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The climate system can be regarded as a dynamic nonlinear system. Thus, traditional linear statistical methods fail to model the nonlinearities of such a system. These nonlinearities render it necessary to find alternative statistical techniques. Since artificial neural network models (NNM) represent such a nonlinear statistical method their use in analyzing the climate system has been studied for a couple of years now. Most authors use the standard Backpropagation Network (BPN) for their investigations, although this specific model architecture carries a certain risk of over-/underfitting. Here we use the so called Cauchy Machine (CM) with an implemented Fast Simulated Annealing schedule (FSA) (Szu, 1986) for the purpose of attributing and detecting anthropogenic climate change instead. Under certain conditions the CM-FSA guarantees to find the global minimum of a yet undefined cost function (Geman and Geman, 1986). In addition to potential anthropogenic influences on climate (greenhouse gases (GHG), sulphur dioxide (SO2)) natural influences on near surface air temperature (variations of solar activity, explosive volcanism and the El Nino = Southern Oscillation phenomenon) serve as model inputs. The simulations are carried out on different spatial scales: global and area weighted averages. In addition, a multiple linear regression analysis serves as a linear reference. It is shown that the adaptive nonlinear CM-FSA algorithm captures the dynamics of the climate system to a great extent. However, free parameters of this specific network architecture have to be optimized subjectively. The quality of the simulations obtained by the CM-FSA algorithm exceeds the results of a multiple linear regression model; the simulation quality on the global scale amounts up to 81% explained variance. Furthermore the combined anthropogenic effect corresponds to the observed increase in temperature Jones et al. (1994), updated by Jones (1999a), for the examined period 1856–1998 on all investigated scales. In accordance to recent findings of physical climate models, the CM-FSA succeeds with the detection of anthropogenic induced climate change on a high significance level. Thus, the CMFSA algorithm can be regarded as a suitable nonlinear statistical tool for modeling and diagnosing the climate system.
Observed global and European spatiotemporal related fields of surface air temperature, mean-sea-level pressure and precipitation are analyzed statistically with respect to their response to external forcing factors such as anthropogenic greenhouse gases, anthropogenic sulfate aerosol, solar variations and explosive volcanism, and known internal climate mechanisms such as the El Niño-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). As a first step, a principal component analysis (PCA) is applied to the observed spatiotemporal related fields to obtain spatial patterns with linear independent temporal structure. In a second step, the time series of each of the spatial patterns is subject to a stepwise regression analysis in order to separate it into signals of the external forcing factors and internal climate mechanisms as listed above as well as the residuals. Finally a back-transformation leads to the spatiotemporally related patterns of all these signals being intercompared. Two kinds of significance tests are applied to the anthropogenic signals. First, it is tested whether the anthropogenic signal is significant compared with the complete residual variance including natural variability. This test answers the question whether a significant anthropogenic climate change is visible in the observed data. As a second test the anthropogenic signal is tested with respect to the climate noise component only. This test answers the question whether the anthropogenic signal is significant among others in the observed data. Using both tests, regions can be specified where the anthropogenic influence is visible (second test) and regions where the anthropogenic influence has already significantly changed climate (first test).
Attribution and detection of anthropogenic climate change using a backpropagation neural network
(2002)
The climate system can be regarded as a dynamic nonlinear system. Thus traditional linear statistical methods are not suited to describe the nonlinearities of this system which renders it necessary to find alternative statistical techniques to model those nonlinear properties. In addition to an earlier paper on this subject (WALTER et al., 1998), the problem of attribution and detection of the observed climate change is addressed here using a nonlinear Backpropagation Neural Network (BPN). In addition to potential anthropogenic influences on climate (CO2-equivalent concentrations, called greenhouse gases, GHG and SO2 emissions) natural influences on surface air temperature (variations of solar activity, volcanism and the El Niño/Southern Oscillation phenomenon) are integrated into the simulations as well. It is shown that the adaptive BPN algorithm captures the dynamics of the climate system, i.e. global and area weighted mean temperature anomalies, to a great extent. However, free parameters of this network architecture have to be optimized in a time consuming trial-and-error process. The simulation quality obtained by the BPN exceeds the results of those from a linear model by far; the simulation quality on the global scale amounts to 84% explained variance. Additionally the results of the nonlinear algorithm are plausible in a physical sense, i.e. amplitude and time structure. Nevertheless they cover a broad range, e.g. the GHG-signal on the global scale ranges from 0.37 K to 1.65 K warming for the time period 1856-1998. However the simulated amplitudes are situated within the discussed range (HOUGHTON et al., 2001). Additionally the combined anthropogenic effect corresponds to the observed increase in temperature for the examined time period. In addition to that, the BPN succeeds with the detection of anthropogenic induced climate change on a high significance level. Therefore the concept of neural networks can be regarded as a suitable nonlinear statistical tool for modeling and diagnosing the climate system.
Die Zunahme der Konzentration von CO2 und anderen "Treibhausgasen" in der Atmosphäre ist unzweifelhaft, und ebenso unzweifelhaft reagiert das Klima darauf. Christian-Dietrich Schönwiese, Professor für Meteorologische Umweltforschung und Klimatologie an der Universität Frankfurt am Main, sieht dringenden politischen Handlungsbedarf und plädiert gleichzeitig dafür, die Debatte rund um den Klimaschutz zu versachlichen.
Die öffentliche Klimadebatte scheint sich zu verselbständigen. Abgehoben von den Erkenntnissen der Fachwissenschaftler reden die einen von der "Klimakatastrophe", die uns demnächst mit voller Wucht treffen wird, wenn wir nicht sofort alles ganz anders machen; Panik ist ihnen das rechte Mittel, Aufmerksamkeit zu erregen. Die anderen sehen im "Klimaschwindel" einen Vorwand für Forschungsgelder und zusätzliche Steuerbelastung der Wirtschaft; ihre Strategie ist Verwirrung und Verharmlosung. Mit der Fixierung auf solche Extrempositionen werden wir den Herausforderungen der Zukunft sicherlich nicht gerecht. Höchste Zeit für eine Versachlichung und für einen klärenden Beitrag zum Verwirrspiel "Klima".
Vielleicht hätte sich außerhalb der Fachwissenschaft niemand für das Weltklimaproblem interessiert, wären da nicht zwei brisante, miteinander gekoppelte Fakten: Die Menschheit ist hochgradig von der Gunst des Klimas abhängig. Es kann uns daher nicht gleichgültig sein, was mit unserem Klima geschieht. Und: Die Menschheit ist mehr und mehr dazu übergegangen, das Klima auch selbst zu beeinflussen. Daraus erwächst uns allen eine besondere Verantwortung. ...
Wenn sich beim Klimagipfel in Den Haag [genauer bei der nun schon 6. Vertragstaatenkonferenz zur Klimaschutzkonvention der Vereinten Nationen] nun wieder die Delegationen aus fast allen Staaten der Welt treffen, um über Klimaschutzmaßnahmen zu beraten, dann schwingt auch immer die Frage mit: Sind solche Maßnahmen wirklich notwendig? Sollen wir nicht einfach warten, bis wir mehr, ja vielleicht alles wissen? ...