Compound extremes in a changing climate - a Markov chain approach

Studies using climate models and observed trends indicate that extreme weather has changed and may continue to change in the future. The potential impact of extreme events such as heat waves or droughts depends not only on their number of occurrences but also on "how these extremes occur", i.e., the interplay and succession of the events. These quantities are quite unexplored, for past changes as well as for future changes and call for sophisticated methods of analysis. To address this issue, we use Markov chains for the analysis of the dynamics and succession of multivariate or compound extreme events. We apply the method to observational data (1951–2010) and an ensemble of regional climate simulations for central Europe (1971–2000, 2021–2050) for two types of compound extremes, heavy precipitation and cold in winter and hot and dry days in summer. We identify three regions in Europe, which turned out to be likely susceptible to a future change in the succession of heavy precipitation and cold in winter, including a region in southwestern France, northern Germany and in Russia around Moscow. A change in the succession of hot and dry days in summer can be expected for regions in Spain and Bulgaria. The susceptibility to a dynamic change of hot and dry extremes in the Russian region will probably decrease

SalaciaML: A Deep Learning Approach for Supporting Ocean Data Quality Control

We present a skillful deep learning algorithm for supporting quality control of ocean temperature measurements, which we name SalaciaML according to Salacia the roman goddess of sea waters. Classical attempts to algorithmically support and partly automate the quality control of ocean data profiles are especially helpful for the gross errors in the data. Range filters, spike detection, and data distribution checks remove reliably the outliers and errors in the data, still wrong classifications occur. Various automated quality control procedures have been successfully implemented within the main international and EU marine data infrastructures (WOD, CMEMS, IQuOD, SDN) but their resulting data products are still containing data anomalies, bad data flagged as good and vice-versa. They also include visual inspection of suspicious measurements, which is a time consuming activity, especially if the number of suspicious data detected is large. A deep learning approach could highly improve our capabilities to quality assess big data collections and contemporary reducing the human effort. Our algorithm SalaciaML is meant to complement classical automated quality control procedures in supporting the time consuming visually inspection of data anomalies by quality control experts. As a first approach we applied the algorithm to a large dataset from the Mediterranean Sea. SalaciaML has been able to detect correctly more than 90% of all good and/or bad data in 11 out of 16 Mediterranean regions

Deglacial patterns of South Pacific overturning inferred from 231Pa and 230Th

The millennial‐scale variability of the Atlantic Meridional Overturning Circulation (AMOC) is well documented for the last glacial termination and beyond. Despite its importance for the climate system, the evolution of the South Pacific overturning circulation (SPOC) is by far less well understood. A recently published study highlights the potential applicability of the 231Pa/230Th‐proxy in the Pacific. Here, we present five sedimentary down‐core profiles of 231Pa/230Th‐ratios measured on a depth transect from the Pacific sector of the Southern Ocean to test this hypothesis using downcore records. Our data are consistent with an increase in SPOC as early as 20 ka that peaked during Heinrich Stadial 1. The timing indicates that the SPOC did not simply react to AMOC changes via the bipolar seesaw but were triggered via Southern Hemisphere processes

Second Data Science Symposium at AWI

The second Data Science Symposium at AWI gathered several data science related talks from AWI, GEOMAR and HZG

Identifikation und statistische Auswertung von globalen Wasserdampftrends aus Satellitenmessungen

