Temperature and CAPE Dependence of Rainfall Extremes in the Eastern United States
(c) Slope coefficients of simple lnI-Td, lnI-lnCAPE and lnCAPE-Td regressions (first row), and for multiple regression (Eq. 2), and gamma (second row; see text for details).
Chiara Lepore (1), Daniele Veneziano (2), Annalisa Molini (3,4)
GC51A-0378
Friday, December 19, 2014
08:00 AM - 12:20 PM Moscone West Poster Hall
!
Results
Abstract
all year
lnIp
DJF+JJA
Sp
lnIp
Sp
all year
Td
DJF+JJA
lnIp
Td
lnIp
We analyze how extreme rainfall intensities in the Eastern United
4
4
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%
1
1
a)
"&!
"&!
CC slope
p
20
20
0.8
0.8
States depend on temperature T, dew point temperature Td and
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"&"'
2
2
#
#
0.6
0.6
10
10
"
"
0.4
0.4
convective available potential energy CAPE, in addition to a) S0.99
0
0
"
"
0.2
0.2
"&"'
"&"'
10
20
30
10
20
30
10 10 20 20 30 30
30
10 10 20 20 30 30
30
!"
#"
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4
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geographic sub-region, season, and averaging duration. When
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%
1
1
"&!
"&!
EUS
20
20
0.8
0.8
using data for the entire year, rainfall intensity has a quasi
2
2
"&"'
"&"'
#
#
0.6
0.6
10
10
North
"
"
0.4
0.4
Clausius-Clapeyron (CC) dependence on T, with super-CC slope in
0
0
"
"
0.2
0.2
"&"'
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10 10 20 20 30 30
10
20
30
10 10 20 20 30 30
10
20
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a limited temperature range and a maximum around 25. While
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%
T
T
T
T
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"&!
lnI
lnI |CAPE
lnCAPE
general, these features vary with averaging duration, season, the
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9
0.15
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4
4
b)
8
"
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4 0.1
quantile of rainfall intensity, and to some extent geographic sub"
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East
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!"
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20.05
Central
region. By using Td and CAPE as regressors, we separate the
p
%
%
6
0
0
"&!
"&!
5
0 0
effects of temperature on rainfall extremes via increased
10
20
30 0.15 75108590 209899 30
99.9 99.99
4
6
8
4
6
8
9
"&"'
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#
#
4
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8
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"
4 0.1
atmospheric water content and via enhanced atmospheric
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2
2
South
7
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20.05
convection. The two contributions have comparable magnitudes, a)
6
T
T
T
T
0
0
EUS
S0.99
0 0
5
Figure 3 Quantile (lnIp) and slope (Sp) plots for the 4 sub-regions for different probabilities levels,
pointing at the need to consider both T and atmospheric stability
4
6
8
4
6
8
10
20
30
75108590 20
9899 30
99.9 99.99
lnCAPE
lnCAPE
T
T
EUS
th p = 0.75, 0.9, 0.99, and 0.999. The dotted vertical lines mark the 9-22C range, the black-dashed
0.12
%&*'
+,-./ 99
parameters when assessing the impact of climate
change on b) Figure
Sp,[9-22] 1 Slope map for the lnI-T
Figure 5 (a) Td-T and lnI-Td quantile plots for all-year (first and second columns)
012.-34
0.1
%&*
percentile for the 270 CONUSNorth
lines (1st and 3rd column) and red dashed lines (2nd and 4th column) show the CC rate (0.068).
rainfall extremes.
536. NCDC
and DJF and JJA superposed (colored and black lines, respectively, third and fourth
NORTH
EAST
EAST
CENTRAL
30
30
CENTRAL
d
d
p
all year
p
SOUTH
EAST
DJF, JJA
CENTRAL
%
d %
d
%
!"#"$%
$#$$ $$&$$$&$$ !"#"$%
$#$$
National Climatic Data Center (NCDC)
Hourly precipitation time series - 270 station from the 6000 the
National Climatic Data Center (NCDC), with not long data gaps, few
missing values, and free of hourly estimates from disaggregated
daily totals. They have data since 1979 and overlap the ERA records.
