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- Author or Editor: Frauke Feser x
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Abstract
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 datasets, a dynamically downscaled high-resolution global atmospheric climate simulation, and observations. The multidecadal 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 datasets have been compared neither 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 multidecadal 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 datasets and they reveal an increasing similarity to observations over time. Strong decadal and multidecadal variability emerged in high wind speed percentiles and storm frequency, but no long-term changes for the last decades were detected.
Abstract
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 datasets, a dynamically downscaled high-resolution global atmospheric climate simulation, and observations. The multidecadal 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 datasets have been compared neither 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 multidecadal 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 datasets and they reveal an increasing similarity to observations over time. Strong decadal and multidecadal variability emerged in high wind speed percentiles and storm frequency, but no long-term changes for the last decades were detected.
Abstract
The impact of spectral nudging on cloud simulation with a regional atmospheric model was examined. Simulated cloudiness of the Regional Model (REMO) and the Spectrally Nudged REMO (SN-REMO) were intercompared and evaluated with satellite-derived cloudiness from the International Satellite Cloud Climatology Project (ISCCP). In general, the additional spectral nudging does not affect the mean cloud simulation. However, for particular weather regimes the introduction of spectral nudging causes notable differences in cloud simulation. Two weather conditions for these large differences in cloud simulation were derived: 1) change of the general circulation patterns, or 2) strong anticyclonic circulation within the model domain. Case studies of these weather situations indicated a better agreement of simulated and satellite-derived cloudiness when spectral nudging has been applied to the regional model.
Abstract
The impact of spectral nudging on cloud simulation with a regional atmospheric model was examined. Simulated cloudiness of the Regional Model (REMO) and the Spectrally Nudged REMO (SN-REMO) were intercompared and evaluated with satellite-derived cloudiness from the International Satellite Cloud Climatology Project (ISCCP). In general, the additional spectral nudging does not affect the mean cloud simulation. However, for particular weather regimes the introduction of spectral nudging causes notable differences in cloud simulation. Two weather conditions for these large differences in cloud simulation were derived: 1) change of the general circulation patterns, or 2) strong anticyclonic circulation within the model domain. Case studies of these weather situations indicated a better agreement of simulated and satellite-derived cloudiness when spectral nudging has been applied to the regional model.
An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties).
However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales.
Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.
An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties).
However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales.
Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.
Abstract
This study analyzes a method of constructing a homogeneous, high-resolution global atmospheric hindcast. The method is the spectral nudging technique, which was applied to a state-of-the-art general circulation model (ECHAM6, T255L95). Large spatial scales of the global climate model prognostic variables were spectrally nudged toward a reanalysis dataset (NCEP-1, T62L28) for the past few decades. The main idea is the addition of dynamically consistent regional weather details to the coarse-grid NCEP-1 reanalysis. A large number of sensitivity experiments was performed, using different nudging e-folding times, vertical profiles, wavenumbers, and variables. Comparisons with observations and several reanalyses showed a high dependency on the variations of the nudging configuration. At the global scale, the accordance is very high for extratropical regions and lower in the tropics. A wavenumber truncation of 30, a relatively short e-folding time of 50 min, and a plateau-shaped nudging profile applied only to divergence and vorticity generally yielded the best results. This is one of the first global spectral nudging hindcast studies and the first applying an altitude-dependent profile to selected prognostic variables. The method can be applied to reconstructing the history of extreme events such as intense storms within the context of ongoing climate change.
Abstract
This study analyzes a method of constructing a homogeneous, high-resolution global atmospheric hindcast. The method is the spectral nudging technique, which was applied to a state-of-the-art general circulation model (ECHAM6, T255L95). Large spatial scales of the global climate model prognostic variables were spectrally nudged toward a reanalysis dataset (NCEP-1, T62L28) for the past few decades. The main idea is the addition of dynamically consistent regional weather details to the coarse-grid NCEP-1 reanalysis. A large number of sensitivity experiments was performed, using different nudging e-folding times, vertical profiles, wavenumbers, and variables. Comparisons with observations and several reanalyses showed a high dependency on the variations of the nudging configuration. At the global scale, the accordance is very high for extratropical regions and lower in the tropics. A wavenumber truncation of 30, a relatively short e-folding time of 50 min, and a plateau-shaped nudging profile applied only to divergence and vorticity generally yielded the best results. This is one of the first global spectral nudging hindcast studies and the first applying an altitude-dependent profile to selected prognostic variables. The method can be applied to reconstructing the history of extreme events such as intense storms within the context of ongoing climate change.
Regional Meteorological–Marine Reanalyses and Climate Change Projections
Results for Northern Europe and Potential for Coastal and Offshore Applications
A compilation of coastal weather analyses and climate change scenarios for the future for northern Europe from various sources is presented. They contain no direct measurements but results from numerical models that have been driven either by observed data in order to achieve the best possible representation of observed past conditions or by climate change scenarios for the near future. A comparison with the limited number of observational data points to the good quality of the model data in terms of long-term statistics, such as multiyear return values of wind speed and wave heights. These model data provide a unique combination of consistent atmospheric, oceanic, sea state, and other parameters at high spatial and temporal detail, even for places and variables for which no measurements have been made. In addition, coastal scenarios for the near future complement the numerical analyses of past conditions in a consistent way. The backbones of the data are regional wind, wave, and storm surge hindcasts and scenarios mainly for the North Sea. We briefly discuss the methodology to derive these data, their quality, and limitations in comparison with observations. Long-term changes in the wind, wave, and storm surge climate are discussed, and possible future changes are assessed. A variety of coastal and offshore applications taking advantage of the data is presented. Examples comprise applications in ship design, oil risk modeling and assessment, or the construction and operation of offshore wind farms.
A compilation of coastal weather analyses and climate change scenarios for the future for northern Europe from various sources is presented. They contain no direct measurements but results from numerical models that have been driven either by observed data in order to achieve the best possible representation of observed past conditions or by climate change scenarios for the near future. A comparison with the limited number of observational data points to the good quality of the model data in terms of long-term statistics, such as multiyear return values of wind speed and wave heights. These model data provide a unique combination of consistent atmospheric, oceanic, sea state, and other parameters at high spatial and temporal detail, even for places and variables for which no measurements have been made. In addition, coastal scenarios for the near future complement the numerical analyses of past conditions in a consistent way. The backbones of the data are regional wind, wave, and storm surge hindcasts and scenarios mainly for the North Sea. We briefly discuss the methodology to derive these data, their quality, and limitations in comparison with observations. Long-term changes in the wind, wave, and storm surge climate are discussed, and possible future changes are assessed. A variety of coastal and offshore applications taking advantage of the data is presented. Examples comprise applications in ship design, oil risk modeling and assessment, or the construction and operation of offshore wind farms.
