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In the last decade record-breaking rainfall events have occurred in many places around the world causing severe impacts to human society and the environment including agricultural losses and floodings. There is now medium confidence that human-induced greenhouse gases have contributed to changes in heavy precipitation events at the global scale. Here, we present the first analysis of record-breaking daily rainfall events using observational data. We show that over the last three decades the number of record-breaking events has significantly increased in the global mean. Globally, this increase has led to 12 % more record-breaking rainfall events over 1981–2010 compared to those expected in stationary time series. The number of record-breaking rainfall events peaked in 2010 with an estimated 26 % chance that a new rainfall record is due to long-term climate change. This increase in record-breaking rainfall is explained by a statistical model which accounts for the warming of air and associated increasing water holding capacity only. Our results suggest that whilst the number of rainfall record-breaking events can be related to natural multi-decadal variability over the period from 1901 to 1980, observed record-breaking rainfall events significantly increased afterwards consistent with rising temperatures.
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Increased record-breaking precipitation events
under global warming
Jascha Lehmann
1,2
&Dim Coumou
1
&Katja Frieler
1
Received: 26 September 2014 /Accepted: 17 May 2015
#Springer Science+Business Media Dordrecht 2015
Abstract In the last decade record-breaking rainfall events have occurred in many places
around the world causing severe impacts to human society and the environment including
agricultural losses and floodings. There is now medium confidence that human-induced
greenhouse gases have contributed to changes in heavy precipitation events at the global
scale. Here, we present the first analysis of record-breaking daily rainfall events using
observational data. We show that over the last three decades the number of record-breaking
events has significantly increased in the global mean. Globally, this increase has led to 12 %
more record-breaking rainfall events over 19812010 compared to those expected in stationary
time series. The number of record-breaking rainfall events peaked in 2010 with an estimated
26 % chance that a new rainfall record is due to long-term climate change. This increase in
record-breaking rainfall is explained by a statistical model which accounts for the warming of
air and associated increasing water holding capacity only. Our results suggest that whilst the
number of rainfall record-breaking events can be related to natural multi-decadal variability
over the period from 1901 to 1980, observed record-breaking rainfall events significantly
increased afterwards consistent with rising temperatures.
1 Introduction
The last decade has produced a large number of extreme weather events worldwide, including
record-breaking rainfall events (Coumou and Rahmstorf 2012). The year 2010 has so far been
the wettest year on record over land in terms of total precipitation (NOAA National Climatic
Data Center 2010), setting new record-breaking rainfall events on different time scales over
Climatic Change
DOI 10.1007/s10584-015-1434-y
Electronic supplementary material The online version of this article (doi:10.1007/s10584-015-1434-y)
contains supplementary material, which is available to authorized users.
*Jascha Lehmann
jascha.lehmann@pik-potsdam.de
1
Potsdam Institute for Climate Impact Research, Telegrafenberg A26, 14473 Potsdam, Germany
2
University of Potsdam, Potsdam, Germany
many parts of the world (Trenberth 2012). This seeming accumulation of weather extremes in
an exceptionally warm decade has raised the question of whether these events are related to
climatic change.
A number of studies have addressed this issue using observational data sets and climate
models (Pall et al. 2007; Zhang et al. 2007,2013; Min et al. 2011;Shiuetal.2012;Benestad
2013;Bergetal.2013;SinghandOGorman 2014). The main finding is that over the recent
past trends towards stronger precipitation extremes can be found over a larger fraction of the
land area than trends towards weaker precipitation extremes (e.g. see references in Seneviratne
et al. 2012). For example, Westra et al. (2013) found a significant increase in annual maximum
daily precipitation extremes on a global scale.
Climate models suggest that the thermodynamic change in saturation vapor pressure as
described by the Clausius-Clapeyron relation is a good predictor for changes in extreme
rainfall intensities (Pall et al. 2007). This relationship predicts an increase in water vapor of
typically 7 % per degree of warming assuming constant relative humidity. However, dynam-
ical changes can also influence the frequency and intensity of precipitation and thus disrupt the
Clausius-Clapeyron expected change (Trenberth 2011). For example, changes in extratropical
storm tracks will affect rainfall in mid-latitude regions (Scaife et al. 2011;Hawcroftetal.
2012). The response of convective precipitation to warming can exceed the Clausius-
Clapeyron rate (Berg et al. 2013) which will have the strongest effects in the tropics.
Most studies used extreme value theory to analyze changes in threshold events, i.e. those
exceeding a specified threshold of the climatological precipitation distribution (e.g., Kharin et al.
2007; Trenberth et al. 2007;Minetal.2011;Westraetal.2013). This usually involves fitting an
extreme value distribution to the tail of the observed distribution. However, small sample sizes in
the tail result in unstable fits which can have strong affects on the results (Frei and Schär 2001).
