marine projections of warming and ocean … › jshess › docs › 2015 › lenton.pdfrent along...

Lenton et al.. Australian Meteorological and Oceanographic Journal (2015) 65:1

Corresponding author address: Andrew Lenton, CSIRO Oceans and Atmospheres, PO Box 1538, Hobart TAS 7000, Australia

Email: Andrew. [email protected].

Marine Projections of Warming and Ocean Acidification in the Australasian Region

Andrew Lenton1, Kathleen L. McInnes

2 and Julian G. O’Grady

2

1 CSIRO Oceans and Atmosphere, Hobart, 7000, Australia

2 CSIRO Oceans and Atmosphere, Aspendale, 3195, Australia

(Manuscript received July 2015; accepted December 2015)

In response to increasing carbon dioxide emissions the oceans have become

warmer and more acidic. In this paper, the ability of Earth System Models to

simulate observed temperature and ocean acidification around Australia is as-

sessed. The model results are also compared with observations collected at sta-

tions around Australia over recent years to assess how representative the model

results are of the coastal domain; and are found to adequately simulate the mean

state at most sites.

Simulations from the Coupled Model Intercomparison Project 5 (CMIP5) under

low, medium and high emissions scenarios (RCPs 2.6, 4.5 and 8.5 respectively)

are then used to project how ocean acidification and sea surface temperature

will change. Under each of these emissions scenarios the oceans around Austral-

ia exhibit warming and continued acidification. However, these changes are

quite heterogeneous, with increases of up to +6 K (under RCP 8.5) above the

pre-industrial value, projected in areas such as the Tasman Sea. We conclude

that the projected changes in SST, aragonite saturation state and pH are likely to

profoundly impact marine ecosystems, and the ecosystem services that they

provide in the Australasian region.

Introduction

In response to increasing carbon dioxide (CO2) emissions the oceans have become warmer and more acidic (IPCC, 2013).

Studies have shown the upper ocean has warmed at a rate of +0.11(0.09-0.13) K/per decade between 1971-2010 (Rhein et

al, 2013). The oceans have also played a critical role in limiting the rate of global warming by absorbing 30% of CO2

emissions since the preindustrial period (IPCC, 2013). Once absorbed, CO2 reacts with seawater, leading to decreases in

pH and dissolved carbonate ion concentration, these changes being collectively referred to as ocean acidification. Over the

past 200 years, it is estimated that there has been a 0.1 unit reduction in the ocean’s surface pH, or a 26% increase in the

concentration of hydrogen ions in seawater (Raven et al., 2005).

The availability of carbonate ions in the marine environment is important as many marine organisms use carbonate ions,

along with calcium, to form hard calcium carbonate shells and skeletons. There are two primary forms of calcium car-

bonate used by organisms: aragonite and calcite. Aragonite is the primary form of calcium carbonate used by corals to

form hard reef structures and by other invertebrate organisms such as oysters, clams, lobsters, crabs and starfish and some

plankton e.g. pteropods. Calcite is primarily used by marine phytoplankton such as coccolithophores and foraminifera and

is more chemically stable than aragonite.

Lenton et al.. Marine projections of warming and ocean acidification S2

The changes in aragonite and calcite are described in terms of their saturation state (Mucci, 1983), which have been ob-

served to be decreasing in recent decades (Feely et al., 2009). This is significant as studies have consistently demonstrated

that as the aragonite saturation state (ΩA) and calcite saturation state (ΩC) continue to fall with increasing atmospheric

CO2 concentrations it will be harder for calcifying organisms to build and repair their shells and skeletons (Doney et al.,

2009).

In the Australian region there has been both a decrease in pH of 0.09 units, close to the global value of 0.1 (Raven et al,

2005), and a decrease in aragonite saturation state of 0.49 units since the preindustrial period (Lenton et al., 2015). The

oceans are projected to continue acidifying; current projections suggesting that the increase in hydrogen ion concentration

is likely to be greater than 100% (compared to the pre-industrial period) by the end of the century under high emissions

trajectories (e.g. Matear and Lenton, 2014). Furthermore, these changes will persist for many millennia (e.g. Frolicher and

Joos, 2010). Within the marine environment, ocean acidification has the potential to impact the entire marine ecosystem,

from microbial communities to top predators. These impacts include organism growth, reproductive health, and physiolo-

gy, species composition and distributions, food web structure and nutrient availability (Aze et al., 2014; Munday et al.,

2009; Munday et al., 2010; Dore et al., 2009; Doney et al., 2012; Iglesias-Rodriguez et al., 2008; Fabry et al., 2008).

The global ocean is also projected to continue warming (IPCC, 2013). This is likely to have significant impacts on atmos-

pheric circulation, precipitation patterns and rates, ocean stratification, mixed-layer depth, vertical mixing, deep-water

circulation, surface mixing, and horizontal and vertical advection (Dave and Lozier, 2013). In the Australian region a

warming of about +0.5 °C has occurred since 1880 (HadiSST; Rayner et al, 2003) but the changes have not been homoge-

nous. Some of the largest changes have been seen in the boundary currents along the east coast of Australia (Holbrook and

Bindoff, 1997) where the warming has been 2 to 3 times faster than the global mean (Wu et al., 2012). The Leeuwin Cur-

rent along the Western Australian Coast has also warmed, especially post-1980 (Zinke et al., 2014). For example, during

the austral summer of 2010/2011 unprecedented temperatures were recorded along the Western Australian coast, with

near-shore temperatures peaking about 5 K above average (Pearce and Feng, 2013).

Upper ocean warming has the potential to impact ocean productivity (Hofmann et al., 2011) and the structure of the entire

marine food web (Pörtner et al., 2014). In the Australian region significant changes in the marine environment have al-

ready been observed (Lough and Hobday, 2011). These changes include a southward shift of tropical fish species along the

east and west coast of Australia, loss of species elsewhere (Verges et al., 2014) and a reduction in coral growth rates in

important areas such as the Great Barrier Reef (De'ath et al., 2009).

It is important to note that ocean warming and acidification are considered two of the key stressors in the marine environ-

ment (Bopp et al. 2013, Boyd and Hutchins, 2012, Pörtner et al., 2014). These are very likely to have significant implica-

tions for the health, longer-term sustainability and biodiversity of marine environments in the Australian region (e.g. Fab-

ricius et al., 2011). Furthermore, it is likely that these changes will impact on the important ecosystems services that these

marine environments provide such as fisheries, tourism, coastal protection and food security (Cooley et al, 2005; Brown, et

al, 2010). In this context, projecting and understanding how Australia’s climate may change, together with its potential

impact on Australia’s marine environment, is critical for the management of marine resources now and into the future.

Here we investigate and quantify how Australia’s marine environment may change in response to different emissions sce-

narios. This analysis extends beyond, and updates, the analysis of Hobday and Lough (2011) who estimated the response

of the Australian marine environment to projected SST changes associated with (now) superseded emissions scenarios. We

use a suite of Earth System Models (ESMs) from the recent Coupled Model Intercomparison Project 5 (CMIP5) to project

warming and ocean acidification under an updated set of emission scenarios. The aims are to: (i) assess how well the

mean state of SST and ocean acidification are captured in ESMs against observed changes over the historical period; (ii)

assess how well the ESMs capture the mean state and variability in the coastal domain; (iii) project future changes around

Australia; (iv) discuss the potential impacts of these changes in the marine environment; and (v) discuss the limitations of

ESMs in projecting future changes. This paper underpins the methods used to provide quantitative information for the

Natural Resource Management regions of Australia (CSIRO and BoM, 2015).


