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

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

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

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

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?

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

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

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

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

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)

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

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

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

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

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

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

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

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

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)

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

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

Controls on Bering Sea Ice Dynamics and Thermodynamics

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

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

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

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

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.)

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….

ECOFOR Workshop Thanks!

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?

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

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

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