US Department of the Interior

Bureau of Ocean Energy Management 

Pacific OCS Region

OCS Study 

BOEM 2020-012

Pacific Continental Shelf Environmental 

Assessment (PaCSEA): 

Characterization of Seasonal Water 

Masses within the Northern California 

Current System Using Airborne Remote 

Sensing off Northern California, 

Oregon, and Washington, 2011–2012
US Department of the Interior 

Bureau of Ocean Energy Management 

Pacific OCS Region 

OCS Study 

BOEM 2020-012 

Pacific Continental Shelf Environmental 

Assessment (PaCSEA): 

Characterization of Seasonal Water 

Masses within the Northern California 

Current System Using Airborne Remote 

Sensing off Northern California, 

Oregon, and Washington, 2011–2012 

February 2020 

Authors: 

Jennifer A. Schulien 

Josh Adams 

Jonathan J. Felis 

Prepared under Intra-Agency Agreement M10PG00081 

By 

U.S. Department of the Interior 

U.S. Geological Survey 

Western Ecological Research Center 

Santa Cruz Field Station 

2885 Mission Street 

Santa Cruz, CA 95060 
DISCLAIMER

This study was funded, in part, by the U.S. Department of the Interior, Bureau of Ocean Energy 

Management (BOEM), Environmental Studies Program, Washington, DC, through Intra-Agency 

Agreement Number M10PG00081 with the U.S. Geological Survey (USGS). This report has been 

technically reviewed by BOEM, and it has been approved for publication. This product has been peer 

reviewed and approved for publication consistent with USGS Fundamental Science Practices 

(http://pubs.usgs.gov/circ/1367/). Any use of trade, firm, or product names is for descriptive purposes 

only and does not imply endorsement by the U.S. Government. 

REPORT AVAILABILITY

To download a PDF file of this report, go to the U.S. Department of the Interior, Bureau of Ocean Energy 

Management Data and Information Systems webpage (http://www.boem.gov/Environmental-StudiesEnvData/), click on the link for the Environmental Studies Program Information System (ESPIS), and 

search on 2020-012. The report is also available at BOEM’s Recently Completed Environmental Studies

- Pacific webpage (https://www.boem.gov/Pacific-Completed-Studies/).

CITATION

Schulien, JA, Adams, J, Felis, J. 2020. Pacific Continental Shelf Environmental Assessment (PaCSEA): 

Characterization of Seasonal Water Masses within the Northern California Current System Using 

Airborne Remote Sensing off Northern California, Oregon, and Washington, 2011–2012. Camarillo 

(CA): U.S. Department of the Interior, Bureau of Ocean Energy Management, Pacific OCS Region. 

OCS Study BOEM 2020-012. 26 p. 

ABOUT THE COVER

Cover image: Remote sensing reflectance hyperspectrograph for PaCSEA Transect 29 depicting a 

bimodal, tidal river plume off the Colombia River, Washington, January 2011. Courtesy of Jennifer 

Schulien (USGS).
ACKNOWLEDGMENTS 

This project was funded by the Bureau of Ocean Energy Management through Intra-Agency Agreement

M10PG00081 with the U.S. Geological Survey (USGS) Western Ecological Research Center (WERC). 

Greg Sanders (BOEM) helped initiate the study and provided constructive advice. David Pereksta

(BOEM) provided contract oversight. Low-elevation aerial survey methods were reviewed by the 

National Oceanic and Atmospheric Administration, National Marine Fisheries Service, who granted a 

Letter of Concurrence (4 January 2011) accepting USGS mitigation measures to avoid marine mammal 

disturbance. Jennifer Schulian was supported by USGS-WERC and gives special thanks to P. Raimondi 

(University of California, Santa Cruz) and J. Ryan (Monterey Bay Aquarium Research Institute). We 

thank the Kudela Lab, especially Sheri Palacios, and Raphe Kudela, and Stephanie Flora (Moss Landing 

Marine Labs) for assistance with programing. David Johncox (USGS) provided consultation regarding 

special-use aerial operations safety and the Department of the Interior’s Interagency Aviation Training 

program provided useful training in advance of safe aerial operations. We are especially grateful to the

pilots at Aspen Helicopters (Oxnard, California) and Gold Aero (Arlington, Washington) for their expert

ability to conduct safe operations at low elevation, often far from land—over the open ocean. 
i

Contents 

List of Figures ................................................................................................................................................ ii

List of Tables ................................................................................................................................................. iii

List of Abbreviations and Acronyms ............................................................................................................. iv

1 Introduction ........................................................................................................................................... 1

1.1 Study region, oceanography, and ecology .................................................................................... 1

2 Methods ................................................................................................................................................ 4

2.1 Aerial surveys and aircraft remote sensing ................................................................................... 4

2.2 Environmental remote sensing ...................................................................................................... 4

2.3 Additional environmental data ....................................................................................................... 7

2.4 K-means cluster analysis .............................................................................................................. 7

3 Results and discussion ......................................................................................................................... 7

3.1 Aerial survey effort ........................................................................................................................ 7

3.2 Remote sensing ............................................................................................................................ 8

3.2.1 Airborne HR3 matchups with MODIS satellite data .............................................................13

3.3 K-means clustering analysis ........................................................................................................14

3.3.1 Winter surveys .....................................................................................................................14

3.3.2 Summer surveys .................................................................................................................17

3.3.3 Autumn surveys ...................................................................................................................19

4 Summary and conclusion ....................................................................................................................21

5 References ..........................................................................................................................................21
ii

List of Figures 

Figure 1. Remote sensing reflectance hyperspectograph over the CRP region ...........................................2

Figure 2. Survey aircraft ................................................................................................................................ 3

Figure 3. Study area ...................................................................................................................................... 5

Figure 4. Survey area, Columbia River flow, and winds ............................................................................... 6

Figure 5. Sea surface temperature .............................................................................................................10

Figure 6. Monthly MODIS satellite imagery ................................................................................................11

Figure 7. Chlorophyll a concentrations derived from HydroRad3 ocean color ...........................................12

Figure 8. MODIS satellite remote-sensing matchups with HydroRad3 ......................................................13

Figure 9. K-means results for winter surveys ..............................................................................................16

Figure 10. K-means results for summer surveys ........................................................................................18

Figure 11. K-means results for autumn surveys .........................................................................................20
iii

List of Tables 

Table 1. Summary of low-elevation aerial survey effort (linear km surveyed) during PaCSEA surveys ...... 8

Table 2. Airborne flights with concurrent environmental data ...................................................................... 9

