Search Results
You are looking at 1 - 10 of 13 items for :
- Author or Editor: Frauke Feser x
- Article x
- Refine by Access: All Content x
Abstract
Regional climate models (RCMs) are a widely used tool to describe regional-scale climate variability and change. However, the added value provided by such models is not well explored so far, and claims have been made that RCMs have little utility. Here, it is demonstrated that RCMs are indeed returning significant added value. Employing appropriate spatial filters, the scale-dependent skill of a state-of-the-art RCM (with and without nudging of large scales) is examined by comparing its skill with that of the global reanalyses driving the RCM. This skill is measured by pattern correlation coefficients of the global reanalyses or the RCM simulation and, as a reference, of an operational regional weather analysis. For the spatially smooth variable air pressure the RCM improves this aspect of the simulation for the medium scales if the RCM is driven with large-scale constraints, but not for the large scales. For the regionally more structured quantity near-surface temperature the added value is more obvious. The simulation of medium-scale 2-m temperature anomaly fields amounts to an increase of the mean pattern correlation coefficient up to 30%.
Abstract
Regional climate models (RCMs) are a widely used tool to describe regional-scale climate variability and change. However, the added value provided by such models is not well explored so far, and claims have been made that RCMs have little utility. Here, it is demonstrated that RCMs are indeed returning significant added value. Employing appropriate spatial filters, the scale-dependent skill of a state-of-the-art RCM (with and without nudging of large scales) is examined by comparing its skill with that of the global reanalyses driving the RCM. This skill is measured by pattern correlation coefficients of the global reanalyses or the RCM simulation and, as a reference, of an operational regional weather analysis. For the spatially smooth variable air pressure the RCM improves this aspect of the simulation for the medium scales if the RCM is driven with large-scale constraints, but not for the large scales. For the regionally more structured quantity near-surface temperature the added value is more obvious. The simulation of medium-scale 2-m temperature anomaly fields amounts to an increase of the mean pattern correlation coefficient up to 30%.
Abstract
Wind vectors over the ocean were extracted from a large number of synthetic aperture radar (SAR) images from the European Remote Sensing Satellites (ERS-1 and ERS-2). The wind directions are inferred from the orientation of wind streaks that are imaged by the SAR, while the wind speeds are retrieved by inversion of the C-band model CMOD4. The derived wind directions and speeds were compared to wind vectors from the numerical Regional Model (REMO) that are available hourly on a 55-km grid. The large number of comparisons and independent weather situations allowed for an analysis of subsets that are classified by SAR-derived wind speed. A strong decrease of the standard deviation of directional differences with increasing wind speed was found. Biases of directional differences depend on SAR wind speed as well. Furthermore, the influence of the temporal difference between SAR overflight and model and an automatic image filtering on the directional error is demonstrated. Overall, reasonable fields of wind vectors were extracted from SAR imagery in 70 of 80 cases. These fields provide valuable information for validation of numerical models of the atmosphere and case studies of coastal wind fields.
Abstract
Wind vectors over the ocean were extracted from a large number of synthetic aperture radar (SAR) images from the European Remote Sensing Satellites (ERS-1 and ERS-2). The wind directions are inferred from the orientation of wind streaks that are imaged by the SAR, while the wind speeds are retrieved by inversion of the C-band model CMOD4. The derived wind directions and speeds were compared to wind vectors from the numerical Regional Model (REMO) that are available hourly on a 55-km grid. The large number of comparisons and independent weather situations allowed for an analysis of subsets that are classified by SAR-derived wind speed. A strong decrease of the standard deviation of directional differences with increasing wind speed was found. Biases of directional differences depend on SAR wind speed as well. Furthermore, the influence of the temporal difference between SAR overflight and model and an automatic image filtering on the directional error is demonstrated. Overall, reasonable fields of wind vectors were extracted from SAR imagery in 70 of 80 cases. These fields provide valuable information for validation of numerical models of the atmosphere and case studies of coastal wind fields.
