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Simulation of global temperature variations and signal detection studies using neural networks
(1998)
The concept of neural network models (NNM) is a statistical strategy which can be used if a superposition of any forcing mechanisms leads to any effects and if a sufficient related observational data base is available. In comparison to multiple regression analysis (MRA), the main advantages are that NNM is an appropriate tool also in the case of non-linear cause-effect relations and that interactions of the forcing mechanisms are allowed. In comparison to more sophisticated methods like general circulation models (GCM), the main advantage is that details of the physical background like feedbacks can be unknown. Neural networks learn from observations which reflect feedbacks implicitly. The disadvantage, of course, is that the physical background is neglected. In addition, the results prove to be sensitively dependent from the network architecture like the number of hidden neurons or the initialisation of learning parameters. We used a supervised backpropagation network (BPN) with three neuron layers, an unsupervised Kohonen network (KHN) and a combination of both called counterpropagation network (CPN). These concepts are tested in respect to their ability to simulate the observed global as well as hemispheric mean surface air temperature annual variations 1874 - 1993 if parameter time series of the following forcing mechanisms are incorporated : equivalent CO2 concentrations, tropospheric sulfate aerosol concentrations (both anthropogenic), volcanism, solar activity, and ENSO (all natural). It arises that in this way up to 83% of the observed temperature variance can be explained, significantly more than by MRA. The implication of the North Atlantic Oscillation does not improve these results. On a global average, the greenhouse gas (GHG) signal so far is assessed to be 0.9 - 1.3 K (warming), the sulfate signal 0.2 - 0.4 K (cooling), results which are in close similarity to the GCM findings published in the recent IPCC Report. The related signals of the natural forcing mechanisms considered cover amplitudes of 0.1 - 0.3 K. Our best NNM estimate of the GHG doubling signal amounts to 2.1K, equilibrium, or 1.7 K, transient, respectively.
During the 1980s and early 1990s, the importance of small firm growth and industrial districts in Italy became the focus of a large number of regional development studies. According to this literature, successful industrial districts are characterized by intensive cooperation and market producer-user interaction between small and medium-sized, flexibly specialized firms (Piore and Sabel, 1984; Scott, 1988). In addition, specialized local labor markets develop which are complemented by a variety of supportive institutions and a tradition of collaboration based on trust relations (Amin and Robins, 1990; Amin and Thrift, 1995). It has also been emphasized that industrial districts are deeply embedded into the socio-institutional structures within their particular regions (Grabher, 1993). Many case studies have attempted to find evidence that the regional patterns identified in Italy are a reflection of a general trend in industrial development rather than just being historical exceptions. Silicon Valley, which is focused on high technology production, has been identified as being one such production complex similar to those in Italy (see, for instance, Hayter, 1997). However, some remarkable differences do exist in the institutional context of this region, as well as its particular social division of labor (Markusen, 1996). Even though critics, such as Amin and Robins (1990), emphasized quite early that the Italian experience could not easily be applied to other socio-cultural settings, many studies have classified other high technology regions in the U.S. as being industrial districts, such as Boston s Route 128 area. Too much attention has been paid to the performance of small and medium-sized firms and the regional level of industrial production in the ill-fated debate regarding industrial districts (Martinelli and Schoenberger, 1991). Harrison (1997) has provided substantial evidence that large firms continue to dominate the global economy. This does not, however, imply that a de-territorialization of economic growth is necessarily taking place as globalization tendencies continue (Storper, 1997; Maskell and Malmberg, 1998). In the case of Boston, it has been misleading to define its regional economy as being an industrial district. Neither have small and medium-sized firms been decisive in the development of the Route 128 area nor has the region developed a tradition of close communication between vertically-disintegrated firms (Dorfman, 1983; Bathelt, 1991a). Saxenian (1994) found that Boston s economy contrasted sharply with that of an industrial district. Specifically, the region has been dominated by large, vertically-integrated high technology firms which are reliant on proprietary technologies and autarkic firm structures. Several studies have tried to compare the development of the Route 128 region to Silicon Valley. These studies have shown that both regions developed into major 2 agglomerations of high technology industries in the post-World War II period. Due to their different traditions, structures and practices, Silicon Valley and Route 128 have followed divergent