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The last interglacial (LIG; Marine Isotope Substage 5e, ~127–117 ka) experienced globally warmer than modern temperatures; however, profound differences in regional climate occurred that are relevant to the assessment of future climate change scenarios. Tropical Atlantic sea surface temperature (SST) and hydrology are intrinsic to the spatiotemporal evolution of past and future climate. We present eight monthly resolved coral Sr/Ca and δ18O records (130–118 ka) to reconstruct mean western tropical Atlantic SST and seawater δ18O changes during the LIG. Cooler and fresher than modern surface waters are indicated for the middle of the LIG at ~126 ka. This was followed by a rapid transition to modern‐like SSTs and salinities that characterized the remaining part of the LIG. Our results, which account for differences found among corals, proxies, and SST calibration uncertainties, agree with western tropical Atlantic sediment records. Together, they suggest that an oceanic regime existed that differed from today.

This paper presents a computational program named BINCOR (BINned CORrelation) for estimating the correlation between two unevenly spaced time series. This program is also applicable to the situation of two evenly spaced time series not on the same time grid. BINCOR is based on a novel estimation approach proposed by Mudelsee (2010) for estimating the correlation between two climate time series with different timescales. The idea is that autocorrelation (e.g. an AR1 process) means that memory enables values obtained on different time points to be correlated. Binned correlation is performed by resampling the time series under study into time bins on a regular grid, assigning the mean values of the variable under scrutiny within those bins. We present two examples of our BINCOR package with real data: instrumental and paleoclimatic time series. In both applications BINCOR works properly in detecting well-established relationships between the climate records compared.

The Red Sea is a deep marine basin often considered as small‐scale version of the global ocean. Hydrographic observations and ocean‐atmosphere modeling indicate Red Sea deep water was episodically renewed by wintertime open‐ocean deep convections during 1982–2001, suggesting a renewal time on the order of a decade. However, the long‐term pacing of Red Sea deep water renewals is largely uncertain. We use an annually resolved coral oxygen isotope record of winter surface water conditions to show that the late twentieth century deep water renewals were probably unusual in the context of the preceding ~100 years. More frequent major events are detected during the late Little Ice Age, particularly during the early nineteenth century characterized by large tropical volcanic eruptions. We conclude that Red Sea deep water renewal time is on the order of a decade up to a century, depending on the mean climatic conditions and large‐scale interannual climate forcing.

River flooding is among the most destructive of natural hazards globally, causing widespread loss of life, damage to infrastructure and economic deprivation. Societies are currently under increasing threat from such floods, predominantly from increasing exposure of people and assets in flood¿prone areas, but also as a result of changes in flood magnitude, frequency, and timing. Accurate flood hazard and risk assessment are therefore crucial for the sustainable development of societies worldwide. With a paucity of hydrological measurements, evidence from the field offers the only insight into truly extreme events and their variability in space and time. Historical, botanical, and geological archives have increasingly been recognized as valuable sources of extreme flood event information. These different archives are here reviewed with a particular focus on the recording mechanisms of flood information, the historical development of the methodological approaches and the type of information that those archives can provide. These studies provide a wealthy dataset of hundreds of historical and palaeoflood series, whose analysis reveals a noticeable dominance of records in Europe. After describing the diversity of flood information provided by this dataset, we identify how these records have improved and could further improve flood hazard assessments and, thereby, flood management and mitigation plans.

The standard approach to flood frequency analysis (FFA) fits mathematical functions to sequences of historic flood data and extrapolates the tails of the distribution to estimate the magnitude and likelihood of extreme floods. Here, we identify the most exceptional floods in the United States as compared against other major floods at the same location, and evaluate how the flood-of-record (Qmax) influences FFA estimates. On average, floods-of-record are 20% larger by discharge than their second-place counterparts (Q2), and 212 gages (7.3%) have Qmax:Q2 ratios greater than two. There is no clear correspondence between the Qmax:Q2 ratio and median instantaneous discharge, and exceptional floods do not become less likely with time. Excluding Qmax from the FFA causes the median 100-year flood to decline by −10.5%, the 200-year flood by −11.8%, and the 500-year flood by −13.4%. Even when floods are modelled using a heavy tail distribution, the removal of Qmax yields significantly “lighter” tails and underestimates the risk of large floods. Despite the temporal extension of systematic hydrological observations in the United States, FFA is still sensitive to the presence of extreme events within the sample used to calculate the frequency curve.

River flooding is among the most destructive of natural hazards globally, causing widespread loss of life, damage to infrastructure and economic deprivation. Societies are currently under increasing threat from such floods, predominantly from increasing exposure of people and assets in flood‐prone areas, but also as a result of changes in flood magnitude, frequency, and timing. Accurate flood hazard and risk assessment are therefore crucial for the sustainable development of societies worldwide. With a paucity of hydrological measurements, evidence from the field offers the only insight into truly extreme events and their variability in space and time. Historical, botanical, and geological archives have increasingly been recognized as valuable sources of extreme flood event information. These different archives are here reviewed with a particular focus on the recording mechanisms of flood information, the historical development of the methodological approaches and the type of information that those archives can provide. These studies provide a wealthy dataset of hundreds of historical and palaeoflood series, whose analysis reveals a noticeable dominance of records in Europe. After describing the diversity of flood information provided by this dataset, we identify how these records have improved and could further improve flood hazard assessments and, thereby, flood management and mitigation plans.

