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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
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?
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
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 !
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
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 export ? Is it clear that bigger phytoplankton result in stronger export?
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
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
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
POSSIBLE DRIVERS
Wind stress u direction (Pa) 16 CMIP5 models average)
Increasing SAM
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, 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)
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
Total clouds (%) IPAR summertime (W/m2)
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
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)
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
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
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 2nd band (40ºS to 50ºS): light and iron co-limited
Fe wintertime trend
MLD summertime trend IPAR summertime trend
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
3rd band (50ºS to 65ºS): light limited
PP trend
MLD summertime trend IPAR summertime trend
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
PP trend Fe wintertime trend
MLD summertime trend IPAR summertime trend
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
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
Δ Iron (nmol/m3)R = 0.821, slope = 5.02E-4
Δ Iron (nmol/m3)R = 0.690, slope = 1.16E-3
Δ Iron (nmol/m3)R = 0.763, slope = 6.19E-4
Rel change in nitrateR = 0.911, slope = 1.144
Δ Iron (nmol/m3)R = 0.767, slope = 2.59E-4
Rel change in nitrateR = 0.872, slope = 0.482
Rel change in nitrateR = 0.876, slope = 0.325
Δ Sea ice fraction (%)R = -0.924, slope = -0.129
Rel
. C
han
ge
PB
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO
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)
Anna Cabre, Shirley Leung & Irina Marinov: Phytoplankton response to climate change in the SO