How will the S. Ocean biological pump respond to climate change? 1). How will phytoplankton...

How will the S. Ocean biological pump respond to climate change?

1). How will phytoplankton productivity respond to climate change in the future and why?

2). How will changes in AABW, AAIW, AAMW affect the Southern Ocean carbon cycle and storage?

Irina MarinovUniv. of Pennsylvania (UPENN)Work with postdocs Anna Cabre, Raffa Bernardello, and former undergrad student Shirley Leung. Thanks to funding from NASA.

DPP with climate change(1980-1999 to 2080-2099, RCP8.5 scenario)

Shirley Leung, Anna Cabre & Irina Marinov: Phytoplankton response to climate change in the SO (submitted)

Primary Production (gC/m2/yr) averaged over 16 CMIP5 models

Historical PP(1980-1999 average)

WHY ?

Phyto

Iron

(Sea ice ↓) IPAR

Phyto

Summer MLD

Cloud cover

IPAR

Phyto

Summer MLD

Iron

Phyto

NO3

What drives 100 year Phytoplankton biomass/productivity changes across the 16 CMIP5 models? Drivers and trends

across latitudinal bands:

65%

73%

56%

43%

59%

26%

68%

59%

27%

65%

34%

20%

D Max Phyto Biomass

D Max Yearly NO3D Min Yearly MLD

D iron

D Max \IPAR

D Cloud Fraction

Anna Cabre, Shirley Leung & Irina Marinov: submitted

75oS 65oS 50oS 40oS 30oS

- Light availability changes result in banded structure. Fe dominated models show less banded structure in the trend. Patterns of change related to increasing SAM.

GFDL-ESM2GHadGEM2-ES

Nitrate (mmol/m3)*

MLD min (m) Iron (nmol/m3)

MLD min (m) IPAR (W/m2)** Iron (nmol/m3)***

Max

Yea

rly

Ph

yto

pla

nkt

on

Bio

mas

s (m

mo

l/m

3)

Iron (nmol/m3)

IPSL-CM5A-MR

Iron (nmol/m3)

30-4

0°S

40-5

0°S

50-6

5°S

S o

f 65

°S

Nitrate (mmol/m3)Nitrate (mmol/m3)

Iron (nmol/m3)

TEMPORAL CORRELATIONS (interannual, 5-year, 10-year):

CONTROL time series (detrended)

(interannual and 5-year mechanisms)

Yearly data (historical 1911-2005)

Yearly data (RCP8.5 2006-2100)

Best linear fit (yearly data)

Best linear fit (5-year data)

CLIMATE CHANGE time series (with trend)

(mechanisms driven by climate warming)

10-year averages (historical 1911-2005)

10-year averages (RCP8.5 2006-2100)

Best linear fit (10-year averages)

Direction of change with climate warming

Sea ice fraction (%)****Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO (submitted)

Each dot represents a moment in the time series (average of value in a given band)

Chl average (1997-2010, SeaWIFS, mg/m3) Chl trend (1997-2010, SeaWIFS, mg/m3/yr)

D PP, 100-year changeSimilar?PP historical (16 CMIP5 models)

OBS

ERVA

TIO

NS

MO

DEL

AVE

RAG

E

CLOUDS TREND 1979-present Reanalysis dataset ERA INTERIM

Summertime MLD trend 1950-2013 UK Met Office Hadley Centre’s monthly global objective analyses fields of seawater potential temperature and salinity

Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO

OBSERVATIONSHISTORICAL TREND

MODELS100-year TREND(16 CMIP5 models)

1980-1999 to 2080-2099

Huge differences among different MLD products !

Climatological MLD, NCEP 2000-2013 Climatological MLD, Hadley 2000-2013

Climatological MLD, Argo floats 2000-2013

Marinov, Cabre et al., in prep.

