changes in floods and droughts in an elevated co 2 climate anthony m. deangelis dr. anthony j....
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Changes in Floods and Changes in Floods and Droughts in an Droughts in an
Elevated COElevated CO22 Climate Climate
Anthony M. DeAngelisDr. Anthony J. Broccoli
Outline of Presentation Introduction and Motivation for Research Model Changes in Floods/ Droughts Scaling Factor Hypothesis Conclusions Future Research References
Importance of Research Floods and droughts are major climatic
events that can significantly impact human life and property.
Previous research has suggested that the frequency of these events has changed over the past century.
The frequency of floods and droughts may continue to change in a warmer climate over the United States.
Projected Changes in Precipitation Extremes
Frequency of Dry Days
Frequency of 95th percentile events
Anomalies in days/year.
Diffenbaugh et al. 2005, RegCM3, Resolution: 25 km
Our Climate Model CM2.1 Developed at NOAA’s Geophysical Fluid
Dynamics Laboratory (GFDL) Resolution: 2° latitude by 2.5° longitude.
Our Data CM2.1U_Control-1860_D4 = Control data.
Coupled (atmosphere + land) and (ocean + sea ice) model with forcing agents consistent with 1860.
CM2.1U-D4_1PctTo4X_J1 = Elevated CO2 data. Increases CO2 from 1860 levels by 1% per year to quadrupling, then holds CO2 constant.
Using P-E Instead of studying precipitation alone, we
study precipitation minus evaporation (P-E).
The negative feedback between soil moisture and surface evaporation affects our results.
As evaporation increases, soil moisture decreases, and reduces the availability of water in the soil. Thus, evaporation increases slow or cease.
Assessing Changes in Extreme Precipitation Events in Elevated CO2 Climate
Calculate 1st and 99th P-E percentiles for control and elevated CO2 data for each location.
Calculate changes in frequencies of <1st, and >99th P-E percentile events between control and elevated CO2 data.
Calculate changes in 99th P-E percentile values between control and elevated CO2 data.
Assessing Changes in Extreme Precipitation Events in Elevated CO2 Climate
We look at changes in >99th percentile P-E events of period lengths 1 and 7 days to assess changes in short and long term floods.
We look at changes in <1st percentile P-E events for period lengths 90 and 360 days to assess changes in short and long term droughts.
Results: Changes in >99th Percentile Frequencies (Floods)
Annual, 1 Day:
Summer, 1 Day:
Winter, 1 Day:
Annual, 7 Day:
Summer, 7 Day:
Winter, 7 Day:
Percent Changes in >99th percentile P-E frequencies ranging from -100 (blue) to 100 (red):
5 10 15 20 25
2
4
6
8
10
12
14
> 99 Percentile Frequency Change (From Control to 4x CO2) (1 Day, Annual)
Longitude Index (i)
Latit
ude
Inde
x (j)
% changemn= -18.3562 mx= 146.0274
-80
-60
-40
-20
0
20
40
60
80
5 10 15 20 25
2
4
6
8
10
12
14
> 99 Percentile Frequency Change (From Control to 4x CO2) (7 Day, Annual)
Longitude Index (i)
Latit
ude
Inde
x (j)
% changemn= -32.1429 mx= 284.3407
-80
-60
-40
-20
0
20
40
60
80
5 10 15 20 25
2
4
6
8
10
12
14
> 99 Percentile Frequency Change (From Control to 4x CO2) (1 Day, Summer)
Longitude Index (i)
Latit
ude
Inde
x (j)
% changemn= -59.4771 mx= 156.2092
-80
-60
-40
-20
0
20
40
60
80
5 10 15 20 25
2
4
6
8
10
12
14
> 99 Percentile Frequency Change (From Control to 4x CO2) (7 Day, Summer)