Here, we present the first global analysis of observed record-breaking daily precipitation
events between 1901 and 2010 and how their frequency differs from that expected in a
stationary climate. Analyzing record-breaking events has the advantage that no assumption
on the underlying probability distribution function has to be made. This also implies that with
this method no statements can be made about the exact changes in the underlying probability
distribution in terms of shifts in the mean or higher order moments. However, here, we are
interested in whether record-breaking rainfall events have increased or not, irrespective of the
exact underlying changes in probability distribution. Thereby, the number of observed record-
breaking rainfall events can be compared to the number expected in a climate with no long-
term trend. This approach has been proven insightful for understanding the increase of heat
extremes in a warming world (Benestad 2003,2004; Redner and Petersen 2005; Meehl et al.
2009; Anderson and Kostinski 2011;Coumouetal.2013).
2Dataandmethod
2.1 Data
We use monthly maximum 1-day precipitation data (Rx1day) from HadEX2 (Donat et al.
2013b), a 3.7 2.5° gridded data set covering 19012010 (Fig. S1 in Supplementary Infor-
mation (SI)). Globally aggregated quantities over land are dominated by the northern
extratropics which account for roughly 2/3 of the total available data (Fig. 1,Fig.S2b). Spatial
coverage varies over time with best coverage between 1960 and 2000 (Fig. S2a).
We analyze record-breaking events in each monthly Rx1day time series and subsequently
take annual (12 calendar months) and boreal winter (Nov-Dec-Jan-Feb-Mar (NDJFM)) and
summer (May-Jun-Jul-Aug-Sep (MJJAS)) averages. To ensure feasible statistics we restrict
analysis to (1) time series with at least 30 years of data and (2) regions and time periods which
had at least 100 non-missing values at each time slice. The maximum time period was found
for which these criteria hold.
To test the robustness of our findings, we applied the analysis also to a second data set, the
Global Historical Climatology Network (GHCNDEX) (Donat et al. 2013a), which has a spatial
resolution of 2.5× 2.5° and covers 19512014. The spatial coverage is similar to that for
HadEX2 (Fig. S3) and thus also the relative share of climatic zones to the global aggregate is
roughly the same (Fig. S4). We repeated the analysis with HadEX2 for the time period 1951
2010 to allow for a direct comparison of both data sets for the overlapping time period. In
general, results are very similar between both data sets and thus confirm the robustness of our
findings (see detailed description in SI, Fig. S5).
2.2 Observed versus iid-expected record-breaking events
A rainfall value (in mm) is defined as record-breaking if it exceeds all previous values in the given
time series. Due to the sparseness of record-breaking events, it is difficult to make statements
about climate change for a particular location only. We therefore aggregate the number of record-
breaking events over seasons and regions as defined in Fig. 4(see also table S1 in SI).
We assume that time series in a stationary climate can be described by independent and
identically distributed (iid) values. For iid time series the number of expected record-breaking
events at time N is equal to
N
n¼1
1=n. We normalize the number of observed record-breaking
events with the analytical solution by defining the record-breaking anomaly:
Ranom ¼
RobsR
1.n
R
1.n
100 %ðÞ;ð1Þ
30 110
number of precipitation values
N. extratropics
Tro p i c s
S. Subtropics
N. Subtropics
Fig. 1 Time series length of monthly maximum 1-day precipitation data covered at each grid point with a value
of 110 indicating full coverage over 19012010. The magnitude is represented by the color and size of the dots
where R
obs
is the sum of all observed record-breaking events in time series within a given
region for a given time period and all calendar months of the given season. Analogously, R
1/n
is the analytically expected number of record-breaking events summed over the same region
and time span. Thereby, missing values in the original time series are also accounted for in the
calculation of R
1/n
, respectively (see Fig. S6 for a schematic illustration). The null-hypothesis
of a stationary climate thus accounts for the same spatial and temporal inhomogeneity as in the
observations and hence for vacancies and the systematic increase of available data over time.
An increase in observing sites will thus increase R
obs
but also R
1/n
in the same way and
therefore this will not lead to a bias in R
anom
. By definition, the first value of each time series is
always a record-breaking event and hence equal to the iid-expected value. To avoid this
artificial start value of R
anom
=0 we start counting record-breaking events at the second time
step.
To test whether the observed number of record-breaking events is significantly different
from those in a stationary climate we create a distribution of simulated record-breaking events
derived from iid time series. The iid assumption for the stationary model is justified because
the detrended observational time series are close to iid (see S1: Testing iid assumptionin SI).
The original month specific observational time series are shuffled (in the process of which any
trend, change in variance, and autocorrelation is removed) to create a set of iid time series
which are based on the original observational data.
This method has the advantage that no assumption on the parametric form of the underlying
distribution is made. The spatial and temporal inhomogeneity of the observations had to be
treated carefully. The number of observing sites systematically increased over time with some
regions only providing observations after a certain time (Fig. 1and Fig. S2). A limitation of
our method is that for a set of time series one can either account for these inhomo-
geneities in the data availability over time (by Bfreezing^missing values during re-
sampling) or for spatial correlation (by synchronous re-sampling) but not both at the
same time. To overcome this, our method accounts for spatial correlation within
regions and for data inhomogeneities between regions. The latter tends to be small
within regions but can be large between regions (Fig. S8S10), i.e. between for example Europe
and Central Africa. The method thus accounts for these large-scale data inhomogeneities as
shown by a similar increasing trend in data coverage in the shuffled data set as compared to
observations (Fig. S8S10). Spatial correlation on the other hand can be pronounced for nearby
grid points and hence primarily within regions. Our method is a balance to account for both
effects when estimating confidence intervals, and it can be considered a conservative estimate
(see sensitivity analysis later).
