Influence of Climate Variability and Change on the Ecosystems of the Sub-Arctic Inland Seas of Canad...
Download
1 / 20

Frédéric Maps & Marina Chifflet - PowerPoint PPT Presentation


  • 65 Views
  • Uploaded on

Influence of Climate Variability and Change on the Ecosystems of the Sub-Arctic Inland Seas of Canada : A modeling approach. Frédéric Maps & Marina Chifflet. Ongoing monitoring programs (visited weekly to yearly). Hudson Bay : MERICA visited yearly

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Frédéric Maps & Marina Chifflet' - moya


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

Influence of Climate Variability and Change on the Ecosystems of the Sub-Arctic Inland Seas of Canada :

A modeling approach.

Frédéric Maps

&

Marina Chifflet


Ongoing monitoring programs Ecosystems of the Sub-Arctic Inland Seas of Canada :

(visited weekly to yearly)

Hudson Bay: MERICA

visited yearly

moorings, T, S, dissolved oxygen, fluorescence, chlorophyll a, nutrients, zooplankton, benthos, fishes larvae

Sea-ice observations (CIS)

Gulf of St Lawrence: Atlantic Zone Monitoring Program

fixed stations visited weekly to monthly, and sections visited twice in the year (spring and autumn)

T, S, dissolved oxygen, fluorescence, chlorophyll a, nutrients, zooplankton abundance

Thermographs

Satellite

Monitoring

(ex. SeaWIFS)


coupled climate - sea-ice - ocean - ecosystem - biological models

Coupler

CRCM / GEM

GCM

GCM

LW

SW

Atmosphere:temperature, winds, clouds, dewpoint, pressure, precipitation

FAI, QAI, M AI

Tides

FAO, QAO, MAO

snow

Sea ice

Ice algae

Runoff

Physical Ocean:

FIO, QIO, M IO

Water level, currents, temperature, salinity, turbulent energy and dissipation

Primary production

NPZD model

Secondary production

copepods population model

Krill aggregation

vertical behaviour

model

Harmful algae

growth and vertical

behaviour model

(OBS or

OGCM)


sea-ice – ocean circulation model models

Salinity

Temperature

Sea ice volume

Observation

Model

Time (years)

Prognostic hindcast solution

for domain-averaged salinity & temperature profiles, and sea ice volume

Saucier et al., in prep


sea-ice – ocean circulation model models

0-30 m 30-100 m 100-200 m

Comparison with annual temperature and salinity indices

in depth ranges

Salinity Temperature (oC)

Depth range

: 0-30 m 30-100 m 100-200 m

MOD

OBS

Saucier et al., in prep


sea-ice – ocean circulation model models

Sea surface salinity Sea surface temperature (oC)

Saucier et al., in prep


Domain-averaged salinity & temperature profiles, and sea ice cover & volume. Anomalies ( solution - standard run)

sea-ice – ocean circulation model

Sea ice cover concentration

Current climate … + 2oC

Simulation with air temperature

+ 2oC (stabilization experiment)

Saucier et al., in prep


coupled ecosystem – sea-ice ocean circulation model cover & volume. Anomalies ( solution - standard run)

Simulated nitrate and chlorophyll a1997

Le Fouest et al., 2005

Chifflet et al., in prep


coupled ecosystem – sea-ice ocean circulation model cover & volume. Anomalies ( solution - standard run)

Comparisons to satellite-derived fields:chl a synoptic events

Le Fouest et al., 2005


coupled ecosystem – sea-ice ocean circulation model cover & volume. Anomalies ( solution - standard run)

Comparisons to satellite-derived fields:St. Lawrence discharge effect

model satellite

SST

Chl a

kCDOM

vs

Chl a

AVHRR

SeaWIFS

SeaWIFS

3rd – 6th of August 1998

Le Fouest et al., submitted


Interannual variability ice cover vs chlorophyll a

coupled ecosystem – sea-ice ocean circulation model cover & volume. Anomalies ( solution - standard run)

Interannual variability : ice cover vs chlorophyll a

April 1997 April 1998 April 1999

Monthly mean ice cover %

Monthly mean chl. a mg/m2

3 different ice cover for the 3 years 3 different patterns for the spring bloom

Chifflet et al., in prep


coupled ecosystem – sea-ice ocean circulation model cover & volume. Anomalies ( solution - standard run)

Interannual variability : chlorophyll a bloom

1997 1998 1999

Gulf

bloom max : end April – end May

bloom max : April

maxbloom : April – end Mai

Estuary

bloom max : end May – end August

bloom max : end May – Sept.

bloom max : May – Sept.

mgChla m-3

  • ice concentration: determinant effect on the bloom timing

  • bloom in the estuary later than in the gulf

  • spring bloom in 1999: early and long, as it was observed

  • autumnal blooms in 1998 & 1999, but not in 1997, as it was observed on Seawifs images

Chifflet et al., in prep


coupled ecosystem – sea-ice ocean circulation model cover & volume. Anomalies ( solution - standard run)

1997

1998

1999

Annual integrated production - gC/m2/an

Mean winter nitrate concentration (mmolN m-2)

Interannual variability in primary production

Chifflet et al., in prep


coupled ecosystem – sea-ice ocean circulation model cover & volume. Anomalies ( solution - standard run)

low freshwater runoff in Nov-Dec 98 & Jan-Feb 99:

North Atlantic water entrance in the St. Lawrence estuary = rich in nutrients ?

tides / storms during the winter ?

