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SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE1533

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Supplementary Information

Mediterranean seagrass vulnerable to regional climate warming

Gabriel Jordà1, Núria Marbà2* and Carlos M. Duarte2,3

1 Department of Ecology and Marine Resources, IMEDEA (CSIC-UIB), Institut

Mediterrani d’Estudis Avançats, Miquel Marquès 21, 07190 Esporles (Illes Balears),

Spain

2 Department of Global Change Research, IMEDEA (CSIC-UIB), Institut Mediterrani

d’Estudis Avançats, Miquel Marquès 21, 07190 Esporles (Illes Balears), Spain

3 The UWA Oceans Institute, The University of Western Australia. 35 Stirling Highway,

6009 - Crawley (WA), Australia

*corresponding author: telephone: +34 971611720; FAX: +34 971611761; e-mail:

[email protected]

© 2012 Macmillan Publishers Limited. All rights reserved.

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Supplementary methods

The Multi Model ensemble

An ensemble of climate models have been used to generate the projections of sea

surface temperature (SST). To generate the ensemble we use ten coupled atmosphere-

ocean global circulation models (AOGCMs) which are included in the IPCC Fourth

Assessment Report [1] . We have only included those models from which we could get

daily SST values. The list of selected AOGCMs are presented in Table S1 and a

complete documentation of each model can be found at the PCMDI web page (http://

www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php). The

ensemble has been completed with two regional models. Unfortunately, there are still

few ocean regional models for the Mediterranean so it was not easy to find regional

simulations (up to our knowledge there are only three published regional simulations

[2-4] and they are run under the A2 scenario). The first regional simulation is the

ENEA simulation performed in the framework of the EU-CIRCE project. This

simulation was obtained with the atmosphere-ocean coupled regional system

PROTHEUS [5] under the A1b scenario for the period 1950-2050. The second regional

simulation is the one performed in the framework of the VANIMEDAT2 Spanish

project. It was obtained with the NEMOMED8 model [6] forced from the outputs of

the ARPEGE-v4 climate model for the period 1950-2100. The spatial resolution of both

regional simulations is 1/8º.

Different authors (e.g. [7] [3] ) have pointed out that the quality of AOGCMs

results in the Mediterranean may be low because their coarse spatial resolution (1-2º)

does not account for key elements of Mediterranean hydrodynamics such as the

complex coastline and the controlling role of the narrow Gibraltar Strait. Regional


http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php




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models also present some deficiencies, even if their resolution is highly increased (see

for instance [8] ). Circulation and dense water formation events are likely to be

misrepresented by climate models in the Mediterranean. However, those models can

also produce reasonable results for other processes. For instance, [9] have shown that

the evaporation and precipitation rates obtained from AOGCMs over the Mediterranean

are close to those obtained by regional models and consistent with the observations in

the range of uncertainty. Oceanic heat waves in the Mediterranean are mainly driven by

the atmospheric conditions. The air temperature in the lower troposphere and the

surface winds, which determines the mixing in the upper ocean, are the parameters that

control the extreme events of sea surface temperature. The ocean dynamics plays a

secondary role in the generation of extreme sea surface temperatures, so the above-

mentioned limitations of the climate models in the Mediterranean are not crucial. Also,

a comparison of the statistics of heat waves from the multi model ensemble and

observations (see section below) shows that the ensemble is able to capture the actual

statistics of heat waves in the Balearic area.

Scaling of temperature time series

Climate models may show biases when compared to observations. Sometimes,

they also show damped variability (i.e. the range of values from models is smaller than

the range from observations). In order to use the same threshold value to characterize

heat waves for all the models and to get comparable statistics in terms of heat wave

intensity we perform a scaling procedure on model outputs. The goal of this procedure

is to ensure that the range of temperatures from the models is consistent with the

observed range.


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The scaling works as follows. We are interested in the time series of surface

temperature around the Balearic Islands. Therefore, for each model we construct a

single time series of temperature averaging temperature outputs from the model in a box

limited by 0-7ºE and 38.5-41ºN. Then, the averaged SST time series can be split into

different components:

€

SST(t) = SST0 + A⋅ sin ωt + φ( )[ ] +η(t) (S1)

where t stands for time, SST0 is the mean value. The seasonal cycle is represented with

a periodic function where Α is its amplitude, ω the annual frequency (2 π/ 365 days-1)

and φ the phase (i.e. the time of the year where the maximum temperature occurs).

Finally, η includes the rest of the signal which is not represented by the former terms

and which basically accounts for the intraseasonal variability (also the interannual

variability but that signal is much smaller).

