher, Wenmin Man, Amanda C. Maycock, Malte Meinshausen, Anders Moberg, Raimund Muscheler, Christoph Nehrbass-Ahles, Bette I. Otto-Bliesner, Steven J. Phipps, Julia Pongratz, Eugene Rozanov, Gavin A. Schmidt, Hauke Schmidt, Werner Schmutz, Andrew Schurer, Alexander I. Shapiro, Michael Sigl, Jason E. Smerdon, Sami K. Solanki, Claudia Timmreck, Matthew Toohey, Ilya G. Usoskin, Sebastian Wagner, Chi-Ju Wu, Kok Leng Yeo, Davide Zanchettin, Qiong Zhang, and Eduardo Zorita
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D. Maraun and M. Widmann
Hydrol. Earth Syst. Sci., 19, 3449–3456, https://doi.org/10.5194/hess-19-3449-2015, https://doi.org/10.5194/hess-19-3449-2015, 2015
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Clim. Past, 11, 203–216, https://doi.org/10.5194/cp-11-203-2015, https://doi.org/10.5194/cp-11-203-2015, 2015
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O. Bothe, J. H. Jungclaus, and D. Zanchettin
Clim. Past, 9, 2471–2487, https://doi.org/10.5194/cp-9-2471-2013, https://doi.org/10.5194/cp-9-2471-2013, 2013
O. Bothe, J. H. Jungclaus, D. Zanchettin, and E. Zorita
Clim. Past, 9, 1089–1110, https://doi.org/10.5194/cp-9-1089-2013, https://doi.org/10.5194/cp-9-1089-2013, 2013
J. Segschneider, A. Beitsch, C. Timmreck, V. Brovkin, T. Ilyina, J. Jungclaus, S. J. Lorenz, K. D. Six, and D. Zanchettin
Biogeosciences, 10, 669–687, https://doi.org/10.5194/bg-10-669-2013, https://doi.org/10.5194/bg-10-669-2013, 2013
S. Tietsche, D. Notz, J. H. Jungclaus, and J. Marotzke
Ocean Sci., 9, 19–36, https://doi.org/10.5194/os-9-19-2013, https://doi.org/10.5194/os-9-19-2013, 2013
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Subject: Climate Modelling | Archive: Historical Records | Timescale: Instrumental Period
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Fiona Williamson
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This paper focuses on the recovery of instrumental weather records available for Singapore and Malaysia (Straits Settlements) from the late 1780s to 1917. Taking a historical approach, the paper explores the types of records available, the circumstances of their production and their value to the scientific community.
Lucas Pfister, Stefan Brönnimann, Mikhaël Schwander, Francesco Alessandro Isotta, Pascal Horton, and Christian Rohr
Clim. Past, 16, 663–678, https://doi.org/10.5194/cp-16-663-2020, https://doi.org/10.5194/cp-16-663-2020, 2020
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This paper aims to reconstruct high-resolution daily precipitation and temperature fields for Switzerland back to 1864 using a statistical approach called the analogue resampling method. Results suggest that the presented method is suitable for weather reconstruction. As illustrated with the example of the avalanche in winter 1887/88, these weather reconstructions have great potential for various analyses of past weather and climate impact modelling.
Bijan Fallah, Emmanuele Russo, Walter Acevedo, Achille Mauri, Nico Becker, and Ulrich Cubasch
Clim. Past, 14, 1345–1360, https://doi.org/10.5194/cp-14-1345-2018, https://doi.org/10.5194/cp-14-1345-2018, 2018
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We try to test and evaluate an approach for using two main sources of information on the climate of the past: climate model simulations and proxies. This is done via data assimilation (DA), a method that blends these two sources of information in an intelligent way. However, DA and climate models are computationally very expensive. Here, we tested the ability of a computationally affordable DA to reconstruct high-resolution climate fields.