Table 3. Summary of k-means clustering results .........................................................................................15
iv

List of Abbreviations and Acronyms

Au Absorbance units

BOEM Bureau of Ocean Energy Management

Chla Chlorophyll a 

CCS California Current System

NCCS Northern California Current System

aCDOM absorption of Colored Dissolved Organic Matter CRP Columbia River Plume

°C Degrees Celsius

GPS Global Positioning System

h Hour

HR3 HyrdoRad3

km Kilometers

Ld Downwelling Radiance

Ed Downwelling Irradiance

Lu Upwelling Radiance

nLw Normalized water-leaving radiance

m Meters

MODIS Moderate Resolution Imaging Spectroradiometer

NASA National Aeronautical and Space Administration

NOAA National Oceanic and Atmospheric Administration

nm Nanometers

NM Nautical Miles

NPP Net Primary Production

OWEI Offshore Wind Energy Infrastructure

OCS Outer Continental Shelf

PaCSEA Pacific Continental Shelf Environmental Assessment

Rrs Remote Sensing Reflectance

SST Sea Surface Temperature

s Second

USGS-WERC U.S. Geological Survey-Western Ecological Research Center

W Watts
1

1 Introduction

Interest in developing renewable energy in the ocean has recently increased, some of which will include 

power generation infrastructure and support activities located within continental shelf (and potentially 

deeper) waters off the U.S. Pacific coast, beyond state waters (i.e., outside three nautical miles [NM]).

The Bureau of Ocean Energy Management (BOEM) is currently considering renewable energy proposals 

off the coast of Oregon, California, and Hawaii (BOEM 2017). The 2011–2012 USGS Pacific Continental 

Shelf Environmental Assessment study (PaCSEA, Adams et al., 2014) was intended to provide up-to-date 

information on species composition, distribution, abundance, seasonal variation, and habitat utilization 

among marine birds and mammals on the outer continental shelf (OCS) within a broad region of the 

northern California Current System (NCCS; herein, the region between Fort Bragg, California [39.3°N] to 

Grays Harbor, Washington [47°N]). The NCCS supports abundant populations of seabirds and marine 

mammals, but comprehensive aerial surveys have not been conducted during certain months (e.g., winter) 

or were conducted two to four decades ago. Briggs et al., (1987) conducted extensive low-elevation 

surveys for marine birds and mammals off central and northern California from February 1980 through 

January 1983, and Bonnell et al., (1992), Briggs et al., (1992), and Green et al., (1992) conducted similar,

extensive aerial surveys from April 1989 to September 1990 off Oregon and Washington. Suryan et al.,

(2012) provided a review of surveys conducted off Oregon and a similar effort also gathered existing 

seabird abundance data off Washington to create species distribution models using environmental 

predictors (Menza et al., 2016). In situ sensing of the ocean environment in the aerial surveys prior to 

PaCSEA was limited to sea surface temperature (SST) measured with pyrometry and composite imagery 

of the ocean collected via earth-orbiting satellites.

Our three primary objectives for PaCSEA were to (1) conduct aerial at-sea surveys of seabirds and marine 

mammals in shelf and slope waters off northern California, Oregon, and Washington and summarize 

species and seasonal at-sea densities, (2) conduct a comparison with existing similar surveys in northern 

California, Oregon, and Washington, and (3) validate and enhance aerial survey data for numerically 

abundant indicator species and certain resident breeding and non-resident migratory seabird species.

Objective 1 included enhancing in situ remote sensing of the ocean by including a hyperspectral 

radiometer to collect continuous, ocean color data during surveys. Data generated using remote sensing 

were intended to assist resource managers in the evaluation of proposed renewable energy sites and

environmental review of specific project proposals received by BOEM. In addition, new data regarding 

seabird density distributions along with data on environmental factors will enhance continuing efforts to 

apply predictive modeling for (1) species distributions (Nur et al., 2011; see also, Data Synthesis and 

High-resolution Predictive Modeling of Marine Bird Spatial Distributions on the Pacific OCS [BOEM 

PC-15-01]) and (2) applied vulnerability assessments among seabirds in the CCS to offshore wind energy 

infrastructure (OWEI; Adams et al., 2016, Kelsey et al., 2018). Here, we report sea surface temperature, 

derived ocean color chlorophyll a (Chla), absorption by colored dissolved organic matter (aCDOM), proxy 

particle load, and statistically-derived water mass classifications for all surveys flown during the 2011–

2012 PaCSEA study.

1.1 Study region, oceanography, and ecology

The California Current System (CCS) is an eastern boundary current spanning the region from Baja 

California, Mexico to British Columbia, Canada and extending up to 1,000 km offshore (Huyer 1983, 

Miller et al., 1999). Despite a poleward decrease in wind-driven Ekman transport, surface concentrations

of Chla increase with latitude (Ware and Thomson, 2005). Intermittent upwelling with periods of 
2

relaxation occur at time scales of 2–10 days (coincident with time-scales of phytoplankton blooms). 

Frontal boundaries related to the Columbia River, submarine canyons that enhance coastal upwelling, 

internal waves generated by remote winds, broad continental shelves, and coastlines without capes (and 

associated local bathymetry) make the NCCS one of the most productive areas within the CCS (Hickey 

and Banas, 2008; Hickey et al., 2006). 

The Columbia River plume (CRP) is one of the most important habitat features in the NCCS, supplying 

77% of the total freshwater discharge from San Francisco, California to the Strait of Juan de Fuca, 

Washington (Hickey et al., 1998). The CRP also is a major source of dissolved and particulate organic 

material, nutrients, and trace metals to the NCCS (Hickey et al., 2005; Aguilar-Islas and Bruland, 2006). 

Although the Columbia River has relatively low iron concentrations compared with other rivers in the

U.S. and is not a major source of nitrogen to the region (nitrogen is primarily utilized in the estuary before 

entering the Pacific Ocean), upwelling and re-suspension of estuarine sediments during low tide supply 

enough iron such that nitrogen ultimately limits phytoplankton growth in the region (Bruland et al., 2008; 

Kudela and Peterson, 2009). The Columbia River provides important nutrients to support marine 

productivity, especially during periods of weak upwelling (Kudela et al., 2010). 

Figure 1. Remote sensing reflectance hyperspectograph over the CRP region

Example of along-transect (#29, Columbia River line) hyperspectrograph of remote sensing reflectance (Rrs)

collected from an aircraft-mounted radiometer during survey of the Columbia River. Plume water is easily

detectable and contrasts markedly with offshore oligotrophic waters of the inner California Current.