Models and Data Sets

Study Domain

In this study we focus on the Australasian region (Figure 1) delineated nominally by latitudes 5°S and 45°S and longitudes

105°E and 160°E. This region encompasses part of the eastern Indian Ocean and Indonesian Seas and a large part of the

Tasman and Coral Seas.

Figure 1 Domain of this study also showing the locations of the National Reference Stations (Lynch et al, 2014).

Climate Model Data

To project future changes around Australia we use simulations of SST and carbonate chemistry from the Climate Model

Intercomparison Project (CMIP5; Taylor et al, 2012). Historical simulations with the CMIP5 models span the period from

1850 to 2005, and future scenarios are based on emissions scenarios or representative concentration pathways (RCPs). The

RCPs are named based on the additional radiative forcing at the end of the century. The emissions scenarios considered

here are representative of high (RCP 8.5 or ‘business–as-usual’), intermediate (RCP 4.5) and low (RCP2.6) emissions,

corresponding to atmospheric CO2 concentrations of 936, 538, and 421 ppm respectively by the end of century (Taylor et

al., 2012). The CMIP5 models used to project future changes in ocean acidification and aragonite saturation are listed in

Table 1. Here we only used CMIP5 models that had projections of all three emissions scenarios available. As seen in Ta-

ble 1 more models were available to assess SST, than ΩA and pH, reflecting that not all ESMs were coupled to ocean bio-

geochemical models.

Monthly data were first interpolated on to a common 1° 1° grid and then averaged to form annual values since it has been

noted that the current class of ESMs do not adequately capture the seasonal cycle of the carbon system (Sasse et al, 2015).

Here we focus on the ensemble median and the range given by the 10th and 90th percentile annual values, approximated


by the results from the 2nd and 16th ranked models at each point in the case of SST, and the 2nd and 5th ranked models at

each point in the case of ΩA and pH. Aragonite saturation state ΩA and pH values were calculated using the simulated

fields of dissolved inorganic carbon, total alkalinity, sea surface temperature and the dissociation constants of Lueker et al.

(2000), following Bopp et al (2013). The values of pH were calculated using the total scale, following the recommenda-

tions of Riebesell et al. (2010).

Table 1 The CMIP5 models used in this study to project changes in sea surface temperature (SST) and ocean acidifi-

cation (OA). OA here denotes aragonite saturation state and pH.

Model Name Institute ID Modelling Group Fields

CCSM4 NCAR National Center for Atmospheric Research SST

CSIRO-Mk3-6-0 CSIRO-BOM Commonwealth Scientific and Industrial Research Organiza-

tion (CSIRO) and Bureau of Meteorology (BOM), Australia

SST

CanESM2 CCCMA Canadian Centre for Climate Modelling and Analysis SST, OA

GFDL-CM3 NOAA GFDL NOAA Geophysical Fluid Dynamics Laboratory SST

GISS-E2-H NASA GISS NASA Goddard Institute for Space Studies SST

GISS-E2-R NASA GISS NASA Goddard Institute for Space Studies SST

HadGEM-ES MOHC(additional

realizations by INPE)

Met Office Hadley Centre (additional HadGEM2-ES realiza-

tions contributed by Instituto Nacional de Pesquisas Espaciais)

SST, OA

GFDL-ESM2G NOAA GFDL NOAA Geophysical Fluid Dynamics Laboratory SST

GFDL-ESM2M NOAA GFDL NOAA Geophysical Fluid Dynamics Laboratory SST, OA

ISPL-CM5A-LR IPSL Institut Pierre-Simon Laplace SST, OA

IPSL-CM5A-MR IPSL Institut Pierre-Simon Laplace SST, OA

MIROC-ESM-

CHEM

MIROC Japan Agency for Marine-Earth Science and Technology, At-

mosphere and Ocean Research Institute (The University of

Tokyo), and National Institute for Environmental Studies

SST

MIROC-ESM MIROC Japan Agency for Marine-Earth Science and Technology, At-

mosphere and Ocean Research Institute (The University of

Tokyo), and National Institute for Environmental Studies

SST

MIROC5 MIROC Atmosphere and Ocean Research Institute (The University of

Tokyo), National Institute for Environmental Studies, and Ja-

pan Agency for Marine-Earth Science and Technology

SST

NorESM1-M NCC Norwegian Climate Centre SST

NorESM1-ME NCC Norwegian Climate Centre SST

MPI-ESM-LR MPI-M Max-Planck-Institut für Meteorologie (Max Planck Institute

for Meteorology)

SST, OA

MPI-ESM-MR MPI-M Max-Planck-Institut für Meteorologie (Max Planck Institute

for Meteorology)

SST, OA


Observational Data

The ensemble ESM results were first compared with observational data in the Australian Region over the historical period

1986-2005. We compared the SSTs with those from the CSIRO Atlas of Regional Seas 2009 (CARS; Ridgway et al.,

2002). CARS is an atlas of seasonal ocean water properties comprising gridded fields of mean ocean properties (including

SST) over the period of modern ocean measurements, averaged over seasonal cycles for that period. It is derived from a

quality-controlled archive of all available historical subsurface ocean property measurements: primarily research vessel

instrument profiles and autonomous profiling buoys (More information on CARS and the data used to produce this product

is available at http://www.marine.csiro.au/~dunn/cars2009/#ovw). The 0.5° 0.5° daily values were averaged onto a 1°

1° grid and then averaged to produce annual mean values. To assess the annual mean state in pH and ΩA in the Australian

region we used the reconstruction of Lenton et al. (2015). With regards to the ESM results at the coast, we compare the

values with annual mean values collected and calculated from National Reference Stations (NRS) around Australia (Lynch

et al., 2014; Figure 1). Darwin was excluded because of the limited number of measurements available at this site.

Results

Assessment of the Mean State

In this section we first assess where the ESMs show good agreement by calculating the ensemble range (defined here as

the difference between the 90th and 10th percentiles) for SST, aragonite saturation state (ΩA) and pH (Figure 2). This pro-

vides insight into where the models show good (low values) and poor (high values) agreement. Figure 2 shows that in

terms of SST, the ESMs show reasonable agreement across much of north Australia but not at higher latitudes where, near

southern Australia and in the Tasman Sea, differences of more than +8 K are simulated. This large range in the Tasman

Sea likely reflects the variability in the strength of Pacific Western Boundary currents in the ESMs (Hu et al, 2015), which

influences the southward extension of the East Australian Current (Ridgway, 2007). In contrast, the differences near

southern Australia likely reflect differences in the location of isotherms related to the coarse resolution of current ESMs

(Lenton et al., 2013).

