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Acknowledgments: Peter Chandler & Mike Foreman

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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Acknowledgments: Peter Chandler & Mike Foreman

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  1. Climatic variability and trends in thesurface waters of coastal BC Patrick Cummins and Diane MassonInstitute of Ocean Sciences, DFO Acknowledgments: Peter Chandler & Mike Foreman

  2. Introduction • 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).

  3. 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)

  4. 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.

  5. Leading principal component (smoothed) from EOF analysis SST SSS PDO 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.

  6. Power spectrum of leading principal components SST Dotted line gives best-fit red-noise power spectrum: months De-correlation time scale: SSS months

  7. 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.

  8. 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 .

  9. 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. 5 10

  10. Influence of coastal freshwater on SSS West Coast of Van. Is. Strait of Georgia SSS – Fraser Rv. SSS – Gold Rv. AR-1 • 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.

  11. Integrate AR-1 model to hindcast SSS time series: • Captures low-frequency variability • reasonably well

  12. Relation with alongshore wind stress Langara Is. SST vs. –Bakun Index AR-1 • 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. AR-1 Langara Is. SST vs. Buoy 205 winds 1% level

  13. 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. AR-1 AR-1 AR-1 Amphitrite Pt. SSS vs. –Bakun Index & vs. modified B.I. AR-1

  14. 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.

  15. Summary • 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.

  16. 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.

  17. Mean annual climatology

  18. Station-pair correlations SST SSS 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.

  19. 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. 20-year running trends in lighthouse SST data Based on SST anomalies with trend retained

  21. 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).

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