Elevated net primary production (NPP) in this region fuels transfer of energy to higher trophic levels 

(Kudela et al., 2010). Zooplankton biomass is enhanced within ‘aged’ plume water and concentrated 

along strong density gradients within the plume’s pycnocline and at the plume boundary (Peterson and 

Peterson, 2008). South of the CRP, approximately 55-km offshore of Oregon (44°N, 125°W), Heceta 

Bank is another ‘hotspot’ in the NCCS where a unique biochemical environment promotes high 

phytoplankton NPP (Hickey and Banas, 2003). Both the CRP and Heceta Bank also support diverse and 

abundant mesopredators including fishes, seabirds, and marine mammals and are particularly important 
3

when normal conditions change and the subsequent nutrient flux is reduced (i.e., during El Niño events; 

Ainley et al., 2005; Adams et al., 2012, Zamon et al., 2014). Many fisheries, including Pacific salmon 

(Onchyrinchus spp.), northern anchovy (Engraulis mordax), and Pacific sardine (Sardinops sagax), exist 

along the coast, making effective management of coastal and marine resources in the NCCS both

ecologically and economically important. To inform management and marine spatial planning, 

classification of regions of similar water properties over appropriate spatial scales may provide useful 

descriptions of species habitats (Longhurst et al., 1995; Devred et al., 2007) and help efforts to better 

predict species distributions (Louzao et al., 2012) and species abundances at sea (Menza et al., 2016). 

Here, we use ocean color measurements (Figure 1) and sea surface temperature (SST) data collected with 

sensors mounted on low-flying aircraft to characterize NCCS water masses and identify patterns among 

seasons and between years. To accomplish this, we applied k-means clustering to measured and derived 

ecologically-relevant physical and bio-optical variables (SST, Chla, absorbance by colored dissolved 

organic matter [aCDOM], proxy particle load). These classifications will be used in the future to evaluate 

species habitat distributions in the NCCS. 

Figure 2. Survey aircraft 

Survey aircraft used during the PaCSEA program. Partenavia P-68 Observer (left; Aspen Helicopters, Oxnard, CA) 

and Commander AC-500 (right; GoldAero, Arlington, WA). Photo courtesy J. Felis, USGS.
4

2 Methods

2.1 Aerial surveys and aircraft remote sensing

We conducted six low-altitude aerial surveys (twin-engine, fixed, high-wing aircraft: Partenavia P-68, 

Aspen Helicopters, Oxnard, California, or Commander AC-500, GoldAero, Arlington, Washington;

Figure 2). We flew 32 east-west-oriented uniform transects (spaced at 15ˈ latitude [27.8-km] intervals) 

when possible to the 2000-m isobath (includes shelf, slope, and rise waters) to ensure comparable spatial 

and temporal coverage with similar historic datasets. The primary survey area extended from Fort Bragg, 

California (39.3°N) to Grays Harbor, Washington (47°N) and focused on federal waters outside of the 3-

mile boundaries (Figure 3). We designed our at-sea surveys so they would be comparable with historic 

transect lines off northern California (Briggs et al., 1987) and off Oregon and Washington (Briggs et al., 

1992). We included six focal-area surveys nested within the overall broad transect survey area at the 

request of BOEM (Figure 3). Each focal-area survey consisted of ten, 25-km long, parallel transect lines 

targeting shelf waters and spaced at 6-km intervals. This pattern (broad survey lines and focal-area survey 

lines) was surveyed during each of three oceanographic seasons: summer during upwelling (23–30 June 

2011 and 1–5 July 2012), autumn during upwelling/relaxation (7–19 October 2011 and 19–24 September 

2012), and winter during downwelling (14–26 January 2011 and 17–27 February 2012). Aerial survey 

methods follow Mason et al. (2007) with slight modifications. Surveys were flown at 160 km h-1, and we

used a Global Positioning System (GPS) unit linked to a laptop computer that allowed us to 

simultaneously collect coordinates (WGS-84 map datum), sea surface temperature (SST, degrees Celsius 

[°C]) determined via a belly-mounted pyrometer, and ocean color data via an onboard radiometer.

Remote sensing data were collected simultaneously with seabird abundance and distribution observations 

(see Adams et al., 2014).

2.2 Environmental remote sensing

Raw HydroRad3 (HR3; HOBI Labs, Bellevue, Washington) measurements of downwelling irradiance

(Ed), downwelling radiance (Ld), and upwelling radiance (Lu) were first calibrated and processed to full 

engineering units using manufacturer-supplied calibration constants (RadSoft Version 1.1, HOBI Labs,

Inc., Bellevue, Washington). These data were imported into Matlab (Version 2011a Student; MathWorks, 

Natick, MA) and attributed with GPS flight data (geographic coordinates) based on date and time. Full 

hyperspectral data (0.3-nm intervals) then were reduced to 5-nm intervals using linear interpolation. We

distinguished spikes representing bad data from very high real values by examining gradients between 

sequential points. 

The downwelling irradiance (Ed) was multiplied by 0.97 following Doxaran et al. (2012) to account for 

air-sea transmittance and Fresnel reflection. The upwelling radiance (Lu) was divided by 0.54 to account 

for the partial reflection and transmission of the upwelled radiance through the sea surface (Mobley 

1999). We corrected Lu for sun angle and calculated water-leaving radiance (nLw) according to Gordon 

and Voss (1999). Because the survey aircraft were flown at low altitude (60-m ASL), absorption and 

scattering in the atmosphere were assumed to be negligible. We calculated remote-sensing reflectance 

(Rrs) according to the SeaWiFS Optics Protocol (Mueller et al., 2003). Chla was calculated using the 

CALFIT algorithm (Kahru et al., 2012), which is an optimization of the fourth-order polynomial 

coefficients of the OCv-type chlorophyll algorithms refined for use in the CCS. Absorbance by CDOM

(aCDOM) was estimated using the algorithm originally developed for use with Sea-Viewing Wide Field-ofView Sensor (SeaWiFS) data (Kahru and Mitchell, 2001). The normalized water-leaving radiance at 555 

nm (nLw[555], scaled 0–1.0) was used as proxy particle load (Otero and Siegel, 2004).
5

Figure 3. Study area 

Pacific Continental Shelf Environmental Assessment (PaCSEA) study area showing 32 broad transects, six 

focal-area transect zones, and geographic place names referenced in text. 
6

Figure 4. Survey area, Columbia River flow, and winds 

A map of the survey area with transect lines is shown in panel a. The 100-m, 1000-m, and 2000-m isobaths are 

shaded. (b) Columbia River flow rates from The Dalles, Oregon. The gray boxes show the survey dates. Data have 

been smoothed using a running mean. Winds (c-g) from the National Data Buoy #46029 located at the mouth of the 

Columbia River plume. No wind data available for the July 2012 survey period (gray panel). 

