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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.
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.