The ranges in ΩA and pH values show a similar pattern. Interestingly, while there is reasonable agreement in ΩA in the

western half of the domain, poorer agreement is evident along the east of Australia. In both ΩA and pH the large differ-

ences off the east coast of Tasmania reflect the range in SST. Importantly, despite differences in the mean state, the rate of

change of ocean acidification over much of the domain (except near the east coast of Tasmania) is very similar due to the

CMIP5 models being run with the same atmospheric concentrations. This suggests that the large range in projected ΩA

and pH is due to differences in the mean state of these variables, rather than differences in the biogeochemistry in ESMs.

Figure 2 The range of the ESMs (90th – 10th percentile) for (a) sea surface temperature in K, (b) aragonite saturation

state, ΩA and (c) pH over the historical period.


We next assess the ability of the median of the ensemble to reproduce the mean state of SST over the IPCC historical ref-

erence period 1986-2005. The median of the models, and its difference to the observed mean state from CARS, is shown

in Figure 3a. This indicates good agreement with the north to south gradients in SST reproduced well in the simulations.

Overall the ensemble shows a negative bias in the Australian region, with the largest differences (greater than -1.5 K) seen

in southern Australian waters and in the Tasman Sea, where the isotherms are shifted further northwards in the ESMs

compared to the observations.

The mean state of aragonite saturation state (ΩA) from the median of ESMs over the IPCC historical reference period

1986-2005 and its difference from the reconstruction of ΩA from Lenton et al. (2015) is shown in Figure 3b. Overall there

is good agreement with the strong north to south gradient in ΩA well captured by ESMs. Most of the spatial differences in

ΩA lie within the uncertainty of the reconstruction (0.1, Lenton et al, 2015) with the exception of parts of the Western

Australian coast and the Tasman Sea.

Figure 3 The median of the CMIP5 ensemble (left) and its difference from the observed values (right) using (a) sea

surface temperature from CARS (Ridgway and Dunn, 2006), (b) aragonite saturation state from Lenton et al

(2015); and (c) pH from Lenton et al (2015).


The large-scale pattern of pH (Figure 3c) is in good agreement with the reconstruction of Lenton et al. (2015) with differ-

ences of order 0.2 largely falling within the uncertainty of the reconstruction, except along parts of the Western Australian

coast and the Tasman Sea, consistent with ΩA. Overall, and consistent with the SST results, we see that the differences in

ocean acidification between models and observations appear to be due to the more northward location of isolines of ΩA

and pH in the ESMs. However the large differences in ocean acidification between the reconstruction and ESMs on the

south coast of New Guinea are more difficult to assess due to limited observations of carbonate chemistry in this region.

Representation of the Coastal Domain in ESMs

To assess how well ESMs represent coastal scale features of SSTs, we compared the data collected at the NRS around

Australia (Figure 1) with the ESM results (Figure 4). Here we find that at all sites the observations sit within the range of

the ESMs (10th-90th percentiles), but that the median values show a small bias at all sites except Yongala and North

Stradbroke Island. At most sites the observed interannual variability shows a similar magnitude as the range of ESMs. It is

important that ESMs represent this variability on the continental shelf, as it can be up to twice that of the deeper ocean

(Lima and Wethey, 2012).

An assessment of how well ESMs capture ΩA and pH at the NRS around Australia (Figure 1) is presented in Figures 5 and

6 respectively. We see that the observed data for ΩA and pH at all sites sits within the range of the ESM simulations ex-

cept Yongala and Ningaloo, where the mean values are less than the range from ESMs. In the latter two regions the differ-

ences between the median values of observations and ESMs are much larger than for SST alone. This suggests that in im-

portant calcifying regions (e.g. Yongala and Ningaloo) the mean state in ocean acidification is not solely driven by open

ocean changes but is modulated by local process that at present are not well resolved by ESMs (Anav et al, 2013). How-

ever away from these important coral calcification regions ESMs appear to capture well the mean state in the coastal do-

main. On longer timescales, while observed interannual variability does fall within the range of the ESMs, this range is

not a good representation of interannual variability at many sites.

Projected Changes: SST

In the following sections we focus on the projected changes averaged over two 20-year time periods centred on 2050

(2040-2059) and 2090 (2080-2099), with reference to the historical period, 1986-2005. The simulated change in SST (me-

dian values) are shown in Figures 7 and 8 respectively. The absolute values are shown in Figures S1 and S2 respectively.

The projected absolute changes at the different NRS are shown in Figure 4.

Under all emissions scenarios a net SST increase around Australia is projected, with the magnitude and rate of this warm-

ing proportional to the emissions scenario i.e. the largest changes in SST are seen under the highest future atmospheric

CO2 concentrations. By 2050 under RCP 2.6 there is a (spatially averaged) net warming of 0.66 (0.38-1.0) K (Figure 7),

increasing to 0.67 (0.31 -1.13) K relative to the historical period by 2090 (Figure 8). The stabilization and subsequent

small reduction of atmospheric CO2 concentrations under RCP 2.6 leads to patterns of change in SST at 2050 and 2090

that are similar in magnitude due to inertia in the climate system e.g. Wigley (2005). By 2050 under RCP 4.5, a warming

of 0.92 (0.61-1.3) K is simulated, rising to 1.28 (0.87-1.84) K by 2090, relative to the historical period. Under RCP 8.5, an

increase of 1.23 (0.88-1.71) K at 2050 is projected and this more than doubles to 2.67 (2.04 -3.7) K by 2090, relative to the

historical period. The smaller changes in RCP 4.5 compared to RCP 8.5 again reflect the quasi-stabilization of atmospheric

CO2 concentrations that occurs under RCP 4.5.

Spatially, the changes in SST are quite heterogeneous as seen in Figures 7 and 8, and in the projections at the individual

NRS sites around Australia (Figure 4). The smallest increases in SST occur in southern Australia, while the largest warm-

ing is projected along the north-west coast of Australia, to the south of Western Australia, and along the east coast of Tas-

mania. Southern Western Australia and the east coast of Tasmania were also identified by Pecl et al., (2014) as two areas

(globally) in which SSTs will increase most rapidly in the future. The Tasmanian SST warming in particular shows large

change across ESMs, with median changes as large as +4 K, and 90th percentile changes exceeding +7 K above the histor-

ical period (1986-2005) under RCP 8.5 by the end of the century. That the 10th and 90th percentiles show the same spatial

pattern as the 50th percentile but with different magnitudes indicates a robust spatial warming pattern.


Projected Changes: Aragonite Saturation State

In this section we focus on the projected changes in aragonite saturation state (ΩA) over two time periods 2050 and 2090

(Figure 9 and 10 respectively), corresponding to a 20-year period centred on each time period, with reference to the IPCC

historical period (1986-2005). The absolute values are shown in S3 and S4 respectively. The projected absolute changes at

the different NRS are shown in Figure 5.

Figure 4 Projections of sea surface temperatures shown are median changes (solid line) and the 10th and 90th percen-

tile range (shading) for the RCPs 2.6, 4.5 and 8.5 at the locations of the National Reference Stations (NRS).

In situ observations collected at the NRS, comprising up to 5 years of data, are represented by the mean and

the 10th and 90th percentiles.


Figure 5 Projections of aragonite saturation state, shown are median changes (solid line) and the 10th

and 90th

percen-

tile range (shading) for the RCPs 2.6, 4.5 and 8.5 at the locations of the National Reference Stations (NRS).

