Abstract 074

Abstract ID: 074

Assessing the Potential Predictability of North Pacific Winter IVT and Precipitation Extremes in ECMWF Subseasonal Forecasts

Lead Author: Timothy Higgins
University of Colorado – Boulder, United States of America

Keywords: Predictability, IVT, Precipitation, North Pacific Jet, Atmospheric rivers

Abstract: Chaos within the atmosphere causes the predictability of weather at a single instant in time to range from a few days to a few weeks depending on existing circulation patterns and the types of phenomena that are being observed (Lorenz 1965). Despite the limit of predictability at singular moments, some processes can create signals in the predictability of broader windows of time that are stronger than the noise of uncertainty caused by chaos. Skillful forecasts of extreme weather can help mitigate future damages that it may cause. It is necessary to determine what useful information can be extracted from forecasts before they can be utilized for decision making. Subseasonal to seasonal (S2S) forecasts focus on weather prediction several weeks to several months into the future. The predictability of weather at the S2S range is heavily reliant on conditions of low-frequency modes of variability. S2S forecasts of extreme weather patterns can have meaningful impacts on water management decisions in regions that are heavily prone to drought and flooding. Potential predictability is a measure of the ability of forecasts to be useful when the model is perfect. In this study, we explore the differences between the potential predictability of integrated vapor transport (IVT) and precipitation under extreme conditions in S2S forecasts that can impact the US west coast. We demonstrate skillful forecasts of both IVT and precipitation into week 4 in some areas when strong anomalies of both anomalously wet and anomalously dry conditions are observed.

Physical quantities that are used to describe the state of the atmosphere often have differences in predictability. There is still considerable uncertainty over the differences in predictability amongst the characteristics that describe our atmosphere. Lavers et al. (2016) demonstrated that IVT has potential predictive skill at longer lead times than precipitation itself in medium-range forecasts. IVT plays a key role in driving Atmospheric rivers (ARs) (Shields et al. 2018, Ralph et al. 2019) and has strong ties to US west coast precipitation (Waliser and Guan 2017, Ricciotti and Cordeira 2022). It can sometimes provide insight into prediction of extreme storms further in advance than precipitation forecasts themselves at medium-range lead times. However, there is still little known of the discrepancies between the predictability of IVT compared to predictability of precipitation at S2S lead times. Investigating these discrepancies will demonstrate the value in predicting IVT-related events in future S2S studies.

Understanding the predictability of IVT and precipitation can help water resource managers make critical decisions relevant to water supply, droughts, and flooding (Das et al. 2013, Mann and Gleick 2015, Williams et al. 2015, Corringham et al. 2019). AR and precipitation extremes on the US west coast are projected to become more severe in the future in both duration and precipitation rate (Dettinger 2011, Payne et al. 2020, Michaelis et al. 2022). The frequency of ARs does have skill in S2S forecasts (Deflorio et al. 2019) and could also be associated with predictability in the region. The extreme nature of IVT and precipitation during periods of high and low AR activity and its impacts on society leads us to assess the predictability of both the top 10% and the bottom 10% of extreme IVT and precipitation conditions. To physically explain the predictability of extreme IVT and precipitation, we examine the connection of the extreme forecasted anomalies to the jet stream over the north Pacific (NPJ) regimes that were shown to have S2S predictive skill in Winters (2021).

Aneesh Subramanian (University of Colorado – Boulder, Boulder, CO, USA)
Will Chapman (National Center for Atmospheric Research, Boulder, CO, USA)
David Lavers (European Centre for Medium-Range Weather Forecasts, Reading, UK)
Andrew Winters (University of Colorado – Boulder, Boulder, CO, USA)