The idea has been to compare the different parameters in (equation S1) from

model and observations for the present climate (1950-1999) and to estimate the optimal

correction factors. Then, these correction factors are applied to model SST projections

for the period 2000-2100. This procedure has been done for each model at every cell

grid.

SST observations are obtained from the reanalysis of satellite infrared AVHRR

data carried out by [10] for the Mediterranean Sea. The scaling is done as follows.

First, we have corrected model biases adding the mean difference between the

observations and the model (SST0obs - SST0model). Second, the amplitude of the seasonal

cycle was also wrong in most models. Their coarse spatial resolution prevented a proper


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representation of vertical mixing processes, so the sea response to surface warming/

cooling is damped/enhanced. Thus, the seasonal cycle amplitude was corrected

multipliying it by the ratio . Finally, it is well known that climate models tend

to generate less variability in the higher frequency bands. So, the intraseasonal

variability in η has also been tuned so that standard deviation (STD) of model and

observations matches. To do that we have multiplyed η by

€

std ηobs( )std ηmod el( )

SST in the Balearic area during this century

All the models project an increase of the annual mean temperature at the end of

the century ranging from 1.12 ºC to 4.17ºC, and with an averaged value of 2.76 ± 1.11

ºC. Also, most of the models project for the end of the century an increase of the

amplitude of the annual cycle (only CGCM3 projects a decrease). The averaged

increase is 0.63 ± 0.53 ºC. Although these results are obtained from the Balearic area,

the same values are obtained from other Mediterranean regions. This is in good

agreement with previous results from [3] and [4] who have found homogeneous SST

warming in the whole basin in their Mediterranean climate change projections.

Discrepancies among models are due to differences in the model physics and the

forcings (surface fluxes and evolution of Atlantic water characteristics). Also, it must be

noted that each model reproduces its own interdecadal variability which can influence

the long term trend. However, [11] have shown that the amplitude of the interdecadal

variability in the Mediterranean is about 0.2ºC for 70-100 year oscillations. This is one


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order of magnitude smaller than the projected changes due to anthropogenic effects, so

it cannot significantly alter the results.

Finally, it is worth mentioning that results from global models are consistent

with the results from regional models (Supplementary Table S1). The trajectories that

regional models predict for sea surface temperature in the Balearic Islands are

consistent and within the bounds of uncertainty of the ensemble of global models (see

Supplementary Table S1). Other studies dealing with projections from regional models

of Mediterranean Sea have assumed a different scenario of GHG emissions (i.e. A2

scenario, which implies higher GHGs concentrations). However, their results are also

consistent with those shown here. They have projected a temperature increase in 100

years of 4ºC [2] , 2.5ºC [3] and 2.6ºC [4]. Moreover, [4] analysed the benefits of

coupled regional models in front of forced regional models. Their conclusion was that

the results of coupled and associated uncoupled regional simulations are very similar as

far as SST is concerned.

Heat waves statistics

The statistics of heat wave events in the Balearic Islands region from

observations and models are compared in Supplementary Table S2. We first compute

the number of extreme warm events, which are identified as the periods when

temperature exceeds a threshold defined by the 99.5 percentile of the observed SST

(27.16 ºC for the Balearic Islands region). Then, we describe the averaged intensity of

those events computing the accumulated excess heat respect to that threshold (in degree

days). The statistics are carried out for a 24 years period (1985-2009 for satellite

observations and 1975-1999 for models).


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It can be seen that there is a large spread among the different models in terms of

number of events per year, ranging from 0.04 to 0.52 events/year. However, the

important point is that the ensemble average (0.22 ± 0.15 events/year) is quite close to

the observed statistics (0.25 events/year). The statistics of the extreme events strength

(averaged intensity) also presents a large spread ranging from 0.06 to 60.57 degree-days

(ºC) but again the ensemble average (19.15 ±16.10 degree-days (ºC)) is close to the

observations (19.27 ± 15.23 degree-days (ºC)). It is interesting to notice that even if the

models are not assimilating real data, and they are only forced with observed GHG

concentrations, the number of extreme warm events per year matches the observations.

Also, it is worth noting that the two regional models used, which contain a far more

detailed representation of oceanographic processes in the Mediterranean basin, as well

as the interactions between the Atlantic and the Mediterranean, show predictions

consistent with those derived from the ensemble of coarser global models. This is not

surprising because maximum sea surface temperature in the Balearic Islands is

dominated by atmospheric processes, rather than oceanographic ones and because the

trajectories for maximum sea surface temperature in the Balearic Islands is consistent

with that across the entire Mediterranean basin.

