Changes in Floods and Droughts in an Elevated CO2 Climate

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Changes in Floods and Droughts in an Elevated
CO2 Climate

• Anthony M. DeAngelis
• Dr. Anthony J. Broccoli

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Outline of Presentation

• Introduction and Motivation for Research
• Model
• Changes in Floods/ Droughts
• Scaling Factor Hypothesis
• Conclusions
• Future Research
• References

3
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.

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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
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Our Climate Model

• CM2.1
• Developed at NOAAs Geophysical Fluid Dynamics
  Laboratory (GFDL)
• Resolution 2 latitude by 2.5 longitude.

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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.

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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.

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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 lt1st, and
  gt99th P-E percentile events between control and
  elevated CO2 data.
• Calculate changes in 99th P-E percentile values
  between control and elevated CO2 data.

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Assessing Changes in Extreme Precipitation Events
in Elevated CO2 Climate

• We look at changes in gt99th 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 lt1st percentile P-E events
  for period lengths 90 and 360 days to assess
  changes in short and long term droughts.

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Results Changes in gt99th Percentile Frequencies
(Floods)
Percent Changes in gt99th percentile P-E
frequencies ranging from -100 (blue) to 100 (red)
Annual, 1 Day
Summer, 1 Day
Winter, 1 Day
Annual, 7 Day
Summer, 7 Day
Winter, 7 Day
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Results Changes in lt1st Percentile Frequencies
(Droughts)
Percent Changes in lt1st percentile P-E
frequencies ranging from -100 (blue) to 100 (red)
Annual, 90 Day
Summer, 90 Day
Winter, 90 Day
Annual, 360 Day
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Results Comparison of Mean Changes with Upper
Percentile Changes
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.
Mean, Annual
Mean, Summer
Mean, Winter
99th, Annual, 1 Day
99th, Summer, 1 Day
99th, Winter, 1 Day
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Agreement with Previous Research

• Diffenbaugh et al. 2005
• RegCM3 model (CO2 from A2 scenario)
• Resolution 25 km, Entire US
• Increases in annual gt95th 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.

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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.

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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.

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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.

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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.

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Testing Our Hypothesis

• Kuipers 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.

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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).
KS Test
KP Test
ALL probabilities near 0
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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).

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Improvements in KS Test D Values and KP Test V
Values After Scaling
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.
KS, 1 Day
KS, 30 Day
KS, 90 Day
KP, 1 Day
KP, 30 Day
KP, 90 Day
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Comparison of Changes in 99th Percentile Before
and After Scaling
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.
99th, Annual, 1 Day
99th, Annual 1 Day
Between Control and Elevated CO2
Between Scaled Control and Elevated CO2
99th, Annual, 90 Day
99th, Annual, 90 Day
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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.

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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.

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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.

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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.

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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.

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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?

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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.