The Giant Sequoia Climate (Cool, Calm, and Clouded)
and Drought Response
Eric Waller
Post-Doctoral Scholar
Laboratory for Conservation Biogeography
Department of Geography
University of Nevada, Reno
eric.waller@gmail.com
Photo Credit:
www.frankiefoto.com
Overview
• Cloud observations from satellites and webcams
• Satellite-derived cloud climatologies
• Giant sequoia species distribution models
• Satellite-based land surface temperature (LST) data
• Meteorological flux tower data
• Plant water use and drought
• Mechanisms for the giant sequoia climate
Fine Print
Giant sequoia appears to be weathering the drought
better than (pretty much) all other tree species.
Fine Print
ALL tree species are doing better within giant sequoia
grove boundaries than outside the groves.
Why might that be?
And could it have anything to do with giant sequoia’s
unusual distribution?
What restricts giant sequoia to groves concentrated in the southern
Sierra Nevada?
A frequent hypothesis is that the groves occur in areas of reliable water
supply, perhaps through cracks in the granite.
California Geology Map
Granitic rocks (red)
Giant Sequoia not Restricted by Precipitation
Giant Sequoia
Vs.
Gray Pine
(Pinus sabiniana)
Tallest
Largest
Biggest Grove
Highest
Lowest
Overview
• Cloud observations from satellites and webcams
• Satellite-derived cloud climatologies
• Giant sequoia species distribution models
• Satellite-based land surface temperature (LST) data
• Meteorological flux tower data
• Plant water use and drought
• Mechanisms for the giant sequoia climate
Giant
Sequoia
MODIS Aqua Satellite Data
RGB = 7-2-1
4/28/2014
~ 1 PM
http://lance-modis.eosdis.nasa.gov/imagery/subsets/?subset=AERONET_Fresno
MODIS Terra
RGB = 7,2,1
4/19/14
~11 AM
http://lance-modis.eosdis.nasa.gov/imagery/subsets/?subset=AERONET_Fresno
Giant
Sequoia
MODIS Terra
RGB = 7,2,1
4/19/14
~11 AM
MODIS Terra
RGB = 7,2,1
7/14/11
~11 AM LST
3-26-10, ~11 AM (MODIS Terra)
3-26-10, ~1 PM (MODIS Aqua)
http://www.nrlmry.navy.mil/sat
-bin/epac_westcoast.cgi
http://rockyags.cr.usgs.gov/dashboards/WebCam.htm?date=20141123
Mountain Home State Forest
Mineral King area mountains
(Sequoia National Park)
Overview
• Cloud observations from satellites and webcams
• Satellite-derived cloud climatologies
• Giant sequoia species distribution models
• Satellite-based land surface temperature (LST) data
• Meteorological flux tower data
• Plant water use and drought
• Mechanisms for the giant sequoia climate
MODIS “Off-the-Shelf” Cloud Frequency,
Annual
Cloud frequency maps derived from
standard MODIS cloud masks (e.g.,
MOD09GA “cloud state” variable)
do not perform well over snow,
bright desert soils, and urban areas.
Custom-developed Cloud Climatology
 Classification of twice-daily MODIS reflectance data at ~500 meter resolution:
Terra (MYD09GA, 2000-2012, ~11 AM) and Aqua (MOD09GA, 2002-2012, ~1 PM)
 Also processed daily ~5-km resolution AVHRR LTDR (AVH09C1) data 1981-1999,
as part of a long-term assessment of trends in Central Valley Tule Fog
 Converted daily products into monthly cloud frequency products
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
MODIS Aqua (~1 PM)
April Cloud Frequency,
2003-2011
Frequency
April Cloud Frequency with Giant Sequoia Overlay
High
Low
Overview
• Cloud observations from satellites and webcams
• Satellite-derived cloud climatologies
• Giant sequoia species distribution models
• Satellite-based land surface temperature (LST) data
• Meteorological flux tower data
• Plant water use and drought
• Mechanisms for the giant sequoia climate
Giant Sequoia Species Distribution Modeling
 Monthly cloud frequencies from MODIS Aqua
 PRISM and Daymet climate variables, as well as derived variables (e.g., Potential
Evapotranspiration and Water Deficit)
 Maxent, logistic regression (R), and classification trees (R)
Maxent Prediction
•
April afternoon cloud frequency
consistently strongest predictor.
• Other monthly cloud frequencies
often found important as well
(strongly correlated with April)
Spring
afternoons
Annual cloud frequency at ~1 PM (MODIS Aqua overpass time)
October to June growth in cloud frequency between ~11 AM (MODIS
Terra overpass time) and ~1 PM (MODIS Aqua overpass time).
