Alaskan heat episode of June 2013-Draft

Transcription

1 Alaskan heat episode of June 2013-Draft Abstract: By Trevor Alcott National Weather Service Western Region, Salt Lake City UT And Richard H. Grumm National Weather Service State College, PA A strong 500 hpa ridge developed over Alaska from June The 500 hpa heights peaked around +4σ above normal June Despite these relative values, data from the climate forecast system indicated that the 500 hpa heights were higher than 500 hpa heights observed in the climate forecast system since A similar signature was observed in the 700 hpa temperature field. The forecast and observed 700 hpa temperatures were at the extreme tail of the probability distribution function relative to the climate forecast system. The resulting large ridge with above normal heights and temperatures lead to a multi-day heat episode over Alaska. Temperatures at several climate sites exceed 90F on at least 1 day and several locations had high temperatures in excess of 80F for three consecutive days, a true Alaskan heat wave. The results shown here suggest that for extreme events and heat episodes that the traditional standardized anomaly provides insights into extreme events. However, the skewed distribution of the height and temperature fields reveals that where a forecast lies relative to the probability distribution can provide a clearer signal when extreme weather events approach historic levels.

2 1. Overview An unseasonably strong ridge affected Alaska in June The 500 hpa heights +2 to +4σ above normal as the ridge peaked in intensity around mid-june. Beneath the ridge, most of Alaska experienced a heat episode and several locations set all-time record highs. The standardized anomalies (Hart and Grumm 2001; Grumm and Hart 2001) provided some insights into the potential for a heat episode. Standardized anomalies offer clues to potential extreme and near record events and have proven successful in identifying historic heat waves (Grumm 2011) and broad range of high impact events (Hart and Grumm 2001; Graham and Grumm 2010). Over the course of the past 14 years, standardized anomalies have proven useful in aiding forecasters in distinguishing high-end weather events. Surges of high precipitable water (PW) accompanied by strong low-level winds with large u- and v-wind anomalies (Stuart and Grumm 2006; Bodner et al 2011;Junker et al 2009;Junker et al 2008;Grumm 2011) have been used to diagnose and forecast extreme rainfall events. The same concepts have been applied to predicted heat waves (Grumm 2011) and significant East Coast snow events (Stuart and Grumm 2007). Many of the aforementioned studies (Graham and Grumm 2011; Hart and Grumm 2001) were biased toward the larger anomalies being associated with deep troughs and cyclonic events. Due to the general lack of large anomalies with anticyclones, heat waves and major heat waves rarely show up in lists of extreme weather events when magnitude of the standardized anomaly is used to identify large events. This implies that the anomalies associated with high impact weather events are skewed and biased toward events with deep cyclones or strong pressure gradients. The European Center for Medium-Range forecasting (ECMWF) developed an extreme forecast index (EFI: Legg and Mylne 200;Lalaurette 2003). The EFI is based on an internal ensemble forecast system (EFS) climatology. The emphasis is on when key fields such as model quantitative precipitation (QPF), winds, temperatures, and other variables depart for the 15-year ensemble forecast. The data are displayed and the index is computed using the model probability distribution function (PDF). This facilitates identifying when a parameter is forecast to exceed the internal model climatology (M-Climate) and thus when the EFS may be forecasting a near extreme, extreme, or potentially extreme event. Using the comparative PDF the data can identify record high temperatures and extreme 500 hpa events where the standardized anomaly approach might not facilitate identifying an extreme event. This paper examines the heat episode of June 2013 as it affected Alaska. The larger scale pattern from a standardized anomaly construct is presented. These data clearly show the successful forecast of the heat episode with 6 days of predictability and a coherent signal of a strong ridge