Longitude Index (i)
Latit
ude
Inde
x (j)
% changemn= -81.6327 mx= 280.9524
-80
-60
-40
-20
0
20
40
60
80
5 10 15 20 25
2
4
6
8
10
12
14
> 99 Percentile Frequency Change (From Control to 4x CO2) (1 Day, Winter)
Longitude Index (i)
Latit
ude
Inde
x (j)
% changemn= -21.1921 mx= 254.9669
-80
-60
-40
-20
0
20
40
60
80
5 10 15 20 25
2
4
6
8
10
12
14
> 99 Percentile Frequency Change (From Control to 4x CO2) (7 Day, Winter)
Longitude Index (i)
Latit
ude
Inde
x (j)
% changemn= 5.5556 mx= 451.3889
-80
-60
-40
-20
0
20
40
60
80
Results: Changes in <1st Percentile Frequencies (Droughts)
Annual, 90 Day:
Summer, 90 Day:
Winter, 90 Day:
Annual, 360 Day:
Percent Changes in <1st percentile P-E frequencies ranging from -100 (blue) to 100 (red):
5 10 15 20 25
2
4
6
8
10
12
14
< 1 Percentile Frequency Change (From Control to 4x CO2) (90 Day, Annual)
Longitude Index (i)
Latit
ude
Inde
x (j)
% changemn= -62.6374 mx= 544.5055
-80
-60
-40
-20
0
20
40
60
80
5 10 15 20 25
2
4
6
8
10
12
14
< 1 Percentile Frequency Change (From Control to 4x CO2) (360 Day, Annual)
Longitude Index (i)
Latit
ude
Inde
x (j)
% changemn= -100 mx= 1191.9668
-80
-60
-40
-20
0
20
40
60
80
5 10 15 20 25
2
4
6
8
10
12
14
< 1 Percentile Frequency Change (From Control to 4x CO2) (90 Day, Summer)
Longitude Index (i)
Latit
ude
Inde
x (j)
% changemn= -100 mx= 1080.9524
-80
-60
-40
-20
0
20
40
60
80
5 10 15 20 25
2
4
6
8
10
12
14
< 1 Percentile Frequency Change (From Control to 4x CO2) (90 Day, Winter)
Longitude Index (i)
Latit
ude
Inde
x (j)
% changemn= -100 mx= 832.7869
-80
-60
-40
-20
0
20
40
60
80
Results: Comparison of Mean Changes with Upper Percentile Changes
Mean, Annual:
Mean, Summer:
Mean, Winter:
99th, Annual, 1 Day:
99th, Summer, 1 Day:
99th, Winter, 1 Day:
Mean P-E changes between control and elevated CO2 data: Ranging from -0.5 (blue) to 0.5 (red). 99th Percentile daily P-E changes: Ranging from -10 (blue) to 10 (red). Units in mm/day.
5 10 15 20 25
2
4
6
8
10
12
14
Mean P-E Change from Control to 4x CO2 (Annual)
Longitude Index (i)
Latit
ude
Inde
x (j)
Mean changemn= -0.42303 mx= 0.43137 (mm/day)
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
5 10 15 20 25
2
4
6
8
10
12
14
Mean P-E Change from Control to 4x CO2 (Summer)
Longitude Index (i)
Latit
ude
Inde
x (j)
Mean changemn= -0.65062 mx= 1.2566 (mm/day)
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
5 10 15 20 25
2
4
6
8
10
12
14
Mean P-E Change from Control to 4x CO2 (Winter)
Longitude Index (i)
Latit
ude
Inde
x (j)
Mean changemn= -1.0176 mx= 0.76744 (mm/day)
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
5 10 15 20 25
2
4
6
8
10
12
14
99 Percentile Change (From Control to 4x CO2) (1 Day, Annual)
Longitude Index (i)
Latit
ude
Inde
x (j)
P-E changemn= -1.532 mx= 11.6056
-8
-6
-4
-2
0
2
4
6
8
5 10 15 20 25
2
4
6
8
10
12
14
99 Percentile Change (From Control to 4x CO2) (1 Day, Summer)
Longitude Index (i)
Latit
ude
Inde
x (j)
P-E changemn= -6.4392 mx= 23.5532
-8
-6
-4
-2
0
2
4
6
8
5 10 15 20 25
2
4
6
8
10
12
14
99 Percentile Change (From Control to 4x CO2) (1 Day, Winter)
Longitude Index (i)
Latit
ude
Inde
x (j)
P-E changemn= -3.0113 mx= 13.927
-8
-6
-4
-2
0
2
4
6
8
Agreement with Previous Research Diffenbaugh et al. 2005 RegCM3 model (CO2 from A2 scenario) Resolution: 25 km, Entire US Increases in annual >95th percentile
precipitation events across east and northwest US.
