Articles | Volume 11, issue 1
Geosci. Model Dev., 11, 351–368, 2018
https://doi.org/10.5194/gmd-11-351-2018
Geosci. Model Dev., 11, 351–368, 2018
https://doi.org/10.5194/gmd-11-351-2018

Model description paper 25 Jan 2018

Model description paper | 25 Jan 2018

Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

Alexander Pasternack et al.

Related authors

Recalibrating Decadal Climate Predictions – What is an adequate model for the drift?
Alexander Pasternack, Jens Grieger, Henning W. Rust, and Uwe Ulbrich
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-191,https://doi.org/10.5194/gmd-2020-191, 2020
Revised manuscript under review for GMD
Short summary

Related subject area

Atmospheric sciences
Novel estimation of aerosol processes with particle size distribution measurements: a case study with the TOMAS algorithm v1.0.0
Dana L. McGuffin, Yuanlong Huang, Richard C. Flagan, Tuukka Petäjä, B. Erik Ydstie, and Peter J. Adams
Geosci. Model Dev., 14, 1821–1839, https://doi.org/10.5194/gmd-14-1821-2021,https://doi.org/10.5194/gmd-14-1821-2021, 2021
Short summary
Evaluation of ECMWF IFS-AER (CAMS) operational forecasts during cycle 41r1–46r1 with calibrated ceilometer profiles over Germany
Harald Flentje, Ina Mattis, Zak Kipling, Samuel Rémy, and Werner Thomas
Geosci. Model Dev., 14, 1721–1751, https://doi.org/10.5194/gmd-14-1721-2021,https://doi.org/10.5194/gmd-14-1721-2021, 2021
Short summary
Influence of biomass burning vapor wall loss correction on modeling organic aerosols in Europe by CAMx v6.50
Jianhui Jiang, Imad El Haddad, Sebnem Aksoyoglu, Giulia Stefenelli, Amelie Bertrand, Nicolas Marchand, Francesco Canonaco, Jean-Eudes Petit, Olivier Favez, Stefania Gilardoni, Urs Baltensperger, and André S. H. Prévôt
Geosci. Model Dev., 14, 1681–1697, https://doi.org/10.5194/gmd-14-1681-2021,https://doi.org/10.5194/gmd-14-1681-2021, 2021
Short summary
Seasonal and diurnal performance of daily forecasts with WRF V3.8.1 over the United Arab Emirates
Oliver Branch, Thomas Schwitalla, Marouane Temimi, Ricardo Fonseca, Narendra Nelli, Michael Weston, Josipa Milovac, and Volker Wulfmeyer
Geosci. Model Dev., 14, 1615–1637, https://doi.org/10.5194/gmd-14-1615-2021,https://doi.org/10.5194/gmd-14-1615-2021, 2021
Short summary
MLAir (v1.0) – a tool to enable fast and flexible machine learning on air data time series
Lukas Hubert Leufen, Felix Kleinert, and Martin G. Schultz
Geosci. Model Dev., 14, 1553–1574, https://doi.org/10.5194/gmd-14-1553-2021,https://doi.org/10.5194/gmd-14-1553-2021, 2021
Short summary

Cited articles

Anderson, J. L.: A method for producing and evaluating probabilistic forecasts from ensemble model integrations, J. Climate, 9, 1518–1530, 1996. a
Arisido, M. W., Gaetan, C., Zanchettin, D., and Rubino, A.: A Bayesian hierarchical approach for spatial analysis of climate model bias in multi-model ensembles, Stoch. Env. Res. Risk A., 31, 1–13, 2017. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Doblas-Reyes, F. J., Hagedorn, R., and Palmer, T. N.: The rationale behind the success of multi-model ensembles in seasonal forecasting – II. Calibration and combination, Tellus A, 57, 234–252, 2005. a
Download
Short summary
We propose a decadal forecast recalibration strategy (DeFoReSt) which simultaneously adjusts unconditional and conditional bias, as well as the ensemble spread while considering the typical setting of decadal predictions, i.e., model drift and a climate trend. We apply DeFoReSt to decadal toy model data and surface temperature forecasts from the MiKlip system and find consistent improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.