Time series within one region are shuffled in time using exactly the same re-sampling order
such that the existing spatial correlation is maintained. Also to calculate seasonal aggregates
the same re-sampling order is used for each monthly time series of the given season to account
for possible correlations from 1 month to the next. The shuffling is repeated 10.000 times to
create a distribution of record-breaking events under the Null hypothesis of the iid-model. The
mean of this distribution follows the analytically expected number of record-breaking events in
iid time series
N
n¼1
1=n

and thus normalizing according to Eq. (1) provides a distribution
centered around 0.
We define the observed record-breaking anomaly to be statistically significant if it is outside
the 95 % confidence range of this distribution of record-breaking anomalies calculated based
on the shuffled time series.
To compute global mean statistics all grid points are separated into 21 smaller regions (see
table S1) to which the shuffling is applied. Subsequently, all regional results are combined to
come up with a global aggregate. Thus, the shuffling method accounts for changes in data
coverage between regions which results in a suitable representation of temporal changes in
global data coverage (Fig. S810).
Other studies which analyzed precipitation extremes have used a similar approach
but shuffled fixed-sized blocks of 23 years to account for autocorrelation (Kiktev
et al. 2003,2007; Alexander et al. 2006). The block size is derived from the variance
inflation factor (V) which depends on the autocorrelation at all time lags and can thus
be interpreted as a measure for the Btime between effectively independent samples^
(Wilks 1997). Similar to Westra et al. (2013), we find that due to little autocorrelation
in our time series (we find a mean Vof ~1.1) a block size greater than one is not
necessary. We tested the sensitivity of our confidence intervals to different block sizes
and found essentially no changes in confidence intervals (see Fig. S1112). Further,
we find that the confidence intervals for regional analyses decrease if we account for
data inhomogeneity rather than spatial correlation within regions (compare Fig. 2and
Fig. S7). Thus, our confidence intervals can be considered as conservative and robust
estimates.
Long-term non-linear trends in record-breaking anomaly time series are computed
using singular spectrum analysis (ssa) with a window length of 15 years. This method
uses eigenvalue decomposition to separate non-linear trends from white noise which
gives similar results as a 30-year moving average (Allen 1997; Golyandina et al.
2001).
2.3 Statistical Clausius-Clapeyron model
We further compare the number of observed record-breaking events to the expected number of
record-breaking events assuming that the intensity of maximum daily precipitation increases
with saturation vapor pressure according to the Clausius-Clapeyron equation. The Clausius-
Clapeyron model consists of ensembles of precipitation time series which are composed of (1)
a thermally induced long-term non-linear trend (pr
therm
trend
) where precipitation changes are
deterministically based on temperature using the Clausius-Clapeyron equation with (2) added
stochastic year-to-year variability (Δpr) based upon the non-linearly detrended original
precipitation time series:
pr ¼prtrend
therm þΔpr:ð2Þ
The thermally induced non-linear trend is calculated for each grid point and each month
using
prtrend
therm ¼prδprtherm;ð3Þ
where pr is the climatological mean of Rx1day time series over the full time period and
δprtherm ¼
esTtrend

esT

esT
 100 ð4Þ
is a time series with changes in precipitation due to the changes in temperature, which arise
from the difference between the temperature averaged over the full time period T

and the
non-linear trend in temperature (T
trend
). The change in precipitation is estimated by an
approximation of the Clausius-Clapeyron equation
esTðÞ¼6:1094exp 17:625 T
Tþ243:04

:ð5Þ
This statistical model thus assumes that extreme precipitation changes with temperature
according to the potential of the atmosphere to hold more moisture at higher temperature. We
used the CRU TS3.21 monthly temperature data (Harris et al. 2014) taken from the Climate
Research Unit (CRU), which provides absolute surface temperatures on a 0.5×0.5° grid. The
record-breaking anomaly for the Clausius-Clapeyron is normalized using Eq. (1) and its
confidence intervals are determined using the same shuffling method as for the iid model.
0 2040608002040608040 20 0 20 40 6050 0 50 10050 0 50 100
1900 1920 1940 1960 1980 2000
0 50 100 150 200
100 200
150
0
50 0 50 100
50 0 50 100 15050 0 50 100 150
1900 1920 1940 1960 1980 2000
20 0 20 40 60200 20406050 0 5050 0 50 100
50 0 50 100
1900 1920 1940 1960 1980 2000
annual NDJFM MJJAS
grid points: 1107
grid points: 1107
grid points: 974
grid points: 766
grid points: 766
grid points: 596
grid points: 243
grid points: 243
grid points: 187
grid points: 138
grid points: 87 grid points: 138
grid points: 124
grid points: 83 grid points: 124
(a) Global
(b) Northern extratropics
(c) Northern subtropics
(d) Tropics
(e) Southern subtropics
(f) Global
(g) Northern extratropics
(h) Northern subtropics
(i) Tropics
(j) Southern subtropics
(k) Global
(l) Northern extratropics
(m) Northern subtropics
(n) Tropics
(o) Southern subtropics
Fig. 2 Annual record-breaking anomaly (grey bars) shown (a)globallyandfor(b) northern extratropics, (c)
northern subtropics, (d) tropics, and (e) southern subtropics. The long-term non-linear trend in record-breaking
anomaly (black line) is calculated using singular spectrum analysis with window length of 15 years. The shaded
areas reflect the 90 % (light blue shading)and95%(dark blue shading) confidence interval for equally
computed long-term non-linear trends of the iid-model. The solid black line is thus directly comparable to the
shaded confidence intervals. (f)(j) and (k)(o)arethesameas(a)(e), respectively, but for seasonal record-
breaking anomalies representing NDJFM (middle panel)andMJJAS(right panel)
3 Record-breaking anomaly over time
3.1 Comparison between observed and iid-expected record-breaking events
On a global scale, the most prominent feature is a strong and consistent increase in
the annual record-breaking anomaly since the 1980s as indicated by the long-term
non-linear trend shown in Fig. 2a (black line). The record-breaking anomaly peaks in
2010, which saw +88 % more record-breaking events (grey bars) than expected by the
iid case. The long-term non-linear trend of the global record-breaking anomaly
significantly increases from 1980 onward reaching +26 % in 2010. A significant
increase in the long-term non-linear trend between 1980 and 2010 is also seen over
the northern extratropics (+31 % in 2010) and in the tropics (+31 % in 2010). The
northern subtropics have also seen an upward trend but it is not statistically signif-
icant (+13 % in 2010).
The long-term non-linear trend of the record-breaking anomaly shows multi-decadal
variability which is most pronounced in the northern extratropics but also seen
globally. Over the first 80 years the observed non-linear trend varies within the
95 % confidence interval of the iid-model with the only exception of a short negative
excursion around 1930 in the northern subtropics and the tropics. This coincides with
a relatively warm period between 1920 and 1940 over the Northern Hemisphere
(Rogers 1985). Note that the northern subtropics and the tropics were only coarsely
sampled in the 1930s (Fig. 2a) so the negative excursion is likely a local phenomenon
only. Due to the applied data requirements the sparse data coverage over the tropics
only allows to compute the record-breaking anomaly for the 19011940 period if all
calendar months are included but not for individual seasons.
During NDJFM, the evolution of the record-breaking anomaly is very similar to
that for the annual results (Fig. 2fj). However, during boreal winter, the year-to-year
variability in the number of record-breaking events in the northern extratropics and
subtropics is generally larger compared to annual results. In addition, the increase in
record-breaking anomaly over the Northern Hemisphere towards the end of the time
series is stronger in NDJFM than in the annual mean. Specifically, the global record-
breaking anomaly peaks in the 2010/11 winter with a value of +230 % (Fig. 2f). Also
the observed long-term non-linear trend of the record-breaking anomaly during
NDJFM is large reaching +30 % (globally), +37 % (northern extratropics), and +
18 % (northern subtropics) by 2010. In the tropics and southern subtropics the non-
linear trend in record-breaking anomaly is similar in both seasons and in annual
analysis.
During MJJAS (Fig. 2ko), the year-to-year variability and the long-term non-linear trend
of the record-breaking anomaly are similar to that of the full year (Fig. 2k and l). Nevertheless,
whereas globally and over the northern extratropics the time series of the annual and boreal
winter record-breaking anomaly show generally positive values between 1950 and 1980, the
record-breaking anomaly during MJJAS remains negative. Over the northern subtropics this
pattern inverts with negative anomalies in NDJFM and positive anomalies in MJJAS during
19501980.
Thus, globally and over the northern extratropics the increase in the long-term non-linear
trend of record-breaking events towards the end of the 20th century is significant at the 5 %
confidence level in both summer and winter season (Fig. 2f, g, k and l).
3.2 Comparing trends in observed, iid-expected, and thermal induced
record-breaking anomalies
The Clausius-Clapeyron model predicts an increase in annual record-breaking rainfall anomaly
starting around 1970 for the northern extratropics, northern subtropics, and globally but little
change in the tropics and southern subtropics (see Fig. 3ae). The model is able to capture the
statistically significant increase of the observed long-term non-linear trend since the 1980s
detected globally and over the northern extratropics and subtropics.
