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- Author or Editor: Frauke Feser x
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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
A two-dimensional discrete spatial filter was developed. It serves as a means to classify meteorological fields on a limited-area grid according to their spatial dimensions by filtering certain wavenumber ranges. Thereby it performs an isotropic spatial-scale separation of the atmospheric fields. A general algorithm was developed, which allows the construction of a filter that closely approximates a specific isotropic response function. The filter is simple in the construction and easy to apply while giving reasonable results. The method allows for considerable flexibility in choosing this specific response. This way, low-, band-, and high-pass filters are obtained. Examples show an effective scale separation of atmospheric fields on limited-area grids that can be used for process studies, model evaluation, or comparisons.
Abstract
A two-dimensional discrete spatial filter was developed. It serves as a means to classify meteorological fields on a limited-area grid according to their spatial dimensions by filtering certain wavenumber ranges. Thereby it performs an isotropic spatial-scale separation of the atmospheric fields. A general algorithm was developed, which allows the construction of a filter that closely approximates a specific isotropic response function. The filter is simple in the construction and easy to apply while giving reasonable results. The method allows for considerable flexibility in choosing this specific response. This way, low-, band-, and high-pass filters are obtained. Examples show an effective scale separation of atmospheric fields on limited-area grids that can be used for process studies, model evaluation, or comparisons.
Abstract
This study explores the possibility of reconstructing the weather of Southeast Asia for the last decades using an atmospheric regional climate model, the Climate version of the Lokal-Modell (CLM). For this purpose global National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalyses data were dynamically downscaled to 50 km and in a double-nesting approach to 18-km grid distance. To prevent the regional model from deviating significantly from the reanalyses with respect to large-scale circulation and large-scale weather phenomena, a spectral nudging technique was used.
The performance of this technique in dealing with Southeast Asian typhoons is now examined by considering an ensemble of one simulated typhoon case. This analysis is new insofar as it deals with simulations done in the climate mode (so that any skill of reproducing the typhoon is not related to details of initial conditions), is done in ensemble mode (the same development is described by several simulations), and is done with a spectral nudging constraint (so that the observed large-scale state is enforced in the model domain). This case indicates that tropical storms that are coarsely described by the reanalyses are correctly identified and tracked; considerably deeper core pressure and higher wind speeds are simulated compared to the driving reanalyses. When the regional atmospheric model is run without spectral nudging, significant intraensemble variability occurs; also additional, nonobserved typhoons form. Thus, the insufficiency of lateral boundary conditions alone for determining the details of the dynamic developments in the interior becomes very clear. The same lateral boundary conditions are consistent with different developments in the interior. Several sensitivity experiments were performed concerning varied grid distances, different initial starting dates of the simulations, and changed spectral nudging parameters.
Abstract
This study explores the possibility of reconstructing the weather of Southeast Asia for the last decades using an atmospheric regional climate model, the Climate version of the Lokal-Modell (CLM). For this purpose global National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalyses data were dynamically downscaled to 50 km and in a double-nesting approach to 18-km grid distance. To prevent the regional model from deviating significantly from the reanalyses with respect to large-scale circulation and large-scale weather phenomena, a spectral nudging technique was used.
The performance of this technique in dealing with Southeast Asian typhoons is now examined by considering an ensemble of one simulated typhoon case. This analysis is new insofar as it deals with simulations done in the climate mode (so that any skill of reproducing the typhoon is not related to details of initial conditions), is done in ensemble mode (the same development is described by several simulations), and is done with a spectral nudging constraint (so that the observed large-scale state is enforced in the model domain). This case indicates that tropical storms that are coarsely described by the reanalyses are correctly identified and tracked; considerably deeper core pressure and higher wind speeds are simulated compared to the driving reanalyses. When the regional atmospheric model is run without spectral nudging, significant intraensemble variability occurs; also additional, nonobserved typhoons form. Thus, the insufficiency of lateral boundary conditions alone for determining the details of the dynamic developments in the interior becomes very clear. The same lateral boundary conditions are consistent with different developments in the interior. Several sensitivity experiments were performed concerning varied grid distances, different initial starting dates of the simulations, and changed spectral nudging parameters.
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.
