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Chlorine monoxide (ClO) plays a key role in stratospheric ozone loss processes at midlatitudes. We present two balloonborne in situ measurements of ClO conducted in northern hemisphere midlatitudes during the period of the maximum of total inorganic chlorine loading in the atmosphere. Both ClO measurements were conducted on board the TRIPLE balloon payload, launched in November 1996 in Le´on, Spain, and in May 1999 in Aire sur l’Adour, France. For both flights a ClO daylight and night time vertical profile could be derived over an altitude range of approximately 15–31 km. ClO mixing ratios are compared to model simulations performed with the photochemical box model version of the Chemical Lagrangian Model of the Stratosphere (CLaMS). Simulations along 24-h backward trajectories were performed to study the diurnal variation of ClO in the midlatitude lower stratosphere. Model simulations for the flight launched in Aire sur l’Adour 1999 show a good agreement with the ClO measurements. For the flight launched in Le´on 1996, a similar good agreement is found, except at around ~ 650 K potential temperature (~26km altitude). However, a tendency is found that for solar zenith angles greater than 86°–87° the simulated ClO mixing ratios substantially overestimate measured ClO by approximately a factor of 2.5 or more for both flights. Therefore we conclude that no indication can be deduced from the presented ClO measurements that substantial uncertainties exist in midlatitude chlorine chemistry of the stratosphere. An exception is the situation at solar zenith angles greater than 86°–87° where model simulations substantial overestimate ClO observations.
Chlorine monoxide (ClO) plays a key role in stratospheric ozone loss processes at midlatitudes. We present two balloon-borne in situ measurements of ClO conducted in northern hemisphere midlatitudes during the period of the maximum of total inorganic chlorine loading in the atmosphere. Both ClO measurements were conducted on board the TRIPLE balloon payload, launched in November 1996 in León, Spain, and in May 1999 in Aire sur l'Adour, France. For both flights a ClO daylight and night-time vertical profile was derived over an altitude range of approximately 15-35 km. ClO mixing ratios are compared to model simulations performed with the photochemical box model version of the Chemical Lagrangian Model of the Stratosphere (CLaMS). Simulations along 24-hour backward trajectories were performed to study the diurnal variation of ClO in the midlatitude lower stratosphere. Model simulations for the flight launched in Aire sur l'Adour 1999 show an excellent agreement with the ClO measurements. For the flight launched in León 1996, an overall good agreement is found, whereas the flight is characterized by a more complex dynamical situation due to a possible mixture of vortex and non-vortex air. We note that for both flights at solar zenith angles greater than 86°-87° simulated ClO mixing ratios are higher than observed ClO mixing ratios. However, the present findings indicate that no substantial uncertainties exist in midlatitude chlorine chemistry of the stratosphere.
Modelling short-term variability in carbon and water exchange in a temperate Scots pine forest
(2015)
The vegetation–atmosphere carbon and water exchange at one particular site can strongly vary from year to year, and understanding this interannual variability in carbon and water exchange (IAVcw) is a critical factor in projecting future ecosystem changes. However, the mechanisms driving this IAVcw are not well understood. We used data on carbon and water fluxes from a multi-year eddy covariance study (1997–2009) in a Dutch Scots pine forest and forced a process-based ecosystem model (Lund–Potsdam–Jena General Ecosystem Simulator; LPJ-GUESS) with local data to, firstly, test whether the model can explain IAVcw and seasonal carbon and water exchange from direct environmental factors only. Initial model runs showed low correlations with estimated annual gross primary productivity (GPP) and annual actual evapotranspiration (AET), while monthly and daily fluxes showed high correlations. The model underestimated GPP and AET during winter and drought events. Secondly, we adapted the temperature inhibition function of photosynthesis to account for the observation that at this particular site, trees continue to assimilate at very low atmospheric temperatures (up to daily averages of −10 °C), resulting in a net carbon sink in winter. While we were able to improve daily and monthly simulations during winter by lowering the modelled minimum temperature threshold for photosynthesis, this did not increase explained IAVcw at the site. Thirdly, we implemented three alternative hypotheses concerning water uptake by plants in order to test which one best corresponds with the data. In particular, we analyse the effects during the 2003 heatwave. These simulations revealed a strong sensitivity of the modelled fluxes during dry and warm conditions, but no single formulation was consistently superior in reproducing the data for all timescales and the overall model–data match for IAVcw could not be improved. Most probably access to deep soil water leads to higher AET and GPP simulated during the heatwave of 2003. We conclude that photosynthesis at lower temperatures than assumed in most models can be important for winter carbon and water fluxes in pine forests. Furthermore, details of the model representations of water uptake, which are often overlooked, need further attention, and deep water access should be treated explicitly.
