Yosemite Hydroclimate Mee1ng 2015 Mountain Cloud Variability in the Western U.S. and How It Affects Sierra Nevada Hydroclimate Edwin Sumargo1 and Daniel R. Cayan1,2 1Scripps Ins<tu<on of Oceanography, University of California San Diego, La Jolla, CA 2United States Geological Survey, La Jolla, CA Funded by: Outline Central Theme: How do snowmelt and streamflow respond to space-­‐2me cloudiness varia2ons in the mountainous western U.S.? •  Background and mo<va<on •  Why study (mountain) cloud variability? •  Why use satellite remote sensing? •  Topics 1.  Spa<al and temporal variability 2.  Influence on spring<me snowmelt and streamflow •  Take home points 2 Why Study Cloud Variability? Net radia<on accounts for 66-­‐90% of the total energy b a l a n c e f o r s p r i n g snowmelt.* hZp://geosci.uchicago.edu/~rtp1/glaciers/EnergyBalanceCartoon.jpg 3 *U.S. Army Corps of Engineers (1956), Marks and Dozier (1992), Cline (1997) Why Study Cloud Variability? Cloud cover is a principal regulator of surface energy budget.* hZp://geosci.uchicago.edu/~rtp1/glaciers/EnergyBalanceCartoon.jpg 4 *Gimeno García et al. (2012) Why Use Satellite Remote Sensing? Tioga Pass Entry Sta<on Tuolumne Meadow White Wolf 4-­‐km x 4-­‐km GOES pixel And 20 years of half-­‐hourly record! Dana Meadow Tenaya Lake Gin Flat Sta<ons that measure snow and solar radia<on in Yosemite cdec.water.ca.gov/stamap.html 5 1.  Spa<al and temporal variability Dataset •  NASA/NOAA GOES-­‐West visible albedo product •  Resolu<on: 4 km spa<al, 30 minute temporal •  Domain: 25°N -­‐ 50°N, 130°W -­‐ 113°W •  Day<me albedo (8-­‐16 LST) is averaged at every pixel, forming 6,940 daily composite maps (1996-­‐2014). hZp://www.goes-­‐r.gov/mission/history.html Surface eleva1on maps (Data source: NOAA NGDC GEODAS) 6 Domain 1.  Spa<al and temporal variability Dataset cont’d : masking out low eleva1ons •  P i x e l s w i t h s u r f a c e eleva<on < 800 meters are masked to exclude coastal and low stratus clouds Surface eleva1on maps (Data source: NOAA NGDC GEODAS) km 7 Cloud and non-­‐cloud albedo components Clear-­‐sky filtering*: αcloud(t) = α(t) – αclear(t) αclear(t) = min(α(t – t0):α(t + t0)) where: Ø α = (raw) GOES albedo Ø αcloud = Cloud albedo Ø αclear = Clear-­‐sky albedo Ø t0 = 7 days Modified from hZp://www.grida.no/publica<ons/vg/africa/page/3111.aspx 8 *Cano et al. (1986), Perez et al. (2002), Paech et al. (2009) Iden<fy dominant paZerns of cloud varia<on Rotated Empirical Orthogonal Func<on (REOF) analysis of de-­‐seasonalized daily cloud albedo (αcloud) N
α cloud (x, y, t) = ∑ EOF(x, y)* PC(t)
n=1
•  The seasonal cycle of αcloud is removed using smoothed daily climatology. •  Period: 1996-­‐2014 •  Domain: 25°N -­‐ 50°N, 130°W -­‐ 113°W, eleva<on ≥ 800 m 9 Spa<al and temporal components of daily cloud albedo modes N
α cloud (x, y, t) = ∑ EOF(x, y)* PC(t)
n=1
10 Cloud albedo modes associated with California More Cloud Less Cloud Monthly Standard Devia1on 11 Rela<onship between the cloud albedo modes and teleconnec<on paZerns Teleconnec1on datasets •  Monthly indices: Niño 3.4*, Pacific Decadal Oscilla<on (PDO)+ •  Daily indices: Pacific North American (PNA)* PNA hZp://www.goes-­‐r.gov/users/comet/tropical/ textbook_2nd_edi<on/navmenu.php_tab_5_page_3.1.0.htm hZps://climate.ncsu.edu/edu/k12/.PNA 12 *NOAA CPC +UW/JISAO Rela<onship between the cloud albedo modes and teleconnec<on paZerns 2 x 2 con1ngency table •  Focus on the winters and springs (Dec-­‐May) •  Only days when PC > 70th percen<le or PC < 30th percen<le •  