Extreme Temperature Regimes during the Cool Season Robert X. Black Rebecca Westby School of Earth and Atmospheric Sciences Georgia Institute of Technology, Atlanta, Georgia MEAS NC State April 25, 2011

Presentation Overview ♠General project objectives & research approach ♥Regional statistical analyses of temperature regimes: ➙ Interannual variability & trends ➙ Modulation by low frequency modes ♣Illustrative synoptic & dynamic analyses: ➙ Jan 2004 Case Study ♦Considerations of recent cold air outbreak behavior: ➙ Winters of 2009/2010 & 2010/2011 ♠Summary & future research directions

Project Overview ♠DOE/Biological & Environmental Research: Regional and Global Climate Modeling Program ♥General project objectives: ➙ Quantify the modulation of extreme temperature regimes (ETRs) by low frequency modes (LFMs) ➙ Assess the representation of ETRs and ETR-LFM linkages in global coupled climate models (CMIP5) ➙ Assess likely future changes in regional ETR behavior and ETR-LFM linkages (CMIP5)

General Research Approach & Datasets ♠Identify extreme temperature regimes (ETRs) in terms of regional anomalies in surface air temperature or wind chill index (Walsh et al 2001; Osczevski and Bluestein 2005) ➙ WCI = F (surface air temperature, wind speed) ♥Basic data: Daily averaged reanalysis data ➙ NCEP/NCAR Reanalyses (1949 – 2010) (Kalnay et al 1996; used for statistical analyses) ➙ NASA-GMAO MERRA (1979 – 2010) (Bosilovich 2008; used for synoptic-dynamic analyses) ♣Anomalies defined in terms of normalized departures of either air temperature or wind chill index from normal during the months of December, January & February

Research Approach: Regional Metrics ♠For each day of the cool season, we first construct areal average of surface air temperature and wind chill index over the following regions (MW, NE, SE, FL):

Research Approach: Regional Metrics ♠The areal average temperature metric is combined among all winters for each calendar day to assess seasonal cycles in the mean and standard deviation. ♥Seasonal cycles are smoothed using Fourier analysis (keep 1 st 6 harmonics) ♣Example: Seasonal cycle for Southeast Region ➙ mean T (μ) ➙ standard deviation (σ)

Research Approach: Regional Metrics ♠Sensitivity Analyses: ♥1) NCEP/NCAR reanalyses vs. NASA-GMAO MERRA ♣2) NCEP/NCAR First 30 years vs. Last 30 years ♦Little Sensitivity found in either analysis ➙ MERRA: Slightly larger amplitude ➙ NCEP: Statistical stationarity

Research Approach: Regional Metrics ♠Smoothed areal average metrics are then used to identify discrete episodes of anomalous temperature/WCI 1)Number of days: N = # days temperature anomaly is: above +nσ (warm events) or below –nσ (cold events) where n = 1, 1.5 or 2 2)Impact Factor: Sum normalized anomaly values for all days exceeding threshold value during each winter. 3)Peak Amplitude: Assess largest magnitude (normalized anomaly) warm and cold event for each winter ➙ work in progress (not shown today)

Results: Number of Cold Days in Southeast Region ♠Assess interannual variability; contrast temperature and wind chill criteria; vary anomaly threshold (-1σ, -1.5σ, -2σ) ♥Temperature and wind chill results almost identical ♣No statistically significant trends ♦Results insensitive to anomaly threshold chosen

Results: Number of Warm Days in Southeast Region ♠Assess interannual variability; contrast temperature and wind chill criteria; vary anomaly threshold (+1σ,+1.5σ,+2σ) ♥Temperature and wind chill results almost identical ♣Significant decreasing trend ♦Very few large amplitude warm events (vs. cold) ➙ negatively skewed T distribution

Cold Days in Southeast: # of Days vs. Impact Factor ♠Relatively little difference observed in interannual behavior ♥Still no significant trend observed ♣Results insensitive to anomaly threshold applied (not shown) ♣2009/2010 winter ranked highest since late 1970s in terms of cold Impact Factor   

Warm Days in Southeast: # of Days vs. Impact Factor ♠Relatively little difference observed in interannual behavior ♥Significant decreasing trend ♣Results insensitive to anomaly threshold (not shown) ♦Employ Impact Factor measure for remaining trend plots

