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Nov 10, 2014 - Cite this article as: D. Brickman, D. Hebert and Z. Wang, Mechanism for the ... could affect the content, and all legal disclaimers that apply to the journal pertain. ... Maine (GoM) form an interconnected shelf sea in Atlantic Canada. ..... 2014 RV survey data (figure 2) shows uniform positive anomalies in bottom ...

Author’s Accepted Manuscript Mechanism for the recent ocean warming events on the Scotian Shelf of eastern Canada D. Brickman, D. Hebert, Z. Wang

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S0278-4343(17)30265-0 https://doi.org/10.1016/j.csr.2018.01.001 CSR3713

To appear in: Continental Shelf Research Received date: 15 May 2017 Revised date: 28 December 2017 Accepted date: 3 January 2018 Cite this article as: D. Brickman, D. Hebert and Z. Wang, Mechanism for the recent ocean warming events on the Scotian Shelf of eastern Canada, Continental Shelf Research, https://doi.org/10.1016/j.csr.2018.01.001 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Mechanism for the recent ocean warming events on the Scotian Shelf of eastern Canada CSR 2017 182 – Revision 2

D. Brickman1 , D. Hebert, Z. Wang Fisheries and Oceans Canada Bedford Institute of Oceanography P.O. Box 1006, Dartmouth, NS, B2Y 4A2 January 4, 2018

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Corresponding author. [email protected]

Abstract In 2012, 2014, and 2015 anomalous warm events were observed in the subsurface waters in the Scotian Shelf region of eastern Canada. Monthly output from a high resolution numerical ocean model simulation of the North Atlantic ocean for the period 1990-2015 is used to investigate this phenomenon. It is found that the model shows skill in simulating the anomaly fields derived from various sources of data, and the observed warming trend over the last decade. From analysis of the model run it is found that the anomalies originate from the interaction between the Gulf Stream and the Labrador Current at the tail of the Grand Banks (south of Newfoundland). This interaction results in the creation of anomalous warm/salty (or cold/fresh) eddies that travel east-to-west along the shelfbreak. These anomalies penetrate into the Gulf of St. Lawrence, onto the Scotian Shelf, and into the Gulf of Maine via deep channels along the shelfbreak. The observed warming trend can be attributed to an increase in the frequency of creation of warm anomalies during the last decade. Strong anomalous events are commonly observed in the data and model, and thus should be considered as part of the natural variability of the coupled atmosphere-ocean system.

Keywords: warming events; mid-depth; temperature and salinity; eastern Canada; numerical model; AZMP

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1

Introduction

The Newfoundland/Labrador Shelf, Gulf of St. Lawrence (GSL), Scotian Shelf (SS), and Gulf of Maine (GoM) form an interconnected shelf sea in Atlantic Canada. The circulation in the region is characterized by a general northeast-southwest flow of water from the Labrador and Newfoundland Shelf areas through the Gulf of St. Lawrence, Scotian Shelf, and Gulf of Maine to the Mid-Atlantic Bight (figure 1). The offshelf region of Atlantic Canada can be looked at as a confluence zone between the warm northeastward flowing Gulf Stream and the cold southwestward flowing Labrador Current (Loder et al., 1998). Resolving the interaction between these two currents at the tail of the Grand Banks (south of Newfoundland) is necessary to correctly simulate properties and variability downstream on the Scotian Shelf and Gulf of Maine. The region between the Scotian Shelf and the Gulf Stream is occupied by a recirculation gyre and the shelf-slope water mass. This water mass is characterized by two main water types - warm (salty) and cold (fresh) slope waters. Warm slope water has a temperature and salinity (TS) signature influenced by the Gulf Stream, while cold slope water has TS characteristics similar to the Labrador Current (Drinkwater et al., 1999). The predominance of these two water masses has been related to the North Atlantic Oscillation (NAO) (Petrie, 2007; Greene and Pershing, 2003). During periods of extended low NAO (e.g. the 1960s) cold slope water predominates, indicating increased transport of the Labrador Current around the tail of the Grand Banks, with associated colder and fresher waters penetrating the onshelf regions downstream. During periods of high NAO (e.g. 1980-1995) the opposite occurs, with warm slope water dominating offshelf, warmer/saltier waters onshelf and, by inference, reduced transport of Labrador Current water around the tail of the Grand Banks (Luo et al., 2006). While the general relationship between the NAO and Labrador Current transport is appealing, there are numerous exceptions to the rule. For example, while the one year of anomalously low NAO in 1996 resulted in a reported invasion of Labrador Current water into the Maritime region (Greene and Pershing, 2003; Drinkwater et al., 1999) numerous other high or low spikes in NAO (e.g. 2000, 2007, 2010) have resulted in no documented significant change in the water mass properties of shelf and slope waters west of the Grand Banks. In 2012, 2014, and 2015 anomalous warm events were reported on the Scotian Shelf, and Gulf of Maine and Mid-Atlantic Bight (MAB) regions. For the GoM-MAB region, anomalous warm/salty water was reported by Chen et al. (2014), Gawarkiewicz et al. (2012), Forsyth et al. (2015) and in the annual NOAA Northeast Fisheries Science Center Reference Documents (Fratantoni et al., 2013 and 2015) which also noted such anomalies entering the GoM at the Northeast Channel. The paper by Gawarkiewicz et al. suggested an abnormal meander in the Gulf Stream as the cause for this anomaly, while Chen et al. connected the anomalous warming in the MAB region to the surface heat flux which in turn was related to the north-south meandering of the atmospheric jet stream. Runge et al. (2015) and Mills et al. (2013) noted the biological effects of the warm events. For the Scotian Shelf region, reports of shelf-wide anomalies can be found in Hebert et al. (2013, 2015), and Devers et al. (2016). The effects on the Scotian Shelf snow crab fishery were first noted by crab fishers at the 2013 Regional Assessment Process meeting in Halifax (attended by author Brickman, and see Cook et al., 2014) where near-complete failure of the western SS snow crab fishery was attributed to anomalously warm fall-winter bottom temperatures in the inshore region of southwest Nova Scotia. To get a sense for the magnitude of the warming on the SS, bottom temperature anomalies averaged over the region of the annual July fisheries survey for 2012, 2014 and 2015 were 1.7, 1.6, and 1.5◦ C respectively – the three highest values in the 46 year timeseries (see below for more details on these 2

