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. S ll, Gregory Husak, Joel Michaelsen Stratus clouds regulate drought
Santa Barbara Effect of stratus on Airport mean daily surface condi ons: May-Sep 2007-2014 1000 Solar clear 800 Radia on
-2 stratus 600 ~50%
W m 400 reduc on 200 0 6:00 12:00 18:00 10 Vapor pressure 8 deficit
6 ~55% hPa 4 reduc on 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 exis ng observa onal 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 sta ons 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 O en Fog in the Coastal Mountains
Los Angeles
From SRTM data h p://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 nega ve trend a ributed to slight posi ve trend in PDO
West Coast Stratus Frequency Correla on 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 Wi w (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 Nigh me 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 Urbaniza on as a driver of nigh me warming
ΔUrban = 2011 Urban – 1950 Urban
2011 Na onal 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 Urbaniza on as a driver of warming
ΔTmin Warming vs ΔTmax Warming vs Urbaniza on Urbaniza on 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 (frac on) ΔUrban (frac on)
Trends Interannual Variability Conclusions 16 Urbaniza on as a driver of cloud-base li ing
7AM ΔBase Height 7AM ΔFog vs vs Urbaniza on Urbaniza on 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 (frac on) ΔUrban (frac on)
Trends Interannual Variability Conclusions 17 What will happen to stratus clouds in the future?
Specific Ques ons to be answered:
Can we use exis ng observa onal 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 Differen a on 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 Characteris cs 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 Characteris cs 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 synop c 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 correla on (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 correla on (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 correla on (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 Con nue Increasing CMIP5 Climate Projec ons through AD 2100 Lower troposphere stability Stronger T inversion Poten al 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 rela onship with large-scale climate is complicated CMIP5 Climate Projec ons through AD 2100 Lower troposphere stability Stronger T inversion Poten al T @ 850 hPa minus T @ 2m No strong trend. Tendency Subsidence @ 700 hPa toward more subsidence
Less radia ve cooling by cloud layer Specific humidity @ 700 hPa
Trends Interannual Variability Conclusions 30 Can the night and early morning cloud-base height affect the day me? Al tude Al tude
Dew point Dew point
More Surface Surface warming warming Dew Point Air T Dew Point Air T Dew-Point Depression Dew-Point Depression
Trends Interannual Variability Conclusions 31 Can the night and early morning cloud-base height affect the day me? Al tude Al tude
Dew point Dew point
More Surface Surface warming warming Dew Point Air T Dew Point Air T Dew-Point Depression Dew-Point Depression
Trends Interannual Variability Conclusions 32 Conclusions
• Urban warming rising clouds less fog
• Increased stability above the marine layer unlikely to compensate for urban effects
• Posi ve feedback: decreased cloudiness more sunlight
• Feedback may have been stalled in last decade due to cold SSTS
Trends Interannual Variability Conclusions 33