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 Marinov
Univ. of Pennsylvania (UPENN)
Work with postdocs Anna Cabre, Raffa Bernardello, and former undergrad
student Shirley Leung. Thanks to funding from NASA.
Primary Production (gC/m2/yr) averaged over 16 CMIP5 models
Historical PP
DPP with climate change
(1980-1999 average)
(1980-1999 to 2080-2099, RCP8.5 scenario)
WHY ?
Shirley Leung, Anna Cabre & Irina Marinov: Phytoplankton response to climate change in the SO (submitted)
What drives 100 year Phytoplankton biomass/productivity
changes across the 16 CMIP5 models? Drivers and trends
across latitudinal bands:
Phyto
65%
Iron
73%
(Sea ice ↓) IPAR
56%
Phyto
43%
Phyto
59%
Summer MLD
59%
Summer MLD
27%
Cloud cover
26%
Iron
65%
IPAR
68%
Phyto
34%
NO3
20%
D iron
D Cloud Fraction
D Max \IPAR
D Min Yearly MLD
D Max Yearly NO3
D Max Phyto Biomass
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.
Anna Cabre, Shirley Leung & Irina Marinov: submitted
TEMPORAL CORRELATIONS (interannual, 5-year, 10-year):
HadGEM2-ES
GFDL-ESM2G
IPSL-CM5A-MR
30-40°S
(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)
Nitrate
(mmol/m3)
Nitrate (mmol/m3)
Nitrate (mmol/m3)*
CLIMATE CHANGE time series (with trend)
40-50°S
(mechanisms driven by climate warming)
10-year averages (historical 1911-2005)
10-year averages (RCP8.5 2006-2100)
Best linear fit (10-year averages)
MLD min (m)
Iron (nmol/m3)
Iron (nmol/m3)
50-65°S
Direction of change with climate warming
MLD min (m)
IPAR (W/m2)**
Sea ice fraction (%)****
Iron (nmol/m3)
Iron (nmol/m3)***
S of 65°S
Max Yearly Phytoplankton Biomass (mmol/m3)
CONTROL time series (detrended)
Each dot represents a
moment in the time
series (average of value
in a given band)
Iron (nmol/m3)
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO (submitted)
Chl average (1997-2010, SeaWIFS, mg/m3)
OBSERVATIONS
Chl trend (1997-2010, SeaWIFS, mg/m3/yr)
Similar?
MODEL AVERAGE
PP historical (16 CMIP5 models)
D PP, 100-year change
Summertime MLD trend
CLOUDS TREND
1979-present
Reanalysis dataset ERA INTERIM
1950-2013 UK Met Office Hadley Centre’s monthly global objective
analyses fields of seawater potential temperature and salinity
OBSERVATIONS
HISTORICAL
TREND
MODELS
100-year TREND
(16 CMIP5 models)
1980-1999 to
2080-2099
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
Climatological MLD, NCEP 2000-2013
Climatological MLD, Hadley 2000-2013
Climatological MLD, Argo floats 2000-2013
Huge differences among
different MLD products !
Marinov, Cabre et al., in prep.
Climatological MLD, NCEP 2000-2013
Regression coefficients (left)
for SeaWiFS period yearly
minimum NCEP MLD
Climatological
MLD,
Hadley
2000-2013
Climatological
MLD,
Hadley
2000-2013
Regression coefficients (left) for
SeaWiFS period yearly minimum
Hadley MLD
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?
- 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
AABW formation areas
from M. England’s web page:
Deep ocean has warmed significantly from the 1990s to the
2000s. We are starting to observe a reduction in the
production rate of AABW …
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
Claim: Freshening of surface waters since 1960s have
made it impossible for open ocean convection to occur
again
Observations over the
past 40 yrs: The polar
Southern Ocean is
freshening, stratifying
and stabilizing:
deLavergne et al., Nature Climate Change 2014
Deep waters accumulate C
stored from the biological C
pump
C storage= f(circ patterns)
Dissolved Inorganic carbon
AAIW
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.
NADW
AABW
28°W in the Atlantic (Key et al., 1996)
Natural ocean carbon components and projected future changes
(2081-2100)
AABW
D Total DIC
NADW
D Preformed DIC
D Remineralized DIC
Bernardello, Marinov et al., Response of the ocean C storage to climate change, J. Climate, 2014
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 Preformed (physical)
Nitrate (in the AABW)
AABW vs remineralized Nitrate (in
the AABW)
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?
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
Biol C loss
If ice thin enough.
Apply salt
perturbation at the
surface:
Rich in biological
carbon
 open sea
convection
 expose deep
CDW to the
surface
 “Burn” ice
 lose heat and
biological
carbon to the
atmosphere
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?
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
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
2081-2100 av.