data). These warm events have also affected the deeper waters of the GSL, where Galbraith et al. (2016) have noted a gradual increase in deep water temperatures advancing toward the northern part of the Gulf. What is common to the reporting of warm anomalies on the SS and GSL region is that they are concentrated below the surface layer (typically in the 100-300m depth interval), pointing to an advective as opposed to a surface forced phenomenon. Historically, the cold/fresh period in the 1960s referred to above was also a deep layer phenomenon, which was attributed to increased advection of Labrador Sea water into the area (Petrie and Drinkwater (1993); Loder et al. (2003)). The contribution of advection to temperature changes along the U.S. east coast was also noted by Shearman and Lentz (2010) and Chen et al. (2016). In this study we analyze the output of a 1990-2015 simulation from a high resolution ocean circulation model of the North Atlantic Ocean. We find that the model shows skill in capturing key aspects of the data related to the anomalous warm events of 2012, 2014, and 2015. Based on this, we use the model output to determine the mechanism for the warming on the shelf regions of Atlantic Canada. The outline of the paper is the following: The Methods section describes the field data used in this study, the setup of the circulation model and analyses of model output. The next section presents the evidence for anomalous warming on the shelf regions of eastern Canada, based on data and model results. The mechanism for the warm events as deduced from the model simulation is presented next. The last section is a summary and discussion.

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Methods Field Data

Since 1999, the Atlantic Zone Monitoring Program (AZMP) of Fisheries and Oceans Canada has conducted regular surveys along standard sections and stations in order to help understand physical and biological processes in the Atlantic Canada region. The AZMP provides a significant database of measurements (mostly T&S) which have been used in numerous studies (for a list see: http://www.meds-sdmm.dfo-mpo.gc.ca/isdm-gdsi/azmp-pmza/publications-eng.html). Model output will be compared to data at section locations shown in figure 1, as well as the fixed station HL2 located along the Halifax line. The broadest spatial temperature and salinity coverage of the Scotian Shelf is obtained during the annual Fisheries and Oceans research vessel (RV) summer trawl survey, which covers the Scotian Shelf from Cabot Strait to the Bay of Fundy. The deep water boundary of the survey is marked roughly by the 200m isobath along the shelfbreak at the Laurentian Channel, at the outer Scotian Shelf, and at the Northeast Channel into the Gulf of Maine towards the Bay of Fundy. The RV survey, which takes about one month to complete (nominally July), typically contains about 175 CTD stations, and has been performed on an annual basis since 1970. The temperatures from the survey are interpolated onto a 0.2-by-0.2 latitude-longitude grid at standard depths and for near the bottom. Only the near bottom temperatures are presented here, as anomalies from the 1981-2010 mean.

