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Biological ramifications of climate-change mediated oceanic multi-stressors

Supplementary Materials

Methods

CESM1 Model

The Community Earth System Model version 1.0, CESM1(BEC), is a coupled ocean-atmosphere model released in April 2010 that includes marine and terrestrial biogeochemistry (Gent et al. 2011; Hurrell et al., 2013). Among more than 50 other coupled climate models, it is part of the Coupled Model Intercomparison Project 5th phase; results are featured in the 5th Assessment Report (AR5) of the IPCC (IPCC, 2013). A comparison of the CESM1(BEC) model simulation for the 20th century revealed good agreement with observational data and an improvement to the previous version of the model. CESM1(BEC) comprises four interlinked components (atmosphere, ocean, land surface, sea-ice) that exchange flux and state information. For the oceanic component, model output from the Parallel Ocean Program version 2 (POP2) was used. Therein, the Biogeochemical Element Cycling (BEC) module tracks plankton dynamics the cycling of key elements (seawater inorganic carbon system and nutrients used in this study); for more details see Moore et al. (2004) and Moore et al. (2013).

For comparison, two model runs were chosen: The 20th century run includes the period 1850-2005 and is used as a reference as described in Lindsay et al. (2014) and Moore et al. (2013). The RCP8.5 simulation simulates conditions under a warming scenario during which the globally averaged radiative forcing at the top of the atmosphere increases to 8.5 W/m² in a 96 year period from 2005 to 2100, and the anthropogenic forcing (i.e., greenhouse gases, aerosols) is orientated following the guidelines for the IPCC and CMIP5 protocols (van Vuuren et al. 2011). In the RCP8.5 scenario, atmospheric CO2 concentrations rise monotonically and rapidly from ~370ppm in 2000 to ~950ppm in 2100. In the CESM1(BEC) simulations, atmospheric iron inputs to the ocean via dust deposition is constant over time with a prescribed spatial deposition pattern (Moore et al., 2013); atmospheric inputs may differ in the future but is difficult to project because of confounding effects of changing winds, hydrology, land-use change, and anthropogenic aerosol sources (Mahowald et al., 2011). Time-varying atmospheric deposition of natural and anthropogenic bioavailable nitrogen is included for the 20th and 21st centuries, but these fluxes have a relatively minor impact on ocean biogeochemistry (Moore et al., 2013). Similar to other CMIP5 models (Bopp et al., 2013), the CESM1(BEC) RCP8.5 simulation exhibits a reduction in global integrated ocean net primary production of ~6% reflecting decreased productivity in the subtropics and North Atlantic and increased productivity in the Equatorial and subpolar North Pacific, Arctic, and Southern Ocean (Moore et al., 2013).

The data for the global analysis of the CESM1(BEC) model output consists of monthly data for each selected variable (15 variables in total) in a global grid of 384 x 320 grid points (~ 1 degree resolution), averaged over the upper 10 m of the ocean. The model output is then transformed as summarised in S-Figure 3. PAR in the model is the average

Biological ramifications of climate-change-mediated oceanic multi-stressors

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2

Photosynthetically Available Radiation for each vertical layer (10 m in the case of the

CESM1(BEC)) averaged over 24 h for each month16,42

.

Factor analysis

Principal component analysis (PCA) is a straightforward mathematical manipulation of a data

covariance matrix with the expectation that related variables will group together when

projected upon the principal components, illuminating their functional relationship. Factor

analysis (FA) builds upon PCA by noting principal components that do not account for a

significant amount of the total variance of the data, and subsequently eliminating those

principal components (factors) creating a reduced dimensional representation of the original

data covariance (i.e., the number of factors are less than the number of variables). These

eliminated factors are often considered random noise that prevents the elucidation of the

relationships contained in the original data set. Once the number of factors is reduced, there is

a certain amount of extra degrees of freedom that can be exploited by rotating the remaining

factors to enhance the relationships between variables and factors, we used the orthogonal

Kaiser Varimax rotation in this study (Glover et al., 2011).

In this study, factor analysis is performed with the anomalies between model output for the

present and for a future projection under a warming scenario. Prior to analysis, the model

output for the 20th

century run was averaged for the period 1981-2000, the RCP8.5 run for the

period 2081-2100 for each grid cell. The anomalies were calculated by subtracting the mean

of period 1981-2000 from the mean of the period 2081-2100 for each grid cell and result in a

global grid of 384 x 320 anomalies for each variable (i.e., a 122,880 × 15 element data

matrix). Out of the 15 variables, 2 were discarded due to their low coefficient of variation

below 0.5 (potential density and pCO2) or their appearance limited to a few regions only (ice

fraction). Temperature, pH and CO32-

were included despite their low coefficient of variation

to represent global warming and ocean acidification, respectively, in the factor analysis. This

reduced the data matrix to 122,880 × 12 elements (locations over land are given a null value).

