Frauke Feser

A Spectral Nudging Technique for Dynamical Downscaling Purposes

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. 1. Background The state of the atmosphere cannot be observed in its entirety. Only samples of mostly point observations irregularly distributed in space are available. They are used by operational weather centers to construct, or ‘‘analyze,’’ a continuous distribution of atmospheric variables. Such analyses are our best guess of the atmospheric state and deviate from the true, unknown state to some extent. Likely, the large scales are best described, simply because they are better sampled. On the other hand, the details on scales of a few tens of kilometers and less are insufficiently sampled and subject to significant uncertainty. In the past, the analyses were prepared by hand. The major breakthrough was the systematic interpretation of observational data aided by quasi-realistic dynamical models. However, the only features that can be well reproduced by these objective analyses with quasi-realistic models are those that are well resolved by the model. For example, while the effect of the Baltic Sea may to some extent be captured, the imprint of Jutland, separating the Baltic Sea from the North Sea, may not. Thus, the missing details in analyses remains at present a major problem in weather analyses. While in days gone by the purpose of weather, or synoptic, analyses was for preparing short-term weather

Regional Climate Models Add Value to Global Model Data: A Review and Selected Examples

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 down...

Northeast Atlantic and North Sea Storminess as Simulated by a Regional Climate Model during 1958-2001 and Comparison with Observations

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 impactrelated 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 yearto-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.

Four decades of gasoline lead emissions and control policies in Europe: a retrospective assessment

Over decades, large amounts of the neurotoxin lead were released into the European environment, mostly from gasoline lead additives. Emissions were growing unabatedly until the 1970s, when a series of regulations on the allowed gasoline lead content were adopted. As a result, in the 1990s most gasoline contained only small amounts of lead. We have examined this case of environmental pollution and regulation, and performed a retrospective assessment of the extent of regional-scale lead pollution and the effects of gasoline lead regulations in Europe. With the help of a regional climate model, NCEP re-analyses, spatially disaggregated lead emissions from road traffic and point sources, and various local data, the airborne pathways and depositions of gasoline lead in Europe since 1958 were reconstructed. It turns out that this approach is successful in describing the time-variable, spatially disaggregated deposition of gasoline lead. Additional data from analyses of concentrations in biota, including plant leaves, mussels and human blood, allows an assessment about the impact of the lead phase-out on the quality of the environment. Demonstrating the success of the lead policies, concentrations in leaves and human blood have steadily declined since the early 1980s. At the same time, the economic repercussions that had been feared did not emerge. Instead, the affected mineral oil and car manufacturing industries in Germany (our case-study) were able to deal with the effort without incurring significant extra costs. We suggest that our method of quantitatively reconstructing and anticipating fluxes and depositions of substances can be applied to other relevant substances as well, such as, for example, Persistent Organic Pollutants, radioactive substances or pollens.

Multi‐decadal atmospheric modeling for europe yields multi‐purpose data

Regional atmospheric models have matured in the past few years. They now find broad applications that range from process studies, reconstruction of recent and paleoclimates, simulation of pathways and deposition of anthropogenic matter, construction of plausible scenarios of future climate change, and regional weather analysis. The initial major problem—namely, the ill-posed boundary value problem—has been efficiently fixed by including a sponge zone that ensures the consistency of the interior flow with the boundary conditions at the outflow boundaries, or by introducing large-scale control in the interior. With these setups, regional models may now be integrated stably over many decades and thereby provide information on spatial and temporal scales not sufficiently resolved by present-day global climate models and weather analyses.

Inconsistencies between Long-Term Trends in Storminess Derived from the 20CR Reanalysis and Observations

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.

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...

Enhanced Detectability of Added Value in Limited-Area Model Results Separated into Different Spatial Scales

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...

A comparison of two identification and tracking methods for polar lows

Abstract In this study, we compare two different cyclone-tracking algorithms to detect North Atlantic polar lows, which are very intense mesoscale cyclones. Both approaches include spatial filtering, detection, tracking and constraints specific to polar lows. The first method uses digital bandpass-filtered mean sea level pressure (MSLP) fields in the spatial range of 200–600 km and is especially designed for polar lows. The second method also uses a bandpass filter but is based on the discrete cosine transforms (DCT) and can be applied to MSLP and vorticity fields. The latter was originally designed for cyclones in general and has been adapted to polar lows for this study. Both algorithms are applied to the same regional climate model output fields from October 1993 to September 1995 produced from dynamical downscaling of the NCEP/NCAR reanalysis data. Comparisons between these two methods show that different filters lead to different numbers and locations of tracks. The DCT is more precise in scale separation than the digital filter and the results of this study suggest that it is more suited for the bandpass filtering of MSLP fields. The detection and tracking parts also influence the numbers of tracks although less critically. After a selection process that applies criteria to identify tracks of potential polar lows, differences between both methods are still visible though the major systems are identified in both.

A Dynamical Downscaling Case Study for Typhoons in Southeast Asia Using a Regional Climate Model

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 ...