Ensemble predictions of the seasonal snowpack over Grand Mesa, CO were conducted for the hydrologic year 2016-2017 using a multilayer snow hydrology model. Ensembles were generated from gridded atmospheric reanalysis, model predictions were evaluated against SnowEx’17 measurements, and the signatures of the weather-dependent variability of snow physics in the behavior of multi-frequency microwave brightness temperatures and backscattering were examined through forward modeling. At sub-daily timescales , the ensemble standard deviation due to atmospheric forcing (i.e., mesoscale spatial variability of weather within the Grand Mesa) is < 3 dB for dry snow, and increases to 8-10 dB at midday when there is surficial melt that also explains the wide ensemble range (~20 dB). Further, the ensemble mean backscatter exhibits robust (R 2 > 0.95) time-varying, weather-dependent linear heuristic relationships with SWE (e.g., 5-6 cm/dB/month in January; 2-2.5 cm/dB/month in late February) as melt-refreeze cycles modify the microphysical structure in the top 50 cm of the snowpack. The nonlinear evolution of ensemble snow physics translates into seasonal hysteresis in the microwave behavior. The backscatter hysteretic offsets between accumulation and melt regimes are robust in the Land C-bands and collapse for wet shallow snow at Ku-band. The ensemble mean emissions behave as a limit-cycles with weak sensitivity in the accumulation regime, and hysteretic behavior during melt that is different for deep (winter-spring transition) and shallow snow (spring-summer) and offsets that increase with frequency. These findings suggest potential for multi-frequency active-passive remote-sensing of SWE conditional on snowpack regime, particularly suited for data-assimilation using coupled snow hydrology-microwave models.