Estimation of accuracy of Posidonia oceanica density projections

The uncertainty in the P. oceanica density due to uncertainties in the projected SST,

mortality (M) and recruitment (R) parameters is estimated with a Montecarlo method.

We did so by adding to the different parameters of the equation (1) (i.e. N=N0 exp [-t .

(M-R)] , where N is shoot density after a time t, N0 initial shoot density) a random noise

ε and introducing in the equation the linear dependence of mortality to temperature


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(equation S2). We run 1000 realizations of the Monte Carlo ensemble where P. oceanica

density is estimated as:

€

Ni = N0 ⋅ exp −t⋅ a +εa( )SSTmaxi + b +εb( ) − R +εR( )[ ]( ) (S3)

where εa, εb and εR are a random noise with zero-mean and variance equal to the

uncertainty of parameters a, b and R , respectively. The index i accounts for the model

used to project temperature in 21st Century. Hence, we have computed an ensemble of a

total of 12000 estimations of P. oceanica density. The average of that ensemble provides

the most probable evolution of Posidonia oceanica density while the ensemble standard

deviation will provide an estimation of the uncertainties. It must be noted that the

empirical relationship between summer SSTmax and P. oceanica mortality rate was

derived for the temperature range 25.6 to 29.3 ºC. Hence, the predicted mortality rates

involved greater uncertainty above this range, which is predicted to be exceeded by year

2072 (Figure 1).

Uncertainty sources on Posidonia oceanica density projections

Additionally, we have checked the impact of the different sources of

uncertainties in the projections. We have run the same experiment as in Figure 2a but

perturbing only one element each time (either temperature projections, mortality

parameters or recruitment estimation), that is, assuming the other parameters are

perfectly estimated. The time series of the uncertainty in each case is plotted in

Supplementary Figure S2. It can be seen that the contribution of the different sources of

uncertainty is similar in magnitude, although their time evolution is different. During


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the first 20 years, uncertainty is mainly brought by inaccuracies in the mortality

equation and by inaccuracies in the recruitment estimation while the dispersion due to

different temperature projections is of second order of importance. After that, all terms

are similar in importance and since 2040, the uncertainties in the projected temperature

lead the ensemble dispersion dominating over the inaccuracies in mortality equation and

recruitment. Towards the end of the century, uncertainty tends to zero because the % of

current P. oceanica shoot density also tends to zero.


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Supplementary Tables

Supplementary Table S1. Annual mean trend and annual cycle amplitude trend

from the different models. (R) indicates a regional model.

MODELAnnual mean trend (oC/100

years)Annual cycle amplitude

trend (oC/100 years)ARPEGE 2.63 ± 0.07 0.90 ± 0.03BCM 2.55 ± 0.10 0.47 ± 0.09CGCM3 1.69 ± 0.20 -0.30 ± 0.08MIROC 4.00 ± 0.07 0.32 ± 0.03ECHAM5R1 3.67 ± 0.09 0.93 ± 0.04ECHAM5R2 3.36 ± 0.08 1.04 ± 0.04KNMI 3.18 ± 0.07 1.00 ± 0.05HADCM3Q0 1.12 ± 0.09 0.38 ± 0.03HADCM3Q3 1.77 ± 0.10 1.33 ± 0.10HADCM3Q16 4.17 ± 0.09 0.57 ± 0.07PROTHEUS (R) 2.17 ± 0.15 0.22 ± 0.13VANIMEDAT-2 (R) 2.92 ± 0.05 0.60 ± 0.03Average 2.77 ± 1.06 0.62 ± 0.51


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Supplementary Table S2. Statistics of extreme events (number and intensity) as

given by observations and IPCC models for present climate and projected for 21st

Century climate. ND indicates there is no available data for that period. (R) indicates a

Regional model. Standard error of average values are provided in brackets when

available.

Dataset

Present ClimatePresent ClimateProjections for

period 2025-2050

Projections for

period 2025-2050

Projections for

period 2075-2100

Projections for

period 2075-2100

DatasetEvents

per year

Averaged

Intensity

(degree-

days (oC))

Events

per year

Averaged

Intensity

(degree-

days (oC))

Events

per year

Averaged

Intensity

(degree-

days (oC))