3 Date New Record High 14-Jun Jun Jun Jun Jun Jun Table 1. NCDC climate data showing record high maximums and record high minimums for June Data includes a list of dates in June 2013 and the number of record high maximums and record high minimums set by date. The highs over 80 value shows the number of sites each day which set new records had daily maximums of 80F or greater. For each record type the new record and tied records for the date are shown. with about 10 days of predictability. A re-analysis based (R-Climate) EFI approach is presented to show how similar to an M-Climate based EFI, R-Climate based EFI indicated that this was an extremely anomalous 500 hpa ridge, despite the modest 500 hpa and 700 hpa height and temperature anomalies respectively. The R-Climate and M-Climate approach offer an opportunity to improve forecasting synoptically forced high impact events and lend themselves well to automate decision support systems applications. 2. Methods and Data High Tied Record High Total Record High Max Tied Record High max Total2 Highs over 80 The large scale pattern was constructed using the GFS 00-hour forecasts which were compared to the standardized anomalies for key fields as described by Hart and Grumm (2001). The pattern with standardized anomalies was extracted from the Climate Forecast System (CFS) and compared to the 30-year climatology. Gridded forecasts and CFS data relative to standardized anomalies were plotted using GrADS (Doty and Kinter 1995). Forecasts from the NCEP Global Ensemble Forecast System (GEFS), Global Forecast System (GFS), and North American Ensemble Forecast System (NAEFS) were used to examine how well the heat episode was predicted. The NAEFS data was plotted against a 30-year climate which included the probability distribution function (PDF) at each grid point. The relative value of the forecast parameter was plotted as to its location in the PDF. The displays using the PDF were made using PYGRIB and only extreme events, in the lower 10 th or upper 90 th percentile were displayed. In section 6 a comparison of PDF and standardized anomalies is presented these data were also displayed using PYGRIB. The high temperature data for the period of the heat episode were retrieved from the National Climatic Data Center. These data were plotted and used to produce tables showing the records

4 set and locations where temperatures were above 80F. The term heat episode is used here as no site in Alaska was able to meet the heat wave criteria of 3 consecutive days of 90F or greater high temperatures. 3. Pattern Overview The 500 hpa pattern over Alaska and the northeastern Pacific basin (Fig. 1) shows the evolution of the large 500 hpa ridge over Alaska from 0000 UTC 15 June through 0000 UTC 20 June The 500 hpa height anomalies peaked June when the anomalies were +3 to +4σ above normal. The accompanying 850 hpa temperatures (Fig. 2) showed 850 hpa temperatures too were in the +3 to +4σ range, but the peak lagged the 500 hpa height anomalies by about 1 day. The precipitable water (PW: mm) and PW anomalies showed (Fig. 3) the well-known pattern associated with heat waves with a plume of high PW air on the west side of the ridge (Grumm 2013). The PW values in west-central Alaska peaked at +3 to +4σ above normal at 0000 UTC 16 June (Fig. 3b) and remained above normal through about 0000 UTC 18 June 2013 (Fig. 3d). The plume of high PW air was associated with a strong 850 hpa southerly jet (not shown) and 250 hpa jet (Fig. 4) over the northern Pacific which brought the high PW air from lower latitudes into Alaska. The National Climatic Data Center (NCDC) climate sites in Alaska (Table 1) indicated that the heat episode peaked between June where 4-10 Stations tied or broke record high temperatures for the date. Fairbanks, Alaska reached 91F on 20 June Only 2 sites reached or exceed 90F including Chulitina River on 17 June (Table 2) and Fairbanks on 19 June (not shown). The high temperatures over much of central Alaska were over 80F on 17 June (Fig. 05). Chulitina River exceeded 80F on 3 consecutive days (Tables 2-4) including 16, 17, and 18 June 2013 but only exceeded 90F on 17 June. Thus, the term heat episode is used here as the definition of a heat wave includes 3 or more consecutive days of 90F or greater temperatures. The 500 hpa heights and height anomalies (Fig. 6) and the 850 hpa temperatures and temperature anomalies (Fig. 7) show that 500 hpa heights peaked over 5820 m over central Alaska during the period of record heat from 0000 UTC 17 to 1200 UTC 18 June The 500 hpa height anomalies peaked in the CFSV2 data in the +3 to +4σ range. The 850 hpa temperatures (Fig. 7) showed the same lag indicated in the 24 hour perspective. The 850 hpa temperatures peaked in the 18 to 20C range at 0000 UTC though 1200 UTC 18 June 2013 with standardized anomalies around +4σ above normal (Figs. 6d-e). 4. PDF analysis of the event 1 6-hourly data showed a 5820 m high at 1800 UTC 16 June.