Modelling short-term variability in carbon and water exchange in a temperate Scots pine forest
(2015)
The vegetation–atmosphere carbon and water exchange at one particular site can strongly vary from year to year, and understanding this interannual variability in carbon and water exchange (IAVcw) is a critical factor in projecting future ecosystem changes. However, the mechanisms driving this IAVcw are not well understood. We used data on carbon and water fluxes from a multi-year eddy covariance study (1997–2009) in a Dutch Scots pine forest and forced a process-based ecosystem model (Lund–Potsdam–Jena General Ecosystem Simulator; LPJ-GUESS) with local data to, firstly, test whether the model can explain IAVcw and seasonal carbon and water exchange from direct environmental factors only. Initial model runs showed low correlations with estimated annual gross primary productivity (GPP) and annual actual evapotranspiration (AET), while monthly and daily fluxes showed high correlations. The model underestimated GPP and AET during winter and drought events. Secondly, we adapted the temperature inhibition function of photosynthesis to account for the observation that at this particular site, trees continue to assimilate at very low atmospheric temperatures (up to daily averages of −10 °C), resulting in a net carbon sink in winter. While we were able to improve daily and monthly simulations during winter by lowering the modelled minimum temperature threshold for photosynthesis, this did not increase explained IAVcw at the site. Thirdly, we implemented three alternative hypotheses concerning water uptake by plants in order to test which one best corresponds with the data. In particular, we analyse the effects during the 2003 heatwave. These simulations revealed a strong sensitivity of the modelled fluxes during dry and warm conditions, but no single formulation was consistently superior in reproducing the data for all timescales and the overall model–data match for IAVcw could not be improved. Most probably access to deep soil water leads to higher AET and GPP simulated during the heatwave of 2003. We conclude that photosynthesis at lower temperatures than assumed in most models can be important for winter carbon and water fluxes in pine forests. Furthermore, details of the model representations of water uptake, which are often overlooked, need further attention, and deep water access should be treated explicitly.
Analysing the composition of ambient ultrafine particles (UFPs) is a challenging task due to the low mass and chemical complexity of small particles, yet it is a prerequisite for the identification of particle sources and the assessment of potential health risks. Here, we show the molecular characterization of UFPs, based on cascade impactor (Nano-MOUDI) samples that were collected at an air quality monitoring station near one of Europe's largest airports, in Frankfurt, Germany. At this station, particle-size-distribution measurements show an enhanced number concentration of particles smaller than 50 nm during airport operating hours. We sampled the lower UFP fraction (0.010–0.018, 0.018–0.032, 0.032–0.056 µm) when the air masses arrived from the airport. We developed an optimized filter extraction procedure using ultra-high-performance liquid chromatography (UHPLC) for compound separation and a heated electrospray ionization (HESI) source with an Orbitrap high-resolution mass spectrometer (HRMS) as a detector for organic compounds. A non-target screening detected ∼200 organic compounds in the UFP fraction with sample-to-blank ratios larger than 5. We identified the largest signals as homologous series of pentaerythritol esters (PEEs) and trimethylolpropane esters (TMPEs), which are base stocks of aircraft lubrication oils. We unambiguously attribute the majority of detected compounds to jet engine lubrication oils by matching retention times, high-resolution and accurate mass measurements, and comparing tandem mass spectrometry (MS2) fragmentation patterns between both ambient samples and commercially available jet oils. For each UFP stage, we created molecular fingerprints to visualize the complex chemical composition of the organic fraction and their average carbon oxidation state. These graphs underline the presence of the homologous series of PEEs and TMPEs and the appearance of jet oil additives (e.g. tricresyl phosphate, TCP). Targeted screening of TCP confirmed the absence of the harmful tri-ortho isomer, while we identified a thermal transformation product of TMPE-based lubrication oil (trimethylolpropane phosphate, TMP-P). Even though a quantitative determination of the identified compounds is limited, the presented method enables the qualitative detection of molecular markers for jet engine lubricants in UFPs and thus strongly improves the source apportionment of UFPs near airports.
