Changes in Floods and Droughts in a Warmer Climate
Anthony M. DeAngelis
Dr. Anthony J. Broccoli
Summer and Fall 2007
Introduction:
In a future climate with elevated CO2 concentrations, research has indicated that the
frequency and intensity of precipitation and precipitation extremes may change. While a change
in mean precipitation may not impose an immediate threat on human life and property, changes
in extreme precipitation can cause serious problems. Floods and droughts are major climatic
events that can quickly alter and destroy many aspects of human existence. In this study, we
take interest in these events and study their potential changes in a warmer climate.
Previous research has shed light on trends in precipitation and precipitation extremes over
the past. Groisman et al. 2004 references earlier papers showing a century long increase in mean
precipitation over much of the United States during the 20th century. They further show that the
increases in heavy and very heavy precipitation were more extreme than the mean increases, that
the contribution of extreme events to total precipitation has increased, and that these trends are
most notable in the eastern two-thirds of the country in the summer. A later study, Groisman et
al. 2005 finds statistically significant increases by 20% in the frequency of very heavy daily
precipitation events (> 99th percentile), all of which have occurred in the last third of the 20th
century. They further attribute these changes to an increase in atmospheric water vapor in a
warming climate, manifesting itself in increased cumulonimbus clouds and thunderstorm
activity.
Here, we study projected changes in mean and extreme precipitation following a global
increase of CO2 concentrations in the future. We look at the United States and analyze how and
by what degree the frequency and intensity of these precipitation characteristics will change. We
look at events of different time scales and during annual, summer, and winter periods. In
addition, we attempt to answer the question of why the climate will change in such a way.
Our hypothesis streams from the scientific concept that a warmer climate will give rise to
the intensification of the hydrologic cycle. The intensification of the hydrologic cycle would
lead to an increase in the intensity of evaporation, allowing for more water vapor in the air, and
ultimately more intense precipitation when the moister air converges. Emori and Brown 2005
and Meehl et al. 2005 show that in mid latitudes, both thermodynamic and dynamic effects
contribute to changes in mean and extreme precipitation. Our hypothesis tests the idea that
changes in only the hydrologic cycle alone, and thus thermodynamic effects alone can explain
changes in precipitation distribution in a warmer climate. Details about how we test this will be
seen later.
Materials and Methods:
Climate Model :
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The climate model predominantly used in this study is the Geophysical Fluid Dynamics
Laboratory Climate Model 2.1 (GFDL CM2.1). The first simulation we utilize is the
CM2.1U_Control-1860_D4 experiment. This simulation is used as our control data and consists
of a coupled atmosphere + land and ocean + sea ice model with forcing agents consistent with
the year 1860 and given a 220 year adjustment period. The data presented is daily and we use
the last 100 years of the precipitation and evaporation output variables. The second simulation is
the CM2.1U-D4_1PctTo4X_J1 experiment and is used as our elevated CO2 data. This
simulation increases CO2 levels at a rate of 1% per year for 140 years or to the point of
quadrupling. All non-CO2 forcing agents are held constant throughout the entire experiment, and
from years 141 through 300, CO2 is held constant. The resolution is 2.5° longitude by 2°
latitude. In our study, we look at the output variables precipitation and evaporation (in daily
form) for the last 100 years of the model. To test the reasonability of our model, we compare
NOAA’s Climate Prediction Center precipitation estimates to precipitation produced from the
model for various locations within our data set (data set region discussed next and model
analysis discussed in following section).
Although the GFDL CM2.1 model simulations produce data for the entire globe, we only
consider a portion of the data including the contiguous United States. Parts of southern Canada
and northern Mexico lie on the borders of our boundary and are included in some of the analyses.
The region stretches from about 127W to 60W and from about 24.3N to 54.6N. Also, we
combine the precipitation and evaporation data by subtracting evaporation from precipitation (PE), and use this quantity for a majority of the project. In the end, the two main data sets utilized
are a 36500x15x27 control P-E matrix and a 36500x15x27 elevated CO2 P-E matrix. Essentially,
there are 27 units of longitude, 15 units of latitude, and 36500 daily P-E values for each location
in time order.
