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Causes of Summer Cloudiness in Coastal Southern California and the Urban Doom of June Gloom

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Causes of Summer Cloudiness in Coastal Southern California and the Urban Doom of June Gloom Causes of Summer Cloudiness in Coastal Southern California and the Urban Doom of June Gloom A. Park Williams, Rachel E. Schwartz, Sam Iacobellis, Richard Seager, Benjamin I. Cook, Christopher J. SBll, Gregory Husak, Joel Michaelsen Stratus clouds regulate drought Santa Barbara Effect of stratus on Airport mean daily surface condions: May-Sep 2007-2014 1000 Solar clear 800 Radia:on -2 stratus 600 ~50% W m 400 reducon 200 0 6:00 12:00 18:00 10 Vapor pressure 8 deficit 6 ~55% hPa 4 reducon 2 0 6:00 12:00 18:00 Time of day 2 Introduc:on Weather sta)on data from www.geog.ucsb.edu/ideas Summer fog provides much-needed water to ecosystems Doug Fischer AP Williams What will happen to stratus clouds in the future? Specific Ques:ons to be answered: Can we use exisng observaonal data to … 1) diagnose MULTI-DECADE TRENDS in stratus frequency? 2) diagnose causes of INTERANNUAL VARIABILITY in stratus frequency? 4 Hourly cloud-base height data from 24 airfields Santa Barbara Methods Los Angeles - Summer stratus season: May-Sep - Hourly cloud-base heights from 24 airfields Islands - Stratus clouds: any cloud with San Diego cloud base <1000 m - Considered only hours 07:00, 10:00, 13:00, 16:00 - Only consider staons with ≥40 years of valid data since 1960 - FOG: lowest 25% of stratus clouds * Cloud height data from NCDC: <p.ncdc.noaa.gov/pub/data/noaa/ at a given airfield 5 The Lowest 25% of Stratus Clouds in the Ci:es are OZen Fog in the Coastal Mountains Los Angeles From SRTM data hp://spaceplace.nasa.gov/topomap-earth/en/ 6 Trends in Summer Stratus Frequency Santa Barbara Los Angeles Islands San Diego May-Sep stratus frequency 0.8 1948-2014 0.6 0.4 frequency 0.2 1950 1960 1970 1980 1990 2000 2010 year Trends Interannual Variability Conclusions 7 Trends in Summer Stratus Frequency From Schwartz et al. (2014, GRL) Slight negave trend aributed to slight posive trend in PDO West Coast Stratus Frequency Correlaon with SST High 1 0 Low -1 Stratus Frequency 1950 1960 1970 1980 1990 2000 2010 Year Trends Interannual Variability Conclusions 8 Trends in Summer Stratus Frequency From LaDochy and Wiw (2012, Pure Appl. Geophys.) Large reduc:on in ground-level fog in Los Angeles Hours of low visibility per year Year Trends Interannual Variability Conclusions 9 Rising Stratus Cloud Base Heights Santa Barbara Los Angeles San Diego Islands 25 * ≥95% significance 20 ** ≥99% significance 15 * * * * 10 * * * * * * 5 * * 0 Base Height (m / decade) -5 Δ * 25 7AM ONLY * 20 * 15 * * 10 * * * * * * * * * * * 5 * * * * 0 Base Height (m / decade) * Δ -5 SLI NSI SEE LAX FUL CLD SBA LGB NZY NRS SNA SAN NKX VNY OXR TOA BUR NFG NTD HHR ONT MYF NUC SMO Airfield Trends Interannual Variability Conclusions 10 Stratus Frequency Time Series for 7:00 AM Fog (lowest 25%) 0.5 Los Angeles 42% increase Upper 75% stratus 0.4 0.3 0.2 Frequency 0.1 64% reduc:on 1950 1960 1970 1980 1990 2000 2010 San Diego 6% increase 0.6 0.5 0.4 0.3 0.2 Frequency 0.1 26% reduc:on 1950 1960 1970 1980 1990 2000 2010 Trends Interannual Variability Conclusions 11 Trends are strongest at night and early morning Stratus Frequency Trends by Hour Los Angeles Interna:onal Airport (LAX) 0.10 0.05 0 Fog -0.05 Upper 75% -0.10 stratus -0.15 01:00 04:00 07:00 10:00 13:00 16:00 19:00 22:00 solid bars: significant with San Diego Lindbergh Field (SAN) ≥95% confidence 0.10 since 1948 0.05 Stratus Frequency 0 Δ -0.05 -0.10 -0.15 01:00 04:00 07:00 10:00 13:00 16:00 19:00 22:00 Hour Trends Interannual Variability Conclusions 12 Why Have Cloud Base Heights Risen? Al:tude Al:tude Inversion top Inversion base cloud base Dew point Dew point Surface warming Dew Point Air T Dew Point Air T Temperature Temperature Trends Interannual Variability Conclusions 13 Nighlme Warming = Rising Cloud Base 7AM ΔBase Height vs Warming 7AM ΔFog vs Warming 25 r = 0.83 r = -0.89 5 20 P<0.001 P<<0.001 15 0 Dots: 10 individual airfields -5 5 Base Height (% / decade) (m / decade) Fog Frequency Δ -10 0 Δ -5 -15 0.1 0.2 0.3 0.4 0.5 0.1 0.2 0.3 0.4 0.5 ΔTmin (°C / decade) ΔTmin (°C / decade) Trends Interannual Variability Conclusions 14 Urbanizaon as a driver of nighme warming ΔUrban = 2011 Urban – 1950 Urban 2011 Naonal Land Cover Based on 1950 US Census Data Dataset - Hammer et al. (2004, Landscape & Urban Planning) - Syphard et al. (2011, J Env Manag) Trends Interannual Variability Conclusions 15 Urbanizaon as a driver of warming ΔTmin