Somewhere over the rainbow ? advantages and pitfalls of colourful visualizations in geosciences

International audienceComputer-generated visualizations of geoscientific data, such as those from climate models are in high demand for a wide variety of usages; these include, among others, scientific publications, reports and graphical aides for the general public's improved understanding of complex developments. The paper will focus on the effects of colours in two-dimensional displays. Practical examples are given from which it becomes clear that considerable confusion or even damage can arise from an uninformed use of colour mapping and if results become, e.g., published in an unchecked manner by the media

STAT-IMM, a statistical approach to determine local and background contributions to PM 10 levels

Abstract. When studying concentrations of particulate matter with a size of 10 µm or below (PM 10 ), measured locally, it becomes evident that two main portions need to be quantified: The concentration produced by sources in the vicinity of the station and the long range transports. The traditional approaches include analyses of the components of PM 10 , comparisons upwind and downwind of a station, investigation of trajectories and complex chemical transport modelling. The development of an independent strategy which makes use of statistical methods, including regression and correlation analysis is a reasonable alternative. This method, presented here, does not apply the concept of PM 10 sources, but, rather, analyzes the relations between times series of PM 10 measurements and atmospheric properties. It is applied to identify the shares of the local portion and the large-scale background plus a stochastic portion that cannot be attributed to either of the two. Using regression analysis, a set of objectively chosen meteorological parameters is used to reconstruct the local PM 10 measurement series, defining the local portion. This weather-dependent part of the series is then removed and the residuum, which contains the large-scale PM 10 background and a stochastic portion is analyzed further with correlations. Results are shown for a three-year set of data which includes well over 250 PM 10 stations across Germany. The data is analyzed according to different stratifications, such as the PM 10 load and the wind direction as well as for the data set as a whole. In a further development of the method, a study of PM 10 transports across several border sections is shown

A robust method to identify cyclone tracks from gridded data

A system to derive tracks of barometric minima is presented. It is deliberately using coarse input data in space (order of 2°×2°) and time (6-hourly to daily) as well as information from just one geopotential level. It is argued that the results are, for one robust in the sense of an assumption of the IMILAST Project that the use of as simple as possible metrics should be strived for and for two tailored to the input from reanalyses and GCMs. The methodology presented is a necessary first step towards an automated storm track recognition scheme which will be employed in a second paper to study the future development of atmospheric dynamics in a changing climate. The process towards obtaining storm tracks is two-fold. In its first step cyclone centers are being identified. The performance of this step requires the existence of closed isolines, i.e., a topology in which a grid-point is surrounded by neighbours which all exhibit higher geopotential. The usage of this topology requirement as well as the constraint of coarse data may lead, though, to limitations in identifying centers in geopotential fields with shallow gradients that may occur in the summer months; moreover, some centers may potentially be missed in case of a configuration in which a small scale storm is located at the perimeter of a deep and very large low (a kind of "dent in a crater wall"). The second step of the process strings the identified cyclone centers together in a meaningful way to form tracks. By way of several examples the capability to identify known storm tracks is shown

Combating the effects of climatic change on forests by mitigation strategies

<p>Abstract</p> <p>Background</p> <p>Forests occur across diverse biomes, each of which shows a specific composition of plant communities associated with the particular climate regimes. Predicted future climate change will have impacts on the vulnerability and productivity of forests; in some regions higher temperatures will extend the growing season and thus improve forest productivity, while changed annual precipitation patterns may show disadvantageous effects in areas, where water availability is restricted. While adaptation of forests to predicted future climate scenarios has been intensively studied, less attention was paid to mitigation strategies such as the introduction of tree species well adapted to changing environmental conditions.</p> <p>Results</p> <p>We simulated the development of managed forest ecosystems in Germany for the time period between 2000 and 2100 under different forest management regimes and climate change scenarios. The management regimes reflect different rotation periods, harvesting intensities and species selection for reforestations. The climate change scenarios were taken from the IPCC's Special Report on Emission Scenarios (SRES). We used the scenarios A1B (rapid and successful economic development) and B1 (high level of environmental and social consciousness combined with a globally coherent approach to a more sustainable development). Our results indicate that the effects of different climate change scenarios on the future productivity and species composition of German forests are minor compared to the effects of forest management.</p> <p>Conclusions</p> <p>The inherent natural adaptive capacity of forest ecosystems to changing environmental conditions is limited by the long life time of trees. Planting of adapted species and forest management will reduce the impact of predicted future climate change on forests.</p

Classification by multiple regression - a new approach towards the classification of extremes

There are numerous algorithmic classification methods that attempt to address the connections between different scales of the atmosphere, such as EOFs, clustering, and neural nets. However, their relative strength lies in the description of the mean conditions, whereas extremes are poorly covered by them. A novel approach towards the identification of linkages between large-scale atmospheric fields and local extremes of meteorological parameters is presented in this paper. The principle is that a small number of objectively selected fields can be used to circumscribe a local meteorological parameter by way of regression. For each day, the regression coefficients form a kind of pattern which is used for a classification based on similarity. As it turns out, several classes are generated which contain days that constitute extreme atmospheric conditions and from which local meteorological parameters can be computed, yielding an indirect way of determining these local extremes just from large-scale information. The range of applications is large. (i) Not only local meteorological parameters can be subjected to such a regression based classification procedure. It can be extended to extreme indicators, such as threshold exceedances, yielding on the one hand the relevant atmospheric fields to describe those indicators, and on the other hand grouping days with “favourable atmospheric conditions”. This approach can be further extended by investigating networks of measurement stations from a region and describing, e.g., the probability for threshold exceedances at a given percentage of the network. (ii) The method can not only be used as a filtering tool to supply days in the current climate with extreme conditions, identified in an objective way. The method can be applied to climate model projections, using the previously found parameter-specific combinations of atmospheric fields. From those fields, as they constitute the modelled future climate, local time series can be generated which are then analysed with respect to the frequency and magnitude of future extremes. The method has sensitivities (i) due to the degree to which there are connections between large-scale fields and local meteorological parameters (measured, e.g., by the correlation) and (ii) due to the varying quality of the different fields (geopotential, temperature, humidity etc.) projected by the climate model

Somewhere over the rainbow &ndash; advantages and pitfalls of colourful visualizations in geosciences

Computer-generated visualizations of geoscientific data, such as those from climate models are in high demand for a wide variety of usages; these include, among others, scientific publications, reports and graphical aides for the general public's improved understanding of complex developments. The paper will focus on the effects of colours in two-dimensional displays. Practical examples are given from which it becomes clear that considerable confusion or even damage can arise from an uninformed use of colour mapping and if results become, e.g., published in an unchecked manner by the media