In situ observations collected at the NRS, comprising up to 5 years of data, are represented by the mean and

the 10th and 90th percentiles.


Figure 6 Projections of pH, shown are the median changes (solid line) and the 10th

and 90th

percentile range (shading)

for the RCPs 2.6, 4.5 and 8.5 at the locations of the National Reference Stations (NRS). In situ observations

collected at the NRS, comprising up to 5 years of data, are represented by the mean and the 10th and

90th percentiles.


All emission scenarios show a net decrease in ΩA, with the magnitude and rate of change proportional to the atmospheric

CO2 concentrations. Under low emissions (RCP 2.6) a (spatially averaged) net decrease in ΩA of 0.36 (0.31-0.4) by 2050

and 0.31 (0.27-0.35) by 2090, relative to the historical period is projected. The decrease between 2050 and 2090 reflects

the stabilization and reduction of atmospheric CO2 concentrations over the latter half of the century under RCP 2.6. Under

RCP 4.5 there is a decrease of 0.51 (0.46-0.55) by 2050 and a decrease of 0.68 (0.62-0.73) by 2090, relative to the histori-

cal period. The changes under RCP 8.5 are the largest, with a decrease of 0.68 (0.61-0.73) by 2050, doubling to 1.35

(1.24-1.44) in 2090, relative to the historical period. That the values of ΩA in RCP 8.5 in 2050 are close to values in 2090

under RCP 4.5, is due to the similarity of the atmospheric CO2 concentrations at this time.

Analogous to spatial changes in SST we find that the decreases in ΩA are also quite heterogeneous, as illustrated by the

time series at the different NRS sites (Figure 5). The largest decreases occur in the northern part of the Australian region.

In particular, under the high emissions (RCP 8.5), median decreases in the Coral Sea of about 1.5 units occur relative to

the historical period (1986-2005). This large-scale pattern of change around Australia is consistent with the projected

change of the chemical buffering capacity of the ocean (Revelle factor). Interestingly, in the Tasman Sea where the largest

warming is simulated, the increase in SST acts to increase ΩA thereby offsetting the decrease of ΩA due to rising atmos-

pheric CO2 concentrations. This is particularly evident under RCP 4.5, at the 90th percentile level. Here we see that the

impact of the large warming of about + 6 K leads to an increase in ΩA that is greater than the decrease over the historical

period. However we also see that by 2090 under RCP 8.5, despite the presence of very strong warming, a net decrease in

ΩA occurs suggesting that the impact of increasing atmospheric CO2 concentrations dominates over the warming by the

end of the century to lead to net reduction of ΩA.

Projected Changes: pH

In this section we focus on the projected changes in pH over two time periods 2050 and 2090 (Figures 11 and 12 respec-

tively), corresponding to a 20-year mean centred on each time period with reference to the IPCC historical period (1986-

2005). The absolute values are shown in Figures S5 and S6 respectively. The projected absolute changes at the different

NRS are shown in Figure 6.

All emission scenarios show a net decrease in pH around Australia, with the magnitude and rate of this change proportion-

al to the atmospheric concentration of CO2. By 2050 a (spatially averaged) decrease in pH of 0.076 (0.072 – 0.079) under

low emissions (RCP 2.6) is projected, while by 2090 the changes seen in 2050 are starting to be reversed, resulting in a net

decrease of 0.1 to 0.066 (0.061-0.069) between 2050 and 2090, as the atmospheric concentrations of CO2 stabilize and

start to decrease. Under RCP 4.5 a decrease of 0.107 (0.102-0.111) is seen by 2050, which continues to decrease to 0.146

(0.140-0.150) by 2090, relative to the historical period. A decrease of pH of 0.144 (0.137-0.150) is seen by 2050 under

high emissions (RCP 8.5), which more than doubles to 0.314 (0.304-0.322) by 2090, relative to the historical period.

Again the similarity between RCP 4.5 in 2090 and RCP 8.5 in 2050 is consistent with atmospheric CO2 concentrations at

these two times. Consistent with ΩA and SST, the spatial decreases in pH are heterogeneous with the largest decreases

seen in southern Australia and at the mid-latitudes. This is evident in the projected time series at the NRS (Figure 6).

Potential Impacts on the Marine Environment

The projected changes in SST in the Australian region highlight the likelihood that warming will continue into the future

with the potential to have significant impacts on the marine environment, and the ecosystem services it provides. How an

organism responds to the projected SST changes will depend on the organism (Howes et al., 2015). Once optimum tem-

peratures for which physiological processes are most efficient have been exceeded, these processes are impacted (Pörtner

et al., 2014). It is this optimum temperature for organisms that strongly influences their distribution (Poloczanska et al.,

2014), and this is reflected in the observed southward migration of marine organisms in the Australian region in response

to observed ocean warming (Verges et al., 2014). In response to projected future warming the southward migration of ma-

rine species is anticipated to continue as the isotherms continue to move southward. For species not able to migrate, their

growth, development and immunity will likely be affected, leading in some cases to local extinctions (Poloczanska et al.,

2014; Wernberg et al., 2011).

One clear impact of this warming will be on coral reefs which support ~50% of the biodiversity in the ocean, and provide

many important ecosystem services. Studies have shown that while calcification is enhanced as temperature increases,

once an optimum value (typically a few degrees below the seasonal maximum temperature) is exceeded, calcification rates


rapidly decline (Al-Horani, 2005; Cantin and Lough, 2014). If this warming continues it will ultimately lead to cor-

als ejecting their symbiont dinoflagellates (zooxanthellae), a process known as coral bleaching, which in many cases can

lead to coral death (McClanahan et al., 2007).

Figure 7 The projected SST changes relative to the IPCC historical period 1986-2005 for 2050 (average 2040-2059).

Shown are the 10th

percentile (left), 50th

percentile (centre) and 90th

percentile (right) of the multi-model

range for RCPs 2.6 (top), 4.5 (middle) and 8.5 (bottom).

Frieler et al. (2013) reported that in order to sustain 50% of existing corals (globally) from coral bleaching SST would

need to stay below a 1.2 K change from the pre-industrial period. Our results suggest that for areas such as the Great Barri-

er Reef, even under RCP 2.6 the warming may be close to this value, suggesting that increased bleaching is likely, and

may lead to long-term degradation. This is further exacerbated under RCP 4.5 where the projected changes are likely to

significantly impact coral viability with close to two-thirds of corals experiencing severe bleaching (globally) within this

century (Frieler et al. 2013). Once a mean threshold of 2 K is reached it is likely that corals will cease to be viable leading

to long-term degradation (Hoegh-Guldberg, 2011). This further suggests that the role corals play in the coastal ecosystem

and the biodiversity they support will be adversely impacted.


Studies with CMIP5 ESMs have suggested there will be a reduction in primary productivity globally as SSTs increase. In

the Australasian region this is estimated to be in the order of 2% under RCP 2.6, and up to 4% under RCP 8.5 but with a

large range of uncertainty (Bopp et al., 2013). Importantly, changes in primary productivity around Australia would likely

have a significant impact on the productivity and return of fisheries, and the biomass of threatened marine animals such as

turtles, sharks and seabirds (Brown et al., 2010). It is also anticipated that this warming will adversely impact other key

species such as seagrasses and macroalgae that provide key habitats for many species (Howes et al., 2015).