Warming vs ΔTmax Warming vs Urbanizaon Urbanizaon 0.5 r = 0.78 0.5 r = -0.10 P<0.005 P=0.46 0.4 0.4 min 0.3 max 0.3 T T Δ 0.2 Δ 0.2 (°C / decade) (°C / decade) Dots: 0.1 individual 0.1 airfields 0 0.2 0.4 0.6 0.8 0 0.2 0.4 0.6 0.8 ΔUrban (fracon) ΔUrban (fracon) Trends Interannual Variability Conclusions 16 Urbanizaon as a driver of cloud-base liing 7AM ΔBase Height 7AM ΔFog vs vs Urbanizaon Urbanizaon 25 r = 0.68 5 r = -0.70 20 P<0.01 P<0.005 0 15 10 -5 5 Base Height (% / decade) -10 (m / decade) Fog Frequency Δ 0 Δ -5 -15 0 0.2 0.4 0.6 0.8 0 0.2 0.4 0.6 0.8 ΔUrban (fracon) ΔUrban (fracon) Trends Interannual Variability Conclusions 17 What will happen to stratus clouds in the future? Specific Ques:ons to be answered: Can we use exisng observaonal data to … 1) diagnose MULTI-DECADE TRENDS in stratus frequency? 2) diagnose causes of INTERANNUAL VARIABILITY in stratus frequency? Trends Interannual Variability Conclusions 18 Strong Interannual Agreement Across the Region De-trended, standardized records of stratus frequency SB 3 LA SD 2 Average 1 0 z-score from mean) -1 σ ( -2 -3 1950 1960 1970 1980 1990 2000 2010 year Trends Interannual Variability Conclusions 19 Strong Differenaon Between Low and High Stratus Cloud Records De-trended, standardized records of stratus frequency by height class 3 Lowest 75% 2 Highest 25% 1 0 z-score from mean) -1 σ ( -2 -3 1950 1960 1970 1980 1990 2000 2010 year Trends Interannual Variability Conclusions 20 Low and High Stratus Promoted by Different Characteriscs of the Temperature Inversion From Iacobellis and Cayan (2013, JGR), we know: Low stratus promoted by a STRONG High stratus promoted by a HIGH inversion layer inversion layer May 19, 2010 May 22, 2014 Trends Interannual Variability Conclusions 21 Low and High Stratus Promoted by Different Characteriscs of the Temperature Inversion Mean T profile at San Diego 1960-2014 5 Inversion Strength 4 3 2 Al:tude (km) Inversion 1 Top Inversion Base 0 0 10 20 Sea level T (°C) Trends Interannual Variability Conclusions 22 Low and high stratus clouds are promoted by different climate processes Interannual variability in stratus frequency versus temperature inversion metrics at San Diego Low stratus promoted by a STRONG High stratus promoted by a HIGH inversion layer inversion layer 3 r = 0.78 r = 0.82 P<<0.001 P<<0.001 2 2 1 1 Stratus Stratus 0 0 (z-score) (z-score) Frequency Frequency Low -1 High -1 -2 -2 5 6 7 8 0.1 0.2 0.3 Mean inversion strength Frac:on of days with (°C) inversion base >800 m Trends Interannual Variability Conclusions 23 Low and high stratus clouds are promoted by different climate processes Correla:on with reanalysis climate: 1979–2014 Coastal upwelling promotes stability High stratus promoted by synopc above marine layer frontal systems Low Stratus VS neAr-surfACe T & wind High Stratus VS upper-level T & wind (surface – 950 hPa T & wind) (500-200 hPa T & wind) -0.8 -0.4 0 0.4 0.8 correlaon (r) *Only correla)ons with P < 0.05 shown Climate fields from NASA’s MERRA Reanalysis Trends Interannual Variability Conclusions 24 Conclusions What does all this mean for the future? Trends Interannual Variability Conclusions 25 How will the relevant climate variables change in the future? How summer frontal ac:vity change? Global climate models suggest DeCreAseD frequency (Seager et al. 2014, J Clim) -0.8 -0.4 0 0.4 0.8 correlaon (r) *Only correla)ons with P < 0.05 shown Climate fields from NASA’s MERRA Reanalysis Trends Interannual Variability Conclusions 26 How will the relevant climate variables change in the future? How will stability above the marine layer change? Global climate models suggest inCreAseD upwelling (Wang et al. 2015, Nature) -0.8 -0.4 0 0.4 0.8 correlaon (r) *Only correla)ons with P < 0.05 shown Climate fields from NASA’s MERRA Reanalysis Trends Interannual Variability Conclusions 27 Stability Above the Marine Layer is Projected to Connue Increasing CMIP5 Climate Projec:ons through AD 2100 Lower troposphere stability Stronger T inversion PotenBal T @ 850 hPa minus T @ 2m Trends Interannual Variability Conclusions 28 Stability Above the Marine Layer has Increased Temperature at 850 hPa (from radiosonde) 22 San Diego 20 Pt. Mugu °C 18 San Nicolas Island Warming 16 1960 1970 1980 1990 2000 2010 Sea Surface Temperature 20 NOAA Ext. 18 °C NCEP/NCAR 16 ECMWF Not Warming 1950 1960 1970 1980 1990 2000 2010 Trends Interannual Variability Conclusions 29 Low stratus relaonship with large-scale climate is complicated CMIP5 Climate Projec:ons through AD 2100 Lower troposphere stability Stronger T inversion PotenBal T @ 850 hPa minus T @ 2m No strong trend.
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