Factor analysis was performed with the anomalies of the remaining 12 variables. The data

were standardized by subtracting the mean and dividing by the standard deviation. Then the

covariance matrix (12 × 12) was computed and 12 eigenvalues and 12 eigenvectors were

extracted. From these principal components, factor loadings (factor–variable correlation

coefficients) and factor communalities (a measure of variable representation by each factor)

were computed. Only the principal components with significant loadings and representative

communalities were kept. Factor analysis is a further step in reducing the dimensionality in

the resulting factors. With an orthogonal rotation technique (Kaiser Varimax rotation), the

remaining factors were adjusted in a way to best obtain Thurston’s Simple Structure solution

(Glover et al., 2011). The original data (anomalies) can be projected onto the rotated factors

forming the factor scores which have the same geographic grid as the model’s original output.

This projection helps to localise the factors in the ocean. Analysis was performed with

MatLab version R2012a using the codes given in Glover et al. (2011).


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Regional vs. global anomaly plots

To further display the regional variations, the mean anomaly for the 14 standard oceanic

regions of the BEC module (e.g., Lima et al., 2014) were calculated and related to the global

mean anomaly of temperature. The relation to temperature was chosen as temperature is a

master variable for global warming. For each variable, the anomalies of each region were

divided by the temperature anomalies of the corresponding region and plotted in a

temperature-variable-anomaly plot.

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Display Items

S-Figure 1 Demarcation of biogeographical provinces or biomes used in the Biogeochemical

Element Cycling (BEC)-model within the CESM1 simulations. BEC has 14 regions which

were analysed (i.e. PCA/FA) separately. The descriptor for each region is abbreviated in

Table 2.

S-Figure 2 Global maps of the change in each ocean property between the decades (mean of

2081-2100) minus present (mean 1981-2000).

S-Figure 3 The five-step procedure employed in this study using principal component and

factor analysis to visualize CESM1(BEC) model output variables as patterns of multi-

stressors, and characterize the main relationships among the stressor variables that contribute

to these patterns.

S-Figure 4 The five-step procedure employed in this study using principal component and

factor analysis to relate CESM1(BEC) model output variables, regionally-distinctive multi-

stressor patterns, and their future ramifications for regional phytoplankton.

S-Tables

S-Table 1 Summary of the projected changes (global and BEC regional) presented in Table 2,

but expressed here as a percentage change relative to the present day value for each ocean

property.

S-Table 2 Summary of the projected changes (global and CESM1(BEC) regional) presented

in Table 2, but expressed here for the three macro-nutrients and one micro-nutrient presented

in Table 2 as the molar stoichiometric changes (future – present day projections).

S-Table 3 The non-lagged, spatial cross-correlation coefficients for the projected rotated

factors 1-6. With the exception of the auto-correlations, any value greater than zero implies a

correlation. These greater than zero correlations indicate factors containing confounded

underlying latent elemental variables (see text and Mulaik, 2010, for discussion).

S-Table 4 Ramifications of regional changes in climate-change properties (year 2100) for the

biota in two illustrative low latitude provinces (SAO and NPSO). The change in each

biologically-influential property is expressed as a % (future minus present day, see S-Table 1)

except for temperature (warming in degrees Celsius). The biological consequences are

examined using a compilation of available laboratory/field manipulation studies and field

surveys for the main phytoplankton groups in these waters – nitrogen fixers and pico-

cyanobacteria. T and C denote Trichodesmium and Crocosphaera diazotrophs, respectively.

Pro and Syn denote Prochlorococcus and Synechococcus, respectively. See Table 3 for a

parallel comparison in two illustrative high latitude provinces. Red and blue arrows denote

increases or decreases in climate-change environmental properties, respectively. Blank cells

denote no available data. PAR denotes mean underwater irradiance for the upper 10 m


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(averaged over 24 h for each month16,42

). Note, the magnitude of the experimental

manipulations presented here closely correspond to those projected by models for

temperature and CO2, whereas iron or nutrient manipulations often exceed model-predicted

changes for 2100.


