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

A. Park Williams, Rachel E. Schwartz, Sam Iacobellis, Richard Seager, Benjamin I. Cook, Christopher J. Sll, 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 Radiaon

-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 Introducon Weather staon data from www.geog.ucsb.edu/ideas Summer provides much-needed water to ecosystems

Doug Fischer

AP Williams What will happen to stratus clouds in the future?

Specific Quesons 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 - Summer stratus season: May-Sep - Hourly cloud-base heights from 24 airfields Islands - Stratus clouds: any cloud with 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 Cies are Oen 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 reducon in ground-level fog in Los Angeles Hours of low visibility per year

Year Trends Interannual Variability Conclusions 9 Rising 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% reducon 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% reducon 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 Internaonal 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? Altude Altude 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 Nighme 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 Quesons 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 Altude (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 Fracon of days with (°C) inversion base >800 m Trends Interannual Variability Conclusions 23 Low and high stratus clouds are promoted by different climate processes Correlaon 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 correlaons 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 acvity 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 correlaons 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 correlaons 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 Projecons through AD 2100 Lower troposphere stability Stronger T inversion Potenal 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 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 Projecons through AD 2100 Lower troposphere stability Stronger T inversion Potenal T @ 850 hPa minus T @ 2m No strong trend. Tendency Subsidence @ 700 hPa toward more subsidence

Less radiave cooling by cloud layer Specific humidity @ 700 hPa

Trends Interannual Variability Conclusions 30 Can the night and early morning cloud-base height affect the dayme? Altude Altude

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 dayme? Altude Altude

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

• Posive feedback: decreased cloudiness more sunlight

• Feedback may have been stalled in last decade due to cold SSTS

Trends Interannual Variability Conclusions 33