Skip to main content Accessibility help
×
Home

A functional regression model for predicting optical depth and estimating attenuation coefficients in sea-ice covers near Resolute Passage, Canada

  • Shaun Mcdonald (a1), Theodoro Koulis (a1), Jens Ehn (a2), Karley Campbell (a2), Michel Gosselin (a3) and C.J. Mundy (a2)...

Abstract

The spectral dependence of natural light transmittance on ice algae concentration and snow depth in Arctic sea ice provides the potential to study the changing bottom-ice ecosystem using optical relationships. In this paper, we consider the use of functional data analysis techniques to describe such relationships. Specifically, we created a functional regression model describing spectral optical depth as a function of chlorophyll a concentration, snow depth and ice thickness. Measurements of the aforementioned covariates and surface and transmitted spectral irradiance were collected on landfast first-year sea ice in the High Arctic near Resolute Passage, Canada, during the spring of 2011 and used as model input. The derived model explains 75–84.5% of the variation in the observed spectral optical depth curves. No prior assumptions of snow/sea-ice optical properties are required in the application of this technique, as the model estimates the attenuation coefficients of each covariate using only the measurements mentioned above. The quality and simplicity of the model highlight the potential of functional data analysis to study the Arctic marine ecosystem.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      A functional regression model for predicting optical depth and estimating attenuation coefficients in sea-ice covers near Resolute Passage, Canada
      Available formats
      ×

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      A functional regression model for predicting optical depth and estimating attenuation coefficients in sea-ice covers near Resolute Passage, Canada
      Available formats
      ×

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      A functional regression model for predicting optical depth and estimating attenuation coefficients in sea-ice covers near Resolute Passage, Canada
      Available formats
      ×