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Tem

pe

ratu

re

Salin

ity

Ice

fra

ctio

n

MLD

PA

R

Win

dst

ress

Po

t. D

en

sity

SiO

3

PO

4

Fe

NO

3

Alk

alin

ity

CO

32-

Aci

dit

y

pC

O2

% % % % % % % % % % % % % % %

Global 0.9 -0.3 -44.1 -5.4 0.7 0.1 -0.1 -21.2 -28.0 6.5 -18.7 -0.3 -41.8 112.8 139.0

South Southern Ocean 0.6 -0.9 -40.5 -6.7 18.5 15.2 0.0 -16.0 -2.9 -1.42 -2.8 -0.1 -53.9 119.5 134.2

North Southern Ocean 1.0 0.1 -99.9 -6.4 1.2 -5.1 -0.1 -32.0 -21.2 11.33 -18.2 -0.2 -44.2 116.9 142.7

South Subtropical Pacific Ocean 0.8 -0.1 0.0 -1.9 -1.5 2.4 -0.1 -38.8 -44.1 2.64 -53.2 -0.3 -38.8 105.4 135.3

West Equatorial Pacific Ocean 0.8 -1.4 0.0 -6.8 -2.2 14.4 -0.1 -38.4 -62.3 16.12 -76.8 -0.4 -43.3 103.7 131.6

East Equatorial Pacific Ocean 0.9 -0.5 0.0 -9.6 -3.6 -7.8 -0.1 -32.1 -44.4 38.54 -53.3 -0.3 -34.8 83.2 103.0

North Subtropical Pacific Ocean 0.8 -0.5 0.0 -0.2 -0.4 -2.7 -0.1 -17.6 -61.1 14.33 -64.5 -0.3 -44.5 109.8 140.0

North Pacific Ocean 1.1 -1.4 0.0 -7.2 2.2 4.7 -0.1 -13.0 -31.3 1.27 -20.0 -0.3 -55.3 122.7 144.2

South Indian Ocean 0.9 -0.6 0.0 2.0 0.6 -2.6 -0.1 -1.7 -53.2 2.43 -30.8 -0.2 -46.1 110.5 141.8

North Indian Ocean 0.9 -0.6 0.0 1.5 -1.0 27.3 -0.1 -12.5 -50.2 3.55 -30.7 -0.3 -41.7 104.1 133.1

South Atlantic Ocean 0.8 0.2 0.0 -1.0 -1.1 -3.0 -0.1 -25.0 -58.7 17.43 -41.4 -0.3 -33.1 101.0 129.8


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North Subtropical Atlantic Ocean 0.8 1.5 0.0 1.2 -1.2 -1.0 0.0 -20.7 -60.5 5.85 -36.3 -0.2 -28.0 103.3 136.6

North Atlantic Ocean 0.7 -0.5 -47.0 -24.0 3.4 -8.3 -0.1 -29.4 -30.5 7.93 -29.5 -0.5 -40.6 126.2 150.5

Arctic Ocean 0.6 -2.5 -45.5 -2.1 124.7 31.4 -0.1 -43.2 -35.7 4.30 -37.0 -0.7 -70.9 156.2 168.5

S-Table 1


10

Fe : Si : NO3 : PO4

Global -3.0 e-4 : 10.0 : 6.2 : 1 South Southern Ocean 4.4 e-5 : 120.6 : 12.8 : 1 North Southern Ocean -2.3 e-4 : 15.9 : 9.7 : 1

South Subtropical Pacific Ocean -6.3 e-5 : 3.2 : 5.4 : 1

West Equatorial Pacific Ocean -7.5 e-4 : 3.7 : 4.9 : 1

East Equatorial Pacific Ocean -9.4 e-5 : 4.5 : 9.3 : 1 North Subtropical Pacific

Ocean -2.9 e-4 : 2.1 : 3.8 : 1

North Pacific Ocean -5.9 e-5 : 5.6 : 5.9 : 1 South Indian Ocean -4.9 e-4 : 1.1 : 1.7 : 1 North Indian Ocean -3.6 e-4 : 4.7 : 0.7 : 1

South Atlantic Ocean -4.8 e-4 : 4.8 : 1.5 : 1 North Subtropical Atlantic

Ocean -9.9 e-4 : 7.6 : 1.8 : 1

North Atlantic Ocean -5.2 e-4 : 9.6 : 9.3 : 1 Arctic Ocean -7.1 e-4 : 9.3 : 10 : 1

S-Table 2


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Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6

Factor 1 1.00 0.28 0.32 0.39 0.73 -0.23

Factor 2 1.00 0.17 0.63 0.53 0.54

Factor 3 1.00 0.05 0.46 -0.19

Factor 4 1.00 0.55 0.38

Factor 5 1.00 0.10

Factor 6 1.00

S-Table 3


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S-Table 4

Low latitude provinces

(SAO; NPSO)

