Articles | Volume 17, issue 11
https://doi.org/10.5194/gmd-17-4689-2024
https://doi.org/10.5194/gmd-17-4689-2024
Methods for assessment of models
 | 
13 Jun 2024
Methods for assessment of models |  | 13 Jun 2024

Multivariate adjustment of drizzle bias using machine learning in European climate projections

Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld

Related authors

Shifts in global atmospheric oxidant chemistry from land cover change
Ryan Vella, Sergey Gromov, Clara M. Nussbaumer, Laura Stecher, Matthias Kohl, Samuel Ruhl, Holger Tost, Jos Lelieveld, and Andrea Pozzer
EGUsphere, https://doi.org/10.5194/egusphere-2025-1800,https://doi.org/10.5194/egusphere-2025-1800, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Influence of ambient NO and NO2 on the quantification of total peroxy nitrates (ΣPNs) and total alkyl nitrates (ΣANs) by thermal dissociation cavity ring-down spectroscopy (TD-CRDS)
Laura Wüst, Patrick Dewald, Gunther N. T. E. Türk, Jos Lelieveld, and John N. Crowley
Atmos. Meas. Tech., 18, 1943–1959, https://doi.org/10.5194/amt-18-1943-2025,https://doi.org/10.5194/amt-18-1943-2025, 2025
Short summary
Enhancement of O₃–CO ratios at tropospheric subtropical latitudes: Photochemistry and stratospheric influence
Linda Ort, Andrea Pozzer, Peter Hoor, Florian Obersteiner, Andreas Zahn, Thomas B. Ryerson, Chelsea R. Thompson, Jeff Peischl, Róisín Commane, Bruce Daube, Ilann Bourgeois, Jos Lelieveld, and Horst Fischer
EGUsphere, https://doi.org/10.5194/egusphere-2025-1477,https://doi.org/10.5194/egusphere-2025-1477, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Global projections of heat stress at high temporal resolution using machine learning
Pantelis Georgiades, Theo Economou, Yiannis Proestos, Jose Araya, Jos Lelieveld, and Marco Neira
Earth Syst. Sci. Data, 17, 1153–1171, https://doi.org/10.5194/essd-17-1153-2025,https://doi.org/10.5194/essd-17-1153-2025, 2025
Short summary
The influence of ammonia emissions on the size-resolved global atmospheric aerosol composition and acidity
Xurong Wang, Alexandra P. Tsimpidi, Zhenqi Luo, Benedikt Steil, Andrea Pozzer, Jos Lelieveld, and Vlassis A. Karydis
EGUsphere, https://doi.org/10.5194/egusphere-2025-527,https://doi.org/10.5194/egusphere-2025-527, 2025
Short summary

Related subject area

Climate and Earth system modeling
ZEMBA v1.0: an energy and moisture balance climate model to investigate Quaternary climate
Daniel F. J. Gunning, Kerim H. Nisancioglu, Emilie Capron, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025,https://doi.org/10.5194/gmd-18-2479-2025, 2025
Short summary
Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025,https://doi.org/10.5194/gmd-18-2443-2025, 2025
Short summary
TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025,https://doi.org/10.5194/gmd-18-2427-2025, 2025
Short summary
The ensemble consistency test: from CESM to MPAS and beyond
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025,https://doi.org/10.5194/gmd-18-2349-2025, 2025
Short summary
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025,https://doi.org/10.5194/gmd-18-2161-2025, 2025
Short summary

Cited articles

Anagnostopoulou, C. and Tolika, K.: Extreme precipitation in Europe: statistical threshold selection based on climatological criteria, Theor. Appl. Climatol., 107, 479–489, 2012. a
Argüeso, D., Evans, J. P., and Fita, L.: Precipitation bias correction of very high resolution regional climate models, Hydrol. Earth Syst. Sci., 17, 4379–4388, https://doi.org/10.5194/hess-17-4379-2013, 2013. a, b
Baigorria, G. A., Jones, J. W., Shin, D.-W., Mishra, A., and O’Brien, J. J.: Assessing uncertainties in crop model simulations using daily bias-corrected regional circulation model outputs, Clim. Res., 34, 211–222, 2007. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001a. a
Breiman, L.: Random forests, Mach Learn., 45, 5–32, 2001b. a
Download
Short summary
This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
Share