Harmonically confined Bose–Einstein condensation on the surface of a cylinder

It is well known that Bose–Einstein condensation cannot occur in a free two-dimensional (2D) system. Recently, several studies have showed that BEC can occur on the surface of a sphere. We investigate BEC on the surface of cylinder on both sides of which atoms are confined in a one-dimensional (1D) harmonic potential. In this work, only the non-interacting Bose gas is considered. We determine the critical temperature and the condensate fraction in the geometry using the semi-classical approximation. Moreover, the thermodynamic properties of ideal bosons are also studied using the grand canonical partition function.

Finite-Size Effects with Boundary Conditions on Bose-Einstein Condensation

We investigate the statistical distribution for ideal Bose gases with constant particle density in the 3D box of volume V=L3. By changing linear size L and imposing different boundary conditions on the system, we present a numerical analysis on the characteristic temperature and condensate fraction and find that a smaller linear size is efficient to increase the characteristic temperature and condensate fraction. Moreover, there is a singularity under the antiperiodic boundary condition.

Gaseous Bose–Einstein condensates

The (gaseous) BECs are introduced: clouds of 106−8 alkali metal atoms, usually 87Rb or 23Na, below ~1 μ‎K. The laser cooling and magnetic trapping are described including the evaporation step needed to reach the conditions for condensation. The magnetooptical and Ioffe–Pritchard traps are described. Imaging methods, both destructive and non-destructive are described. Evidence of condensation is presented; and of interference between separated clouds, thus confirming the coherence of the condensates. The measurement of the condensate fraction is recounted. The Gross–Pitaevskii analysis of condensate properties is given in an appendix. How Bragg spectroscopy is used to obtain the dispersion relation for excitations is detailed. Finally the BEC/BCS crossover is introduced and the role therein of Feshbach resonances.

Arctic cloud annual cycle biases in climate models

Arctic clouds exhibit a robust annual cycle with maximum cloudiness in fall and minimum cloudiness in winter. These variations affect energy flows in the Arctic with a large influence on the surface radiative fluxes. Contemporary climate models struggle to reproduce the observed Arctic cloud amount annual cycle and significantly disagree with each other. The goal of this analysis is to quantify the cloud-influencing factors that contribute to winter–summer cloud amount differences, as these seasons are primarily responsible for the model discrepancies with observations. We find that differences in the total cloud amount annual cycle are primarily caused by differences in low, rather than high, clouds; the largest differences occur between the surface and 950 hPa. Grouping models based on their seasonal cycles of cloud amount and stratifying cloud amount by cloud-influencing factors, we find that model groups disagree most under strong lower tropospheric stability, weak to moderate mid-tropospheric subsidence, and cold lower tropospheric air temperatures. Intergroup differences in low cloud amount are found to be a function of lower tropospheric thermodynamic characteristics. Further, we find that models with a larger low cloud amount in winter have a larger ice condensate fraction, whereas models with a larger low cloud amount in summer have a smaller ice condensate fraction. Stratifying model output by the specifics of the cloud microphysical scheme reveals that models treating cloud ice and liquid condensate as separate prognostic variables simulate a larger ice condensate fraction than those that treat total cloud condensate as a prognostic variable and use a temperature-dependent phase partitioning. Thus, the cloud microphysical parameterization is the primary cause of inter-model differences in the Arctic cloud annual cycle, providing further evidence of the important role that cloud ice microphysical processes play in the evolution and modeling of the Arctic climate system.