Time scales of physics vs. biology ENSO effects on biology Pacific Decadal Oscillation (PDO)
The El Nino Southern Oscillation (ENSO) varies its effect on Atlantic hurricane formation in a...
1
• The El Nino Southern Oscillation (ENSO) varies its effect on Atlantic hurricane formation in a multi-decadal manner. • ENSO varies between being positively and negatively correlated with number of hurricanes. • The Multivariate ENSO Index (MEI) is used to represent the ENSO effects (Figure 4). b) Comparison with Other Models • We took the prediction for each year from 2001-2013 using the method described above (only data available up to May of the year predicted was used). We compared our model with several others in terms of average error. 1950 1960 1970 1980 1990 2000 2010 -1 0 1 Ten-year Running Correlation Between MEI and # of Hurricanes c) Uncertainty Range We developed two methods to compute a prediction range that captures the true number of hurricanes more accurately than using a standard method of adding/subtracting a standard deviation of the forecasting errors. They are: 1. Add/subtract error standard deviation of years with positive (negative) May AMO to corresponding years. 2. Create upper (lower) bound by regressing on years with high (low) errors. A new statistical model to predict seasonal North Atlantic hurricane activity Kyle Davis, Xubin Zeng, and Elizabeth Ritchie Department of Atmospheric Sciences, University of Arizona Contact: [email protected] Why do we care? • Atlantic hurricanes rank #1 among insured losses from 1970-2002 in the US, more than the combined losses from earthquakes and human-caused disasters (including terrorist attacks) over the same period. • Atlantic hurricane numbers, as of late, have been wildly variable, as shown by Figure 1. • Other models have failed to show sufficient skill above a five-year running average climatology in recent years (Figure 5). 1950 1960 1970 1980 1990 2000 2010 0 4 8 12 16 Historical Atlantic Hurricane Counts Figure 1 Since the 1980s, annual hurricane counts have shown marked multi-decadal variability. Low Variability High Variability Figure 2 Number of Atlantic hurricanes correlated with the average March, April, May SSTs. The area in the box is the region used for the model. Figure 3 The correlation of zonal pseudo wind stress (ZPS) with the number of hurricanes. The white spaces in the ocean are for locations that do not have data for the whole period. Figure 4 Running ten-year correlations using data from 1950- 2013 (correlation computed by taking the five years after and the four years before the year shown). 1 The UA Model 2 • Warm, tropical waters above 26⁰ C are one of the most important ingredients for hurricane formation. • We correlated Atlantic sea surface temperatures (SSTs) with hurricane numbers between 1950 and 2013 (Figure 2). • To incorporate these effects, the model uses the MEI when AMO is negative and zero when AMO is positive. Table 1 A comparison between climatology (based on the average hurricane number of prior five years) and the UA model based on data available up to that point for predictions after 1980, or data after that year if before 1981. Model MAE Climatolog y MAE Model % Improvement 1950-1980 1.55 1.97 21.32% 1981-2013 1.85 2.55 27.45% TSR CSU CPC UA 5-year avg 0 2 4 Average Error, 2001-2013 Figure 5 Comparison of the average error of four models with the 5-year average (the no-skill metric) for the period of 2001-2013. Figure 6 The upper and lower limits of the prediction ranges as well as the actual number of hurricanes (in gray circles) for the year. Method 1 was created by adding and subtracting one standard deviation based on the sign of the May AMO. Method 2 was created by using separate regressions for the upper and lower bounds. The standard method was created by adding/subtracting one standard deviation of the The UA model uses a Poisson regression a) Robustness Test • We divided the whole period into a 1950- 1980 period and a 1981-2013 period because of the difference in hurricane number dispersion for these two periods (Figure 1). • The 1981-2013 data were used to train the model and issue a prediction for 1980 using the data from the spring of 1980. The prediction for the next year, 1979, would use the model calibrated for 1980- 2013. The same procedure is used going backwards until 1950 and then, starting with data from 1950-1980, going forward until 2013. • The mean absolute errors (MAE) are shown in Table 1 compared to the MAE of a five- year running average climatology for the same periods. • Zonal psuedo wind stress (ZPS) affects the horizontal and vertical distribution of ocean temperature and is also highly correlated with sea level pressure (- 0.66 over region shown in Figure 3). • We used ZPS raised to the 3/2 power, which is proportional to the turbulent dissipation rate in the ocean mixed layer, because it predicts better than just ZPS. 1950 1960 1970 1980 1990 2000 2010 0 10 20 A Comparison of Three Range Types Actual Method 1 Method 2 Standard Method Reference Davis, K., X. Zeng, and E. Ritchie, 2014: A new statistical model to predict seasonal North Atlantic hurricane activity. Submitted to Wea. Forecasting Performance 3 Conclusions 4 • We have developed a statistical model to make more accurate predictions of seasonal North Atlantic hurricane numbers by 1 June of each year. • The model uses average SSTs, the MEI index conditioned upon the AMO, and the average zonal psuedo wind stress over the Atlantic raised to the 3/2 power. • Our model has a smaller average error than other models over the period 2001- 2013. • We created two new ways to issue a prediction range that captures more accurately the true number of hurricanes than adding/subtracting a standard deviation of the errors.
