We theoretically investigate the role of multiple impurity atoms on the ground-state properties of Bose polarons. The Bogoliubov approximation is applied for the description of the condensate resulting in a Hamiltonian containing terms beyond the Fröhlich approximation. The many-body nature of the impurity atoms is taken into account by extending the many-body description for multiple Fröhlich polarons, revealing the static structure factor of the impurities as the key quantity. Within this formalism various experimentally accessible polaronic properties are calculated such as the energy and the effective mass. These results are examined for system parameters corresponding to two recent experimental realizations of the Bose polaron, one with fermionic impurities and one with bosonic impurities.

We compare insights provided by local and large‐scale perspectives of extreme heat events in ERA5 near‐surface temperature data. Heat waves where temperatures exceed four standard deviations about the climatological‐mean are expected less than once a century locally but occur roughly once every 10 days somewhere in the Northern Hemisphere midlatitudes. The high frequency of occurrence indicated by the hemispheric perspective is not well represented by normal statistics because it strongly depends on the shapes of the local temperature distributions. The large effective sample size afforded by the hemispheric perspective provides robust evidence of trends in the frequency of occurrence of extreme heat events integrated over the Northern Hemisphere. It also confirms that trends in heat events summed over the hemisphere can be explained by changes in mean temperature alone.

The sensitivity of the radiative flux at the top of the atmosphere to surface temperature perturbations cannot be directly observed. The relationship between sea surface temperature (SST) and top‐of‐atmosphere radiation can be estimated with Green's function (GF) simulations by locally perturbing the sea surface temperature boundary conditions in atmospheric climate models. We perform such simulations with the Ai2 Climate Emulator (ACE), a machine learning‐based emulator trained on ERA5 reanalysis data (ACE2‐ERA5). This produces a sensitivity map of the top‐of‐atmosphere radiative response to surface warming that aligns with our physical understanding of radiative feedbacks. However, ACE2‐ERA5 likely underestimates the radiative response to historical warming. We compare to two additional versions of ACE and traditional climate models. We argue that GF experiments can be used to evaluate the performance and limitations of machine learning‐based climate emulators by examining if causal physical relationships are correctly represented and testing their capability for out‐of‐distribution predictions.

We investigate the effects of quantum fluctuations on the low-energy collective modes of two-dimensional (2D) $s$-wave Fermi superfluids from the BCS to the Bose limit. We compare our results to recent Bragg scattering experiments in 2D box potentials, with very good agreement. We show that quantum fluctuations in the phase and modulus of the pairing order parameter are absolutely necessary to give physically acceptable chemical potential and dispersion relation of the low-energy collective mode throughout the BCS to Bose evolution. Furthermore, we demonstrate that the dispersion of the collective modes change from concave to convex as interactions are tuned from the BCS to the Bose regime, and never crosses the two-particle continuum, because arbitrarily small attractive interactions produce bound states in two dimensions.

We derive a generalization of the Kalman filter that allows for non‐Gaussian background and observation errors. The Gaussian assumption is replaced by considering that the errors come from a mixed distribution of Gaussian, lognormal, and reverse lognormal random variables. We detail the derivation for reverse lognormal errors and extend the results to mixed distributions, where the number of Gaussian, lognormal, and reverse lognormal state variables can change dynamically every analysis time. We test the dynamical mixed Kalman filter robustly on two different systems based on the Lorenz 1963 model, and demonstrate that non‐Gaussian techniques generally improve the analysis skill if the observations are sparse and uncertain, compared with the Gaussian Kalman filter.

Predicting forced, long‐term radiative feedbacks from internal climate variability has been a decades‐long quest in climate science. We train a convolutional neural network (CNN) to predict annual‐ and global‐mean top of the atmosphere radiation anomalies from time‐varying maps of near‐surface temperature in climate models. Trained on internal variability alone, the nonlinear CNN can predict radiation under strong climate change, outperforms a regularized linear regression approach, and works within and across different climate models. We show with explainable artificial intelligence methods that the CNN draws predictive skill from physically meaningful regions but at much smaller spatial scales than currently assumed.