impact of pacific climate variability on ocean circulation, marine ecosystems & living resources...
TRANSCRIPT
Impact of Pacific Climate Variability on Ocean Circulation,
Marine Ecosystems & Living Resources
Francisco Chavez MBARI Lead PI
Dick Barber, Duke University Co PI
Fei Chai, University of Maine Co PI
Yi Chao, JPL of Calif. Inst. of Tech. Co PI
David Foley, NOAA SW Fish. Center Collaborating PI
Supported by Earth Science Research Program;
Carbon Cycle and Ecosystems Focus Area;
Biodiversity and Ecological Forecasting Section.
Usual* justification of ocean ecosystem development:
- better management of living resources
- helping to achieve sustainability, and
- optimizing societal investment in fishery infrastructure
But…
For example, a proposal funded by NSF in May 1975 said:
“The goal of the Coastal Upwelling Ecosystems Analysis Program (CUEA) is to understand the coastal upwelling ecosystem well enough to predict its response far enough in advance to be useful to mankind.”
What was CUEA?
A big, multi-institutional, multi-disciplinary, multi-agency, long-term project…
Big = 14 mil $ from 1972 to 1980; ~24 PI’s, ~14 institutions, four agencies (NSF/NOAA/ONR/NASA)
Peru field work in 1976/1977 with 4 US ships, an NCAR plane, a NASA radiometer, several shore-based met stationsplus a lot support from Peru in people, logistics and diplomacy
CUEA was a successful interdisciplinary basic research project; but did not deliver any “useful to mankind” product.
Why?
three sources of systemic model error:
theory - understanding, equations
resolution - time/space realism
initialization - initial state realism
In the 70’s there were:
Two specific biological “theory” deficiencies:
food web structure + Fe,
Two specific physical “theory” deficiencies:
remote forcing + decadal variability
Plus 3 fatal technical constraints*:
computing power,
observing power (satellites)
information handling infrastructure
*Tech constraints unconceivable, at the time Absolutely fatal Orders of magnitude changes required to fix
In 2008 we think this is the status of ocean forecast modeling
Two specific biological “theory” deficiencies: = FIXED
food web structure + Fe
Two specific physical “theory” deficiencies: = ½ FIXED
remote forcing + decadal variability
Plus 3 fatal technical constraints: = ALL FIXED
computing power, observing power (satellites and moorings)
information infrastructure
computational power revolution.
The potential consequences of Moore’s Law for operational forecast modeling are impressive:
increased time and space resolution,
new concepts (i.e., assimilation),
scale convergence, scale expansion, spatial nesting,
reanalysis,
model complexity, near real-time modeling, retrospective modeling,
etc., etc., etc.
PhysicalModel
Nitrate[NO3]
Advection& Mixing
SmallPhytoplankton
[P1]NO3
Uptake
Micro-Zooplankton
[Z1]
Grazing
Ammonium[NH4]
Excretion
NH4Uptake
Detritus-N[DN]
FecalPellet
Sinking Silicate[Si(OH)4]
Diatoms[P2]
Si-Uptake
N-UptakeMeso-zooplankton
[Z2]
Sinking
Detritus-Si[DSi]
GrazingFecalPellet
Sinking
Predation
Lost
Total CO2[TCO2]
BiologicalUptake
Air-Sea Exchange
Dissolution
Carbon, Silicate, Nitrogen Ecosystem ModelCoSiNE, Chai et al. 2002; Dugdale et al. 2002
Chai et al., DSR 1996
Iron
Iron
Iron
diatoms
picophytoplankton
Days since first Fe Addition
-1 0 1 2 3 4 5 6 7 8 9 10 11
Chl
(m
g m
-3)
Model complexity re need for diatom and picophytoplankton response to perturbations
From Barber and Hiscock, GBC, 2006
Diatoms bloom, crash and export; rates and biomass change (first +, then --)
pico-micro steady-state rates shift-up, but with small biomass change
Remote Forcing: El Nino’s influence California Current System
(J. Ryan, MBARI)
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Mo
nth
ly S
ST
An
om
aly
(S
D)
f
or
Nin
o 1
+2
-3
-2
-1
0
1
2
3
4
5
6
Year
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Pe
ruvi
an
An
cho
vy C
atc
h
(x
10
6 m
etr
ic t
on
s)
0
2
4
6
8
10
12
14
1925 1950 1975 2000
Japan
California
Peru
South Africa
Sardine Landings
1925 1950 1975
Chavez et al. Science (2003)
Scale convergence of eddy kinetic energy of model and observations in a coastal upwelling system
2.5-km5-km
10-km
20-km
ObservationDrifter
Model
Resolution (km)
Ed
dy k
ineti
c en
erg
y (
cm2
s-2
)
Different physical and biological response to the same initial physical perturbation
A Nine Month Forecast of Peru Coastal Chl that is PDG for the first 5 months!
The same nine month forecast embedded in a longer record
European Centre for Medium-Range Weather Forecasts (ECMWF)
Conclusion:
The deficiencies in theory and technology that prevented “useful” operational marine ecosystem forecasts for resource management in the past are now being surmounted.
Its time to test this new, and still primitive, forecasting capability in ecosystem-based management of living resources.
Peru is the best place to start.