Figure 8 The projected SST changes relative to the IPCC historical period 1986-2005 for 2090 (average 2080-2099).

Shown are the 10th

percentile (left), 50th

percentile (centre) and 90th

percentile (right) of the multi-model


The projected decreases in aragonite saturation state (ΩA) are likely to significantly impact marine calcifiers making it

harder for these species to build their hard skeletons. Observational studies have shown that corals are sensitive to ΩA,

although consensus on the exact form of the response has not yet been reached (Pandolfi et al., 2011). Nevertheless

Guinotte et al. (2003) suggested, based on historical data, that in tropical coral growth regions ΩA values less than 3.5

would be marginal for corals, less than 3 very marginal and for values less than 2.5, corals are not historically found.


While this is far from the point that the chemical dissolution of calcium carbonate occurs (when ΩA < 1), it does highlight

the balance between dissolution processes and calcification rates. Under RCP 2.6 the values in the Coral Sea, an important

coral calcifying region, remain annually around ~3.5 (Figures S1 and S2) which is considered to be the transition point

between healthy and marginal conditions. In contrast under RCP 4.5 by 2050 the values of ΩA are considered marginal

for corals (3-3.5). By 2090, despite the quasi-stabilization of atmospheric CO2 concentrations the values of ΩA continue to

decline and the range of change indicates conditions that are marginal to extremely marginal for corals at the end of the

century.

Figure 9 The projected aragonite saturation state changes relative to the IPCC historical period 1986-2005 for 2050

(average 2040-2059). Shown are the 10th percentile (left), 50th percentile (centre) and 90th percentile (right)

of the multi-model range for RCPs 2.6 (top), 4.5 (middle) and 8.5 (bottom).


Figure 10 The projected aragonite saturation state changes relative to the IPCC historical period 1986-2005 for 2090

(average 2080-2099). Shown are the 10th percentile (left), 50th percentile (centre) and 90th percentile (right)

of the multi-model range for RCPs 2.6 (top), 4.5 (middle) and 8.5 (bottom).

Under RCP 8.5 by 2050, consistent with RCP4.5 at 2090, the conditions in the Coral Sea will be marginal for corals. By

2090 the median of the ESMs suggest that the Coral Sea will experience conditions in which corals are not historically

found. This suggests that corals will likely become unviable in the longer-term, and may start to experience net loss of

calcium carbonate (Hoegh-Guldberg, 2011; Silverman et al., 2009). If the range is considered under the most optimistic

simulations (90th percentile) the conditions at best could be considered to be very marginal. Other important calcifying

organisms such as benthic forams, may become extinct by the end of this century under high emissions (Uthicke et al.,

2013). Other species of calcifying organisms show weakened calcified structures and reduced rates of calcification repair


under high emissions (Coleman et al., 2014; Fitzer et al., 2014) potentially making these organisms more vulnerable to

other environmental stressors.

Figure 11 The projected pH changes relative to the IPCC historical period 1986-2005 for 2050 (average 2040-2059).

Shown are the 10th percentile (left), 50th percentile (centre) and 90th percentile (right) of the multi-model


Some species such as macroalgae and sea grasses are projected to be more productive under lower pH (Pörtner et al.,

2014). However, many other species are expected to be adversely impacted, with observational studies reporting deleteri-

ous effects on organisms such as reduced reproductive health, impaired navigation and reduced sensory ability (Aze et al.,

2014; Munday et al., 2009; Munday et al., 2010). Due to the limited observational studies and the vast species diversity

that exists in the Australian region it is unclear what the full implications of falling pH will be, and how these may play out


under different scenarios. Branch et al. (2013) suggested that pH may be directly translatable into changes in commercially

targeted fish, with implications for the fisheries these species support. For example, in south-eastern Australia under high

emissions and fishing pressure, a restructuring of the pelagic and demersal food webs is anticipated, as well as major re-

gime shifts (Griffith et al., 2012). Together these studies suggest that ocean acidification, particularly under high emissions

(RCP 4.5 and 8.5) may have significant implications for the stewardship and management of commercial fisheries in the

Australian region.

Figure 12 The projected pH changes relative to the IPCC historical period 1986-2005 for 2090 (average 2080-2099).

Shown are the 10th percentile (left), 50th percentile (centre) and 90th percentile (right) of the multi-model



Based on studies from a natural CO2 seep (Milne Bay, Papua New Guinea; Fabricius et al., 2011), observations suggest

that once the pH falls below 7.7 coral reefs cease developing, but importantly before this point is reached the decrease in

pH leads to a large reduction in the biodiversity, abundance and structural complexity in the marine ecosystem (Fabricius

et al., 2011). Under RCP 8.5 we see that the values of pH are projected to be between 7.7-7.8 (Figure S6) for the entire

Australian region, suggesting that in response to ocean acidification the structure and diversity of the marine ecosystems

will be impacted.

It is important to note that stresses beyond ocean acidification and warming exist in the marine environment, such as

changes in nutrient supply and light, riverine input, storm damage, disease and human pressures (Burke et al., 2011),

which can impact the ability of organisms to buffer these environmental stressors, thereby reducing their resilience

(Edinger et al., 2000). Nevertheless studies have identified ocean acidification and warming as the two changes in the

physical environment that will have the most direct impact on biological systems (Howes et al., 2015). Recent studies have

also shown that ocean warming can further exacerbate the impacts of ocean acidification (Kroeker et al., 2013), and equal-

ly ocean acidification can exacerbate the impacts of warming (Anthony et al., 2008) on biological systems.

For many organisms it will be a trade-off between ocean acidification and warming, for example some organisms that mi-

grate southward may experience conditions that are more favourable in terms ocean temperature but less attractive in terms

of ocean pH. For other organisms such as seagrasses there is potential to do better in the future due to ocean acidification

but worse under ocean warming. At present, how this will play out remains unknown, as historically most studies have

focused on impacts to either warming or acidification. Therefore more studies that consider the physical-biological interac-

tions are urgently needed (Boyd and Brown, 2015). This will require new models informed by new observations that con-

sider the impacts of multiple stressors in a mechanistic framework (e.g. Evenhuis et al., 2015).

In this study the seasonal changes in the Australasian region were not investigated, primarily because many models do a

poor job reproducing dynamics and ocean biogeochemical changes at the seasonal scale (Sasse et al., 2015). Sasse et al.

(2015) showed that if the seasonal cycle is taken into account changes in ocean acidification may occur much sooner than

projected at the annual scale. Better representation of seasonal changes in SST and acidification will be needed to im-

prove projections in the future.

This paper is primarily focussed on the projected large-scale spatial changes that occur in the Australian region from

ESMs. However, in addition to large-scale spatial changes, marine species are also strongly influenced by local hydrody-

namic processes (e.g. Mongin and Baird, 2014). Our comparison of ESMs with the NRS illustrates that variability can be

considerably larger in the coastal domain than the ocean. Further many important features, such as boundary currents, are

poorly resolved in ESMs (Matear et al, 2013). Capturing changes in the coastal domain with greater confidence will re-

quire ESMs with higher resolution and improved representation of local processes.