Regression coefficients (left)for SeaWiFS period yearly

minimum NCEP MLD

Regression coefficients (left) for SeaWiFS period yearly minimum

Hadley MLD

Climatological MLD, Hadley 2000-2013 Climatological MLD, NCEP 2000-2013 Climatological MLD, Hadley 2000-2013



- Fe supply and light (controlled by cloud cover, MLD depth during blooms, and sea ice) are the

most important limiting factors in the subpolar and polar Southern Ocean, while NO3 is most

important in the subtropical Southern Ocean. Light changes result in banded structure. Iron

dominated models show less banded structure in the trend.

- Changes in these variables are governed by changes in ocean circulation and dynamics and

an increasingly positive Southern Annular Mode (SAM) index.

- Needed: time series for Fe, MLD, IPAR, cloud coverage, sea ice fraction, Si, phyto

biomass. Fe and PAR obs are critical!

- What is the “best” MLD data out there for the S Ocean ? Why do different MLD products look so different from each other? I am confused …

- Are there more relevant stratification indices that we can connect to biology?

2). How will changes in AABW, AAIW, AAMW affect the Southern Ocean C cycle and storage?

North Atlantic

from M. England’s web page:

AABW formation areas

Heat flux required to warm AABW beneath 4000 m in the 1990s and 2000s (constructed from observations by Purkey and Johnson 2010).

Purkey & Johnson 2010, 2012, 2013

Deep ocean has warmed significantly from the 1990s to the 2000s. We are starting to observe a reduction in the

production rate of AABW …

Observations over the past 40 yrs: The polar

Southern Ocean is freshening, stratifying

and stabilizing:

Claim: Freshening of surface waters since 1960s have made it impossible for open ocean convection to occur again

deLavergne et al., Nature Climate Change 2014

28°W in the Atlantic (Key et al., 1996)

Deep waters accumulate C stored from the biological C

pumpC storage= f(circ patterns)

AABW

AAIW

NADW

Dissolved Inorganic carbon

Preformed nutrients/C

The efficiency of C sequestration by the biological pump is set to a large degree by the pattern and strength of the global ocean ventilation.

Natural ocean carbon components and projected future changes (2081-2100)

D Total DIC D Preformed DIC D Remineralized DIC

Bernardello, Marinov et al., Response of the ocean C storage to climate change, J. Climate, 2014

AABW NADW

Decreases in AABW over the 21st century in the CMIP5 models and biogeochemical implications.

AABW vs DIC remin in AABW AABW vs oxygen in AABW

AABW volume vs. Biological Efficiency = (16/170)*(AOU/NO3)

AABW vs remineralized Nitrate (in the AABW)

AABW vs Preformed (physical) Nitrate (in the AABW)



Needed: time series for Fe, MLD, IPAR, cloud coverage, sea ice fraction, Si, phyto biomass. Fe and PAR obs are critical! We need to unify MLD obs, why do different MLD products look so different from each other? Are there more relevant stratification indices that we can connect to biology?

2). How will changes in AABW, AAIW, AAMW affect the Southern Ocean C cycle and storage?

Needed: monitor properties of watermasses in formation regions and along these watermasses (preformed nutrients, Fe, O2, heat and CO2 fluxes).

~ AABW formation regions critically important for climate~ Biological productivity and efficiency of air-sea exchange in these regions determines Preformed nutrient and DIC properties. Need to measure these !