We considered excluding all data collected during cloudy to partially cloudy conditions (above the 

survey aircraft), but this would have drastically reduced the number of remote sensing observations. 

Clouds alter both the magnitude and spectral shape of incident light, which consequently affected nLw.

Therefore, we divided spectral nLw by the integrated area for the full wavelength range to account for the

effect of changing magnitude of incidental light. This normalization was necessary because survey data 

were collected during both cloudy and cloud-free conditions. Although no correction could be made 

when the spectral quality of light was altered by clouds, dividing by the integral effectively normalized

the spectra to a common scale.

To evaluate coarse-scale, seasonal and inter-annual variability in ocean conditions, we present monthly

composite averages of Chla concentrations determined using the National Aeronautics and Space 

Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, we 

compared two sub-regions of daily 1-km MODIS Chla values to HR3-derived measurements to evaluate 

the ability of the HR3 to retrieve meaningful ocean color data from our low-elevation survey aircraft. To 

do this, we downloaded MODIS L1B data from the LAADS website

(https://ladsweb.nascom.nasa.gov/data/) and processed these to L2 data using SeaDAS Version 7.2. To 

correct MODIS L1B data for atmospheric effects, we used a modified atmospheric correction scheme

(aer_opt = 1; relative humidity = 75%; aerosol fraction = 0.01) appropriate for the CCS (Ryan et al., 

2009). 
7

2.3 Additional environmental data 

For 2011–2012, we compiled USGS stream gage river discharge rates for the Columbia River at The 

Dalles, Oregon (45.608°N, 121.172°W; http://waterdata.usgs.gov/nwis/inventory/?site_no=1410570; 

Figure 4). Wind speed and direction data were acquired from the National Data Buoy Center #46029, 

located near the Columbia River mouth (46.159°N, 124.514°W) 

(http://www.ndbc.noaa.gov/station_history.php?station=46029; Figure 4), and in situ Chla measurements 

for the Columbia River estuary were made available by the Center for Coastal Margin Observation and 

Prediction (CMOP) SATURN-03 platform (46.208°N, 123.943°W) 

(http://www.stccmop.org/datamart/observation_network/fixedstation?id=saturn07; Figure 4). 

2.4 K-means cluster analysis 

We used k-means cluster analysis (Matlab Student Version 2011a), a feature-based classification 

technique, to delineate and characterize water masses in the NCCS. Feature-based classification uses the 

inherent variability in multivariate data to reveal structure, making no a priori knowledge necessary 

(Martin Traykovski and Sosik, 2003; Jain, 2010) and thus, introducing less bias (Palacios et al., 2012). 

Input variables used in our k-means analysis included SST, log10 transformed Chla, aCDOM, and proxy 

particle load (i.e, nLw[555]). All variables were re-scaled from 0–1, to weight them equally for our kmeans water mass classifications. The k-means clustering analysis computed clusters based on the 

squared Euclidean distance from the mean of each cluster. For this analysis, with the exception of January 

2011 (regionally-limited data set, k = 3), we set k = 4, following Thomas and Weatherbee (2006), who 

found four water masses to persist in the CRP region of the CCS. We chose k-means cluster analysis 

rather than hierarchical, or fuzzy c-means clustering, because k-means is considered more robust for 

clustering data with lower signal-to-noise ratios (Martin Traykovski and Sosik, 2003; Jain, 2010).

3 Results and discussion 

3.1 Aerial survey effort 

We conducted replicated surveys off northern California, Oregon, and southern Washington on 14–26 

January 2011, 23–30 June 2011, 7–19 October 2011, 17–27 February 2012, 1–5 July 2012, and 19–24 

September 2012 (Table 1). Inclement weather occasionally interfered with survey effort. For example, we 

were not able to complete focal-area surveys during February 2012 within the Nehalem and Grays Harbor 

target zones, although we were able to complete surveys along several broad lines that overlapped these 

areas. Also, U.S. Department of the Interior aviation flight restrictions for extended overwater operations 

constrained our maximum offshore extent to 50 NM during February, July, and September 2012 surveys. 

This restriction only affected survey coverage of the far offshore slope waters (>200-m depth) off 

northern Oregon and southern Washington, where the continental shelf broadens. We completed a total 

26,752 km of standardized survey effort (not including coastal and offshore deadhead segments between 

transect lines; Table 1). Effort allocated according to bathymetric domain (i.e., depth strata) was 

approximately proportional to areas occupied by the bathymetric domains of the shelf-slope region, with 

additional effort targeting regions of the continental shelf (mostly <200-m depth) within the focal areas. 

Effort was similar among seasons (winter, summer, and autumn) and between years. With respect to 

depth strata, 47% (12,646 km) covered the slope domain (200–2000-m depth), 33% (8887 km) covered 

the inner-shelf (0–100-m depth), and 20% (5219 km) covered the outer-shelf (Table 1). 
8

Table 1. Summary of low-elevation aerial survey effort (linear km surveyed) during PaCSEA 

surveys off northern California, Oregon, and southern Washington. 

Year Month Survey Dates

Depth Stratum

All Depths

0 –100 m 100–200 m 200–2000 m

2011

January January 14–26 1,780 991 2,285 5,056

June June 23–30 1,572 874 2,382 4,828

October October 7–19 1,487 893 2,277 4,657

2012

February February 17–27 1,114 752 1,626 3,492

July July 1–5 1,451 846 2,055 4,352

September September 19–24 1,483 863 2,021 4,367

All Surveys 8,887 5,219 12,646 26,752

3.2 Remote sensing

Winter surveys revealed the least amount of regional variability in SST (Figure 5). In January 2011,

coolest temperatures (5–6°C) were confined to the relatively small region nearshore off the Columbia 

River, Willapa Bay, and Grays Harbor. The region of coolest water was more extensive in February 2012 

and reached beyond the shelf into the slope domain (i.e., >200 m depth) and farther south off northern

Oregon. The onset of regional upwelling during spring and summer caused increased heterogeneity in the 

SST of the PaCSEA region. In June 2011, relatively warmer waters (16–18°C) emanating from the 

Columbia River appeared to be transported toward the southwest and extended at least to the slope 

domain (>200 m depth) off central Oregon (Figure 5). Closer to shore, cold waters (11–12°C) occurred 

off south-central Oregon north of Cape Blanco (Figure 5). There was no distinct CRP in July 2012, and 

the region of upwelling did not extend north of Cape Blanco (Figure 5). Fall surveys during October 2011 

and September 2012 revealed SST distributions associated with low river flow and regional, autumn 

relaxation events, though there was a signature of sustained coastal upwelling during September 2012 off 

central-southern Oregon (Figure 5). October 2011 surveys revealed incursions of warmer waters north of 

Cape Blanco, Oregon, and south of Cape Mendocino, with small pockets of cooler waters near Cape 

Mendocino. In September 2012, the signal from warmer offshore waters was evident over the slope 

domain north of Cape Blanco outside a thin band of cooler waters adjacent to the coast (Figure 5).