Flow of nutrients at Québec city

Temperature profile at Rimouski station

Winter 1998/1999

no major differences of irradiance

nutrients fluxes in the St. Lawrence estuary and the north-western region

Chifflet et al., in prep


coupled ecosystem – sea-ice ocean circulation model cover & volume. Anomalies ( solution - standard run)

Observed vs simulated nitrate concentration

1997

1998

1999

Spring

Fall

observed

predicted

Chifflet et al., in prep


coupled ecosystem – sea-ice ocean circulation model cover & volume. Anomalies ( solution - standard run)

Observed vs simulated chlorophyll a biomass

1997

1998

1999

Spring

Fall

observed

predicted

Chifflet et al., in prep


10 cover & volume. Anomalies ( solution - standard run)1

100

10-1

10-2

101

100

10-1

10-2

Chla modèle (mgChla.m-3)

Chla modèle (mgChla.m-3)

10-2 10-1 100 101

10-2 10-1 100 101

Chla observée (mgChla.m-3)

Chla observée (mgChla.m-3)

101

100

10-1

10-2

101

100

10-1

10-2

Chla modèle (mgChla.m-3)

Chla modèle (mgChla.m-3)

10-2 10-1 100 101

10-2 10-1 100 101

Chla observée (mgChla.m-3)

Chla observée (mgChla.m-3)

Spring

RMS=0,69 mgChla.m-3

MRD= -421,7%

Ratio=5,22

RMS=0,68 mgChla.m-3

MRD= -367,5 %

Ratio=4,67

C/N et C/Chla constants

C/N variable

RMS=0,46 mgChla.m-3

MRD= -216,0 %

Ratio=3,16

RMS=0,31 mgChla.m-3

MRD= -111,7%

Ratio=2,11

C/N et C/Chla variables

C/Chla variable


Autumn cover & volume. Anomalies ( solution - standard run)

101

100

10-1

10-2

101

100

10-1

10-2

RMS=0,49 mgChla.m-3

MRD= -95,49 %

Ratio=1,96

RMS=0,43 mgChla.m-3

MRD= -11,12 %

Ratio=1,11

Chla modèle (mgChla.m-3)

Chla modèle (mgChla.m-3)

C/N et C/Chla constants

C/N variable

10-2 10-1 100 101

10-2 10-1 100 101

Chla observée (mgChla.m-3)

Chla observée (mgChla.m-3)

101

100

10-1

10-2

101

100

10-1

10-2

RMS=0,45 mgChla.m-3

MRD= -74,29%

Ratio=1,74

RMS=0,44 mgChla.m-3

MRD= -4,39%

Ratio=0,95

Chla modèle (mgChla.m-3)

Chla modèle (mgChla.m-3)

C/N et C/Chla variables

C/Chla variable

10-2 10-1 100 101

10-2 10-1 100 101

Chla observée (mgChla.m-3)

Chla observée (mgChla.m-3)


Conclusions

In situ cover & volume. Anomalies ( solution - standard run) data used for initial and boundaries conditions

Validation: in situ data and satellite images

Conclusions

  • Detailed regional climate models can predict the synoptic to interannual variability of the general sea ice - ocean circulation

  • Fully coupled ecosystem model predict also the synoptic to interannual variability, especially primary production variability and nutrients concentration. Principal results:

    • high spatial and temporal variability: heterogeneous entity

    • ice cover: determinant effect on the spring bloom timing

    • pre-conditioning of the bloom during the winter? impact of synoptic events? role of the estuary and north-western region?

Future steps

  • Improvement of the ecosystem model: St. Lawrence discharge of nutrient, C/chla, C/N, remineralization, carbon cycle, sulfur cycle…

  • Operational reanalyses of physical and biogeochemical fields for forcing / coupling to upper trophic levels

  • Climate change scenarios and seasonal forecasting


The team cover & volume. Anomalies ( solution - standard run)

Principal investigators

F. Saucier physical model

B. Zakardjian coupled physical-ecosystem model

Post-doc

M. Chifflet NPZD model – Gulf of St. Lawrence

Z.-P. Mei carbon cycle – Hudson Bay

M. Sourrisseau krill model, harmful algae model – Gulf of St. Lawrence

PhD

M. Defossez physical model – Hudson Bay

J. Fauchot harmful algae model – Gulf of St. Lawrence

V. Le Fouest NPZD model – Gulf of St. Lawrence

F. Maps copepods population model – Gulf of St. Lawrence

V. Sibert sea-ice algae and NPZD models – Hudson Bay

G. Smith physical model – Gulf of St. Lawrence

Research assistants

J. Caveen, F. Roy, S. Senneville

Collaborators

M. Gosselin, P. Larouche, D. Lavoie, D. Lefaivre

S. Plourde, Y. Simard, M. Starr


ad