State and Perspectives of the Concept of Large‐Scale Conditioning of Regional Climate Modelling

Storch, Hans von; Cavicchia, Leone; Feser, Frauke; Li, Delei

doi:10.20944/preprints201807.0533.v1

Cited by 2 publications

(3 citation statements)

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“…Nevertheless, this still leaves a lot of freedom for the model to simulate various equivalent weather states, which could all have happened with the same probability. Here, we also use additional simulations where the observed large-scale circulation patterns are simulated by applying spectral nudging (von Storch et al 2000). This means that large-scale weather states of the GCM are adjusted to be more similar to the forcing reanalysis data.…”

Section: B Downscaled High-resolution Simulationmentioning

confidence: 99%

“…Observations such as wind speed measurements often suffer from inhomogeneities over time as they are very dependent on station location, its surroundings, and measurement techniques (e.g., Lindenberg et al 2012;von Storch et al 2017). To overcome these potential inhomogeneities, geostrophic wind speed proxies based on surface pressure measurements can be used.…”

Section: Introductionmentioning

confidence: 99%

“…For that a global high-resolution atmospheric climate model was spectrally nudged at large spatial scales toward reanalysis data. Spectral nudging is a well-established method to constrain the large spatial scales of climate models for climate hindcast simulations (von Storch et al 2000;Waldron et al 1996;von Storch et al 2018). The climate model remains free to develop regional-scale weather phenomena and processes, while larger-scale features are nudged toward reanalyses or global model data.…”

Section: Introductionmentioning

confidence: 99%

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North Atlantic Winter Storm Activity in Modern Reanalyses and Pressure-Based Observations

et al. 2021

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This study analyzes changes in extratropical windstorms over the North Atlantic during the last decades. We assessed and compared North Atlantic winter storm activity in a comprehensive approach from three different data sources: modern reanalysis data sets, a dynamically downscaled high-resolution global atmospheric climate simulation, and observations. The multi-decadal observations comprise both a storm index derived from geostrophic wind speed triangles and an observational record of low pressure systems counted from weather analyses. Both observational data sets have neither been compared to the most recent reanalyses nor to the downscaled global climate simulation with respect to North Atlantic winter storms before.The similarity of the geostrophic wind speed storm index to reanalyzed high wind speed percentiles and storm numbers confirms its suitability to describe storm frequencies and intensities for multi-decadal time scales. The results show that high wind speeds, storm numbers, and spatial storm track distributions are generally alike in high-resolution reanalyses and downscaled data sets and they reveal an increasing similarity to observations over time. Strong decadal and multi-decadal variability emerged in high wind speed percentiles and storm frequency, but no long-term changes for the last decades were detected.

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Section: B Downscaled High-resolution Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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North Atlantic Winter Storm Activity in Modern Reanalyses and Pressure-Based Observations

et al. 2021

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Internal Model Variability of the Regional Coupled System Model GCOAST-AHOI

et al. 2020

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Simulations of a Regional Climate Model (RCM) driven by identical lateral boundary conditions but initialized at different times exhibit the phenomenon of so-called internal model variability (or in short, Internal Variability—IV), which is defined as the inter-member spread between members in an ensemble of simulations. Our study investigates the effects of air-sea coupling on IV of the regional atmospheric model COSMO-CLM (CCLM) of the new regional coupled system model GCOAST-AHOI (Geesthacht Coupled cOAstal model SysTem: Atmosphere, Hydrology, Ocean and Sea Ice). We specifically address physical processes parameterized in CCLM, which may cause a large IV during an extreme event, and where this IV is affected by the air-sea coupling. Two six-member ensemble simulations were conducted with GCOAST-AHOI and the stand-alone CCLM (CCLM_ctr) for a period of 1 September–31 December 2013 over Europe. IV is expressed by spreads within the two sets of ensembles. Analyses focus on specific events during this period, especially on the storm Christian occurring from 27 to 29 October 2013 in northern Europe. Results show that simulations of CCLM_ctr vary largely amongst ensemble members during the storm. By analyzing two members of CCLM_ctr with opposite behaviors, we found that the large uncertainty in CCLM_ctr is caused by a combination of two factors (1) uncertainty in parameterization of cloud-radiation interaction in the atmospheric model. and (2) lack of an active two-way air-sea interaction. When CCLM is two-way coupled with the ocean model, the ensemble means of GCOAST-AHOI and CCLM_ctr are relatively similar, but the spread is reduced remarkably in GCOAST-AHOI, not only over the ocean where the coupling is done but also over land due to the land-sea interactions.

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State and Perspectives of the Concept of Large‐Scale Conditioning of Regional Climate Modelling

Cited by 2 publications

References 46 publications

North Atlantic Winter Storm Activity in Modern Reanalyses and Pressure-Based Observations

North Atlantic Winter Storm Activity in Modern Reanalyses and Pressure-Based Observations

Internal Model Variability of the Regional Coupled System Model GCOAST-AHOI

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