Conclusions

In the future the ocean in the Australasian region is projected to become warmer and more acidic. The magnitude and rate

of these changes is strongly tied to the emissions trajectory followed. The projected changes in warming and ocean acidifi-

cation have the potential to profoundly impact the marine ecosystem, and the ecosystem services that it provides. Howev-

er, assessing the response of these changes in the Australasian region remains difficult due to limited observational data

with which to confidently project the impacts on the marine ecosystem and associated ecosystem services. In particular

many studies have focussed on the higher emission scenarios, with little focus on the lower emission scenario, the latter

also being critically important to inform policy makers and to manage risk and adaptation. Therefore we suggest that fu-

ture work is needed, that considers different emission scenarios and brings together physical and biological modelling

with observational expertise to more fully explore the economic, social and food security implications of ocean acidifica-

tion and warming for Australia. Such efforts will facilitate future marine planning and policy development.

Acknowledgements

This work was supported by the Australian Department of the Environment through their funding for the Natural Resource

Management (NRM) project and the Australian Climate Change Science Program (ACCSP). We acknowledge the World

Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the

climate modelling groups (listed in Table 1 of this paper) for producing and making available their model output. For


CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating

support and led development of software infrastructure in partnership with the Global Organization for Earth System Sci-

ence Portals.

References

Al-Horani, F.A. 2005. Effects of changing seawater temperature on photosynthesis and calcification in the scleractinian

coral Galaxea fascicularis, measured with O-2, Ca2+ and pH microsensors. Scientia Marina, 69, 347-354. DOI:

10.3989/scimar.2005.69n3347

Anthony, K.R.N., Kline, D.I., Diaz-Pulido, G., Dove, S., and Hoegh-Guldberg, O. 2008. Ocean acidification causes

bleaching and productivity loss in coral reef builders. Proc. Nat. Acad. Sci., 105, 17442-17446. DOI:

0.1073/Pnas.0804478105.

Anav, A., Friedlingstein, P., Kidston, M., Bopp, L., Ciais, P., Cox, P., Jones, C., Jung, M., Myneni, R., and Zhu, Z., 2013.

Evaluating the Land and Ocean Components of the Global Carbon Cycle in the CMIP5 Earth System Models. J. Cli-

mate, 26, 6801–6843. DOI: 10.1175/JCLI-D-12-00417.1

Aze, T. and co-authors. 2014. (Eds.) An Updated Synthesis of the impacts of impacts of ocean acidification on Marine Bi-

odiversity, Montreal.

Bopp, L., Resplandy, L., Orr, J.C., Doney, S.C., Dunne, J.P., Gehlen, M., Halloran, P., Heinze, C., Ilyina, T., Seferian, R.,

Tjiputra, J., and Vichi, M. 2013. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5

models. Biogeosciences, 10, 6225-6245. DOI:10.5194/Bg-10-6225-2013.

Boyd, P. and Brown, C. 2015. Modes of interactions between environmental drivers and marine biota. Frontiers in Marine

Science, 2. DOI: 10.3389/fmars.2015.00009.

Boyd P.W. and Hutchins, D.A. 2012. Understanding the responses of ocean biota to a complex matrix of cumulative an-

thropogenic change. Marine Ecology Progress Series, 470, 125-135. DOI: 10.3354/Meps10121.

Branch, T.A., DeJoseph, B.M., Ray, L.J., Wagner, C.A. 2013. Impacts of ocean acidification on marine seafood. Trends in

Ecology & Evolution, 28, 178-186. DOI: 10.1016/J.Tree.2012.10.001.

Brown C.J. and co-authors. 2010. Effects of climate-driven primary production change on marine food webs: implications

for fisheries and conservation. Global Change Biology, 16, 1194-1212. DOI: 10.1111/J.1365-2486.2009.02046.X.

Burke, L., Reytar, K., Spalding, M., and Perry, A. 2011. Reefs at risk revisited. World Resources Institute, Washington,

DC, USA.

Cantin, N.E. and Lough, J.M. 2014. Surviving Coral Bleaching Events: Porites Growth Anomalies on the Great Barrier

Reef. Plos One, 9, e88720. DOI:10.1371/journal.pone.0088720.

Coleman, D.W., Byrne, M., and Davis, A.R. 2014. Molluscs on acid: gastropod shell repair and strength in acidifying

oceans. Marine Ecology Progress Series, 509, 203-211. DOI: 10.3354/Meps10887.

Cooley, S.R., Kite-Powell, H.L. and Doney, S.C. 2009. Ocean acidification's potential to alter global marine ecosystem

services. Oceanography, 22, 172-181, DOI: 10.5670/oceanog.2009

CSIRO and Bureau of Meteorology. 2015. Climate Change in Australia Information for Australia’s Natural Resource

Management Regions, CSIRO and Bureau of Meteorology. 222 pp.

Dave, A.C. and Lozier, M.S. 2013. Examining the global record of interannual variability in stratification and marine

productivity in the low-latitude and mid-latitude ocean. J. Geophys. Res.-Oceans, 118, 3114-3127. DOI:

10.1002/Jgrc.20224.

De'ath G., Lough J.M., and Fabricius K.E. 2009. Declining coral calcification on the Great Barrier Reef. Science, 323,

116-119. DOI: 10.1126/science.1165283

Doney, S.C., Fabry, V.J., Feely, R.A., and Kleypas, J.A. 2009. Ocean Acidification: The Other CO2 Problem. Annual Re-

view of Marine Science, 1, 169-192. DOI: 10.1146/annurev.marine.010908.163834.

Doney, S.C. and coauthors. 2012. Climate Change Impacts on Marine Ecosystems. Annual Review of Marine Science, 44,

11-37. DOI: 10.1146/Annurev-Marine-041911-111611.

Dore, J.E., Lukas, R., Sadler, D.W., Church, M.J., and Karl, D.M. 2009. Physical and biogeochemical modulation of ocean

acidification in the central North Pacific. Proc. National Academy of Sciences of the United States of America, 106,

12235-12240. DOI: 10.1073/Pnas.0906044106.

Edinger, E.N., Limmon, G.V., Jompa, J., Widjatmoko, W., Heikoop, J.M., and Risk, M.J. 2000. Normal coral growth rates

on dying reefs: are coral growth rates good indicators of reef health? Marine Pollution Bulletin 40:404-425.

Evenhuis, C., Lenton, A., Cantin, N.E., and Lough, J.M. 2015. Modelling coral calcification accounting for the impacts of

coral bleaching and ocean acidification. Biogeosciences, 12, 2607-2630, DOI: 10.5194/bg-12-2607-2015


Fabricius, K.E., Langdon, C., Uthicke, S., Humphrey, C., Noonan, S., De'ath, G., Okazaki, R., Muehllehner, N., Glas,

M.S., and Lough, J.M. 2011. Losers and winners in coral reefs acclimatized to elevated carbon dioxide concentrations.

Nature Climate Change, 1, 165-169. DOI: 10.1038/Nclimate1122.