Global water vapour total column amounts have been retrieved from spectral data provided by the Global Ozone Monitoring Experiment (GOME) flying on ERS-2, which was launched in April 1995, and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard ENVISAT launched in March 2002. For this purpose the Air Mass Corrected Differential Optical Absorption Spectroscopy (AMC-DOAS) approach has been used. The combination of the data from both instruments provides a long-term global data set spanning more than 12 years with the potential of extension up to 2020 by GOME-2 data on MetOp. Using linear and non-linear methods from time series analysis and standard statistics the trends of water vapour columns and their errors have been calculated. In this study, factors affecting the trend such as the length of the time series, the variance of the noise and the autocorrelation of the noise are investigated. Special emphasis has been placed on the calculation of the statistical significance of the observed trends, which reveal significant local changes from -5 % per year to 5 % per year. These significant trends are distributed over the whole globe. Increasing trends have been calculated for Greenland, East Europe, Siberia and Oceania, whereas decreasing trends have been observed for the northwest USA, Central America, Amazonia, Central Africa and the Arabian Peninsular. The idea of the comprehensive trend and significance analysis is to get evidence for the truth of these observed changes. While the significance estimation is based on intrinsic properties such as the length of the data sets, the noise and the autocorrelation, an important aspect of assessing the probability that the real trends have been observed is a validation with independent data. Therefore an intercomparison of the global total column water vapour trends retrieved from GOME and SCIAMACHY with independent water vapour trends measured by radiosonde stations provided by the Deutsche Wetter Dienst DWD (German Weather Service) is presented. The validation has been performed in a statistical way on the basis of univariate time series. Information about the probability of agreement between the two independently observed trends, conditional on the respective data, is revealed. On the one hand a standard t-test is used to compare the trends and on the other hand a Bayesian model selection approach has been developed to derive the probability of agreement. The hypothesis of equal trends from satellite and radiosonde water vapour data is preferred in 85 % of compared pairs of trends. Substantial evidence for the hypothesis of agreeing trends is found in 26 % of analysed trends. However, also disagreement has been observed, where the main reason has been identified on the one hand as the different spatial resolutions of the instruments. This means, that the radiosonde measurements can resolve very localised events, which is not possible with the satellite instruments. On the other hand, in contrast to the in principle continuously available (on a monthly mean basis) GOME/SCIAMACHY data, missing data in the radiosonde time series lead to trend discrepancies. The identification and validation of water vapour trends is an important step for a better understanding of climate change, but water vapour is not the only contributing quantity. Beside water vapour, decisive parameters are temperature, clouds, precipitation, vegetation and many more. A promising framework for the investigation of a multivariate data set of environmental variables is given by the Markov chain analysis. As a first approach, the Markov chain analysis has been applied to a bivariate water vapour -- temperature data set, where the global near surface temperatures are provided by the Goddard Institute of Space Studies (GISS) and cover a time span from 1880 to 2005. The temperature data are retrieved from ground stations and are mainly based on the Global Historical Climatology Network (GHCN). In the framework of a Markov chain analysis, the bivariate set of data is reduced to a univariate sequence of symbols, which can be described as a discrete stochastic process, a Markov chain. This Markov chain represents the water vapour -- temperature interaction or state of a region. Several descriptors have been calculated, such as persistence, replacement of and entropy. This approach is new in environmental science. Exemplarily two climate systems, the Iberian Peninsular and a region at the islands of Hawaii in the central Pacific Ocean, are investigated. The Markov chain analysis is able to retrieve significant differences between the two climate systems in terms of the characteristic descriptors, which reflect properties such as climate stability, rate of changes and short term predictability

Identification and statistical analysis of global water vapour trends based on satellite data

Global water vapour total column amounts have been retrieved from spectral data provided by the Global Ozone Monitoring Experiment (GOME) flying on ERS-2, which was launched in April 1995, and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard ENVISAT launched in March 2002. For this purpose the Air Mass Corrected Differential Optical Absorption Spectroscopy (AMC-DOAS) approach has been used. The combination of the data from both instruments provides a long-term global data set spanning more than 12 years with the potential of extension up to 2020 by GOME-2 data on MetOp. Using linear and non-linear methods from time series analysis and standard statistics the trends of water vapour columns and their errors have been calculated. In this study, factors affecting the trend such as the length of the time series, the variance of the noise and the autocorrelation of the noise are investigated. Special emphasis has been placed on the calculation of the statistical significance of the observed trends, which reveal significant local changes from -5 % per year to 5 % per year. These significant trends are distributed over the whole globe. Increasing trends have been calculated for Greenland, East Europe, Siberia and Oceania, whereas decreasing trends have been observed for the northwest USA, Central America, Amazonia, Central Africa and the Arabian Peninsular. The idea of the comprehensive trend and significance analysis is to get evidence for the truth of these observed changes. While the significance estimation is based on intrinsic properties such as the length of the data sets, the noise and the autocorrelation, an important aspect of assessing the probability that the real trends have been observed is a validation with independent data. Therefore an intercomparison of the global total column water vapour trends retrieved from GOME and SCIAMACHY with independent water vapour trends measured by radiosonde stations provided by the Deutsche Wetter Dienst DWD (German Weather Service) is presented. The validation has been performed in a statistical way on the basis of univariate time series. Information about the probability of agreement between the two independently observed trends, conditional on the respective data, is revealed. On the one hand a standard t-test is used to compare the trends and on the other hand a Bayesian model selection approach has been developed to derive the probability of agreement. The hypothesis of equal trends from satellite and radiosonde water vapour data is preferred in 85 % of compared pairs of trends. Substantial evidence for the hypothesis of agreeing trends is found in 26 % of analysed trends. However, also disagreement has been observed, where the main reason has been identified on the one hand as the different spatial resolutions of the instruments. This means, that the radiosonde measurements can resolve very localised events, which is not possible with the satellite instruments. On the other hand, in contrast to the in principle continuously available (on a monthly mean basis) GOME/SCIAMACHY data, missing data in the radiosonde time series lead to trend discrepancies. The identification and validation of water vapour trends is an important step for a better understanding of climate change, but water vapour is not the only contributing quantity. Beside water vapour, decisive parameters are temperature, clouds, precipitation, vegetation and many more. A promising framework for the investigation of a multivariate data set of environmental variables is given by the Markov chain analysis. As a first approach, the Markov chain analysis has been applied to a bivariate water vapour -- temperature data set, where the global near surface temperatures are provided by the Goddard Institute of Space Studies (GISS) and cover a time span from 1880 to 2005. The temperature data are retrieved from ground stations and are mainly based on the Global Historical Climatology Network (GHCN). In the framework of a Markov chain analysis, the bivariate set of data is reduced to a univariate sequence of symbols, which can be described as a discrete stochastic process, a Markov chain. This Markov chain represents the water vapour -- temperature interaction or state of a region. Several descriptors have been calculated, such as persistence, replacement of and entropy. This approach is new in environmental science. Exemplarily two climate systems, the Iberian Peninsular and a region at the islands of Hawaii in the central Pacific Ocean, are investigated. The Markov chain analysis is able to retrieve significant differences between the two climate systems in terms of the characteristic descriptors, which reflect properties such as climate stability, rate of changes and short term predictability