Analysing the composition of ambient ultrafine particles (UFP) is a challenging task due to the low mass and chemical complexity of small particles, yet it is a prerequisite for the identification ofparticle sources and the assessment of potential health risks. Here, we show the molecular characterization of UFP, based on cascade impactor (Nano-MOUDI) 10samples that were collected at an air quality monitoring station nearby one of Europe`s largest airports in Frankfurt, Germany. At this station, particle-size-distribution measurements show enhanced number concentration of particles smaller than 50nm during airport operating hours. We sampled the lower UFP fraction (0.010-0.018 μm; 0.018-0.032 μm; 0.032-0.056 μm) when the air masses arrived from the airport. We developed an optimized filter extraction procedure, used ultra-high performance liquid chromatography (UHPLC) for compound separation, and a heated electrospray ionization (HESI) source with an 15Orbitrap high-resolution mass spectrometer (HRMS) as a detector for organic compounds. A non-target screening detected ~200 organic compounds in the UFP fraction with sample-to-blank ratios larger than five. We identified the largest signals as homologous series of pentaerythritol esters (PEE) and trimethylolpropane esters (TMPE), which are base stocks of aircraft lubrication oils. We unambiguously attribute the majority of detected compounds to jet engine lubrication oils by matching retention times, high-resolution/accurate mass (HR/AM) measurements, and comparing MS/MS fragmentation patterns between both ambient samples and commercially available jet oils. For each UFP stage, we created molecular fingerprints to visualize the complex chemical composition ofthe organic fraction and their average carbon oxidation state. These graphs underline the presence of the homologous series of PEE and TMPE, and the appearance of jet oil additives (e.g. tricresyl phosphate (TCP)). Targeted screening on TCP confirmed the absence of the harmful tri-orthoisomer, while we identified a thermal transformation product of TMPE-based lubrication oil (trimethylolpropane phosphate (TMP-P)). Even though a quantitative determination of the identified compounds is limited, the presented method enables the qualitative detection of molecular markers for jet engine lubricants in UFP and thus strongly improves the source apportionment of UFP near airports.
As part of two drilling campaigns of the International Continental Scientific Drilling Program (ICDP), several geophysical borehole measurements were carried out by the Leibniz Institute for Applied Geophysics (LIAG) in two lakes. The acquired data was used to answer stratigraphic and paleoclimatic research questions, including the establishment of robust age-depth models and the construction of continuous lithological profiles.
Lake Towuti is located on Sulawesi (Indonesia), within the "Indo-Pacific Warm Pool" (IPWP), a globally important region for atmospheric heat and moisture budgets. The lake exists for approximately one million years, but its exact age is uncertain. We present the first agedepth model for the approximately 100 m continuous sediment sequence from the central part of the lake. The basis for this model is the magnetic susceptibility measured in the borehole and a tephra layer with an age of about 797 ka at 72 m depth. Our age-depth model is inferred from cyclostratigraphic analysis of borehole data and covers a period from 903 ± 11 to 131 ± 67 ka. We suggest that orbital eccentricity and/or changes between global cold and warm periods are responsible for hydroclimatic changes in the IPWP, that these changes affect sedimentation processes in Lake Towuti, and that we can measure and observe this effect in the sediment properties today. Additionally, we created a continuous artificial lithological profile from a series of different borehole data using cluster analysis. This provides information from parts of the borehole where no sediment is available due to core loss.
Lake Ohrid is 1.36 million years old and is located on the Balkan Peninsula on the border between Albania and North Macedonia. The primary hole 'DEEP' in the central part of the lake has been the subject of several investigations, but information about sediments of the marginal locations 'Pestani' and 'Cerava' have not been published yet. In our study, we use natural gamma radiation (GR) measured in the borehole to generate an age-depth model for DEEP. This is performed using the correlation of GR to the global LR04 reference record of Lisiecki and Raymo (2005).
The age information is then transferred via prominent seismic marker horizons to the other two sites, Pestani and Cerava, where it provides the first age-control points for the construction of age-depth models from correlation of GR to LR04. The generated age-depth models are tested using cyclostratigraphic methods, but the limits of this approach are revealed. At DEEP, sedimentation rates (SR) from the cyclostratigraphic method and the correlative approach differ by 2.8 %, at Pestani this difference is 16.7 %, and at Cerava the quality of the data does not allow a reliable evaluation of SR using the cyclostratigraphic approach. We used cluster analysis to construct artificial lithological profiles at all three sites and integrated them into the respective age-depth models. This enables us to determine which sediment types were deposited at what time, and we recognize the change between warm and cold periods in the sediment properties at all three locations. The analyses in this study were all performed on borehole and seismic data and thus do not involve sediment core data. Especially at Pestani and Cerava, new insights into the sedimentological history of Lake Ohrid could be obtained.
In the last part we discuss the occurrence of the half-precession (HP) signal in the European region during the last one million years. The focus is on Lake Ohrid, but a range of other proxies, from the eastern Mediterranean, across the European continent, up to Greenland are analyzed in regards to HP. Applying filters, we focus on the frequency range with a period of 13-8.5 ka and only HP remains in the records. We use correlative methods to determine the clarity of the HP signal in proxies distributed across the European realm. Additionally, we determined the development of HP over time. The HP signal is clearest in the southeast and decreases toward the north. It is further more pronounced in interglacial periods and in the younger part (<621 ka) of most proxies. We suggest that there are mechanisms that transmit the HP signal from its origin near the equator to higher latitudes via different processes. In this context, for instance, the African monsoon, the Nile River and the Mediterranean outflow via the Strait of Gibraltar can be important factors.