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Transcript of The El Nino Southern Oscillation (ENSO) varies its effect on Atlantic hurricane formation in a...
• The El Nino Southern Oscillation (ENSO) varies its effect on Atlantic hurricane formation in a multi-decadal manner.
• ENSO varies between being positively and negatively correlated with number of hurricanes.
• The Multivariate ENSO Index (MEI) is used to represent the ENSO effects (Figure 4).
b) Comparison with Other Models• We took the prediction for each year from 2001-
2013 using the method described above (only data available up to May of the year predicted was used). We compared our model with several others in terms of average error.
1950 1960 1970 1980 1990 2000 2010-1
-0.5
0
0.5
1
Ten-year Running Correlation Between MEI and # of Hurri-
canes
c) Uncertainty RangeWe developed two methods to compute a prediction range that captures the true number of hurricanes more accurately than using a standard method of adding/subtracting a standard deviation of the forecasting errors. They are: 1. Add/subtract error standard deviation of years
with positive (negative) May AMO to corresponding years.
2. Create upper (lower) bound by regressing on years with high (low) errors.
A new statistical model to predict seasonal North Atlantic hurricane activity
Kyle Davis, Xubin Zeng, and Elizabeth RitchieDepartment of Atmospheric Sciences, University of Arizona
Contact: [email protected]
Why do we care?
• Atlantic hurricanes rank #1 among insured losses from 1970-2002 in the US, more than the combined losses from earthquakes and human-caused disasters (including terrorist attacks) over the same period.
• Atlantic hurricane numbers, as of late, have been wildly variable, as shown by Figure 1.
• Other models have failed to show sufficient skill above a five-year running average climatology in recent years (Figure 5).
1950 1960 1970 1980 1990 2000 20100
4
8
12
16
Historical Atlantic Hurricane Counts
Figure 1 Since the 1980s, annual hurricane counts have shown marked multi-decadal variability.
Low Variability High Variability
Figure 2 Number of Atlantic hurricanes correlated with the average March, April, May SSTs. The area in the box is the region used for the model.
Figure 3 The correlation of zonal pseudo wind stress (ZPS) with the number of hurricanes. The white spaces in the ocean are for locations that do not have data for the whole period.
Figure 4 Running ten-year correlations using data from 1950-2013 (correlation computed by taking the five years after and the four years before the year shown).
1
The UA Model2
• Warm, tropical waters above 26⁰ C are one of the most important ingredients for hurricane formation.
• We correlated Atlantic sea surface temperatures (SSTs) with hurricane numbers between 1950 and 2013 (Figure 2).
• To incorporate these effects, the model uses the MEI when AMO is negative and zero when AMO is positive.
Table 1 A comparison between climatology (based on the average hurricane number of prior five years) and the UA model based on data available up to that point for predictions after 1980, or data after that year if before 1981.
Model MAE ClimatologyMAE
Model % Improvement
1950-1980 1.55 1.97 21.32%1981-2013 1.85 2.55 27.45%
TSR CSU CPC UA 5-year avg
01234
Average Error, 2001-2013
Figure 5 Comparison of the average error of four models with the 5-year average (the no-skill metric) for the period of 2001-2013.
Figure 6 The upper and lower limits of the prediction ranges as well as the actual number of hurricanes (in gray circles) for the year. Method 1 was created by adding and subtracting one standard deviation based on the sign of the May AMO. Method 2 was created by using separate regressions for the upper and lower bounds. The standard method was created by adding/subtracting one standard deviation of the forecasting errors.
The UA model uses a Poisson regression
a) Robustness Test• We divided the whole period into a 1950-1980
period and a 1981-2013 period because of the difference in hurricane number dispersion for these two periods (Figure 1).
• The 1981-2013 data were used to train the model and issue a prediction for 1980 using the data from the spring of 1980. The prediction for the next year, 1979, would use the model calibrated for 1980-2013. The same procedure is used going backwards until 1950 and then, starting with data from 1950-1980, going forward until 2013.
• The mean absolute errors (MAE) are shown in Table 1 compared to the MAE of a five-year running average climatology for the same periods.
• Zonal psuedo wind stress (ZPS) affects the horizontal and vertical distribution of ocean temperature and is also highly correlated with sea level pressure (-0.66 over region shown in Figure 3).
• We used ZPS raised to the 3/2 power, which is proportional to the turbulent dissipation rate in the ocean mixed layer, because it predicts better than just ZPS.
1950 1960 1970 1980 1990 2000 20100
5
10
15
20
A Comparison of Three Range Types
Actual Method 1Method 2 Standard Method
ReferenceDavis, K., X. Zeng, and E. Ritchie, 2014: A new statistical model to predict seasonal North Atlantic hurricane activity. Submitted to Wea. Forecasting
Performance3
Conclusions4
• We have developed a statistical model to make more accurate predictions of seasonal North Atlantic hurricane numbers by 1 June of each year.
• The model uses average SSTs, the MEI index conditioned upon the AMO, and the average zonal psuedo wind stress over the Atlantic raised to the 3/2 power.
• Our model has a smaller average error than other models over the period 2001-2013.
• We created two new ways to issue a prediction range that captures more accurately the true number of hurricanes than adding/subtracting a standard deviation of the errors.