In the tropics, the Clausius-Clapeyron model predicts no change in contrast to a consistent
(but not significant) increase in the observed record-breaking anomaly (Fig. 3d, i and n). In
time series with a linear trend, the number of record-breaking events scales with the ratio of the
magnitude of the linear trend to the short term variability (Rahmstorf and Coumou 2011). In
the warm tropics, the absolute increase in thermal induced precipitation per degree of warming
as described by Eq. (5) is larger compared to cooler regions. On the other hand, the short term
variability in tropical time series is about four times larger than in the extratropics leading to a
−20 0 20 40
(a) Global
−20 0 20 40 60 80
(b) Northern extratropics
−20 0 20 40
(c) Northern subtropics
−40 0 20 40 60
(d) Tropics
−40 0 20 40 60
(e) Southern subtropics
1900 1920 1940 1960 1980 2000
−20 0 20 40
(f) Global
−20 0 20 40 60 80
(g) Northern extratropics
−20 0 20 40
−20
−20
−20
(h) Northern subtropics
−40 0 20 40 60
(i) Tropics
−40 0 20 40 60
(j) Southern subtropics
1900 1920 1940 1960 1980 2000
20 0 20 40
(k) Global
−20 0 20 40 60 80
(l) Northern extratropics
−20 0 20 40
(m) Northern subtropics
−40 0 20 40 60
(n) Tropics
−40 0 20 40 60
(o) Southern subtropics
1900 1920 1940 1960 1980 2000
SAJJMMFJDNlaunna
Fig. 3 Long-term non-linear trends in annual record-breaking anomaly (black line)shown(a) globally and for (b)
northern extratropics, (c) northern subtropics, (d) tropics, and (e) southern subtropics. The shaded areas reflect the
90 % (light shading)and95%(dark shading) confidence interval derived from long-term non-linear trends of the iid-
model (blue color) and Clausius-Clapeyron model (red color). (f)(j)and(k)(o)arethesameas(a)(e), respectively,
but for seasonal record-breaking anomalies representing NDJFM (middle panel)andMJJAS(right panel)
relatively small trend-to-variability ratio. As a consequence, the Clausius-Clapeyron model
projects an increase in record-breaking anomaly in the northern extratropics but only little
change in the tropics.
In the tropics we instead find indications for super Clausius-Clapeyron scaling, i.e. that
record-breaking rainfall increases at a higher rate than expected by the Clausius-Clapeyron
relation. Accordingly, the observed record-breaking anomaly shows an increase that reaches
the upper end of the 95 % confidence level of the Clausius-Clapeyron model. Over the
southern subtropics the Clausius-Clapeyron model shows little trend consistent with the
observations.
The Clausius-Clapeyron model predicts larger trends for boreal winter (Fig. 3f, g and h)
compared to boreal summer (Fig. 3k, l and m). This is explained by the trend-to-variability
ratio and follows the same argumentation as given for the comparison between the northern
extratropics and the tropics: The larger thermal induced trend in boreal summer is counter
acted by the up-to three times larger year-to-year variability in its Rx1day time series.
4 Regional analysis of recent past (19812010)
The most distinct feature of the global record-breaking anomaly is a robust increase over the
last 30 years. However, we showed that this trend is differently expressed across the latitudes.
We therefore analyze the time period 19812010 in more detail on a smaller regional scale
using a spatial division of the land area similar to that in Field et al. (2012)(TableS1 in SI).
Time-averaged record-breaking anomalies between 1981 and 2010 show distinct regional
patterns. While the record-breaking anomaly is positive globally and over all latitudinal belts
(see bottom panels of Fig. 4), it is more diverse regionally with values ranging from 27 %
(Mediterranean) to +56 % (South East Asia). The box panels in the map of Fig. 4show the
regional mean record-breaking anomaly (+ symbol) with confidence intervals of the iid-model
(blue bars) and Clausius-Clapeyron model (red bars). On a global scale, the mean record-
breaking anomaly has significantly increased to +12 % more rainfall extremes compared to iid-
expected in 19802010. Significant increases are also found over the northern extra-/subtrop-
ics and tropics (see bottom panels of Fig. 4). The magnitude of the increase is largest for the
tropics (+18 %) and moderate for the northern extra-/subtropics (+12, +9 %). Consistent with
this, most subcontinental regions also show an increased record-breaking anomaly over the last
30 years. Significant increases can be found over Central North America (+24 %), Europe (+
31 %), Northern Asia (+21 %), the Tibetan Plateau (+31 %), and South East Asia (+56 %).
Some regions show exceptionally high increases in record-breaking anomalies (e.g. South-East
Asia) which reach the upper 95 % confidence limits of the Clausius-Clapeyron model.
Conversely, also significant negative record-breaking anomalies are found, notably in the
Mediterranean region (27 %) and Western North America (21 %).
The Clausius-Clapeyron model projects an increase in record-breaking anomaly compared
to the iid-model for all regions as all regions have warmed. Thus, significant decreases as
found in Western North America and the Mediterranean region cannot be explained by this
model. However, for all regions with significant increases in record-breaking anomaly the
Clausius-Clapeyron model is able to capture this increase. Regional record-breaking anomalies
for 19802010 during NDJFM are similar to those for the full year. The largest exception
exists for Southern Africa, which exhibits a record-breaking anomaly of +30 % during
NDJFM compared to 7 % for the full year. In addition, the decrease in record-breaking
anomaly over the Mediterranean region is more pronounced during NDJFM (36 %) than for
the full year (27 %). South East Asia experienced a higher record-breaking anomaly during
NDJFM (+80 %) than during the full year (+56 %). Some regions in the tropics do not provide
results for the seasonal record-breaking anomaly due to lack of data (Fig. 5a and b).