Extremereignisse zeigen am augenfälligsten, wie verletzlich Deutschland gegenüber dem Klima und seinen Veränderungen ist. Betrachtet man Extremereignisse genauer, verursachten in den vergangenen 20 Jahren Hochwasser die größten Schäden (Ernst Rauch, Münchener Rückversicherungs-Gesellschaft, persönliche Mitteilung). In der Wissenschaft herrscht Einigkeit darüber, dass sich der zukünftige globale Wasserkreislauf durch steigende atmosphärische Treibhausgaskonzentrationen verändern wird (Kirtman et al. 2013). Doch selbst bei der vergleichsweise guten Datenlage für Deutschland ist es unsicher, ob sich die Auftrittsrate – die Anzahl an Ereignissen pro Jahr – von Hochwasser verändert (Trend), wie stark eventuell vorliegende Trends sind und wie stark der Klimawandel ursächlich einwirkt. Diese Zuschreibung der Ursachen wird als Attribution bezeichnet. Gleichzeitig bilden diese Informationen eine wichtige Grundlage für Entscheidungsträger, die über Mitigations- und Anpassungsstrategien befinden. Die damit verbundenen Unsicherheiten müssen daher möglichst transparent kommuniziert werden, um einen Umgang damit zu ermöglichen. Ihre Quellen und Ausmaße werden im Folgenden am Beispiel der Elbehochwasser ausführlich illustriert. Für die Elbe ist der Wissensstand aufgrund der guten Datenqualität und umfangreicher wissenschaftlicher Untersuchungen relativ hoch. Für andere Flüsse (▶ Kap. 10) und andere Ereignistypen sind die Unsicherheiten zum Teil wesentlich größer.

Granger (1980) found that the aggregation of m short-memory AR(1) processes yields a long-memory process. Thereby he assumed m -> ∞, Gaussian shape and betadistributed AR(1) parameters over (0; 1). To test hypotheses that long memory in climate time series comes from aggregation, the finding of Granger (1980) cannot be directly applied. First, the number of \"microclimatic\" processes to be aggregated is finite. Second, climatic processes often produce right-skewed data. Third, the AR(1) parameters of the microclimatic processes could be restricted to a narrower interval than (0; 1). We therefore perform Monte Carlo simulations to study aggregation in climate time series under realistic conditions. The long-memory parameter, H, is estimated by fitting an ARFIMA model to various types of aggregations. Our results are as follows. First, for m above a few hundred, H approaches saturation. Second, the distributional shape has little influence, as noted by Granger (1980). Third, the upper limit of the interval for the AR(1) parameter has a strong influence on the saturation value of H, as noted by Granger (1980)., Granger (1980) fand heraus, dass die Summe von m schwach seriell abhängigen AR(1)-Prozessen einen stark seriell abhängigen Prozess ergibt. Er nahm dabei an, dass m -> ∞ geht, die Verteilungen Gaußsch sind und die AR(1)-Parameter beta-verteilt über (0; 1) sind. Um die Hypothese zu testen, daß starke serielle Abhängigkeit in Klimazeitreihen von dieser \"Aggregation\" rührt, kann das Ergebnis von Granger (1980) jedoch nicht direkt angewendet werden. Erstens: die Anzahl \"mikroklimatischer\", zu summierender Prozesse is endlich. Zweitens: Klimaprozesse erzeugen oft rechtsschief verteilte Daten. Drittens: die AR(1)-Parameter der mikroklimatischen Prozesse mögen auf ein engeres Intervall begrenzt sein als (0; 1). Wir f¨uhren deshalb Monte-Carlo-Simulationen durch, um die Aggregation in Klimazeitreihen für realistische Bedingungen zu studieren. Der Parameter H, der die starke serielle Abhängigkeit beschreibt, wird geschätzt durch die Anpassung eines ARFIMA-Modelles an unterschiedliche Aggregations-Typen. Unsere Ergebnisse sind wie folgt. Erstens: für m oberhalb einiger hundert erreicht H S¨attigung. Zweitens: die Verteilungsform hat geringen Einfluß, wie von Granger (1980) bemerkt. Drittens: die obere Grenze des Intervalles für den AR(1)-Parameter hat einen starken Einfluß auf den Sättigungwert von H, wie von Granger (1980) bemerkt.