Copyright

References

Hide All
Alou-Font, E, Mundy, C-J, Roy, S, Gosselin, M and Agusti, S (2013) Snow cover affects ice algal pigment composition in the coastal Arctic Ocean during spring. Mar. Ecol. Progr. Ser., 474, 89104 (doi: 10.3354/meps10107)
Arrigo, KR Sullivan CWand Kremer JN (1991) A bio-optical model of Antarctic sea ice. J. Geophys. Res., 96(C6), (10581–10 592) (doi: 10.1029/91JC00455)
Arrigo, KR Mock, T and Lizotte MP (2010)Primary producers and sea ice. In Thomas DN and Dieckmann GS eds. Sea ice. Wiley-Blackwell, Chichester
Bradstreet, MSW and Cross, WE (1982) Trophic relationships at High Arctic ice edges. Arctic, 35(1), 112 (doi: 10.14430/ arctic2303)
Campbell, K, Mundy C-J, Barber DG and Gosselin, M (2014) Remote estimates of ice algae biomass and their response to environmental conditions during spring melt. Arctic, 67(3), 375387 (doi: 10.14430/arctic4409)
Craven, P and Wahba, G (1978) Smoothing noisy data with spline functions. Numer. Math., 31(4), 377403 (doi: 10.1007/ BF01404567)
Ehn, JK Mundy, CJJ (2013) Assessment of light absorption within highly scattering bottom sea ice from under-ice light measurements: implications for Arctic ice algae primary production. Limnol. Oceanogr., 58(3), 893902 (doi: 10.4319/lo.2013. 58.3.0893)
Ehn, JK Mundy, CJ and Barber, DG (2008a) Bio-optical and structural properties inferred from irradiance measurements within the bottommost layers in an Arctic landfast sea ice cover. J. Geophys. Res., 113(C3), C03S03 (doi: 10.1029/ 2007JC004194)
Ehn, JK Papakyriakou, TN and Barber DG (2008b) Inference of optical properties from radiation profiles within melting landfast sea ice. J. Geophys. Res., 113(C9), C09024 (doi: 10.1029/ 2007JC004656)
Fritsen, CH Ackley, SF Kremer JN and Sullivan CW (1998)Flood– freeze cycles and microalgal dynamics in Antarctic pack ice. In Lizotte MP and Arrigo KR eds. Antarctic sea ice: biological processes, interactions and variability. (Antarctic Research Series 73) American Geophysical Union, Washington, DC, 1–21
Fritsen, CH Wirthlin, ED Momberg, DK Lewis MJ and Ackley SF (2011) Bio-optical properties of Antarctic pack ice in the early austral spring. Deep-Sea Res. II, 58(9–10), (1052–1061) (doi: 10.1016/j.dsr2.2010.10.028)
Galindo, V and 9 others (2014) Biological and physical processes influencing sea ice, under-ice algae, and dimethylsulfonio-propionate during spring in the Canadian Arctic Archipelago. J. Geophys. Res., 119(6), 37463766 (doi: 10.1002/ 2013JC009497)
Garrison, DL and Buck KR (1986) Organism losses during ice melting: a serious bias in sea ice community studies. Polar Biol., 6(4), 237239 (doi: 10.1007/BF00443401)
Grenfell, TC (1991) A radiative transfer model for sea ice with vertical structure variations. J. Geophys. Res., 96(C9), (16 991–17 001) (doi: 10.1029/91JC01595)
Grenfell, TC and Maykut GA (1977) The optical properties of ice and snow in the Arctic Basin. J. Glaciol., 18(80), 445463
Grossi, SM Kottmeier, ST Moe, RL Taylor GT and Sullivan CW (1987)Sea ice microbial communities. VI. Growth and primary production in bottom ice under graded snow cover. Mar. Ecol. Progr. Ser., 35(1–2), 153–164
Hamre, B, Winther J-G, Gerland, S, Stamnes JJ and Stamnes, K (2004) Modeled and measured optical transmittance of snow-covered first-year sea ice in Kongsfjorden, Svalbard. J. Geophys. Res., 109(C10), (C10006) (doi: 10.1029/2003JC001926)
Horner, R and Schrader GC (1982) Relative contribution of ice-algae, phytoplankton, and benthic micropalgae to primary production in nearshore regions of the Beaufort Sea. Arctic, 35(4), 485503
Horner, R and 9 others (1992) Ecology of sea ice biota. Polar Biol., 12(3–4), (417–427) (doi: 10.1007/BF00243113)
Landy, J, Ehn, JK Shields, M and Barber DG (2014) Surface and melt pond evolution on landfast first-year sea ice in the Canadian Arctic Archipelago. J. Geophys. Res., 119(5), 30543075 (doi: 10.1002/2013JC009617)
Legendre, L and Gosselin, M (1991) In-situ spectroradiometric estimation of microalgal biomass in first-year sea ice. Polar Biol., 11(2), 113115 (doi: 10.1007/BF00234273)
Leu, E, Søreide, JE Hessen, DO Falk-Petersen, S and Berge, J (2011) Consequences of changing sea-ice cover for primary and secondary producers in the European Arctic shelf seas: timing, quantity, and quality. Progr. Oceanogr., 90(1–4), (18–32) (doi: 10.1016/j.pocean.2011.02.004)