Increases in annual mean precipitation across eastern US.
Similar patterns in direction of mean and precipitation extreme anomalies.
Agreement with Previous Research Leung et al. 2004 PCM model (Doubling CO2 from 1995-
2100) Resolution: 40 km, Western US Increases in winter 95th percentile
precipitation values across parts of the northwestern US.
Decreases in winter mean precipitation across the western US.
Why does extreme precipitation change? Our hypothesis: An intensification of the
hydrologic cycle only. Warmer temperatures Increased
evaporation Increased water vapor Heavier precipitation in areas and time periods of convergence Increased droughts in areas and time periods of dry weather.
Scaling the hydrologic cycle by a constant factor may explain the changes.
Testing Our Hypothesis Multiply control data by constant scaling
factor of 1.0581 (globally and time averaged percent increase in precipitation and evaporation between control and quadrupled CO2 climate).
Perform Kolmogorov-Smirnov (KS) and Kuiper (KP) statistical tests on distributions of scaled control and elevated CO2 data for all locations.
Testing Our Hypothesis Kolmogorov-Smirnov Test (KS)
Yields D value: The maximum distance between cumulative distribution functions of scaled control and elevated CO2 data.
Yields Probability: Ranging from 0 to 1 where small values show that the cumulative distribution functions of both data sets are significantly different.
Testing Our Hypothesis Kuiper’s Statistic (KP)
Variant on Kolmogorov-Smirnov statistic Yields V value: Sum of the absolute value of
maximum negative and positive distances between the cumulative distribution functions of the scaled control and elevated CO2 data.
Yields Probability: Same as for KS statistic.
KS and KP Statistical Test Results for P-E 1 Day Annual Data
Scaled control and elevated CO2 distribution tested. Probability values ranging from 0 (blue) to 1 (red).
5 10 15 20 25
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4
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14
KS Test between Scaled Control and 4x CO2 (1 Day, ALL)
Longitude Index (i)
Latit
ude
Inde
x (j)
Probmn= 0
mx= 0.0011965
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
5 10 15 20 25
2
4
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10
12
14
KP Test between Scaled Control and 4x CO2 (1 Day, ALL)
Longitude Index (i)
Latit
ude
Inde
x (j)
Probmn= 0
mx= 1.094e-006
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
KS Test KP Test
ALL probabilities near 0
Annual Statistical Test Results for All Period Lengths The KS test yields an overall lowest D value of
about 0.0085, corresponding to a probability of 0.14.
The KP test yields an overall lowest V value of above 0.012, corresponding to a probability below 0.10.
These low probabilities indicate that the cumulative distribution functions between the scaled control and elevated CO2 data are different for all locations and all period lengths (1, 2, 3, 7, 30, 60, 90, 180, 360 days).
Improvements in KS Test D Values and KP Test V Values After Scaling
KS, 1 Day:
KS, 30 Day: KS, 90 Day:
KP, 1 Day:
KP, 30 Day: KP, 90 Day:
Change in D before and after scaling. ∆D values ranging from -0.05 (blue) to 0.05 (red). Positive values (yellow, orange, red) indicate improvement.
5 10 15 20 25
2
4
6
8
10
12
14
KS Test Control-Scaled Map (1 Day, Annual)
Longitude Index (i)
Latit
ude
Inde
x (j)
dDmn= -0.0097808
mx= 0.017479
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
5 10 15 20 25
2
4
6
8
10
12
14
KP Test Control-Scaled Map (1 Day, Annual)
Longitude Index (i)
Latit
ude
Inde
x (j)
dVmn= -0.019644
mx= 0.019616
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
5 10 15 20 25
2
4
6
8
10
12
14
KS Test Control-Scaled Map (90 Day, Annual)
Longitude Index (i)
Latit
ude
Inde
x (j)
dDmn= -0.022164
mx= 0.060806
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
5 10 15 20 25
2
4
6
8
10
12
14
KP Test Control-Scaled Map (90 Day, Annual)
Longitude Index (i)
Latit
ude
Inde
x (j)
dVmn= -0.030321
mx= 0.054324
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
5 10 15 20 25
2
4
6
8
10
12
14
KS Test Control-Scaled Map (30 Day, Annual)
Longitude Index (i)
Latit
ude
Inde
x (j)
dDmn= -0.013518
mx= 0.037262
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
5 10 15 20 25
2
4
6
8
10
12
14
KP Test Control-Scaled Map (30 Day, Annual)
Longitude Index (i)
Latitu
de I
ndex (
j)
dVmn= -0.016369
mx= 0.035946
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
Comparison of Changes in 99th Percentile Before and After Scaling
99th, Annual, 1 Day:
99th, Annual 1 Day:
99th, Annual, 90 Day:
99th, Annual, 90 Day:
Absolute changes in P-E annual data: Ranging from -10 (blue) to 10 (red) in 1 day and from -2 (blue) to 2 (red) in 90 day. Units in mm/day.