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Figure 1: Schematic of the circulation in the study region. Blue/red arrows denote cold/warm current streams. Thick black lines are the AZMP sections used in this paper (BB = Browns Bank; HFX = Halifax; LB = Louisbourg; CS = Cabot Strait; BBAY = Bonne Bay; se-GB = southeast Grand Banks). The fixed station, HL2, is the red dot along the HFX section. The thick red line is the Laurentian Channel-Bonne Bay (LC-BBAY) transect. The brown dashed box highlights the tail of the Grand Banks region. Other abbreviations are: MAB = Mid-Atlantic Bight; GoM = Gulf of Maine; BoF = Bay of Fundy; NEC = Northeast Channel; SS = Scotian Shelf; LC = Laurentian Channel; GSL = Gulf of St. Lawrence. Bathymetry contours are: 50m (dashed), 100m (black), 200m (purple), 500m (cyan), 1000m (thick black).

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2.1.1

Other data used

Other data used come from the Ocean Tracking Network (OTN) – an interdisciplinary oceanographic program designed to monitor the oceanographic conditions along the Halifax Line at high spatial and temporal resolutions. The data (monthly averaged T bottom), come from 5 locations along the northern part of the Halifax line. The data start in 2008 at one location, and 2011 at the other 4 locations presented.

2.2

Circulation model

The model simulation used in this study has been reported on elsewhere (Wang et al., 2016; Brickman et al., 2016) so here we provide only basic information. The model is a version of NEMO-OPA (Madec et al., 2008), with the LIM sea ice module (Fichefet and Morales Maqueda, 1997). The domain covers the North Atlantic ocean from 8-75N latitude, 100W-30E longitude, at a resolution of 1/12◦ . In the region of interest, this results in characteristic grid cell dimensions of 5-6km, allowing resolution of the interaction between the Labrador Current and the Gulf Stream near the tail of the Grand Banks, as well as other shelf scale processes. Open boundary conditions are a monthly climatology based on the 18-year GLORYS reanalysis run (Ferry et al., 2010). The model was spun-up for 10 years using the Coordinated Ocean-ice Reference Experiments (CORE) “Normal Year Forcing” (Griffies et al., 2009; Large and Yeager, 2004). Then, the simulation switched to interannual surface forcing for the 1990 to 2015 period, derived from a combination of CORE and NCEP/NCAR reanalysis forcing. The simulation includes runoff from major rivers, specified according to a monthly climatology compiled for the DRAKKAR project (Barnier et al., 2006). Due to computational constraints, model output for the 26 year simulation was limited to monthly mean fields. Anomalies for a given year and month were computed by subtracting the chosen field from the model climatology. The latter was computed by averaging over each month for a period of years. The period chosen was 1990-2009, as a compromise between the averaging periods found in a variety of data sources. In practice, no qualitative differences in the results were found so long as the averaging period was at least 15 years. We note that the model is run on an annual basis, when the forcing becomes available, which limited the model termination year to 2015 at the time of writing, even though some 2016 data were available for analysis.

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Evidence for warm events: data and model

In this section we present evidence for the warm events as depicted by available data and simulated by the model. Both these sources have their limitations. The data provide limited space-time coverage of the region, thus missing aspects of the process. On the other hand, the model provides a more continuous version of events, but is not expected to match the data in all instances. The premise is that the skill exhibited by the model in simulating key aspects of the data suggests that further analysis of model output can be used to understand the processes responsible for the observed anomalies evident in the Maritime Canadian shelf seas.

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3.1

July survey data

The spatial pattern of bottom temperature anomalies derived from the July RV survey for 20122015, and the model simulation, is shown in figure 2. The data for years 2012, 2014, and 2015 show strong positive anomalies on the SS which is well simulated by the model. By contrast, 2013 had weaker, but generally positive, anomalies with this change also followed by the model, although with differences in the spatial pattern. To quantify this, the bottom temperature anomaly averaged over the SS (SS mean T bottom anomaly, < (Tb ) >) was computed for both the data and the model, for 1990-2015 (figure 3). While there are significant differences between the model and data, the model generally follows the data (correlation = 0.67) and in particular captures the variability from 2012-2015. The timeseries of < (Tb ) > show that anomalous events are not uncommon on the SS in July. For example, 14 out of 26 of the data from 1990-2015 have | < (Tb ) > | ≥ 0.5 deg. Thus patterns such as those seen in figure 2 are not unique to the years 2012, 2014, and 2015. What is more significant is that the 3 highest anomalies have occurred in these years, and there is an increasing trend in anomalies starting around 2005 in both data (red dashed line) and model (blue dashed line). (For analysis purposes, the slope from the last 10 years (2006-2015) of all series longer than 10 years has been computed and plotted as dashed lines on the various figures. All reported slopes are different from zero at the 95% confidence level.) Recently, satellite derived annually averaged SST anomalies on the SS have been predominantly positive, with an increasing trend over the last 10 years (Hebert et al., 2015). However, the July 2014 satellite image (BIO-OCG, 2017) shows mostly negative SST anomalies on the SS while the 2014 RV survey data (figure 2) shows uniform positive anomalies in bottom T for that month. The fact that the surface and bottom T anomalies can be of opposite sign suggests that surface forcing is not the cause of the bottom T anomalies.