182 are in the EUS region we study. [270 Colored%&*'dots in Figure 1]
%&*
ERA Interim - ECMWF
%&%#
Data span the period 1979-2008 and were bi-linearly
interpolated
%&%)
to a spatial resolution of 0.125 from the original %&%(
grid (ECMWF, 2013).
Daily time series of T and CAPE for the NCDC %&%'
stations were taken
%
from the closest interpolated reanalysis cell.
!"#"$% $#$$ $$&$$$&$$ !"#"$% $#$$
Mean daily air temperatures T - Data were extracted from the subdaily screen level (2 m) temperatures of ERA-Interim.
Mean daily convective available potential energy CAPE were derived
from the ERA 12-hour predicted fields.
NCDC Integrated Global Radiosonde Archive (IGRA)
The radiosonde stations are matched to the nearest NCDC gauge
station and grouped by sub-region in analogy with the other
variables. [49 black circles in Figure 1]
Mean daily Dewpoint Temperature Td - From the NCDC IGRA (Durre
et al., 2006) from bi-daily radio-soundings (00 and 12UTC) at the
m.s.l.
Bi-daily CAPE from soundings - For completeness, we consider
CAPE values from soundings (data and documentation available at
http://www.ncdc.noaa.gov/oa/climate/igra/).
Methods
Rainfall Intensity, I, and Temperature, T,
data are matched and binned into 2deg temperature intervals 0-2, 2-4,..oC.
We extracted precipitation quantiles for
several non-exceedance probabilities p,
resulting in a matrix of intensities Ip,T.
The dependence of rainfall intensity on
T is displayed through quantile plots,
which are plots of Ip,T against T for
different p.
Through regression of lnIp,T vs. T, we
infer, for each p, different measures of
sensitivity of Ip to T: the overall slope Sp
based on all temperature bins, the
average slope Sp,[T1-T2] within a selected
range of temperatures [T1-T2] , and the
local slope Sp,T.
12
7,8./
0 0
0.08
0.06
6
0.04
0.02
$#$$ $$&$$$&$$ !"#"$%
0
East
South
C C
0.08
8
0.06
$$&$$$&$$ !"#"$%
Central
0.1
10
75 8590
4
0.04
2
0.02
5
$#$$ $$&$$$&$$
98 99
p%
99.9 99.99
75 8590
98 99
Figure 2 Slope values for the lnIp-T
regressions, for various percentiles for
the 4 macro regions presented in Fig. 1.
These slopes are calculated for a
temperature range 9-22 C. In this range
we observe no inflection (see Fig. 3).
The overall regional slopes increase with
the percentile levels and they reach
values that are 150% CC-slope.
99.9 99.99
10
15
20
25
0T
758590
d=2 and988hr
75 8590
99 - Within
99.9 storm
99.99
lnIp
Sp
4
30
5
10
15
20
25
30
75 8590
10
T
20
30
15
20
25
30
T
T
9899 99.999.99 758590 9899 99.999.99
d=2
- block
98 99and 8hr
99.9
99.99averaging
5
758590
10
4
20
T
25
daily values
10
T
20
30
10
T
20
30
0.05
0
0
10
T
20
30
10
T
20
30
0.05
T
20
0.2
75 8590
758590
p 98 99
%
%
lnI
p -Td
9899 99.999.99
0.1
99.9
75 8590 p
98 99
%%
758590
9899
0.1
0.4
0.4
0.08
75
75
90
90
99
99
0.02
00
lnIp - lnCAPE
99.999.99 758590 9899 99.999.99
0.15
0.15
1 758590
75
75
75
p 98 99
!