Capturing small-scale variability in Chla derived from remote sensing satellite data is particularly 

challenging in regions like PaCSEA that have persistent seasonal cloud cover. Monthly 4-km MODIS 

composite Chla images during January 2011 and February 2012 revealed the least amount of variability 

and the least concentrations overall in regional surface Chla throughout the NCCS (Figure 6). The 

airborne HR3 measurements show the opposite pattern, with greater median Chla concentrations (Figure 

7, Table 3). This discrepancy likely results from small-scale (daily, ≤1-km) variability missed when data 

are averaged over greater spatiotemporal scales (e.g., MODIS composite imagery). Additionally, the

relatively short snapshots acquired from in situ airborne measurements are not necessarily representative 

of average seasonal conditions, and therefore monthly MODIS composites are useful for providing 

seasonal context.

Although summer Chla concentrations were greatest over the inner-shelf with some discrete hotspot 

regions, satellite and airborne data demonstrated important differences in distribution patterns. During 

June 2011, the greatest satellite-based Chla concentrations were associated with the Cape Blanco/Heceta 

Bank region off Oregon and the Cape Mendocino region off northern California (Figure 6). In situ HR3 

data revealed comparable Chla data to satellite Chla concentrations in the Cape Blanco/Heceta Bank 
9

region bur lesser magnitude Chla off the Columbia River mouth (Figures 6, 7). Satellite Chla during July 

2012 displayed a similar pattern as June 2011, but areas of relatively greater Chla concentrations extended 

farther north through central Oregon, but not south of Cape Mendocino (Figure 6). The SW offshore 

bulge of the CRP observed during June 2011 is not as extensive during July 2012 and slightly greater 

Chla concentrations were instead elevated to the north and south of the Columbia River mouth along the 

inner shelf (Figures 6, 7). 

Patterns in autumn Chla distributions were generally consistent between MODIS and HR3 platforms 

(Figures 6, 7). October 2011 satellite and HR3 data revealed greatest surface Chla concentrations near the

shelf break off Grays Harbor, Washington, near the Columbia River Plume, near the California/Oregon 

border, and south of Cape Mendocino, California (Figures 6, 7). During September 2012, greatest Chla 

concentrations were observed off south-central Oregon near Heceta Bank and over the inner-shelf from 

Cape Blanco, Oregon south to Cape Mendocino, California (Figures 6, 7). 

We observed important differences between satellite and airborne Chla measurements in the winter and 

summer surveys, which highlights the benefits of collecting ocean color data from low-flying aircraft. The 

ability to survey during periods when cloud cover would obscure satellite remote sensing is particularly 

critical in regions of persistent cloud cover. Furthermore, airborne data collected during in situ sampling 

should be more closely associated with simultaneous observations of marine birds and mammals, thereby 

providing an alternative to more limited and spatiotemporally mismatched composite satellite data. 

Table 2. Airborne flights with concurrent environmental data. USGS stream gage river discharge 

rates from The Dalles, Oregon are in units of cubic feet per second. Wind speed and direction are 

observations from the National Data Buoy #46029, located near the Columbia River mouth. Wind 

speed (m s‐1) is averaged over an eight-min period and wind direction is the direction the wind is 

coming from in degrees clockwise from true north. Median values are in parentheses. 

Flight Period Latitude 

Range 

Longitude 

Range 

% Usable 

HR3 Data River Flow Wind Speed 

Wind 

Direction 

25–27 January 

2011

45.496N–

47.069N 

125.327W–

123.956W 99 1.86X105–2.67X105

0–10.6 

(3.9) 4–359 (176) 

23–30 June 

2011

39.250N–

47.050N 

125.899W–

123.791W 96 4.16X105–4.79X105

0.2–10.5 

(5.4) 1–358 (277) 

7–19 October 

2011

39.250N–

47.051N 

125.889W–

123.780W 99 1.10X105–1.44X105

0.1–12.6 

(6.2) 1–360 (184) 

18–27 February 

2012

39.249N–

47.006N 

125.362W–

123.794W 94 1.30X105–1.63X105

0.1–14.1 

(6.5) 1–358 (201) 

1–5 July 2012 39.247N–

47.053N 

125.416W–

123.783W 96 3.63X105–3.97X105

1.4–10.1 

(6.4) 1–360 (334) 

19–24 

September 2012 

39.250N–

47.052N 

125.421W–

123.784W 99 1.05X105–1.24X105

0–11.3 

(5.2) 1–359 (172) 
10

Figure 5. Sea surface temperature Figure 5. Sea surface temperature

Sea surface temperature (SST, °C) from aircraft-mounted infrared pyrometer collected along replicated, 

broad transect surveys during winter (A, D), summer (B, E), and fall (C, F). Gaps in (A) off southern Oregon 

represent missing temperature data only and do not reflect patterns in continuous survey trackline effort. 
11

Figure 6. Monthly MODIS satellite imagery

Monthly composite Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery (NASA Aqua 

MODIS Chlorophyll a, log10 mg Chla m-3; 4-km pixel resolution) for the northern California Current Region 

during each of the six PaCSEA survey windows. NASA's OceanColor Web distributes chlorophyll a

concentration data from NASA's Aqua Spacecraft. Available via https://oceancolor.gsfc.nasa.gov/ (accessed 28

August 2019).
12

Figure 7. Chlorophyll a concentrations derived from HydroRad3 ocean color

Chlorophyll a concentrations (log10[mg Chla m-3]) derived from airborne HR3 data shown on the same scale for all 

surveys, and with the same color-ramp as Figure 6.
13

3.2.1 Airborne HR3 matchups with MODIS satellite data 

The best two MODIS to HR3 matchups are shown in Figure 8. Only subsets of the MODIS images were 

cloud-free and thus available for matchup to HR3 data. Using the modified atmospheric correction 

scheme, the distributions of Chla values between sensors were similar (Figure 8c, h) and the structure and 

gradients of Chla in the HR3 data were realistic and comparable with MODIS images (Figure 8d, e, i, j). 