Comparing the CM2.1 model output to estimated precipitation:
We use NOAA’s Climate Prediction Center (CPC) Daily United States Unified
Precipitation to compare with our CM2.1 control and quadrupled carbon dioxide precipitation
output. The purpose is to assess the ability of the CM2.1 model to portray a realistic distribution
of precipitation (and thus precipitation minus evaporation). The CPC precipitation data is
derived from the following sources: NOAA's National Climate Data Center (NCDC) daily co-op
stations (1948-...), CPC dataset (River Forecast Centers data + 1st order stations - 1992-...), and
daily accumulations from hourly precipitation dataset (1948-...). For more details on the
construction of CPC data, see http://www.cdc.noaa.gov/cdc/data.unified.html. The initial
resolution is 0.25° longitude by 0.25° latitude and the temporal coverage is a 51 year period from
1948 to 1998 with daily data. In order to successfully compare precipitation data between
sources, we convert the CPC data to have the same resolution as the CM2.1 model (2.5°
longitude by 2° latitude), and only consider the first 51 years of CM2.1 data.
We develop QQ plots (where sorted CM2.1 data is plotted on the y-axis of a graph and
sorted CPC precipitation on the x-axis) for this analysis. We perform this technique for nine
locations geographically scattered across the United States (each having an area of 2.5° longitude
by 2° latitude) and use both CM2.1 control and elevated CO2 precipitation data. We are limited
to only analyzing points within the continental United States because the CPC precipitation data
does not guarantee full data for points that are just outside of US borders.
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Changes in floods and droughts in an elevated CO2 climate:
We choose to use P-E for this analysis because it gives one variable that combines the
influence of precipitation and evaporation for 1 day. High values of P-E in 1 day data represent
flash flooding events for a particular location. With the use of Matlab 7.0, an elaborate
calculating and graphing tool, we combine the 1 day data into 2, 3, 7, 30, 60, 90, 180, and 360
day period lengths by taking rolling averages of the date ordered data with length corresponding
to the period length. High P-E values in the 2, 3, 7, and 30 day data represent flood events of the
corresponding period length. Similarly, low P-E values in the 30, 60, 90, 180, and 360 day data
represent drought events of the corresponding period length. We also look at annual, summer,
and winter data. Annual data is simply the entire data set, summer data is obtained by pulling
out data points congruent with the months May through September, and winter data is obtaining
by pulling out data points corresponding to November through March.
We attempt to see how extreme precipitation (floods and droughts) is changing between
the control and elevated CO2 climate over the region. We produce maps that show the change in
frequency in extreme precipitation events between the different climate types (hereafter referred
to as “percentile frequency change maps”). The primary method of doing this is to order the P-E
data from lowest to highest and discover the values of fundamental percentiles in the control
data. The percentiles we use are the 1st, 2nd, 5th, 95th, 98th, and 99th. We then calculate the
frequency of events that lie <1st, <2nd, <5th, >95th, >98th, and >99th percentiles for each point for
both the control and elevated CO2 data while keeping the percentile values constant (obtained
from the control data). Finally, we calculate the percent change in frequency for each percentile
range between the control and elevated CO2 data, and plot these numbers on a map. This
procedure is done for the higher percentiles in lower period lengths (1, 2, 3, 7, and 30 day), and
for the lower percentiles in higher period lengths (30, 60, 90, and 180 day). In the summer and
winter analysis, the 180 and 360 day period lengths are omitted.
We also look at how the absolute intensity of precipitation extremes changes between the
different climates in a similar fashion. After ordering the data from lowest to highest, we obtain
the values of the same percentiles as above for both the control and elevated CO2 data (but also
include the median in the annual analysis) and calculate the absolute change in the percentile
values between the control and elevated CO2 climate for each location. This analysis also shows
how the distribution of P-E changes. These numbers are then mapped.
Departing from a broad look at the map and zooming closely on specific regions, we pick
individual grid boxes that show distinct frequency change trends between the control and
elevated CO2 climate for annual, summer, and winter seasons. These trends include: increased
floods and increased droughts, increased floods and decreased droughts, decreased floods and
increased droughts, and decreased floods and decreased droughts. The locations themselves are:
Maryland, the Carolinas, northern Maine, eastern Colorado, southwest Ontario, central Texas,
south-central Ontario, northern Texas, southwest Michigan, central Utah, southern Alberta/
Saskatchewan, southern Quebec, and northern Mexico. We then develop histograms using bins
that are consistent with the unique control percentile values for each location. The percentile
values used are the minimum, 1st, 2nd, 5th through 95th in increments of 5, 98th, 99th, and the
maximum. By holding the bins constant and changing the data (i.e. from control to elevated CO2
or to scaled control), we produce different histograms for these different data sets. The
information obtained is similar to that in the “percentile frequency change” map analysis, but
uses more bins and covers all period lengths, making it more detailed.
