Climatologicalscale science from sparse data Michael Steele Wendy

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Climatological-scale science from sparse data Michael Steele & Wendy Ermold Polar Science Center /

Climatological-scale science from sparse data Michael Steele & Wendy Ermold Polar Science Center / Applied Physics Laboratory / University of Washington, Seattle WA USA # of profiles Kara Sea

Michael Steele Polar Science Center / APL University of Washington tp: //psc. apl. washington.

Michael Steele Polar Science Center / APL University of Washington tp: //psc. apl. washington. edu Example: A study of Arctic Ocean surface warming over the past 100 years ia ss Ru N. Pole 1979 N. Ameri ca ia ss Ru N. Pole 2003 N. Ameri ca September sea ice cover How much ocean warming?

Temperature (ºC) WOD’ 05 data distribution, colored by: Temperature Years Earliest Year 50 N

Temperature (ºC) WOD’ 05 data distribution, colored by: Temperature Years Earliest Year 50 N – 90 N 0 -10 m July, Aug, Sept

Temperature (ºC) Data Handling details: Temperature 1. ACQUIRE THE DATA The data were downloaded

Temperature (ºC) Data Handling details: Temperature 1. ACQUIRE THE DATA The data were downloaded off the web as “wrapped” ascii files: World Ocean Database, 2005 http: //www. nodc. noaa. gov/OC 5/WOD 05/pr_wod 05. html 2. REFORMATING Data were reformatted into the following ascii columns: Profile_ID# latitude longitude depth temperature salinity month year

Instrument Counts for some Arctic shelves Drifting Buoy Data ocean station data: bottle, low

Instrument Counts for some Arctic shelves Drifting Buoy Data ocean station data: bottle, low res CTD e. Xpendable bathythermograph data mechanical bathythermograph data

Temporal Bias Raw data histograms

Temporal Bias Raw data histograms

SPATIAL BIAS Example: East Siberian + eastern Laptev Seas Raw Data Mean SST =

SPATIAL BIAS Example: East Siberian + eastern Laptev Seas Raw Data Mean SST = 2. 2 C 50 km bin averaged Mean SST = 1. 9 C sparse COLD profiles dense WARM profiles Temperature (ºC)

Fake trends Year 1 Year 2 Year 3 data cold warmer hot Steady state

Fake trends Year 1 Year 2 Year 3 data cold warmer hot Steady state ocean + spatially biased sampling fake warming trend!

The solution: Multiple Regression ^ T = a + b x + c y

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

Long-term N/AO trends Are these trends reflected in the SST data?

Long-term N/AO trends Are these trends reflected in the SST data?

(b) SST: 1965 -1995 (a) SST: 1930 -1965 No M. R. here: just smoothed,

(b) SST: 1965 -1995 (a) SST: 1930 -1965 No M. R. here: just smoothed, 300 km binned trends (c) SST: 1930 -1995 (d) OHC: 1965 -1995 SST trend -0. 5 -0. 4 -0. 3 -0. 2 -0. 1 0. 2 0. 3 0. 4 0. 5 OHC trend -100 -80 -60 -40 -20 0 20 40 60 80 100 °C per decade MJ/m 2 per decade

SST anomalies using M. R. (b) Beaufort-W (c) Bering (d) ESS+Laptev (e) Kara (f)

SST anomalies using M. R. (b) Beaufort-W (c) Bering (d) ESS+Laptev (e) Kara (f) Barents Ice thickness (m) (a) Beaufort-E