1. Recent Climate Variations in the California Current System, Including Impacts on the Ecosystem
Art Miller
Scripps Institution of Oceanography
Gordon Research Conference
Coastal Ocean Circulation
Univeristy of New England
Biddeford, Maine
June 9-14, 2013

2. Recent Climate Variations in the California Current System, Including Impacts on the Ecosystem
California Current Ecosystem –
Long-Term Ecological Research Program
Motivation for today’s talk:
CCE-LTER founded in 2004
Special Issue of DSR (~2013): Long-term CCE observational time series
- Augmented CalCOFI
- Process Cruises

3. Issues Concerning Pacific Climate Variations Affecting the Coastal Ocean, Including Impacts on the Ecosystem
Outline
1) California Current drivers of ecosystem response
- Key new findings that are influencing our research
2) Mesoscale coupled ocean-atmosphere feedbacks
- CCS, Peru-Humboldt, and Kuroshio
3) Bering Sea ice
- Controls on sea ice behavior

4. Recent Climate Variations in the California Current System, Including Impacts on the Ecosystem
Outline
1) Changes in Perspective of CCS Dynamics since 2004
- Key new findings that are influencing our research

5. Recent Climate Variations in the California Current System, Including Impacts on the Ecosystem
The physical-biological observational datasets
motivate many modeling studies with a
Unifying Scientific Motivation:
How do changes in surface forcing (heat fluxes, wind stresses)
alter stratification, upwelling cells and mesoscale eddy statistics
and the consequent upward nutrient fluxes
and subsequent biological response?

6. Recent Climate Variations in the California Current System, Including Impacts on the Ecosystem
Brief Review of Two Classes of Modeling:
1) Long-term climate hindcasts
- Deterministic: Explain observed changes in forced physical structures
- Stochastic: Identify relations among variables and input forcing
2) Data assimilation runs
- Enhance observations in space and time for process diagnostics
- Initialize predictions of eddies and forced components

7. Regional and Coastal Circulation Modeling: California Current System
Brief Review of Two Classes of Modeling:
1) Long-term climate hindcasts
- Deterministic: Explain observed changes in forced physical structures
- Stochastic: Identify relations among variables and input forcing

8. Key new developments that are influencing our research
Submesoscale variability
North Pacific Gyre Oscillation
Wind stress-curl vs. Ekman upwelling: Nutrient flux
Long-term observed decreases in Dissolved Oxygen at depth
Persistent poleward subsurface jets (200m, 200km offshore)
Long regional physical-biological mesoscale simulations
Global physical-biological GHG-forced projections
Data assimilation and Generalized Stability Analysis tools

9. Coastal upwelling regions controlled by PDO and NPGO
Di Lorenzo et al., GRL, 2008

10. Adjoint runs of passive tracer in upwelling zon: Surface warming causes shallower coastal upwelling cell
Model Adjoint backward runs of passive tracer in upwelling zone:
Reveal how weaker upwelling winds cause shallower coastal upwelling cell
Negative PDO Phase
Positive PDO Phase
Surface layer
transport into
coastal upwelling
zone
Mid-depth (150m)
transport into
coastal upwelling
zone
More nutrient flux to surface
Less nutrient flux to surface
(Chhak and Di Lorenzo, 2007)

13. Regional and Coastal Circulation Modeling: California Current System
Brief Review of Two Classes of Modeling:
2) Data assimilation runs
- Enhance observations in space and time for process diagnostics
- Initialize predictions of eddies and predictable forced components

14. Near-Real-Time CCS Data Assimilation by UC, Santa Cruz
Broquet et al. (2009)
7-day fits using mostly surface data
with 10km
May 2, 2012

15. SCCOOS 3DVar ROMS model (JPL-UCLA)
Yi Chao et al.
Surface CODAR is a key variable
Daily updates with 1km resolution every 6 hrs
72-hour forecasts executed daily

16. Data Assimilation “Fits” for April 2002 and 2003
- Strong constraints over 30-day periods allows
diagnosis of 4D physical processes that help
explain the large disparity in sardine spawning
Offshore spawning, fewer eggs: La Nina
Nearshore spawning, many eggs: El Nino
Data includes: T-S (CalCOFI, Argo, CUFES), SLH (AVISO), SST (AVHRR)
Song et al., 2012, JGR

17. Red: Egg density Grey Scale Arrows: Surface Currents
Data Assimilation Model Fits: (1) Quantifying Transport Stronger offshore transport and upwelling in 2002 Weaker offshore transport and stronger convergence in 2003
Red: Egg density Grey Scale Arrows: Surface Currents
Song et al., 2012, JGR

