Predictability Studies Using the Intraseasonal Variability Hindcast Experiment (ISVHE)

Wednesday, 17 December 2014: 4:00 PM
Duane Edward Waliser1,2, Neena Joseph Mani3, Bin Wang4, Xianan Jiang5, June-yi Lee6 and sun-Seon Lee6, (1)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (2)Jet Propulsion Laboratory, Pasadena, CA, United States, (3)University of California Los Angeles, Pasadena, CA, United States, (4)Univ Hawaii, Honolulu, HI, United States, (5)JIFRESSE/UCLA, Pasadena, CA, United States, (6)Busan National University, Busan, South Korea
Intraseasonal variability (ISV) in the tropics represents a primary source of predictability at time scales in between those typically associated with weather and seasonal climate variations. With our growing awareness of the modulations to weather and extreme events by organized modes of ISV, efforts to develop and improve subseasonal predictions have been a focus of intense research for the past two decades. While the last decade has witnessed marked improvement in dynamical MJO prediction, timely assessment of the practical and potential MJO prediction capabilities from dynamic models is crucial for guiding future research and development priorities. In this presentation, we report on our predictability and prediction skill studies using the Intraseasonal Variability Hindcast experiment (ISVHE). ISVHE is multi-model experiment consisting of set of extended range hindcasts from eight different coupled models. Specifically, we present our studies’ findings regarding boreal winter Madden-Julian Oscillation (MJO), and the boreal summer eastern Pacific and south Asian monsoon ISV MJO. Predictability and prediction skill is estimated from both the deterministic and ensemble mean hindcasts, with an emphasis on quantifying the gap between prediction skill and estimates of predictability for these dominant modes of ISV. In addition, we analyze the forecast “spread-error” relationship in the different ensemble prediction systems (EPS) as an illustration of how the fidelity of the combined model, assimilation system and structural fidelity of existing EPSs influence our prediction capabilities of ISV.