Characteristics of large scale climate indices

Nate Mantua

University of Washington

Aquatic and Fishery SciencesGLOBEC/PICES/ICES ECOFOR

Workshop, Friday Harbor

Sept 7-11

Motivation for identifying large-scale climate patterns?

Capture large fractions of field variability with a small number of spatial patterns and associated time series

•Large-scale climate indices are tailored to questions of large-scale climate variations, not the climate variability of any single location or any particular region

Modes of variability in the atmosphere

Various analyses of historical data fields (like Sea Level Pressure or upper atmosphere pressure fields) have identified a relatively small number of geographically fixed patterns that explain significant fractions of the total monthly or seasonal field variance (mostly in N. Hemisphere winter). Prominent modes include: • The North Atlantic Oscillation and Artic Oscillation• The Aleutian Low/Pacific North America Pattern• The North Pacific Oscillation• The Southern Oscillation

Cool season Aleutian Low variability (Nitta and Yamada 1989, J. Met. Soc. Japan; Trenberth 1990,

BAMS)

November-April SLP change (1977-88) - (1965-76)

The NP index is the area weighted SLP anomaly from 30-65N, 160E-140W (Trenberth and Hurrell 1994, Clim Dyn)

A simplified ocean’s red noise response to the atmosphere’s white noise forcing

(Frankignoul and Hasselman 1977: Tellus)

• Here, the predictability or persistence of SST anomalies is limited to the timescale associated with the thermal inertia of the mixed layer

• Ocean mixed-layer acts as a low pass filter with an enhanced response at low frequencies, but no preferred time scale

Figure from Deser et al. 2010: Ann. Rev. Mar. Sci.

0 20 40 60

0 20 40 60

0 20 40 60

year

SST (H=500m)

SST (H=50m)

The PDO pattern and index are derived from an EOF analysis of monthly North Pacific SSTa from 1900-93 after removing the monthly global average anomaly.

Schematic of Pacific Oceanic Response to Decadal Forcing by the Aleutian Low

(Miller and Schneider, 2000, Prog. Oceanogr.)

CanonicalSST Pattern

2 - 5 yrsLagged KOESST Pattern

Rossby waves

Canonical Pattern of Decadal SST Response

SST Cooling

SSTWarming

Driven by surface atmospheric forcing

Canonical Pattern of Decadal SST Response (Aleutian Low Strengthening)

From Miller, Chai, Chiba, Moisan and Neilson (2004, J Oceanogr.)

Schematic

Equator

sCoolingSST Cooling

Lagged Pattern of Decadal SST Response

Driven by thermocline changes via wind-stress curl

Schematic

From Miller, Chai, Chiba, Moisan and Neilson (2004, J Oceanogr.)

Lagged Pattern of Decadal SST Response (Aleutian Low Strengthening)

Equator

Thermocline Shallowing

Thermocline

Deepening

Basin-Scale Pattern of Decadal Thermocline Response

Lagged response in west due to Rossby wave propagation

Schematic

From Miller, Chai, Chiba, Moisan and Neilson (2004, J Oceanogr.)

Basin-Scale Pattern of Decadal Thermocline Response (Aleutian Low Strengtherning)

Equator

The PDO SST pattern is a consequence of multiple processes

ENSO Impacts ENSO Impacts on cool-season on cool-season

climateclimate

Two El Niño-related processes promote warming and poleward coastal currents in the NE Pacific: Atmospheric teleconnection: the Aleutian Low tends to be more intense, and its location shifted south and east

Oceanic teleconnection: Northward propagating coastally-trapped kelvin waves originating in the equatorial Pacific also alter nearshore currents over the continental shelf.

http://www.o3d.org/npgo/

The scale issueThe scale issue

• Soay Sheep population dynamics are better correlated with the NAO than with “local climate”?– local weather events drive winter mortality: yet cold

temperatures, high winds, and heavy rainfall all appear as causal factors in different years

– One-dimensional view of climate (e.g. temperature) is simply too narrow to capture climate impacts on Soay sheep, and the NAO index (roughly) captures many dimensions

Vol 430, 1 July 2004

Key points• Large scale indices are not meant to represent local/regional

variability in any single place

• Large scale indices for atmospheric patterns typically look like white-noise, with substantial intrinsic variability

– Coordinated atmospheric forcing over large regions with a broad spectrum of time scales

• Large-scale indices for upper ocean patterns (PDO, NPGO, AMO, ENSO, etc.) have more variance at lower frequencies

– typically integrate atmospheric forcing and involve ocean processes too – multiple processes at work, some with time lags

• Large-scale indices correlate with multiple dimensions of habitat, and this may favor improved correlations with biological/ecological variables