ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-9-259-2017The Sub-Polar Gyre Index – a community data set for application in fisheries and environment researchBerxBarbarab.berx@marlab.ac.ukhttps://orcid.org/0000-0001-5459-2409PayneMark R.https://orcid.org/0000-0001-5795-2481Marine Scotland Science, 375 Victoria Road, Aberdeen, AB11 9DB, UKCentre for Ocean Life, Technical University of Denmark, National
Institute of Aquatic Resources, 2920 Charlottenlund, DenmarkBarbara Berx (b.berx@marlab.ac.uk)18April20179125926622October20161November201614March201715March2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://essd.copernicus.org/articles/9/259/2017/essd-9-259-2017.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/9/259/2017/essd-9-259-2017.pdf
Scientific interest in the sub-polar gyre of the North Atlantic Ocean has
increased in recent years. The sub-polar gyre has contracted and weakened,
and changes in circulation pathways have been linked to changes in marine
ecosystem productivity. To aid fisheries and environmental scientists, we
present here a time series of the Sub-Polar Gyre Index (SPG-I) based on
monthly mean maps of sea surface height. The established definition of the
SPG-I is applied, and the first EOF (empirical orthogonal function) and PC
(principal component) are presented. Sensitivity to
the spatial domain and time series length are explored but found not to be
important factors in terms of the SPG-I's interpretation. Our time
series compares well with indices presented previously. The SPG-I time series
is freely available online (http://dx.doi.org/10.7489/1806-1), and we
invite the community to access, apply, and publish studies using this index
time series.
Introduction
The sub-tropical and sub-polar gyres are the
dominant features of the surface circulation of the North Atlantic Ocean
(Fig. ). Both are driven by the combination of permanent wind
features, heat-input variation with latitude, and the global overturning
circulation. The sub-tropical gyre is formed by the synthesis of the Gulf
Stream, North Atlantic Current, Canary Current, and North Equatorial Current
to yield a nearly continuous, anticyclonic circulation in the sub-tropical
North Atlantic Ocean. Its equivalent in the sub-polar region can be
considered as a cyclonic gyre encompassing the North Atlantic, East
Greenland, and Labrador Currents (Fig. ). Within the North
Atlantic, changes in the strength and extent of the sub-polar gyre have been
linked to changes in the advection of water masses and
changes in their properties . These changes
in the strength and extent of the sub-polar gyre have been attributed to the
strong overturning circulation observed in the preceding years
. More recently, marine ecologists have
reported changes in the ecosystem associated with changes in circulation in
the North Atlantic and particularly the sub-polar gyre region (e.g.
; ).
Based on a survey of research scientists within the International Council on
the Exploitation of the Seas (ICES) community, the Working Group on
Operational Oceanographic products for Fisheries and Environment (WGOOFE;
) identified a need from fisheries and environmental
scientists for freely accessible oceanographic data, in a suitable data
format and with operational delivery. Within the climate community, the need
for large volumes of data to be distilled into readily accessible,
user-friendly data sets has recently driven the development of the Climate
Data Guide . This site provides a
community-based overview of available data products and includes some expert
guidance on the strengths and weaknesses of the products as well as
information on their derivation. In its work as an interface between the ICES
community and the operational oceanography community, WGOOFE found that
index-based products – where a complex spatio-temporal data set, process, or
state estimate may be reduced to a single time series, such as the North
Atlantic Oscillation Index (NAO; Hurrell and NCAR Staff, 2013) – remain a
major gap in the available oceanographic data products .
Recently, presented three index-based oceanographic
data products developed by the MyOcean project: the El Niño indicator, the
Kuroshio Extension indicator, and the Ionian Surface Circulation indicator.
However, these index time series have not yet been made readily available to
the wider community. To date, no readily accessible up-to-date data set exists
summarizing the sub-polar gyre's dynamics. This limits some researchers in
the field of ecosystem science in the search for drivers of ecosystem
variability within the region and therefore also the development of improved
fisheries management tools.
Map of the sub-tropical and sub-polar North Atlantic with a schematic
representation of the ocean circulation.
The sub-polar gyre index has been applied recently in a number of studies
investigating ecosystem variability. present a decline
in nutrient concentrations (particularly nitrate and phosphate
concentrations) in the Rockall Trough related to the strength of the
sub-polar gyre. Recent work has even highlighted potential linkages between
sub-polar gyre dynamics and higher trophic levels, including commercially
important fish stocks . In
2012, the ICES Working Group on Widely Distributed Stocks (WGWIDE) emphasized
the absence of such a data product as a key obstacle when studying
distribution and abundance changes in economically important fish stocks,
such as mackerel and blue whiting .
