Published

Reconstructed Arctic Sea-Ice Conditions in the Common Era Using Data Assimilation (Under review)

Do multi-model ensembles improve reconstruction skill in paleoclimate data assimilation?

Reconstructing past climates remains a difficult task because pre‐instrumental observational networks are composed of geographically sparse and noisy paleoclimate proxy records that require statistical techniques to inform complete climate fields. …

Exploring the Application of Machine Learning for Downscaling Climate Projections

Policy makers need information about future climate change on spatial scales much finer than is available from typical climate model grids. New and creative methods are being advanced to downscale climate change projections with statistical methods. …

Magnitudes and spatial patterns of interdecadal temperature variability in CMIP6

Attribution and prediction of global and regional warming requires a better understanding of the magnitude and spatial characteristics of internal global-mean surface air temperature (GMST) variability. We examine interdecadal GMST variability in …

Arctic Sea-Ice Variability During the Instrumental Era

Arctic sea‐ice extent (SIE) has declined drastically in recent decades, yet its evolution prior to the satellite era is highly uncertain. Studies using SIE observations find little variability prior to the 1970s; however, these reconstructions are …

Measuring Lipid Membrane Viscosity Using Rotational and Translational Probe Diffusion

The two-dimensional fluidity of lipid bilayers enables the motion of membrane-bound macromolecules and is therefore crucial to biological function. Microrheological methods that measure fluid viscosity via the translational diffusion of tracer …