18. Orange indicates location of water 30 days before arriving in BOX
Data Assimilation Model Fits: (2) Quantifying Upwelling Sources Adjoint tracer model (run backwards) for source waters (boxes) of surface ocean 2002 source waters in offshore spawning area transported from more productive upwelled surface water near the coast
Orange indicates location of water 30 days before arriving in BOX
Song et al., 2012, JGR

19. Orange indicates location of water 30 days before arriving in BOX
Data Assimilation Model Fits: (2) Quantifying Upwelling Sources Adjoint tracer model (run backwards) for source waters (boxes) of surface ocean 2003 source waters in nearshore spawning area transported from more productive deep water in the central California Current
Orange indicates location of water 30 days before arriving in BOX
Song et al., 2012

20. Regional and Coastal Circulation Modeling: California Current System
Brief Review of Two Classes of Modeling:
1) Long-term climate hindcasts
- Deterministic: Explain observed changes in forced physical structures
- Stochastic: Identify relations among variables and input forcing
2) Data assimilation runs
- Enhance observations in space and time for process diagnostics
- Initialize predictions of eddies and forced components

21. Wind Stress divergence
Regional Coupled Ocean-Atmosphere Feedback Processes Affecting Oceanic and Atmospheric Climate
Coupling of SST with Atmospheric Boundary Layer is observed and modeled in the CCS region over eddy scales
How does this coupling affect statistics of ocean eddies, and the overlying atmospheric flows?
Latent heat flux
SST, winds
RSM
Atmos
model:
16 km
ROMS
Ocean
7 km
Wind Stress divergence
Wind Stress curl
SCOAR simulation
Seo, Miller
and Roads
(2007, J. Climate)

22. Do mesoscale coupled ocean-atmosphere feedbacks
affect the Sea Breeze? (in an anomalous sense)
June 2000
Average
10:00pm winds
June 2000
Average
10:00am winds

23. Regional Coupled Ocean-Atmosphere Feedback Processes Affecting Oceanic and Atmospheric Climate
How does mesoscale coupling affect
the marine layer, clouds and coastal
atmospheric flows
How far inland does the
Anomalous ocean state influence?
From Seo, Miller and Roads, J. Climate, 2007

24. Controls on Bering Sea Ice Dynamics and Thermodynamics

25. Controls on Bering Sea Ice Dynamics and Thermodynamics
Ice-ocean hindcasts of Bering Sea ice (POP-CICE and ROMS) exhibit strong correlations with observed sea ice

26. Controls on Bering Sea Ice Dynamics and Thermodynamics
How can the model be so good?

27. Controls on Bering Sea Ice Dynamics and Thermodynamics
First, we have diagnosed the seasonal cycle balances of ice volume thermodynamic and dynamic teendencies….

28. Controls on Bering Sea Ice Dynamics and Thermodynamics
First, we have diagnosed the seasonal cycle balances of ice volume thermodynamic and dynamic teendencies….to come up with a nice little sketch of the basic processes

29. Controls on Bering Sea Ice Dynamics and Thermodynamics
Second, we are looking at the anomalies and their relation to specified atmospheric fields (air temp; winds, clouds, etc.)

30. Controls on Bering Sea Ice Dynamics and Thermodynamics
Future, we will compare this eddy-permitting run with a coarse 1-deg run to try to determine if the eddies are causing significant differences in the ice response….

31. ECOFOR Workshop
Thanks!

32. Predictability in Trophic Level
Next NSF proposal
We all love to consider predictability in time (and sometime space)
What about predictability of ecosystem response to physical forcing as the forced signal cascades upwards?

33. Predictability in Trophic Level
An ecosystem model response can consist of two parts: Intrinsic biological variations and a physically forced part due to the ocean environment
Quantifying the physically forced part is vital, since it is unlikely that the intrinsic biological part will have useful skill

34. Predictability in Trophic Level
But how much skill is even possible in the physically forced part?
Imagine a complicated physical-biological model:
Physics Nutrient Phytopl Zoopl Sardines Tuna
Each “level” has its own degree of non-linearity
Consider a “balanced” state of ecosystem for a fixed physical forcing
Introduce “small-scale” error(s) in the physical state
Determine the new “balanced” state of the ecosyste
Quantify error growth for each trophic level

35. Predictability in Trophic Level
Is there any skill at all in the determination of “managed species” for a given phsyical state? Or do non-linearities prevent this?
Physics Nutrient Phytopl Zoopl Sardines Tuna
Each “level” has its own degree of non-linearity
Consider a “balanced” state of ecosystem for a fixed physical forcing
Introduce “small-scale” error(s) in the physical state
Determine the new “balanced” state of the ecosyste
Quantify error growth for each trophic level