2
99.9
%
%
1
North
Central
East
0
South
C C
1
7585
90 9899 99.9
99.99
%
γg
9899 99.999.99
0.1
0.1
0.5
0.05
0.05
0.06
0.2
0.2
0.04
0.05
0.05
90
90
90
99
99
99
000
75
7575
90
9090
99
9999
30
90
9090
p
99
9999
Figure 4 (top row) Variation of average storm duration D (hr) with temperature T for (i) all year,
(ii) DJF, (iii) JJA , (iv) SON (solid) and MAM (dashed); (bottom row) Quantile and slope plots for 2
averaging durations: 2hr (colored) and 8 hr (black) within storms (Columns 1 and 2), using block
averaging (Columns 3 and 4), and for daily average values (last two columns). The grey shading
reproduces Figure 3. The storms are identified using a fractal method (Veneziano and Lepore,
2012). Through this method, we reduce dry periods, especially inside longer storms and the
averaging duration doesn’t affect the shape of lnIp. When using block averaging the inclusion of
dry periods reduces the intensities values, which are much lower than those for 1hr (grey lines).
This is especially evident for d = 8 e daily averages.
0.1
0.4
0.4
0.05
0.05
00
10
0.3
lnCAPE - T
0.14
0.12
0.1
0.08
0.06
0.04
0.02
99.9
75 8590
0.3
10.3
0.05
0
0
0.4
0.1
0.1
0.1
2
lnIp - lnCAPE
0.5
0.1
0.1
00
Sp
4
0.05
0
30
lnIp
0.1
2
15
lnIp -Td
0.14
0.12
0.12
0.1
0.1
0.08
0.08
0.06
0.06
0.04
0.02
0.04
0.02
9899 99.999.99
Sp
0
0.05
10
lnIp
0.05
0
5
9899 99.999.99 758590
0.1
2
Simple
%&%'
Multiple
reanalysis
Data
%&%(
columns). (b) lnI-lnCAPE plots for all-year (first column) and DJF+JJA (second
column). The third column shows lnCAPE-Td quantiles and last column shows lnI-Td
relationships for different CAPE classes.
Td-T is a 1:1 relationship up to 22-25C. lnI-Td regressions result in a less peak-shaped
relationships compared to Figure 3, but some curvature persists. lnI-lnCAPE is a
very regular regression, with slope values all below 0.5. lnI-Td for different CAPE
classes results in a decreasing slope value as CAPE increases, suggesting a different
role of the potential energy for differentRegression
CAPEcoefficients
ranges.
Multiple
sounding
%&%)
7,8./
0.08(colored circles); the black
stations
circles The first two columns are for the all-year analysis. The last two columns are for winter (DJF,
0 0
are the0.06
location of the sounding stations.
colored lines) and summer (JJA, black lines). Results for the spring and fall seasons are close to
The EUS
the all-year results and are omitted.
0.04 values are mostly CC or slightly
higher 0.02
than a CC slope. The rest of the US
East Most of the super-CC slopes are within the 9-22 range, but even the maximum local slopes
$$&$$$&$$ !"#"$% $#$$ $$&$$$&$$ !"#"$% $#$$ $$&$$$&$$
Central with slopes mostly below
is very variable,
remain below 1.5CC except for isolated cases. The slopes for the whole year have a local minimum
0
8590
98 99
99.9 99.99
75 8590
98 99
99.9 99.99
75 8590
99.9 99.99
75 8590
98 99
99.9 99.99
oC. 98 99
CC rate. 75
The
rest
of
the
analysis
are
around
8-10
p%
focused on the EUS of the CONUS, The Sp plots for the winter (after the initial local minimum) and the summer have a “parabolic”
South
divided in 4 regions: North, Central, South, shape. The “zeros” of the parabola, i.e. at about T=10 and 25 oC in the North during summer,
EUS
East.
correspond to local minima and maxima of the lnIp plots. The parabolic shape of the Sp curves
corresponds to an “S” shape of the lnIp plots. The peak of the parabola tends to decrease as p
+,-./
b) Sp,[9-22] 0.12
012.-34
0.1
0.12
increases.
i)
ii)
iii)
North
iv)
E[D] 14
536.
NORTH
%&%#
0.2
0.2
0.5
0.1
0.1
0.05
0.2
0.2
75
75
90
90
99
99
00
75
75
p
90
90
p
%
%
99
99
0 00
75
7575
Figure 6 We considered the following equations:
lnIp = c +aTd + blnCAPE (1)
T=dlnCAPE (2)
g = a/(a+db) (3)!
The first row shows the slope coefficients of simple lnI-Td, lnI-lnCAPE and lnCAPE-T
regressions; in the second row we show the coefficients for multiple regression (Eq. 1),
and g (Eq. 3) The third row repeats the analysis using sounding data.
In the second row The values are highly consistent across regions and vary from
about 0.5 for p=0.75 (equal contribution from water vapor content and the intensity
of convection) to about 0.8 for p = 0.99 (predominance of water vapor variation).
When we consider sounding data, the main effect is a decrease in the lnIp-lnCAPE
slope and an increase of g towards 1, with a weaker dependence of rainfall on CAPE.
Conclusions
• We have evaluated the rainfall intensity-temperature (lnI-T) relation in the EUS using ground observations, soundings, and reanalysis products.
• Our focus is on rainfall intensity quantiles with p ≥ 0.75. Within the EUS, the variation in the lnI-T relationships is relative small, except in winter. The differences between the EUS and
the western part of the country are much sharper.
• We compared global and local slopes of the lnI-T relationships to the CC rate. The overall range of these slopes is in line with prior literature for the EUS (Shaw et al., 2011; Utsumi et
al., 2011). This includes the “peak-like structure” with maximum rainfall intensity at about 22oC. We rarely see super-CC rates. Such rates occur almost exclusively in an intermediate
range of temperatures and only for the highest quantiles and rarely exceed 150% of the CC value.
• We separated the two main pathways through which temperature affects precipitation (increased water vapor and enhanced atmospheric convection) by jointly regressing lnI
against Td and CAPE;
• Between rainfall intensity quantiles and CAPE, there is a power-law relationship with coefficients depending on region. These exponents are below the value 0.5 (from w ~ CAPE1/2 ),
hence the energy-conversion mechanism is less efficient when CAPE is higher.
• We investigated the peak-like structure of the lnI-T curves to determine whether the temperature T at the peak varies seasonally and regionally (it does) and whether the peak is
due to a limit in the amount of moisture in the atmosphere or to the fact that at higher temperatures a larger fraction of rainstorms have sub-hourly duration (the primary cause is
the limit on Td).
• To quantify extreme rainfall under possible future warmer climates it is important to assess the seasonal and annual limits of Td . It is also important to quantify how CAPE will change,
although CAPE is more influential on the lower quantiles of rainfall intensity.
• Understanding the root causes of the inefficiencies of the moisture-rainfall and potential-to-kinetic energy conversions and consideration of frontal versus convective precipitation
could help explain the regional and seasonal patterns of rainfall intensity with temperature;
• Differences were found when using CAPE values from soundings instead of ERA Interim estimates, suggesting the need to make a more detailed analysis of CAPE-related indices.
%
This material has been published:
C. Lepore, D. Veneziano, A. Molini (2014), Temperature and
CAPE Dependence of Rainfall Extremes in the Eastern United
States, GRL doi: 10.1002/2014GL062247
Funding:
C. Lepore acknowledges fundings from the DEES and LDEO at
Columbia University.
D. Veneziano and C. Lepore acknowledge NSF National Science
Foundation under Grant # EAR-0910721.
A. Molini acknowledges fundings from the Masdar Institute
(One-to-One MIT-MI, #12WAMA1 and Flagship #12WAMC1).
We are grateful to Dr. Tanvir Ahmed and Seonkyoo Yoon for the
processing and quality control of the NOAA data.
References
Durre, I., Vose, R.S., Wuertz, D.B. (2006), Overview of the Integrated Global
Radiosonde Archive, J. Clim., 19, 53– 68.
ECMWF (2013), IFS DOCUMENTATION, 40 ed., ECMWF, Shinfield Park,
Reading, RG2 9AX, England
Shaw, S. B., A. A. Royem and S. J. Riha, (2011), The Relationship between
Extreme Hourly Precipitation and Surface Temperature in Different
Hydroclimatic Regions of the United States. J. of Hydr., 12(2), 319–325.
Utsumi, N., Seto, S., Kanae, S., Maeda, E.E., and T. Oki (2011), Does higher
surface temperature intensify extreme precipitation?, Geophysical Research
Letters, doi:10.1029/2011GL048426.