Figure 8. MODIS satellite remote-sensing matchups with HydroRad3 

MODIS satellite remote-sensing matchups to HydroRad3 in situ chlorophyll a data. Panels a-e show data from 

26–27 June 2011 and panels f–j show data from 23 February 2012 included are zoomed visible images (b, g), 

histograms showing frequency (y-axes) distributions (c, h), and survey and MODIS chlorophyll a concentrations 

(log10 [mg m-3]) (d, e) and (I, j), respectively. 
14

3.3 K-means clustering analysis

3.3.1 Winter surveys 

The winter 2011 survey period lasted from 14–26 January, but HR3 ocean color data were only available 

for the northern section of the survey region, including the CRP sampled during 25–27 January. Columbia

River discharge during the survey period was moderate and relatively constant, and wind stress was 

relatively weak (Table 2, Figure 4). K-means clustering identified a bi-directional CRP water mass 

(hereafter, italics are used to identify water mass descriptors) that bulged at the Columbia River mouth

(Figure 9). CRP was the coldest water mass (7.8°C median value; Table 3) and had the greatest median

Chla (5.3 mg m-3), aCDOM (0.6 Au), and proxy particle load (0.7) (Table 3). Offshore water was the

warmest (9.4°C median value; Table 3) and had the lowest median Chla (1.3 mg m-3), aCDOM (0.4 Au) and

proxy particle load (0.2) (Table 3, Figure 9). Aged CRP water had intermediate values for all variables, 

and clustering analysis likely identified waters where CRP water mixed with offshore water (SST = 

8.6°C, Chla = 2.6 mg m-3, aCDOM = 0.5 Au, proxy particle load = 0.5) (Table 3). The boundary between

aged CRP and offshore waters was located over the continental shelf except for the transect intersecting 

the Columbia River mouth, where CRP water extended beyond the shelf break (>200-m depth; Figure 9). 

The 2012 winter survey period (18–27 February) was characterized by moderate river flow (less flow than 

during the 2011 winter survey period) and variable, strong winds (Table 2, Figure 4). K-means clustering 

again identified a bi-directional CRP water mass (44.5°N to 47°N, the northern extent of survey area). 

Similar to the 2011 winter survey, CRP water was the coldest water mass (7.1°C) and had the greatest

median Chla (3.9 mg m-3) (Table 3, Figure 9). More extensive regional coverage and best clustering with 

k = 4 allowed identification of more variable watermasses during winter 2012. Aged CRP identified 

during 2011 were better described by nearshore and intermediate waters, with the boundary between the 

two types located over the outer continental shelf between the 100-m and 200-m isobaths. Kudela et al. 

(2006) found the continental shelf was a natural boundary between optically-distinct shelf and open ocean 

waters, consistent with the frontal region identified here using k-means clustering. The intermediate water 

mass had similar SST compared with nearshore and offshore waters, and extended over Heceta Bank off 

Oregon. The SST of nearshore water (8.9°C) was similar to intermediate (9.4°C) and offshore (9.1°C)

waters and had greater Chla (2.4 mg m-3), aCDOM (0.6 Au), and proxy particle load

(0.7) compared with intermediate and offshore waters (Table 3, Figure 9). This pattern indicated that 

upwelling did not appear to be the primary mechanism supplying nutrients to this water mass, consistent 

with expectations during no/low upwelling during winter (Banas et al., 2009). We suggest stream/river

discharge and interaction with the bottom through enhanced mixing during storm events were the primary 

sources of nutrient input during this winter survey period.
15

Table 3. Summary of k-means clustering results. Color ID refers to water mass descriptors presented in Figures 9–11. Percent of pixels 

(a measure of area), and ranges for sea surface temperature (SST), chlorophyll a (Chla; mg Chla m-3), absorption coefficient of colored 

dissolved organic material (aCDOM) and detritus, and a proxy particle load (nLw[555) are shown. aCDOM and proxy particle load values have

been rescaled from 0–1.0; median values are in parentheses.

Survey Water Mass # Water mass Descriptor Color ID % Pixels SST (C) Chla (mg m-3

) aCDOM (Au) Particle load 

Jan-2011 

1 

2 

3 

Offshore 

CRP

Aged CRP 

Blue 

Green 

Red 

46

30

24

7.3–10.5 (9.4) 

5.3–9.3 (7.8) 

6.8–9.8 (8.6) 

0.4–2.3 (1.3) 

2.1–19.8 (5.3) 

1.0–5.6 (2.6) 

0.3–0.5 (0.4) 

0.5–0.7 (0.6) 

0.4–0.6 (0.5) 

0–0.4 (0.2) 

0.5–1.0 (0.7) 

0.2–0.7 (0.5) 

Jun-2011 

1 

2 

3 

4 

Aged CRP 

CRP

Offshore 

Nearshore 

Yellow 

Green 

Blue 

Red 

25

10

29

36

13.0–16.3 (14.6) 

10.0–16.7 (15.0) 

10.4–15.9 (13.8) 

7.8–13.4 (11.9) 

1.0–8.3 (2.3) 

1.9–18.4 (4.9) 

0.3–2.5 (1.1) 

1.0–10.0 (2.7) 

0.4–0.5 (0.5) 

0.5–0.7 (0.6) 

0.3–0.5 (0.4) 

0.4–0.6 (0.5) 

0.3–0.7 (0.3) 

0.5–1.0 (0.6) 

0–0.5 (0.2) 

0.2–0.9 (0.3) 

Oct-2011

1 

2 

3 

4 

Warm offshore 

CRP & nearshore 

Offshore 

Intermediate 

Red 

Green 

Blue 

Yellow 

37

11

34

18

13.5–16.8 (14.8) 

11.4–15.7 (13.3) 

10.5–17.3 (13.8) 

9.8–14.8 (13.3) 

0.6–4.9 (1.5) 

1.0–19.4 (2.7) 

0.3–1.5 (0.7) 

0.7–6.1 (1.4) 

0.4–0.5 (0.4) 

0.4–0.7 (0.5) 

0.3–0.5 (0.4) 

0.4–0.5 (0.4) 

0.1–0.6 (0.3) 

0.4–1.0 (0.6) 

0–0.4 (0.2) 

0.2–0.8 (0.4) 

Feb-2012 

1 

2 

3 

4 

Nearshore 

Offshore 

CRP

Intermediate 

Red 

Blue 

Green 

Yellow 

19

34

11

35

6.4–10.3 (8.9) 