Results for MJJAS are qualitatively similar. Notable differences only exist for Southern
Asia, Australia, and the Mediterranean region. Over Southern Asia, the increase in record-
breaking anomaly is significant and about three times larger in MJJAS (+45 %) compared to
NDJFM (+15 %, not significant). Over Australia the decrease in record-breaking anomaly is
much larger during winter (MJJAS, 24 %) than during summer (NDJFM, 2 %). Similarly, in
the Mediterranean the decrease in record-breaking anomaly is only significant during winter
(NDJFM, 36 %), but not during summer (MJJAS, 9 %). On a global scale, the 19812010
record-breaking anomaly increases significantly in both seasons, i.e. +15 % during NDJFM
and +11 % during MJJAS.
5 Relation with ENSO
One major contributor to natural variability in precipitation patterns is the interplay between El
Niño and La Niña events (Hurrell 1995; Dai and Wigley 2000; Trenberth et al. 2003). In the
Southern Hemisphere, a clear spatial pattern of the correlation between the year-to-year
variability of the record-breaking anomaly and ENSO (represented by the nino3.4 index) is
observed (Fig. 6). Over South East Asia and Australia we see a significant anti-correlation
during all seasons implying that these regions experience significantly more record-breaking
*Land only
LegendLegend
-40 -20 0 20 40
Record-breaking anomaly (%)
-60 60
40
20
0
20
40
Alaska/N.W. Canada
40
20
0
20
40
Australia
40
20
0
20
40
Central America/Mexico
40
20
0
20
40
E. Canada/Greenl./Icel.
40
20
0
20
40
C. North America
40
20
0
20
40
W./C. Asia
40
20
0
20
40
E. Asia
40
20
0
20
40
E. North America
40
20
0
20
40
Europe
20
0
20
40
Globe*
40
20
0
20
40
Mediterranean
40
20
0
20
40
N. Asia
20
0
20
40
N. Extratropics*
20
0
20
40
N. Subtropics*
40
20
0
20
40
S. Africa
40
20
0
20
40
S. Asia
40
20
0
20
40
S.E. Asia
20
0
20
40
S. Subtropics*
40
20
0
20
40
Tibetan Plateau
20
0
20
40
Tropics*
40
20
0
20
40
W. North America
Fig. 4 Annual observed record-breaking anomaly between 1981 and 2010. The magnitude is indicated by
different colors at grid cells which contributed to the regional record-breaking anomaly. For each region a
separate diagram is shown which includes the observed record-breaking anomaly (+ symbol) and the 90 and
95 % confidence interval estimates from the iid-model (blue bars) and the Clausius-Clapeyron model (re d bars).
Lower panels show the same results for the global mean and the four latitudinal belts (same regions as in Figs. 2
and 3)
rainfall during La Niña events than during El Niño. The opposite pattern can be seen over
Central and South America. Here, El Niño results in more record-breaking rainfall compared
to La Niña events.
In the Northern Hemisphere a separation can be seen over North America with the western
areas (Central and Western North America and Alaska) exhibiting more record-breaking
rainfall during El Niño events and the eastern regions (East North America, East Canada,
and Greenland) experiencing more record-breaking events during La Niña. However,
(a) Record-breaking anomaly during NDJFM
(b) Record-breaking anomaly during MJJAS
Fig. 5 Same as in Fig. 4, but for record-breaking anomalies during (a) NDJFM and (b)MJJAS
regressions are not significant in most cases. Significant results are found over central west
Asia and over the Tibetan Plateau, both of which show a positive correlation between ENSO
and record-breaking anomaly.
Over the latitudinal belts, correlations between record-breaking anomaly and ESNO are less
obvious (bottom box in Fig. 6). This is due to the fact that regressions with opposite signs in
different regions can cancel each other out, as e.g. in the southern subtropics. Nevertheless,
rainfall in the Northern Hemisphere (and especially in the subtropics) tends to be intensified
during El Niño years leading to a surplus of record-breaking rainfall events.
6 Summary and discussion
The number of record-breaking rainfall events between 1901 and 2010 is investigated using
observations from the HadEX2 data set. We find an increase of +12 % in the globally
aggregated number of record-breaking rainfall events compared to that expected in a stationary
climate over the time period 1981 to 2010. This implies that over the last 30 years, roughly one
in ten record-breaking events would not have occurred without climate change. The increase in
record-breaking anomaly peaks in 2010 with +88 % more record-breaking events than
expected in a climate with no long-term change. We show that the long-term increase in
record-breaking anomaly cannot be explained by (multi-decadal) natural variability alone but
that it is consistent with what would be expected from rising temperatures.
A large and consistent increase in the long-term trend of the record-breaking anomaly since
the 1980s is found over the northern extratropics (+37/+27 % in 2010), the tropics (+25/+30 %
in 2010), and partly over the northern subtropics (+19/+11 % in 2010), independent of the
season (NDJFM/MJJAS). Over the southern subtropics the long-term record-breaking anom-
aly shows no trend.
