REMO2020: a modernised modular regional climate model

Abstract. This paper introduces REMO2020, a modernised version of the well-known and widely used REgional climate MOdel (REMO). REMO2020 has undergone fundamental changes in its code structure to provide a more modular, operationally focused design, facilitating the inclusion of new components and updates. Here, we describe the default configuration of REMO2020, which includes the following updates compared to the previous version, REMO2015: (i) the FLake lake model, (ii) a state-of-the-art MACv2-SP aerosol climatology, (iii) a newly developed three-layer snow module, (iv) a prognostic precipitation scheme, (v) an updated time filter, and (vi) a new tuning approach. Additionally, we describe some optional modules that can be activated separately, such as the interactive MOsaic-based VEgetation model iMOVE. REMO2020 outperforms its predecessor REMO2015 with regard to nearly all evaluation metrics used to evaluate simulations of Europe's climate. The persistent warm-temperature bias over central Europe and the cold-temperature bias over northern Europe have been significantly reduced in REMO2020. Similarly, the previously modelled dry bias in central Europe has been nearly eliminated, and the extent of the wet bias in eastern Europe has been reduced. The precipitation distribution in REMO2020 is much more realistic, especially in terms of heavy-precipitation extremes. Statistically, REMO2020 aligns better with long-term measurements than with older versions. Mountainous areas still present a challenge in REMO2020, especially with a higher vertical resolution. In this paper, we demonstrate why REMO2020 will be our new model version for future dynamical downscaling activities.

Authors

Publication Details

Journal
Geoscientific model development
Published
2025-10-28
DOI
https://doi.org/10.5194/gmd-18-7907-2025
Primary Topic
Climate variability and models
Type
article
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article

REMO2020: a modernised modular regional climate model

Joni‐Pekka Pietikäinen, Diana Rechid, Christine Nam, Peter Hoffmann et al.
Geoscientific model development
Climate variability and models
article

REMO2020: a modernised modular regional climate model

Joni‐Pekka Pietikäinen, Diana Rechid, Christine Nam, Peter Hoffmann, Lars Buntemeyer, Thomas Frisius, Kevin Sieck, Daniela Jacob, Christina Asmus
article en

Abstract

Abstract. This paper introduces REMO2020, a modernised version of the well-known and widely used REgional climate MOdel (REMO). REMO2020 has undergone fundamental changes in its code structure to provide a more modular, operationally focused design, facilitating the inclusion of new components and updates. Here, we describe the default configuration of REMO2020, which includes the following updates compared to the previous version, REMO2015: (i) the FLake lake model, (ii) a state-of-the-art MACv2-SP aerosol climatology, (iii) a newly developed three-layer snow module, (iv) a prognostic precipitation scheme, (v) an updated time filter, and (vi) a new tuning approach. Additionally, we describe some optional modules that can be activated separately, such as the interactive MOsaic-based VEgetation model iMOVE. REMO2020 outperforms its predecessor REMO2015 with regard to nearly all evaluation metrics used to evaluate simulations of Europe's climate. The persistent warm-temperature bias over central Europe and the cold-temperature bias over northern Europe have been significantly reduced in REMO2020. Similarly, the previously modelled dry bias in central Europe has been nearly eliminated, and the extent of the wet bias in eastern Europe has been reduced. The precipitation distribution in REMO2020 is much more realistic, especially in terms of heavy-precipitation extremes. Statistically, REMO2020 aligns better with long-term measurements than with older versions. Mountainous areas still present a challenge in REMO2020, especially with a higher vertical resolution. In this paper, we demonstrate why REMO2020 will be our new model version for future dynamical downscaling activities.

Geoscientific model developmentVol. 18(20)
Openalex Percentile: Top 71%
Climate variability and models
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