Southern Florida: Cold Days vs. Warm Days ♠Many more cold events than warm events (-ve skewness) ♥No statistically significant trends ♣2009/2010 winter ranked 2 cd overall in terms of cold Impact Factor (!) ♦No evidence of decreasing trend in warm events (unlike Southeast)    

Upper Midwest: Cold Days vs. Warm Days ♠Less skewness evident in event distribution ♥Decrease in cold events (but not significant) ♣Weakly significant increase in Impact Factor for warm events ♦High levels of interannual variability

Northeast Region: Cold Days vs. Warm Days ♠Less skewness evident in event distribution (as in Midwest) ♥No statistically significant trends ♣High levels of interannual variability (similar to the Midwest Region) ♦None of the 4 regions exhibit significant down- ward trends in Cold Events

Distinction between T & WCI Events ♠Generally identify the same events but the relative magnitude (ranking) of events typically varies

Relevant Modes of Low Frequency Variability Arctic Oscillation (AO): Regressed 500 hPa Heights (Z)

Relevant Modes of Low Frequency Variability North Atlantic Oscillation (NAO): Regressed 500 hPa Z

Relevant Modes of Low Frequency Variability Pacific North-American (PNA): Regressed 500 hPa Z

Relevant Modes of Low Frequency Variability Nino 3.4 SST (Nino 3.4): Regressed 500 hPa Z

Low Frequency Modulation of Temperature Regimes Interannual Variability in Cold Air Events in Atlanta ♠Downward trend in cold air events until last 2-3 winters ♥ Greatest number of cold days occurred in 2009/2010 (!) ♣ Significant negative correlation with the AO (r = -0.55)

Low Frequency Modulation of Temperature Regimes Correlation Assessment for the Southeast Region

Low Frequency Modulation of Temperature Regimes Correlation Assessment for Southern Florida

Low Frequency Modulation of Temperature Regimes Correlation Assessment for the Northeast Region

Low Frequency Modulation of Temperature Regimes Correlation Assessment for the Midwest Region

Correlate LF Mode Indices with Air Temperature AO Nino 3.4 PNA NAO

♠Cold front passes through Atlanta ~12Z January 5, 2004 ♥ Highs in the 70s Jan 5 -> Lows in the 10s on Jan 7 1/05/20041/07/2004 Cold Air Outbreak: 12Z Jan 5, 2004 (NOAA/HPC)

Cold Air Outbreak: 12Z Jan 5, 2004 (Winds/EPV) 1/05/20041/07/2004

p Remote Influence of Local PV Anomalies (‘Charges’)  Poisson-like PV balance condition indicates nonlocal effects analogous to induction of electric field by localized charges x,y Spheroids of constant Z’ associated with isolated q anomalies [e.g., Hoskins et al. 1985] Vertical extent related to L/N; Large scales & weak N favor a downward influence

Piecewise PV Inversion: Quasi-Geostrophic Form

Cold Air Outbreak: Jan 5, 2004 (QGPV Anomalies) ♠Diagnose contributions of PV anomalies within different vertical layers to the northerly flow in lower troposphere ♥ Anomalies defined as deviations from monthly mean flow ♣ Divide PV anomaly field into three parts: 1) Upper tropospheric PV ( hPa) 2) Lower tropospheric PV ( hPa) 3) Surface theta at lower boundary ( hPa)

QGPV Inversions: Invert Entire PV Anomaly Field ♠Generally excellent quantitative correspondence over most regions ♥ Notable errors near base of trough where strong curvature exists ♣Actual wind is subgeostrophic due to locally large Rossby number ♦ Supergeostrophic flow in ridge 300 hPa vector wind anomalies

QGPV Inversions: Invert Entire PV Anomaly Field ♠Generally excellent quantitative correspondence over most regions (including over midwest US) ♥ Some errors near cold front ♣No differences where 925 hPa surface dips below ground ♦ Proceed to piecewise PV inversion 925 hPa vector wind anomalies

QGPV Inversions: Invert PV “Pieces” ♠Upper tropospheric PV induces southwesterly flow over midwest ♥ Lower tropospheric PV induces northeasterly flow over midwest ♣Strong cancellation among the contributions of interior PV ♦ Surface theta induces northerlies 925 hPa vector wind anomalies

