Articles | Volume 16, issue 10
https://doi.org/10.5194/gmd-16-2899-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-2899-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
26 May 2023
Methods for assessment of models |
| 26 May 2023
| 26 May 2023
Various ways of using empirical orthogonal functions for climate model evaluation
Rasmus E. Benestad
CORRESPONDING AUTHOR
The Norwegian Meteorological Institute, P.O. Box 43 Blindern, 0313 Oslo, Norway
Abdelkader Mezghani
The Norwegian Meteorological Institute, P.O. Box 43 Blindern, 0313 Oslo, Norway
Julia Lutz
The Norwegian Meteorological Institute, P.O. Box 43 Blindern, 0313 Oslo, Norway
Andreas Dobler
The Norwegian Meteorological Institute, P.O. Box 43 Blindern, 0313 Oslo, Norway
Kajsa M. Parding
The Norwegian Meteorological Institute, P.O. Box 43 Blindern, 0313 Oslo, Norway
Oskar A. Landgren
The Norwegian Meteorological Institute, P.O. Box 43 Blindern, 0313 Oslo, Norway
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Cited
17 citations as recorded by crossref.
- Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach S. Moe et al.
- Downward Mediterranean Cloudiness Beyond Little Ice Age Background Variability N. Diodato et al.
- Summertime Arctic and North Atlantic–Eurasian circulation regimes under climate change J. Müller et al.
- Atmospheric circulation and moisture budget drivers of dominant modes of winter extreme precipitation variability over North America D. Jeong et al.
- Downscaling the probability of heavy rainfall over the Nordic countries R. Benestad et al.
- Validation of ERA5 rainfall data over the South Pacific Region: case study of Fiji Islands P. Sagero et al.
- Investigating reduced-dimensional variability in aircraft-observed aerosol–cloud parameters K. Butler et al.
- Global record-breaking recurrence rates indicate more widespread and intense surface air temperature and precipitation extremes R. Benestad et al.
- Contribution of relative humidity on aerosol optical depth role in influencing the regionalization pattern of precipitation over West Africa A. Arowolo et al.
- Long-term variability in thermospheric mass density (TMD): insights from Singular Spectrum Analysis (SSA) and model comparisons S. Korlaelci et al.
- Optimization algorithms for multivariate sampling reduction using spatial-temporal data T. Maltauro et al.
- Svalbard’s 2024 record summer: An early view of Arctic glacier meltdown? T. Schuler et al.
- Analysis of the Spatio-Temporal Evolution Characteristics and Influencing Factors of Extreme Climate Events in Jilin Province from 1970 to 2020 S. Zhang et al.
- Incorporating climate projections in the environmental risk assessment of pesticides in aquatic ecosystems R. Oldenkamp et al.
- Bergen metrics: composite error metrics for assessing performance of climate models using EURO-CORDEX simulations A. Samantaray et al.
- A dataset of gridded precipitation intensity-duration-frequency curves in Qinghai-Tibet Plateau Z. Ren et al.
- Machine Learning as a Lens on NWP ICON Configurations Validation over Southern Italy in Winter 2022–2023—Part I: Empirical Orthogonal Functions D. Cinquegrana & E. Bucchignani
17 citations as recorded by crossref.
- Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach S. Moe et al.
- Downward Mediterranean Cloudiness Beyond Little Ice Age Background Variability N. Diodato et al.
- Summertime Arctic and North Atlantic–Eurasian circulation regimes under climate change J. Müller et al.
- Atmospheric circulation and moisture budget drivers of dominant modes of winter extreme precipitation variability over North America D. Jeong et al.
- Downscaling the probability of heavy rainfall over the Nordic countries R. Benestad et al.
- Validation of ERA5 rainfall data over the South Pacific Region: case study of Fiji Islands P. Sagero et al.
- Investigating reduced-dimensional variability in aircraft-observed aerosol–cloud parameters K. Butler et al.
- Global record-breaking recurrence rates indicate more widespread and intense surface air temperature and precipitation extremes R. Benestad et al.
- Contribution of relative humidity on aerosol optical depth role in influencing the regionalization pattern of precipitation over West Africa A. Arowolo et al.
- Long-term variability in thermospheric mass density (TMD): insights from Singular Spectrum Analysis (SSA) and model comparisons S. Korlaelci et al.
- Optimization algorithms for multivariate sampling reduction using spatial-temporal data T. Maltauro et al.
- Svalbard’s 2024 record summer: An early view of Arctic glacier meltdown? T. Schuler et al.
- Analysis of the Spatio-Temporal Evolution Characteristics and Influencing Factors of Extreme Climate Events in Jilin Province from 1970 to 2020 S. Zhang et al.
- Incorporating climate projections in the environmental risk assessment of pesticides in aquatic ecosystems R. Oldenkamp et al.
- Bergen metrics: composite error metrics for assessing performance of climate models using EURO-CORDEX simulations A. Samantaray et al.
- A dataset of gridded precipitation intensity-duration-frequency curves in Qinghai-Tibet Plateau Z. Ren et al.
- Machine Learning as a Lens on NWP ICON Configurations Validation over Southern Italy in Winter 2022–2023—Part I: Empirical Orthogonal Functions D. Cinquegrana & E. Bucchignani
Saved (final revised paper)
Latest update: 24 May 2026
Short summary
A mathematical method known as common EOFs is not widely used within the climate research community, but it offers innovative ways of evaluating climate models. We show how common EOFs can be used to evaluate large ensembles of global climate model simulations and distill information about their ability to reproduce salient features of the regional climate. We can say that they represent a kind of machine learning (ML) for dealing with big data.
A mathematical method known as common EOFs is not widely used within the climate research...