5 Figure 8 shows the North American Ensemble Forecast System (NAEFS) 500 hpa height forecasts leading up to the event and the verifying GFS 00-hour forecast. The GFS analysis indicated that the 500 hpa heights over most of Alaska were extreme end of the CFS 500 hpa height climatological PDF at 0000 UTC and all time periods in the CFS centered on 18 June. The analysis clearly shows that the 500 hpa heights analyzed represented a record event relative to the CFS.. Figure 9 shows the NAEFS 700 hpa temperature forecasts and the verifying GFS 00 hour forecasts. These data too show that the 700 hpa temperatures at 0000 UTC 18 June were at alltime values for the time of year. Comparing the analysis to the PDF clearly shows this was the record event at both 500 hpa and 700 hpa in terms of height and temperature extremes respectively. Relative to traditional anomalies, these data can highlight extreme and in this case, record events. 5. Ensemble forecasts NCEP GEFS forecast of 500 hpa heights for 6 forecast cycles valid at 0000 UTC 18 and 0000 UTC 19 June 2013 are shown in Figures 10 & 11. These data show that forecasts of a ridge over Alaska had at least 7 days of lead-time (Fig. 10a). The convergence of forecasts on a strong ridge with 500 hpa height anomalies in excess of +3σ above normal showed about 5 days of predictability. An examination of intervening GEFS runs suggested about a 5.5 to +6 day leadtime for a strong ridge (not shown). Forecasts valid at 0000 UTC 19 June (Fig. 11), the afternoon hours where the highest temperatures over the widest region were experience, showed a similar success as those valid 24 hours earlier. Once the ridge was established in the GEFS the predictions of its duration were relatively good and in this timeframe the ridge showed good predictability at least 8 days in advance and the strong ridge was predicted with about 6-7 days lead-time. The 850 hpa forecasts for the 0000 UTC 18 June (Fig. 12) and 19 June (Fig. 13) showed similar skill. The 850 hpa temperature anomalies in several forecasts showed a wide area of +3 to +4σ 850 hpa temperature anomalies (Figs. 11d-f) and a few areas where the 850 hpa temperature anomalies were forecast to over +5σ above normal. The broad region of over +4σ 850 hpa temperature anomalies were in the forecasts valid at 0000 UTC 18 June 2013 too (Fig. 13). These latter forecasts lacked the extreme areas of over 5σ 850 hpa temperature anomalies. 6. Forecast relative to the full PDF The 500 hpa and 700 hpa forecasts from the NAEFS (Figs. 8 & 9) showed the evolution of the forecast of a record event over 10 days. Due to position and intensity issues in the forecasts, the 500 hpa heights were unable to show a strong signal until about 144 hours prior to the event. It should be noted that the NAEFS ensemble mean 500 hpa heights at 240 hours forecast the heights in the 90 th percentile relative to the CFS climatology. The 96-hour and shorter forecasts