Then iden<fy days when teleconnec<on indices >90th percen<le or <10th percen<le 13 Table from hZps://www.boundless.com/sta<s<cs/textbooks/boundless-­‐sta<s<cs-­‐textbook/other-­‐hypothesis-­‐tests-­‐13/nonparametric-­‐sta<s<cs-­‐63/ comparing-­‐two-­‐popula<ons-­‐paired-­‐difference-­‐experiment-­‐314-­‐2772/ Con<ngency table for REOF/PC-­‐1 Sta<s<cally significant at 99% confidence level when χ2 > 6.64 χ2 = 7.02 +PC1 -­‐PC1 +PDO 116 96 -­‐PDO 91 126 14 Con<ngency tables for REOF/PC-­‐4 Sta<s<cally significant at 99% confidence level when χ2 > 6.64 χ2 = 11.34 χ2 = 25.65 χ2 = 15.02 +PC4 -­‐PC4 +Niño 3.4 130 109 -­‐Niño 3.4 101 156 +PC4 -­‐PC4 +PNA 86 136 -­‐PNA 124 71 +PC4 -­‐PC4 +PDO 126 87 -­‐PDO 88 130 15 2.  Influence on spring<me snowmelt and streamflow Datasets •  USDA Snow Telemetry (SNOTEL) and California Coopera<ve Snow Surveys snow water equivalent records (SWE) •  Domain: 25°N -­‐ 50°N, 130°W -­‐ 113°W •  Period: 19 years, 1996-­‐2014 •  USGS Hydro-­‐Clima<c Data Network (HCDN) stream gauge records (Q) •  Compiled by Stewart et al. (2005) for snow-­‐dominated gauges •  Domain: 25°N -­‐ 50°N, 130°W -­‐ 113°W •  Period: 19 years, 1996-­‐2014 16 Snowmelt and Runoff terms Processing i.  Snowmelt ΔSWE = SWE(t–1) – SWE(t+1) ≥ 0 ii.  Runoff ΔQ = Q(t+1) – Q(t–1) The seasonal cycles of ΔSWE and ΔQ are removed using smoothed daily climatology. 17 Correlate the leading cloud albedo mode (PC-­‐1) to snowmelt (ΔSWE) and runoff (ΔQ) hZp://meteora.ucsd.edu/~deznge/pulse/ 18 Correlate the leading cloud albedo mode (PC-­‐1) to snowmelt (ΔSWE) and runoff (ΔQ) R(PC-­‐1, ΔSWE) R(PC-­‐1, ΔQ) R > 0 R < 0 19 Cloudy and clear spring composites 2 scenarios •  5 cloudiest WY (most posi<ve Feb-­‐Jun PC-­‐1 averages) •  5 clearest WY (most nega<ve Feb-­‐Jun PC-­‐1 averages) Cloudiest Clearest 1998 2013 2011 2002 2010 2007 2005 2004 2006 2014 20 Lagged responses of snowmelt (ΔSWE) and runoff (ΔQ) to daily cloud albedo varia<ons Higher cloud albedo = More snowmelt R(PC-­‐1, ΔSWE) Higher cloud albedo = Less snowmelt Peak responses at lag > 0 ‘x’ denotes where p-­‐values < 0.01 Higher cloud albedo = More precipita1on-­‐runoff R(PC-­‐1, ΔQ) Higher cloud albedo = Less snowmelt-­‐runoff lag > 0 means cloud leads snowmelt 21 Different signatures of snowmelt (ΔSWE) response to cloudiness varia<on (PC-­‐1) in cloudy and clear years Mar Apr May Clear Years R2 Apr Cloudy Years Mar May R > 0 R < 0 Bold circles are where p-­‐values < 0.01 22 Different signatures of runoff (ΔQ) response to cloudiness varia<on (PC-­‐1) in cloudy and clear years Mar Apr May Clear Years R2 Apr Cloudy Years Mar May R > 0 R < 0 Bold circles are where p-­‐values < 0.01 23 Take Home Points •  Daily cloud variability modes associated with California (REOF-­‐1 and REOF-­‐4) account for 31% of the total variance in the western U.S., and are ac<ve throughout the winter and spring. •  ENSO, PDO and PNA have a sizeable influence on the cloud varia<ons in Californian mountains. •  Snowmelt and streamflow fluctua<ons on one day are cumula<ve responses to cloudiness varia<ons from several days before. •  Snowmelt and streamflow ac<vi<es last longer throughout the spring<me in cloudier years, indica<ng the importance of cloud shading effect. 24 Thank you! CNN Paul Duginski/LA Times 25 Rela<onship between the cloud albedo modes and teleconnec<on paZerns Chi square test for con1ngency table •  Sta<s<cally significant at 99% confidence level when χ2 > 6.64 χ2 PC-­‐1 PC-­‐4 Niño 3.4 0.80 11.34 PNA 4.38 25.65 PDO 7.02 15.21 AO 3.49 0.00 26