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Analysis of spatial and temporal qualities of extreme precipitation events:
We are also interested in the characteristics of ubiquitousness and seasonality of extreme
precipitation events events. In doing this, we use our same CM2.1 P-E data set and select a
region including 9 grid points to discover the spatial extent and seasonal quality of the events.
The region chosen is centered over the middle of Pennsylvania and includes surrounding grid
boxes on all sides. The result is a contiguous 9 box square having an area of 7.5° longitude by 6°
latitude and covering approximately 81.75W to 75.25W and 38.5N to 44.5N. To perform the
analysis, we use P-E control and elevated CO2 data and determine the top 10 (10 wettest) P-E
events for each grid box. We then study the dates of these events and determine if there are
overlaps among the boxes.
To assess the ability to which the CM2.1 model portrays spatial and temporal qualities of
extreme precipitation events, we perform the same analysis using the CPC precipitation data
discussed earlier and compare that to the above analysis. For the CM2.1 model, P-E is very
similar to precipitation for the top 10 events so a direct comparison can be made. Still, a bit of
uncertainly arises because only 51 years of CPC data are available for the analysis, while 100
years are present in CM2.1. This difference in sample size may influence the results.
We also seek to understand this spatial distribution within a 2.5° by 2° box. We look at
historical daily precipitation data for the city of New Brunswick, New Jersey, and select the top
ten events between January 1979 and December 2006. We then compare the dates of these
events with National Centers for Environmental Prediction (NCEP)- DOE Reanalysis II data for
the same temporal coverage and a grid box with area 2° longitude by 2° latitude including New
Brunswick. In short, Reanalysis II is an updated version of Reanalysis I with minor errors fixed.
Reanalysis in general is a model reconstructing the past, outputting various atmospheric
parameters that are influenced by observations to varying extents, depending on the parameter.
For more information, see Kanamitsu et al. 2002 about Reanalysis II and Kistler et al. 2001
about Reanalysis I (both fully cited in references section). Use of reanalysis data for this
analysis provides a great deal of uncertainty which will be discussed later.
Comparing scaled control climate with elevated CO2 climate:
Another facet of our research is to attempt to explain why the observed changes in
precipitation distribution are happening as a result of the changing level of CO2. Our hypothesis
is that simply scaling control P-E data by a constant factor will change its distribution enough to
reasonably match that of the elevated CO2 atmosphere. We calculate scaling factors based on the
percent changes in saturation vapor pressure and obtain a different scaling factor for each
location put under the analysis. The locations used here are the same discussed in the “changes
in floods and droughts” section. More specifically about the calculation, we use the change in
annual mean temperature between the control and elevated CO2 environment to determine a
subsequent change in annual mean saturation vapor pressure. Data for temperature is taken from
the same GFDL CM2.1 model. The equation used to calculate saturation vapor pressure (es) is es
≈ 6.11 * e ^ {5.24E3 *[(1/273) – (1/T)]}. A scaling factor is then determined for each location
under the analysis by dividing the annual mean es from the elevated CO2 run by that of the
control run. Scaling factors range from 1.39 to 1.62 depending on the location. At this point in
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our research project, we begin to turn our attention to only precipitation data (rather than
precipitation minus evaporation) for reasons elaborated on in the discussion section.
To quantitatively test our hypothesis, we use the Kolmogorov-Smirnov (KS) statistical
test. This metric returns D- the absolute maximum distance between the cumulative distribution
functions of the control and elevated CO2 data (percentage of data to the left of a value vs. data)and the probability that the distributions are from the same population. Values of D closer to
zero and probabilities closer to 1 indicate close distribution agreement.
Departing from quantitative assessment of our hypothesis, we also qualitatively compare
agreement in distributions between scaled control and elevated CO2 data. To do this we produce
QQ-plots for each location where the elevated CO2 data is plotted against the control data, and
the line [y = (es scaling factor) * control data] is also plotted. We then eyeball the extent to
which the raw plotted data follows the es scaling factor line.
