Subseasonal-to-Seasonal Research Priorities

Á.G. Muñoz

Atmospheric and Oceanic Sciences (AOS) and NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL). Princeton University. Princeton, NJ. USA

and International Research Institute for Climate and Society (IRI). The Earth Institute of

Columbia University. New York, NY. USA

Sources of Predictability at sub-

seasonal scale

• Tropical-extratropical interactions (Rossby waves dispersion due to tropical tropospheric warming [e.g., Hoskins y Karoly, 1981]).

• Persistence of atmospheric regimes (e.g., blocking [e.g., Hoskins y Woolings, 2015]).

• Persistence of oceanic conditions in the tropics [e.g., Alexander, 1992] and extra-tropics [e.g., Hartmann, 2015].

• Persistence of soil moisture anomalies, modifying surface fluxes and planetary boundary layer stability [e.g., Koster et al., 2011; Guo et al., 2012]

• Large-scale climate modes at sub-seasonal timescale, especially the Madden-Julian Oscillation (MJO), regional modes like SACZ/SALLJ, and interactions between these and other modes at multiple timescales [e.g., Muñoz et al., 2015, 2016]

Tony Barnston + IRI Communications

• Improve forecast skill and understanding on the sub-seasonal to seasonal timescale with special emphasis on high-impact weather events

• Promote the initiative’s uptake by operational centres and exploitation by the applications community

• Capitalize on the expertise of the weather and climate research communities to address issues of importance to the Global Framework for Climate Services

The project focuses on the forecast range between 2 weeks and a season.

The S2S Database, hosted by ECMWF and CMA, went online in May 2015. International Coordination Office hosted by KMA.

Co-chairs: Frédéric Vitart (ECMWF) Andrew Robertson (IRI)

General Priorities • Sources of predictability. • Predictions of the

temporal distribution of rainfall.

• Prediction of high societal impact events.

• Evaluation of skill of the models which are part of the S2S Database.

• Societal and economic applications.

S2S Task Force

Goals “To advance NOAA’s and the Nation’s capability to model and predict sources of S2S predictability. The ultimate goal of this initiative is to help close the gap in prediction skill and products between traditional weather and seasonal lead times.”

S2S Task Force

S2S Task Force

Sources of Predictability at sub-

seasonal scale

• Tropical-extratropical interactions (Rossby waves dispersion due to tropical tropospheric warming [e.g., Hoskins y Karoly, 1981]).

• Persistence of atmospheric regimes (e.g., blocking [e.g., Hoskins y Woolings, 2015]).

• Persistence of oceanic conditions in the tropics [e.g., Alexander, 1992] and extra-tropics [e.g., Hartmann, 2015].

• Persistence of soil moisture anomalies, modifying surface fluxes and planetary boundary layer stability [e.g., Koster et al., 2011; Guo et al., 2012]

• Large-scale climate modes at sub-seasonal timescale, especially the Madden-Julian Oscillation (MJO), regional modes like SACZ/SALLJ, and interactions between these and other modes at multiple timescales [e.g., Muñoz et al., 2015, 2016]

Can we “bump” skill from other timescales? It seems so!

Muñoz et al. J. Clim. 2015, 2016

Modified from Tony Barnston + IRI Communications

Muñoz, 2017 (NOAA’s ENSO blog; https://www.climate.gov/news-features/blogs/enso/la-niña-did-you-orchestrate)

Cross-timescale Interference

Potential predictability Realtime predictability

Cross-timescale Interference and Forecast Skill

Muñoz et al., J Clim. 2016

s2s scenarios: temporal distribution of rainfall

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s2s extreme rainfall scenario Forecast DJF 2015/16 96% for scenario I

Composite analysis/analogs

Multinomial logistic model

Spatial distribution Temporal distribution

Selection: 95th, 99th,…

Predictor Predictand

s2s state’s probabilities

Extremes more likely during these days:

Muñoz et al., J Clim. 2016

Summary

• Top research priority is to improve forecast skill at sub-seasonal timescale.

• In order to do that we need to understand better the related physical processes, and thus identify best sources of predictability.

• Emphasis in dynamical models. More could be done using statistical models and hybrid approaches.

• The social component needs to have a stronger presence in the research priorities.

• Sub-seasonal timescales provide an additional frame to explore new ways to provide actionable information; e.g., subseasonal-to-seasonal rainfall scenarios.