Light, B, Grenfell, TC and Perovich, DK (2008) Transmission and absorption of solar radiation by Arctic sea ice during the melt season. J. Geophys. Res., 113(C3), (C03023) (doi: 10.1029/ 2006JC003977)
Markus, T, Stroeve, JC and Miller, J (2009) Recent changes in Arctic sea ice melt onset, freezeup, and melt season length. J. Geophys. Res., 114(C12), (C12024) (doi: 10.1029/ 2009JC005436)
Maykut, GA and Grenfell TC (1975) The spectral distribution of light beneath first-year sea ice in the Arctic Ocean. Limnol. Oceanogr., 20(4), 554563
Müller, H-G, Wu, S, Diamantidis, AD Papadopoulos NT and Carey JR (2009) Reproduction is adapted to survival characteristics across geographically isolated medfly populations. Proc. R. Soc. Lond., Ser. B, 276(1677), 44094416 (doi: 10.1098/ rspb.2009.1461)
Mundy, CJ Barber, DG and Michel, C (2005) Variability of snow and ice thermal, physical and optical properties pertinent to sea ice algae biomass during spring. J. Mar. Syst., 58(3–4), (107–120) (doi: 10.1016/j.jmarsys.2005.07.003)
Mundy, CJ Ehn, JK Barber, DG and Michel, C (2007) Influence of snow cover and algae on the spectral dependence of transmitted irradiance through Arctic landfast first-year sea ice. J. Geophys. Res., 112(C3), C03007 (doi: 10.1029/2006JC003683)
Nicolaus, M and Katlein, C (2013) Mapping radiation transfer through sea ice using a remotely operated vehicle (ROV). Cryosphere, 7(3), 763777 (doi: 10.5194/tc-7-763-2013)
Nicolaus, M, Katlein, C, Maslanik, J and Hendricks, S (2012)Changes in Arctic sea ice result in increasing light transmittance and absorption. Geophys. Res. Lett., 39(24), (L24501Palmisano, AC Beeler SooHoo, J and Sullivan CW) (1987)Effects of four environmental variables on photosynthesis–irradiance relationships in Antarctic sea-ice microalgae. Mar. Biol., 94(2), 299–306 (doi: 10.1007/BF00392944)
Perovich, DK (1990) Theoretical estimates of light reflection and transmission by spatially complex and temporally varying sea ice covers. J. Geophys. Res., 95(C6), (9557–9567) (doi: 10.1029/ JC095iC06p09557)
Perovich, DK (2005) On the aggregate-scale partitioning of solar radiation in Arctic Sea Ice during the SHEBA field experiment. J. Geophys. Res., 110(C3), (C03002) (doi: 10.1029/ 2004JC002512)
Perovich, DK Cota, GF Maykut, GA and Grenfell, TC (1993) Bio-optical observations of first-year Arctic sea ice. Geophys. Res. Lett., 20(11), 10591062 (doi: 10.1029/93GL01316)
Perovich, DK Roesler, CS and Pegau, WS (1998) Variability in Arctic sea ice optical properties. J. Geophys. Res., 103(C1), (1193–1208) (doi: 10.1029/97JC01614)
R Core, Team (2013) R: a language and environment for statistical computing.. R Foundation for Statistical Computing, Vienna http://www.r-project.org
Ramsay, JO (2006) Functional data analysis. In Kotz, S, Read CB and Banks DL eds. Encyclopedia of statistical sciences. Wiley (doi: 10.1002/0471667196.ess3138)
Ramsay, JO and Silverman, BW (2005) Functional data analysis. 2nd edn. Springer, New York
Ramsay, JO Hooker, G and Graves, S (2009) Functional data analysis with R and MATLAB (use R!).. Springer, Dordrecht
Ramsay, J, Wickham, H, Graves, S and Hooker G. (2012)fda: Functional data analysis, R package. Version 2.3.2. http://cran .r-project.org/web/packages/fda/
Smith, REH, Anning, J, Clément, P and Cota GF (1988) The abundance and production of ice algae in Resolute Passage, Canadian Arctic. Mar. Ecol. Progr. Ser.. 48, 251263
Sørensen, H, Goldsmith, J and Sangalli LM (2013) An introduction with medical applications to functional data analysis. Stat. Med., 32(30), 52225240 (doi: 10.1002/sim.5989)
Stroeve, JC and 6 others (2012) Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations. Geophys. Res. Lett., 39(16), (L16502) (doi: 10.1029/2012GL052676)
Vines, BW Krumhansl, CL Wanderley MM and Levitin DJ (2006) Cross-modal interactions in the perception of musical performance. Cognition, 101, 80113 (doi: 10.1016/j.cognition.2005.9.003)

Keywords

A functional regression model for predicting optical depth and estimating attenuation coefficients in sea-ice covers near Resolute Passage, Canada

  • Shaun Mcdonald (a1), Theodoro Koulis (a1), Jens Ehn (a2), Karley Campbell (a2), Michel Gosselin (a3) and C.J. Mundy (a2)...

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed