Environmental Data Summary C-MORE BioLINCS 2011 Cruise

Environmental Data Summary C-­MORE BioLINCS 2011 Cruise John Ryan (ryjo@mbari.org; 831.775.1978) 1. Purpose The purpose of this brief report is to summarize some environmental data from remote sensing and in situ measurements, toward facilitating interdisciplinary synthesis of results from the BioLINCS cruise north of Hawaii in September 2011. Some of these results are familiar, but it is useful to organize them, compare them in one place, and identify further work. Having never studied this region, my interpretations are basic (version 1). 2. Overview A satellite-­‐based overview of regional patterns during the cruise begins to illustrate processes that influenced regional ecology and the trajectory of the drifting ESP (Figure 1). ï15 ï10 ï5
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c) Chlorophyll (mg mï3)
b) Sea Surface Temperature (°C)
a) Sea Level Anomaly (cm)
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Figure 1. BioLINCS environmental overview from remote sensing. Averages of (a) AVISO sea level anomaly (SLA), (b) RSS microwave / infrared sea surface temperature (SST), and (c) MODIS-­‐Aqua chlorophyll concentration. All averages are for the cruise period, 6-­‐20 September 2011. SLA contours are overlaid on all panels to illustrate influences of eddies on the transport of heat and phytoplankton. Station ALOHA is marked by the circle and +. The >-­‐shaped track shows the drift path of the ESP. Altimetry indicates that two anticyclonic eddies (A1, A2 in Figure 1a) influenced the ESP drift path. Along their eastern flank, the eddy anomalies merged into a larger-­‐scale gradient in SLA that spanned the combined latitudinal extent of both eddies. Signifying eddy transport anomalies, SST and chlorophyll patterns show anticyclonic “wrapping” of relatively warm, chlorophyll-­‐enriched waters from the area west of the eddies, around / through A1, southeastward along the eastern periphery of both eddies, and southwestward along the southern periphery of A2. At a larger spatial scale, we may interpret that transport patterns driven by eddy pairs (A1/C1, A1/A2, A2/C2 in Figure 1a) caused deformation of zonal thermal and biological gradients. Entrainment of relatively warm, chlorophyll-­‐enriched waters from the area west of A1/A2 evidently developed in the A1/C1 “conveyer” and extended along the A1/A2 eastern boundary into the A2/C2 “conveyer”. These waters also apparently branched eastward between A1 and A2 to extend along the southern periphery of C1. Entrainment of relatively cool, chlorophyll-­‐
poor waters from the area east of A1/A2 evidently developed in the A2/C2 “conveyer”, extended along the southern periphery of A2, and entered the interior of A2. Mean patterns indicate that the ESP drift took place in the center of an eddy stirring zone involving confluence of different water types, and that the vertex in the ESP trajectory was in a frontal zone of this regional stirring process (Figure 1b). 3. The ESP drift relative to time-­dependent eddy signals The cruise-­‐average surface properties provide an effective overview for describing regional patterns and inferring dynamics. Time-­‐dependent maps during the ESP drift period define temporal variability and spatial patchiness (Figure 2). Sea Level Anomaly (cm)
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a) September 7ï9
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Figure 2. 3-­‐day mean maps of SLA and SST relative to corresponding ESP drift segments. The full ESP drift track is white; 3-­‐day segments are black. SLA contours are overlaid on SST. The final maps (e) cover the end of the cruise, after ESP was recovered but ship operations continued. The dashed black line in the SLA maps serves as a fixed reference to describe eddy movement. The September 7-­‐9 maps (Figure 2a) illustrate a warm filament (yellow-­‐orange) extending from the area west of A1, around the northern and eastern periphery of A1. Although contiguous, this filament had a patch of warmest SST in the middle (along the northeastern periphery of A1). Warm patches within this region, and within the extended warm-­‐
filament trajectory, were observed throughout the remainder of the time-­‐series (Figure 2b-­‐
e), indicating time-­‐varying transport / mixing. At the southern extent of the A1/A2 warm-­‐
filament trajectory, i.e. along the southern periphery of A2, patchiness in SST developed at the interface with cool waters entrained between A2/C2 (Figure 2), suggesting that this confluence of regional waters was an area of active mixing that was feeding into the ESP drift domain. Structure in the first map (Figure 2a) suggests entrainment of relatively cool waters from A2 toward the southern periphery of A1. This is relevant to interpreting the locally enhanced heterogeneity in temperature and salinity observed in the A1 / A2 interaction zone (Section 4). The maps also suggest that some warm waters from west of A1/A2 may have been entrained along the northern periphery of A2 (Figure 2c,d). However, northeastward transport along the northwestern periphery of A2 was likely inhibited by southwestward flow along the southeastern periphery of A1. That is, opposing flows within this zone of anticyclonic eddy interaction may have inhibited eastward flow of warm waters between A1 and A2. Further, lateral shear may have locally enhanced mixing of regional water types entrained by the eddy circulation field. This may be part of the framework for interpreting the microbial patterns observed by ship and ESP sampling (Sections 5, 6). 4. ESP-­drift attributes and local environmental signals During the 8-­‐18 September deployment (Figure 3a), the ESP drift speed varied from < 10 cm/s to > 40 cm/s (Figure 3b). The fastest drift occurred along the western flank of A1. The slowest drift occurred at and around the turning point from northward to westward drift (Figure 3b), in the boundary interaction region between A1 and A2 (Figures 1, 2). Water velocity measured by the WH300 ADCP on the ship (not shown) was consistent with the velocity defined by the drifter track (Figure 3b). While the ESP-­‐float drift trajectory is representative of surface currents, the first good bin for the ship ADCP data was at 16 m. A slight clockwise rotation of velocity vectors between the surface (ESP float) and subsurface (ADCP, 16 m) was apparent. This is consistent with rotation of the wind-­‐driven component of circulation (Ekman spiral). There was also an upward looking WH300 ADCP on the ESP itself. Current velocity relative to the ESP is an important part of describing “Lagrangian-­‐
ness” of the experiment, and this will be examined in detail following correction of an error in the data caused by an incorrect parameter in the ADCP configuration. As noted, the relatively warm (cool) SST anomalies across A1 (A2) are consistent with deformation of the regional SST gradients by the eddy circulation field (Sections 2, 3). These anomalies were similarly detected in situ by sensors onboard ESP (Figure 3c). The low salinity waters toward the south are consistent with September-­‐average surface salinity from the Aquarius satellite sensor (online1). Temperature and salinity were relatively homogeneous toward the southern and western reaches of the drift, where they were closer to the periphery of the individual eddies (Figure 3c, 3e). Temperature and salinity were relatively heterogeneous and patchy in the A1/A2 interaction zone, where drift velocity was lowest (Figure 3b, 3c, 3e), consistent with mixing of water types (Sections 2 and 3). Considering the anticyclonic flow of A1 and A2, their interaction would presumably create a zone of confluence and enhanced shear. Enhanced lateral and vertical 1 http://aquarius.nasa.gov/images/gallery/monthlysss/aq_global_sep2011.png We may consider cal/val of this data for the study region and adding to Figure 1. mixing may have developed in the A1/A2 interaction zone. Analysis of ESP, ship, and glider data may inform this question, with relevance to microbial ecology (e.g. Section 6). Temperature (°C)
ESP drift speed (cm/s)
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Figure 3. ESP drift summary; all variables are averaged to hourly resolution. Although the signal range is quite small, chlorophyll concentrations at the depth of ESP were higher in A2 (Figure 3d), in contrast to what would be predicted from satellite-­‐based observations (Figure 1c). This is not explained by the vertical profile of chlorophyll between the depth of ESP and the surface (based on examination of the ship CTD profiles). If these small bio-­‐optical signals are of interest, further analysis is required, e.g. to consider mixing the DCM biomass up to the depth of the ESP. Fluorometric chlorophyll profiles from CTD casts indicate that the DCM was centered below 100 m throughout the survey domain. However, along the periphery of A2 there was evidently greater vertical perturbation and horizontal discontinuity in the DCM, and relatively elevated chlorophyll concentrations extended shallower in the water column. 5. Glider sections Mission 10 of SeaGlider-­‐148 included three sections across the BioLINCS study region. These sections passed along the western, northern, and eastern periphery of A1 (Figure 4). That is, it was somewhat of a square dance, and it appears that none of the glider sections provided a synoptic view across the core of eddy A1. An initial look at the CTD data suggests a need for QC, so I am checking with Lance Fujieki on this. Sea Level Anomaly (cm)
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Figure 4. The surface track of SeaGlider sections relative to SLA. Sections were occupied during (a) 29 August -­‐ 7 September, (b) 10 -­‐ 15 September, and (c) 16 -­‐ 28 September. Maps show the most temporally appropriate 3-­‐day mean SLA for each glider section. 6. T-­S diagrams and biological signals It is useful to place the properties of waters sampled by ESP, which drifted at a nearly constant depth along its trajectory, within the context of properties throughout the upper water column (Figure 5a). In some cases, it may also be useful to interpret the biological signals in “water property space” (e.g. Figure 5b). Group A (x 105 copies Lï1)
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Figure 5. (a) T-­‐S diagram representing water properties along the ESP trajectory (dark gray) relative to water properties within the upper 100 m (light gray, from ship CTD). (b) An example of biological results from ESP in the context of a water-­‐property diagram. The ESP “water property envelope” exhibited a greater temperature range toward the high-­‐
salinity side (Figure 5a). One of the BioLINCS target microbes exhibited a distribution organized relative to this envelope (Figure 5b). Group-­‐A populations were most abundant in the cool/fresh domain, and they decreased toward the warm/salty domain. Toward the high-­‐salinity end, this biological gradient was most apparent along the warm side of the T-­‐S envelope, whereas homogeneously low signal was observed within the cool/salty domain. The distribution of the biological gradient in T-­‐S space suggests the role of lateral mixing of water types (Sections 2-­‐4). Additionally, the extension of a “T-­‐S bridge” between the highest levels of Group-­‐A signal (red box in Figure 5b) and intermediate levels of Group-­‐A signal (green box in Figure 5b) extended across isopycnals. There is some indication of this cross-­‐isopycnal signal in the more sparsely sampled ship CTD profiles. If substantiated (e.g. with the more densely sampled glider data set), we may more carefully consider interpretation of vertical mixing relevant to microbial ecology. 7. SST Complementary to the eddy-­‐field description from SLA, SST maps can be used to infer related circulation patterns and their temporal variability (Sections 2, 3). Not all satellite SST data sets are measured or created equally. In an effort to determine which satellite SST product would best serve this study, I examined four options: 1. daily OI from a single microwave sensor 2. daily OI maps from two microwave sensors 3. daily maps from multiple geostationary infrared sensors (GOES; I computed daily averages from hourly de-­‐clouded maps, night-­‐time only because of known problems with daytime data). 4. daily OI maps that integrate microwave and infrared. Theoretically, option 4 would provide the best product because it integrates the benefits of each form of remote sensing: through-­‐cloud visibility of microwave provides better temporal resolution, and infrared provides better spatial resolution. Plus, combined use of more sensors means better sampling to produce more robust mean maps. Examining 3-­‐
day and whole-­‐cruise averages relative to SLA maps, I found that option 4 exhibited the greatest degree of consistency between SST and SLA patterns. The description of OI mapping for this SST product2, and discussion with the NOAA person who was actively involved in developing this product with RSS (Dave Foley), convinced me that it is the best option for this study. However, the comparisons of all four products are available to those interested. 2 http://www.ssmi.com/sst/microwave_oi_sst_data_description.html