Over the last several decades, spinel-structured minerals with the chemical formula AB2O4 (where A and B stand for divalent and trivalent cations, respectively) have attracted more and more attention, particularly with regards to their breakdown at high pressures and temperatures and the nature of the so-called "post-spinel" phases. Spinel-structured phases with different endmember compositions, like magnetite (Fe3O4), hercynite (FeAl2O4) or spinel (MgAl2O4), are known to breakdown differently at high pressure-temperature conditions (e.g., Akaogi et al. 1999; Schollenbruch et al. 2010; Woodland et al. 2012). Such phases are of particular interest when they incorporate ferric (Fe3+) and ferrous (Fe2+) cations as this makes their stability sensitive to redox conditions. Since magnetite and magnesioferrite (MgFe3+ 2O4) have been found as inclusions in diamond (e.g., Stachel et al. 1998; Harte et al. 1999; Wirth et al. 2014; Palot et al. 2016; Jacob et al. 2016), understanding their phase relations is important for setting constraints on the conditions of their formation.
This study aimed to experimentally investigate the phase relations of Fe-Mg spinel-structured phases at conditions of the deep upper mantle and transition zone. Exploring the stability of new post-spinel phases and their characterization were also major goals of this study. Approaching a pyrolitic mantle composition by adding amounts of SiO2 in the system allowed constraints on the relevance of Fe-Mg post-spinel phases coexisting with mantle silicates to be made. ...
Long-term average groundwater recharge representing the sustainable groundwater resources is modeled as a 0.5° by 0.5° grid on global scale by the WaterGAP Global Hydrology Model. Due to uncertainties of estimating groundwater recharge, especially in semiarid and arid regions, independent estimates are used for calibrating the model. This work compiled a new set of independent groundwater recharge estimates based on a work of Scanlon et al. (2006). The 59 independent estimates, together with an already existing independent estimates compilation, are used for the evaluation of two WGHM variants; one variant is modeling with an improved more realistically distributed daily precipitation dataset.
The objective of this thesis is the evaluation of the modeled data of the WaterGAP Global Hydrology Model (WGHM). The analysis of the impact of the new Watch Forcing Data (WFD) precipitation dataset on the modeled groundwater recharge tends to result in lower values in humid and higher values in (semi-)arid regions compared to the WGHM standard variant. Comparing both WGHM variants to the independent estimates compilations, representing (semi-)arid regions, the WGHM variant shows over- and underestimations especially of the low values and the WGHM WFD variant shows a bias toward overestimation especially for values below 4 mm/yr. The analysis of texture, hydrogeology and vegetation/ land cover could not give satisfying explanations for the discrepancies, but derived from the groundwater recharge measurement methods analysis indirect/ localized recharge seems to be a significant factor causing underestimations, as resulted in the comparison of the independent estimates based on Scanlon et al. 2006 with the WGHM variants.
Derivation and characterization of a new filter for nonlinear high-dimensional data assimilation
(2015)
Data assimilation (DA) combines model forecasts with real-world observations to achieve an optimal estimate of the state of a dynamical system. The quality of predictions in nonlinear and chaotic systems such as atmospheric or oceanic circulation is strongly sensitive to the initial conditions. Therefore, beyond the consistent reconstruction of past states, a primary relevance of advanced DA methods concerns the proper model initialization. The ensemble Kalman filter (EnKF) and its deterministic variants, mostly square root filters such as the ensemble transform Kalman filter (ETKF), represent a popular alternative to variational DA schemes. They are applied in a wide range of research and operations. Their forecast step employs an ensemble integration that fully respects the nonlinear nature of the analyzed system. In the analysis step, they implicitly assume the prior state and observation errors to be Gaussian. Consequently, in nonlinear systems, the mean and covariance of the analysis ensemble are biased and these filters remain suboptimal. In contrast, the fully nonlinear, non-Gaussian particle filter (PF) relies on Bayes' theorem without further assumptions, which guarantees an exact asymptotic behavior. However, it is exposed to weight collapse, particularly in higher-dimensional settings, known as the curse of dimensionality.
This work presents a new method to obtain an analysis ensemble with mean and covariance that exactly match the corresponding Bayesian estimates. This is achieved by a deterministic matrix square root transformation of the forecast ensemble, and subsequently a suitable random rotation that significantly contributes to filter stability while preserving the required second-order statistics. The forecast step remains as in the ETKF. The algorithm, which is fairly easy to implement and computationally efficient, is referred to as the nonlinear ensemble transform filter (NETF). The limitation with respect to fully-nonlinear filtering is that the NETF only considers the mean and covariance of the Bayesian analysis density, neglecting higher-order moments.
