Talks

Over the past decade, climate networks have emerged as a powerful tool to characterise high dimensional weather and climate datasets. Climate networks are a sparse representation of the dynamical similarities between weather time series from different geographical locations. Nodes represent the locations themselves, and network edges represent high dynamical similarity between pairs of locations. The topology of the resulting complex network encodes information about how atmospheric and oceanic dynamics “connect” different locations. For instance, strong monsoon years might yield a different network structure than weak monsoon years. With the tools of graph theory and complex networks at our disposal, we can characterise climate dynamics in novel and interesting ways, which yield, in part, results that corroborate what meteorologists already know, and, in part, results that generate new hypotheses about how atmospheric and oceanic processes influence different weather patterns. In this...

The main focus of these pedagogical talks will be on discussing the interplay between statistics and climate science as a two-way street. On one hand, thinking about the climate helps us understand many aspects of statistics, from the fundamental to conceptual to practical. On the other, statistical thinking is crucial and indispensable in studying climate. I will also emphasize that statistics plays an important role not just in climate studies, but more generally in understanding any complex system such as those from biological and social sciences as well. Another thread will be the discussion of interplay between uncertainty and dynamics, with an emphasis on the role of dynamical instabilities.

Oceanic flows stir and mix tracers such heat, salt, carbon, and plankton and understanding the details of the tracer dispersion is key to developing effective parameterizations for large climate-scale models. Unfortunately, the flow structure in the ocean is highly variable as a function of spatial scales. For instance O(100 km) mesoscale flows are significantly different from O(10 km) submesoscale flows. In this talk I'll use results from a recent study to explain how tracer dispersion characteristics change as we move from large mesoscales to small submesoscales in the oceans.

The exactly solvable spin-1/2 Kitaev model on a honeycomb lattice has drawn significant interest, as it offers a pathway to realizing the long-sought after quantum spin liquid. Building upon the Kitaev model, Yao and Lee introduced another exactly solvable model on an unusual star lattice featuring non-abelian spinons. The additional pseudospin degrees of freedom in this model could provide greater stability against perturbations, making this model appealing. However, a mechanism to realize such an interaction in a standard honeycomb lattice remains unknown. I will present a microscopic theory to obtain the Yao-Lee model on a honeycomb lattice by utilizing strong spin-orbit coupling of anions edge-shared between two eg ions in the exchange processes. This mechanism leads to the desired bond-dependent interaction among spins rather than orbitals, unique to our model, implying that the orbitals fractionalize into gapless Majorana fermions and fermionic octupolar excitations emerge. Since the conventional Kugel-Khomskii interaction also appears, the phase diagram including these interactions using classical Monte Carlo simulations and exact diagonalization techniques will be presented. Several open questions will be also discussed.

Thin enough black strings are unstable to rippling along their length, and the instability threshold indicates that static inhomogeneous black strings exist. These have indeed been constructed with increasing inhomogeneity until a high-curvature singular pinch appears. We study the string-scale version of this phenomenon: “string-ball strings”, which are linearly extended, self-gravitating configurations of string balls obtained within the Horowitz- Polchinski (HP) approach to near-Hagedorn string states. We construct inhomogeneous HP strings in spatial dimension d ≤ 6, and show that, as the inhomogeneity increases, they approach localized HP balls when d ≤ 5 or cease to exist when d = 6. We then discuss how string theory can smooth out the naked singularities that appear in the Kaluza-Klein black hole/black string transition, and we propose scenarios for the final stage of the evolution of the black string instability after string theory takes over.

The thorium-229 nucleus has a unique, low-lying isometric state allowing for laser spectroscopic investigations that are otherwise only accessible in electronic transitions. Here, we report on the first resonant laser excitation of the Th-229 nucleus. The fluorescence signal is observed from two Th-229 doped CaF2 crystals that enable us to determine the center frequency of 2020.409(7) THz corresponding to 148.3821(5) nm of the nuclear transition. The fluorescence lifetime in the crystal is 630(15) s, corresponding to an isomer half-life of 1740(50) s for a nucleus isolated in vacuum. These results pave the way towards high-resolution nuclear laser spectroscopy of Th-229 and an optical nuclear clock with high sensitivity in fundamental tests. This is work done in a cooperation of PTB and TU Wien: J. Tiedau et al., Phys. Rev. Lett. 132, 182501 (2024)

We are entering the golden age of multi-wavelength astronomical surveys. In the 2020s, a plethora of surveys (such as Euclid, eROSITA, Rubin-LSST, Simons Observatory, and CMB-S4) are underway or planned to provide unprecedented insights into cosmology and astrophysics. In this talk, I will discuss the significant scientific opportunities and challenges that arise in the era of big data, highlighting recent advances in computational modeling and the roles of artificial intelligence and machine learning.

I present the recurrence analysis of temperature and relative humidity data from various locations spread over India, including the mountainous region, coastal region, and central and north eastern parts of India. This study reveals the spatiotemporal pattern underlying the climate dynamics and captures the variations in the complexity of the dynamics over the period 1948 to 2022. By reconstructing the dynamics from data, the recurrence pattern is studied using recurrence networks and the measures of the networks computed using a sliding window analysis on the data sets. This brings out the climate variability in different spatial locations and the heterogeneity across the locations chosen. The variations observed in dynamics can be correlated with reported shifts in the climate related to strong and moderate El Niño–Southern Oscillation events.