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Climatic variability and trends in the surface waters of coastal BC Patrick Cummins and Diane Masson Institute of Ocean Sciences, DFO. Acknowledgments: Peter Chandler & Mike Foreman. Introduction.

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Climatic variability and trends in thesurface waters of coastal BC Patrick Cummins and Diane MassonInstitute of Ocean Sciences, DFO

Acknowledgments: Peter Chandler & Mike Foreman

  • Data collected on SST and SSS at lighthouse stations along the coast of British Columbia represent some of the longest records available from the coastal waters of Canada.
  • We use these records to characterize the climatic variability and trends over the region.
  • Previous examination of these data given in, for example, Freeland (1990, 2013), Freeland et al. (1997), Emery & Hamilton (1985), McKinnell & Crawford (2007), Moore et al. (2008; only Race Rocks ).
  • Special attention is paid to the representation of the variability using low-order (AR-1) climate models.
  • Such models have previously been considered to have limited applicability to the coastal ocean (e.g., Hall & Manabe, 1997).

Lighthouse stations

  • (monthly means)
  • Amphitrite Pt – 78 yrs
  • Kains Is.– 78 yrs
  • Entrance Is. – 76 yrs
  • Pine Is. – 76 yrs
  • Langara Is. – 73 yrs
  • Race Rocks – 71 yrs
  • Bonilla Is. – 53 yrs
  • Chrome Is. – 50 yrs

Met buoys

+ Bakun upwelling index

Wind observations

Freshwater fluxes

Gold River (proxy for coastal

freshwater flux)

Fraser River

Estevan Pt (rain)

data processing
Data processing
  • Monthly anomalies are formed by removing climatological mean for 1981-2010.
  • Except where noted, no smoothing of data is done.
  • Detrended anomalies are formed by removing least-square fitted linear trends.
  • EOFs of lighthouse temperature (T) and salinity (S) used to characterize coast-wide, regional variability, and related to climate indices.
  • Cross-correlation analysis used to relate SST and SSS to forcing (wind & freshwater flux).
  • Long and shorter trends are examined.
leading principal component smoothed from eof analysis
Leading principal component (smoothed) from EOF analysis




r = 0.72

  • Coastal BC SSTs co-vary with the PDO and large scale, NE Pacific SSTs.
  • SSS is not linked to large-scale climate indices.
  • PC1SST and PC1SSS only weakly related.
power spectrum of leading principal components
Power spectrum of leading principal components


Dotted line gives best-fit red-noise

power spectrum:


De-correlation time scale:



relation to freshwater discharge and alongshore wind stress
Relation to freshwater discharge and alongshore wind stress

Stochastic climate model (Hasselmann, 1976)

  • Model equation: with

- the SST or SSS anomalies

- a damping time scale

- forcing under consideration (e.g., freshwater discharge, alongshore wind stress)

- additional, uncorrelated noise forcing (e.g., eddy noise)

  • The ‘null hypothesis’ for climate studies: low frequency variability in ocean variable due to integration of noisy weather fluctuations. Two time-scale assumption.
  • Response has a red noise spectrum, given white noise forcing
  • In discrete form we have: , a first-order, auto-regressive process (AR-1),

with (for example) . . This is a measure of the ‘memory’ of the process.

ar 1 model lagged cross correlation
AR-1 model: lagged cross-correlation
  • Defined as : with

- discrete time lag, positive for forcing leading the response

- standard deviations of and

  • For white noise forcing we have

where and

  • Cross-correlation has a highly asymmetrical form
  • Inclusion of additional white noise forcing (G) reduces the amplitude, but does not affect the form of
  • In the following, is determined from the lag-1 autocorrelation of the SST or SSS anomalies and we compare with the form of .
forcing autocorrelations
Forcing autocorrelations

For white noise

  • Gold River is fed by a rainfall dominated
  • (pluvial) watershed, and anomalies are
  • well correlated with rainfall at Estevan Pt.
  • (r=0.58, p<<0.01).
  • WCVI – a rainy coast. Gold Rv. is taken as a
  • proxy for freshwater runoff along the WCVI.
  • Autocorrelation shows that discharge anomalies
  • are well represented as a white noise process
  • Fraser Rv. drains a snowmelt-driven (nival-
  • glacial) watershed; discharge anomalies
  • have significant autocorrelation (not white noise).
  • Autocorrelation structure of alongshore wind
  • stress is well approximated by white noise.



influence of coastal freshwater on sss
Influence of coastal freshwater on SSS

West Coast of Van. Is.

Strait of Georgia

SSS – Fraser Rv.

SSS – Gold Rv.


