The Contribu-on of ENSO to Hail and Tornado Seasonal Variability

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1 The Contribu-on of ENSO to Hail and Tornado Seasonal Variability John T. Allen Michael K. Tippett, Adam H. Sobel 2 nd Workshop on Severe Convection & Climate, Columbia University, 9 th March 2016

2 The Problem with Severe Thunderstorm Reports: Reality All Measures of Severity and Reference Objects are Value-added!

3 2016 Forecast (Based on Ensemble Predicted Nino 3.4 SST) Relies only on ENSO state (so what can we do with no ENSO?), hopes for no outbreaks, and is a below normal forecast really that useful?

4 US Hail Reports Increase, but not Hail Days US Wide Top and Middle: Trends in observed hail reports stratified by size for the period Only significant trends shown. Bottom: Trends in number of days in which at least 1 hail event is reported somewhere in the US. (on a regional scale hail days are also less stable) Allen & Tippe* 2015 (EJSSM)

5 Temporal Changes not Spatially Uniform Spatial Changes in Hail Report Density ( 0.75In.) (uniform scale). Allen & Tippe* 2015 (EJSSM)

6 Non-Meteorological Bias: Arbitrary Change Arbitrary Result The influence of the severe hail criteria change in Allen & Tippe* 2015 (EJSSM)

7 Cleaning Up - An Environmental Index for Hail Occurrence 25N (a) Mean Hail Index (b) Mean Large Hail Events N 130W 115W 100W 85W 70W Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Hail Environmental Index (NARR reanalysis) compared to Observed Hail Days Period: Allen et al (JAMES) CONUS Hail Events CONUS Hail Events Seasonal Variability Annual Total Hail Events and Index Observations Index Adjusted Obs. Observations Index Year

8 MAM Climatology of Hail (Obs. & Index) a) MAM Hail Index b) 25N c) MAM Hail Events d 25N 130W 115W 100W 85W 70W 130W 115W Mean MAM Climatology for Hail and Tornado Index, Tornadoes and Hail Events for the period Allen et al (Nat. Geoscience)

9 Modulation of Hail by ENSO Teleconnection - MAM a) MAM Hail Index El Nino e) MAM Hail Events El Nino 25N g) MAM c) MAM Lag Hail Index Events La Nina La Nina 25N g) MAM Hail Events La Nina 25N 25N 130W 115W 100W 85W 70W 25N 130W 115W 100W 85W 70W Composites of El Niño and La Niña MAMs based on 7 and 6 year composites respectively selected using ONI ±0.5. Stippling is for Monte Carlo derived significance at the two-tailed 95% level. Allen et al (Nat. Geoscience)

10 25N 25N ENSO Teleconnection MAM Synoptic and Environment a) MAM 300hPa Wind El Nino b) MAM 300hPa Wind La Nina L c) MAM 10m Wind El Nino d) MAM 10m Wind La Nina L e) MAM MLCAPE El Nino f) MAM MLCAPE La Nina N 130W 115W 100W 85W 70W 130W 115W 100W 85W 70W MAM Composites of Environmental variables for El Niño and La Niña Allen et al (Nat. Geoscience) 250

11 Application Seasonal Predictability: DJF forecasting MAM NOAA Climate Regions 25N 130W 115W 100W 85W 70W South SE NE UMW Central Plains Climate Regions Define a box around area with most noticeable signal bounded by W and N what does frequency look like compared to climatology for each year? Allen et al (Nat. Geoscience)

12 ENSO Signal Seasonal Predictability: DJF forecasting MAM MAM ONI vs MAM index/events Hail Index Tornado Index Hail Events Tornadoes a) DJF ONI vs MAM index/events Hail Index Tornado Index Hail Events Tornadoes 1 1 ONI 0.5 ONI % of Climatology % of Climatology Scatter of box (bounded by W and N) total index for hail and observed hail events against seasonal ONI for a) MAM ONI b) Preceding DJF ONI. Shapes are as indicated in the legend. Red shapes correspond to the 7 El Niño seasons and blue icons correspond to the 6 La Niña seasons used in the composite respectively. Allen et al (Nat. Geoscience)

13 ENSO Signal Seasonal Predictability: DJF forecasting MAM b) Categorical Probabilities Conditional on ONI 100 Normal 90 Below Above 80 Hail Tornado 70 Probability (%) ONI Extended Logistic Regression for MAM tornado and hail index probability conditional on the ONI state of the prior DJF for the defined area Note these aren t your ordinary terciles (25%/50%/25%) Allen et al (Nat. Geoscience)

14 ENSO Derived Seasonal Predictability: DJF forecasting MAM Forecast (March Issued) Verifica-on? Probabilistic forecast of severe weather occurrence based on DJF ONI data for the 2015 spring severe weather season.

15 ENSO Derived Seasonal Predictability: DJF forecasting MAM Verifica)on Lessons so far: Hail and Tornado don t necessarily match. According to official figures in this box 107% tornadoes, 121% hail. (EF1+ tornado, 1 Inch Hail). Forecasting for marginal ENSO conditions requires other predictors.

16 2015/2016 El Niño Forecast Plume Evolution NMME Forecast Plume NMME for ENSO State Spring Season: Forecast to be ~1.0C anomaly Dynamic Average and Statistical Average Ranks Mid-High Range for El Nino events in MAM period (Relative Anomaly Matters)

17 2016 MAM Simple Probabilistic Forecast Seasonal Forecast: 2016 Severe Weather Season (Southern US) Hail Tornado CAVEATS: Assumes we don t see synoptically-driven outbreak events that oppose the climatological signal (which are less likely in strong El Niño but certainly not unprecedented). Uses DJF Niño 3.4 (in this case forecast from the mean ensemble NMME) as the sole predictor for the ELR.

18 DJF Hail Comparable Years (Analog forecast NMME Nino3.4) % Anomaly (Relative to Climatology) Hail index proxy days

19 MAM Hail Comparable Years (Analog NMME Nino3.4) % Anomaly (Relative to Climatology) Hail index proxy days

20 2016 Analog Map Forecast Spatial Hail Seasonal Forecast: 2015/2016 DJF & 2016 MAM Based on NMME ONI forecast analogs for both Winter and Spring. DJF Outlook MAM Outlook 82/83,91/92,97/ ,1987,1992,1993,1997 >=2 seasons with signal of category >=3 >=4 seasons with signal of category Categories: Well >±50% of Normal, Above/Below >±25% of Normal, -25% - 25% < N <+25%

21 TEI Analogs 1982/1983 & 1997/1998 (Proxy for all Tornadoes) Anomaly Seasonal TEI (Relative to Climatology)

22 2016 Spatial Forecast (SST Analog Informed Climatology ) Consensus forecast is for a below normal over the entire season but that doesn t help you if your house is hit by a tornado.

23 Summary Observations of hail cannot be taken at face value for climate problems (but sample size is bigger than tornadoes). Caution check the meteorological environment is the driving signal, and that the climate signal can influence it. ENSO (ONI) relationship for strong ENSO signal is statistically robust not necessarily transitions (but there are flavors and transitions which explain a smaller fraction). El Niño produces significant negative anomalies (25-50%). La Nina has strong positive anomalies (50-150%) Is there really a use for a below normal forecast? The one tornado problem Seasonal Prediction is for below normal activity for MAM (25-50% below normal compared to Climatology).

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