Results
Comparing the CM2.1 model output to estimated precipitation:
For this analysis we use QQ plots (as discussed above) comparing CM2.1 and CPC
precipitation data. Qualitatively, if the plotted data appears to follow a straight line (especially
the line y = x), it shows that the distributions of both data sets are similar, and that the CM2.1
model calculates precipitation successfully.
We find that for most locations, the image formed by the plotted raw data is essentially a
straight line that is reasonably close to the line y = x when using the CM2.1 control data. Of the
nine locations; Maryland, southwest Ontario, northern Maine, southwest Michigan, the
Carolinas, central Texas, and eastern Colorado have this characteristic. Northern New Mexico
and southern Minnesota are two particular locations that show less impressive correspondence
between the model and CPC data. Figure 1 (seen below) compares the QQ plot of a “good”
location such as the Carolinas, with a “poorer” location such as northern New Mexico.
Another thing to point out is a noticeable difference in pattern as to how the CM2.1
model control precipitation distribution differs from that of precipitation estimates by the CPC.
In one pattern, small precipitation events are overestimated by the model and large precipitation
events are underestimated. The locations following this pattern are Maryland, southwest
Michigan, the Carolinas, central Texas, and southern Minnesota, with southern Minnesota being
the most extreme. The other pattern shows that the model output overestimates precipitation for
all event sizes. These locations are northern Maine, eastern Colorado, southwestern Ontario, and
northern New Mexico. Looking at the same graphs in Figure 1, we can obtain a demonstration
of the difference in patterns, where the former pattern in the left and the latter on the right. It
appears that geographic region (northeast, southeast, northwest, etc.) within the United States
does not influence the pattern.
When comparing CM2.1 elevated CO2 precipitation with CPC precipitation, nearly all
locations show an overestimation of precipitation by the model, with the exception of central
Texas (predominantly shows underestimation), and southern Minnesota (follows the first pattern
described in the previous paragraph). For almost all locations, KS and KP statistical tests
indicate that agreement in distributions between the CPC and CM2.1 quadrupled CO2
precipitation is slightly worse than that between CPC and CM2.1 control precipitation. Visually,
this difference is very hard to discern on the QQ plots.
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Figure 1: Comparison of QQ plots showing model distribution correspondence with CPC
precipitation
Figure 1: Left: QQ plot comparing distributions of CM2.1 control precipitation with CPC precipitation for the
Carolinas. Right: Same but for northern New Mexico.
(Not all QQ plots are shown here but can be accessed via
http://envsci.rutgers.edu/~toine379/Model%20Analysis.html)
Overall, we conclude that the CM2.1 model, though has tendencies to depart from reality
in some locations for certain precipitation events, shows reasonable enough consistency with
Climate Prediction Center precipitation distributions to be used confidently in our research
project. In other words, it is expected that the CM2.1 model generates a distribution of
precipitation (or P-E) events that is realistic and can be trusted.
Changes in floods and droughts in an elevated CO2 climate
The “percentile frequency change” charts showing the change in extreme precipitation
(from CM2.1 data) between a control and elevated CO2 climate- at the annual level- show a
general increase in the frequency of floods across the much of the northern part of the United
States, including the northern Rockies and a majority of the east coast. Elsewhere, there is little
or no distinct trend (see Figure 2). Longer period lengths show a more intense frequency
increase and a spreading of positive values. Also, frequency increases are more intense and
widespread in bins that are narrower and closer to the maximum, such as the >98th and >99th
percentile bins, indicating a greater increase in more extreme floods.
Figure 2: Frequency changes in 1 day flood events in an elevated CO2 climate
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> 99 Percentile Frequency Change (From Control to 4x CO2) (1 Day, Annual)
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80
60
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Latitude Index (j)
40
10
20
8
0
-20
6
-40
4
-60
-80
2
5
mn= -18.3562
10
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Longitude Index (i)
20
25 % change
mx= 146.0274
The “percentile frequency change” charts for droughts at the annual level show an
increase in the frequencies of droughts across many parts of the northern and eastern sections of
the region, with no change or decreases near the central US/ Canadian border. This pattern more
or less holds for period lengths up to 90 days. For long period droughts of 360 days, parts of the
east show frequency decreases as well as parts of the northwest, which is mainly due to a
decrease in droughts in winter for these areas. Figure 3 contrasts short and long period droughts.
