Large Scale Weather Patterns Associated with California Extreme Heat Waves and their Link to Daily Summer Maximum Temperatures in the Central Valley

Transcription

1 Large Scale Weather Patterns Associated with California Extreme Heat Waves and their Link to Daily Summer Maximum Temperatures in the Central Valley Richard Grotjahn Atmospheric Science Program Dept. of LAWR, UCD, Davis, CA, USA

2 Outline of Talk: Some extremes are a forecast problem Short term events Examples of short term extremes CAOs, Heat waves, Windstorms Misleading monthly means Florida Dec California, July 1991 Applying forecast experience to a climate issue CV Forecast composites CAOs Heat waves Northwinds (Diablos) Large scale air mass displacements A prototype analysis scheme How it works How well it does Conclusions & future work

3 Consider: Short term extreme events. Only last a few days Can have controlling influence upon crops, infrastructure. May not show up on monthly means Though ephemeral, they can be important for climate. Will make application to California Central Valley (CV)

4 Example 1: Dec CAO Minimum Temperature (anomaly) Winter Haven, Fl (Dec. 1983) Mean = +0.8C (+1.44F) degrees F Series Date CAO: Cold Air Outbreak One of the coldest outbreaks on record for the much of U.S. In Florida, >80% of the juicing oranges were spoiled; more than half the citrus trees were killed. Winter Haven, Florida is heart of citrus growing region: Daily anomaly temperatures show the 4 day deep freeze Rest of month was generally above average. The mean for the month? Above normal! +0.8C (+1.44 F) So an above normal December wiped out the crop. Conclusion: The monthly mean misses this important event!

5 Example 2: Jul CV-HW July Daily anomaly temperatures at 4 CV stations June-Sept Normalized by each stations STD. Pink line is LTDM. CV-HW: California Central Valley Heat Wave Hottest outbreaks in at least 70 years. Daily anomaly temperatures show 4 days of extreme heat Rest of month was generally below average. The mean for the month? -0.2 STD (Standard deviations) below normal! A cooler than normal July had the state s hottest heat wave. Conclusion: The monthly mean misses this important event!

6 Forecast experience: CV Cold Air Outbreaks CV Cold Air Outbreaks Some examples of target composites from severe CAOs affecting California CV. T at 850 hpa V at 700 hpa Z at 500 hpa Conclusions: very large scale pattern Not surprising; large airmass displacements

7 Forecast experience: Some examples of target composites from severe heat waves affecting California CV. T at 850 hpa V at 700 hpa Z at 700 hpa Conclusions: very large scale pattern Not surprising; large airmass displacements CV Heat waves

8 Some climate extremes are like a forecast problem Conclusions Important impacts on longer term climate. (though may not show up in time means) Their short period means they are studied with forecast tools Climate models resolve only large scale features directly, so need events with large scale. Temperature and wind related extremes often have a large scale, these result from large displacements of air masses The large scale patterns can be resolved by medium resolution (e.g. T42) climate models. How to use this information? Test with pilot project

9 Pilot Project, part 1 Purpose: find extreme surface events from large scale upper air data. First find the events daily anomalies of 28 summers (3416 days) Average 3 stations spaced along the CV, (avoid Delta sea breezes) Choose threshold to find hottest ~1% 33 target dates of extreme heat waves found. Make daily anomaly fields from NCEP/DOE AMIP data: 2.5x2.5 grid. Make target composites of many variables on the target dates using anomaly gridded data (improve by using only even or odd years) Then..

10 Pilot Project, part 2 Compare target composite with 1000 random composites, (each from 33 randomly-chosen dates). Bootstrap resampling identifies highly significant features Develop a daily circulation index from the odd years Correlate highly significant areas of select target composites (or leading extreme EOF) with same areas in daily data. Correlations from more than one variable are combined to get an overall daily circulation index for the date. Index optimizes combination to hit most number of target dates How well do the daily indices perform for all years? Example: Target composite and sign counts for 33 events. T850 hot consistently 10 o west of CV

11 Pilot Project Results Study the daily indices found Compare the distribution with the max T distribution Compare dates of highest 1%: actual T versus circulation index. Animation of time series Observed anomaly (red) Circulation index (blue) Extreme events (circles) Performance Highest 33 values of index match 25 of the 33 (76%) observed extreme events. All the highest 33 values of index are in top 2% of observed events Correlation between index and surface obs: 0.84 Index picks up cold and near normal events very well, too. Conclusion: picks up most observed surface events.

