• Energy Research

Regional flood frequency analysis (RFFA) is a powerful method for interrogating hydrological series since it combines observational time series from several sites within a region to estimate risk-relevant statistical parameters with higher accuracy than from single-site series. Since RFFA extreme value estimates depend on the shape of the selected distribution of the data-generating stochastic process, there is need for a suitable goodness-of-distributional-fit measure in order to optimally utilize given data. Here we present a novel, least-squares-based measure to select the optimal fit from a set of five distributions, namely Generalized Extreme Value (GEV), Generalized Logistic, Gumbel, Log-Normal Type III and Log-Pearson Type III. The fit metric is applied to annual maximum discharge series from six hydrological stations along the Sava River in South-eastern Europe, spanning the years 1961 to 2020. Results reveal that (1) the Sava River basin can be assessed as hydrologically homogeneous and (2) the GEV distribution provides typically the best fit. We offer hydrological-meteorological insights into the differences among the six stations. For the period studied, almost all stations exhibit statistically insignificant trends, which renders the conclusions about flood risk as relevant for hydrological sciences and the design of regional flood protection infrastructure.

The dataset includes speleothem carbon isotopes, oxygen isotopes, U-Th ages, and fluid inlcusion hydrogen isotopes used to reconstruct climate in southeastern Alaska during the past ~3,500 years. Speleothem WB-21-5-A was collected in Wishbone Cave (55.774 N, -133.191 E; 420 m.a.s.l.) and speleothem WA-21-6-A was collected in Walkabout Cave (55.776° N, 133.195° W; 350 m.a.s.l.) on May 21, 2022 and June 21, 2022, respectfully. The data collection was completed between September 2021-March 2022. U-Th ages were measured on a ThermoFisher Neptune Plus multi-collector inductively coupled plasma mass spectrometer, and stable/fluid inclusion isotopes were measured on a ThermoFisher Delta V isotope ratio mass spectrometer.

The deuterium excess of polar ice cores documents past changes in evaporation conditions and moisture origin. New data obtained from the European Project for Ice Coring in Antarctica Dome C East Antarctic ice core provide new insights on the sequence of events involved in Termination II, the transition between the penultimate glacial and interglacial periods. This termination is marked by a north–south seesaw behavior, with first a slow methane concentration rise associated with a strong Antarctic temperature warming and a slow deuterium excess rise. This first step is followed by an abrupt north Atlantic warming, an abrupt resumption of the East Asian summer monsoon, a sharp methane rise, and a CO2overshoot, which coincide within dating uncertainties with the end of Antarctic optimum. Here, we show that this second phase is marked by a very sharp Dome C centennial deuterium excess rise, revealing abrupt reorganization of atmospheric circulation in the southern Indian Ocean sector.

The dataset includes speleothem carbon isotopes and oxygen isotopes used to reconstruct climate in southeastern Alaska during the past ~3,500 years. Speleothem WA-21-6-A was collected in Walkabout Cave (55.776° N, 133.195° W; 350 m.a.s.l.) on June 21, 2022. The data collection was completed between September 2021-March 2022. Stable isotopes were measured on a ThermoFisher Delta V isotope ratio mass spectrometer.

The dataset includes speleothem fluid inlcusion hydrogen isotopes used to reconstruct climate in southeastern Alaska during the past ~3,500 years. Speleothem WB-21-5-A was collected in Wishbone Cave (55.774 N, -133.191 E; 420 m.a.s.l.) on May 21, 2022. The data collection was completed between September 2021-March 2022. Stable/fluid inclusion isotopes were measured on a ThermoFisher Delta V isotope ratio mass spectrometer.

Since the publication of the IPCC's Fifth Assessment Report in 2013, there has been increasing evidence that the social and ecological impacts of global warming depend more on seasonal extremes (e.g. peak summer temperatures) than on trends in annual averages. This is particularly true in the tropics, where extremes have become the greatest threat to ecosystems. However, little is known about the current and future rates of change in means and extremes. Lack of high-resolution data from past warm climates (which serve as analogues) and lack of advanced data analysis methods explain this knowledge deficit. The SEARCH project (Seasonal Extremes and Rates of Change in Past Warm Climates: Insights from Advanced Statistical Estimations on High-Resolution Coral Proxy Records) aims to advance our knowledge by means of (1) using a database of high-resolution coral proxy records and (2) applying advanced simulation techniques from statistical science. SEARCH uses a database of about 50 existing and new (bi-)monthly resolved coral proxy records during the (a) Anthropocene, (b) Medieval Climate Anomaly-Medieval Warm Period, (c) Holocene Thermal Maximum, (d) Last Interglacial and (e) Mid-Pliocene Warm Period. In the first part of our presentation series, we explain the methodological foundations (Mudelsee 2014, 2023): proxy calibration, nonparametric kernel estimation of the first derivative of the climate proxy series and linear regression. The methods take into account typical peculiarities of paleoclimate time series: non-Gaussian distributions, autocorrelation, uneven spacing and uncertain timescales. We present some initial results. Based on the (preliminary) results of SEARCH, we also consider the lessons for navigating the climate future within the framework of the GreenSCENT project (Smart Citizen Education for a Green Future). Acknowledgements: This work has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project number 468589022 (SEARCH), within the SPP 2299, project number 441832482; and by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101036480 (GreenSCENT). References: Mudelsee M (2014) Climate Time Series Analysis: Classical Statistical and Bootstrap Methods. Second Edition. Springer, Cham. xxxii + 454 pp [https://www.manfredmudelsee.com/book/index.htm] Mudelsee M (2023) Unbiased proxy calibration. Mathematical Geosciences. (doi:10.1007/s11004-023-10122-5).