NASA Biodiversity and Ecological Forecasting Team Meeting 29 – 31 August 2005 Washington, DC Fig.2

NASA Biodiversity and Ecological Forecasting Team Meeting29 – 31 August 2005 Washington, DC Fig.2

We are: NASA GRT # NNG04GM64G

“Pacific climate variability and its impact on ecosystems and fisheries: a multi-scale

modeling and data assimilation approach for nowcasting and forecasting.”

My name is Dick Barber ([email protected])

NASA Biodiversity and Ecological Forecasting Team Meeting29 – 31 August 2005 Washington, DC Fig. 3

Dick Barber – Duke University Fei Chai - University of Maine Yi Chao - Jet Propulsion Lab of Cal Tech

Francisco Chavez - Monterey Bay Aquarium Research Institute Joaquim Goes - Bigelow Laboratory for Ocean Sciences Michael Alexander -

NOAA Climate Diagnostics Center

NASA Biodiversity and Ecological Forecasting Team Meeting29 – 31 August 2005 Washington, DC Fig.4

Goal: Deliver forecasts of circulation and ecosystem function in the Pacific Ocean which can force fish population models to make forecasts of fish abundance with 6 to 9 months lead-time.

NASA Biodiversity and Ecological Forecasting Team Meeting29 – 31 August 2005 Washington, DC

30 years ago (1975) we had an NSF project,Coastal Upwelling Ecosystems Analysis (CUEA),

based on the premise that:

“Prediction of the response of the coastal upwelling ecosystem to natural variations, man-made environmental perturbations or to different harvesting strategies is possible from knowledge of a few biological, physical and meteorological variables...”


“The goal of the Coastal Upwelling Ecosystems Analysis Program is to understand the coastal upwelling ecosystem well enough to predict its response far enough in advance to be useful to mankind.”

Written in May 1975 Little did we know…………


CUEA was successful by NSF’s basic research criteria, but the goal was never achieved …... the technology, vision and science of 30 years ago was simply inadequate:

- undersampling in time and space- omission of remote forcing in the CU paradigm- complete unawareness of decadal variability

- over-simplified linear food web theory- limited to 2D and uncoupled modeling

1. Two conceptual advances in ocean ecological theory: 1a. two-path food web (need for

picophytoplankton and micrograzers)

1b. role of Fe in ocean ecosystems


What has changed ?

The ecology of 1975 was pretty good, only two conceptual advances in ocean ecological theory: 1a. two-path food web (need for

picophytoplankton and micrograzers)

1b. role of Fe in ocean ecosystems


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



But the big change is: 2. A revolution in observing systems in mode, resolution & quantity. 3. An even more profound revolution in computational power.


2. The observational revolution (satellites and moorings) gives continuous access to the time and space scales of variability forcing ocean ecosystems.


3. The computational revolution makes possible and enormous increases in both time and space resolution (physical) and model complexity (ecology).

Project Columbia and ROMS at NASA Ames

Computer at NASA Advanced Supercomputing Facility: 20 interconnected SGI® Altix® 512-processor systems

a total of 10,240 Intel Itanium 2 processorsPacific basin-scale ROMS: (1520x1088x30)

12.5-km horizontal resolutions & 30 vertical layers50-year (1950-2000) integration

12.5-km


…that is, NASA satellites and computational-power enable us to:

observe the ocean (earth) continuously, initialize and assimilate the observations into

models with the time/space resolution and complexity needed for accurate ecosystem and fisheries forecasts.


Scale convergence of eddy kinetic energy of model and observations in a coastal upwelling system

Internal/intrinsic variability– Features (<10 km, days)– Model resolution (~1 km,

hours)

2.5-km5-km

10-km

20-km

ObservationDrifter

Model

Resolution (km)

Eddy

kin

etic

ener

gy (c

m2s-

2)


J = 0.5 (x-xf)T B-1 (x-xf) + 0.5 (h x-y)T R-1 (h x-y)

3-dimensional variational (3DVAR) method: y: observationx: model

Week 1 Week 4Week 3Week 2

Initialcondition

1-week forecast

Week 5

Xa = xf + xf

Xa

xf

Weeklyassimilation cycle

analysis

Assimilation and Initialization

Now, for a look at the Peru coastal upwelling ecosystem:Time series: 1955 – 2005Anchovy catch by the Peruvian fisheryTemperature anomaly (°C) in Niño 1 and 2

regions (3-month running mean of monthly SST

anomalies)

Niño 2 Niño 1


Year1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Per

uvia

n A

ncho

vy C

atch

(x

106

met

ric to

ns)

0

2

4

6

8

10

12

14

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

3mrm

Mon

thly

SS

T A

nom

aly

(deg

C)

f

or N

ino

1+2

-3

-2

-1

0

1

2

3

4

5

6


• Natural interannual (ENSO) and decadal (PDO, NAO) climate variability, together with fishing itself, drive large variations in fish stocks.

• Inability to account for externally-driven variability in fish stocks prevents successful management.

• Tools are now available to incorporate climate effects into ecosystem-based management models.

• Lead time is needed for economic usefulness. • Operational 6 to 9 month physical forecasts (for the Pacific)

are now available.


Show movie.

We have:1. Improved food web theory (Fe and 2-path)2. Realistic and validated physical and food web models3. Observing tools, satellites, moorings, TOGA-TAO, etc.

for initialization and assimilation4. Computational power needed for scale convergence,

fine time steps and many model compartments

5. Operational 6 and 9 month ENSO forecasts



Summary:The capability exists to deliver

operational marine ecosystem forecasts with

enough lead time, accuracy and precision to be socially and

economically beneficial.

Documents

NASA Biodiversity and Ecological Forecasting Team Meeting 29 – 31 August 2005 Washington, DC Fig.2