regional and coastal circulation modeling: california current system art miller scripps institution...
TRANSCRIPT
Regional and Coastal Circulation Modeling: California Current System
Regional and Coastal Circulation Modeling: California Current System
Art MillerScripps Institution of Oceanography
ECOFOR WorkshopFriday Harbor, WA
September 7-10, 2012
ECOFOR WorkshopFriday Harbor, WA
September 7-10, 2012
Regional and Coastal Circulation Modeling: California Current System
Regional and Coastal Circulation Modeling: California Current System
The physical-biological observational datasetsmotivate 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?
Regional and Coastal Circulation Modeling: California Current System
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 forcing2) Data assimilation runs
- Enhance observations in space and time for process diagnostics- Initialize predictions of eddies and forced components
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 forcing2) Data assimilation runs
- Enhance observations in space and time for process diagnostics- Initialize predictions of eddies and forced components
Regional and Coastal Circulation Modeling: California Current System
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
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
Examples of deterministic CCS hindcasting
- Curchitser et al. 2005 used a basin-scale hindcast to show thatThe 2002 cold/fresh anomaly in the northeast Pacific was due toenhanced mixing during the preceding winter in the center of the Alaska gyre
- Hermann et al. 2009 used a regional and basin-scale modelto isolate remotely from locally driven sea level and current ENSOvariability in the CCS and ACS regions
- Rykaczewski and Dunne 2010 used a global greenhouse-gasforced run to predict that nitrate and primary productionwill increase in the CCS due to differences in subductionand age of upwelled waters
Coastal upwelling regions controlled by PDO and NPGO
Di Lorenzo et al., GRL, 2008
Less nutrient flux to surface
Positive PDO Phase
Model Adjoint backward runs of passive tracer in upwelling zone:Reveal how weaker upwelling winds cause shallower coastal upwelling cell
(Chhak and Di Lorenzo, 2007)
Negative PDO Phase
Surface layertransport into coastal upwellingzone
Mid-depth (150m) transport into coastal upwellingzone
More nutrient flux to surface
Regional and Coastal Circulation Modeling: California Current System
Regional and Coastal Circulation Modeling: California Current SystemBrief 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
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
Near-Real-Time CCS Data Assimilation by UC, Santa Cruz
May 2, 20127-day fits using mostly surface datawith ROMS @ 10km
Broquet et al. (2009)
SCCOOS 3DVar ROMS model (JPL-UCLA)
Surface CODAR is a key variableDaily updates with 1km resolution every 6 hrs 72-hour forecasts executed daily
Yi Chao et al.
Data Assimilation “Fits” for April 2002 and 2003- Strong constraints over 30-day periods allowsdiagnosis of 4D physical processes that helpexplain the large disparity in sardine spawning
Nearshore spawning, many eggs: El Nino
Song et al., 2012, JGR
Offshore spawning, fewer eggs: La Nina
Data includes: T-S (CalCOFI, Argo, CUFES), SLH (AVISO), SST (AVHRR)
Data Assimilation Model Fits: (1) Quantifying TransportStronger offshore transport and upwelling in 2002
Weaker offshore transport and stronger convergence in 2003
Song et al., 2012, JGR
Red: Egg density Grey Scale Arrows: Surface Currents
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
Song et al., 2012, JGR
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
Song et al., 2012
Orange indicates location of water 30 days before arriving in BOX
Regional and Coastal Circulation Modeling: California Current System
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 forcing2) Data assimilation runs
- Enhance observations in space and time for process diagnostics- Initialize predictions of eddies and forced components
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 forcing2) Data assimilation runs
- Enhance observations in space and time for process diagnostics- Initialize predictions of eddies and forced components
Thanks!
ECOFOR Workshop