Heat content and temperature trends in the Mediterranean Sea as derived from Argo float data

The Mediterranean Sea is very sensitive to climatic changes due to its semi-enclosed nature and is therefore defined as one of the hotspots in future climate change projections. In this study, we use Argo float data to assess climatologies and trends in temperature and Ocean Heat Content (OHC) throughout the Mediterranean Sea and for specific sub-basins (e.g. Western and Eastern Mediterranean, Gulf of Lion, South Adriatic). The amount of the OHC, spatially averaged in bins of 1°x1° over the period from 2001 to 2020, increases from west to east in the Mediterranean Sea. Time series of temperature and OHC from 2005 to 2020, estimated in the surface and intermediate layers (5-700 m) and deeper layer (700-2000 m), reveal significant warming trends and an increase of OHC. The upper 700 m of the Mediterranean Sea show a temperature trend of 0.041 ± 0.012°C·yr-1, corresponding to an annual increase in OHC of 3.59 ± 1.02 W·m-2. The Western Mediterranean Sea (5-700 m) is warming fastest with an increase in temperature at a rate of 0.070 ± 0.015°C·yr-1, corresponding to a yearly increase in OHC of 5.72 ± 1.28 W·m-2. Mixing and convection events within convection sites and along boundary currents transport and disperse the temperature and OHC changes. Significant warming trends are evident in the deeper layers (700-2000 m) of the two deep convection sites in the Mediterranean Sea (Gulf of Lion, South Adriatic), with an exceptionally strong warming trend in the South Adriatic from 2013 to 2020 of 0.058 ± 0.005°C·yr-1, corresponding to a yearly increase in OHC of 9.43 ± 0.85 W·m-2. The warming of the different water masses will show its feedback on ocean dynamics and air-sea fluxes in the next years, decades, and even centuries as these warming waters spread or re-emerge. This will provide more energy to the atmosphere, resulting in more extreme weather events and will also stress ecosystems and accelerate the extinction of several marine species. This study contributes to a better understanding of climate change in the Mediterranean region, and should act as another wake-up call for policy makers and society

The regional MiKlip decadal forecast ensemble for Europe

Funded by the German Ministry for Education and Research (BMBF) a major research project called MiKlip (Mittelfristige Klimaprognose, Decadal Climate Prediction) was launched and global as well as regional predictive ensemble hindcasts have been generated. The aim of the project is to demonstrate for past climate change whether predictive models have the capability of predicting climate on time scales of decades. This includes the development of a decadal forecast system, on the one hand to support decision making for economy, politics and society for decadal time spans. On the other hand, the scientific aspect is to explore the feasibility and prospects of global and regional forecasts on decadal time scales. The focus of this paper lies on the description of the regional hindcast ensemble for Europe generated by COSMO-CLM and on the assessment of the decadal variability and predictability against observations. To measure decadal variability we remove the long term bias as well as the long term linear trend from the data. Further, we applied low pass filters to the original data to separate the decadal climate signal from high frequency noise. The decadal variability and predictability assessment is applied to temperature and precipitation data for the summer and winter half-year averages/sums. The best results have been found for the prediction of decadal temperature anomalies, i.e. we have detected a distinct predictive skill and reasonable reliability. Hence it is possible to predict regional temperature variability on decadal timescales, However, the situation is less satisfactory for precipitation. Here we have found regions showing good predictability, but also regions without any predictive skill

Down-core measurements of SiO2 for sediment core SO213/2_76-2

SiO2 measurements were conducted to analyze a potential impact on opal on our 231Pa/230Th ratios, that in turn were used to reconstruct past changes in South Pacific Overtruning Circulation. Opal measurements followed the method of Müller and Schneider 1993