Reconstructing Climate Using Ensemble Kalman Fitting
Knowledge of the climate past is indispensable for a better understanding of current and future changes in the climate system. However, past climate information is often very sparse. Interpretations of spatial patterns or circulation changes are not possible directly. In recent years, new numerical techniques have been developed that make it possible to link sparse climate information from the past with climate model simulations. Such data assimilation methods provide optimal estimates of the spatio-temporal variability of the past climate, which are at the same time physically consistent and consistent with the assimilated climate information. In the REUSE project such a data set is being created.
The aim is a monthly, global, 3-dimensional reconstruction of the atmosphere back to 1600, combining climate information from measurements, historical documents and tree rings with 30 simulations of a climate model. The method is available, but needs to be adapted and refined in order to achieve a good reconstruction. The new method brings a significant improvement compared to conventional reconstruction methods; a global monthly climate reconstruction does not yet exist. On the basis of this data set, the transition from the climate of the "Little Ice Age" to the present climate will be examined more closely. A particular focus is on decadal fluctuations in climate and the large-scale atmospheric circulation.
Veronika Haller, Dr. Jörg Franke, Prof. Dr. Stefan Brönnimann
June 2016 - November 2019
EKF400 version 1.9 ensemble mean and ensemble members
EKF400 for ensemble member with corrected land-surface bug
Best ensemble member
Franke, J., S. Brönnimann, J. Bhend, and Y. Brugnara (2017) A monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations. Scientific Data 4, 170076. doi: 10.1038/sdata.2017.76.
Brönnimann, S., S. White, and V. Slonosky (2018) Climate from 1800 to 1970 in North America and Europe, in: White, S., C. Pfister, and F. Mauelshagen (Eds.) The Palgrave Handbook of Climate History. Palgrave Macmillan, pp. 309-320.
CH2018 (2018), CH2018 – Climate Scenarios for Switzerland, Technical Report, National Centre for Climate Services, Zurich, 271 pp. ISBN: 978-3-9525031-4-0.
Hegerl, G. C., S. Brönnimann, A. Schurer, and T. Cowan (2018) The early 20th century warming: Anomalies, causes, and consequences. WIREs Clim. Change, 9, e522, doi: 10.1002/wcc.522
Brönnimann, S. (2019) Temps et climat en Suisse dans les années 1810 (Weather and climate in Switzerland in the 1810s). Annales Valaisannes 2019, 49-60.
Brönnimann, S., J. Franke, S. U. Nussbaumer, H. J. Zumbühl, D. Steiner, M. Trachsel, G. C. Hegerl, A. Schurer, M. Worni, A. Malik, J. Flückiger, and C. C. Raible (2019a) Last phase of the Little Ice Age forced by volcanic eruptions. Nature Geoscience, 12, 650-656.
Brönnimann, S., L. Frigerio, M. Schwander, M. Rohrer, P. Stucki, and J. Franke (2019b) Causes for increased flood frequency in central Europe in the 19th century. Climate of the Past 15, 1395–1409.
Burgdorf, A., S. Brönnimann, and J. Franke (2019) Two types of North American droughts related to different atmospheric circulation patternsc. Clim. Past, 15, 2053–2065.
Delaygue, G., S. Brönnimann, P. Jones, J. Blanchet, and M. Schwander (2019) Reconstruction of Lamb weather type series back to the 18th century. Clim. Dyn., 52, 6131-6148.
Franke, J., V. Valler, S. Brönnimann, R. Neukom, and F. Jaume Santero (2019) The importance of input data quality and quantity in climate field reconstructions – results from a Kalman filter based paleodata assimilation method, Clim. Past Discuss., https://doi.org/10.5194/cp-2019-80, in review.
Labbé, T., C. Pfister, S. Brönnimann, D. Rousseau, J. Franke and B. Bois (2019) The longest homogeneous series of Grape Harvest Dates, Beaune 1354-2018, and its significance for the understanding of past and present climate. Clim. Past, 15, 1485–1501.
Valler, V., J. Franke, J., and S. Brönnimann (2019a) Impact of different estimations of the background-error covariance matrix on climate reconstructions based on data assimilation, Clim. Past, 15, 1427-1441.
Valler, V., Y. Brugnara, J. Franke, and S. Brönnimann (2019b) Assimilating monthly precipitation data in a paleoclimate data assimilation framework. Clim. Past. Discuss., https://doi.org/10.5194/cp-2019-137, in review.