Articles | Volume 6, issue 3
https://doi.org/10.5194/gmd-6-849-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-6-849-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
22 Jun 2013
Development and technical paper |
| 22 Jun 2013
Impacts of using spectral nudging on regional climate model RCA4 simulations of the Arctic
Rossby Centre, Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
R. Döscher
Rossby Centre, Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
T. Koenigk
Rossby Centre, Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
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- What is the surface mass balance of Antarctica? An intercomparison of regional climate model estimates R. Mottram et al. https://doi.org/10.5194/tc-15-3751-2021
- The impact of wave number selection and spin‐up time in spectral nudging B. Gómez & G. Miguez‐Macho https://doi.org/10.1002/qj.3032
- Wave height return periods from combined measurement–model data: a Baltic Sea case study J. Björkqvist et al. https://doi.org/10.5194/nhess-20-3593-2020
- An algorithm based on sea-level pressure fluctuations to identify major Baltic inflow events S. Schimanke et al. https://doi.org/10.3402/tellusa.v66.23452
- Compound Flooding in Halmstad: Common Causes, Interannual Variability and the Effects of Climate Change M. Hieronymus et al. https://doi.org/10.16993/tellusa.4068
- On the effects of constraining atmospheric circulation in a coupled atmosphere-ocean Arctic regional climate model P. Berg et al. https://doi.org/10.1007/s00382-015-2783-y
- Model Consistent Pseudo-Observations of Precipitation and Their Use for Bias Correcting Regional Climate Models P. Berg et al. https://doi.org/10.3390/cli3010118
- Arctic budget study of intermember variability using HIRHAM5 ensemble simulations A. Sommerfeld et al. https://doi.org/10.1002/2015JD023153
- Reliability of Antarctic air temperature changes from Polar WRF: A comparison with observations and MAR outputs Y. Zhang et al. https://doi.org/10.1016/j.atmosres.2021.105967
- Future Projections of Fire Occurrence in Brazil Using EC-Earth Climate Model P. Silva et al. https://doi.org/10.1590/0102-778631320150142
- An assessment of the Polar Weather Research and Forecasting (WRF) model representation of near‐surface meteorological variables over West Antarctica P. Deb et al. https://doi.org/10.1002/2015JD024037
- Projected changes in near-surface wind speed in the Arctic by a regional climate model M. Akperov et al. https://doi.org/10.1016/j.polar.2024.101162
- Impact of Ice Data Quality and Treatment on Wave Hindcast Statistics in Seasonally Ice-Covered Seas L. Tuomi et al. https://doi.org/10.3389/feart.2019.00166
- Biogeophysical feedbacks enhance the Arctic terrestrial carbon sink in regional Earth system dynamics W. Zhang et al. https://doi.org/10.5194/bg-11-5503-2014
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- Evaluation of the atmosphere–land–ocean–sea ice interface processes in the Regional Arctic System Model version 1 (RASM1) using local and globally gridded observations M. Brunke et al. https://doi.org/10.5194/gmd-11-4817-2018
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- Sea-ice evaluation of NEMO-Nordic 1.0: a NEMO–LIM3.6-based ocean–sea-ice model setup for the North Sea and Baltic Sea P. Pemberton et al. https://doi.org/10.5194/gmd-10-3105-2017
- Nemo-Nordic 1.0: a NEMO-based ocean model for the Baltic and North seas – research and operational applications R. Hordoir et al. https://doi.org/10.5194/gmd-12-363-2019
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- A Novel Machine Learning Based Bias Correction Method and Its Application to Sea Level in an Ensemble of Downscaled Climate Projections M. Hieronymus & F. Hieronymus https://doi.org/10.16993/tellusa.3216
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