EXTRAS:

Regression coefficients (left) and significant (p<0.05) coefficients for SeaWiFS period yearly minimum NCEP MLD

Regression coefficients (left) and significant (p<0.05) coefficients for SeaWiFS period yearly minimum Hadley MLD

Polynya kept open by mixing with relatively Warm Deep Water

O2 If ice thin enough. Apply salt perturbation at the surface: open sea

convection expose deep

CDW to the surface

“Burn” ice lose heat and

biological carbon to the atmosphere

Rich in biological carbon

Biol C loss


1). How will phytoplankton productivity respond to climate change in the future and why? Needed: time series for Fe, MLD, IPAR, cloud coverage, sea ice fraction, Si, phyto biomass. Fe and PAR obs are critical! We need to unify MLD obs, why do different MLD products look so different from each other? Are there more relevant stratification indices that we can connect to biology?2). How will changes in AABW, AAIW, AAMW affect the Southern Ocean C cycle and storage?Needed: monitor properties of watermasses in formation regions and along these watermasses (preformed macronutrients, Fe, O2, heat and CO2 fluxes). Deep water formation regions critically important !3). How will the export of organic matter and the subsequent remineralization change with climate change?Needed: understand dependence of OM export and remin. on temperature and phytoplankton size groups. New methods to observe PFTs from space (e.g., backscattering) needed. How do we link the surface signature of PFTs with export ? Is it clear that bigger phytoplankton result in stronger export?

DIC components zonal averages in preindustrial 2081-2100 av.

Bernardello

Global zonal mean of changes in DIC components 2081-2100 av.

+17 Pg C

-35 Pg C

-20 Pg C

R. Bernardello15

2081-2100 av.Numbers are Pg C of storage change

Results

AABW volume vs. average NITRATE (in AABW) CMIP5 RCP8.5

Climate change (CM2Mc model, RCP8.5) convection collapse the deep ocean & AABW store heat and remineralized carbon

Temp (Weddell Sea)

Bernardello, Marinov et al. 2014.

Periodic deep convection in the Weddell Sea occurs regularly throughout the long preindustrial spin-up in CM2Mc

Annual mean T (°C)

Satellite Sept Sea Ice (1974-1976)Model Sept. MLD

(3 convective winters)

Annual mean Mixed Layer Depth

c c c c

Bernardello, Marinov et al., GRL, in review

Salt anomaly Periodic deep convection burns sea ice; more AABW; more deep O2; outgass remineralized Carbon

AABW volume

surf salinity

Sea ice

deep O2

c c c c

MLD

T (°C)

ccc c coutgassing

25 of 36 models IPCC-2013 models simulate

open S.Ocean convection under

preindustrial forcing

Some caveatsClimate models generally do not properly represent shelf processes, so the deep ocean is too poorly stratified and open ocean convection is favored

Convection is parameterized, introducing uncertainties

DeLavergne, et al., 2014.

Most models show a marked decrease in the strength of deep convection over the course of the 20th and 21st centuries

Huge variability in timing of cessation. Open ocean convection completely ceases before 2030 in 7 models.

Simulations run to 2300 show no return to convective activity over this period

Convection collapses under anthropogenic forcing (RCP8.5)

Note huge variability in convection regime (area, frequency and duration)

deLavergne et al., 2014

Under pre-industrial atmospheric concentrations of CO2 most models simulate deep Southern Ocean convection

Conclusions• Consistent with previous studies, iron supply and light availability (controlled by

cloud cover, minimum yearly mixed layer depth during blooms, and sea ice) are

the most important limiting factors in the subpolar and polar Southern Ocean,

while nitrate is most important in the subtropical Southern Ocean. Light availability

drives the latitudinal banded patterns.

• Iron dominated models: GFDL-ESM2G, GFDL-ESM2M, IPSLs, CMCC-CESM, and

GISS-E2-H-CC (less banded structure in the trend).

• Shifts in these limiting variables drive changes in phytoplankton abundance and

production on not only interannual, but also decadal and 100-year timescales: the

timescales most relevant to 21st Century climate change.

• Changes in these driving variables are in turn governed by first-order adjustments

in ocean circulation and dynamics associated with elevated greenhouse gas

concentrations and perhaps an increasingly positive Southern Annular Mode

(SAM) index.