3.2

Section data

The 3 sections on the SS – Louisbourg, Halifax, and Browns Bank (figure 1) – have been occupied twice annually (spring and fall) since the start of the AZMP (1999) and irregularly before this. In practice, this has provided up to 2 data points per year since 1997, with a long enough timeseries to construct spring and fall climatologies for the 3 sections. A typical positive anomaly result for the HFX section, and the model simulation, is shown in figure 4. Features to note are that the anomalies are concentrated at mid-depth (50-300m layer), penetrate onto the shelf, and are TS compensating (warm/salty or cold/fresh). The model simulation is seen to capture these basic features. In order to get an idea of the timeseries nature of these anomaly patterns the sections were divided into onshelf and offshelf subsections and the mean T anomalies computed in a layer from 50-300m for the onshelf subsection (data and model). The dividing line between the onshelf and offshelf was taken to be the location of the first 100m isobath found proceding from deep water to shallow. The mean onshelf T anomaly data series for the 3 SS sections is shown in figure 5a. All series show significant variability in the sign of the anomalies, indicating that alternations of positive/negative anomalies across the SS sections are not uncommon. The frequency of occurrence of positive anomalies (defined as (# positive anomalies)/(# points)) is ∼ 0.6 for each series. For all sections, the frequency of occurrence of positive anomalies increases in the last 10 years of the series. Breaking 6

Figure 2: Comparison of the spatial pattern of bottom T anomalies for the July RV survey for 2012-2015 (LHS: data; RHS: model). The 2015-07 data panel shows the colorscale (-5C to 5C). For clarity, model depths >500m are masked out. The grey line is the zero contour. Bathymetry contours are: 100m (dashed), 200m (black), and 500m (thick black). the series into sliding 4 year periods, all series have the highest occurrence of positive anomalies in the last 4 years (i.e. 2012-2015), with HFX and BB having the highest mean T anomalies in the last 4 years as well. All series exhibit positive trends in the last 10 years similar to the SS mean T bottom anomaly data. The model monthly onshelf T anomaly series for the 3 SS sections (figure 5b) show general 7

Figure 3: Timeseries of SS mean T bottom anomalies from the July RV survey and model simulation. 2006-2015 slopes are 0.19C/yr (data, dashed red line) and 0.16C/yr (model, dashed blue line). similarities to the data. The frequency of occurrence of positive anomalies (using all points) is 0.62, 0.56, 0.53 for Louisbourg, Halifax, and Browns Bank respectively, and increases in the last 10 years of the series for all sections. All series have the highest occurrence of positive anomalies and highest mean T anomaly in the last 4 years, and all series show an increasing trend in the anomalies since 2006.

3.3

High frequency data

The best continuous timeseries data we have comes from the fixed station along the Halifax section (HL2, figure 1). This station has been occupied irregularly starting in 1969 and at least once per month since 1999, allowing a monthly climatology to be derived. Data available for this study were monthly anomalies from 2009-2015. Figure 6a shows a time-depth plot of the T anomaly data for the 2009-2015 period. Strong anomalies are found for most of 2012, concentrated in depths below 50m, with 2013-2015 showing predominately positive anomalies at depth but not as strong as 2012. The model (figure 6b) is

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(a)

(b)

Figure 4: TS anomalies (dT, dS) along the HFX section for spring 2012 (a) data and (b) model. Contours every 1 deg-C for T, 0.5 PSU for S. The thick grey line is the zero contour.

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(a)

(b)

Figure 5: Mean onshelf T anomalies for the Louisbourg, Halifax, and Browns Bank (LB, HFX and BB) sections. (a) data. (b) model. Slopes for the last 10 years are annotated.