12 Climate Model Application, part 1 Apply daily circulation index of AMIP model output Correlate highly significant areas of observed target composites with corresponding areas in daily simulation data (historical, AMIP runs). Combine correlations from more than one variable to get an overall daily circulation index for the date. Study & adjust the daily indices found for historical simulations Compare the distribution with the observed distribution Is the model capturing the variability? Apply extreme statistical analysis to the peak values. Rescale to bring the index based on the model variability into line with the observed variability. Assess impact of using optimal levels (to allow study of archived output)

13 Climate Model Application part 2 Apply daily circulation index to future climate scenario model output Correlate highly significant areas of observed select target composites (or leading extreme EOF) with corresponding areas in daily anomaly simulation data (AR4 runs). Correlations from more than one variable are combined to get an overall daily circulation index for the date. Study the daily indices found Compare the future climate distribution with the historical simulation distribution How has the variability changed? (duration, frequency, intensity) How much has the mean shifted? Apply extreme statistical analysis to the peak values. (GPD, PP, GEV, etc.)

14 Summary & future work Some extremes are a forecast problem Short term events can have bit impact, may not be captured by monthly means Examples of short term extremes with large scale patterns Windstorms, CAOs Heat waves Described pilot project of California Central Valley heat waves Example CV-HW composites shown Developed index based on similarity between daily pattern & composites Index picks up variability (hot, cold, near normal pattern) Future application to examine climate model simulations Assess IPCC scenarios (local runs & archived) Various issues described (rescaling, statistics)

15 Questions? Thank you for your attention.

16 Storage

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18 Example 2: Jul CV-HW July Daily anomaly temperatures, 3-station CV average June-Sept Green line is 1.6 STD above the LTDM. CV-HW: California Central Valley Heat Wave Hottest outbreaks in at least 70 years. Daily anomaly temperatures show 4 days of extreme heat Rest of month was generally below average. The mean for the month? -0.2 STD (Standard deviations) below normal! A cooler than normal July had the state s hottest heat wave. Conclusion: The monthly mean misses this important event!

19 Pilot Project Purpose: find extreme surface events from large scale upper air data. First find the events daily anomalies of 28 summers (3416 days) Average 3 stations spaced along the CV, (avoid Delta sea breezes) Choose threshold to find hottest ~1% 33 target dates of extreme heat waves found. Make daily anomaly fields from NCEP/DOE AMIP data: 2.5x2.5 grid. Make target composites of many variables on the 14 target dates using anomaly gridded data on odd years only. Compare target composite with 1000 random composites, (each from 14 randomlychosen dates). Bootstrap resampling identifies highly significant features Develop a daily circulation index from the odd years Correlate highly significant areas of select target composites (or leading extreme EOF) with same areas in daily data. Correlations from more than one variable are combined to get an overall daily circulation index for the date. Index optimizes combination to hit most number of target dates How well do the daily indices perform for all years?

20 Example 6: California Heat Wave of 2006 July 2006 Affected California, Western US Sacramento: 11 straight days with Tmax > 100 (new record for consecutive days: July) More than 104 deaths statewide (140?) Electric demand soared to record levels (60TW) 1,000 PGE transformers fail High overnight minimum T Global warming may raise the overnight minimum T more than raise the daytime max T. Are more severe heat waves in our future? (very likely)

21 Pilot Project Purpose: find extreme surface events from large scale upper air data. First find the events daily anomalies of 28 summers (3416 days) Average 3 stations spaced along the CV, (avoid Delta sea breezes) Choose threshold to find hottest ~1% 33 target dates of extreme heat waves found. Find daily anomaly fields of gridded data Find the target composites of many variables on the 14 target dates using the anomaly data on odd years only. Compare the target composite with 1000 random composites, (each from 14 randomly-chosen dates). Bootstrap resampling to identify highly significant features of the target composites. Develop a daily circulation index from the odd years Correlate highly significant areas of select target composites (or leading extreme EOF) with corresponding areas in daily data. Correlations from more than one variable are combined to get an overall daily circulation index for the date. Study the daily indices found for all years Compare the distribution with the max T distribution Compare dates of highest 1%: actual T versus circulation index.