Abstract
Geostrophic wind speeds calculated from mean sea level pressure readings are used to derive time series of northeast Atlantic storminess. The technique of geostrophic wind speed triangles provides relatively homogeneous long-term storm activity data and is thus suited for statistical analyses. This study makes use of historical air pressure data available from the International Surface Pressure Databank (ISPD) complemented with data from the Danish and Norwegian Meteorological Institutes. For the first time, the time series of northeast Atlantic storminess is extended until the most recent year available, that is, 2016. A multidecadal increasing trend in storm activity starting in the mid-1960s and lasting until the 1990s, whose high storminess levels are comparable to those found in the late nineteenth century, initiated debate over whether this would already be a sign of climate change. This study confirms that long-term storminess levels have returned to average values in recent years and that the multidecadal increase is part of an extended interdecadal oscillation. In addition, new storm activity uncertainty estimates were developed and novel insights into the connection with the North Atlantic Oscillation (NAO) are provided.
Abstract
Geostrophic wind speeds calculated from mean sea level pressure readings are used to derive time series of northeast Atlantic storminess. The technique of geostrophic wind speed triangles provides relatively homogeneous long-term storm activity data and is thus suited for statistical analyses. This study makes use of historical air pressure data available from the International Surface Pressure Databank (ISPD) complemented with data from the Danish and Norwegian Meteorological Institutes. For the first time, the time series of northeast Atlantic storminess is extended until the most recent year available, that is, 2016. A multidecadal increasing trend in storm activity starting in the mid-1960s and lasting until the 1990s, whose high storminess levels are comparable to those found in the late nineteenth century, initiated debate over whether this would already be a sign of climate change. This study confirms that long-term storminess levels have returned to average values in recent years and that the multidecadal increase is part of an extended interdecadal oscillation. In addition, new storm activity uncertainty estimates were developed and novel insights into the connection with the North Atlantic Oscillation (NAO) are provided.
Abstract
The “spectral nudging” method imposes time-variable large-scale atmospheric states on a regional atmospheric model. It is based on the idea that regional-scale climate statistics are conditioned by the interplay between continental-scale atmospheric conditions and such regional features as marginal seas and mountain ranges. Following this “downscaling” idea, the regional model is forced to satisfy not only boundary conditions, possibly in a boundary sponge region, but also large-scale flow conditions inside the integration area.
In the present paper the performance of spectral nudging in an extended climate simulation is examined. Its success in keeping the simulated state close to the driving state at larger scales, while generating smaller-scale features is demonstrated, and it is also shown that the standard boundary forcing technique in current use allows the regional model to develop internal states conflicting with the large-scale state. It is concluded that spectral nudging may be seen as a suboptimal and indirect data assimilation technique.
Abstract
The “spectral nudging” method imposes time-variable large-scale atmospheric states on a regional atmospheric model. It is based on the idea that regional-scale climate statistics are conditioned by the interplay between continental-scale atmospheric conditions and such regional features as marginal seas and mountain ranges. Following this “downscaling” idea, the regional model is forced to satisfy not only boundary conditions, possibly in a boundary sponge region, but also large-scale flow conditions inside the integration area.
In the present paper the performance of spectral nudging in an extended climate simulation is examined. Its success in keeping the simulated state close to the driving state at larger scales, while generating smaller-scale features is demonstrated, and it is also shown that the standard boundary forcing technique in current use allows the regional model to develop internal states conflicting with the large-scale state. It is concluded that spectral nudging may be seen as a suboptimal and indirect data assimilation technique.
Abstract
An analysis of the storm climate of the northeast Atlantic and the North Sea as simulated by a regional climate model for the past 44 yr is presented. The model simulates the period 1958–2001 driven by the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis. Comparison with observations shows that the model is capable of reproducing impact-related storm indices such as the number of severe and moderate storms per year or the total number of storms and upper intra-annual percentiles of near-surface wind speed. The indices describe both the year-to-year variability of the frequency, as well as changes in the average intensity of storm events. Analysis of these indices reveals that the average number of storms per year has increased near the exit of the North Atlantic storm track and over the southern North Sea since the beginning of the simulation period (1958), but the increase has attenuated later over the North Sea and the average number of storms per year has been decreasing over the northeast Atlantic since about 1990–95. The frequency of the most severe storms follows a similar pattern over the northeast North Atlantic while too few severe storms occurred in other areas of the model domain, preventing a statistical analysis for these areas.