Fabry, V.J., Seibel, B.A., Feely, R.A., and Orr, J.C. 2008. Impacts of ocean acidification on marine fauna and ecosystem

processes. Ices Journal of Marine Science, 65, 414-432. DOI: 10.1093/Icesjms/Fsn048.

Feely, R.A., Doney, S.C., and Cooley, S.R. 2009. Ocean Acidification: Present Conditions and Future Changes in a High-

CO2 World. Oceanography, 22, 36-47. DOI: 10.5670/Oceanog.2009.95.

Fitzer, S.C., Cusack, M., Phoenix, V.R., and Kamenos, N.A. 2014. Ocean acidification reduces the crystallographic con-

trol in juvenile mussel shells. Journal of Structural Biology, 188, 39-45. DOI: 10.1016/J.Jsb.2014.08.007.

Frieler, K., Meinshausen, M., Golly, A., Mengel, M., Lebek, K., Donner, S.D., and Hoegh-Guldberg, O. 2013. Limiting

global warming to 2 degrees C is unlikely to save most coral reefs. Nature Climate Change, 3, 165-170. DOI:

10.1038/Nclimate1674.

Frolicher, T.L. and Joos, F. 2010. Reversible and irreversible impacts of greenhouse gas emissions in multi-century projec-

tions with the NCAR global coupled carbon cycle-climate model. Climate Dynamics, 35:1439-1459. DOI:

10.1007/S00382-009-0727-0.

Griffith, G.P., Fulton, E.A., Gorton, R., and Richardson A.J. 2012. Predicting Interactions among Fishing, Ocean Warm-

ing, and Ocean Acidification in a Marine System with Whole-Ecosystem Models. Conservation Biology, 26, 1145-

1152. DOI: 10.1111/J.1523-1739.2012.01937.X.

Guinotte, J.M., Buddemeier, R.W., and Kleypas, J.A. 2003. Future coral reef habitat marginality: temporal and spatial ef-

fects of climate change in the Pacific basin. Coral Reefs, 22, 551-558. DOI: 10.1007/S00338-003-0331-4.

Hobday, A. J. and Lough, J.M. 2011. Projected climate change in Australian marine and freshwater environments, Marine

and Freshwater Research, 62, 1000-1014, DOI:10.1071/MF10302

Hoegh-Guldberg, O. 2011. The Impact of Climate Change on Coral Reef Ecosystems, in: Z. Dubinsky and N. Stambler

(Eds.), An Ecosystem in Transition. 391-403. Springer Netherlands. DOI: 10.1007/978-94-007-0114-4_22.

Hofmann, M., Worm, B., Rahmstorf, S., and Schellnhuber, H.J. 2011. Declining ocean chlorophyll under unabated an-

thropogenic CO2 emissions. Environ. Res. Lett., 6, 1–8. DOI:10.1088/1748-9326/6/3/034035

Holbrook, N.J. and Bindoff, N.L. 1997. Interannual and decadal temperature variability in the southwest Pacific Ocean

between 1955 and 1988. J. Climate, 10, 1035-1049. DOI: 10.1175/1520-0442

Howes, E.L., Joos, F., Eakin, M., and Gattuso, J.-P. 2015. An updated synthesis of the observed and projected impacts of

climate change on the chemical, physical and biological processes in the oceans. Frontiers in Marine Science, 2. DOI:

10.3389/fmars.2015.00036.

Hu, D.X and co-authors.. 2015. Pacific western boundary currents and their roles in climate. Nature. 522, 299-308, DOI:

10.1038/nature14504

Iglesias-Rodriguez, M.D., Halloran, P.R., Rickaby, R.E.M., Hall, I.R., Colmenero-Hidalgo, E., Gittins, J.R., Green,

D.R.H., Tyrrell, T., Gibbs, S.J., von Dassow, P., Rehm, E., Armbrust, E.V., and Boessenkool, K.P. 2008. Phytoplankton

calcification in a high-CO2 world, Science, 320, 336-340. DOI: 10.1126/science.1154122

IPCC. 2013. Summary for Policymakers, in: T. F. Stocker, et al. (Eds.), Climate Change 2013: The Physical Science Ba-

sis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate

Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Kroeker, K.J., Kordas, R.L., Crim, R., Hendriks, I.E., Ramajo, L., Singh, G.S., Duarte, C.M., and Gattuso, J.P. 2013. Im-

pacts of ocean acidification on marine organisms: quantifying sensitivities and interaction with warming. Global

Change Biology 19, 1884-1896. DOI: 10.1111/Gcb.12179.

Lenton, A., and co-authors. 2013. Sea-air CO2 fluxes in the Southern Ocean for the period 1990-2009. Biogeosciences

10:4037-4054. DOI: 10.5194/Bg-10-4037-2013.

Lenton, A., Tilbrook, B., Matear, R.J., Sasse, T., and Nojiri, Y. 2015. Historical reconstruction of ocean acidification in

the Australian region, Biogeosciences Discussion, 12, 8265-8297. DOI:10.5194/bgd-12-8265-201

Lima F.P., and Wethey D.S. 2012. Three decades of high-resolution coastal sea surface temperatures reveal more than

warming. Nature Communications, 3. 704. DOI: 10.1038/Ncomms1713.

Lueker, T.J., Dickson, A.G., and Keeling, C.D. 2000. Ocean pCO(2) calculated from dissolved inorganic carbon, alkalini-

ty, and equations for K-1 and K-2: validation based on laboratory measurements of CO2 in gas and seawater at equilib-

rium. Marine Chemistry, 70, 105-119. DOI: 10.1016/S0304-4203(00)00022-0.

Lynch T.P. and co-authors. 2014. IMOS National Reference Stations: A Continental-Wide Physical, Chemical and Biolog-

ical Coastal Observing System. Plos One 9. DOI: 10.1371/journal.pone.0113652.

https://scripps.ucsd.edu/biblio?page=2&s=year&o=desc&f%5Bterm_id%5D=225&f%5Bauthor%5D=9614

https://scripps.ucsd.edu/biblio/pacific-western-boundary-currents-and-their-roles-climate


Matear R.J. and Lenton A. 2014. Quantifying the impact of ocean acidification on our future climate. Biogeosciences, 11,

3965-3983. DOI: i 10.5194/Bg-11-3965-2014.

Matear R.J., Chamberlain, M.A., Sun, C., and Feng, M. 2013. Climate change projection of the Tasman Sea from an Eddy-

resolving Ocean Model. J. Geophys. Res.-Oceans, 118, 2961-2976. DOI: 10.1002/Jgrc.20202.

McClanahan, T.R., Ateweberhan, M., Graham, N.A.J., Wilson, S.K., Sebastian, C.R., Guillaume, M.M.M., and Brug-

gemann, J.H. 2007. Western Indian Ocean coral communities: bleaching responses and susceptibility to extinction. Ma-

rine Ecology Progress Series, 337, 1-13. DOI: 10.3354/Meps337001.

Mongin, M., and Baird, M. 2014. The interacting effects of photosynthesis, calcification and water circulation on carbon

chemistry variability on a coral reef flat: A modelling study. Ecological Modelling, 284, 19-34. DOI:

10.1016/J.Ecolmodel.2014.04.004.