On a regional scale the mean record-breaking anomalies between 1981 and 2010 are more
diverse and in some cases in the opposite direction. For example, Australia experiences a
slope < 0 slope > 0
p-val < 0.05
0.05 < p-val < 0.50
0.50 < p-val
--
0
-- +
+
0
Global N. extratropics N. subtropics Tropics S. subtropics
Legend
Annual
NDJFM
MJJAS
+
0
0
+
0
+
+
0
--
0
+
0
+
+
+
+
+
+
+
0
--
--
--
+
--
+
--
--
--
--
0
0
--
0
0
--
0+
+
+
na na
na
na
0
+
++
+
--
+
+
+
+
0
+
+
+
-- --
0
+
+
+
--
0
0
0
+++
na
na
na
na
Fig. 6 Correlation between the year-to-year variability of the record-breaking anomaly and ENSO represented
by the nino3.4 index. For each region and season symbols indicate the sign and strength of the correlation with
the B+^symbol indicating more records during El Niño compared to La Niña years and vice versa for the B^
symbol (see legend for details)
decline in the number of record-breaking rainfall of 24 % (during winter season) at the same
time as the record-breaking anomaly over South East Asia increases by +80 % (during summer
season). Our results support previous findings (Seneviratne et al. 2012), but some new insights
are also obtained. For example, we find a significant decrease in the winter record-breaking
anomaly over the Mediterranean region. So far, there has only been low confidence about
changes in extreme precipitation in this region due to inconsistent trends within domains and
across studies (Kiktev et al. 2003; Caballero 2005; Alexander et al. 2006;Garcíaetal.2007;
Pavan et al. 2008). Confidence has also been low for the Asian continent in general except for
Western Asia where medium confidence exists for an increase in extreme precipitation
confirming our results (Kwarteng et al. 2009; Rahimzadeh et al. 2009). However, we also
find significant results for other Asian regions, i.e. significant increases in record-breaking
rainfall over Northern Asia, the Tibetan Plateau, India and South East Asia.
Further, we show that thermally driven moisture increase has significantly contributed to
the intensification of extreme rainfalls since the 1980s. In particular, the number of record-
breaking events in the last three decades is quantitatively consistent with those projected by the
Clausius-Clapeyron model. This model assumes that changes in extreme rainfall intensities
scale with temperature changes as given by the Clausius-Clapeyron equation, implying that the
maximum moisture in the atmosphere limits the intensity of rainfall extremes. Observations
and model results agree best over the northern mid-latitudes and northern subtropics. Con-
versely, over the tropics, the observed record-breaking anomaly is at the upper end of the 95 %
range for the Clausius-Clapeyron model, indicating that, here, a super Clausius-Clapeyron
scaling is required to explain the observed changes in record-breaking rainfall. This is not
unreasonable given that the composition of precipitation types is different between the tropics
and sub-/extratropics. Whilst in the tropics daily precipitation is largely convective it can be a
mixture of convective and stratiform in the sub-/extratropics. Stratiform precipitation extremes
increase with temperature at approximately the Clausius-Clapeyron rate whereas the intensity
of convective rainfall tends to be more sensitive to temperature changes and can thus exceed
the Clausius-Clapeyron rate (Berg et al. 2013). Our results indicate that thermodynamics are
able to explain much of the observed increase in the record-breaking rainfall which cannot be
related to natural climate variability. However, other factors such as changes in dynamics
which were not addressed in this study likely also play a role.
On a regional scale we find examples of decreasing record-breaking anomalies which
cannot be attributed to either natural climate variability or to changes in atmospheric moisture
content. One example is the negative Mediterranean record-breaking anomaly in winter
between 1981 and 2010 which is significant at the 5 % confidence level and also well outside
the confidence range of the Clausius-Clapeyron model. Hoerling et al. (2012) analyzed
Mediterranean rainfall in detail showing that the drying is likely related to changes in sea
surface temperature through external radiative forcing.
Results from the linear regression analysis suggest that over some regions the number of
record-breaking events is strongly influenced by the ENSO cycle. In particular, we find that
South East Asia and Australia exhibit significantly more record-breaking rainfall events during
La Niña years, whilst the correlation is reversed for South America, the Tibetan Plateau and the
adjacent region of Western Asia. This is in good agreement with results for global patterns of
ENSO-induced precipitation shown by Dai and Wigley (2000).
We argue that the multi-decadal variability of the record-breaking anomaly can partly be
explained by the multi-decadal variability in the ENSO cycle. In particular, the drop in the
global record-breaking anomaly between 1920 and 1950 as well as the increase during 1950
1980 coincide with years of strong La Niña and El Niño years, respectively (Fig. S14). This is
consistent with the positive correlation we find for the global record-breaking anomaly and
ENSO. However, over the last 30 years natural climate variability (and in particular ENSO)
cannot explain the large and consistent increase in record-breaking anomaly. Instead, over this
period, changes in temperature seem to have favored the increased number of record-breaking
precipitation events globally.