Overview
• Cloud observations from satellites and webcams
• Satellite-derived cloud climatologies
• Giant sequoia species distribution models
• Satellite-based land surface temperature (LST) data
• Meteorological flux tower data
• Plant water use and drought
• Mechanisms for the giant sequoia climate
Average MODIS (Aqua)
2012 Diurnal Land
Surface Temperature
Range (Day – Night)
{Clear Sky Days!}
Δ 0C
Average Annual MODIS
Cloud Frequency (%)
Sequoia
Points
R2 = 0.58
Other
Sierra Nevada
Points
Average Annual MODIS Diurnal
Land Surface Temperature Range (C)
Color Composite:
Red color gun = MODIS Aqua annual cloud frequency
Green color gun = Oct to June daytime cloud growth (Aqua-Terra)
Blue color gun = MODIS Aqua annual average dLST (Day-Night)
Overview
• Cloud observations from satellites and webcams
• Satellite-derived cloud climatologies
• Giant sequoia species distribution models
• Satellite-based land surface temperature (LST) data
• Meteorological flux tower data
• Plant water use and drought
• Mechanisms for the giant sequoia climate
405 meters
2015 meters
1160 meters
2700 meters
Red Channel = - dLST (annual average)
Green, Blue = April Cloud Frequency
Los
Angeles
San Francisco
Red Channel = - dLST (annual average)
Green, Blue = April Cloud Frequency
Los
Angeles
San Francisco
Sequoia groves
Fresno
Tower sites
Sequoia groves
Tower sites
Sequoia groves
Tower sites
Average ½ Hourly Temperature by Month: 2015 meter site
January
April
Noon
March
February
Average ½ Hourly Dew Point Temperature by Month: 2015 meter site
Afternoon
peaks
Noon
Average ½ Hourly Relative Humidity by Month: 2015 meter site
Noon
Average ½ Hourly Relative Humidity by Month: 2015 m site, Clear Afternoons
Noon
Average ½ Hourly Relative Humidity for 4 Towers: April
Noon
Average ½ Hourly Temperature for 4 Towers: April
No daytime warm-up at tower at 2015 meters
Noon
Overview
• Cloud observations from satellites and webcams
• Satellite-derived cloud climatologies
• Giant sequoia species distribution models
• Satellite-based land surface temperature (LST) data
• Meteorological flux tower data
• Plant water use and drought
• Mechanisms for the giant sequoia climate
Relationship between vapor pressure deficit, relative humidity, and temperature
Credit: Kunkel
Average ½ Hourly Vapor Pressure Deficit by Month, 2015 m site
Noon
Average Hourly Vapor Pressure Deficit by Month, 2015 m (surface)
Noon
Average Hourly Difference in Vapor Pressure Deficit by Month: VPD(2m) – VPD(55m) at the 2015 m site
Noon
Blodgett Tower: 1300 meters, northern Sierra Nevada
Average ½ Hourly Vapor Pressure Deficit by Month, 2006: 10.5 m height
Noon
Crescent Meadow Sequoia Grove: Why is all the sequoia canopy so high?
Giant Sequoias
Mostly fir trees
Credit: sgainsboro
www.panoramio.com
Credit: Jacek, Bozena Macias
www.panoramio.com
Relationships between vapor pressure
deficit (VPD) and carbon dioxide flux in
old-growth Douglas-fir forest at Wind
River Valley site in Washington.
Average ½ Hourly Vapor Pressure Deficit by Month, 2015 m site
~ 1.3 kPa
Blodgett Forest, 10.5 m
Chen et al., Ecosystems, 2004
> 2 kPa
2015 m Site, surface
> 1.5 kPa
Average ½ Hourly CO2 Flux (NEE) by Month: 2015 m Site
Noon
Noon
Average ½ Hourly Wind, Annual: 4 Towers
Noon
Overview
• Cloud observations from satellites and webcams
• Satellite-derived cloud climatologies
• Giant sequoia species distribution models
• Satellite-based land surface temperature (LST) data
• Meteorological flux tower data
• Plant water use and drought
• Mechanisms for the giant sequoia climate
Vertical Velocity, 850 hPa (~1500 m)
Vertical Velocity, 500 hPa (~ 5500 m)
Negative values = rising air
Positive values = subsidence
October minus April maximum temperature
(California mountains unusually cold in April)
°C
Where’s this cold spring air coming from?
•
Still cold North Pacific
•
Pacific High migrating north: winds/weather generally coming from
northwest rather than the west (confirmed by NCEP/NARR data)
• Increased meridional flow: cutoff or closed lows
850 hPa
January
Geopotential
Height
700 hPa
April
Climate Summary
 Unusually cloudy, throughout the year but particularly on spring
afternoons (strongest predictor of giant sequoia distribution)
• Reduces daytime temperature and therefore vapor pressure
deficit
• Reduces evapotranspiration
• Increases diffuse light
• Retains later snowpack
 Clouds a visible manifestation of complex climate
• Unusual seasonal and daily patterns of temperature, humidity,
and wind
• All reduce evapotranspiration
• Mostly only evident with measurements from towers (at
particular locations?)
What does the future hold for
Giant Sequoia?
The Sequoia cloud pattern
and its frequency appear to
have been consistent in the
last thirty-plus years. But…
The answer is up in the air.
Credit: Machaca
www.panoramio.com