The properties and performance of the proposed algorithm are investigated via a set of experiments. The results indicate that such a filter formulation can increase the analysis quality, even for relatively small ensemble sizes, compared to other ensemble filters in nonlinear, non-Gaussian scenarios. They also confirm that localization enhances the applicability of this PF-inspired scheme in larger-dimensional systems. Finally, the novel filter is coupled to a large-scale ocean general circulation model with a realistic observation scenario. The NETF remains stable with a small ensemble size and shows a consistent behavior. Additionally, its analyses exhibit low estimation errors, as revealed by a comparison with a free ensemble integration and the ETKF. The results confirm that, in principle, the filter can be applied successfully and as simple as the ETKF in high-dimensional problems. No further modifications are needed, even though the algorithm is only based on the particle weights. Thus, it is able to overcome the curse of dimensionality, even in deterministic systems. This proves that the NETF constitutes a promising and user-friendly method for nonlinear high-dimensional DA.
Das Klima, insbesondere der Niederschlag ist einer der wichtigsten natürlichen Gestaltungsfaktoren für die Savannenregion Westafrikas. Morphodynamik, Bodenbildung, Abflußregime sowie Wasserhaushalt werden direkt vom Klima bestimmt. Der Niederschlag ist zudem das begrenzende Element für das Wachstum von Flora und Fauna. Jede Änderung der Niederschlagsmenge hat gravierende Folgen für die Landschaft und seine Bewohner. Die Untersuchung langfristiger klimatischer Veränderungen ist ein Beitrag die Entstehung und den Wandel der Landschaft zu verstehen. Hierdurch können parallele Entwicklungen zwischen Natur- und Kulturraum im langfristigen Zusammenhängen gesehen werden. Ziel ist, das Klima des Tschadseegebietes seit dem Beginn regelmäßiger Aufzeichnung von Klimadaten mit Hilfe verschiedener statistischer Verfahren zu beschreiben. Des weiteren sollen Wechselwirkungen und Zusammenhänge zu externen Faktoren (Globale Zirkulation, Ozeantemperatur, Solarstrahlung,...) aufgezeigt werden.
Über gewundene Bergkrystalle
(1894)
About half of present-day cloud condensation nuclei originate from atmospheric nucleation, frequently appearing as a burst of new particles near midday1. Atmospheric observations show that the growth rate of new particles often accelerates when the diameter of the particles is between one and ten nanometres2,3. In this critical size range, new particles are most likely to be lost by coagulation with pre-existing particles4, thereby failing to form new cloud condensation nuclei that are typically 50 to 100 nanometres across. Sulfuric acid vapour is often involved in nucleation but is too scarce to explain most subsequent growth5,6, leaving organic vapours as the most plausible alternative, at least in the planetary boundary layer7,8,9,10. Although recent studies11,12,13 predict that low-volatility organic vapours contribute during initial growth, direct evidence has been lacking. The accelerating growth may result from increased photolytic production of condensable organic species in the afternoon2, and the presence of a possible Kelvin (curvature) effect, which inhibits organic vapour condensation on the smallest particles (the nano-Köhler theory)2,14, has so far remained ambiguous. Here we present experiments performed in a large chamber under atmospheric conditions that investigate the role of organic vapours in the initial growth of nucleated organic particles in the absence of inorganic acids and bases such as sulfuric acid or ammonia and amines, respectively. Using data from the same set of experiments, it has been shown15 that organic vapours alone can drive nucleation. We focus on the growth of nucleated particles and find that the organic vapours that drive initial growth have extremely low volatilities (saturation concentration less than 10−4.5 micrograms per cubic metre). As the particles increase in size and the Kelvin barrier falls, subsequent growth is primarily due to more abundant organic vapours of slightly higher volatility (saturation concentrations of 10−4.5 to 10−0.5 micrograms per cubic metre). We present a particle growth model that quantitatively reproduces our measurements. Furthermore, we implement a parameterization of the first steps of growth in a global aerosol model and find that concentrations of atmospheric cloud concentration nuclei can change substantially in response, that is, by up to 50 per cent in comparison with previously assumed growth rate parameterizations.
