Abstract ID: 173
Subseasonal predictability from atmospheric, land, and ocean initial states
Lead Author: Sasha Glanville
National Center for Atmospheric Research, United States
Keywords: predictability, initial conditions, attribution
Abstract: Subseasonal prediction fills the gap between weather forecasts and seasonal outlooks. There is a general understanding that the predictability on the subseasonal timescale comes from the atmospheric, land, and ocean initial conditions, with predictability from land initial conditions being related primarily to slowly varying changes in soil moisture and snow pack, and ocean variability such as the El Niño Southern Oscillation and the Madden-Julian Oscillation providing potential predictability. Here we use a unique set of subseasonal reforecast experiments to quantify the role of atmospheric, land, and ocean initial conditions on subseasonal predictability with a focus on surface temperature over land. We find that in the global average the majority of predictive skill for surface temperature in the weeks 3-4 window comes from the atmosphere, and the ocean initial state becomes important after week 4. In this subseasonal prediction system the land initial state does not contribute to surface temperature predictability in the weeks 3-6 window, and climatological land initialization leads to higher skill. These vary by region, with surface temperature predictability in South America benefiting most from ocean initialization. Subseasonal precipitation prediction skill comes primarily from the atmospheric initial state, except for few regions for which the ocean state becomes important.
Co-authors:
Jadwiga H. Richter (NCAR)
Teagan King (NCAR)
Sanjiv Kumar (Auburn University)
Nicholas Davis (NCAR)
Paul Dirmeyer (George Mason University)
Steven Yeager (NCAR)
Gokhan Danabasoglu (NCAR)