For all period lengths, the relative pattern of change in less extreme droughts holds as the
droughts become more extreme (<2nd and <1st percentile), although positive and negative values
become more intense. The southwestern parts of the region and some coastal points have been
omitted from this analysis. The reason for this is discussed later.
The “percentile frequency change” charts for winter and summer show some regions
with different patterns in the change of floods and droughts from the annual. The pattern of
increases in the frequency of floods is confined further north in the summer. In the winter, the
pattern of increases extends further south than the annual, and the southwest sees frequency
increases. The geographic pattern of droughts between the annual and summer data is very
similar, except for changes in magnitude. In winter, the north and east see frequency decreases
in droughts and in the summer these regions show frequency increases.
Figure 3: Contrast between projected changes in short and long period droughts.
< 1 Percentile Frequency Change (From Control to 4x CO2) (360 Day, Annual)
< 1 Percentile Frequency Change (From Control to 4x CO2) (90 Day, Annual)
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14
80
60
12
80
60
12
10
20
8
0
-20
6
40
Latitude Index (j)
Latitude Index (j)
40
10
20
8
0
-20
6
-40
4
-60
-80
2
5
mn= -62.6374
10
15
Longitude Index (i)
20
25 % change
mx= 603.5714
-40
4
-60
-80
2
5
mn= -100
10
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Longitude Index (i)
20
25 % change
mx= 2087.2576
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Figure 3: Left: Frequency change of <1st percentile 90 day droughts in elevated CO2 climate. Right: Same but for
360 day droughts.
The “absolute percentile change” maps generally agree with the trends shown in the
“percentile frequency change” maps for floods. Areas that see an increase in the frequency of
floods between the control and elevated CO2 climate also see an increase in the intensity of the
95th, 98th, and 99th percentiles. This is true for annual, summer, and winter seasons. For
droughts, the changes in the 1st, 2nd, and 5th percentile values are very little compared to changes
in the upper percentiles, therefore there is not much similarity between frequency changes and
“absolute percentile changes” for droughts. Another apparent trend with the “absolute percentile
change” maps is that the median changes very little if at all between the control and elevated
CO2 data for all period lengths in the annual analysis. Overall, it appears that lower and middle
percentiles change very little everywhere, and upper percentiles increase significantly in some
locations.
An analysis of the mean P-E change between the control and elevated CO2 climate for
annual, summer, and winter seasons shows that in many parts of the north and east, the direction
of change in the mean is consistent with that of the upper percentiles. However, the magnitude
of the mean change is much smaller than that of the upper percentiles. In few places, changes in
mean P-E aren’t as consistent in direction with changes in upper or lower percentiles. For
instance, in summer, the upper northeastern US shows decreases in the mean but increases in
upper percentiles, which is also true in winter in the southwest.
In the individual location analyses, the elevated CO2 histogram (with bins being the
control data percentile values and data being that of quadrupled CO2) generally show the same
pattern of frequency changes of flood and drought events as is indicated by the “percentile
frequency change” maps for all periods lengths. There are exceptions though, where some
locations show a different trend in droughts for period lengths that are shorter than is typical for
a drought event. For example, southwest Ontario, south-central Ontario, and southern Quebec
show an increase in the frequency of floods and a decrease in the frequency of droughts in an
elevated CO2 climate for annual, summer, and winter seasons respectively. In the full
histograms, all three Canadian locations show an increase in the frequencies of extreme
evaporation events for short period lengths. Similarly, in winter northern Mexico shows a
decrease in floods and an increase in droughts in an elevated CO2 climate, but shows a decrease
in extreme evaporation events (droughts) for short period lengths.
(Not all maps and figures are shown but can be accessed via http://envsci.rutgers.edu/~toine379/changesfd.html)
Analysis of spatial and temporal qualities of extreme precipitation events:
In the control P-E CM2.1 analysis (discussed in the “materials and methods” section),
we find that out of a possible 90 different dates that could be present in the top 10 P-E events for
9 contiguous grid boxes, there are actually 60 different dates. 40 of the 60 dates are unique to
only one grid box, while 20 out of 60 are shared among 2 or more grid boxes. The highest
frequency of one date occurring is 4. When this analysis is performed on elevated CO2 P-E data,
we find similar results: 62 different dates, 39 of 62 are individualized, 23 of 62 cover two or
more boxes, and the highest frequency of one date occurring is 3.