Für eine möglichst vollständige analytische Beschreibung werden in der statistischen Klimatologie beobachtete Klimazeitreihen als Realisation eines stochastischen Prozesses, das heißt als eine Folge von Zufallsvariablen verstanden. Die Zeitreihe soll im wesentlichen durch eine analytische Funktion der Zeit beschrieben werden können und die Beobachtung nur durch Zufallseinflüsse von dieser Funktion abweichen. Diese analytische Funktion setzt sich aus der Summe zeitlich strukturierter Komponenten zusammen, welche aus klimatologischem Blickwinkel interpretierbar erscheinen. Es werden Funktionen zugelassen, die den Jahresgang, Trends, episodische Komponenten und deren Änderung beschreiben. Die Extremereignisse sind als eine besondere weitere Komponente in die Zeitreihenanalyse aufgenommen und als von Änderungen in den Parametern der Verteilung unabhängige, extreme Werte definiert. Die Zufallseinflüsse sollen zunächst als Realisierungen unabhängiger normalverteilter Zufallsvariablen mit dem Erwartungswert Null und im Zeitablauf konstanter Varianz interpretiert werden können. In diesem Fall beschreibt die analytische Funktion der Zeit, die Summe detektierter strukturierter Komponenten, den zeitlichen Verlauf des Mittels. Ein zu einem bestimmten Zeitpunkt tatsächlich beobachteter Wert kann dann als eine mögliche Realisation einer Zufallsvariablen interpretiert werden, die der Gaußverteilung mit dem Mittelwert µ(t) zur Zeit t und konstanter Varianz genügt. Da die zugrundeliegenden Annahmen, unter Verwendung klimatologisch interpretierbarer Basisfunktionen, in der Analyse von Klimazeitreihen, die nicht die Temperatur betreffen, zumeist nicht erfüllt sind, wird in eine Verallgemeinerung des Konzepts der Zeitreihenzerlegung in einen deterministischen und einen statistischen Anteil eingeführt. Zeitlich strukturierte Änderungen werden nun in verschiedenen Verteilungsparametern frei wählbarer Wahrscheinlichkeitsdichtefunktionen gesucht. Die gängige Beschränkung auf die Schätzung einer zeitlich veränderlichen Lokation wird aufgehoben. Skalenschätzer sowie Schätzer fär den Formparameter spielen ebenso relevante Rollen fär die Beschreibung beobachteter Klimavariabilität. Die Klimazeitreihen werden wieder als Realisation eines Zufallprozesses verstanden, jedoch genügen die Zufallsvariablen nun einer frei wählbaren Wahrscheinlichkeitsdichtefunktion. Die zeitlich strukturierten Änderungen in den Verteilungsparametern werden auf Basis der gesamten Zeitreihe für jeden Zeitpunkt geschätzt. Die aus der Analyse resultierende analytische Beschreibung in Form einer zeitabhängigen Wahrscheinlichkeitsdichtefunktion ermöglicht weiterhin die Schätzung von Über- und Unterschreitungswahrscheinlichkeiten beliebig wählbarer Schwellenwerte für jeden Zeitpunkt des Beobachtungszeitraums. Diese Methode erlaubt insbesondere eine statistische Modellierung monatlicher Niederschlagsreihen durch die Zerlegung in einen deterministischen und einen statistischen Anteil. In dem speziellen Fall von 132 Reihen monatlicher Niederschlagssummen deutscher Stationen 1901-2000 gelingt eine vollständige analytische Beschreibung der Reihen durch ihre Interpretation als Realisation einer Gumbel-verteilten Zufallsvariablen mit variablem Lage- und Streuparameter. Auf Basis der gewonnenen analytischen Beschreibung der Reihen kann beispielsweise im Westen Deutschlands auf Verschiebungen der jährlichen Überschreitungsmaxima des 95%-Perzentils von den Sommer- in die Wintermonate geschlossen werden. Sie werden durch relativ starke Anstiege in der Überschreitungswahrscheinlichkeit (bis 10%) in den Wintermonaten und nur geringe Zunahmen oder aber Abnahmen in den Sommermonaten hervorgerufen. Dies geht mit einer Zunahme der Unterschreitungswahrscheinlichkeit in den Winter- und einer Abnahme in den Sommermonaten einher. Monte-Carlo-Simulationen zeigen, daß jahreszeitlich differenzierte Schätzungen von Änderungen im Erwartungswert, also gebräuchliche Trends, auf Basis der Kleinst-Quadrate-Methode systematischen Bias und hohe Varianz aufweisen. Eine Schätzung der Trends im Mittel auf Basis der statistischen Modellierung ist somit ebenso den Kleinst-Quadrate-Schätzern vorzuziehen. Hinsichtlich der Niederschlagsanalysen stellen jedoch aride Gebiete, mit sehr seltenen Niederschlägen zu bestimmten Jahreszeiten, die Grenze der Methode dar, denn zu diesen Zeitpunkten ist eine vertrauenswürdige Schätzung einer Wahrscheinlichkeitsdichtefunktion nicht möglich. In solchen Fällen ist eine grundsätzlich andere Herangehensweise zur Modellierung der Reihen erforderlich.