Abstract
An analysis of the storm climate of the northeast Atlantic and the North Sea as simulated by a regional climate model for the past 44 yr is presented. The model simulates the period 1958–2001 driven by the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis. Comparison with observations shows that the model is capable of reproducing impact-related storm indices such as the number of severe and moderate storms per year or the total number of storms and upper intra-annual percentiles of near-surface wind speed. The indices describe both the year-to-year variability of the frequency, as well as changes in the average intensity of storm events. Analysis of these indices reveals that the average number of storms per year has increased near the exit of the North Atlantic storm track and over the southern North Sea since the beginning of the simulation period (1958), but the increase has attenuated later over the North Sea and the average number of storms per year has been decreasing over the northeast Atlantic since about 1990–95. The frequency of the most severe storms follows a similar pattern over the northeast North Atlantic while too few severe storms occurred in other areas of the model domain, preventing a statistical analysis for these areas.
Abstract
Global atmospheric reanalyses have become a common tool for both validation of climate models and diagnostic studies, such as assessing climate variability and long-term trends. Presently, the Twentieth Century Reanalysis (20CR), which assimilates only surface pressure reports, sea ice, and sea surface temperature distributions, represents the longest global reanalysis dataset available covering the period from 1871 to the present. Currently the 20CR dataset is extensively used for the assessment of climate variability and trends. Here, the authors compare the variability and long-term trends in northeast Atlantic storminess derived from 20CR and from observations. A well-established storm index derived from pressure observations over a relatively densely monitored marine area is used. It is found that both variability and long-term trends derived from 20CR and from observations are inconsistent. In particular, both time series show opposing trends during the first half of the twentieth century: both storm indices share a similar behavior only for the more recent periods. While the variability and long-term trend derived from the observations are supported by a number of independent data and analyses, the behavior shown by 20CR is quite different, indicating substantial inhomogeneities in the reanalysis, most likely caused by the increasing number of observations assimilated into 20CR over time. The latter makes 20CR likely unsuitable for the identification of trends in storminess in the earlier part of the record, at least over the northeast Atlantic. The results imply and reconfirm previous findings that care is needed in general when global reanalyses are used to assess long-term changes.
Abstract
Global atmospheric reanalyses have become a common tool for both validation of climate models and diagnostic studies, such as assessing climate variability and long-term trends. Presently, the Twentieth Century Reanalysis (20CR), which assimilates only surface pressure reports, sea ice, and sea surface temperature distributions, represents the longest global reanalysis dataset available covering the period from 1871 to the present. Currently the 20CR dataset is extensively used for the assessment of climate variability and trends. Here, the authors compare the variability and long-term trends in northeast Atlantic storminess derived from 20CR and from observations. A well-established storm index derived from pressure observations over a relatively densely monitored marine area is used. It is found that both variability and long-term trends derived from 20CR and from observations are inconsistent. In particular, both time series show opposing trends during the first half of the twentieth century: both storm indices share a similar behavior only for the more recent periods. While the variability and long-term trend derived from the observations are supported by a number of independent data and analyses, the behavior shown by 20CR is quite different, indicating substantial inhomogeneities in the reanalysis, most likely caused by the increasing number of observations assimilated into 20CR over time. The latter makes 20CR likely unsuitable for the identification of trends in storminess in the earlier part of the record, at least over the northeast Atlantic. The results imply and reconfirm previous findings that care is needed in general when global reanalyses are used to assess long-term changes.
Abstract
Spectral nudging is a method that was developed to constrain regional climate models so that they reproduce the development of the large-scale atmospheric state, while permitting the formation of regional-scale details as conditioned by the large scales. Besides keeping the large-scale development in the interior close to a given state, the method also suppresses the emergence of ensemble variability. The method is mostly applied to reconstructions of past weather developments in regions with an extension of typically 1000–8000 km. In this article, the authors examine if spectral nudging is having an effect on simulations with model regions of the size of about 700 km × 500 km at midlatitudes located mainly over flat terrain. First two pairs of simulations are compared, two runs each with and without spectral nudging, and it is found that the four simulations are very similar, without systematic or intermittent phases of divergence. Smooth fields, which are mainly determined by spatial patterns, such as air pressure, show hardly any differences, while small-scale and heterogeneous fields such as precipitation vary strongly, mostly on the gridpoint scale, irrespective if spectral nudging is employed or not. It is concluded that the application of spectral nudging has little effect on the simulation when the model region is relatively small.