6 all predicted a record event in terms of 500 hpa heights and the GFS analysis verified this record forecast. The 700 hpa temperature forecasts showed a similar pattern with the ensemble mean forecast close to 90 th percentile outcome 10 days in advance and a 97.5 percentile event, in the correct approximate location, about 6 days in advance of what was a record event in terms of 700 hpa temperatures. The verifying analysis suggests that the 500 hpa height forecasts were more successful than the 700 hpa temperature forecasts. An examination of the GFS and GEFS forecasts is used to illustrate the limitations of predicting record events at longer ranges. The 144 hour GFS and GEFS control forecasts (Fig. 14) show the differences in the pattern within the two forecasts systems and the GEFS ensemble mean verse the GFS. The spaghetti plot from all members and the GFS shows the differences. The key to a successful forecasts of a record event within an ensemble forecast system requires that the system correctly predicted the feature, the geographic location of the feature, and the intensity of the feature. In these 144 hour forecasts the GFS and GEFS correctly forecast a ridge over central Alaska. There were slight differences in the intensity and location of the ridge (lower panel). These issues in the GEFS and CMC EFS (shown), likely lead to the strong ridge in the NAEFS over Alaska (Fig. 8) at 144 hours and the heights in the 97 percentile. The 240 hour GEFS (Fig. 15) showed far more uncertainty with the location and intensity of the ridge over central Alaska. Thus, the NAEFS showed as strong ridge and high end event, though less confidence in the event. A comparison of the NAEFS 500 hpa heights and 700 hpa temperatures valid at 0000 UTC 18 June 2013 showing the forecasts of the standardize anomalies and using the PDF illustrates the power of the PDF in forecasting extreme events. These data show that over portions of central Alaska, the NAEFS was forecasting both 500 hpa heights (Fig. 18) and 700 hpa temperatures (Fig. 19) which exceed all the analyzed values in the CFSR since The NAEFS forecasts, relative to the CFSR climatology, were predicting an historic event. The standardized anomalies for this event showed at +3 and +5σ event for 700 hpa temperatures and 500 hpa heights respectively. These data suggest that meteorological data are highly skewed and in ridges and high temperatures the PDF is better able to identify extreme events than standardized anomalies. The data also imply that use of climate data relative to ensemble forecasts contains uncertainty information when the system is forecasting a relative extreme event. The 240 hour forecasts correctly predicted the ridge and that the heights in the ridge would be in the 90 th percentile. Due to uncertainty, this signal appeared weak, though as the forecasts converged these skewed forecasts proved to have correctly predicted a strong ridge, underestimating the actual intensity 7. Summary

7 A record heat event impacted Alaska in June 2013 and the number of record high temperatures tied or broken peaked on the 17 th and 18 th of June. The data presented here indicate that the NCEP GEFS and NAEF when displayed using the 30-year CFS data, forecast a potential extreme weather event. The 500 hpa height, 850 hpa temperature and 700 hpa temperature anomalies all indicated values at least +3σ above normal. The CFS and 00-hour GFS data were used to verify the forecasts of the heights and temperatures within the Alaskan ridge. These data indicated a successful forecast of an anomalously strong ridge with above normal temperatures. The GEFS forecasts of the 500 hpa heights and height anomalies (Figs. 10 & 11) along with the forecasts of the 850 hpa temperatures and temperature anomalies (Fig. 12 & 13) implied that the GEFS correctly predicted the Alaskan heat episode. The skill in the general pattern at 500 hpa was on the order of 8 days. The skill in predicting the potential for both 500 hpa heights and 850 hpa temperatures to have anomalies on the order of +3 to +5σ above normal showed considerable lead-time, on the order of 6-7 days. Other modeling and ensemble prediction systems showed comparable skill at forecasting the strong ridge and resulting warm episode. This event and the ridge associated with central European heat wave of 2010 (Grumm 2011) suggest that larger scale ridges are relatively well predicted by synoptic scale models and EFS. The standardized anomaly approach provided useful insights into the potential for a significant heat episode over Alaska. When used with EFS data, the standardized anomalies provide at least 6 days of lead-time for the onset of this record event. However, the standardize anomalies were primarily on the order of +3 to +4σ above normal in the CFS and most EFS s. Unlike deep cyclones and in regions of strong gradients, it is often difficult to get large standardized anomalies, much over +4 to +5σ in ridges and features more typically associated with droughts and heat waves. Cases such as this show why an EFI and more PDF based approach may add information to the forecast, analysis, and decision making processes associated with extreme synoptic events. The PDF of the 500 hpa heights from the 42-member NAEFS system (Fig. 8) showed that a broad region of central Alaska was forecast to experience 500 hpa heights that were higher than the highest 500 hpa heights in the CFSR over a 30-year period for forecasts of less than 144 hours in duration. Unlike the standardized anomalies, these data showed that the 500 hpa heights in the NAEFS were forecast to be higher than observed since Even at 240 hours, the heights in the NAEFS were forecast to be in the 90 th percentile. The signal looked weak relative to the strong and coherent signal in the shorter range forecasts. Relative to traditional anomalies, these PDFs of the forecast, relative to the CFS climatology can help identify the potential for both high impact and record events. This case suggests that i. PDF data can show both the potential for a high and a record event. ii. A weak signal at long ranges, of a high impact event can be useful data. iii. Higher anomalies and PDFs near the tails in the ensemble mean contain confidence information when the EFS is forecasting a high-end event. iv. The lack of a signal at longer ranges does not facilitate knowing weather uncertainty or conditions close to normal produce the resulting signal.