Hans Visser, Sönke Dangendorf, Detlef P. van Vuuren, Bram Bregman, and Arthur C. Petersen
Clim. Past, 14, 139–155, https://doi.org/10.5194/cp-14-139-2018, https://doi.org/10.5194/cp-14-139-2018, 2018
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In December 2015, 195 countries agreed in Paris to hold the increase in global temperature well below 2.0 °C. However, the Paris Agreement is not conclusive as regards methods to calculate it. To find answers to these questions we performed an uncertainty and sensitivity analysis where datasets, model choices, choices for pre-industrial and warming definitions have been varied. Based on these findings we propose an estimate for signal progression in global temperature since pre-industrial time.
Rüdiger Glaser, Iso Himmelsbach, and Annette Bösmeier
Clim. Past, 13, 1573–1592, https://doi.org/10.5194/cp-13-1573-2017, https://doi.org/10.5194/cp-13-1573-2017, 2017
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This paper presents the extent to which climate, harvest and prices influenced the major migration waves from southwest Germany into North America during the 19th century, a century of dramatic climatic and societal changes.
Geir Hestmark and Øyvind Nordli
Clim. Past, 12, 2087–2106, https://doi.org/10.5194/cp-12-2087-2016, https://doi.org/10.5194/cp-12-2087-2016, 2016
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The detailed and continuous meteorological observations of Jens Esmark from the capital of Norway in the period 1816 to 1838 are evaluated, homogenized and reanalysed with modern methods to characterize the weather in Oslo in this period.
Laurie Caillouet, Jean-Philippe Vidal, Eric Sauquet, and Benjamin Graff
Clim. Past, 12, 635–662, https://doi.org/10.5194/cp-12-635-2016, https://doi.org/10.5194/cp-12-635-2016, 2016
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This paper describes a daily high-resolution reconstruction of precipitation and temperature fields in France from 1871 onwards. A statistical method linking atmospheric circulation to local precipitation is refined for taking advantage of recently published global long-term atmospheric and oceanic reconstructions. The resulting data set allows filling in the spatial and temporal data gaps in historical surface observations, and improving our knowledge on the local-scale climate variability.
Cited articles
Annan, J. D. and Hargreaves, J. C.: Identification of climatic state with limited proxy data, Clim. Past, 8, 1141–1151, https://doi.org/10.5194/cp-8-1141-2012, 2012.
Bhend, J., Franke, J., Folini, D., Wild, M., and Brönnimann, S.: An ensemble-based approach to climate reconstructions, Clim. Past, 8, 963–976, https://doi.org/10.5194/cp-8-963-2012, 2012.
Brohan, P., Kennedy, J. J., Harris, I., Tett, S. F. B., and Jones, P. D.: Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850, J. Geophys. Res.-Atmos., 111, D12106, https://doi.org/10.1029/2005jd006548, 2006.
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.
Fernández-Donado, L., González-Rouco, J. F., Raible, C. C., Ammann, C. M., Barriopedro, D., García-Bustamante, E., Jungclaus, J. H., Lorenz, S. J., Luterbacher, J., Phipps, S. J., Servonnat, J., Swingedouw, D., Tett, S. F. B., Wagner, S., Yiou, P., and Zorita, E.: Large-scale temperature response to external forcing in simulations and reconstructions of the last millennium, Clim. Past, 9, 393–421, https://doi.org/10.5194/cp-9-393-2013, 2013.
Goosse, H., Renssen, H., Timmermann, A., Bradley, R. S., and Mann, M. E.: Using paleoclimate proxy-data to select optimal realisations in an ensemble of simulations of the climate of the past millennium, Clim. Dynam., 27, 165–184, 2006.
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.-Atmos., 115, D09108, https://doi.org/10.1029/2009jd012737, 2010.
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, 2012.
Hakim, G. J., Annan, J., Bronnimann, S., Crucifix, M., Edwards, T., Goosse, H., Paul, A., van der Schrier, G., and Widmann, M.: Overview of data assimilation methods, PAGES News, 21, 72–73, 2013.