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seen to capture the year long anomaly pattern at depth in 2012, but is colder in 2010 and 2013 with the pattern of anomalies improving after early 2014 relative to the data. (Note that the model depth at this location (110m) is shallower than the actual station depth – likely due to bathymetric smoothing and grid cell averaging effects.) Analyses presented above indicate a trend of increasing positive anomalies during the last decade. Figure 7 shows timeseries of monthly HL2 110m temperature (the depth of the model bottom layer) for 2009-2015. Both the data and the model show similar increasing trends during this period (0.26C/yr, 0.31C/yr respectively) with the mean model bias being -1.1 degrees (i.e. cooler). As an exercise, if we consider HL2 as a proxy for the shelf-averaged July RV survey data for the same period (green dots in this figure and see figure 3) we find that there is general consistency in the 2 subsampled datasets, although the 3 warmest years in the survey record (2012, 2014 and 2015) are not as clear in the HL2 data. As a more extreme example, if the survey occurred in March (the black symbols in figure 7), the HL2 proxy data would lead to the conclusion that 2013 was the warmest year on the record. Although this type of behavior is not unexpected, it does highlight that caution should be taken when making deductions from limited data.

Figure 6: Time depth plots of T anomaly (dT) at HL2, for 2009-2015. (a) Data; (b) Model. Note that model depth is shallower than actual HL2 depth. Contour interval is 1 deg-C with negative contours dashed and positive contours solid. The zero contour is gray. Continuous timeseries of T bottom from the central SS, within a radius of about 40 km of HL2 are shown in figure 8. T2 (166m deep, 12km from HL2), the longest series, has a slope similar to the HL2 110m depth. Three of the 4 other (shorter) series are similar to T2 with similar slopes from 2012-2015. OTN028 (111m deep) has a significant negative slope over its record, but is seen 11

to be very similar to the HL2 data (6km away) for that period. While there are differences in these monthly series, they all support the idea that there has been a gradual increase in temperature on the SS during the last decade.

Figure 7: Model:data bottom temperature comparison at HL2, for 2009-2015. Because the model bottom is shallower than the actual HL2 station, the data are extracted for the 110m depth. The red dots are the July data, the black squares the March data. The green dots are the SS mean T bottom anomaly data (from figure 3), scaled to fit on this plot. The analyses in this section have pointed out the difficulties in determining definitively whether extended warm events have occurred using data that is limited in either time or spatial extent. Nevertheless, what is consistent in the various data is that there has been a trend of increasing anomalies during the last decade. Although model/data differences exist, we consider that key properties of these data are captured by the model simulation. Notably, the trend of anomalies is seen in the model simulation, as well as other characteristic features such as the mid-depth penetration of anomalies onto the shelf region. This suggests that further analysis of model output can be used to understand the process(es) responsible for anomalous events in the shelf regions.

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Mechanism for the warm events

In this section we take advantage of the enhanced spatial and temporal output of the model (compared to data) to investigate the mechanism for the creation and propagation of the anomalies reported on the shelf seas of Maritime Canada (GSL, SS, GoM). A key source of information is 12

Figure 8: Available bottom temperature timeseries data for 2008-2015 period, with associated slopes. Color-coded location are shown on the inset map, with HL2 shown as the black square, and the municipality of Halifax as the filled black circle. found in spatial plots of TS anomalies at fixed depth. These will be supplemented by plots of anomalies along sections to help understand how anomalies invade shelf areas via deep channels at the shelfbreak. To introduce the analysis, model TS fields at 131m for July 2014 are shown in figure 9. This level is chosen because it is shallow enough to illustrate the penetration of water through deep channels but deep enough to lie beneath the bottom for much of the shelf area. The top panel shows the model climatology for July. Features to note are the juxtaposition of cold/fresh Labrador Sea water and warm/salty Gulf Stream water at the tail of the Grand Banks (denoted as tail of the Banks). The former water type occupies the shelf break along the SS and penetrates into the GSL via the Laurentian Channel. The Gulf Stream water is situated offshelf of the colder water. In between the two, south of the SS, is the recirculation zone whose water is a mixture of these two water types. Note the smoothness of the climatological fields. The middle panel is the field for July 2014. 13

Figure 9: Example of model temperature and salinity fields at 130m depth for July. The top panels are the model climatology. The middle panels are the fields for July 2014. The bottom panels are the TS anomalies (denoted dT and dS). Bathymetry contours are: 100m (dashed), 200m (black), and 500m (thick black). It is characterized by wave-like perturbations in the Gulf Stream that have pushed warm/salty water up against the shelfbreak in the tail of the Banks region, effectively shutting off the flow of cold/fresh Labrador Current water around the bank. Warm/salty water is also seen penetrating up the Laurentian Channel toward the GSL, and onto the SS and into the GoM. The bottom panel shows the TS anomalies for that year and month, derived from the top 2 panels, clearly illustrating the penetration of warm/salty water onto the shelf regions just mentioned. Note that the patterns of T and S anomalies are almost perfectly compensating (warm/salty or cold/fresh). Because this is the case we focus mostly on T anomalies in this analysis. The best way to follow the development and propagation of TS anomalies is by viewing animations of the monthly model output at a given depth (see supplementary material). In the text we present the results as 12 panel plots. Figure 10 shows the sequence of T anomalies for 2012, the year that was anomalously warm on the SS for most of the year. The year starts (January, bottom left panel) with weak anomalies on the SS and penetrating up the Laurentian Channel, with a pattern of small positive and negative eddies near and west of the tail of the Banks. As the year proceeds through May, positive anomalies develop near the tail of the Banks and propagate downstream (i.e. westward) along the shelfbreak, penetrating the deep channels along the way. Beginning in June, a cold eddy occupies the tail of the Banks and starts to propagate downstream,