List of models


POSSIBLE DRIVERS

Wind stress u direction (Pa) 16 CMIP5 models average)

Increasing SAM


100-year change with climate change(1980-1999 to 2080-2099, RCP8.5 scenario)

Historical(1980-1999 average)

MLD summertime (m) 100-year change with climate change (16 CMIP5 models average) (1980-1999 to 2080-2099)


Total clouds (%) IPAR summertime (W/m2)

100-year change with climate change (16 CMIP5 models average) (1980-1999 to 2080-2099)


Summertime Fe (nmol/m3)Summertime NO3 (mmol/m3)

100-year change with climate change (1980-1999 to 2080-2099)

(16 CMIP5 models average) (12 CMIP5 models average)

Misumi et al. 2013 (Changes in iron in CESM1-BGC)


A 2004

H 2005

G 2005

S 2013

JG 2011

JG 2011

LG 2005

90W

120W

150W

180E

150E

120E

90E

60E

30E

0E

30W

60W

30ºS

40ºS

50ºS

60ºS

SC 2008

A 2008

A 2008

T 2012

MG 2009

Compilation of observed trends over historical period

G2005 Greg et al. 2005S2013 Siegel et al. 2013A2004 Atkinson 2004JG2011 Johnston & Gabric 2011LG2005 Lovenduski & Gruber 2005SC2008 Smith and Comiso 2008T2012 Takao et al. 2012MH2009 Montes-Hugo et al. 2009A2008 Arrigo et al. 2008

RED INCREASE IN CHLOROPHYLL, PHYTOPLANKTON, KRILL

BLUE DECREASE


1st band (30ºS to 40ºS): nitrate limited

PP trend NO3 wintertime trend


PP trend 2nd band (40ºS to 50ºS): light and iron co-limited

Fe wintertime trend

MLD summertime trend IPAR summertime trend


3rd band (50ºS to 65ºS): light limited

PP trend



PP trend Fe wintertime trend



4th band (South of 65ºS): light and iron co-limited

SPATIAL CORRELATIONS: Scatter plots of 100-year changes in max annual SPB vs. 100-year changes in listed variable at every masked grid point

GFDL-ESM2GHadGEM2-ES

Δ P

B (

mm

ol/

m3)

IPSL-CM5A-MR

30-4

0°S

40-5

0°S

50-6

5°S

S o

f 65

°S

a)

b)

c)

d)

Δ MLD min (m)R = -0.901, slope = -0.219

Δ Iron (nmol/m3)R = 0.841, slope = 1.07E-3

Δ MLD min (m)R = -0.820, slope = -0.184




Rel change in nitrateR = 0.911, slope = 1.144




Δ Sea ice fraction (%)R = -0.924, slope = -0.129

Rel

. C

han

ge

PB


30S-40S

40S-50S

50S-65S

>65S

Relative change in production vs. relative changes in variables of interest, by model and latitudinal band

Relative change in variables of interest

Min yearly summertime

MLD

Max yearly wintertime

NO3

Max yearly wintertime

iron

Max yearly IPAR

Avg yearly cloud fraction

Re

lati

ve

ch

an

ge

in

p

rod

uc

tio

n

30-40°S where phytomax decrease

40-50°S where phytomax increase

50-65°S where phytomax decrease

S of 65°S where phytomax increase

Masked latitudinalband colors:

Model symbols:

NorESM1-ME

MRI-ESM1

CMCC-CESM

GISS-E2-H-CC

GISS-E2-R-CC

MIROC-ESM

MIROC-ESM-CHEM

IPSL-CM5A-LR

IPSL-CM5A-MR

MPI-ESM-LR

MPI-ESM-MR

CanESM2

CESM1-BGC

GFDL-ESM2G

GFDL-ESM2M

HadGEM2-CC

HadGEM2-ES

a) b) c) d) e)


Date post:	29-Dec-2015
Category:	Documents
Upload:	paulina-lamb
View:	218 times
Download:	0 times

How will the S. Ocean biological pump respond to climate change? 1). How will phytoplankton...

Documents