Abstract
Spectral nudging is a method that was developed to constrain regional climate models so that they reproduce the development of the large-scale atmospheric state, while permitting the formation of regional-scale details as conditioned by the large scales. Besides keeping the large-scale development in the interior close to a given state, the method also suppresses the emergence of ensemble variability. The method is mostly applied to reconstructions of past weather developments in regions with an extension of typically 1000–8000 km. In this article, the authors examine if spectral nudging is having an effect on simulations with model regions of the size of about 700 km × 500 km at midlatitudes located mainly over flat terrain. First two pairs of simulations are compared, two runs each with and without spectral nudging, and it is found that the four simulations are very similar, without systematic or intermittent phases of divergence. Smooth fields, which are mainly determined by spatial patterns, such as air pressure, show hardly any differences, while small-scale and heterogeneous fields such as precipitation vary strongly, mostly on the gridpoint scale, irrespective if spectral nudging is employed or not. It is concluded that the application of spectral nudging has little effect on the simulation when the model region is relatively small.
Abstract
Tropical cyclone (TC) activity for the last three decades shows strong discrepancies, deduced from different best track datasets (BTD) for the western North Pacific (WNP). This study analyzes the reliability of BTDs in deriving climate statistics for the WNP. Therefore, TC lifetime, operational parameters [current intensity (CI) number], and tracks are compared (for TCs identified concurrently) in BTD provided by the Joint Typhoon Warning Center (JTWC), the Japan Meteorological Agency (JMA), and the China Meteorological Administration (CMA).
The differences between the BTD are caused by varying algorithms used in weather services to estimate TC intensity. Available methods for minimizing these discrepancies are not sufficient. Only if intensity categories 2–5 are considered as a whole, do trends for annually accumulated TC days show a similar behavior. The reasons for remaining discrepancies point to extensive and not regular usage of supplementary sources in JTWC. These are added to improve the accuracy of TC intensity and center position estimates. Track and CI differences among BTDs coincide with a strong increase in the number of intense TC days in JTWC. These differences are very strong in the period of intensive improvement of spatiotemporal satellite coverage (1987–99).
Scatterometer-based data used as a reference show that for the tropical storm phase JMA provides more reliable TC intensities than JTWC. Comparisons with aircraft observations indicate that not only homogeneity, but also a harmonization and refinement of operational rules controlling intensity estimations, should be implemented in all agencies providing BTD.
Abstract
Tropical cyclone (TC) activity for the last three decades shows strong discrepancies, deduced from different best track datasets (BTD) for the western North Pacific (WNP). This study analyzes the reliability of BTDs in deriving climate statistics for the WNP. Therefore, TC lifetime, operational parameters [current intensity (CI) number], and tracks are compared (for TCs identified concurrently) in BTD provided by the Joint Typhoon Warning Center (JTWC), the Japan Meteorological Agency (JMA), and the China Meteorological Administration (CMA).
The differences between the BTD are caused by varying algorithms used in weather services to estimate TC intensity. Available methods for minimizing these discrepancies are not sufficient. Only if intensity categories 2–5 are considered as a whole, do trends for annually accumulated TC days show a similar behavior. The reasons for remaining discrepancies point to extensive and not regular usage of supplementary sources in JTWC. These are added to improve the accuracy of TC intensity and center position estimates. Track and CI differences among BTDs coincide with a strong increase in the number of intense TC days in JTWC. These differences are very strong in the period of intensive improvement of spatiotemporal satellite coverage (1987–99).
Scatterometer-based data used as a reference show that for the tropical storm phase JMA provides more reliable TC intensities than JTWC. Comparisons with aircraft observations indicate that not only homogeneity, but also a harmonization and refinement of operational rules controlling intensity estimations, should be implemented in all agencies providing BTD.
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.