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The above results show us that many of the extreme events in which we are interested
exhibit a small enough scale that only one 2.5° by 2° region of land is affected. The fact that this
quality does not change significantly in the elevated CO2 analysis shows that the scale of these
events does not change in a warmer climate, and hints that the mechanism contributing to the
increase in frequency of these events is less influenced by synoptic scale atmospheric processes.
One might still wonder, however, if the CM2.1 model is accurately portraying the spatial quality
of extreme P-E events. In the results using the same grid boxes and methods from CPC
precipitation, we find that 70 different dates are present; where 13 of 70 are shared dates, 57 are
individualized, and the most locations sharing one particular date is 4. We conclude that CPC
precipitation (which is a reasonable representation of reality) shows a more mesoscale quality of
extreme precipitation events than the CM2.1 model. However, the CPC results are close enough
to that from the CM2.1 model that we can trust CM2.1 in spatial and temporal qualities of large
precipitation events.
In the New Brunswick region analysis we find that out of a possible 20 different events
between the New Brunswick historical and Reanalysis II (2° by 2° grid box) precipitation data
sets, there are actually 19 different events. Only one event, rain from Tropical Storm Floyd on
September 16th to 17th, 1999 is shared as a top 10 by the city and surrounding grid box. In one
light, these results show that even for a small 2° by 2° grid box, major precipitation could occur
on an even smaller scale and only affect a fraction of the box. The event of July 25th, 1997 is a
demonstration of this, shown by CPC precipitation in Figure 4 below. In another light, some
events appear widespread by CPC precipitation maps (such as September 20th, 1989- also shown
below) but are still deemed insignificant by Reanalysis II data, contributing to the large number
of “different dates”. The latter phenomenon results from the unreliability of reanalysis
precipitation and will be mentioned further in the discussion section. Our final conclusion from
this analysis is that some large precipitation events are widespread within the New Brunswick
grid box, and others only affect a fraction of the region.
Figure 4: Comparison of CPC precipitation between small-scale and widespread events
Figure 4: Left: CPC precipitation estimate in inches (close to reality) for 7/25/97. Right: Same but for 9/20/89.
New Brunswick grid box outlined in white.
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(Not all maps are shown but can be accessed via http://envsci.rutgers.edu/~toine379/newbrunsprec.html)
Finally, we take a look at the seasonality of the top 10 precipitation events for: the 9 grid
boxes in CM2.1 control and quadrupled CO2 P-E data, the same 9 grid boxes for the CPC
precipitation data, Reanalysis II precipitation data for the New Brunswick box, and the top 10
historical New Brunswick precipitation events. In both the CM2.1 control and risen CO2 P-E
analyses; August, September, and October are the most frequently appearing months in the top
10 events. This statistic holds when looking at total different dates, individual dates alone, and
widespread event dates alone. Although the sample of data used in the Reanalysis II and
historical New Brunswick precipitation sets is significantly smaller than that of CM2.1, we also
find a climax of frequencies occurring in September here. We attribute this seasonality to a
combination of warm ocean temperatures feeding precipitation events and tropical activity.
We come across disparate results when discovering the seasonality of the top 10 CPC
precipitation events for the 9 grid boxes used in the CM2.1 analysis. Here, the highest
frequencies occur in July, August, and September. This makes sense with our previously
discussed findings that CPC precipitation shows a more mesoscale quality of large events when
compared to CM2.1 P-E. One explanation is that smaller mesoscale events tend to occur in the
summer when large synoptic scale forcing for precipitation is shifted toward the poles. Looking
back to historical New Brunswick precipitation data, July and August also occur rather frequency
(just behind September). This further backs the contention that, in reality, large precipitation
events tend to occur in the mid to late summer. Applying these results to assess the ability of the
CM2.1 model to represent proper seasonality, the clash between CPC and CM2.1 is not severe
enough to make the CM2.1 seem substantially unrealistic.