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effectively pushing the remnant warm anomalies deeper onto the shelf regions. By October, the cold anomaly at the tail of the Banks is replaced by smaller +/- anomalies. In this year, the cold anomalies are mostly trapped along the shelfbreak and in the recirculation zone, and the result of this process is an extended period of time where warm anomalies populate the shelf regions. Note that these eddies evolve while drifting so it is difficult to follow an individual eddy downstream and thus estimate the propagation speed. Nevertheless, an estimate (based on assessing plots such as figure 10) is that it takes about 8 months to travel the roughly 1200km from the tail of the Banks to the Northeast Channel, which translates into a propagation speed of about 150km/month. Also note that anomalies at the tail of the Banks can change sign on a 1-2 month timescale, and in extreme cases this process can result in a complete flow reversal at the tail of the Banks whereby the westward flowing shelfbreak Labrador Current has been replaced by the eastward flowing Gulf Stream (figure 11). It is difficult to devise a metric to quantify the properties of the anomalies over the entire model simulation. In order to do this we note that the mouth of the Laurentian Channel would appear to be a good indicator of the existence of anomalies that both propagate downstream and penetrate the shelf regions (figure 10). As such, the mean T anomaly at 131m depth for a line across the Laurentian Channel at the shelfbreak was computed for each month of the simulation (figure 12). We find that the 1990’s were dominated by negative anomalies, switching to mostly positive anomalies around 2004, with strong positive anomalies in the last 4 years of the timeseries. The fit to the last 10 years of the series has a positive slope of 0.33C/yr. The anomalies across the Laurentian Channel were assigned strong positive/negative values of +1.5 and -1.0 deg-C respectively (based on histograms, and shown by the red and blue dots in figure 12) and analyzed to determine periods of persistent strong anomalies of at least 3 consecutive months. We see that between 1991 and 2003 there were 8 instances of persistent negative anomalies, and from 2004-2015 there were 6 instances of persistent positive anomalies, including 15 and 9 month periods during the last 28 months of the simulation. We postulate that the persistence of one-signed anomalies along the shelfbreak will result in deeper penetration onshelf via the deep channels – a sort of integrative effect. Based on this we would expect to find a more extensive spreading of positive anomalies onto the shelf regions during the last years of the model simulation. To investigate this we look at the propagation of anomalies along the Laurentian Channel. Figure 13 shows an example of TS anomalies along the LC-BBAY section (figure 1) for May 2015. At this time an 800km tongue of anomalous warm/salty water has penetrated up the Laurentian Channel, concentrated in a layer from 50-200m. To get an idea of the time progression of these anomalies, time-depth plots of T anomaly profiles taken from the center of the CS and BBAY sections (blue and red lines in figure 13) were extracted for 20042015. The timeseries of anomalies at mid-depth (100-200m) at the CS section (figure 14a) reflects the anomaly pattern at the mouth of the Laurentian Channel (figure 12), with a period of weak positive anomalies (2004-2006) followed by negative or neutral anomalies from 2007 until late 2011. The first positive anomaly arrives at CS in late 2011, with all of 2012 experiencing warm anomalies. From late 2013 to the end of 2015 the CS section is populated by warm anomalies at mid-depth. The timeseries at the shallower BBAY section (figure 14b) is similar to CS, with a suggestion of a time delay of about a year. For example the late 2011 anomaly seen at CS seems to appear at BBAY in late 2012. However, inspection of animations at 131m (see figure 10, and supplementary material) indicate that the anomalies that appear at BBAY are not the result of a single pulse that takes about a year to travel from CS to BBAY, but rather they are related to (i.e. require) the 15

Figure 10: Temperature anomalies at 131m depth for 2012. The sequence starts at the bottom left (month 1) and proceeds clockwise up the left column and then down the right column. The last panel contains the colorscale. Bathymetry contours are: 100m (dashed), 200m (black), and 500m (thick black). 16

Figure 11: Temperature and salinity anomalies (dT, dS) and normal velocity (V) at the tail of the Grand Banks (along the se-GB section) for July 2014, showing a flow reversal at the shelfbreak accompanied by warm/salty anomalies concentrated in the top 300m. Negative velocities denote flow through the section from west-to-east, i.e in the predominate direction of the Gulf Stream. The thick grey lines are the zero contours. Colorscale units (top to bottom) are deg-C, PSU, and m/s.

