Presentation on theme: "Characteristics of large scale climate indices Nate Mantua University of Washington Aquatic and Fishery Sciences GLOBEC/PICES/ICES ECOFOR Workshop, Friday."— Presentation transcript:
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
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. 0204060 0204060 0204060 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.) Canonical SST Pattern 2 - 5 yrs Lagged KOE SST Pattern Rossby waves
Canonical Pattern of Decadal SST Response SST Cooling SST Warming 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
sCooling SST 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 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.
The 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