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AR Science Gaps/Objectives Duane Waliser on behalf of Calwater 2 SSG Jet Propulsion Laboratory/Caltech Pasadena, CA

Jet Propulsion Laboratory California Institute of Technology. AR Science Gaps/Objectives Duane Waliser on behalf of Calwater 2 SSG Jet Propulsion Laboratory/Caltech Pasadena, CA. CalWater 2015 – ACAPEX Campaign Planning Workshop Scripps Institution of Oceanography La Jolla, California

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AR Science Gaps/Objectives Duane Waliser on behalf of Calwater 2 SSG Jet Propulsion Laboratory/Caltech Pasadena, CA

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  1. Jet Propulsion Laboratory California Institute of Technology AR Science Gaps/ObjectivesDuane Waliser on behalf of Calwater 2 SSGJet Propulsion Laboratory/CaltechPasadena, CA CalWater 2015 – ACAPEX Campaign Planning Workshop Scripps Institution of Oceanography La Jolla, California 22-24 April 2014

  2. AR Science Considerations • Landfall, overland and topography considerations • Water Budgets over ocean and land • Energy and Momentum budgets • Synoptic Meteorology & Vapor Sources • Multi-scale interactions (e.g. ENSO to mesoscale) • Climate change implications • Microphysical considerations • Modeling Improvements (microphysical to multi-scale) • Predictability and Prediction Skill • Air Sea Interaction

  3. AREX* Landfall Science Targets • Barrier Jet occurrence, dynamics & modulation of precipitation • Variations and factors influence the “melting layer” • “Dividing Streamline” • Soil moisture pre-conditioning (HMT & SMAP) * A SIO/UCSD & JPL proposal to NASA

  4. New Measurements of AR processes are key to improving the model forecasts Scripps/UCSD & JPL are proposing AREX* to NASA’s Earth Ventures Suborbital Campaign opportunity to use advanced remote sensing instruments to directly measure the processes that determine the flow and direction of AR moisture in order to improve model forecasts. * A SIO/UCSD & JPL proposal to NASA

  5. Water Vapor Transport = Wind * Water Vapor Precipitation Total Through Saturday Forecast of Water Vapor Transport on Wednesday In this regard shouldn’t an understanding of the Momentum Budget be at least as relevant as the Water Vapor Budget What conditions effect different aspects of the source/sink terms? NCEP Weather Prediction Center: http://www.hpc.ncep.noaa.gov/qpf/p120i.gif Imagery generated by Prof. Jason Cordeira (Plymouth State University) and Dr. Marty Ralph (CW3E/UCSD) unless otherwise noted.

  6. cold air Enhanced vapor flux in Atmos. river warm air Warm, Humid cold air IWV > 2 cm Atmos. river 400 km • Meteorology of an AR • Observational studies by Ralph et al. (2004, 2005, 2006) extend model results: • Long, narrow plumes of IWV >2 cm measured by SSM/I satellites considered proxies for ARs. • These plumes (darker green) are typically situated near the leading edge of polar cold fronts. • P-3 aircraft documented strong water vapor flux in a narrow (400 km-wide) AR; See section AA’. • Airborne data also showed 75% of the vapor flux was below 2.5 km MSL in vicinity of LLJ. Courtesy Marty Ralph, U. Calif, San Diego

  7. Canonical Water Vapor Sources via Back Trajectories • Water vapor source regions / processes still to be fully characterized including dependencies on • synoptic conditions • & • target rainfall region Ryoo, Waliser, Waugh, Wong, Fetzer, 2014, TBD

  8. Common Climate Modes Linked toAtmospheric River Frequency 500 mb Geopotential Height Anomalies “Pacific North American” (PNA) “Arctic Oscillation” (AO) Circulation anomaly when both PNA and AO in “negative” phase • On average 6-7 Atmospheric River (AR) events provided 30-40% of total seasonal snow water equivalent (SWE) accumulation in most years • When PNA & AO are in “negative” phases, there is a doubling of the frequency of ARs • Since PNA&AO exhibit some predictability from days to months, it may be possible to extend forecasts of ARs Typical frequency of ARs How does this influence/relate to predictability on synoptic and seasonal timescales? Changes in AR frequency by AO & PNA Guan, Molotch, Waliser, Fetzer, Neiman, 2014, Water Resources Review, in press.

  9. Forecast Skill, Dependencies & What about Predictability? For example: at 5-6 day lead time, global weather forecasts cannot determine if it will hit LA or San Francisco Wick et al. 2013 What is the upper bound? What synoptic/climate factors influence predictability?

  10. ARM Cloud Aerosol Precipitation Experiment (ACAPEX) PI : R. Leung/PNNL/DOE • AR Science Questions: • What influences the evolution and structure of AR and its associated cloud and precipitation? • To what extent does water vapor in ARs originate from the tropics? What role does tropical convection play in this? • What are the roles of air-sea fluxes and ocean mixed layer processes in AR evolution? • What are the key dynamical processes that modulate cloud and precipitation from landfalling ARs?

  11. AR Science Considerations • Landfall, overland and topography considerations • Water Budgets over ocean and land • Energy and Momentum budgets • Synoptic Meteorology & Vapor Sources • Multi-scale interactions (e.g. ENSO to mesoscale) • Climate change implications • Microphysical considerations • Modeling Improvements (microphysical to multi-scale) • Predictability and Prediction Skill • Air Sea Interaction

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