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Figure 12: Mean temperature anomalies at 131m depth across the mouth of the Laurentian Channel for 1990-2015, with anomalies > 1.5 deg-C in red and < −1 deg-C in blue. Large dots indicate beginning of sequences longer than 2 months of persistent anomalies of the same sign. Text at top and bottom of plot is the number of months in the sequences. 2006-2015 slope (dashed blue line) is 0.33C/yr. persistence of anomalies at the mouth of the Laurentian Channel. Note that below 200m in the BBAY timeseries there is evidence of a gradual warming starting in early 2010. Warming of the deep waters of this part of the GSL has been reported by Galbraith et al. (2016, figure 56).

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Discussion

In this study the output from a 26 year high resolution numerical model simulation of the North Atlantic Ocean was analyzed to investigate the recent subsurface warming events reported in the SS-GoM region in 2012, 2014 and 2015. The data analyzed consist of shelf cross sections (occupied twice annually), RV survey data (broad spatial coverage, occupied once per year), and point source locations (providing high frequency coverage). These data are characterized by large spatial and temporal variability and indicate that strong warm/salty or cold/fresh anomalies are not uncommon on the shelf region. The gaps in either space or time in the measurements make it difficult to definitively deduce the persistence and spatial extent of observed anomalous events. The data support 2012 as being anomalously warm/salty for most of the year. However, while 2014 and 2015 exhibited strong anomalies in some months, whether these years should be considered warm on an annual basis is not clear. Simulating these events is a difficult task for a model but, nevertheless, the model does exhibit some skill. For example, the timeseries of SS mean T bottom anomalies from 1990-2015 (figure 3) is reasonably simulated, and the spatial pattern of July T bottom anomalies from 2012-2015 (figure 2) is clearly captured by the model. The mid-depth concentration of the anomalies (figure 4) is also clear in the model simulation. For illustrative purposes the last 10 years (or less) of all timeseries data presented were fit to straight lines. Notably, all the data suggest that there has been a significant increasing trend of warm (salty) anomalies during the last decade, of magnitude 0.2 − 0.3C/yr. This behavior is also found in the model simulation (figures 3, 5, 7, 8). While individually these trends may not be noteworthy, taken together they argue for a significant decadal warming event. Note that these trends are considerably larger than the long term trends available for the study region, and globally 18

Figure 13: TS anomalies (dT, dS) along the Laurentian Channel-Bonne Bay (LC-BBAY) section for 2015.05. The blue and red lines mark the intersection of the LC-BBAY section with the Cabot Strait and Bonne BAY sections respectively (figure 1). dT contours at 1, 3, 5 deg-C. dS contours at 0.5, 1.0 PSU. The thick grey line is the zero contour. (typically (1 − 3)C/century). This large rate of temperature increase during recent years, relative to climatology, has also been observed in the GoM-MAB region (Pershing et al. (2015); Mills et al. (2013); Forsythe et al. (2015); Shearman and Lentz (2010)), and in the SS-GSL region (Galbraith et al. (2016); Hebert et al. (2015)). However, if we consider the cold 1960s period on the SS reported by Petrie and Drinkwater (1993) and Loder et al. (2003) we find that both the onset and retreat of the cold period, each of which took about a decade, were also characterized by temperature trends of magnitude 0.2 − 0.3C/yr (see Petrie and Drinkwater (1993) figure 2; Loder et al. (2003) figure 10; and also Hebert et al. (2015) figures 16 and 24). This suggests that changes associated with the current phenomenon are part of the natural variability in the region and caution is required before interpreting these changes as evidence of global warming. The mechanism for the creation of anomalies is evident from looking at time sequences of model anomalies at a mid-depth level (figure 10). The interaction of the Gulf Stream and the Labrador

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Figure 14: Time-depth plots of T anomalies (dT) for profiles taken at central locations from the (a) Cabot Strait (CS) and (b) Bonne-Bay (BBAY) sections, for 2004-2015. Note that the BBAY section is shallower than CS. The zero contour is grey; 1 deg-C solid black; -1 deg-C dashed black. Current at the tail of the Grand Banks results in the pinching off of warm/salty or cold/fresh eddies that propagate westward along the shelfbreak toward the mid-Atlantic Bight penetrating onto the Maritime shelf region via deep channels along the way. The propagation speed for these eddies is estimated to be about 150km/month. The process is seen to occur in pulses as opposed to continuous streams of anomalous water. The propagation speed estimate implies that it takes about 8 months for anomalies to transit from the tail of the Grand Banks to the Northeast Channel. This is consistent with the progress of cold-fresh anomalies through the region associated with the 1996 low NAO event, reported by Drinkwater et al. (1999) (see also figure 1b of Greene and Pershing (2003)). Also of interest is that their reported anomaly arrival times at various locations on the SS-GoM shows instances where the signal arrives later at an upstream location than a downstream one. This is consistent with the process occurring in pulses, with associated high spatio-temporal 20

variability that brings an element of chance to the monitoring of these events. The model results show that the last decade is characterized by an increased frequency of creation of warm/salty anomalies with increased persistence (i.e. continuous sequences) of samesigned anomalies (figure 12). This results in deeper penetration of anomalous water through deep channels into the GSL (figures 13, 14) and a buildup of anomalies on the SS and GoM regions. This process can explain the trend of increasing temperature over the last decade observed in Maritime Canadian waters. The longer period data series presented herein indicate that significant anomalies are common in the region, and beg the question of the forcing responsible for these observations. For the NW Atlantic Ocean the NAO is often considered, but correlations with the presented series showed varied results. For example, no significant correlation with the (djfm) NAO index (Hurrell, 2016) (at various lags) was found between both the observed mean bottom temperature anomalies on the SS (figure 3) and the annually averaged onshelf temperature anomaly data for the Louisbourg, Halifax, and Browns Bank sections (figure 5a). With respect to model output, the annually averaged mean temperature anomaly across Laurentian Channel (figure 12) was found to be uncorrelated with the NAO. The correlation between the NAO and the transport anomaly at the shelfbreak of the se-GB section was -0.5, which is consistent with Luo et al. (2006) who found reduced transport of the Laurentian Channel during persistent positive NAO forcing. Similarly, a determination of the annual occurrences of reversed flow at the tail of the Grand Banks (such as shown in figure 11) has a correlation of 0.48 with the NAO, indicating a weak tendency toward increased flow reversals when the NAO is positive. While these correlations are not outstanding, they are similar in magnitude to those reported in Petrie (2007). Petrie (2007) found significant correlation between bottom TS anomalies in the Maritime region and the NAO when the latter were grouped into persistent periods of same-sign (3 or more years) and the former were extracted from the last year of each persistent 3 year period. The idea of an integrative effect is consistent with our finding that recent persistent positive (model) anomalies can explain the observed trend and spreading of anomalies found in the data. Note that there is evidence that the system may now be responding differently. For example Mountain (2012), investigating the relation between Labrador slope water entering the GoM and the NAO, found a change in that relationship starting in the mid 1990s, with no notable response to the 2000, 2007, and 2010 NAOs. Similarly, Wang et al. (2016), who analyzed output from the same model as this study but in the Labrador Sea region, found a noticible increase in the transport of the western part of the Labrador Current around 2001 (data and model) which could be attributed to a change in the surface forcing pattern associated with the NAO at that time, but not to the simple NAO timeseries. So while the NAO is a popular indicator of ocean variability, it seems that recent studies point to a more complicated (although not necessarily unrelated) relationship with this climate variable. Relating the model response to external forcing is a subject of further work.

6

Conclusion

This study has used circulation model output to investigate reported subsurface warming events on the shelf seas of Maritime Canada. Analysis of available timeseries data support a general trend of warming in the region over the last decade. This trend is observed in comparable model output. The model simulation reveals that these events are due to the interaction of the Gulf Stream and Labrador Current at the tail of the Grand Banks which leads to the formation of anomalous 21

warm/salty (or cold/fresh) eddies that propagate from east-to-west along the shelfbreak toward the Gulf of Maine, invading the shelf region along the way. Analysis of model output shows that penetration of anomalies deep onto the shelf region requires periods of persistent generation of samesigned anomalies which also results in longer timescale temperature (and salinity) trends. This can account for the presently observed warming trend and, by inference, the cooling and warming trends that were observed during the 1960s. These strong anomalous events are commonly observed in the data and model, and thus should be considered as part of the natural variability of the coupled atmosphere-ocean system. While the model simulation is of course not perfect, this study shows that, in concert with observations, detailed analysis of model output can help in understanding difficult to observe processes occurring in the ocean.

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