Satellite

observations (SE)0.25

19.27

(15.23)ND ND ND ND

ARPEGE 0.08 4.50 0.80 692.02 1.00 3270.96

BCM 0.24 17.60 0.12 49.02 1.00 1213.02

CGCM3 0.04 0.06 0.44 107.69 ND ND

MIROC 0.52 19.51 1.00 1328.36 1.00 5394.72

ECHAM5r1 0.16 14.86 0.84 467.96 1.00 4264.36

ECHAM5r2 0.20 30.28 0.80 379.21 1.00 3773.38

ECHAM5r3 0.16 17.60 0.80 394.71 1.00 3124.91

HADCM3Q0 0.22 12.30 1.00 180.71 ND ND

HADCM3Q3 0.14 15.24 0.92 718.06 1.00 2794.30

HADCM3Q16 0.20 7.38 0.92 877.50 1.00 3567.21

PROTHEUS (R) 0.40 60.57 0.96 449.95 ND ND


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VANIMEDAT2 (R) 0.16 5.75 0.96 695.50 1.00 3073.60

Models average

(SE)

0.22

(0.15)

19.15

(16.10)

0.78

(0.27)

513.20

(324.20)

1.00

(0.01)

3425.36

(1202.89)


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Supplementary Figures

Supplementary Figure S1. Relationship between annual SSTmax over the Balearic

Islands region (Spain) and observed annual mortality rate of Posidonia oceanica

shoots at Cabrera Archipelago National Park (Balearic Islands, Spain) between

years 2002 and 2007. Summer SSTmax data were obtained from satellite images.

Annual shoot mortality was quantified annually in 3 replicated permanent plots (area

ranging from 0.09 m2 to 0.25 m2) installed at 9 stations distributed between 5 and 25 m

water depth [12] . Each data point represents the average annual shoot mortality rate

quantified at the 9 stations in a particular year. Bars indicate the standard error of

average values. The solid line represents the fitted regression equation:

shoot mortality rate (yr-1) = a· annual SST max (ºC) - b (S2)

a (average ± SE) = 0.021 ± 0.002, b (average ± SE) = 0.471 ± 0.065; N = 6; R2 = 0.95 ;

p < 0.001.


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Supplementary Figure S2. Time series of uncertainty of the percentage of current

Posidonia oceanica shoot density including different sources of uncertainty

(models, mortality rate, recruitment rate, all).


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Supplementary References

[1] IPCC. Climate Change 2007: The Physical Science Basis (eds Solomon,

S. et al.) (Cambridge Univ. Press, 2007)

[2] Thorpe, R.B. & Bigg, G.R. Modelling the sensitivity of Mediterranean

Outflow to anthropogenically forced climate change. Clim. Dyn. 16, 355-368

(2000).

[3] Somot, S., Sevault, F. & Déqué, M. Transient climate change scenario

simulation of the Mediterranean Sea for the 21st century using a high-

resolution ocean circulation model. Clim. Dyn. 27, 851-879 (2006).

[4] Somot, S., Sevault, F., Déqué, M. & Crépon M. 21st century climate

change scenario for the Mediterranean using a coupled atmosphere-ocean

regional climate model, Global Planet. Change 63, 112–126 (2008).

[5] Dell'Aquila A. et al. Impacts of long wave fluctuations of the seasonal

cycle in an A1B scenario over the Euro-Mediterranean area using a regional

earth system model. Clim. Res. doi:10.3354/cr01037 (2011).

[6] Beuvier, J. et al. Modelling the Mediterranean Sea inter-annual

variability during 1961-2000: Focus on the Eastern Mediterranean Transient,

J. Geophys. Res. 115, C08017, doi:10.1029/2009JC005950 (2010).

[7] Marcos, M. & Tsimplis, M. Comparison of results of AOGCMs in the

Mediterranean Sea during the 21st century J. Geophys. Res. 113 C12028

(2008).

[8] Calafat, F.M, Jordà, G., Gomis, D. & Marcos, M. Comparison of

Mediterranean sea level variability as given by three baroclinic models. J.

Geophys. Res. 117, C02009, doi:10.1029/2011JC007277 (2012).


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[9] Sanchez-Gomez, E., Somot, S. & Mariotti, A. Future changes in the

Mediterranean water budget projected by an ensemble of Regional Climate

Models. Geophys. Res. Lett. 36, L21401, doi:10.1029/2009GL040120

(2009).

[10] Marullo, S., Buongiorno Nardelli, B., Guarracino, M. & Santoleri, R.

Observing the Mediterranean Sea from space: 21 years of Pathfinder-

AVHRR sea surface temperatures (1985 to 2005): re-analysis and validation.

Ocean Sci. 3, 299-310 (2007).

[11] Marullo, S., Artale, V. & Santoleri, R. The SST multidecadal variability

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24, 4385-4401, doi:10.1175/2011JCLI3884.1 (2011).

[12] Marbà, N. & Duarte, C.M. Mediterranean Warming Triggers Seagrass

(Posidonia oceanica) Shoot Mortality. Glob. Change Biol. 16: 2366-2375

(2010).


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