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Preparation of this report was made possible by financial support of the UK Ocean
Acidification research programme, co-funded by the Natural Environment Research
Council (NERC), the Department for Environment, Food and Rural Affairs (Defra)
and the Department of Energy and Climate Change (DECC); main funding codes
R8/H12/73, NE/N012585/1 and NE/H/017046/1. The Defra-supported PLACID
project at Cefas (MF1113), and other funders and institutions, have also supported
the data collection.
Cover photo: RRS Discovery CTD view of salps.
June 2016
Carbon dioxide and ocean acidification observations in UK waters
Synthesis report with a focus on 2010 - 2015
Clare Ostle1,2,3, Phil Williamson1,4 , Yuri Artioli5, Dorothee C. E. Bakker1, Silvana
Birchenough2, Clare E. Davis6, Stephen Dye2, Martin Edwards3, Helen S.
Findlay5, Naomi Greenwood2, Susan Hartman7, Matthew P. Humphreys8,
Tim Jickells1, Martin Johnson1,2 , Peter Landschützer9, Ruth Parker2, David
Pearce2, John Pinnegar2, Carol Robinson1, Ute Schuster10, Briony Silburn2,
Rob Thomas11, Sarah Wakelin12, Pamela Walsham13, and Andrew J. Watson10.
1Centre for Ocean and Atmospheric Sciences (COAS), School of Environmental
Sciences, University of East Anglia, Norwich, UK.
2Centre for Environment Fisheries and Aquaculture Science (CEFAS), Lowestoft, UK.
3Sir Alister Hardy Foundation for Ocean Science (SAHFOS), Plymouth, UK.
4Natural Environmental Research Council (NERC), Swindon, UK.
5Plymouth Marine Laboratory (PML), Plymouth, UK.
6Department of Earth, Ocean, and Ecological Sciences, University of Liverpool, UK.
7National Oceanography Centre (NOC), Southampton, UK.
8Ocean and Earth Science, University of Southampton, UK.
9Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, Zürich, Switzer-
land.
10Life and Environmental Sciences, University of Exeter, Exeter, UK.
11British Oceanographic Data Centre (BODC), Liverpool, UK.
12National Oceanography Centre (NOC), Liverpool, UK.
13Marine Scotland Science (MSS), Aberdeen, UK.
Recommended citation: Ostle C., P. Williamson, Y. Artioli, D. C. E. Bakker, S.
Birchenough, C. E. Davis, S. Dye, M. Edwards, H. S. Findlay, N. Greenwood, S.
Hartman, M. P. Humphreys, T. Jickells, M. Johnson, P. Landschützer, R. Parker,
D. Pearce, J. Pinnegar, C. Robinson, U. Schuster, B. Silburn, R. Thomas, S.
Wakelin, P. Walsham, and A. J. Watson (2016) Carbon dioxide and ocean
acidification observations in UK waters: Synthesis report with a focus on 2010
- 2015.
Contents
Acknowledgements ........................................ 5
Executive Summary ......................................... 6
1Introduction .................................................. 8
1.1 Rationale 8
2Methods .................................................... 13
2.1 Carbonate system measurements 13
2.2 Calculation of carbonate system parameters 14
3Findings ..................................................... 15
3.1 Sampling locations 15
3.2 Results 17
3.2.1 Long-term trends ................................................ 17
3.2.2 Seasonal trends ................................................. 20
3.2.3 Depth distributions .............................................. 27
3.2.4 Future estimates ................................................ 32
3.3 Data and products available 36
4Conclusions ................................................ 37
4.1 Concluding remarks 37
Acknowledgements
Many thanks to the people and organisations who contributed to this re-
port, and their effort in collecting, measuring, analysing and maintaining
the many different datasets used.
Executive Summary
Key messages:
1.1 The process of ocean acidification is now relatively well-documented
at the global scale as a long-term trend in the open ocean. How-
ever, short-term and spatial variability can be high.
1.2 New datasets made available since Charting Progress 2 make it
possible to greatly improve the characterisation of CO2and ocean
acidification in UK waters.
3.1 Recent UK cruise data contribute to large gaps in national and global
datasets.
3.2 The new UK measurements confirm that pH is highly variable, there-
fore it is important to measure consistently to determine any long
term trends.
3.3 Over the past 30 years, North Sea pH has decreased at 0.0035
±0.0014 pH units per year.
3.4 Upper ocean pH values are highest in spring, lowest in autumn. These
changes reflect the seasonal cycles in photosynthesis, respiration
(decomposition) and water mixing.
3.5 Carbonate saturation states are minimal in the winter, and lower in
7
more northerly, colder waters. This temperature-dependence could
have implications for future warming of the seas.
3.6 Over the annual cycle, North-west European seas are net sinks of
CO2. However, during late summer to autumn months, some coastal
waters may be significant sources.
3.7 In seasonally-stratified waters, sea-floor organisms naturally experi-
ence lower pH and saturation states; they may therefore be more
vulnerable to threshold changes.
3.8 Large pH changes (0.5 - 1.0 units) can occur in the top 1 cm of sedi-
ment; however, such effects are not well-documented.
3.9 A coupled forecast model estimates the decrease in pH trend within
the North Sea to be -0.0036±0.00034 pH units per year, under a high
greenhouse gas emissions scenario (RCP 8.5).
3.10 Seasonal estimates from the forecast model demonstrate areas of
the North Sea that are particularly vulnerable to aragonite under-
saturation.
iIf you are viewing this document as a soft-copy, the numbers next to the
above key messages are hyper-linked and will take you to the relevant
section of the document.
1. Introduction
Take-home messages
1.1 The process of ocean acidification is now relatively well-documented
at the global scale as a long-term trend in the open ocean. However,
short-term and spatial variability can be high
1.2 New datasets made available since Charting Progress 2 make it pos-
sible to greatly improve the characterisation of CO2and ocean acidifi-
cation in UK waters
1.1 Rationale
Ocean acidification is the large-scale, long-term fall in pH (increase in hy-
drogen ion concentration) occurring as an inevitable consequence of in-
creasing carbon dioxide (CO2) in the atmosphere. Other chemical changes
include a decrease in carbonate ion concentration, and increase in bicar-
bonate ions (see Box 1, and figure 1.1).
iA useful list of frequently asked questions and answers about ocean
acidification can be found here www.epoca-project.eu
1.1 Rationale 9
Figure 1.1: Schematic of ocean acidification, adapted from a graphic by the
University of Maryland, taken from www.oceanacidification.org.uk.