Huntley, H. S. and Hakim, G. J.: Assimilation of time-averaged observations in a quasi-geostrophic atmospheric jet model, Clim. Dynam., 35, 995–1009, 2010.
Jansen, E., Overpeck, J., Briffa, K., Duplessy, J.-C., Joos, F., Masson-Delmotte, V., Olago, D., Otto-Bliesner, B., Peltier, W., Rahmstorf, S., Ramesh, R., Raynaud, D., Rind, D., Solomina, O., Villalba, R., and Zhang, D.: Palaeoclimate, in: Climate change 2007: the physical science basis. Contribution of working group 1 to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K., Tignor, M., and Miller, H., Cambridge University Press, Cambridge, 433–497, 2007.
Jones, P. D. and Mann, M. E.: Climate over past millennia, Rev. Geophys., 42, RG2002, https://doi.org/10.1029/2003RG000143, 2004.
Jungclaus, J. H., Lohmann, K., and Zanchettin, D.: Enhanced 20th-century heat transfer to the Arctic simulated in the context of climate variations over the last millennium, Clim. Past, 10, 2201–2213, https://doi.org/10.5194/cp-10-2201-2014, 2014.
Marsland, S. J., Haak, H., Jungclaus, J. H., Latif, M., and Roske, F.: The Max-Planck-Institute global ocean/sea ice model with orthogonal curvilinear coordinates, Ocean Model., 5, 91–127, https://doi.org/10.1016/s1463-5003(02)00015-x, 2003.
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.
Matsikaris, A., Widmann, M., and Jungclaus, J.: Assimilating continental mean temperatures to reconstruct the climate of the late pre-industrial period, Clim. Dynam., 46, 3547–3566, https://doi.org/10.1007/s00382-015-2785-9, 2016.
Moberg, A. and Brattstrom, G.: Prediction intervals for climate reconstructions with autocorrelated noise-An analysis of ordinary least squares and measurement error methods, Palaeogeogr. Palaeocl., 308, 313–329, https://doi.org/10.1016/j.palaeo.2011.05.035, 2011.
PAGES 2K Consortium: Continental-scale temperature variability during the past two millennia, Nat. Geosci., 6, 339–346, 2013.
Pendergrass, A. G., Hakim, G. J., Battisti, D. S., and Roe, G.: Coupled air-mixed layer temperature predictability for climate reconstruction, J. Climate, 25, 459–472, 2012.
Raddatz, T. J., Reick, C. H., Knorr, W., Kattge, J., Roeckner, E., Schnur, R., Schnitzler, K. G., Wetzel, P., and Jungclaus, J.: Will the tropical land biosphere dominate the climate-carbon cycle feedback during the twenty-first century?, Clim. Dynam., 29, 565–574, https://doi.org/10.1007/s00382-007-0247-8, 2007.
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.
Stevens, B., Giorgetta, M., Esch, M., Mauritsen, T., Crueger, T., Rast, S., Salzmann, M., Schmidt, H., Bader, J., Block, K., Brokopf, R., Fast, I., Kinne, S., Kornblueh, L., Lohmann, U., Pincus, R., Reichler, T., and Roeckner, E.: Atmospheric component of the Earth System Model: ECHAM6, Journal of Advances in Modeling Earth Systems, 5, 146–172, https://doi.org/10.1002/jame.20015, 2013.
Van Den Dool, H. M.: Searching for analogues, how long must we wait, Tellus A, 46, 314–324, 1994.
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.
Short summary
We have assimilated proxy-based (PAGES 2K) and instrumental (HadCRUT3v) observations into a General Circulation Model (MPI-ESM-CR). Assimilating instrumental data improves the performance of Data Assimilation. No skill on small spatial scales is however found for either of the two schemes. Errors in the assimilated data are therefore not the main reason for this lack of skill; continental mean temperatures cannot provide skill on small spatial scales in palaeoclimate reconstructions.
We have assimilated proxy-based (PAGES 2K) and instrumental (HadCRUT3v) observations into a...
