Anthony Rosati

TL;DR: In this article, a new approach implements sea ice data assimilation in enthalpy space where a sea ice model represents a nonlinear function that transforms a positive-definite space into the sea ice concentration subspace, and results from observation-assimilation experiments using a conceptual pycnocline prediction model that characterizes the influences of sea ice on the decadal variability of the climate system show that the new scheme efficiently assimilates “sea ice observations” into the model: while improving sea ice variability itself, it consistently improves the estimates of all climate components.

TL;DR: In this article, the authors explored the response of climate conditions in the Atlantic hurricane main development region (MDR) to doubling of atmospheric CO2 using the new high-resolution coupled climate model, version 2.5.

TL;DR: In this paper, the authors used a statistical optimization method, the Average Predictability Time (APT), to identify three large-scale components of the frequency of North American summertime heat extremes that are predictable with significant correlation skill.

TL;DR: In this article , the authors evaluate regional Arctic sea ice predictions made with the FLOR and SPEAR_MED dynamical seasonal forecast systems developed at the NOAA Geophysical Fluid Dynamics Laboratory.