8 v. Probabilistic displays of the data, such as the probability of 90 (10),95 (5),97 (3) and 100 (0) percent could be of value and would provide additional confidence information. The term heat episode is used here as the definition of a heat wave includes 3 or more consecutive days of 90F or greater temperatures. In Alaska, high temperature of 90F or greater are difficult to achieve and reaching these values for 3 consecutive days is likely even more difficult to achieve. Thus, for some regions of the world, based on latitude and altitude a more generic definition of may be required. Though the extreme heat achieved in Alaska in June 2013 was not likely a high impact heat event relative to heat waves such as the Central European Heat Wave of 2010 (Grumm 2011) or the 2003 western European heat wave. 8. Acknowledgements Randy Graham for feedback and information related to rare events. 9. References Bodner, M. J., N. W. Junker, R. H. Grumm, and R. S. Schumacher, 2011: Comparison of atmospheric circulation patterns during the 2008 and 1993 historic Midwest floods. Natl. Wea. Dig., 35, Doty, B.E. and J.L. Kinter III, 1995: Geophysical Data Analysis and Visualization using GrADS. Visualization Techniques in Space and Atmospheric Sciences, eds. E.P. Szuszczewicz and J.H. Bredekamp, NASA, Washington, D.C., Grumm, R.H. 2011: New England Record Maker rain event of March NWA, Electronic Journal of Operational Meteorology, EJ4. Graham, R A. and R. H. Grumm, 2010: Utilizing Normalized Anomalies to Assess Synoptic-Scale Weather Events in the Western United States. Wea. Forecasting, 25, Grumm, R.H. and R. Hart. 2001: Standardized Anomalies Applied to Significant Cold Season Weather Events: Preliminary Findings. Wea. and Fore., 16, Grumm, Richard H., 2011: The Central European and Russian Heat Event of July August Bull. Amer. Meteor. Soc., 92, Hart, R. E., and R. H. Grumm, 2001: Using normalized climatological anomalies to rank synoptic scale events objectively. Mon. Wea. Rev., 129, Junker, N.W, M.J.Brennan, F. Pereira,M.J.Bodner,and R.H. Grumm, 2009:Assessing the Potential for Rare Precipitation Events with Standardized Anomalies and Ensemble Guidance at the Hydrometeorological Prediction Center. Bulletin of the American Meteorological Society,4 Article: pp

9 Junker, N. W., R. H. Grumm, R. Hart, L. F. Bosart, K. M. Bell, and F. J. Pereira, 2008: Use of standardized anomaly fields to anticipate extreme rainfall in the mountains of northern California. Wea. Forecasting,23, Knight, P., J. Ross, B. Root, G.S. Young, R.H. Grumm, 2005: Fingerprinting significant weather events. Proceedkngs of the Fourth Conference on Artificial Intelligence, San Diego, CA, January 9-13, Stuart, N. and R. Grumm 2009, "The Use of Ensemble and Anomaly Data to Anticipate Extreme Flood Events in the Northeastern United States",NWA Digest,33, Stuart, N. and R. Grumm 2009, "The Use of Ensemble and Anomaly Data to Anticipate Extreme Flood Events in the Northeastern United States", 33, Stuart,N.A and R.H. Grumm 2007: Using Wind Anomalies to Forecast East Coast Winter Storms.Wea. and Forecasting, 21,

10 Figure 1. Return to text.

11 Figure 2. Return to text.

12 Figure 3. Return to text.

13 Figure 4. Return to text.

14 Figure 5. The 24 hour maximum temperatures (F) from the Pennsylvania State University ewall-site for the period covering 17 June Return to text.