  • On the WCVI, SSS variability is consistent with integration of noisy freshwater discharge, as in AR-1.
  • In the Strait of Georgia, SSS is highly correlated to Fraser Rv. discharge. Because the river is snow-melt dominated, the response differs from the cross-correlation of a white noise driven AR-1 process.
integrate ar 1 model to hindcast sss time series
Integrate AR-1 model to hindcast SSS time series:
  • Captures low-frequency variability
  • reasonably well
relation with alongshore wind stress
Relation with alongshore wind stress

Langara Is. SST

vs. –Bakun Index


  • Relation to lighthouse SST anomalies is similar for the (sign-reversed) Bakun index and wind stress from Buoy 205.
  • SST variability at Langara Is. is accounted for, in part, as an integration of noisy alongshore wind stress, according to an AR-1 process.


Langara Is. SST

vs. Buoy 205 winds

1% level

relation with alongshore wind stress cont d
Relation with alongshore wind stress (cont’d)
  • AR-1 model is not unreasonable for SST and SSS at Amphitrite Pt. and Kains Is. on the outer coast.
  • Bakun index is correlated with Gold Rv. discharge (r=-0.29).
  • To isolate the influence of wind stress, a modified Bakun Index was constructed in which the freshwater-related component is removed.
  • The SST-wind stress relation is essentially unaffected, but the SSS-wind stress cross-correlation is no longer significant.

Amphitrite Pt. SST

vs. –Bakun Index &

vs. modified B.I.




Amphitrite Pt. SSS

vs. –Bakun Index &

vs. modified B.I.


seasonal correlations alongshore wind stress and sss on wcvi
Seasonal correlations: Alongshore wind stress and SSS on WCVI

Removing influence of fresh water forcing

  • Spring is the only season with a meaningful relation between alongshore winds and SSS,
  • likely reflecting variations in timing of onset of upwelling season in Spring.
  • Removing freshwater influence weakens this relation, especially at Amphitrite Pt. station.
  • Nearshore signal associated with upwelling appears generally weak.
  • Leading principal component (PC1) of SST variability represents an index of variability for coastal BC waters.
  • This PC1 is very well correlated with the PDO index, which is the leading PC of SST variability over the entire extra-tropical N. Pacific.
  • SSS anomalies have smaller spatial scales than SST and appear to be locally driven, displaying a clear relation with run-off anomalies.
  • Along the WCVI, salinity anomalies are consistent with integration of forcing by white noise freshwater flux anomalies as an AR-1 process.
  • Along the outer coast, SST anomalies also appear to integrate noisy atmospheric forcing represented by longshore winds. However, influence of upwelling on nearshore SSS is relatively weak.
  • BC coastal waters are warming (0.89 oC/century) and generally freshening.
  • On time scales of concern to the management of marine resources, natural variability can easily overwhelm secular trends associated with global warming. This variability has a white spectrum at low frequencies.
seasonal correlations sst and alongshore wind stress
Seasonal correlations: SST and alongshore wind stress

Bold: significant at the 1% (*5%) level

  • Seasonal relation between SST and wind stress is strongest in Winter & Fall, weak to non-existent in Spring
  • and Summer.
  • Poleward winds (+’ve wind stress) in Winter drives warm water poleward. This also are associated with
  • warm air masses and enhanced air/sea heat fluxes.
  • In Spring and Summer these effects tend to cancel as poleward winds (cyclonic air flow) are associated with
  • cold air.
  • The similar relation seen at Entrance Island in the Strait of Georgia suggests that alongshore advection is not
  • the only process involved.
  • n the Strait of Georgia, SSS follows Fraser River discharge anomalies throughout the year.
  • Positive SST anomalies in Strait during Summer associated with reduced Fraser Rv. streamflow.
  • Here the strongest relation is between SSTs during spring and streamflow anomalies in the
  • following Summer: r(SSTAMJ, QJAS) = -0.50.
station pair correlations
Station-pair correlations



SST correlations generally larger than SSS, indicating larger spatial scales of

variability for temperature.

All entries significant at the 1% level,

except (*) – 5% level, and (ns) entry.

long term trends
Long-term trends

Overall warming and offshore freshening consistent with Freeland (1990, 2013) and Freeland et al. (1997). Average SST trend for the 6 longest records is 0.89±0.62 oC/century.

Bold: significant at the 5% level

20 year running trends in lighthouse sst data
20-year running trends in lighthouse SST data

Based on SST anomalies with trend retained

average histograms for sst trends
Average histograms for SST trends

Probability of trend ≤ 0

over 20 years: 39%

over 30 years: 34%

over 40 years: 17%

Similar behaviour seen in results

from climate models, albeit with

weaker variability (Easterling

& Wehner, 2009).