5 10 15 20 25
2
4
6
8
10
12
14
99 Percentile Change (From Control to 4x CO2) (1 Day, Annual)
Longitude Index (i)
Latit
ude
Inde
x (j)
P-E changemn= -1.532 mx= 11.6056
-8
-6
-4
-2
0
2
4
6
8
5 10 15 20 25
2
4
6
8
10
12
14
99 Percentile Change (From Scaled to 4x CO2) (1 Day, Annual)
Longitude Index (i)
Latit
ude
Inde
x (j)
P-E changemn= -2.8264 mx= 9.7074
-8
-6
-4
-2
0
2
4
6
8
5 10 15 20 25
2
4
6
8
10
12
14
99 Percentile Change (From Scaled to 4x CO2) (90 Day, Annual)
Longitude Index (i)
Latit
ude
Inde
x (j)
P-E changemn= -1.3622 mx= 4.8934
-1.5
-1
-0.5
0
0.5
1
1.5
5 10 15 20 25
2
4
6
8
10
12
14
99 Percentile Change (From Control to 4x CO2) (90 Day, Annual)
Longitude Index (i)
Latit
ude
Inde
x (j)
P-E changemn= -1.1809 mx= 5.1223
-1.5
-1
-0.5
0
0.5
1
1.5
Bet
wee
n C
ontr
ol a
nd E
leva
ted
CO
2
Bet
wee
n S
cale
d C
ontr
ol a
nd E
leva
ted
CO
2
Does Using a Higher Scaling Factor Yield Better Results? Increasing the scaling factor improves
agreement in cumulative distribution functions for many locations.
However, the improvement is not significant enough to conclude that the scaled control and elevated CO2 distributions come from the same population.
Does Scaling Precipitation Alone Yield Better Results? Scaling precipitation alone and comparing
its cumulative distribution function with that of the elevated CO2 data gives higher probabilities.
However, these probabilities are still close to zero, even when scaling factors are increased beyond 1.0581.
Conclusions Frequency of floods increases across the
north and east annually and in summer, and nearly everywhere in winter.
Frequency of droughts increases in east annually and in summer, and decreases in winter.
With the exception of a few regions, the direction of mean change is overall similar to the direction of upper percentile changes.
Conclusions Magnitude of mean increases are
significantly smaller than those of upper percentiles.
Cumulative Distribution functions between scaled control and elevated CO2 data are different for all locations.
Increasing scaling factors and performing the analysis on precipitation alone improves distribution agreement, but not significantly.
Conclusions It appears that one reason for the large
differences in cumulative distribution functions is the inability for the scaling factor to account for the large absolute increases in upper P-E percentiles (99th) between the control and elevated CO2 data.
Future Research We seek to further understand how the
scaled control distributions differ from the elevated CO2 distributions.
If a simple linear scaling of the hydrological cycle alone cannot explain changes in extreme precipitation in a warmer climate, what can?
References Diffenbaugh NS, Pal JS, Trapp RJ, et al., 2005: Fine-scale
processes regulate the response of extreme events to global climate change. Proceedings of the National Academy of Sciences of the United States of America, 102, 15774-15778.
Leung LR, Qian Y, Bian XD, et al., 2004: Mid-century ensemble regional climate change scenarios for the western United States. Climatic Change, 62, 75-113.