Lecture 12 PDO: From fish to climate and beyond

How was Pacific Decadal Oscillation discovered?

• By fisheries scientist Steven Hare and colleagues in 1996

• Noticed connections between Alaskan salmon production cycle/fisheries and the Pacific climate, with a cyclical link

What is Pacific Decadal Oscillation (PDO)?

• Recurring climate event centred in the Pacific, over period of 20 to 30 years

• Horse shape around the Pacific and the equatorial region

• Positive = warm phase

• Negative = cold phase

• Phase identified by longer-term average of PDO Index

• Teleconection

PDO Index

Calculated based on SST in the Pacific Ocean, shows PDO condition

What happens when PDO is in a “positive” or “warm” phase?

• Higher than normal SST extends from North America towards, and across, equator

• More rapid global warming

• Horseshoe warm front, warmer off Western USA

What happens when PDO is in a “negative” or “cold” phase?

• Lower than normal SST extends from North America towards, and across, equator

• Slow down in Global Warming

  • Due to more upwelling/mixing of water in the Pacific region

  • Cooler, so absorbs more heat from the atmosphere

• Horseshoe becomes a colder area, colder off the Western USA

How does PDO change with climate change?

• PDO seems to be switching phase at a higher frequency in recent years

• Climate change models predict a reduced PDO amplitude, due to weakened temperature gradients and wind-stress related gyre movements (Zhang and Delworth, 2016)

• The predictability of the PDO also likely to be reduced e.g harder to predict weather events (already hard to predict)

PDO effects on biology

• Copepods have a short lifespan, and their species composition responds quickly to changes in PDO

• Copepod richness off of Washington and Oregon mirrors the changing of PDO

• 2016 = warm phase, followed by an increase in copepod species richness

How does PDO interact with the seasons?

• Change the type of species that thrive in an area e.g. If cold phase PDO during the summer, cold-water copepod species dominate off the coast of Washington/Oregon, and vice versa

• The southern copepod species that dominate in warmer water are smaller and have less lipid reserves

• Lower fat content passed to higher trophic levels - Knock on impacts for the food web e.g. small pelagic fish have a lower fat content after feeding

• Copopods = short-lived species, so react quickly with PDO, harder to predict effects on longer-lived species

PDO effects on Salmon

• Salmon abundance and composition changes

• Higher than average catch of Alaska salmon during warm phases, lower during colder/negative phases

• Not as common in Salmon species in the specific North West, further south = difference in how PDO affects the area - hard to predict for fisheries management

What is Climate change causing more of?

• ‘Novel’ climates, making predictions harder

• E.g. the relationship between Salmon catch and PDO phase is hard to identify (pic) - Makes fisheries management more challenging

PDO Effects on precipitation in N. America

• Caused by changes in pressure

• Warm phase: higher/more precipitation

• Cold phase: drier than normal = less

• Causes changes in vegetation

What happends to ENSO during PDO?

• Both have similar profiles, ENSO helps to drive PDO, overlapping effects

• Switches many times within each PDO phase

• When the two are “in-phase”...

  • El Niño + PDO warm phase, overlap = effects amplified

  • La Niña + PDO cold phase, overlap = effects amplified

• Total effects on temperature and precipitation will be stronger

What do climate indices tell us?

• Many e.g. ENSO, NAO, PDO (positive/negative)

• Don’t tell us causes or effects of climate patterns or weather phenomena

• Describe large-scale (in time and space) phenomena

• Should not be used to make local or short-term predictions

• Don’t give an idea on the effect of longer-living species

Common problems of using climate indices to understand weather

• Spatial variation: indices “average out” finer scale spatial variations

• Seasonality: indices “average out” finer scale temporal variations

• Non-stationarity: Climate and weather are not a fixed relationship

• Nonlinearity: Weather response is not always proportional to changes in climate indices

• Lack of correlation: Local climate/weather can be influenced by multiple indices

Common advantages of using climate indices to understand ecology

• Spatial variation: Help understand large geographic patterns

• Modelling: Reduce local climate/ weather variables to a more manageable scale

• Predictability: Recurring phenomena over a long stretch of time

• Biological effects: Many biological processes respond to changes in climate indices

• Availability: Long-term data sets are easily accessible

• Can be pulled apart to identify anthropogenic causes of climate change