Identifying source of predictability for vapor pressure deficit variability in the southwestern United States

Abstract

Abstract Atmospheric vapor pressure deficit (VPD) measures the difference between saturation vapor pressure and actual vapor pressure, and its variability is closely related to fire activity in the western United States (US). Here, we assess the forecast skill of monthly VPD variability using a state-of-the-art dynamical forecast system and statistical predictions, such as the persistence forecast and model-analog forecasts. In the model-analog framework, we select analog states resembling the observed initial conditions from the model space, and the subsequent evolution of those initial model-analogs yields forecast ensembles. Dynamical forecasts demonstrate skillful predictions of VPD variability in the western US, exceeding the persistence forecast skill, which indicates additional sources of VPD predictability within the climate system. To quantify the contribution of different climate variables to VPD prediction, we develop a weighted model-analog forecast and evaluate its skill in comparison to VPD-only and unweighted forecasts. Our findings suggest that sea surface temperature is a critical source of VPD predictability over the western US. The optimally weighted model-analog exhibits forecast skill for VPD variability comparable to that of the dynamical forecast system.