development paths which have resulted in a different regional specialization (Dorfman, 1983; Saxenian, 1985; Kenney and von Burg, 1999). In the mid 1970s, both regions were almost equally important in terms of the size of their high technology sectors. Since then, however, Silicon Valley has become more important and has now the largest agglomeration of leading-edge technologies in the U.S. (Saxenian, 1994). Saxenian (1994) argues that the superior performance of high technology industries in Silicon Valley over those in Boston is based on different organizational patterns and manufacturing cultures which are embedded in those socio-institutional traditions which are particular to each region. Despite the fact that Saxenian (1994) has been criticized for basing her conclusions on weak empirical research (i.e. Harrison, 1997; Markusen, 1998), she offers a convincing explanation as to why the development paths of both regions have differed.1 Saxenian s (1994) study does not, however, identify which structures and processes have enabled both regions to overcome economic crises. In the case of the Boston economy, high technology industries have proven that they are capable of readjusting and rejuvenating their product and process structures in such a way that further innovation and growth is stimulated. This is also exemplified by the region s recent economic development. In the late 1980s, Boston experienced an economic decline when the minicomputer industry lost its competitive basis and defense expenditures were drastically reduced. The number of high technology manufacturing jobs decreased by more than 45,000 between 1987 and 1995. By the mid 1990s, however, the regional economy began to recover. The rapidly growing software sector compensated for some of the losses experienced in manufacturing. In this paper, I aim to identify the forces behind this economic recovery. I will investigate whether high technology firms have uncovered new ways to overcome the crisis and the extent to which they have given up their focus on self-reliance and autarkic structures. The empirical findings will also be discussed in the context of the recent debate about the importance of regional competence and collective learning (Storper, 1997; Maskell and Malmberg, 1998). There is a growing body of literature which suggests that some regional economies During the 1980s and early 1990s, the importance of small firm growth and industrial districts in Italy became the focus of a large number of regional development studies. According to this literature, successful industrial districts are characterized by intensive cooperation and market producer-user interaction between small and medium-sized, flexibly specialized firms (Piore and Sabel, 1984; Scott, 1988). In addition, specialized local labor markets develop which are complemented by a variety of supportive institutions and a tradition of collaboration based on trust relations (Amin and Robins, 1990; Amin and Thrift, 1995). It has also been emphasized that industrial districts are deeply embedded into the socio-institutional structures within their particular regions (Grabher, 1993). Many case studies have attempted to find evidence that the regional patterns identified in Italy are a reflection of a general trend in industrial development rather than just being historical exceptions. Silicon Valley, which is focused on high technology production, has been identified as being one such production complex similar to those in Italy (see, for instance, Hayter, 1997). However, some remarkable differences do exist in the institutional context of this region, as well as its particular social division of labor (Markusen, 1996). Even though critics, such as Amin and Robins (1990), emphasized quite early that the Italian experience could not easily be applied to other socio-cultural settings, many studies have classified other high technology regions in the U.S. as being industrial districts, such as Boston s Route 128 area. Too much attention has been paid to the performance of small and medium-sized firms and the regional level of industrial production in the ill-fated debate regarding industrial districts (Martinelli and Schoenberger, 1991). Harrison (1997) has provided substantial evidence that large firms continue to dominate the global economy. This does not, however, imply that a de-territorialization of economic growth is necessarily taking place as globalization tendencies continue (Storper, 1997; Maskell and Malmberg, 1998). In the case of Boston, it has been misleading to define its regional economy as being an industrial district. Neither have small and medium-sized firms been decisive in the development of the Route 128 area nor has the region developed a tradition of close communication between vertically-disintegrated firms (Dorfman, 1983; Bathelt, 1991a). Saxenian (1994) found that Boston s economy contrasted sharply with that of an industrial district. Specifically, the region has been dominated by large, vertically-integrated high technology firms which are reliant on proprietary technologies and autarkic firm structures. Several studies have tried to compare the development of the Route 128 region to Silicon Valley. These studies have shown that both regions developed into major 2 agglomerations of high technology industries in the post-World War II period. Due to their different traditions, structures and practices, Silicon