The data set presented here aims to fill this gap in index-based operational
oceanographic data products by presenting a time series of the Sub-Polar Gyre
Index (SPG-I) extending from the start of satellite altimetry records
(January 1993) to the present. The data set presented is freely available and
easily citable. In Sect. , we outline the underlying data
set and methodology for our SPG-I calculation; in Sect.
we present the data product and its sensitivities and compare our time series
with other published results of SPG-I variability; and finally we present how
to access the SPG-I data product and acknowledge its use (Sect. 4), followed
by a brief conclusion and outlook (Sect. ).
MethodologySea surface height data
The altimeter products used to create the SPG-I were obtained through the
Copernicus Marine Environment Monitoring Service (CMEMS; product identifier:
SEALEVEL_GLO_SLA_MAP_L4_REP_OBSERVATIONS _008_027). For our analysis,
we obtained the delayed time, global, daily Maps of Sea Level Anomaly (MSLAs)
on a 1/4∘ by 1/4∘ grid. The product is the result of
merging all available satellite missions at a given time, resulting in a
better-quality product (particularly in recent years). Monthly mean maps were
created by averaging the multimission daily maps by month, while seasonal
climatology maps were calculated by averaging the monthly mean maps within
the same month for all complete years (1993–2015). The climatological maps
therefore represent the average conditions for each month of the year
throughout the observation period. To avoid issues with observations in grid
points on land, a land–sea mask was obtained by interpolating the
1/12∘ by 1/12∘ TerrainBase database
on to the same grid as the altimeter data. The land–sea mask was also used to
remove altimeter data in the Pacific Ocean, Great Lakes, and Mediterranean
Sea. The altimeter data set starts in January 1993, and the latest update
obtained from CMEMS extends to April 2016.
Calculation of SPG-I
We followed the method of to calculate the SPG-I, which
has been defined as the first principal component (PC1) of an empirical
orthogonal function analysis (EOFA) of the sea level anomaly field in the
North Atlantic. In Sect. , our results are compared to
similarly defined gyre indices based on altimeter data, although alternative
indices based on sea surface temperature and wind stress curl have also been
defined by and , respectively.
In our analysis, we restricted the geographical extent to a rectangular area
focused on the North Atlantic's sub-polar gyre (delimited by the
60∘ W and 10∘ E meridians and the 40 and 65∘ N
parallels). The exact choice of spatial domain varies between authors – here
we have chosen a region focused on the sub-polar gyre region itself. However,
we also perform sensitivity analyses to examine the effect of this choice on
the resulting index time series. Seasonality in the monthly mean observations
of sea level anomaly was removed by subtracting the relevant climatological
sea level anomaly map. We calculated the monthly SPG-I based on these
deseasonalized maps of sea level anomaly. A yearly SPG-I is calculated based
on the average of the deseasonalized sea level anomalies by calendar year,
with only complete years included in the analysis.
EOFA is a well-established analysis technique, but for completeness a short
description follows. For more in-depth information, we refer the reader to
.
Overview of defined regions to investigate sensitivity of SPG-I to
the chosen spatial coverage (regions are also shown in
Fig. ). The abbreviations in the first column correspond
to those used in figure legends. S: S. Häkkinen time series; B: time series
presented here; R: time series based on different regions; 1X1: time series
presented here based on a 1∘ by 1∘ grid; 2X2: time series
presented here based on a 2∘ by 2∘ grid; all other time
series presented here are based on a
1/4∘ by 1/4∘ grid.
First mode of the EOFA of sea surface height: (a) empirical orthogonal function (spatial field, in centimetres); (b) principal component
(temporal variability, dimensionless).
Map of defined regions to investigate sensitivity of SPG-I to the
chosen spatial coverage. Details of boundaries are in Table .
S: S. Häkkinen time series; B: time series presented here; R: time series
based on different regions; 1X1: time series presented here based on a
1∘ by 1∘ grid; 2X2 grid: time series presented here based on
a 2∘ by 2∘; all other time series presented here are based on a 1/4∘ by
1/4∘ grid.