6.9–11.1 (9.1) 

5.3–9.4 (7.1) 

6.7–11.0 (9.4) 

0.9–6.6 (2.4) 

0.3–1.6 (0.6) 

1.9–14.4 (3.9) 

0.5–4.1 (1.2) 

0.5–0.9 (0.6) 

0.3–0.6 (0.4) 

0.5–0.9 (0.6) 

0.4–0.6 (0.5) 

0.4–1.0 (0.7) 

0–0.4 (0.2) 

0.5–1.0 (0.8) 

0.2–0.6 (0.4) 

Jul-2012 

1 

2 

3 

4 

Intermediate 

CRP & nearshore 

Offshore 

Nearshore south 

Yellow 

Green 

Blue 

Red 

31

21

22

26

12.7–16.4 (15.2) 

11.8–16.7 (15.1) 

11.6–15.8 (14.8) 

7.9–13.2 (11.2) 

0.8–2.5 (1.6) 

1.3–7.3 (2.7) 

0.2–1.2 (0.7) 

0.5–7.1 (2.0) 

0.4–0.5 (0.4) 

0.4–0.6 (0.5) 

0.3–0.4 (0.4) 

0.4–0.7 (0.5) 

0.3–0.6 (0.4) 

0.4–0.9 (0.5) 

0–0.4 (0.2) 

0.2–1.0 (0.5) 

Sep-2012 

1 

2 

3 

4 

Intermediate 

Nearshore 

Offshore north 

CRP

Yellow 

Red 

Blue 

Green 

32

26

19

23

10.1–16.5 (13.6) 

9.2–12.9 (11.6) 

11.3–16.9 (15.4) 

12.1–15.9 (13.8) 

0.4–2.0 (1.1) 

0.6–5.8 (1.8) 

0.3–1.1 (0.4) 

0.9–6.8 (1.8) 

0.3–0.5 (0.4) 

0.4–0.5 (0.4) 

0.3–0.4 (0.4) 

0.4–0.6 (0.5) 

0.2–0.5 (0.3) 

0.3–1.0 (0.5) 

0–0.4 (0.2) 

0.3–0.9 (0.6) 
16

Figure 9. K-means results for winter surveys

Water mass classifications are indicated by color. The box plots on the right show the distribution of sea surface 

temperature (SST), chlorophyll a (Chla), absorption of colored dissolved organic material (aCDOM) and detritus, and

proxy particle load (nLw[555], scaled 0–1.0) for each water mass. For whisker plots, red horizontal line indicates the 

the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The 

whiskers extend to the most extreme data points not considered outliers, and the outliers are plotted individually 

using the red '+' symbol.
17

3.3.2 Summer surveys 

During summer 2011, we sampled from 23–30 June. River discharge was the greatest recorded 

throughout any of the survey periods, and the wind was relatively strong, changing direction partway 

through the survey (Table 2, Figure 4). CRP water clustered separately from aged plume water, which 

extended about 200 km southwest from the Columbia River mouth (Figure 10). The SST of the CRP

water was similar to aged plume water (15.0°C versus 14.6°C), but CRP water had greater Chla, aCDOM,

and proxy particle load (4.9 mg m-3, 0.6 Au, and 0.6 versus 2.3 mg m-3, 0.5 Au, and 0.3, respectively; 

Table 3, Figure 10). Offshore water extended the length of the survey area and had the lowest median 

Chla, aCDOM, and proxy particle load (1.1 mg m-3, 0.4 Au, and 0.2, respectively). The median SST

(13.8°C, Table 3, Figure 10) was intermediate compared with the other water masses identified. 

Nearshore (newly upwelled) water was the coldest (11.9°C) and had similar Chla, aCDOM, and proxy

particle load as the aged plume (2.7 mg m-3, 0.5 Au, and 0.3, respectively, versus SST = 14.6°C, 2.3 mg

m-3, 0.5 Au, and 0.3, respectively; Table 3, Figure 10). The boundary between these two water masses was

located at the shelf break or offshore (Figure 10).

During the July 2012 survey period, the boundaries of the CRP were less distinct and different from 

clusters observed in 2011. We identified a unified cluster as plume/nearshore water (Figure 10). This 

water mass formed a bulge at the mouth of the Columbia River and extended northward (Figure 10). Very 

nearshore water south to Cape Blanco (<100 m depth) also clustered with this water mass. Intermediate

water bordered the plume and extended south along the coast to Cape Blanco and across the shelf break 

(Figure 11). South of Cape Blanco, nearshore south water was colder (11.2°C) and had greater Chla,

aCDOM, and proxy particle load (2.0 mg m-3, 0.5 Au, and 0.5, respectively; Table 3, Figure 10), indicative

of strong upwelling at Cape Mendocino and with the presence of a frontal boundary extending offshore. 

This thermal frontal boundary is well documented (Barth et al., 2000; Huyer et al., 2005; Venegas et al., 

2008) and is hypothesized to be a response to differential forcing combined with bathymetry and coastline 

orientation. Offshore water, unlike the two nearshore water masses, extended the entire length of the 

survey and had the lowest median Chla, aCDOM, and proxy particle load (0.7 mg m-3, 0.4 Au, and 0.2,

respectively; Table 3; Figure 10). 

CRP Chla concentrations in June 2011 and July 2012 ranged from 1.6–18.4 mg m-3 (median = 4.9 mg 

m-3) and 12.3–16.7 mg m-3 (median = 15.2 mg m-3), respectively. Chla at SATURN-03, located inside the

Columbia River mouth (Figure 4), in June 2011 ranged from 2.5–8.0 mg m-3 and July 2012 ranged from

2.5–6.0 mg m-3. In June 2011, river flow was high and the core plume was relatively small. Although

upwelling was occurring (indicated by low coastal SST in the region), the range of plume Chla values

overlapped with Chla concentrations measured in the estuary. However, the range of Chla in aged plume

water was lower than the CRP perhaps indicating mixing effects or consumption by grazers. This is

consistent with Peterson and Peterson (2008) who observed the greatest zooplankton concentrations in

older, aged plume water. Likewise, we might expect to find greater relative abundances of mesotrophic

fish and seabirds within aged plume water.

Conversely in July 2012, CRP & nearshore Chla concentrations were much higher than in the estuary. 