Valley and Route 128 have followed divergent development paths which have resulted in a different regional specialization (Dorfman, 1983; Saxenian, 1985; Kenney and von Burg, 1999). In the mid 1970s, both regions were almost equally important in terms of the size of their high technology sectors. Since then, however, Silicon Valley has become more important and has now the largest agglomeration of leading-edge technologies in the U.S. (Saxenian, 1994). Saxenian (1994) argues that the superior performance of high technology industries in Silicon Valley over those in Boston is based on different organizational patterns and manufacturing cultures which are embedded in those socio-institutional traditions which are particular to each region. Despite the fact that Saxenian (1994) has been criticized for basing her conclusions on weak empirical research (i.e. Harrison, 1997; Markusen, 1998), she offers a convincing explanation as to why the development paths of both regions have differed.1 Saxenian s (1994) study does not, however, identify which structures and processes have enabled both regions to overcome economic crises. In the case of the Boston economy, high technology industries have proven that they are capable of readjusting and rejuvenating their product and process structures in such a way that further innovation and growth is stimulated. This is also exemplified by the region s recent economic development. In the late 1980s, Boston experienced an economic decline when the minicomputer industry lost its competitive basis and defense expenditures were drastically reduced. The number of high technology manufacturing jobs decreased by more than 45,000 between 1987 and 1995. By the mid 1990s, however, the regional economy began to recover. The rapidly growing software sector compensated for some of the losses experienced in manufacturing. In this paper, I aim to identify the forces behind this economic recovery. I will investigate whether high technology firms have uncovered new ways to overcome the crisis and the extent to which they have given up their focus on self-reliance and autarkic structures. The empirical findings will also be discussed in the context of the recent debate about the importance of regional competence and collective learning (Storper, 1997; Maskell and Malmberg, 1998). There is a growing body of literature which suggests that some regional economies an develop into learning economies which are based on intra-regional production linkages, interactive technological learning processes, flexibility and proximity (Storper, 1992; Lundvall and Johnson, 1994; Gregersen and Johnson, 1997). In the next section of this paper, I will discuss some of the theoretical issues regarding localized learning processes, learning economies and learning regions (see, also, Bathelt, 1999). I will then describe the methodology used. What follows is a brief overview of how Boston s economy has specialized in high technology production. The main part of the paper will then focus on recent trends in Boston s high technology industries. It will be shown that the high technology economy consists of different subsectors which are not tied to a single technological development path. The various subsectors are, at least partially, dependent on different forces and unrelated processes. There is, however, tentative evidence which suggests that cooperative behavior and collective learning in supplierproducer- user relations have become important factors in securing reproductivity in the regional structure. The importance of these trends will be discussed in the conclusions.
The assumption that mankind is able to have an in uence on global or regional climate, respectively, due to the emission of greenhouse gases, is often discussed. This assumption is both very important and very obscure. In consequence, it is necessary to clarify definitively which meteorological elements (climate parameters) are in uencend by the anthropogenic climate impact, and to which extent in which regions of the world. In addition, to be able to interprete such an information properly, it is also necessary to know the magnitude of the different climate signals due to natural variability (for example due to volcanic or solar activity) and the magnitide of stochastic climate noise. The usual tool of climatologists, general circulation models (GCM) suffer from the problem that they are at least quantitatively uncertain with regard to the regional patterns of the behaviour of climate elements and from the lack of accurate information about long-term (decadal and centennial) forcing. In contrast to that, statistical methods as used in this study have the advantage to test hypotheses directly based on observational data. So, we focus to the very reality of climate variability as it has occurred in the past. We apply two strategies of time series analyis with regard to the observed climate variables under consideration. First, each time series is splitted into its variation components. This procedure is called 'structure-oriented time series separation'. The second strategy called 'cause-oriented time series separation' matches various time series representing various forcing mechanisms with those representing the climate behaviour (climate elements). In this way it can be assessed which part of observed climate variability can be explained by this (combined) forcing and which part remains unexplained.
Die vorliegende Arbeit liefert einen Beitrag zum Verständnis der Rolle des RO x bei der troposphärischen Ozonbildung. Troposphärisches Ozon (O 3 ) spielt eine wichtige Rolle bei der Selbstreinigung der Atmosphäre. Andererseits führen erhöhte Ozonkonzentrationen zu gesundheitlichen Beeinträchtigungen beim Menschen und Schäden an Pflanzen und Umwelt. Die Anwesenheit von flüchtigen organischen Verbindungen (VOCs) führt zur Bildung von Peroxyradikalen (RO x ), die das normale photochemische Gleichgewicht zwischen Ozon und Stickoxiden zu Gunsten erhöhter OzonKonzentrationen verschieben. Im Rahmen der Arbeit wurde ein chemischer Verstärker zur Messung der GesamtPeroxyradikalkonzentration gebaut. RO x reagiert im Einlass des Gerätes mit hinzugefügtem NO und CO in einer Kettenreaktion und bildet dabei NO 2 . Dieses wird mit einem Luminoldetektor nachgewiesen. Der Detektor wird alle 2 Stunden kalibriert. Die Kettenlänge wird durch eine Kalibrierung des Gerätes mit HO 2 Radikalen bestimmt, die durch die Photolyse von H 2 O gebildet werden. Der Verstärkungsfaktor wurde in Bezug auf eine Querempfindlichkeit gegen Wasserdampf korrigiert. Die Messgenauigkeit ist etwa 70% bei 60% relativer Feuchte. Messungen am Taunus Observatorium auf dem Kleinen Feldberg in den Sommermonaten der beiden Jahre 1998 und 1999 werden vorgestellt. Die Ozon und RO x Konzentrationen sind gut miteinander korreliert. Allerdings ist die Tagestemperatur die für die Ozon und RO x Konzentrationen bei weitem wichtigste Einflussgröße und ist daher der beste Parameter zur statistischen Beschreibung von photochemischen Vorgängen. Auf der Grundlage der Messungen am Kleinen Feldberg wurde ein einfaches statistisches Modell zur Vorhersage des Ozonmaximums erstellt. Mit den Parametern Temperatur und Ozonkonzentration am Vortag konnte das statistische Modell bereits 80% der Variation der Ozonkonzentration erklären. Durch die Berücksichtigung der RO x Messungen am Vormittag konnte lediglich eine Verbesserung der erklärten Varianz um 0.5% erzielt werden. Um einen Hinweis auf den Einfluss anthropogener Emissionen zu bekommen, wurde der Wochengang von Ozon, RO x und NO x ebenfalls untersucht. Die Zunahme des Ozonmischungsverhältnisses am Wochenende bei gleichzeitigem Rückgang des Mischungsverhältnisses der Stickoxide wird damit erklärt, dass am Kleinen Feldberg eine VOClimitierte Situation vorgefunden wurde. Die Ozonbildungsrate auf Basis der Reaktion zwischen RO x und NO wurde für Tage mit einem Maximum der Globalstrahlung über 600 W m tdatensatz niedrig (r = 0,46). Die beobachtete Änderung des Ozonmischungsverhältnisses wurde mit dem berechneten mittleren Tagesgang der Ozonbildungsrate verglichen. Die Ozonbildungsrate lag um die Mittagszeit bei etwa 5 ppbv h Verlustprozesse zu erklären. Am Abend werden etwa 2 ppbv O 3 pro Stunde abgebaut. Im Rahmen einer Messkampagne im Juni/Juli 2000 am Meteorologischen Observatorium Hohenpeißenberg fanden Messungen der Konzentrationen von RO x , OH, einer Reihe von VOCs, und anderen relevanten Spurengasen statt. Die Messdaten werden mit Hilfe eines auf der Annahme des lokalen photostationären Gleichgewichts der Radikale basierenden Modells interpretiert. Die Modellergebnisse stimmten sehr gut mit den Messungen überein. Die Überschätzung der Konzentration an 2 Tagen wurde durch den Einfluss sauerstoffhaltiger VOCs erklärt. Das '' Recycling" der HO 2 Radikale (die Reaktion zwischen HO 2 und NO) ist die wichtigste Quelle für OH und die wichtigste Senke für RO x . Durch die erhöhte NOKonzentration am Vormittag wird HO 2 sehr schnell in OH umgewandelt, das wiederum für die VOCOxidation und RO x Bildung verantwortlich ist. Die wichtigste OHSenke und RO x Quelle ist die Oxidation von Isopren und den Terpenen. Um die Rolle der photochemischen Ozonbildung auf regionaler Skala zu untersuchen, wurden Ozonmessungen aus ganz Deutschland auf unterschiedlichen zeitlichen und räumlichen Skalen statistisch untersucht. Die Netto Änderungsrate der Ozonkonzentration war tagsüber an 3 nahe zusammenliegenden Stationen sehr ähnlich. Die OzonMessdaten von 277 deutschen Messstationen wurden mit den an einer Waldmessstelle nahe Königstein gemessenen Ozonwerten korreliert. Die Ozonmessungen in Königstein erklären 50% der Varianz der sommerlichen Ozonmessungen zwischen 11:00 und 16:00 MEZ an Stationen, die in einem Umkreis von etwa 250 km von Königstein liegen. Auf das ganze Jahr bezogen, liegt diese ''charakteristische Entfernung" bei etwa 350 km. Diese Ergebnisse deuten darauf hin, dass die Prozesse, die einen wichtigen Einfluss auf die Ozonkonzentration ausüben, auf regionalen Skalen von einigen hundert Kilometern aktiv sind. Zusammenfassend lässt sich sagen, dass die gemessenen RO x Konzentrationen mit den aufgrund der Oxidation der VOCs durch OH berechneten Konzentrationen konsistent sind. Obwohl die RO x Konzentationen für die chemische Modellierung von Bedeutung sind, tragen RO x Messungen nur wenig zu einer Verbesserung der Qualität von kurzfristigen statistischen Ozonprognosen bei. Keywords: Ozone, Troposphere, Peroxy Radicals, Free Radicals, Photochemistry, Chemical Amplifier
Crustal structure at the western end of the North Anatolian Fault Zone from deep seismic sounding
(2001)
The first deep seismic sounding experiment in Northwestern Anatolia was carried out in October 1991 as part of the "German - Turkish Project on Earthquake Prediction Research" in the Mudurnu area of the North Anatolian Fault Zone. The experiment was a joint enterprise by the Institute of Meteorology and Geophysics of Frankfurt University, the Earthquake Research Institute (ERI) in Ankara, and the Turkish Oil Company (TPAO). Two orthogonal profiles, each 120 km in length with a crossing point near Akyazi, were covered in succession by 30 short period tape recording seismograph stations with 2 km station spacing. 12 shots, with charge sizes between 100 and 250 kg, were fired and 342 seismograms out of 360 were used for evaluation. By coincidence an M b = 4.5 earthquake located below Imroz Island was also recorded and provided additional information on Moho and the sub-Moho velocity. A ray tracing method orginally developed by Weber (1986) was used for travel time inversion. From a compilation of all data two generalized crustal models were derived, one with velocity gradients within the layers and one with constant layer velocities. The latter consists of a sediment cover of about 2 km with V p » 3.6 km/s, an upper crystalline crust down to 13 km with V p » 5.9 km/s, a middle crust down to 25 km depth with V p » 6.5 km/s, a lower crust down to 39 km Moho depth with V p » 7.0 km/s and V p » 8.05 km/s below the Moho. The structure of the individual profiles differs slightly. The thickest sediment cover is reached in the Izmit-Sapanca-trough and in the Akyazi basin. Of particular interest is a step of about 4 km in the lower crust near Lake Sapanca and probably an even larger one in the Moho (derived from the Imroz earthquake data). After the catastrophic earthquake of Izmit on 17 August 1999 this significant heterogeneity in crustal structure appears in a new light with regard to the possible cause of the Izmit earthquake. Heterogeneities in structure are frequently also heterogeneities in strength and stress that impede or even lock rupture. The Izmit earthquake is discussed in relation to a large stepover or jog at the North Anatolian Fault.