Das Jahr 1816 ist in die Klimatologie als das "Jahr ohne Sommer" eingegangen. Der 1815 erfolgte Ausbruch des Vulkans Tambora auf der indonesischen Insel Sumbawa führte im darauffolgenden Jahr in vielen Teilen der Welt zu katastrophalen Missernten. Während in Nordamerika daran besonders die außerordentlich tiefen Sommertemperaturen Schuld waren, scheinen in Europa dafür sowohl die unter dem Durchschnitt liegenden Temperaturen wie auch die überdurchschnittlichen Regenmengen verantwortlich zu sein. Zeitgenössische Berichte wie auch Niederschlags- und Pegelaufzeichnungen aus verschiedenen Teilen Europas belegen eine besonders hohe Niederschlagstätigkeit im "Erntemonat" Juli des Jahres 1816 und eine hohe Wasserführung der großen europäischen Ströme in den Jahren 1816 und 1817., The year 1816 is known in climatology as the "Year without Summer". The eruption of the Tambora volcano on the Indonesian island Sumbawa in 1815 led to catastrophic crop failures over many parts of the world in the following year. Whereas in North America those failures originated from extraordinarily low summer temperatures, causes in Europe seem to have been below-normal temperatures as well as above-normal rainfall. Contemporary reports and also precipitation and river gauge measurements from different parts of Europe document enhanced precipitation in July 1816 ("Erntemonat") and high discharges of large European rivers in years 1816 and 1817.

XTREND consists of the following methodical Parts. Time interval extraction (Part 1) to analyse different parts of a time series; extreme events detection (Part 2) with robust smoothing; magnitude classification (Part 3) by hand; occurrence rate estimation (Part 4) with kernel functions; bootstrap simulations (Part 5) to estimate confidence bands around the occurrence rate. You work interactively with XTREND (parameter adjustment, calculation, graphics) to acquire more intuition for your data. Although, using “normal” data sizes (less than, say, 1000) and modern machines, the computing time seems to be acceptable (less than a few minutes), parameter adjustment should be done carefully to avoid spurious results or, on the other hand, too long computing times. This Report helps you to achieve that. Although it explains the statistical concepts used, this is generally done with less detail, and you should consult the given references (which include some textbooks) for a deeper understanding.

Speleothem δ18O records provide valuable information about past continental environmental and climatic conditions, although their interpretation is often not straightforward. Here we evaluate a compilation of late Holocene speleothem δ18O records using a Monte Carlo based Principal Component Analysis (MC-PCA) method that accounts for uncertainties in individual speleothem age models and for the variable temporal resolution of each δ18O record. The MC-PCA approach permits not only the identification of temporally coherent changes in speleothem δ18O; it also facilitates their graphical depiction and evaluation of their spatial coherency. The MC-PCA method was applied to 11 Holocene speleothem δ18O records that span most of the European continent (apart from the circum-Mediterranean region). We observe a common (shared) mode of speleothem δ18O variability that suggests millennial-scale coherency and cyclicity during the last 4.5ka. These changes are likely caused by variability in atmospheric circulation akin to that associated with the North Atlantic Oscillation, reflecting meridionally shifted westerlies. We argue that these common large-scale variations in European speleothem δ18O records are in phase with changes in the North Atlantic Ocean circulation indicated by the vigour of the Iceland Scotland Overflow Water (ISOW), the strength of the subpolar gyre (SPG) and an ocean stacked North Atlantic ice rafted debris (IRD) index. Based on a recent modelling study, we conclude that these changes in the North Atlantic circulation history may be caused by wind stress on the ocean surface driven by shifted westerlies. However, the mechanisms that ultimately force the westerlies remain unclear.

Die Jahresmitteltemperatur-Zeitreihen dreier Stationen (Berlin, Leipzig und Stockholm) werden auf ihre langfristigen Trends im Zeitbereich 1830-1980 untersucht. Dazu wird die neuartige, parametrische Methode der Rampenregression (Mudelsee 1999a) verwendet. Die Vorteile gegenüber bisher verwendeten Verfahren sind (1) eine realistischeres Übergangsmodell und (2) Angaben des statistischen Fehlers geschätzter Übergangs-Zeitpunkte und -Niveaus. Leipzig (Erwärmung um 0.86±0.13 °C von 1889±7bis1911±7) und Stockholm (Erwärmung um 1.01±0.22 °C von 1879±23 bis 1945±21) zeigen beide einen rampenförmigen Trendverlauf, Berlin dagegen einen noch komplizierteren Trend. Im Falle von Leipzig liegt wahrscheinlich ein deutlicher Urbanisierungseinfluß vor. Die Rampenregression bietet die Möglichkeit, einen globalen Klimawechsel genauer zu quantifizieren., Timeseries of annual average temperature from three stations (Berlin, Leipzig and Stockholm) are investigated with regards to their long-term trends in the time interval 1830-1980. For that, the new, parametric method of ramp function regression (Mudelsee 1999a) is used. The advantages against other previously employed methods are (1) a more realistic transition model and (2) information about the statistical accuracy of estimated transition dates and levels. Both Leipzig (warming by 0.86±0.13 °C, from 1889±7 to 1911±7) and Stockholm (warming by 1.01±0.22 °C, from 1879±23 to 1945±21) show a ramp-form trend, whereas Berlin\'s trend is even more complicated. In the case of Leipzig a significant contribution by urbanization is likely. Ramp function regression has the potential to quantify a global climate change more accurately.