Box 1: What is ocean acidification?
When CO2dissolves in seawater, it forms carbonic acid - that then dis-
sociates to release carbonate and hydrogen ions (see figure 1.1). The lat-
ter decreases pH (a logarithmic, inverse measure of H+concentration),
causing acidification. However, most seawater pH is, and will remain,
well above 7.0, the chemists’ boundary between acidic and alkaline so-
lutions. Associated chemical changes include increased concentrations
of bicarbonate ions, but decreased levels of carbonate ions. Reductions
in carbonate ions lower the saturation state () of the two main forms
of calcium carbonate, aragonite and calcite, used by many marine or-
ganisms for their shells and other external coverings. Low carbonate sat-
uration states can cause the dissolution of these structures, particularly
in deeper and/or colder water, where dissolved CO2levels are naturally
higher (and pH lower). The water depth at which carbonate dissolution
occurs is called the saturation horizon.
Since the beginning of the industrial revolution, pH of surface seawater
has globally decreased by 0.1 units, which corresponds to an increase in
hydrogen ion concentration of around 26% (Stocker et al.,2013). A wide
range of other process affect seawater pH at the local and regional level.
1.1 Rationale 10
The potential for these chemical changes to have serious biological,
ecological and socio-economic consequences was first identified 10 - 15
years ago. Many experimental studies have since been carried out on ma-
rine species in the laboratory, with focus on the mean pH (and CO2) values
expected to occur in surface seawater in future, under ’business as usual’
climate change scenarios – involving a decrease of 0.4 pH units.
The research effort on ocean acidification increased greatly around 2010,
with the implementation of UK, German, US and European programmes.
The UK Ocean Acidification research programme (UKOA, 2010 - 16; co-
supported by NERC, Defra and DECC) supported inter alia experimental
studies that investigated interactions of temperature and pH, including for
coldwater corals; palaeo-ocean acidification events; regional and global
modelling; and observations in European waters and polar seas. The Defra-
funded PLACID project at Cefas (Placing Ocean Acidification in a wider
Fisheries Context, 2013 - 16) extended the observational studies, and also
carried out additional experimental and modelling work.
Measurements made by other groups of decadal-scale changes have
confirmed that ocean acidification is a real event globally (IPCC,2014).
However, there is considerable natural variability around the long-term trends
for the upper ocean (figure 1.2), with an annual range of up to 0.4 pH units
at open ocean sites. Variability at coastal sites may be greater, with ad-
ditional influences of watershed processes, nutrient inputs, and changes in
ecosystem structure (Duarte et al.,2013).
1.1 Rationale 11
Figure 1.2: pH at seven time series stations, mostly representing open ocean con-
ditions in the Northern Hemisphere: European Station for Time series in the Ocean
at the Canary Islands (ESTOC), Bermuda Atlantic Time-series Study (BATS), CArbon
Retention In A Colored Ocean (CARIACO), Iceland Sea, Hawaii Ocean Time-series
(HOT), Station Papa (PAPA) and Munida (New Zealand). Figure created by Ute
Schuster, adapted from Bates et al. (2014).
1.1 Rationale 12
This report brings together relevant data on CO2and ocean acidifica-
tion measurements for UK waters, from UKOA, PLACID and other sources
(SOCAT, GLODAP, ICES, SSB, see table 3.1 for details). It updates Charting
Progress 2 (Defra,2010), based on Hydes et al. (2011), by focussing on mea-
surements made between 2010 and 2015. Coverage in Charting Progress
2 was limited, with that report stating (p.20): "Because there are currently
no baseline measurement of pH against which changes in UK waters can
be judged, it will be some time before we can make accurate judgements
about the rate of acidification relative to natural annual and interannual
cycles of pH. We also need a better understanding of the physical, chemi-
cal and biological processes controlling the ocean’s ability to absorb CO2".
The current synthesis was initiated in response to policy requirements
identified in early 2015 by the Ocean Processes Evidence Group (OPEG),
part of the Defra-led UK Marine Monitoring and Assessment Strategy (UKM-
MAS). Updated information on variability and trends in marine CO2and
ocean acidification was considered necessary for several upcoming re-
views and updates on the status of UK seas. These include the Initial Assess-
ment of the EU Marine Strategy Framework Directive (MSFD) in 2016/2017;
UK inputs to future OSPAR (Convention for the Protection of the Marine Envi-
ronment of the North-East Atlantic) Quality Status Reports for the North At-
lantic; and other national reviews of marine climate change; e.g. planned
reports of the Marine Climate Change Impacts Partnership (MCCIP) and
the Climate Change Risk Assessment (CCRA).
The collection, collation and interpretation of data on ocean acidifi-
cation and associated marine CO2is not a statutory requirement under
the EU Marine Strategy Framework Directive (MSFD). Nevertheless, ocean
acidification parameters are mentioned in Annex III, with measurement of
"pH, pCO2profiles or equivalent information used to measure ocean acidi-
fication" included within the "indicative list of characteristics, pressures and
impacts" (as a "physical and chemical feature").
We do not here provide any assessment of potential biological or socio-
economic impacts of the observed changes in water chemistry. Although
a wide range of marine organisms are potentially sensitive to ocean acidi-
fication (IGBP et al.,2013;CBD,2014), most adverse impacts occur outside
the pH range currently experienced in UK waters. Whilst it is possible that
deleterious effects are already underway, it is currently difficult to unam-
biguously separate the consequences of ocean acidification from other
environment changes, such as temperature (Beaugrand et al.,2012), and
there is not yet agreement on the most suitable indicator species for pH
change (ICES,2014). Instead, a wide range of biological measurements
are considered necessary to assess the ecological response (Newton et al.,
2015).
2. Methods
2.1 Carbonate system measurements
Although pH is the parameter of greatest concern for ocean acidification, it
is rarely measured directly – since most sensors are not sufficiently accurate.
Instead it is usually calculated from measurements of other components of
the closely-linked ocean ‘carbonate system’. These other measurements
also give additional ecologically-important information, e.g. on CO2levels
and carbonate saturation state. There are a number of different methods
for measuring carbonate system parameters in seawater, the more widely
used methods are well described in Riebesell et al. (2011). Briefly, the def-
initions of the marine carbonate system parameter used within this report,
and the common techniques used are given below:
pH can be defined using different scales (Hydes et al.,2013), the more
common scale used within seawater is the hydrogen ion scale, there-
fore for the purpose of this report pH is defined as the negative log-
arithm of hydrogen ions. pH is commonly calculated from two other
carbonate system parameters (see section 2.2). However, it can also
be measured directly using electrometric or spectrophotometric de-
termination.
pCO2and fCO2are the sea surface partial pressure of carbon dioxide
(pCO2), and the fugacity of CO2(fCO2), which takes into account the
non-ideal nature of the gas. These parameters are most commonly
measured using infrared determination.
DIC is the sum of all of the dissolved forms of inorganic carbon, this is
2.2 Calculation of carbonate system parameters 14
often measured using a coulometric method.
TA is the total alkalinity, which is the balance of all of the ionic charges
in the marine carbonate system. TA is usually measured using an acidi-
metric titration.
ar is the aragonite saturation state, which is a measure of the poten-
tial for carbonate in the form of aragonite to form or dissolve. If ar is
less than 1 aragonite will readily dissolve. ar is most commonly calcu-
lated from two measurements of the carbonate system, see section
2.2.
2.2 Calculation of carbonate system parameters
Using two measured carbonate system parameters (DIC, TA, pCO2/fCO2
and pH), together with sea surface temperature (SST), salinity, sea level
air pressure, silicate and phosphate, the remaining carbonate parameters
can be calculated. DIC and TA measurements are most commonly used to
calculate the other parameters, as they are relatively easy to collect and
preserve for later analysis. However there are advantages/disadvantages
and error associated with using different combinations for such calculations
(Riebesell et al.,2011). Currently only pH and CO2can be measured contin-
uously over time (with appropriate calibration). More compact and reliable
sensors to measure pH and CO2are currently being developed in the UK
and elsewhere.
To calculate the carbonate parameters within this report a Matlab tool-
box called CO2SYS was used which is based on the program developed
by Lewis et al. (1998) for DOS and Excel. When using the CO2SYS toolbox
there are a number of dissociation constant and formulation options that
have to be selected. For this report the dissociation constants for pK1were
taken from Mehrbach et al. (1973) that were refitted by Dickson and Millero
(1987), and the dissociation constant for pK2was taken from Dickson (1990).
Lueker et al. (2000) have estimated the root-mean-square-error (RMSE) for
pK1as ±0.0055 and for pK2as ±0.01. It is important to note that when cal-
culating carbonate parameters the error associated with each individual
measurement input in to CO2SYS and with the dissociation constants used
propagates, increasing the uncertainty associated with the output.
iA user-friendly seawater-carbon calculator app can be downloaded
from here gcmd.nasa.gov/USGS-CO2calc
3. Findings
3.1 Sampling locations
Take-home message
3.1 Recent UK cruise data contribute to large gaps in national and global
datasets
Figure 3.1: Map of the spatial coverage showing the locations of a) the fixed-point
observatories (coloured diamonds, top-left legend) and b) the sampling locations
from cruises that took place between 2010 and 2015, with the cruise ID given in
the bottom-right legend.
3.1 Sampling locations 16
Figure 3.1 shows the region that this report is focussed on, the light blue
area of the seas represents shallower waters, and dark blue areas are deeper
than 1000 m. Because of the nature of the processes involved, and the
relative scarcity of data, it does not make scientific sense to analyse sep-
arately for the 8 sub-regions defined in Charting Progress 2 (Defra,2010).
However, additional datasets likely to become available in future (e.g. from
the NERC-Defra Shelf Sea Biogeoechemistry programme) are expected to
make greater geographical discrimination possible.
Comparisons of the data shown in figure 3.1 with GLODAP (GLobal Ocean
Data Analysis Project) V2 data (Key et al.,2015) show that the UK data im-
proves spatial coverage of DIC and TA samples within OSPAR North-East
Atlantic regions II (Greater North Sea) and III (Celtic Seas). The temporal
coverage is also improved within these regions, as when the GLODAP V2
dataset is combined with the UK cruise data shown in figure 3.1 a com-
plete monthly seasonal pH cycle is resolved. Data used within this report
are listed within table 3.1 in section 3.3, with information or links to data
access provided.
iSee www.ospar.org for an interactive map of the North-East Atlantic
OSPAR regions.
3.2 Results 17
3.2 Results
3.2.1 Long-term trends
Take-home messages
3.2 The new UK measurements confirm that pH is highly variable, there-
fore it is important to measure consistently to determine any long term
trends
3.3 Over the past 30 years, North Sea pH has decreased at 0.0035 ±0.0014
pH units per year
Figure 3.2: pH calculated from DIC and TA samples collected at fixed-point obser-
vatories. L4 = black circles, Stonehaven = red circles, SmartBuoys = yellow circles,
see figure 3.1 for locations. See table 3.1 for data access and information.
Figure 3.2 shows pH values calculated from DIC and TA samples (see
methods section 2.2) collected from three different sources. Although these
fixed-point observatories are positioned at different locations around the UK
(see figure 3.1), and all show high variability, they compare reasonably well.
Thus pH shows clear seasonality, and particularly strong variability in some
years; for example 2013 has a larger range in pH than previous years. This
variability not only highlights the importance of consistently measuring pH
throughout the year to determine any long term trends, but also the mea-
3.2 Results 18
surement of other environmental parameters likely to affect pH (Newton
et al.,2015).
One of the longest historical records of pH data (including information
for UK waters) can be found on the International Council for the Exploration
of the Seas (ICES) (www.ices.dk) website. Some of these pH values (mea-
sured and calculated) were recorded as far back as 1910; however, there
are some difficulties with interpreting this dataset as metadata on how
the values were determined are not available. Fay and McKinley (2013)
suggest that in order to determine a long-term trend in seawater carbon
data, a dataset of >25 years is required. Currently, fixed-point observatory
datasets within UK/European waters do not have the temporal coverage
to match that difficult to achieve criterion, therefore the ICES dataset con-
sidered over the period 1984-2014 provides the best available information
to determine a long-term trend in UK/European waters.
Plot 3.3a shows a map of the locations of ICES pH data, with the bound-
aries of the OSPAR regions shown in black. OSPAR region II (the Greater
North Sea, including the English Channel) was the only OSPAR region con-
sidered to be adequately sampled. For the purpose of this report, the
trend was calculated from 1984 to 2014 using measurements made within
the top 20 m (plot 3.3b and 3.3c, covering 31 years of sampling effort).
This period was selected for analysis because before 1984 there were not
enough pH measurements recorded to obtain annual means. The trend of
-0.0035±0.0014 pH units per year (shown in plot 3.3c) is consistent with find-
ings from recent literature (see figure 1.2 and Bates et al. (2014)). On the
basis of more limited information, Hydes et al. (2011) estimated the north
west European shelf trend to be between -0.002 to -0.004 pH units per
year from 1995 to 2009.
3.2 Results 19
Figure 3.3: a) Map of the ICES pH data with OSPAR boundaries shown. b) pH data
collected within the top 20 m in OSPAR region II (the greater North Sea) from 1984
- 2014. c) The mean annual pH within the top 20 m in OSPAR region II (the greater
North Sea) from 1984 - 2014 with the trend and standard deviation shown.
3.2 Results 20
Other analyses of multi-annual changes in North Sea upper ocean car-
bonate chemistry are provided by Beare et al. (2013); Duarte et al. (2013);
Salt et al. (2013); Clargo et al. (2015). Beare et al. (2013) focussed on ICES
data for the central North Sea; their fitted, curvilinear trend for 1963- 2010 in-
dicated an increase in pH until the early 1990s, and a subsequent decline in
the following years. Figures in Duarte et al. (2013) show a similar pattern for
both the southern North Sea (coastal station influenced by river discharge)
and Danish Straits, although with the change from increase to decrease
occurring earlier, 1985. Additional observations in the southern North Sea,
including estuaries (Provoost et al.,2010), indicate that changes in nutrient
levels, causing increasing (and subsequently) decreasing eutrophication
were probably responsible for the decadal-scale changes occurring in ad-
dition to the atmospherically-driven overall trend.
Salt et al. (2013) and Clargo et al. (2015) both focussed on data since
2001, with the former identifying the North Atlantic Oscillation as an impor-
tant physical driver, in addition to atmospheric CO2, through its effects on
North Sea water masses.
3.2.2 Seasonal trends
Take-home messages
3.4 Upper ocean pH values are highest in spring, lowest in autumn. These
changes reflect the seasonal cycles in photosynthesis, respiration (de-
composition) and water mixing
3.5 Carbonate saturation states are minimal in the winter, and lower
in more northerly, colder waters. This temperature-dependence could
have implications for future warming of the seas
3.6 Over the annual cycle, North-west European seas are net sinks of
CO2. However, during late summer to autumn months, some coastal
waters may be significant sources.
The seasonal cycle of pH at Stonehaven and L4 is shown on the following
page in figure 3.4. Both fixed-point observatories have a similar seasonal cy-
cle with high pH in the spring (April-June) and low pH in the autumn months
(August-October).
3.2 Results 21
Figure 3.4: Box and whisker plots of calculated pH data within each month from
Stonehaven and L4 between 2008 to 2015. The red lines within the boxes represent
the median for each month, while the edge of box = 25th and 75th percentile, the
whiskers are the extremes (2.7 standard deviations) and outliers are outside of this
range represented as red crosses. The number of observations within each month
are shown in the bottom panel as bar charts, with the mean temperature plotted
as a bounded red line with the shaded area showing the standard deviation. See
table 3.1 for data access and information.
This increase in pH during the spring is primarily due to the spring bloom
of phytoplankton (mostly microscopic algae), with high photosynthetic ac-
tivity, decreasing the amount of dissolved CO2and hence hydrogen ions.
The lower pH in the autumn is likely due to the increased abundance and
activity of non-photosynthetic marine organisms, including phytoplankton
predators (zooplankton), decomposers (mostly bacteria) and benthic in-
vertebrates. Respiration by these organisms returns CO2to the seawater,
decreasing the pH.
3.2 Results 22
Figure 3.5: Monthly mean total phytoplankton counts = green, and total
zooplankton counts = red, from Continuous Plankton Recorded (CPR) data
(doi:10.7487/2014.44.1.10) collected within 5radius of L4, and calculated pH at
L4 = blue (Cummings et al.,2015) from 2008 to 2013. The shaded area around
each mean represents the standard deviation.
The succession between the spring and the summer/autumn bloom of
phytoplankton and zooplankton at L4 can be seen in figure 3.5. Stratifica-
tion starts to break down during autumn, deepening the mixed layer and
mixing carbon rich waters to the surface which continues to lower the pH.
The above interpretation is supported by findings from Kitidis et al. (2012)
and Marrec et al. (2013), who demonstrated that the seasonal changes
in carbonate chemistry in the western English Channel are dominated by
changes in biology, rather than advection.
3.2 Results 23
Figure 3.6: Monthly mean saturation state for aragonite (ar) calculated from DIC
and TA samples between 2008 to 2015, collected at Stonehaven = blue, and L4 =
red. The shaded area around each mean represents the standard deviation. See
table 3.1 for data access and information.
The mean seasonal cycle of carbonate (aragonite) saturation state at
L4 and Stonehaven are shown in figure 3.6. The saturation state is high-
est during the summer months and lowest during winter at both sites, with
Stonehaven having a lower monthly mean saturation state than L4. Whilst
this seasonality in aragonite saturation state is primarily driven by the sum-
mer decrease in DIC as primary production by phytoplankton increases, it
is re-inforced by the seasonal change in temperature. Thus saturation state
is higher at warmer temperatures due to lower solubility, which explains the
difference between the two sites: water temperatures at Stonehaven are
3.1C cooler than L4 throughout the year. Future warming could therefore
reduce the impact of increased CO2on saturation state and the depth at
which aragonite or calcite dissolves; however, models that take account of
both factors indicate that directly-driven CO2effects predominate.
3.2 Results 24
Figure 3.7: Mean seasonal surface ocean pCO2between 2010 and 2014. Esti-
mated using SOCAT V3 pCO2data (Bakker et al.,2014,2016) interpolated using
the ETH SOM-FFN method (Landschützer et al.,2015a,b).
Figure 3.7 describes the seasonal cycle of CO2in surface waters of the
north-west European shelf and adjacent Atlantic Ocean. These maps are
based on direct observations of upper ocean CO2, that are interpolated
using the ETH SOM-FNN method (using additional indirectly-derived infor-
mation on sea surface temperature, chlorophyll and mixed layer depth)
(Landschützer et al.,2015a,b). High concentrations of CO2are indicated
off the south coast of Iceland and around the UK during the winter months,
with the mixing of high carbon deep waters considered to be the main
driver of such effects. During the spring and summer months the concen-
tration of CO2decreases, due to increased productivity by phytoplankton
taking up CO2via photosynthesis. In the autumn (figure 3.7c) there are high
concentrations of CO2around the coast of the UK and in the south North
Sea: This is likely to be an effect of marine respiration and decomposition,
as previously noted for L4 and riverine contribution, from watershed run off
of high carbon waters and organic matter inputs (Kitidis et al.,2012).
3.2 Results 25
Figure 3.8: Mean residuals (difference) between monthly surface ocean pCO2
interpolated using the ETH SOM-FFN method (Landschützer et al.,2015a,b) and
monthly SOCAT V3 pCO2data (Bakker et al.,2014,2016) between 2010 and 2014.
Light grey areas of the map are where no data were available.
The model-derived interpolated data of figure 3.7 was subtracted from
the directly observed data (residuals) to indicate areas of greater uncer-
tainty (figure 3.8). In most areas the interpolated data agrees well with the
observational data, there is a good fit between the observations and the
model-derived data in the open ocean, but less so in the shelf regions as
the south North Sea and coastal areas show larger uncertainty.
3.2 Results 26
Figure 3.9: Mean seasonal surface ocean fCO2(fCO2in seawater - fCO2in at-
mosphere) between 2010 and 2014. Estimated using SOCAT V3 fCO2data (Bakker
et al.,2014,2016). Red = CO2source, blue = CO2sink. Light grey areas of the
maps are where no measurements were available (in SOCAT).
The seasonal fCO2maps shown in figure 3.9 indicate that throughout
most of the year the north-east Atlantic is a sink for CO2, i.e. net uptake.
During July to December there is a region of high CO2that creates a source
of CO2to the atmosphere around the south coast of the UK (subplots 3.9c
and 3.9d). This is likely due to the high riverine run-off that occurs during this
period, creating high concentrations of CO2(seen in figure 3.7). Note that
information for some UK waters is sparse or lacking for this analysis, based
on different sources from those shown in figure 3.1. Additional coverage for
the period 2014-2016 is expected to become available through the Shelf
Sea Biogeochemistry programme.
3.2 Results 27
3.2.3 Depth distributions
Take-home messages
3.7 In seasonally-stratified waters, sea-floor organisms naturally experi-
ence lower pH and saturation states; they may therefore be more vulner-
able to threshold changes
3.8 Large pH changes (0.5 - 1.0 units) can occur in the top 1 cm of sedi-
ment; however, such effects are not well-documented
Figure 3.10: In stratified waters, pH values at the a) sea surface can be 0.2 units
higher than at the b) seafloor, as shown here for a North Sea survey in summer
2011. Figure created by Naomi Greenwood (Greenwood et al.,2012).
The data considered so far overwhelmingly relates to the upper ocean
(0 - 20 m), where most ocean acidification measurements have been made.
But marine organisms of commercial and ecological significance live through-
out the water column, and seafloor habitats in UK waters are of particular
importance, supporting both high biomass and high biodiversity. Wherever
seasonal stratification occurs (typically at water depths > 50 m for UK wa-
ters, e.g. northern North Sea and deeper parts of the Celtic Sea), strong ver-
tical gradients in pH and other carbonate chemistry parameters develop
during the summer (figure 3.10, figure 3.11).
3.2 Results 28
Figure 3.11: Seasonal depth profiles of dissolved inorganic carbon (DIC) from a
transect within the Celtic Sea for the year 2014. Figure adapted from Humphreys
et al. (2015).
The full annual cycle is shown for dissolved inorganic carbon (DIC) in the
Celtic Sea for the year 2014 (figure 3.11) – from complete mixing (April) to
the onset of stratification (June), its strengthening (August) and subsequent,
wind-driven breakdown and beginning of re-mixing (November) (Humphreys
et al.,2015).
3.2 Results 29
Figure 3.12: Maps of surface dissolved inorganic carbon (DIC) and DIC at depth
below 20 m. a) DIC from UK cruise data from 2010 to 2015 (see figure 3.1 for sam-
pling locations) between 0 m and 20 m b) DIC from UK cruise data from 2010 to
2015 deeper than 20 m. c) DIC from GLODAP V2 climatology (Lauvset et al.,2016)
from 2000 to 2013 between 0 m and 20 m. d) DIC from GLODAP V2 climatology
(Lauvset et al.,2016) from 2000 to 2013 deeper than 20 m. Light grey areas of the
maps are where no data were available.
The increased concentration of DIC at depth seen in figure 3.11 can
also be seen in the maps of the cruise data DIC measurements and the
estimates from GLODAP V2 (Lauvset et al.,2016) shown in figure 3.12. The
GLODAP V2 (Lauvset et al.,2016) climatology agrees well with the cruise
data.
3.2 Results 30
Figure 3.13: Maps of pH and aragonite saturation state (ar) from GLODAP V2
climatology (Lauvset et al.,2016). a) pH from 2000 to 2013 between 0 m and 20
m. b) pH from 2000 to 2013 deeper than 20 m. c) ar from 2000 to 2013 between
0 m and 20 m. d) ar from 2000 to 2013 deeper than 20 m.
There are lower pH values at depth compared with surface measure-
ments (see figure 3.13) particularly off the shelf where the depth increases
(figure 3.1). The saturation states are also lower at depth, however the spa-
tial distribution is different to that of pH because saturation state is more
influenced by temperature, therefore the region between Iceland and Nor-
way has the lowest saturation states of aragonite as temperatures are cold-
est here (subplot 3.13d).
3.2 Results 31
Figure 3.14: Sediment profiles from the North Sea. a) S = Southern region (blue),
N = Northern region (pink), C = central region (green), M = Mud (brown), as de-
termined by EHUs (Eco Hydrodynamic Units) calculated from Sediment (% fines),
depth and temperature difference (stratification). Selected b) pH and c) oxygen
profiles from contrasting stations in the North Sea. Figure adapted from Parker
et al. (2012) and Greenwood et al. (2012).
At the seafloor itself, further pH and carbonate chemistry gradients oc-
cur on the millimetre to centimetre scale. The sediment profiles shown in
figure 3.14 were collected using a microelectrode to measure box core sub
cores during research cruises in the North Sea in the summer of 2011 and
January 2012. There was a consistent feature of a 0.5 to 1 unit decrease
in pH in the upper 1 cm (muddy sands/sandy muds) recorded, which can
be seen in plot 3.14b (Parker et al.,2012;Greenwood et al.,2012). The
above data illustrate the range of pH values that organisms that live either
on or in the seafloor experience on a regular basis. The former include
many invertebrates (molluscs, polychaetes and crustacea, as well as cold-
water corals) that provide either food or habitat for benthic fish. Whist it is
possible that most/all seafloor organisms are genetically adapted to such
conditions, there may also be the risk that physiological thresholds (e.g.
relating to saturation state) may be crossed as a consequence of future
water chemistry changes. To date, experimental studies would seem to
have given insufficient attention to the range of conditions naturally expe-
rienced.
3.2 Results 32
3.2.4 Future estimates
Take-home messages
3.9 A coupled forecast model estimates the decrease in pH trend within
the North Sea to be -0.0036±0.00034 pH units per year, under a high
greenhouse gas emissions scenario (RCP 8.5)
3.10 Seasonal estimates from the forecast model demonstrate areas of
the North Sea that are particularly vulnerable to aragonite undersatura-
tion
Figure 3.15: Historical and projected changes in global surface ocean pH from
1870 - 2100 for the for IPCC AR5 RCP scenarios, annotated with information from
the December 2015 Paris Agreement. INDC = Indicative Nationally Determined
Contribution. Adapted from Bopp et al. (2013).
Whilst there are many global-scale models of ocean acidification in the
upper ocean, full-depth modelling for regional seas – including coastal
components, terrestrial inputs and seafloor exchanges – is not so well de-
veloped. In the Regional Ocean Acidification Modelling (ROAM) project
of UKOA, a coupled physical-ecosystem model was used to project the fu-
ture pH values and saturation state of the North Western European Shelf.
The circulation model used was the Nucleus for European Modelling of the
Ocean (NEMO; Madec (2008)) and it included a wide range of processes
3.2 Results 33
considered important in the shelf environment (e.g. tidal currents, variable
sea surface height). The ecosystem model implemented is the European
Regional Sea Ecosystem Model (ERSEM; Blackford et al. (2004); Butenschön
et al. (2016)) that has been widely used to study ecosystem dynamics and
impact of climate change and Ocean Acidification in the area (e.g. Holt
et al. (2012); Artioli et al. (2014); Wakelin et al. (2015)). The ERSEM carbonate
system module has been intensively validated against observational data
for this domain (Artioli et al.,2012). To date, the model has been forced
with data representative of the IPCC AR5 RCP 8.5 (RCP=Representative
Concentration Pathway), as simulated by the UKMO HADGEM model. RCP
8.5 describes a possible climate scenario based on continued high green-
house gas emissions, as shown as the red line in figure 3.15.
Figure 3.16: Top panel = Mean surface water pH from a) 1990 to 2009, b) 2080
to 2099 and the c) difference between these two periods. Bottom panel = Mean
surface water aragonite saturation state (ar) from d) 1990 to 2009, e) 2080 to 2099
and the f) difference between these two periods. These projections are modelled
based on IPCC AR5 RCP 8.5. Note: The model projects an increase in pH and
ar close to Iceland, but this is an artefact due to boundary conditions within the
model.
3.2 Results 34
Figure 3.16 shows the mean pH and aragonite saturation state (ar) in
surface waters around the UK using the ROAM model and the RCP 8.5 sce-
nario. The model projects an increase in pH and ar close to Iceland, but
this is an artefact due to boundary conditions within the model. There is
a clear decrease in both pH and ar between the two periods with ar-
eas around the south coast of Norway showing the strongest decrease,
and becoming undersaturated in aragonite (red area in figure 3.16e). The
model estimates that surface waters will start to become occasionally un-
dersaturated gradually from around 2030 and more rapidly from 2080. By
the end of the century the model estimates that an area of surface water
of 300,000 km2could become undersaturated for at least a month. The
pH trends estimated from this model output for OSPAR regions II (Greater
North Sea) and III (Celtic Seas) were -0.0036±0.00034 and -0.0033±0.00019
pH units per year, respectively. These trends are within the standard devia-
tion of each other, and are closely similar to the recent trend (0.0035 pH
units per year) calculated from the ICES pH data in section 3.2.1.
iA free, simple to use, interactive app for looking at different CO2sce-
narios and consequent changes in temperature, sea level rise and pH
can be found here www.co2modeller.info.
3.2 Results 35
Figure 3.17: Seasonal mean surface water aragonite saturation state (ar) be-
tween 2080 to 2099 for a) January to March b) April to June c) July to September
d) October to December. The red area highlights regions of undersaturation (< 1)
of aragonite. These projections are modelled based on IPCC AR5 RCP 8.5.
Sasse et al. (2015) highlight the importance of accounting for season-
ality when looking at saturation states from future scenarios. This is clear
in figure 3.17 as the seasonal undersaturation of aragonite (red areas) are
larger than when averaging over the whole period from 2080 to 2099. The
cooler periods between January - March (figure 3.17a) and October - De-
cember figure (3.17d) are when undersaturation in aragonite is greatest,
particularly off the Norwegian coast, and many areas are very close to the
undersaturation value of 1. There are also some areas around the UK that
show undesaturation in aragonite between January - March (figure 3.17a).
This seasonality of ar in surface waters is in agreement with the observa-
tional data shown in section 3.2.2 in figure 3.6, with low values during the
winter months.
3.3 Data and products available 36
3.3 Data and products available
Table 3.1: Links and information for the data used within this report.
Organisation/Project PI/Ref Region Period used Parameters info/link/doi
GLODAP V2 Key et al. (2015) Global 2000 - 2013 DIC/TA Data available from CDIAC
Olsen et al. (2016)
Lauvset et al. (2016)
SOCAT V3 Bakker et al. (2014,2016) Global 2010 - 2014 fCO2Data available from SOCAT
ETH SOM FFN Peter Landschützer Global 2010 - 2014 pCO2Data available from CDIAC
Landschützer et al. (2015a,b)
PML/WCO Helen Findlay L4 - English Channel 2008 - 2014 DIC/TA Data available from BODC doi:10/7dj
Cummings et al. (2015)
Cefas Naomi Greenwood Warp, West Gabbard, 2011-2014 DIC/TA Data available from BODC
Liverpool Bay (smartbuoys)
Marine Scotland Pamela Walsham Stonehaven 2009-2015 DIC/TA Data available from MSS
UKOA Toby Tyrrell (Ribas-Ribas et al. (2014b) UK Seas 2011-2012 DIC/TA/pCO2/pH Data available from BODC doi:10/thr
Ribas-Ribas et al. (2014a)) doi:10/sbz
Cefas/UKOA/PLACID Naomi Greenwood UK Seas 2010-2013 DIC/TA Data available from BODC
PML/AMT Andrew Rees, Atlantic 2012 DIC/TA/pCO2/pH Data available from BODC
Vassilis Kitidis, Ian Brown
NOCS Susan Hartman North-East Atlantic 2011-2012 DIC/TA Data available from BODC doi:10/bb89
Hartman et al. (2016)
SAHFOS Clare Ostle B-route North-East Atlantic 2008-2013 phytoplankton Data available from SAHFOS
Stevens (2014) zooplankton doi:10.7487/2014.44.1.10
PML/NEMO-ERSEM Yuri Artioli (Madec (2008), North Western European Shelf 1990 - 2099 pH/ar Data available form Yuri Artioli
Blackford et al. (2004); Butenschön et al. (2015))
ICES ICES Historical pH dataset 2015. North Western European Shelf 1984 - 2014 pH Data available from ICES
ICES, Copenhagen
iPathfinders Ocean Acidification project, lead by Jamie Shutler, provides a useful site for downloadable
ocean acidification datasets here www.pathfinders-oceanacidification.org.
4. Conclusions
4.1 Concluding remarks
This report identifies many new observations of carbonate chemistry in UK
waters, whilst also extending previously-existing time-series. These recent
datasets greatly advance our understanding of the dynamic nature of CO2
exchanges and ocean acidification from a national perspective, improving
on the knowledge base available to Charting Progress 2.
The evidence presented for 2010 - 2015 is fully consistent with the global
trend for ocean acidification, as driven by atmospheric CO2. Nevertheless,
the variability in conditions is now much better appreciated, both spatially
(including with water depth) and temporally. Whilst there is consistent infor-
mation from three UK time series (for L4, Stonehaven and SmartBuoys), no
assumptions can necessarily be made that all UK waters will respond simi-
larly, since other data sources, including from ship-based surveys, indicate
significant spatial differences.
Further datasets are known to have been collected, and are currently
being analysed. The future continuation of such measurements cannot be
assumed, since there is no statutory requirement for such monitoring. Nev-
ertheless, it is highly desirable that relevant, well-focussed data collection
continues, not only in the context of OSPAR and MSFD Assessments, as iden-
tified, but also to address UN Sustainable Development Goal 14 (with target
14.3 "to minimise and address the impacts of ocean acidification including
through enhanced scientific cooperation at all levels") - and to test the ef-
fectiveness of the Paris Agreement in minimising such impacts, by slowing
(and ideally, halting) future ocean acidification.
4.1 Concluding remarks 38
Five specific recommendations are made:
1. High-quality observations of ocean acidification should continue to
be supported, with adequate resources for data analysis and inter-
pretation, in view of the importance of such datasets in assessing
local conditions and long-term changes.
2. Additional, non-UK datasets should be brought together with those
presented here, to further improve our understanding of variability
and its causes on a European scale, whilst also contributing to the
wider global research effort.
3. Additional effort should be made to make seafloor measurements of
ocean acidification at seasonally-stratified sites, including at habi-
tats (e.g. coldwater corals) of high conservation value.
4. New sensors and platforms (gliders and profiling floats) currently un-
der development should be further tested, and used to increase the
cost-effectiveness and spatial coverage of UK ocean acidification
measurements.
5. Future national modelling effort should include sea-floor conditions,
and investigate the implications of emission scenarios consistent with
the full and partial implementation of the Paris Agreement.
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... Unfortunately, we were unable to directly assess changes in seawater pH due to a lack of suitable long-term data. The reliable tracing of long-term trends in the seawater carbonate system requires data sets of at least 25 years 85 which are not available for the (Southern) North Sea 86 . However, as we explain in the discussion, the discharge of anthropogenic effluent over many decades had a considerable influence on the carbonate system of the Dutch and Belgian nearshore making the assessment of pH changes less relevant for the formation of biogenic CaCO 3 in this region 87 . ...
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... In the European shelf seas, the impacts of climate change on fisheries have been noted for several important commercial species, notably nephrops, mussels, oysters, and lobster (e.g., Styf et al., 2013;Ostle et al., 2016). Fernandes et al. (2017) quantified the potential effects of ocean warming and acidification on fisheries catches, resulting revenues and employment in the United Kingdom of Great Britain and Northern Ireland under different greenhouse gas emission scenarios. ...
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... Broad scale changes in phytoplankton life-forms have been recorded across the north west European shelf (Bedford et al., 2020). The carbonate chemistry of the oceans is also changing (Ostle et al., 2016) and the community of HAB researchers and scientists is at an early stage in understanding the effects of these changes on HAB species dynamics and biogeography and their impacts (Fu et al., 2012, Riebesell et al., 2018, Raven et al., 2020. Wells et al. (2019) discuss the increasing concern that human-mediated environmental parameters may alter the patterns, distribution and intensity of HABs. ...
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... Following from UKOA, the UK Shelf Sea Biogeochemistry research programme (SSB) has further added to the carbonate chemistry dataset available for this region ( Figure 4; Hartman et al., 2019;Humphreys et al., 2019). As a result, we are now able to more accurately constrain air-sea CO2 fluxes across the North-West European shelf and the associated changes in seawater pH and other carbonate chemistry parameters, building on previous knowledge summarised by Ostle et al. (2016). Detailed assessments of how physical and biological processes combine to enable CO2 uptake in the North Sea (Clargo et al., 2015) can therefore now be extended to other parts of the shelf, particularly the Celtic Sea. ...
... This long-term absorption of atmospheric CO 2 , and resultant increase in DIC is causing ocean acidification on the NWES. Over the last 30 years this has decreased pH by 0.0035 pH units per year in the North Sea (Ostle et al., 2016). Increasing DIC also increases the DIC:TA ratio, lowering the buffering capacity of seawater and reduces the potential for future CO 2 uptake on the shelf (Thomas et al., 2007;Clargo et al., 2015). ...
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... Following from UKOA, the UK Shelf Sea Biogeochemistry research programme (SSB) has further added to the carbonate chemistry dataset available for this region ( Figure 4; Hartman et al., 2019;Humphreys et al., 2019). As a result, we are now able to more accurately constrain air-sea CO2 fluxes across the North-West European shelf and the associated changes in seawater pH and other carbonate chemistry parameters, building on previous knowledge summarised by Ostle et al. (2016). Detailed assessments of how physical and biological processes combine to enable CO2 uptake in the North Sea (Clargo et al., 2015) can therefore now be extended to other parts of the shelf, particularly the Celtic Sea. ...
... Whilst this is not an input term, it does not contribute to export via the CSP+ advection and must therefore be accounted for as the latter is the remainder of input vs output terms. In our study, this DIC accumulation rate was estimated from annual OA rates in the range of -0.0013 to −0.0035 pH-units 42,43,69 . We estimated a mean DIC accumulation rate of 1.01 µmol kg −1 y −1 using the CO2SYS software 48 with pH T = 8.05 and Total Alkalinity of 2283.1 µmol kg −1 70 as baseline. ...
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Shelf seas play an important role in the global carbon cycle, absorbing atmospheric carbon dioxide (CO2) and exporting carbon (C) to the open ocean and sediments. The magnitude of these processes is poorly constrained, because observations are typically interpolated over multiple years. Here, we used 298500 observations of CO2 fugacity (fCO2) from a single year (2015), to estimate the net influx of atmospheric CO2 as 26.2 ± 4.7 Tg C yr−1 over the open NW European shelf. CO2 influx from the atmosphere was dominated by influx during winter as a consequence of high winds, despite a smaller, thermally-driven, air-sea fCO2 gradient compared to the larger, biologically-driven summer gradient. In order to understand this climate regulation service, we constructed a carbon-budget supplemented by data from the literature, where the NW European shelf is treated as a box with carbon entering and leaving the box. This budget showed that net C-burial was a small sink of 1.3 ± 3.1 Tg C yr−1, while CO2 efflux from estuaries to the atmosphere, removed the majority of river C-inputs. In contrast, the input from the Baltic Sea likely contributes to net export via the continental shelf pump and advection (34.4 ± 6.0 Tg C yr−1).
... In the European shelf seas, both observations and modelling show that CO 2 levels in near-surface can vary between 200 ppm and 450 ppm, contributing to a pH variability of as much as 1.0 pH unit over an annual cycle (Provoost et al., 2010). Collated pH measurements from waters around the United Kingdom of Great Britain and Northern Ireland suggest a long-term decline over the past 30 years, and North Sea pH has decreased at a rate of around 0.0035 pH units per year (Ostle et al., 2016). ...
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This article provides an overview of the approach taken by the Marine Knowledge Exchange Network (M-KEN) and an assessment of its activities in valorizing and generating impact from research. M-KEN was formed in 2014 in response to a call for projects to accelerate impact generated from environmental research in the United Kingdom (UK). M-KEN was university-led and focused in the eastern region of the UK but its approach to fostering impact has had international reach. Over the course of its first five years, M-KEN has leveraged substantial additional funding; spawned numerous spin-off projects; influenced policy and practice; and supported a range of marine research projects in the delivery of their research to stakeholders. This article demonstrates that the reach of M-KEN has been international and has led to substantial ripples of activity radiating out from the core activity of the network. We reflect on the strengths and weaknesses of the approach taken by M-KEN in the context of key research questions around Knowledge Exchange. Finally, we propose recommendations for endeavors from regional to global scale that wish to develop impact from a portfolio of research.
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The potential response of the marine ecosystem of the northwest European continental shelf to climate change under a medium emissions scenario (SRES A1B) is investigated using the coupled hydrodynamics-ecosystem model POLCOMS-ERSEM. Changes in the near future (2030–2040) and the far future (2082–2099) are compared to the recent past (1983–2000). The sensitivity of the ecosystem to potential changes in multiple anthropogenic drivers (river nutrient loads and benthic trawling) in the near future is compared to the impact of changes in climate. With the exception of the biomass of benthic organisms, the influence of the anthropogenic drivers only exceeds the impact of climate change in coastal regions. Increasing river nitrogen loads has a limited impact on the ecosystem whilst reducing river nitrogen and phosphate concentrations affects net primary production (netPP) and phytoplankton and zooplankton biomass. Direct anthropogenic forcing is seen to mitigate/amplify the effects of climate change. Increasing river nitrogen has the potential to amplify the effects of climate change at the coast by increasing netPP. Reducing river nitrogen and phosphate mitigates the effects of climate change for netPP and the biomass of small phytoplankton and large zooplankton species but amplifies changes in the biomass of large phytoplankton and small zooplankton.
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The CO2 system in the North Sea over the 2001-2011 decade was investigated using four comprehensive basin-wide datasets covering the late summer periods of 2001, 2005, 2008 and 2011.We find that rises in surface water DIC and pCO2 exceeded concurrent rises in atmospheric pCO2, which we attribute primarily to biological activity in late summer. After accounting for this biological signal, the observed ocean acidification occurs at a rate that is consistent with concurrent atmospheric and open ocean CO2 increases over the 2001-2011 decade. Nevertheless, we do find a consistent reduction in CO2 undersaturation in the NNS and an increase in CO2 supersaturation in the SNS. We propose that the synergistic effects of increasing atmospheric pCO2 and subsequent decrease in seawater buffering capacity, together with rising sea surface temperatures in the future oceans, may reduce the strength of the North Sea as a CO2 sink. Such a reduction would diminish the efficiency of this region as a continental shelf pump with respect to uptake of CO2 by the sea. Ultimately this would constitute a positive feedback mechanism, i.e. enhancing the airborne fraction of anthropogenic CO2 and thus the net rate of increase of atmospheric pCO2 and subsequent global climate change.
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The observation-based pCO2 fields were created using a 2-step neural network technique. In a first step, the global ocean is divided into 16 biogeochemical provinces using a self organizing map. In a second step, the non-linear relationship between variables known to drive the surface ocean carbon system and gridded observations from the SOCATv2 dataset (Bakker et al. 2014) is reconstructed using a feed-forward neural network within each province separately. The final product is then produced by projecting these driving variables, i.e., surface temperature, chlorophyll, mixed layer depth, and atmospheric CO2 onto oceanic pCO2 using these non-linear relationships. This results in monthly pCO2 fields at 1°x1° resolution covering the entire globe with the exception of the Arctic Ocean and few marginal seas. The air-sea CO2 flux is then computed using a standard bulk formula. More details can be found in Landschützer et al. 2013 and Landschützer et al. 2014. Compared to Landschützer et al. 2014 we now have extended the time series back in the past from 1982 through 2011. More details can be obtained from Landschützer et al. 2015 and the manuscript supplement.