Temp. (2.38C ; 2.44C)

CO2 (130% ; 140%)

PAR (1% ; 0.4%)

Iron (17% ; 14%)

Silicate (25% ; 18%) NO3 (41% ; 65%)

PO4 (59% ; 62%)

Nitr

ogen

Fix

ers

Effects of individual stressors Warming enhances growth by 25% (C, SAO 20)

High CO2 increases N fixation (20%, T/C, SAO27); reduces P requirements (C, NPSO, Garcia et al., 2013)

Effects of interactive stressors Complex interplay with PAR and PO4 (NPSO, C, Garcia et al., 2013) High CO2 and high causes 100% increase in growth and N fixation (NPSO, C, Fu et al., 2008)

Complex interplay with CO2 and PO4 (see right, Garcia et al., 2013); High PAR offsets high CO2 enhanced growth (T, Hutchins et al., 2007)

Iron, PAR and CO2 interactions (C) (Fu et al., 2008); Iron and PO4 limit growth (T, Mills et al., 2004)

Interactions with PAR and CO2 (Garcia et al., 2013, see right); Interactions with iron (Mills et al., 2004, see right)

Pcio

-cya

noba

cter

ia

Effects of individual stressors Warming enhanced growth by 35% (Pro, 25). Warming enhances growth by 20% (Syn, 23)

Stimulation of growth (Syn, Pro) during large scale iron-enrichments34

Effects of interactive stressors High CO2 and warming enhance photosynthetic rates by 300% (Syn) with no change for Pro23

Interplay with warming (see left)

Warming and reduced NO3 reported to promote Pro over Syn25

Nitrogen Fixers vs. Pico-cyanobacteria Biogeography

Temperature and PAR exert controls on spatial distributions (Pro, Martiny et al., 2009)

Fe controls distribution of N fixers (T) in Atlantic (SAO19)

Warming and lower NO3 may enhance Pro biome over Syn biome25)

Alters NO3:PO4:Fe stoichiometry influences biogeography of N fixers (NPSO26)


13

S-Fig. 1


14

S-Fig. 2


15

S-Fig. 3

1. Calculate Change Subtract present conditions (time mean for 1981-2000) from future conditions (time mean for 2081-2100) for each gridcell.

2. Standardization

Subtract global mean climate change signal and divide by spatial

standard deviation.

3. Principal Component Analysis

Compute covariance matrix of standardized fields. Extract eigenvalues λ and vectors U of covariance matrix. Calculate factor loadings.

4. Factor Rotation

Rotate 6 principal components (> 80 % of total variance) with

orthogonal Kaiser Varimax rotation to generate factor loading. Variables with similar spatial patterns of climate change are

grouped onto specific factors.

5. Re-projection

Reproject factors onto original spatial grid (1.) to generate factor score maps.

Magnitude and sign of local anomalies, relative to global mean change, due to a specific factor reflects factor loading (4.) times factor score map.

Example: Blue areas have stronger percentage increase of iron and decrease of phosphate compared to the global mean change.


16

S-Fig. 4

1. Model simulations

Use CESM with embedded biological BEC module (NPP, three PFT‘s) to provide simulations of physical, biogeochemical and carbonate system variables.

Subtract mean present conditions (1981-2000) from mean future conditions (2081-2100) for each gridcell.

2. Standardization and PCA Subtract global mean climate change signal and divide by spatial standard deviation. Use PCA to calculate principal component loadings.

3. Factor Rotation

Select 6 principal components (> 80 % of total variance) for orthogonal Kaiser Varimax rotation

to optimize factor loadings. Variables with similar spatial patterns of climate change are grouped onto specific factors.

4. Regionally- Distinctive Climate Change Patterns

Summarise changes in ocean properties (future minus present day simulations) globally and regionally.

Collate factors across regions for each of the physical, biogeochemical and carbonate system variables.

Scale all arrows according to the regional deviation from the global mean climate change signal. Distinguish the environmental properties that elicit taxon-specific phytoplankton responses to

regionally distinctive patterns.

5. Ramifications of Future Multi-stressor patterns for Regional Biota

Compile all available published data from laboratory and field manipulation experiments and field observations for selected provinces.

Cross-link reported individual and interactive effects of environmental variables with regional multi-stressors for important resident phytoplankton groups.

Appraise the likely outcome of the influence of regional multi-stressors on resident phytoplankton.


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