Acknowledgments We thank the Met Office Hadley Center, GHCN, and CRU for making their data available.
The work was supported by the German Federal Ministry for the Environment, Nature Conservation and Nuclear
Safety (11 II 093 Global A SIDS and LDCs), by the German research Foundation (CO994/2-1), and the German
Federal Ministry of Education and Research (01LN1304A).
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... [Insert Figure 6] To test whether the observed number of record-breaking events is significantly different from those in a stationary climate, we applied the procedure implemented in Lehmann, Coumou, and Frieler (2015) based on simulated record-breaking events derived from iid time series. ...
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Extreme rainfall events are increasing in both number and intensity at global scale; however, it is hard to quantify the impact of climate change at local scale given the strong temporal and spatial heterogeneity of this process. Moreover, limited data availability and its spatial variability requires significant effort to identify specific trends at the regional level. In this study, we attempt to construct a detailed description of rainfall patterns and trends over the Campania Region, southern Italy. For this reason, the dataset of rainfall annual maxima in pre-assigned durations was constructed using all available records and extended using interpolation methods such as Inverse Distance Weighting and Ordinary Kriging methods. The rainfall dataset allowed actual trends over the region to be quantified using the Mann-Kendall trend test and the record-breaking analysis. The trend test reveals that most of the rainfall stations display no statistically significant trend, however, an increasing trend of extreme rainfall for short durations, in specific portions of the region, is observed. This article is protected by copyright. All rights reserved.
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A disastrous cloudburst and associated floods in Kerala during the 2019 monsoon season raise the hypothesis that rainfall over the west coast of India, much of which is stratiform, may be trending towards being more convective. As a first exploration, we sought statistically significant differences in monthly ERA-5 reanalysis data for the monsoon season between two epochs, 1980–1999 and 2000–2019. Results suggest a more convective (deeper, ice-rich) cloud population in recent decades, with patterns illustrated in ERA-5 spatial maps. Deepening of convection, above and beyond its trend in amount, is also indicated by the steeper regression slope of outgoing longwave radiation trends against precipitation than that exhibited in interannual variability. Our reanalysis results are strengthened by related trends manifested in more direct observations from satellite and gauge-based rainfall and a CAPE index from balloon soundings data.
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[1] Attributing changes in extreme daily precipitation to global warming is difficult, even when based on global climate model simulations or statistical trend analyses. The question about trends in extreme precipitation and their causes has been elusive because of climate models' limited precision and the fact that extremes are both rare and occur at irregular intervals. Here a newly discovered empirical relationship between the wet-day mean and percentiles in 24 h precipitation amounts was used to show that trends in the wet-day 95th percentiles worldwide have been influenced by the global mean temperature, consistent with an accelerated hydrological cycle caused by a global warming. A multiple regression analysis was used as a basis for an attribution analysis by matching temporal variability in precipitation statistics with the global mean temperature.
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[1] This study provides estimates of the human contribution to the observed widespread intensification of precipitation extremes. We consider the annual maxima of daily (RX1day) and 5 day consecutive (RX5day) precipitation amounts over the Northern Hemisphere land area for 1951–2005 and compare observed changes with expected responses to external forcings as simulated by multiple coupled climate models participating in Coupled Model Intercomparison Project Phase 5. The effect of anthropogenic forcings can be detected in extreme precipitation observations, both individually and when simultaneously estimating anthropogenic and naturally forced changes. The effect of natural forcings is not detectable. We estimate that human influence has intensified annual maximum 1 day precipitation in sampled Northern Hemisphere locations by 3.3% [1.1% to 5.8%, >90% confidence interval] on average. This corresponds to an average intensification in RX1day of 5.2% [1.3%, 9.3%] per degree increase in observed global mean surface temperature consistent with the Clausius-Clapeyron relationship.
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Precipitation changes can affect society more directly than variations in most other meteorological observables, but precipitation is difficult to characterize because of fluctuations on nearly all temporal and spatial scales. In addition, the intensity of extreme precipitation rises markedly at higher temperature, faster than the rate of increase in the atmosphere's water-holding capacity, termed the Clausius-Clapeyron rate. Invigoration of convective precipitation (such as thunderstorms) has been favoured over a rise in stratiform precipitation (such as large-scale frontal precipitation) as a cause for this increase, but the relative contributions of these two types of precipitation have been difficult to disentangle. Here we combine large data sets from radar measurements and rain gauges over Germany with corresponding synoptic observations and temperature records, and separate convective and stratiform precipitation events by cloud observations. We find that for stratiform precipitation, extremes increase with temperature at approximately the Clausius-Clapeyron rate, without characteristic scales. In contrast, convective precipitation exhibits characteristic spatial and temporal scales, and its intensity in response to warming exceeds the Clausius-Clapeyron rate. We conclude that convective precipitation responds much more sensitively to temperature increases than stratiform precipitation, and increasingly dominates events of extreme precipitation.
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Cambridge Core - Environmental Policy, Economics and Law - Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation - edited by Christopher B. Field
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