Comparing scaled control climate with elevated CO2 climate:
In this part of our research project, we test the idea that scaling CM2.1 control data by
location unique saturation vapor pressure ratios (discussed in “materials and methods”) will
change its distribution enough to match that of the CM2.1 elevated CO2 data. We first use the
KS statistical test (also described in “materials and methods”) to quantitatively assess this. The
results for this analysis are simple and straightforward; no locations (out of the 13 tested) show
agreement in P-E distribution between scaled control and elevated CO2 environments. The
highest probability is 4.3043e-023 (D= 0.037781) in southwest Ontario, indicating that the scaled
control and elevated CO2 distributions are very different.
Since the KS statistic is dependent on cumulative distribution functions and thus is highly
sensitive to values near the median of these functions, we come to the conclusion that it might be
enlightening to qualitatively asses the ability of es scaling factors to explain changes in
hydrologic extremes. In other words, perhaps the KS statistical test is not the best metric for
testing our hypothesis. Here, precipitation data is used exclusively and we produce QQ plots
(discussed in “materials and methods”). From the results of these images, we gain an entirely
different viewpoint of the effectiveness of es scaling factors. We find that for many locations,
raw data follows the es line very closely. This implies that multiplying control precipitation by a
linear constant that is derived from a unique increase in saturation vapor pressure is enough to
reasonably explain distribution changes in an elevated CO2 climate for these locations.
Still, some locations exhibit poor qualitative agreement between raw data and their es
line; including eastern Colorado, central Texas, northern Texas, southern Alberta/ Saskatchewan,
southern Quebec, and northern Mexico. For all of these locations except northern Mexico, the
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reason for this is that precipitation does not increase as much in a quadrupled CO2 atmosphere as
would be anticipated by the es scaling factor. The pattern from these locations might be
explained by the fact that the CM2.1 model underestimates lower tropospheric moisture in these
regions, resulting in underestimated precipitation upon convective events. In northern Mexico,
perhaps the opposite is true.
Figure 5, seen below, contrasts a successful QQ plot (one where data follows the
respective es line) with an unsuccessful QQ plot. On each plot, the line with a smaller slope
represents the line y= x. If data follows this line (which it essentially does for eastern Colorado),
it indicates that there is virtually no change in distribution of precipitation between the control
and quadrupled CO2 environments. The line with the larger slope represents the es scaling factor.
Figure 5: “Good” versus “Bad” QQ plots with es lines
Figure 5: Left: QQ with es and global scaling factor line for southwest Michigan. Right: Same but for eastern
Colorado.
(Not all QQ plots are show here but can be accessed via http://envsci.rutgers.edu/~toine379/qqplotses.html )
Discussion
Shortcomings:
Reanalysis-
When we look at the seasonality and spatial extent of the top 10 precipitation events
within a 2° by 2° grid box surrounding New Brunswick, we observe that NCEP DOE Reanalysis
II severely underestimates precipitation when compared to CPC precipitation estimates for
certain regions and dates. For instance, a CPC precipitation map shows a widespread region of
0.5 to 4 inches of rain covering the entire 2° by 2° New Brunswick grid box on September 20,
1989 (see Figure 4). This same region reveals no more than 0.5 inches from September 19th and
20th combined in the Reanalysis II model. We look at both 9/19 and 9/20 because “precipitation
days” are defined 12 hours earlier for CPC precipitation (i.e. precipitation falling between 8AM
and 8PM September 19th would be recorded for September 20th in CPC data and for September
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19th in Reanalysis II), thus to “catch” all CPC precipitation we must assess two dates from
Reanalysis II.
Although the discrepancy in defining “precipitation days” certainly helps explain
apparent differences between Reanalysis II and CPC estimates for some events, it is not enough
to reconcile severe underestimation of precipitation by reanalysis for others (such as 9/20/87).