In der hier vorliegenden Arbeit wurde der troposphärische Kreislauf von Carbonylsulfid (COS) untersucht. COS ist ein Quellgas des stratosphärischen SulfatAerosols, das die Strahlungsbilanz beeinflussen und den chemischen Abbau des stratosphärischen Ozons beschleunigen kann. Trotz zahlreicher Studien sind die Quellen und Senken des atmosphärischen COS bisher nur unzulänglich quantifiziert. Insbesondere bestehen große Unsicherheiten in den Abschätzungen der Beiträge des Ozeans und der anthropogenen Quellen, sowie der Senkenstärke der Landvegetation. Schiffs und flugzeuggetragene Messungen des atmosphärischen COS ergaben kein einheitliches interhemisphärisches Verhältnis (IHR=MNH /M SH ). Während die Messungen von Bingemer et al. (1990), StaubesDiederich (1992) und Johnson et al. (1993) ein IHR zwischen 1.10 und 1.25 zeigten, fanden die Messungen von Torres et al. (1980), StaubesDiederich (1992), Weiss et al. (1995) und Thornton et al. (1996) keinen oder nur einen geringfügigen N/SGradienten. Die Untersuchung von Chin und Davis (1993) zeigt ein N/SVerhältnis der COS Quellstärke von 2.3, das hauptsächlich auf die stärkeren anthropogenen Quellen auf der Nordhalbkugel zurückzuführen ist. Es ist unklar, ob der zeitweilige Konzentrationsüberschuß der Nordhemisphäre Zeichen anthropogener Quellen dort oder Teil eines durch die Senkenfunktion der Landpflanzen verursachten saisonalen Signals ist. Die Konsistenz der Breitenverteilung des COSMischungsverhältnisses mit den geographischen bzw. saisonalen Variationen der COSQuellen und Senken muß überprüft werden. Dazu werden genaue Kenntnissen der Quell und Senkenstärken des atmosphärischen COS und ihrer raumzeitlichen Variabilität benötigt. Vor dem obigen Hintergrund ergeben sich als Schwerpunkte dieser Arbeit: (1) der Austausch von COS zwischen Atmosphäre und Ozean sowie (2) zwischen Atmosphäre und terrestrischer Vegetation und (3) die raumzeitliche Variabilität des atmosphärischen COS. Zur Untersuchung des Austausches von COS zwischen Atmosphäre und Ozean wurde das KonzentrationsUngleichgewicht von COS zwischen Ozean und Atmosphäre durch Messungen des COS im Seewasser und in der Meeresluft ermittelt und die resultierenden Austauschflüsse mit einem Modell berechnet. Die Messungen fanden an Bord des Forschungsschiffs Polarstern während der Fahrten ANT/XV1 (15.10.6.11.1997, BremerhavenKapstadt) und ANT /XV5 (26.5.6.20.1998, KapstadtBremerhaven) statt. Die Konzentration des gelösten COS und das Sättigungsverhältnis von COS zwischen Ozean und Atmosphäre zeigen ausgeprägte Tagesgänge und saisonale und geographische Variationen. Die mittlere Konzentration von COS im Seewasser beträgt 14.7 pmol L -1 für die HerbstFahrt bzw. 18.1 pmol L -1 für die SommerFahrt. Höchste COSKonzentrationen werden in der jeweiligen SommerHemisphäre und in Gebieten mit hoher biologischer Produktivität beobachtet, d.h. im BenguelaStrom im November, im NordostAtlantik im Juni und in den Auftriebgebieten vor Westafrika im Oktober bzw. Juni. In den übrigen Gebieten sind die Konzentrationen um eine Größenordnung niedriger. Die Konzentration von COS im Seewasser steigt frühmorgens von ihrem tiefsten Stand an. Um ca. 15 Uhr Ortszeit erreicht sie ihr Maximum, danach nimmt sie ab. Der Tagesgang unterstützt die Theorie, daß COS im Seewasser photochemisch produziert wird. Während der Tagesstunden wird eine Übersättigung des offenen Ozean für COS gefunden. Dagegen ist eine Untersättigung des Ozeans in den späten Nachtstunden zu beobachten. Der Ozean wirkt in den Tagesstunden als COSQuelle, in der späten Nacht als COSSenke. Die Untersättigung tritt sogar im Sommer in produktiven Meeresgebieten regelmäßig auf. Eine Konsequenz dieser Beobachtung ist die weitere Reduzierung der ozeanischen Quelle von COS gegenüber bisher publizierten Abschätzungen. Methylmercaptan (CH 3 SH) ist in allen Seewasserproben zu beobachten. Der Tagesmittelwert der CH 3 SHKonzentration variiert zwischen 29 und 303 pm L -1 und ist 316 fach größer als der der COSKonzentration. Der Tagesgang der CH 3 SHKonzentration zeigt ein Minimum um die