15 Location Lat Lon POR Max Difference Date Old record Date2 CHULITNA RIVER F 12 6/17/ F 6/17/1983 GLENNALLEN KCAM F 6.1 6/17/ F 6/17/1986 NORTH POLE F 5 6/17/ F 6/17/2005 TONSINA F 4 6/17/ F 6/17/1907 SKAGWAY F 2 6/17/ F 6/17/1948 COLLEGE OBSERVATORY JUNEAU DOWNTOWN F 0 6/17/ F 6/17/ F 0.9 6/17/ F 6/17/1967 CRAIG F 7.1 6/17/ F 6/17/2005 WISEMAN F 2 6/17/ F 6/17/1948 ELFIN COVE F 5.1 6/17/ F Table 2. As in Table 1 for daily data for 17 June 2013 including the location, latitude, longitude, the length of the climate period of record, the attained maximum temperatures, how much the daily maximum exceed the previous record by and the date of the current and previous records. Return to text.

16 Location Lat Lon POR Max Difference Date Old record Date2 NORTH POLE F 5.1 6/18/ F 6/18/1996 CHULITNA RIVER F 8.1 6/18/ F 6/18/2011 TONSINA F 4.2 6/18/ F 6/18/2005 GLENNALLEN KCAM F 2 6/18/ F 6/18/2005 SKAGWAY F 0 6/18/ F 6/18/1961 ALYESKA F /18/ F 6/18/2005 WISEMAN F 0.9 6/18/ F 6/18/1948 Table 4. As in Table 3 except for 18 June Return to text. Location Lat Lon POR Max Difference Date Old record Date2 CHULITNA RIVER F 9 6/16/ F 6/16/2002 SKAGWAY F 2.2 6/16/ F 6/16/2002 ELFIN COVE F 16 6/16/ F 6/16/2006 CRAIG F 4.8 6/16/ F 6/16/1944 Table 3. As in Table 3 except for 16 June Return to text.

17 Figure 6. Return to text.

18 Figure 7. Return to text.

19 Figure 8. NAEFS forecasts and GFS analysis of 500 hpa heights (m) and the percentile of the 500 hpa verse the climatological 500 hpa height probability density function. Data show NAEFS forecasts at a) 240 hours, b) 192 hours, c) 144 hours, d) 96 hours, f) 48 hours, and d) the verifying GFS 00-hour forecasts. Return to text.

20 Figure 9. As in Figure 8 except for 700 hpa temperatures. Return to text.

21 Figure 10. GEFS forecasts of 500 hpa heights and 500 hpa height anomalies (shaded) from 6 GEFS forecasts valid at 0000 UTC 18 June 2013 from GEFS forecasts initialized at a) 1200 UTC 11 June, b) 1200 UTC 13 June, c) 1200 UTC 14 June, d) 1200 UTC 15 June, e) 1200 UTC 16 June, f) 1200 UTC 17 June Return to text.

22 Figure 11. As in Figure 9 except valid at 0000 UTC 19 June Return to text.

23 Figure 12. As in Figure 9 except for GEFS forecasts of 850 hpa temperatures ( C) and 850 hpa temperature anomalies. Return to text.

24 Figure 13. As in Figure 9 except valid at 0000 UTC 19 June Return to text.

25 Figure 15.

26 Figure 16.

27 Figure 15. As in Figure 13 except for 700 hpa temperature and comparative temperature anomalies. Return to text. Figure 14. NAEFS 500 hpa heights valid at 0000 UTC 18 June Upper panels shows the NAEFS forecasts of 500 hpa heights relative to the 30-year CFSR data. The lower panels shows the 500 hpa heights with the traditional standardized anomalies. Return to text.