Climatological-scale science from sparse data

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Climatological-scale science from sparse data
Michael Steele Wendy Ermold Polar Science
Center / Applied Physics Laboratory / University
of Washington, Seattle WA USA
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Michael Steele Polar Science Center /
APL University of Washington
Example A study of Arctic Ocean surface warming
over the past 100 years
http//psc.apl.washington.edu
How much ocean warming?
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WOD05data distribution,colored by
Temperature
Earliest Year
50?N 90?N 0-10 m July, Aug, Sept
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• Data Handling details
• 1. ACQUIRE THE DATA
• The data were downloaded off the web as wrapped
  ascii files
• World Ocean Database, 2005
• http//www.nodc.noaa.gov/OC5/WOD05/pr_wod05.html
• 2. REFORMATING
• Data were reformatted into the following ascii
  columns
• Profile_ID latitude longitude depth
  temperature salinity month year

Temperature
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Instrument Counts for some Arctic shelves
Drifting Buoy Data
mechanical bathythermograph data
ocean station data bottle, low res CTD
eXpendable bathythermograph data
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Temporal Bias
Raw data histograms
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SPATIAL BIAS
Example East Siberian eastern Laptev Seas
Raw Data
50km bin averaged
Mean SST 2.2 C
Mean SST 1.9 C
sparse COLD profiles
dense WARM profiles
Temperature (ºC)
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Fake trends
Year 1
Year 3
Year 2
data
cold
warmer
hot
Steady state ocean spatially biased
sampling ? fake warming trend!
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• The solution Multiple Regression
• T a b?x c?y d?PHC(x,y) e?year

spatial field
Anomalies are defined relative to the mean
spatial field over a given time period.
no intra-year predictor
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Long-term N/AO trends
Are these trends reflected in the SST data?
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(b) SST 1965-1995

• SST
• 1930-1965

No M.R. here just smoothed, 300 km binned trends
(c) SST 1930-1995
(d) OHC 1965-1995
SST trend
C per decade
-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2
0.3 0.4 0.5
MJ/m2 per decade
OHC trend
-100 -80 -60 -40 -20 0 20 40
60 80 100
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SST anomalies using M.R.
(a) Beaufort-E
(b) Beaufort-W
(c) Bering
(d) ESSLaptev
Ice thickness (m)
(e) Kara
(f) Barents