Presentation on theme: "Characteristics of large scale climate indices"— Presentation transcript:
1 Characteristics of large scale climate indices Nate MantuaUniversity of WashingtonAquatic and Fishery Sciences GLOBEC/PICES/ICES ECOFOR Workshop, Friday HarborSept 7-11
2 Motivation for identifying large-scale climate patterns? Capture large fractions of field variability with a small number of spatial patterns and associated time seriesLarge-scale climate indices are tailored to questions of large-scale climate variations, not the climate variability of any single location or any particular region
3 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 OscillationThe Aleutian Low/Pacific North America PatternThe North Pacific OscillationThe Southern Oscillationatmospheric modes, generally speaking, appear to be intrinsic to the atmosphereSome arising in part as instabilities in the mean climatological flow and/or arising from eddy/mean flow interactionsHowever, 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
4 November-April SLP change Cool season Aleutian Low variability (Nitta and Yamada 1989, J. Met. Soc. Japan; Trenberth 1990, BAMS)November-April SLP change( ) - ( )The NP index is the area weighted SLP anomaly from 30-65N, 160E-140W (Trenberth and Hurrell 1994, Clim Dyn)
5 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 layerOcean mixed-layer acts as a low pass filter with an enhanced response at low frequencies, but no preferred time scale204060SST (H=50m)204060These mld values span the typical range of observed MLD in the extratropical oceans.SST (H=500m)204060yearFigure from Deser et al. 2010: Ann. Rev. Mar. Sci.
6 The PDO pattern and index are derived from an EOF analysis of monthly North Pacific SSTa from after removing the monthly global average anomaly.
7 Schematic of Pacific Oceanic Response to Decadal Forcing by the Aleutian Low CanonicalSST PatternRossby waves2 - 5 yrsLaggedKOESSTPattern(Miller and Schneider, 2000, Prog. Oceanogr.)
8 Canonical Pattern of Decadal SST Response (Aleutian Low Strengthening) SchematicSSTWarmingSST CoolingDriven by surface atmospheric forcingCanonical Pattern of Decadal SST ResponseEquatorFrom Miller, Chai, Chiba, Moisan and Neilson (2004, J Oceanogr.)
9 Lagged Pattern of Decadal SST Response (Aleutian Low Strengthening) SchematicSST CoolingsCoolingDriven by thermocline changesvia wind-stress curlLagged Pattern of Decadal SST ResponseEquatorFrom Miller, Chai, Chiba, Moisan and Neilson (2004, J Oceanogr.)
10 Basin-Scale Pattern of Decadal Thermocline Response (Aleutian Low Strengtherning)SchematicThermoclineThermocline ShallowingDeepeningLagged response in west due toRossby wave propagationBasin-Scale Pattern of Decadal Thermocline ResponseEquatorThe PDO SST pattern is a consequence of multiple processesFrom Miller, Chai, Chiba, Moisan and Neilson (2004, J Oceanogr.)
11 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 eastOceanic teleconnection: Northward propagating coastally-trapped kelvin waves originating in the equatorial Pacific also alter nearshore currents over the continental shelf.
13 The scale issueVol 430, 1 July 2004Soay 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 yearsOne-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 dimensionsHallett 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.
14 Key pointsLarge scale indices are not meant to represent local/regional variability in any single placeLarge scale indices for atmospheric patterns typically look like white-noise, with substantial intrinsic variabilityCoordinated atmospheric forcing over large regions with a broad spectrum of time scalesLarge-scale indices for upper ocean patterns (PDO, NPGO, AMO, ENSO, etc.) have more variance at lower frequenciestypically integrate atmospheric forcing and involve ocean processes too – multiple processes at work, some with time lagsLarge-scale indices correlate with multiple dimensions of habitat, and this may favor improved correlations with biological/ecological variables