QGPV Inversions: Invert Surface PV “Pieces” ♠Isolate cold surface theta anomalies over the western US/Canada ♥ Invert cold surface theta anomalies ♣Provides a large contribution to northerly flow over midwest US ♦ Cold anomalies east of Rockies promote northerlies to the east 925 hPa vector wind anomalies

Average surface air temperature anomalies 12/15 – 01/14 1/07/2004 Winters of 2009/10 & 2010/11: Unusual Behavior! AO index → (NOAA/CPC) Composite T Anomalies → (NOAA/ESRL)

Winters of 09/10 & 10/11: North Atlantic Jet Structure ♠Climo characterized by two jets: Subtropical jet & eddy-driven jet ♥ North-South jet anomaly dipole found during 2009/10 with strong westerly anomalies near 30N ♣Net impact: Effective merger of subtropical & eddy-driven jet ♦ High latitude eddy-driven jet? Zonal wind averaged from 300W-360W (12/15 – 1/14)

2010/11: Nov 1 to Jan /10: Nov 1 to Jan hPa Zonal Wind Evolution over North Atlantic ♠Eddy driven jet strengthens during Fall and early winter ♥ Subtropical jet develops beginning in January ♣ Eddy driven jet abruptly collapses during Spring onset Climo: July 1 to June 30Climo: Nov 1 to Jan 20

Composite 500 hPa Geopotential Height Field Total heights: Left: climo Right: 2009/10 (12/15 – 1/14) Stationary eddies: Left: climo Right: 2009/10 (12/15 – 1/14)

Composite 500 hPa Geopotential Height Field Total heights: Left: climo Right: 2010/11 (12/15 – 1/14) Stationary eddies: Left: climo Right: 2010/11 (12/15 – 1/14)

Stationary Wave Activity Fluxes: 2009/ hPa Horizontal Flux Climatology(12/15 – 1/14) 500 hPa Horizontal Flux 2009/2010(12/1 5 – 1/14)

Stationary Wave Activity Fluxes: 2010/ hPa Horizontal Flux Climatology(12/15 – 1/14) 500 hPa Horizontal Flux 2010/2011 (12/15 – 1/14)

Forcing Mechanisms?

Summary ♠Cold Air Outbreaks are evidently alive and well (no evidence for decreasing trends) ♥ Warm waves have decreased over Southeast Region ♣ Warm waves have increased over Midwest Region ♦ January 2004 case study: Southward surge of cold air through the midwest is primarily effected by cold surface theta anomalies positioned east of Rocky Mountains ♠ Recent winter behavior: Possible alterations in the seasonal cycle of the North Atlantic jetstream?

Summary and Future Research Directions ♠ Future work: More fully explore the low frequency modulation of ETRs in different geographical regions ♦ Future work: Examine the behavior of ETRs and their low frequency modulation in coupled climate models

PV Balance Condition:  Large-scale atmospheric disturbances are governed by the linear balance condition:  Poisson-like => nonlocal response in Z’ [e.g., Black 2002]

Boundary Conditions  Polar Continuity  Longitudinally cyclic  Z’ = 0 at low latitude boundary (10 0 N)  Upper and lower boundaries: a)Boundary q’ not included: b)If boundary q’ is included: [Black 2002]

EAS Quasi-Geostrophic Theory  Given a 3-D distribution of q’ and boundary conditions for Φ’, one can invert the QG balance to infer the 3-D Φ’ distribution. (From which the temperature and horizontal windfields can be deduced via hydrostatic & geostrophic balance, respectively)  Note: Laplacian-like operator L localized q anomalies are associated with a Φ anomaly distribution that may extend horizontally and vertically away into the far field (from q’).  Permits dynamic interaction of spatially separated q anomalies Quasi-Geostrophic Potential Vorticity

WCI ( o F) = T – 35.75V TV 0.16 Where V is the wind speed in m/s & T is air temperature in o F Following Osczevski and Bluestein 2005 Wind Chill Definition

Relevant Modes of Low Frequency Variability Pacific Decadal Oscillation (PDO): Regressed 500 hPa Z