A major strength of EOFA is the reduction in data volume: a large data set can
be reduced to a smaller one containing the most significant fraction of
variability contained in the original data. In particular, it can often
reduce large spatial data sets to a more manageable size. We can consider the
altimeter data set as a time series of I time instances at J spatial
locations, defined by the point's latitude and longitude on the grid. During
EOFA, the data matrix is standardized (for each location, the mean is removed
from the time series and the remaining anomalies then scaled by dividing by
their standard deviation) and then decomposed into mutually uncorrelated
(orthogonal) modes which have a spatial pattern (these are called the
eigenvectors or empirical orthogonal functions) and a temporal amplitude
(these are called the eigenfunctions or principal components). The first mode
extracted using the EOFA technique explains the largest fraction of
variability in the data set, and each subsequent mode explains the largest
fraction of the remaining variability. By extracting the first mode, we
obtain the time series of SPG-I based on the MSLA data. The sign of an EOF (empirical orthogonal function)/PC
is not determined explicitly during the calculation process and may vary
between machines and software versions. We therefore define the sign of the
EOF and PC in 1993 to be positive and make adjustments as required.
Comparison of yearly SPG-I for different domains within the North
Atlantic: (a) yearly SPG-I time series; (b–d) corresponding empirical orthogonal function (spatial field,
in centimetres) for three domains. Table and Fig.
show the defined areas used in the EOFA. Colours in (a) correspond
to those used in Fig. .
Sensitivity of yearly SPG-I to time series length:
(a) principal component (temporal variability, dimensionless) for
time periods from 1993 to 2002, increasing 1 year in length (grey lines)
with periods shown in (b–d) in bold black lines; empirical orthogonal function (spatial field, in centimetres) for four time periods:
(b) 1993 to 2002, (c) 1993 to 2007, (d) 1993 to
2012, and (e) 1993 to 2015.
Comparison of SPG-I calculated here with that calculated by S. Häkkinen, based on
monthly time series
The addition of data points either in space or time also changes the EOF/PC
results, which is briefly explored here (Sect. ). For the
user, this means the entire time series needs to be updated when the temporal
coverage of the data is updated. To investigate the impact of the chosen
spatial extent of data included in the EOFA, we performed the analysis on a
number of different regions centred on the sub-polar North Atlantic. These
results were based on the 1/4∘ by 1/4∘ grid, although
time series based on the 1∘ by 1∘ grid and 2∘ by
2∘ grids are also presented. The longitude and latitude limits of these
regions are listed in Table and shown in
Fig. .
SPG-I time seriesInterpretation
Figure shows the first principal component (i.e. temporal
variability) of the SPG-I and corresponding empirical orthogonal function
(i.e. the spatial field). The spatial field shows the signature of the Gulf
Stream–North Atlantic Current variability (eddy-like features in
Fig. a) as well as the lower sea level in the sub-polar
gyre region compared to the sub-tropical Atlantic. This first mode of the
EOFA explains 25.9 % of the total variance. The second and third mode
explain 8.6 and 6.4 % of the variance, respectively. Their interpretation
is less studied, and as they are unrelated to published work on the sub-polar
North Atlantic, they are not discussed further here.
In the SPG-I time series (Fig. b), positive values of the
index are associated with a strong sub-polar gyre circulation with a wide
spread. In comparison, negative values of SPG-I are associated with a weak
sub-polar gyre and westward retraction. The commonly reported weakening and
contraction of the sub-polar gyre can be seen in the mid to late 90s . More recently the sub-polar gyre has been
variable but remains weak (Fig. b).
The exact mechanism whereby changes in the sub-polar gyre state influence
circulation across the North Atlantic basin continues to be an area of active
research. present a schematic representation of how a
weaker sub-polar gyre allows for greater advection of sub-tropical water
masses (see their Fig. 2). The changes in the sub-polar gyre have recently
been proposed to be a response to a prolonged positive NAO phase
. The atmospheric forcing and
storm track variability strengthen the Atlantic Meridional Overturning
Circulation, enhancing the northward transport of heat in the oceans and
subsequent negative feedback on the sub-polar gyre circulation. Both
and highlight the importance of the
ocean's heat transport in the dynamics of the sub-polar gyre.
Sensitivity to spatial extent
To investigate the sensitivity of the SPG-I to the chosen spatial domain for
the EOFA, the analysis was repeated for a number of different regions
(Fig. and Table ). The results for the
annual SPG-I, shown in Fig. a, show that a
separation does occur between indices focused solely on the sub-polar region
(time series B, R6, R7, and R8 in Fig. a) and those
incorporating a wider region of the North Atlantic. In particular, when
including the region in the tropical North Atlantic Ocean, the SPG-I becomes
strongly linear. The correlations between time series based on these various
regions are high (all greater or equal to 0.90, p<0.001). The correlation
coefficients also highlight that indices calculated from regions restricted
to the sub-polar North Atlantic correlate well to each other but less well to
those calculated over the wider North Atlantic, with r values dropping from
0.99 to approx. 0.92. Linear detrending of the index time series prior to the
correlation analysis does not influence this outcome: the lower correlation
coefficients are between 0.53–0.98, but all remain statistically significant
and exhibit the same pattern. This pattern is due to the fact that the SPG-I
calculated for regions narrowly confined to the sub-polar North Atlantic has
a higher level of inter-annual variability.