Numbers are
Pg C of storage
change
15
Results
R. Bernardello
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
Model Sept. MLD
Satellite Sept Sea Ice (1974-1976)(3 convective winters)
Annual mean Mixed Layer Depth
c
c
c
c
Bernardello,
Marinov et al.,
GRL, in review
Annual mean
T (°C)
Salt anomaly  Periodic deep convection  burns sea ice;
more AABW; more deep O2; outgass remineralized Carbon
c
c
c
AABW volume
c
outgassing
c
deep O2
surf salinity
Sea ice
T (°C)
MLD
c
c
c
c
25 of 36 models IPCC2013 models simulate
open S.Ocean
convection under
preindustrial forcing
Some caveats
Climate 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
Convection collapses under anthropogenic forcing (RCP8.5)
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
DeLavergne, et al., 2014.
Under pre-industrial atmospheric concentrations of CO2 most
models simulate deep Southern Ocean convection
Note huge variability in
convection regime
(area, frequency and
duration)
deLavergne et al., 2014
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, CMCCCESM, 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
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
Increasing SAM
POSSIBLE DRIVERS
Wind stress u direction (Pa) 16 CMIP5 models average)
Historical
(1980-1999 average)
100-year change with climate change
(1980-1999 to 2080-2099, RCP8.5 scenario)
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
MLD summertime (m) 100-year change with climate change (16 CMIP5 models average)
(1980-1999 to 2080-2099)
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
100-year change with climate change (16 CMIP5 models average) (1980-1999 to 2080-2099)
Total clouds (%)
IPAR summertime (W/m2)
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
100-year change with climate change (1980-1999 to 2080-2099)
(16 CMIP5 models average)
Summertime NO3
(mmol/m3)
(12 CMIP5 models average)
Summertime Fe (nmol/m3)
Misumi et al. 2013
(Changes in iron in CESM1-BGC)
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
0E
30W
Compilation of
observed trends over
historical period
30E
30ºS
S 2013
40ºS
60W
60E
50ºS
A 2004
60ºS
A 2008
G2005 Greg et al. 2005
S2013 Siegel et al. 2013
A2004 Atkinson 2004
JG2011 Johnston & Gabric 2011
LG2005 Lovenduski & Gruber 2005
SC2008 Smith and Comiso 2008
T2012 Takao et al. 2012
MH2009 Montes-Hugo et al. 2009
A2008 Arrigo et al. 2008
LG 2005
MG 2009
90W
90E
SC 2008
T 2012
H 2005
JG 2011
A 2008
120W
G 2005
120E
BLUE DECREASE
JG 2011
150W
150E
180E
RED INCREASE IN
CHLOROPHYLL,
PHYTOPLANKTON, KRILL
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
1st band (30ºS to 40ºS): nitrate limited
PP trend
NO3 wintertime trend
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
PP trend
MLD summertime trend
2nd band (40ºS
to 50ºS):
light and iron
co-limited
IPAR
summertime
trend
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
Fe wintertime trend
PP trend
3rd band (50ºS to 65ºS):
light limited
MLD summertime trend
IPAR
summertime
trend
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
PP trend
MLD summertime trend
4th band
(South of 65ºS):
light and iron
co-limited
IPAR
summertime
trend
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
Fe wintertime trend
SPATIAL CORRELATIONS: Scatter plots of 100-year changes in max annual SPB vs. 100year changes in listed variable at every masked grid point
GFDL-ESM2G
IPSL-CM5A-MR
30-40°S
30S-40S
Rel. Change PB
HadGEM2-ES
a)
Rel change in nitrate
R = 0.911, slope = 1.144
Rel change in nitrate
R = 0.872, slope = 0.482
Δ MLD min (m)
R = -0.901, slope = -0.219
Δ Iron (nmol/m3)
R = 0.767, slope = 2.59E-4
Rel change in nitrate
R = 0.876, slope = 0.325
40-50°S
b)
Δ Iron (nmol/m3)
R = 0.841, slope = 1.07E-3
c)
50-65°S
50S-65S
Δ PB (mmol/m3)
40S-50S
Δ MLD min (m)
R = -0.820, slope = -0.184
Δ Iron (nmol/m3)
R = 0.821, slope = 5.02E-4
Δ Iron (nmol/m3)
R = 0.690, slope = 1.16E-3
>65S
S of 65°S
d)
Δ Sea ice fraction (%)
R = -0.924, slope = -0.129
Δ Iron (nmol/m3)
R = 0.763, slope = 6.19E-4
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
Relative change in production vs. relative changes in
variables of interest, by model and latitudinal band
b)
e)
d)
c)
Relative change in
production
a)
Min yearly
summertime
MLD
Max yearly
wintertime
NO3
Max yearly
wintertime
iron
Max yearly
IPAR
Avg yearly cloud
fraction
Relative change in variables of interest
Model
symbols:
CanESM2
CESM1-BGC
GFDL-ESM2G
GFDL-ESM2M
HadGEM2-CC
HadGEM2-ES
MIROC-ESM
MIROC-ESM-CHEM
IPSL-CM5A-LR
IPSL-CM5A-MR
MPI-ESM-LR
MPI-ESM-MR
NorESM1-ME
MRI-ESM1
CMCC-CESM
GISS-E2-H-CC
GISS-E2-R-CC
Masked
latitudinal
band
colors:
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
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO