Results From Comparative Ecosystem Studies within GLOBEC: Examples from
SPACC, CCC and ESSAS
Ken DrinkwaterInstitute of Marine Research and Bjerknes Center
for Climate Research, Bergen, Norway
US GLOBEC MeetingBoulder, Colorado Feb. 17, 2009
Outline
•Why comparative studies?•Types of comparisons•Examples
Species –Cod, LobsterEcosystems – Upwelling, Subarctic Seas
•Problems•Concluding Remarks
Why Comparative Studies of Ecosystems?
3. Ecosystems are complex – can help determine what is a fundamental process and what is unique.
2. Provides insights that one cannot obtain by looking at a single ecosystem
4. Increases statistical degrees of freedom
5. Sharing approaches and methodologies
1. Cannot run controlled experiments on ecosystems
Types of Comparisons
3. Same species but different geographic (hydrographic) regions, e.g. Atlantic cod, American lobster.
1. Same ecosystem type but different geographic regions (upwelling regions, subarctic seas)
There are many types of comparative studies.
4. Same ecosystem (geographic area) but at different times (forcing), e.g. cold period vs. warm period.
2. Different ecosystems, e.g. tropics vs. polar ecosystems.
Atlantic Cod (Gadus morhua)
CCC (Cod and Climate Change)
Cod Distribution
Temperature plays a large role in determining the Growth Rate of cod
The relative size of a 4-year old as a function of mean bottom temperature.
Brander 1994
The same information shown graphically, i.e. the relative size of the age 4 cod at different temperatures.
Cod Recruitment and TemperatureCod Recruitment and Temperature
Mean Annual Bottom Temperature11
10
9
8
7
6
4
3
2
Temperature Anomaly
Warm Temperatures
decrease Recruitment
Warm Temperatures
increase Recruitment
Log2 Recruitment Anomaly
Faroes
Iceland
Newfoundland
North Sea
Irish Sea
-2 -1 0 1 2
-2 -1 0 1 2
-2 -1 0 1 2
-2 -1 0 1
-2 -1 0 1 2 -2 -1 0 1 2
-2 -1 0 1 2
-2 -1 0 1 2
Georges Bank
4
W. Greenland
4
2
0
-2
-
4
2
0
-2 -1 0 1 2
4
2
0
-2
Celtic Sea
-2
4
2
0
-2
4
2
0
-2
4
2
0
-2
4
2
0
-2
4
2
0
-2
4
2
0
-2
Barents Sea
American Lobster (Homarus americanus)
Lobster Landings Magdalen Island
In the late 1980s landings rose dramatically with suggestions that it was due to good management.
Again a dramatic rise in landings in the late 1980s-early 1990s and claims that management was working well.
US and Canadian Lobster LandingsUS and Canadian Lobster Landings
The US and Canadian landings for each of the lobster management regions showed similar trends (except for 4 out of 34) with different management strategies. Could not have been management strategies. It was not increased effort.
SPACC (Small Pelagics and Climate Change)
Kawasaki, 1983;Bakun, 1997
Bakun, 1989; 1997
Synchrony in Upwelling Areas
Lead to much research on ecological teleconnections.
RIS
0
150
300
450
0
350
700
1050
HumboldtA
nchovy x10 4 (tons)
Sar
dine
x 1
0 4 (to
ns) 0
20
40
60
80
0
10
20
30
40
California
0
150
300
450
0
15
30
45Japan
-2.5
0
2.5
1920 1940 1960 1980
Sardine
Anchovy
Also found tendency for anchovy and sardines to be out of phase (but not always or everywhere).
Lluch-Belda et al., 1989
ESSAS (Ecosystem Studies of Sub-Arctic Seas)
Coccolithophore Bloom at the eastern entrance to the Barents Sea
SSTs in the Labrador Sea
NORCAN (Norway-Canada Comparison of Marine Ecosystems)
NORCAN
• Workshop Funded by NRC and DFO• Held in Bergen in December 2005•Meeting in St. John’s May 2006 and writing groups met in Bergen 2007•Decided to write papers along discipline lines – physical oceanography, phytoplankton, zooplankton, fish (3) and marine mammals.•Drafts nearing completion and hope to submit mid-2009
-1.5
-1.0
-0.5
0.0
0.5
1.0
1975 1980 1985 1990 1995 2000 2005
YEAR
TE
MP
AN
OM
AL
Y (O
C)
-20
-15
-10
-5
0
5
10
15
20
25
CIL
AN
OM
AL
Y (
KM
2)
FUGLØYA-BJØRNØYA NEWFOUNDLAND CIL LABRADOR CIL
-1.5
-1.0
-0.5
0.0
0.5
1.0
1975 1980 1985 1990 1995 2000 2005
YEAR
TE
MP
AN
OM
AL
Y (O
C)
-20
-15
-10
-5
0
5
10
15
20
25
CIL
AN
OM
AL
Y (
KM2
)
VARDØ-N NEWFOUNDLAND CIL LABRADOR CIL
65 62 59 56 53 50 47 44 41
LONG ITUDE
42
44
46
48
50
52
54
56
58
LATITUDE
NEW FOUNDLAND
LABRADOR SEA
LABRADOR
STANDARD SECTIONS200 M500 M1000 M2000 M3000 M4000 M
DEPTH
STATION 27
FUGLØYA - BJØRNØYAVARDØ-N
KOLASEAL ISLAND
BONAVISTA
•Ocean Temperature Trends between two regions Out-Of-Phase prior to mid-1990s
•Similar Trends Recently
-1.0
-0.5
0.0
0.5
1.0
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
YEAR
TE
MP
AN
OM
AL
Y(°
C)
-25
-20
-15
-10
-5
0
5
10
15
20
25
CIL
AN
OM
AL
Y (
MB
)
KOLA SECTION TEMP TRENDS NEWFOUNDLAND CIL LABRADOR CIL
INDEX 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960
NAO -0.25 -0.09 -0.56 0.60 -0.40 0.98 1.25 -0.48 0.64 0.94 1.29
NUUK 0.80 0.51 1.09 0.43 0.57 1.32 0.18 0.98 1.48 0.72 1.60
IQUALUIT 0.36 0.16 0.78 0.18 0.17 1.92 -0.03 -0.06 1.02 -0.04 1.10
CARTWRIGHT 0.24 1.27 1.72 0.81 0.42 1.50 0.22 -0.05 1.48 0.62 1.50
ST JOHN'S -0.42 1.88 1.44 1.09 0.74 -0.26 0.04 -0.72 1.20 -1.01 0.87
NL SEA ICE
ICEBERGS 0.44 1.09 1.08 1.02 0.65 1.02 0.99 -0.25 1.11 0.10 0.73
S27 SURFACE T -0.15 1.13 1.16 0.37 -0.92 -1.73 -0.14 -1.57 0.52 -0.84 0.40
S27 BOTTOM T -1.11 1.01 1.06 1.05 0.58 0.75 0.77 -0.03 1.10 -0.60 1.19
S27 AGERAGED T 0.31 1.92 0.90 2.83 0.14 1.27 1.28 -1.10 2.47 0.59 0.52
HAMILTON BANK SURFACE T 0.50 1.65 0.74 -0.15 1.45 -1.67 -0.86 -1.16 0.12 -0.21 -0.06
HAMILTON BANK BOTTOM T -0.63 1.14 -0.87 0.34 -1.23 -0.09 -0.13 -0.73 0.47 -0.47 -0.10
FLEMISH CAP SURFACE T -1.27 1.06 1.42 0.13 0.19 0.08 -0.30 -0.62 0.50 -0.16 0.36
FLEMISH CAP BOTTOM T -0.44 0.60 0.32 0.33 0.92 1.22 0.11 -0.54 0.90 -0.45 0.69
SEAL ISLAND AVG T 1.13 1.13 -0.17 0.56 -0.86 0.39 -0.07 0.00 0.02 0.83 1.61
BONAVISTA AVG T -0.57 -0.05 -0.52 -0.07 0.09 0.09 0.71 0.82 1.06 0.40 0.73
FLEMISH CAP AVG T 1.41 2.01 1.24 0.10 0.03 0.32 0.03 1.29 0.45 1.14
ST. PIERRE BANK BT -0.46 0.64 1.84 -0.84 1.30 -1.16 -1.99 -0.54 1.29 -1.51 -0.51
SEAL ISLAND CIL 0.23 0.85 0.06 0.21 -0.68 0.95 -0.19 -0.68 0.28 -0.32 -0.20
BONAVISTA CIL -0.02 0.52 -0.39 0.74 0.05 -0.03 1.06 1.37 0.91 0.38 0.64
FLEMISH CAP CIL 0.90 0.94 1.86 -0.08 -0.28 0.58 -0.52 1.78 1.47 0.94
S27 SURFACE S 0.50 -0.25 -0.69 0.32 1.09 0.48 0.84 -0.33 -0.05 0.77 0.13
S27 AVERAGED S 0.64 0.74 -0.58 0.12 1.03 -0.54 1.63 -0.43 -1.08 -0.45 -0.14
SEAL ISLAND AVG S 1.05 1.05 -0.06 0.60 -0.12 0.92 0.60 0.66 -0.77 0.14 0.47
BONAVISTA AVG S 1.39 0.17 0.04 0.53 1.63 -0.08 1.63 2.24 0.65 -0.08 0.53
FLEMISH CAP AVG S -0.83 0.15 0.34 1.02 0.44 1.61 1.71 -0.73 0.83 0.34
2.26 19.63 12.91 14.66 7.86 7.53 10.10 -1.98 17.66 2.09 15.77
Normalized indices (blue-cold, fresh; red-warm, saline) used to estimate overall index for both Labrador and Norwegian regions.
These standardized anomalies also show change from out of phase prior to the mid-1990s and in phase since then.
NORTH ATLANTIC WINTER SLP FIELDS
MEAN ANOMALY
1991
2000
2003
HISTORICAL PATTERN- COLD IN WEST WARM IN EAST
EASTWARD DISPLACEMENT
WESTWARD DISPLACEMENT
(Marine Ecosystem Comparisons of Norway and the United States)
MENU
NOAA Fisheries
MENU• Workshop Funded by NRC• Held outside Bergen in March 2007• Brought data to the table•Divided into 2 groups: (1) response to recent changes and (2) structure and function of ecosystem. •Five papers have been accepted for publication in PiO.
Eastern Bering Sea (EBS)
Gulf of Alaska (GOA)
(NOR/BAR)
Alaska
Russia
Canada
USA
Greenland
GB
Norwegian Sea
Barents Sea
GOM0
250,000
500,000
750,000
1,000,000
1,250,000
1,500,000
EBS GOA GOM GB NOR BAR
are
a (
sq
km
)
35
45
55
65
75
85
EBS GOA GOM GB NOR BAR
lati
tud
e (
de
gre
es
N)
Area
LatitudeGulf of Maine / Georges Bank (GOM/GB)
Menu Regions
Highly Advective Systems
Strong Tidal Currents and Mixing in subregions
Mean Mean CirculationCirculation
Monthly meanMonthly meansea-surfacesea-surface
temperaturetemperatureanomaliesanomalies1900-20061900-2006
Correlations between annual heat fluxes and SST temperatures
Pacific Ecosystems: Significant correlations, 25-30% of SST variance accounted for.
Atlantic Ecosystems: Weak and non-significant correlations.
Suggests that warming due to advection in the Atlantic while in Pacific air-sea fluxes play a significant role.
Gulf of Alaska
Gulf of Maine
Barents Sea
In GoA surface freshening due to local runoff
In GoM freshening due to advection from the North (Arctic?)
In Barents Sea increasing salinity due to higher salinity in Atlantic Water.
Salinity
0
100
200
300
400
500
600
Bering GOA GOM / GB Norwegian Barents
Prim
ary
prod
uctio
n (g
C m
-2y-1
Total annual net PP 1998-2006 average (± 2 SD)
SeaWiFS climatology – Chl. a (Apr-Jun)
Bering Sea / Gulf of Alaska Norwegian Sea / Barents Sea
Gulf of Maine /Georges Bank
Source: http://oceancolor.gsfc.nasa.gov/cgi/level3.pl Mueter et al., in press
EBS GOA GOM/GB NOR BAR
10
20
30
40
Nitr
ate
co
nce
ntr
atio
n (
µM
)
Productivity increases with nitrate content of deep source waters
Approximate rangeNitrate in source waters and
total annual primary production
GOM/GB
Mueter et al., in press
Effect of SST on primary production, 1998-2006
2 4 6 8 10
200
300
400
500
Annual mean SST (°C)
Total annual net
primary production(gC m-2)
Barents Sea(P = 0.093)
Norwegian Sea(n.s.)
Bering Sea(P = 0.039)
Gulf of Maine/Georges Bank(P < 0.001)
Gulf of Alaska(n.s.)
Mueter et al., in press
MENUII
NOAA Fisheries
MENUII• With the success of MENU, NOAA and
IMR administrators encouraged MENU participants to submit full proposals
• Decided that emphasis would be model comparisons and ecosystem indicators
• 4 types of models: ECOPATH, production models, biophysical models (3-D hydrodynamic models up to zooplankton) and system models (includes fish and fisheries (ATLANTIS)
Ecosystems are created in Atlantis three-dimensionally, using linked polygons that represent major geographical features. Information is added on local oceanography, chemistry and biology such as currents, nutrients, plankton, invertebrates and fish.
The model then simulates ecological processes such as:consumption and production, waste production, migration,Predation, habitat dependency, mortality.
The Atlantis framework used for management strategy evaluation incorporates a range of sub-models for each major step in the management cycle. They simulate the marine environment, the behaviour of industry, fishery monitoring and assessment processes, and management actions and implementation.
ATLANTIS
MENUII• Same model different regions• Different models for same region• Determine what we learned from each
of the models
A good forecaster (modeller) is not smarter than everyone else, he merely has his ignorance better organised. -Anonymous
MENUII•Norwegian component funded by RCN (2009-2011)
•US component submitted to CAMEO but not funded in first round. Hoping to obtain funds to carry out work from other sources.
Prediction is difficult, especially if it involves the future.
Nils Bohr
Prediction is easy, getting it right is the difficult part!
Prediction is easy, getting it right is the difficult part!
What does ”right” imply?
-Some quantifiable measure of how well the model fits the observations
-For future projections where we won’t have observations need some quantifiable measure of the uncertainty.
Observationalists and Modellers need to work closer together
- Modeller’s to help determine what, where and how often observationalists should measure.
- Observationalists should provide more feedback on model results (requires available model results, positive criticisms)
- All motherhood statements but not generally done
Some Problems
• For data comparisons, the data or datasets should be similar. Not always possible.•Forcing is based on large model and data based datasets (e.g. NCEP) that usually have some problems • When using different models there is the difficulty of knowing if one is comparing ecosystems or models
Concluding Remarks
• Comparative studies are a useful way to gain insights into marine ecosystems• They often lead to shifts in our thinking about what is important and what is not.• Bring comparative datasets to the table•For models need to develop new and better measures of uncertainty• Need to make sure that observationalists and modelers do not work independently.
Thank You!
SST Anomalies in the North Atlantic during 1990-1994
HISTORICAL PATTERN- COLD IN
WEST WARM IN EAST
SST Anomalies in the North Atlantic
during 2004
BROAD-SCALE WARMING
NOAA Optimum Interpolation SST, NOAA-CIRES Climate Diagnostics Center
Maximum SST 1997-2006
Sea-Ice Cover Sea-Ice Cover AnomaliesAnomalies
Bering Sea
Barents Sea
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
-0.4
0
0.4
-1
-0.5
0
0.5
1
1.5
Zooplankton anomalies: Evidence of top-down and bottom-up control
Barents Sea
Norwegian Sea
Gulf of Maine /Georges Bank
Bering Sea
Nor
mal
ized
ano
mal
y(B
iovo
lum
e)N
orm
aliz
ed a
nom
aly
(Bio
mas
s )
Napp & Shiga (unpublished)
Based on Valdés et al. (2006)
r = 0.60P = 0.002
Mueter et al., in press
-1.0 -0.5 0.0 0.5
-1.5
0.0
1.5
-1.0 0.0 1.0
-20
1
Fish: SST & cod recruitment
Barents Sea(Atlantic cod)
Georges Bank (Atlantic cod)
Bering Sea(Pacific cod)
Gulf of Alaska(Pacific cod)
SST anomaly
Log(
Recr
uitm
ent)
ano
mal
y 1977-2005 only!
Georges Bank and Barents Sea figures from:Planque & Frédou (1999)
-1.0 0.0 1.0
-20
1
-1.0 -0.5 0.0 0.5
-1.5
0.0
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Mueter et al., in press