Sensitivity to time series length
To highlight the impact of increasing time series length on the SPG-I, the
analysis was performed on the first 10 years of altimeter data (1993 to
2012), increasing time series length by time increments of 1 year
(Fig. ). The analysis highlights to the user the need
to access the latest SPG-I time series: although the overall pattern of the
SPG-I in preceding years remains unchanged, there are minor changes in the
values of the time series. When updating the index, users should always
download the entire SPG-I time series. The consistency of the index time
series with increasing time series length, as seen in
Fig. , confirms that the SPG-I can be defined
robustly as the first PC of an EOFA of SLA (sea level
anomaly) and
that the statistical mode of the analysis has a dynamical meaning.
Comparison to previous results
The SPG-I presented here has been compared to similar indices previously
presented by S. Häkkinen and H.
Hátún . A first comparison
(Fig. ) shows the comparison of the monthly
resolution SPG-I time series to that previously presented by S. Häkkinen
(S. Häkkinen, personal communication, 2013). These two time series show
close agreement, and minor differences are likely due to different underlying
altimeter data products and the change in time series extent. There is also a
difference in scaling between these two indices by 2 orders of magnitude,
most likely due to differing units (centimetres vs. metres). However, as the
typical usage of the data set is in terms of its correlations with other
variables, rather than interpreting its absolute value, this discrepancy is
not seen as important. In a second comparison (Fig. ),
the annual filtered time series by S. Häkkinen has been compared to the
annual SPG-I calculated here and the time series estimated by H.
Hátún based on annual mean SSH (sea surface
height) data
(H. Hátún, personal communication, 2012). Again, the overall pattern
of these three indices shows good agreement, with all showing a clear
reduction in the SPG-I in the mid to late nineties. The difference between
this third index time series in comparison to the previous two is due to a
different underlying altimeter product (a 1/3∘ by 1/3∘
Mercator grid, which placed additional emphasis on the sub-polar gyre
region).
Comparison of yearly SPG-I with that calculated independently by S.
Häkkinen and H. Hátún. See text for more details.
Following the recommendations of , we would like to ensure
the SPG-I time series presented here is easily accessible and available to
all. The data can therefore be downloaded from
http://dx.doi.org/10.7489/1806-1 in ASCII format, and researchers can
also access the supporting information there (including code to recreate the
index). We are committed to updating the time series within 6 months from the
time the data provider (CMEMS) publishes its updates. As the time series is
based on the EOFA technique, we recommend users download the entire index
time series when updating the time series they use. This will ensure the most
relevant time series is used in any analyses. Version numbering will
facilitate users identifying which version of the time series they are
accessing. Finally, we would appreciate it if all those making use of this
time series appropriately acknowledged its use by citing this paper and the
digital object identifier (DOI) of the data set.
Summary
We have presented a time series of SPG-I based on monthly mean SLA maps
obtained from CMEMS. Our time series compares well with indices presented
previously. The variability in the index time series is influenced by the
chosen spatial extent of the sea surface height data included in the EOFA,
and inter-annual variability is suppressed when including the wider North
Atlantic region. We also presented an indication of changes with temporal
coverage of the data series, and users need to ensure downloading the entire
time series when accessing future updates. The index data product we present
is freely available from http://dx.doi.org/10.7489/1806-1, and we
encourage all scientists interested in establishing linkages between North
Atlantic climate variability and ecosystem function to access, apply, and
publish.
The authors declare that they have no conflict of
interest.
Acknowledgements
We would like to thank Sirpa Häkkinen and Hjalmar
Hátún for providing their time series of SPG-I and ancillary
information relating to their calculation; we are grateful to Hjalmar Hátún and
Stuart Cunningham for useful discussions on sub-polar gyre dynamics. The
research leading to these results has received funding from the European
Union 7th Framework Programme (FP7 2007–2013) under grant agreement number
308299 (NACLIM).Edited by: G. M. R. Manzella
Reviewed by: three anonymous referees
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