From a paper discussing Reanalysis I by Kistler et al., we discover that the precipitation
parameter contrived by Reanalysis I and II comes only from the model itself. The influence of
observations only affects precipitation (a “type C” variable) through variables which are partly
determined by observations (“type A” and “type B” variables). Because models in general are
not guaranteed to give accurate output, precipitation data produced by Reanalysis should be used
with caution. A comparison study between NCEP Reanalysis I and CMAP estimations of
precipitation between 1979 and 1995 reveals an annual mean underestimation of precipitation by
Reanalysis I over parts of the same Pennsylvania region used in our analysis. Although
Reanalysis II is not compared, the results of this study are in the same direction as the disparities
observed in our analysis. (Trenberth and Guillemot, 1998)
The use of evaporation instead of potential evaporation-
In our analysis studying changes in extreme hydrologic events in a warmer climate, we
are forced to omit the results obtained from the southwestern parts of the region. The results for
short term annual and summer droughts show a decrease in the frequency of extremely low P-E
events between the control and elevated CO2 climate, which is atypical of a dry and desert-like
area (Diffenbaugh et al. 2006). Because we look at evaporation rather than potential
evaporation, the amount of soil moisture present greatly affects the magnitude of surface
evaporation that contributes to P-E. The reason is that there exists a negative feedback between
the level of soil moisture and the amount of evaporation over the soil. As evaporation increases,
soil moisture decreases, and the amount of water available to evaporate decreases. Soon, a lack
of soil moisture leads to little or no evaporation and evaporation quantities contributing to P-E
approach zero. We believe this is happening in our data in the southwest United States, resulting
in an increase in P-E values in a warmer climate, and thus the impression that droughts are
decreasing in frequency and magnitude. An analysis of soil moisture changes between the
control and elevated CO2 climate collected from GFDL CM2.1 model data confirms that soil
moisture is indeed decreasing in the southwestern United States.
Comparison of flood/drought change results with previous research:
Diffenbaugh et al. 2006 find increases in the frequency of >95th percentile daily
precipitation across parts of the Pacific Northwest in an elevated CO2 climate. Though our
results do not agree on the areas of most intense increase, we also find increases in the same
measure in P-E across the same general region. In a more extended study of the entire United
States, Diffenbaugh et al. 2005 find increases in >95th percentile events across the east and
northwest, and statistically significant patterns in anomalies of mean and extreme precipitation to
be similar. Our results generally agree on both findings.
Leung et al. 2004 find increases in 95th percentile precipitation values across parts of the
northwestern United States. Our results show the same trend in P-E. Bell 2004 finds decreases
in the frequency of extreme precipitation events across much of the state of California. Our
results show decreases in the central and southern parts of the state, and little or no change
13
elsewhere, which is similar to this finding. Finally, Tebaldi et al. 2006 find increases in the
contribution of >95th percentile precipitation events to total precipitation for parts of the
northeastern and northwestern United States, and little or no change elsewhere. Our results show
increases in >95th percentile P-E events and 95th percentile P-E values in the same areas, and
small changes or decreases elsewhere, which also agrees with their findings.
Shifting attention to mean precipitation, Diffenbaugh et al. 2005 find increases in annual
mean precipitation across the eastern US, which is consistent with our findings in P-E. Leung et
al. 2004 find increases in mean daily summer precipitation and decreases in mean daily winter
precipitation across the western US. Our results converge better on the finding of decreases in
mean daily winter precipitation in this region. Finally, Bell 2004 finds little change in mean
daily annual precipitation across much of the state of California. Our results show overall
decreases in this measure in P-E over California, which is inconsistent with this study.
Comparison of hypothesis results with previous studies:
As discussed in the introduction, previous research has attributed both thermodynamic
and dynamic effects to changes in precipitation in a warmer climate. Thermodynamic effects
include changes in atmospheric moisture, and contribute to changes in precipitation when a
change in atmospheric moisture is carried to areas of mean moisture convergence (Meehl et al.
2005). Dynamic effects involve changes in atmospheric circulation. Emori and Brown 2005
find that in middle and high latitudes, both thermodynamic and dynamic effects contribute to
changes in mean and extreme precipitation. Furthermore, Meehl et al. 2005 find that advective
effects, indicated by sea level pressure changes, contribute to greatest precipitation intensity
increases over northeastern and northwestern North America.
Since our region covers mid-latitudes as well as the same regions in North America, our
finding that the saturation vapor pressure scaling of the hydrologic cycle qualitatively explains
distribution changes of precipitation to a satisfying extent, would contradict these studies. The
idea that the importance of changes in the hydrologic cycle alone is greater than that of
dynamical atmospheric changes (for the increase in extreme precipitation) is supported by our
findings. However, our analysis qualitatively assessing es scaling factors will need to be
expanded further if we are to gain more confidence in our hypothesis.
14
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