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]

SinkingDetritus-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

(mg

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

Mon

thly

SS

T A

nom

aly

(SD

)

for

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

Per

uvia

n A

ncho

vy C

atch

(x

106 m

etric

tons

)

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)

Eddy

kin

etic

ener

gy (c

m2s-

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.