Für eine möglichst vollständige analytische Beschreibung werden in der statistischen Klimatologie beobachtete Klimazeitreihen als Realisation eines stochastischen Prozesses, das heißt als eine Folge von Zufallsvariablen verstanden. Die Zeitreihe soll im wesentlichen durch eine analytische Funktion der Zeit beschrieben werden können und die Beobachtung nur durch Zufallseinflüsse von dieser Funktion abweichen. Diese analytische Funktion setzt sich aus der Summe zeitlich strukturierter Komponenten zusammen, welche aus klimatologischem Blickwinkel interpretierbar erscheinen. Es werden Funktionen zugelassen, die den Jahresgang, Trends, episodische Komponenten und deren Änderung beschreiben. Die Extremereignisse sind als eine besondere weitere Komponente in die Zeitreihenanalyse aufgenommen und als von Änderungen in den Parametern der Verteilung unabhängige, extreme Werte definiert. Die Zufallseinflüsse sollen zunächst als Realisierungen unabhängiger normalverteilter Zufallsvariablen mit dem Erwartungswert Null und im Zeitablauf konstanter Varianz interpretiert werden können. In diesem Fall beschreibt die analytische Funktion der Zeit, die Summe detektierter strukturierter Komponenten, den zeitlichen Verlauf des Mittels. Ein zu einem bestimmten Zeitpunkt tatsächlich beobachteter Wert kann dann als eine mögliche Realisation einer Zufallsvariablen interpretiert werden, die der Gaußverteilung mit dem Mittelwert µ(t) zur Zeit t und konstanter Varianz genügt. Da die zugrundeliegenden Annahmen, unter Verwendung klimatologisch interpretierbarer Basisfunktionen, in der Analyse von Klimazeitreihen, die nicht die Temperatur betreffen, zumeist nicht erfüllt sind, wird in eine Verallgemeinerung des Konzepts der Zeitreihenzerlegung in einen deterministischen und einen statistischen Anteil eingeführt. Zeitlich strukturierte Änderungen werden nun in verschiedenen Verteilungsparametern frei wählbarer Wahrscheinlichkeitsdichtefunktionen gesucht. Die gängige Beschränkung auf die Schätzung einer zeitlich veränderlichen Lokation wird aufgehoben. Skalenschätzer sowie Schätzer fär den Formparameter spielen ebenso relevante Rollen fär die Beschreibung beobachteter Klimavariabilität. Die Klimazeitreihen werden wieder als Realisation eines Zufallprozesses verstanden, jedoch genügen die Zufallsvariablen nun einer frei wählbaren Wahrscheinlichkeitsdichtefunktion. Die zeitlich strukturierten Änderungen in den Verteilungsparametern werden auf Basis der gesamten Zeitreihe für jeden Zeitpunkt geschätzt. Die aus der Analyse resultierende analytische Beschreibung in Form einer zeitabhängigen Wahrscheinlichkeitsdichtefunktion ermöglicht weiterhin die Schätzung von Über- und Unterschreitungswahrscheinlichkeiten beliebig wählbarer Schwellenwerte für jeden Zeitpunkt des Beobachtungszeitraums. Diese Methode erlaubt insbesondere eine statistische Modellierung monatlicher Niederschlagsreihen durch die Zerlegung in einen deterministischen und einen statistischen Anteil. In dem speziellen Fall von 132 Reihen monatlicher Niederschlagssummen deutscher Stationen 1901-2000 gelingt eine vollständige analytische Beschreibung der Reihen durch ihre Interpretation als Realisation einer Gumbel-verteilten Zufallsvariablen mit variablem Lage- und Streuparameter. Auf Basis der gewonnenen analytischen Beschreibung der Reihen kann beispielsweise im Westen Deutschlands auf Verschiebungen der jährlichen Überschreitungsmaxima des 95%-Perzentils von den Sommer- in die Wintermonate geschlossen werden. Sie werden durch relativ starke Anstiege in der Überschreitungswahrscheinlichkeit (bis 10%) in den Wintermonaten und nur geringe Zunahmen oder aber Abnahmen in den Sommermonaten hervorgerufen. Dies geht mit einer Zunahme der Unterschreitungswahrscheinlichkeit in den Winter- und einer Abnahme in den Sommermonaten einher. Monte-Carlo-Simulationen zeigen, daß jahreszeitlich differenzierte Schätzungen von Änderungen im Erwartungswert, also gebräuchliche Trends, auf Basis der Kleinst-Quadrate-Methode systematischen Bias und hohe Varianz aufweisen. Eine Schätzung der Trends im Mittel auf Basis der statistischen Modellierung ist somit ebenso den Kleinst-Quadrate-Schätzern vorzuziehen. Hinsichtlich der Niederschlagsanalysen stellen jedoch aride Gebiete, mit sehr seltenen Niederschlägen zu bestimmten Jahreszeiten, die Grenze der Methode dar, denn zu diesen Zeitpunkten ist eine vertrauenswürdige Schätzung einer Wahrscheinlichkeitsdichtefunktion nicht möglich. In solchen Fällen ist eine grundsätzlich andere Herangehensweise zur Modellierung der Reihen erforderlich.
Die konventionelle Extremwertstatistik die sich an der Über- bzw. Unterschreitungshäufigkeit bestimmter Schwellenwerte orientiert, beinhaltet den Nachteil, daß Änderungen der Parameter der Häufigkeitsverteilung die Extremwertwahrscheinlichkeit beeinflussen. So kann allein das Vorhandensein eines Trends für derartige Veränderungen verantwortlich sein. Die hier gewählte Methodik vermeidet diesen Nachteil, indem sie eine Zerlegung der betrachteten Zeitreihen in einen strukturierten und einen unstrukturierten Anteil durchführt. Dabei setzt sich der strukturierte Anteil aus einer Trend-, Saison- und glatten Komponente zusammen. Aus der Summe dieser in der Zeitreihe signifikant enthaltenen Komponenten läßt sich die Eintrittswahrscheinlichkeit von Extremwerten ableiten. Ähnliches gilt für den unstrukturierten Anteil insbesondere für die Varianz des Residuums. Das Residuum kann aber auch Werte enthalten, die nicht zu ihrer ansonsten angepaßten Häufigkeitsverteilung passen. Solche Werte werden als Extremereignisse bezeichnet und sind von den Extremwerten zu unterschieden. In der vorliegenden Arbeit werden nun, getrennt voneinander, durch Änderungen in den Parametern der Häufigkeitsverteilung hervorgerufene Variationen der Extremwertwahrscheinlichkeit als auch parameterunabhängige Extremereignisse der bodennahen Lufttemperatur betrachtet. Als Datenbasis dienten 41, wahrscheinlich homogene, europäische Stationszeitreihen von Monatsmitteltemperaturen, die den Zeitraum von 1871 bis 1990 abdecken. In den untersuchten Temperaturzeitreihen wurde an 37 von 41 Stationen ein positiver Trend detektiert, woraus ein Anstieg der Extremwertwahrscheinlichkeit mit der Zeit resultiert. Die glatten, niederfrequenten Schwingungen wirken sich in den meisten Fällen um 1890 und 1975 negativ und um 1871, 1940 und 1990 positiv auf die Extremwertwahrscheinlich keit aus. Desweiteren treten Änderungen in der Saisonfigur bezüglich der Amplitude und der Phasenlage auf. Detektierte Zunahmen in der Amplitude des Jahresgangs führen zu einer positiven Änderung der Extremwertwahrscheinlichkeit. Signifikante Änderungen in der Phasenlage der Saisonfigur erzeugen in den Anomaliezeitreihen einen saisonal unterschiedlichen Trend, dessen Amplitude, in den betrachteten Fällen, in der Größenordnung der Trendkomponente liegt. Saisonal unterschiedliche Trends beeinflussen saisonal unterschiedlich die Wahrscheinlichkeit für das Eintreten von Extremwerten. Die Residuen von fünf Temperaturzeitreihen weisen signifikante Varianzinstationaritäten auf, wobei in nur einem Fall die Varianz mit der Zeit zunimmt und somit einen Anstieg der Extremwertwahrscheinlichkeit erzeugt. Extremereignisse treten vorwiegend in Form besonders kalter Winter auf und können wahrscheinlich als Realisation eines Poisson-Prozesses interpretiert werden. Sie erscheinen zufällig über den Beobachtungszeitraum verteilt mit einer mittleren Wiederkehrzeit von mehr als 10 Jahren.
Ecophysiological studies on Antarctic cryptophytes to assess whether climatic changes such as ocean acidification and enhanced stratification affect their growth in Antarctic coastal waters in the future are lacking so far. This is the first study that investigated the combined effects of increasing availability of pCO2 (400 and 1000 µatm) and irradiance (20, 200 and 500 μmol photons m−2 s −1) on growth, elemental composition and photophysiology of the Antarctic cryptophyte Geminigera cryophila. Under ambient pCO2, this species was characterized by a pronounced sensitivity to increasing irradiance with complete growth inhibition at the highest light intensity. Interestingly, when grown under high pCO2 this negative light effect vanished and it reached highest rates of growth and particulate organic carbon production at the highest irradiance compared to the other tested experimental conditions. Our results for G. cryophila reveal beneficial effects of ocean acidification in conjunction with enhanced irradiance on growth and photosynthesis. Hence, cryptophytes such as G. cryophila may be potential winners of climate change, potentially thriving better in more stratified and acidic coastal waters and contributing in higher abundance to future phytoplankton assemblages of coastal Antarctic waters.