Characteristics of large scale climate indices Nate Mantua University of Washington Aquatic and Fishery Sciences GLOBEC/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 atmospheric modes, generally speaking, appear to be intrinsic to the atmosphere Some arising in part as instabilities in the mean climatological flow and/or arising from eddy/mean flow interactions However, they can be excited via external forcing (solar, greenhouse gases, volcanic aerosols, stratospheric ozone depletion …), or changes in surface boundary conditions (SST, sea-ice), which can alter their temporal patterns and potentially enhance their predictability

November-April SLP change 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 20 40 60 SST (H=50m) 20 40 60 These mld values span the typical range of observed MLD in the extratropical oceans. SST (H=500m) 20 40 60 year Figure from Deser et al. 2010: Ann. Rev. Mar. Sci.

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 Canonical SST Pattern Rossby waves 2 - 5 yrs Lagged KOE SST Pattern (Miller and Schneider, 2000, Prog. Oceanogr.)

Canonical Pattern of Decadal SST Response (Aleutian Low Strengthening) Schematic SST Warming SST Cooling Driven by surface atmospheric forcing Canonical Pattern of Decadal SST Response Equator From Miller, Chai, Chiba, Moisan and Neilson (2004, J Oceanogr.)

Lagged Pattern of Decadal SST Response (Aleutian Low Strengthening) Schematic SST Cooling sCooling Driven by thermocline changes via wind-stress curl Lagged Pattern of Decadal SST Response Equator From Miller, Chai, Chiba, Moisan and Neilson (2004, J Oceanogr.)

Basin-Scale Pattern of Decadal Thermocline Response (Aleutian Low Strengtherning) Schematic Thermocline Thermocline Shallowing Deepening Lagged response in west due to Rossby wave propagation Basin-Scale Pattern of Decadal Thermocline Response Equator The PDO SST pattern is a consequence of multiple processes From Miller, Chai, Chiba, Moisan and Neilson (2004, J Oceanogr.)

ENSO Impacts on cool-season climate 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 issue Vol 430, 1 July 2004 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 Hallett et al. (2004) describe an interesting case study of the links between Soay Sheep population changes, local climate, and changes in the North Atlantic Oscillation (NAO) index. The NAO index is based on anomalous sea level pressure changes between Iceland and the Azores, a see-saw in atmospheric pressure that is the dominant pattern of SLP variability over the North Atlantic sector (e.g. Hurrell et al. 19xx). Over the period from 19xx-20xx, the NAO index is better correlated with Soay Sheep population variability than are indices tracking Soay Islands precipitation, winds, or temperatures, respectively. Examining the local climate records with more scrutiny than provided by correlation analysis reveals the source for this apparent paradox. The link between the NAO index and Saoy Sheep comes with a range of different weather impacts on the limited food resources that sustain Soay Sheep in the winter season. In each case, local weather events influence wintertime mortality events for Soay Sheep; in some years it is extreme cold temperatures, in others it is high winds, and in others it is heavy precipitation events that appear as causal factors for mortality events. The NAO index is modestly correlated with each of those factors, and it therefore offers a better correlation with Soay Sheep population numbers than any one local weather does index alone. A take home message from this study of climate impacts on Soay Sheep is that a one-dimensional view of climate (e.g. temperature) is simply too narrow to identify and understand climate impacts on Soay Sheep.

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