Relatively homogenous SST values across the continental shelf indicated that weak or no upwelling 

occurred north of Cape Blanco just prior to or during the July 2012 survey period. Elevated Chla 

concentrations observed in the CRP may have resulted from a northward flowing plume that blocked 

southward flow of California Current water, causing this water mass to be retained on the continental 

shelf, and consequently creating a more stable environment to promote phytoplankton growth. However, 

these data only provided a snapshot for the cumulative effects of multiple, dynamic ocean processes (i.e., 

tides, internal waves, upwelling preceding survey period) which also may have contributed to the 

observed pattern. 
Figure 10. K-means results for summer surveys 

Water mass classifications are indicated by color. The box plots on the right show the distribution of sea surface 

temperature (SST), chlorophyll a (Chla), absorption of colored dissolved organic material (aCDOM) and detritus, and

proxy particle load (nLw[555], scaled 0–1.0) for each water mass. For whisker plots, red horizontal line indicates the

the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The 

whiskers extend to the most extreme data points not considered outliers, and the outliers are plotted individually 

using the red '+' symbol. 

18
19

3.3.3 Autumn surveys 

The Autumn surveys (7–19 October 2011) occurred during the lowest Columbia River flow rates when 

wind speeds were among the greatest observed (Figure 4). K-means clustering identified CRP water 

extended offshore and north of the river mouth (Figure 11). Very nearshore water south of the plume and 

water around Cape Mendocino (CRP & nearshore water) had the greatest Chla, aCDOM and proxy particle

load, and clustered with CRP water (13.3C, 2.7 mg m-3, 0.5 Au, and 0.6, respectively; Table 3, Figure 

11). Intermediate water (13.3C, 1.4 mg m-3, 0.4 m-1, and 0.4), both at the CRP boundary and around 

Cape Mendocino, likely resulted from mixing of CRP & nearshore water with warm offshore water 

(14.8C, 1.5 mg m-3, 0.4 m-1, and 0.3; Table 3, Figures 5, 11). A second offshore water mass extended the 

full latitudinal range of the survey area, and had the lowest median Chla, aCDOM, and proxy particle load

(13.8C, 0.7 mg m-3, 0.4 Au, and 0.2, respectively; Table 3, Figure 11). We observed high Chla values 

north of the CRP during October 2011 (<100 m depth; >15 mg m-3). This pattern is similar to July 2012, 

and also indicates a northward flowing plume retained on the Washington shelf. It is possible that an 

additional nutrient source was entrained in the plume, allowing accumulation of phytoplankton biomass 

and greater Chla at the surface.

In contrast to October 2011, during 19–24 September 2012, we sampled during some of the weakest wind 

speeds observed. The CRP was bi-directional and extended southwestward near Heceta Bank. Isolated 

observations of similar waters occurred very nearshore between Capes Blanco and Mendocino (Figure 

11). CRP water (13.8C, 1.8 mg m-3, 0.5 Au, and 0.6) had similar median SST as intermediate water

(13.6C, 1.1 mg m-3, 0.4 Au, 0.3), but had greater Chla, aCDOM, and proxy particle load (Table 3, Figure

11). Nearshore water extended from south of the Columbia River to south of Cape Mendocino over a 

large portion of the continental shelf. This was the coldest water mass, with Chla, aCDOM, and proxy 

particle load similar to the CRP (11.6C, 1.8 mg m-3, 0.4 Au, and 0.5, respectively; Table 3, Figure 11).

Offshore water was located predominantly north of Cape Blanco, and had the warmest SST with the 

lowest Chla, CDOM, and proxy particle load (15.4C, 0.4 mg m-3, 0.4 m-1, and 0.2, respectively; Table 3, 

Figure 11). The majority of observations were classified as intermediate, with SST similar to the CRP, 

and Chla, aCDOM, and proxy particle load all of intermediate values, between offshore and CRP water. This 

water mass was located at the CRP boundary, where it mixed with offshore water, and extended 

throughout the length of the survey area.

During September 2012, weak, southerly, downwelling-favorable winds persisted for most of the survey, 

then switched to upwelling favorable winds. The plume region was sampled 23–24 September after 

several days of moderate NW winds (Figure 4g). A bi-directional CRP was consistent with alongshore 

wind stress that switched from southerly to northerly partially through the survey. Nearshore water had 

slightly greater median Chla (1.80 mg m-3) than CRP water (1.75 mg m-3), resulting from elevated Chla 

concentrations over Heceta Bank. Lower SSTs were observed nearshore north of Cape Blanco indicating 

nutrients in this water mass likely were at least in-part, upwelling-derived.
20

Figure 11. K-means results for autumn surveys

Water mass classifications are indicated by color. The box plots on the right show the distribution of sea surface 

temperature (SST), chlorophyll a (Chla), absorption of colored dissolved organic material (aCDOM) and detritus, and

proxy particle load (nLw[555], scaled 0–1.0) for each water mass. For whisker plots, red horizontal line indicates the 

the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The 

whiskers extend to the most extreme data points not considered outliers, and the outliers are plotted individually 

using the red '+' symbol.
21

4 Summary and conclusion 

This study has demonstrated that airborne ocean color and SST data can be used to characterize water 

masses associated with the NCCS, including the CRP. We observed variability in water mass boundaries 

between years and during all seasons, consistent with the expectation of a dynamic, bi-modal CRP 

(Hickey et al. 2005) and variable forcing driving upwelling. Aircraft-mounted sensors flown at relatively 

low altitude (60 m) allowed data to be collected when satellite data would have otherwise been 

unavailable due to cloud cover. For example, of 46 survey days, none had satellite remotely-sensed ocean 

color data (daily) in >50% of the image. 

Aerial surveys have been used extensively to monitor seabird and marine mammal species abundance and 

distribution patterns at sea (Camphuysen et al., 2004; Buckland et al., 2012). Including an ocean color 

sensor and aerial pyrometer provides a means to collect in situ environmental data useful for investigating 

some of the underlying mechanisms driving these abundance patterns. Additionally, ocean color data 

collected from low-flying survey aircraft can be used for long-term monitoring of ocean properties, such 

as chlorophyll a concentrations and other derived properties. For example, during the past two decades, in 

the Chesapeake Bay, Harding et al., (1992), Harding et al., (1995), and Miller and Harding (2007) used 

low-flying aircraft to monitor phytoplankton dynamics during cloudy conditions at high spatial resolution. 

Including ocean color data sensed from low-flying aircraft during future surveys in the Pacific could 

provide important information on phytoplankton and water mass dynamics over multiple time-scales. 

Additional study in the future will use these data to assist in the quantification of NCCS seabird 

community patterns and evaluation of environmental predictors that may prove useful for predicting atsea abundance. 

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