Mucci, A. 1983. The Solubility of Calcite and Aragonite in Seawater at Various Salinities, Temperatures, and One Atmos-

phere Total Pressure. American J. Science, 283, 780-799, DOI: 10.2475/ajs.283.7.780

Munday, P.L., Donelson, J.M., Dixson, D.L., and Endo, G.G.K. 2009. Effects of ocean acidification on the early life histo-

ry of a tropical marine fish. Proc. Royal Society B-Biological Sciences, 276, 3275-3283. DOI: 10.1098/Rspb.2009.0784.

Munday, P.L., Dixson, D.L., McCormick, M.I., Meekan, M., Ferrari, M.C.O., and Chivers, D.P. 2010. Replenishment of

fish populations is threatened by ocean acidification. Proc. Nat. Acad. Sci., 107, 12930-12934. DOI:

10.1073/Pnas.1004519107.

Pandolfi, J.M., Connolly, S.R., Marshall, D.J., and Cohen, A.L. 2011. Projecting Coral Reef Futures Under Global Warm-

ing and Ocean Acidification. Science, 333, 418-422. DOI: 10.1126/Science.1204794.

Pearce, A.F. and Feng, M. 2013. The rise and fall of the "marine heat wave" off Western Australia during the summer of

2010/2011. J. Marine Systems, 111, 139-156. DOI: 10.1016/J.Jmarsys.2012.10.009.

Pecl, G.T., Hobday, A.J., Frusher, S., Sauer, W.H.H., and Bates, A.E. 2014. Ocean warming hotspots provide early warn-

ing laboratories for climate change impacts. Reviews in Fish Biology and Fisheries, 24, 409-413. DOI:

10.1007/S11160-014-9355-9.

Poloczanska, E.S., Hoegh-Guldberg, O., Cheung, W., Pörtner, H.O., and Burrows, M. 2014. Cross-chapter box on ob-

served global responses of marine biogeography, abundance, and phenology to climate change, in: C. B. Field, et al.

(Eds.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribu-

tion of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel of Climate Change, Cambridge

University Press, Cambridge, United Kingdom and New York, NY, USA, 123-127.

Pörtner, H.-O., Karl, D., Boyd, P.W., Cheung, W., Lluch-Cota, S.E., Nojiri, Y., Schmidt, D., Zavialov, P. 2014. Ocean

systems, in: C. B. Field, et al. (Eds.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global

and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel

of Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 411-484.

Raven, J., Caldeira, K., Elderfield, H., Hoegh-Guldberg, O., Liss, P., Riebesell, U. 2005. Ocean acidification due to in-

creasing atmospheric carbon dioxide, The Royal Society, Policy Document, London, UK.

Rayner, N., Parker, D., Horton, E., Folland, C., Alexander, L., Rowell, D., Kent, E., and Kaplan, A. 2003. Global analyses

of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res.

– Atmospheres, 108. DOI: 10.1029/2002JD002670

Rhein, M. and co-authors. 2013. Observations: Ocean. In: Climate Change 2013: The Physical Science Basis. Contribu-

tion of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker,

T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)].

Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Ridgway, K.R., Dunn, J.R., and Wilkin, J.L. 2002. Ocean interpolation by four-dimensional weighted least squares - ap-

plication to the waters around Australasia. J. Atmos. Ocean. Tech., 19, 1357-1375. DOI: 10.1175/1520-0426.

Ridgway, K. R. 2007. Long-term trend and decadal variability of the southward penetration of the East Australian Current,

Geophys. Res. Lett., 34, L13613. DOI:10.1029/2007GL030393.

Riebesell, U., Fabry, V.J., Hansson, L., and Gattuso, J.-P. 2010. Guide to best practices for ocean acidification research

and data reporting, Publications Office of the European Union., Luxembourg. pp. 260.

Sasse, T.P., McNeil, B.I., Matear, R.J., and Lenton, A. 2015. Quantifying the influence of CO2 seasonality on future ocean

acidification. Biogeosciences, 12, 5907-5940. DOI:10.5194/bg-12-6017-2015

Silverman, J., Lazar, B., Cao, L., Caldeira, K., and Erez, J. 2009. Coral reefs may start dissolving when atmospheric CO2

doubles. Geophys. Res. Lett., 36. L05606. DOI 10.1029/2008gl036282.

Taylor, K. E., Stouffer, R.J., and Meehl, G.A. 2012. An overview of CMIP5 and the experiment design, Bull. Am. Meteor-

ol. Soc., 90,485–498, DOI:10.1175/BAMS-D-11-00094.1.

http://dx.doi.org/10.1029/2007GL030393


Uthicke, S., Momigliano, P., and Fabricius, K.E. 2013. High risk of extinction of benthic foraminifera in this century due

to ocean acidification. Scientific Reports 3. DOI: 10.1038/Srep01769.

Verges, A. and co-authors. 2014. The tropicalization of temperate marine ecosystems: climate-mediated changes in her-

bivory and community phase shifts. Proc. Royal Soc. B-Biological Sciences, 281. DOI: 10.1098/Rspb.2014.0846.

Wernberg, T., Russell, B.D., Thomsen, M.S., Gurgel, C.F.D., Bradshaw, C.J.A., Poloczanska, E.S., and Connell, S.D.

2011. Seaweed Communities in Retreat from Ocean Warming. Current Biology, 21, 1828-1832. DOI:

10.1016/J.Cub.2011.09.028.

Wigley, T.M.L. 2005. The climate change commitment. Science 307, 1766-1769. DOI: 10.1126/Science.1103934.

Wu, L.X., Cai, W.J., Zhang, L.P., Nakamura, H., Timmermann, A., Joyce, T., McPhaden, M.J., Alexander, M., Qiu, B.,

Visbecks, M., Chang, P., and Giese, B. 2012. Enhanced warming over the global subtropical western boundary currents.

Nature Climate Change, 2, 161-166. DOI 10.1038/Nclimate1353.

Zinke, J., Rountrey, A., Feng, M., Xie, S.P., Dissard, D., Rankenburg, K., Lough, J.M., and McCulloch, M.T. 2014. Corals

record long-term Leeuwin current variability including Ningaloo Nino/Nina since 1795. Nature Communications, 5,

3607. DOI: 10.1038/Ncomms4607.


Supplementary Figures

Figure S1 The projected SST for 2050 (average 2040-2059) for the median (50th percentile) and 10th and 90th percen-

tiles for RCPs 2.6, 4.5 and 8.5.


Figure S2 The projected SST for 2090 (average 2080-2099) for the median (50th percentile) and 10th and 90th percen-



Figure S3 The projected aragonite saturation state for 2050 (average 2040-2059) for the median (50th percentile) and

10th and 90th percentiles for RCPs 2.6, 4.5 and 8.5.


Figure S4 The projected aragonite saturation state for 2090 (average 2080-2099) for the median (50th percentile) and

10th and 90th percentiles for RCPs 2.6, 4.5 and 8.5.


Figure S5 The projected pH for 2050 (average 2040-2059) for the median (50th percentile) and 10th and 90th percen-



Figure S6 The projected pH for 2090 (average 2080-2099) for the median (50th percentile) and 10th and 90th percen-


marine projections of warming and ocean … › jshess › docs › 2015 › lenton.pdfrent along...

Documents