Mittagszeit. Die Tagesmittel der CH 3 SH und COSKonzentrationen sind signifikant miteinander korreliert. Diese Daten liefern den Beweis dafür, daß CH 3 SH eine der wichtigen Vorgängersubstanzen von COS ist. Die Regressionslinie der Korrelation zwischen den mittleren COS und CH 3 SHKonzentrationen weist nur einen geringfügigen Achsenabschnitt auf. Somit kann die CH 3 SHKonzentration als ein Indikator der Konzentration von COSVorgängern benutzt werden. Es besteht außerdem eine Korrelation zwischen der CH 3 SHKonzentration und dem Logarithmus der Konzentration des gelösten Chlorophyll a. Diese Korrelation deutet darauf hin, daß der Gehalt von CH 3 SH im Seewasser eine enge Beziehung zur marinen Primärproduktion hat. COS wird im Seewasser durch Hydrolyse abgebaut. Die Abbaurate hängt von der Temperatur des Seewassers ab. Je wärmer das Seewasser ist, desto schneller wird COS abgebaut, und um so kürzer ist die Lebenszeit von COS im Seewasser. Die Lebenszeit kann einerseits durch das ReaktionsgeschwindigkeitsGesetz von Arrhenius berechnet werden, andererseits läßt sie sich durch exponentielle Anpassung an den nächtlichen Konzentrationsverlauf (d.h. bei Abwesenheit von Photoproduktion) abschätzen. Eine solche Anpassung des exponentiellen Abklingens wurde anhand von dicht gestaffelten Messungen während einiger Nächte vorgenommen. Die gefitteten Lebenszeiten stimmen mit den theoretischen Werten gut überein, obwohl die gefittete Lebenszeit neben Hydrolyse noch von anderen Prozessen (z.B. Transport nach unten, AirSeaAustausch, usw.) beeinflußt wird. Diese gute Übereinstimmung unterstützt die Aussage, daß die Hydrolyse eine bedeutende Rolle beim Abbau von COS im Seewasser spielt. Die berechnete HydrolyseLebenszeit ist mit dem Tagesmittel der COSKonzentration korreliert. Da die Tagesmittelwerte sowohl zeitliche wie auch räumliche Mittelwerte der COSKonzentrationen darstellen, zeigt diese Korrelation, daß Hydrolyse eine bedeutende Rolle in der raumzeitlichen Variabilität der COSKonzentration einnimmt. Da die Konzentration des gelösten COS von mehreren Faktoren abhängig ist, scheint eine multivariable Betrachtung sinnvoll. Hierfür wurde eine "Multiple Linear Regression Analysis'' (MLRA) ausgeführt. Diese Analyse ergibt ein empirisches Modell der folgenden Form für die Berechnung des Tagesmittels der COSKonzentration: [COS] = 1.8# 13log[Chl] - 1.5W s 0.057G - 0.73, mit [COS] = mittlere Konzentration von COS in pmol L -1 # = HydrolyseLebenszeit in Stunde [Chl] = mittlere Konzentration von Chlorophyll a in mg m -3 W s = Windgeschwindigkeit in m s -1 G = Intensität der Globalstrahlung in W m -2 . Die Parameter auf der rechten Seite der Gleichung können direkt oder indirekt von Satelliten aus gemessen werden, deshalb kann dieses Modell für die Abschätzung der Konzentration von COS im Seewasser anhand von Satelliten Daten verwendet werden. Das empirische Modell soll noch durch weitere Messungen bestätigt bzw. verbessert werden. Der Austauschfluß von COS zwischen der Atmosphäre und dem offenen Ozean wurde mit dem AirSeaFlußModell von Liss and Slater (1974) zusammen mit dem Modell von Erickson (1993) f
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.
Excitation functions for quasi-elastic scattering have been measured at backward angles for the systems 32,34S+197Au and 32,34S+208Pb for energies spanning the Coulomb barrier. Representative distributions, sensitive to the low energy part of the fusion barrier distribution, have been extracted from the data. For the fusion reactions of 32,34S with 197Au couplings related to the nuclear structure of 197Au appear to be dominant in shaping the low energy part of the barrier distibution. For the system 32S+208Pb the barrier distribution is broader and extends further to lower energies, than in the case of 34S+208Pb. This is consistent with the interpretation that the neutron pick-up channels are energetically more favoured in the 32S induced reaction and therefore couple more strongly to the relative motion. It may also be due to the increased collectivity of 32S, when compared with 34S.