Download Global changes in seasonal means and extremes of precipitation

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, D01108, doi:10.1029/2011JD016260, 2012
Global changes in seasonal means and extremes of precipitation
from daily climate model data
Simone Russo1,2 and Andreas Sterl3
Received 17 May 2011; revised 2 November 2011; accepted 5 November 2011; published 12 January 2012.
[1] We investigate simulated changes of seasonal precipitation maxima and means in a
future, warmer climate. We use data from the ESSENCE project, in which a 17-member
ensemble of climate change simulations in response to the SRES A1b scenario has been
carried out using the ECHAM5/MPI-OM climate model. The large size of the data set
gives the opportunity to detect the changes of climate extremes and means with high
statistical confidence. Daily precipitation data are used to calculate the seasonal
precipitation maximum and the seasonal mean. Modeled precipitation data appear
consistent with observation-based data from the Global Precipitation Climatology Project.
The data are split into six time periods of 25 years to get independent time series. The
seasonal peaks are modeled by using the generalized extreme value distribution, while
empirical distributions are used to study changes of the seasonal precipitation mean.
Finally, we use an empirical method to detect changes of occurrence of very wet and dry
periods. Results from these model simulations indicate that over most of the world
precipitation maxima will increase in the future. Seasonal means behave differently. In
many regions they are decreasing or not increasing. The occurrence of very wet periods is
strongly increasing during boreal winter in the extratropics and decreasing in the tropics. In
summary, wet regions become wetter and dry regions become drier.
Citation: Russo, S., and A. Sterl (2012), Global changes in seasonal means and extremes of precipitation from daily climate
model data, J. Geophys. Res., 117, D01108, doi:10.1029/2011JD016260.
1. Introduction
[2] Changes in extreme weather and climate events have
significant impacts and are among the most serious challenges to society in coping with a changing climate [U.S.
Climate Change Science Program, 2008]. Indeed, “confidence has increased that some extremes will become more
frequent, more widespread and/or more intense during the
21st century” [Intergovernmental Panel on Climate Change,
2007]. Of particular interest are changes in rainfall distribution, as water-related disasters, i.e., floods and droughts,
can cause devastating impacts on human lives and economic
damages.
[3] The intensity of precipitation extremes is projected to
increase under global warming in many parts of the world,
even in regions where mean precipitation decreases [e.g.,
Kharin and Zwiers, 2000, 2005; Semenov and Bengtsson,
2002; Voss et al., 2002; Wilby and Wigley, 2002; Wehner,
2004]. Simulations with global coupled ocean–atmosphere
general circulation models (CGCMs) forced with projected
greenhouse gas and aerosol emissions are the primary tools
1
Laboratoire de Physique des Oceans, UMR6523, IRD/IFREMER/
UBO, Université de Bretagne Occidentale-UFR, Brest, France.
2
Now at Institute for Environmental Protection and Research, Rome,
Italy.
3
Royal Netherlands Meteorological Institute, De Bilt, Netherlands.
Copyright 2012 by the American Geophysical Union.
0148-0227/12/2011JD016260
for studying possible future changes in climate mean, variability and extremes [Kharin et al., 2007]. Some results
on the ability of atmospheric general circulation models to
simulate temperature and precipitation extremes are documented by, e.g., Kharin and Zwiers [2005] and Kiktev et al.
[2003, 2007].
[4] Contrary to many recent studies that focus on changes
of yearly precipitation means and/or maxima [e.g., Kharin
and Zwiers, 2005, Kharin et al., 2007, Min et al., 2011],
we here zoom in on the seasonal scale. Using the daily
precipitation output of a 17-member model ensemble of the
ECHAM5/MPI-OM climate model run under the SRES A1b
emission scenario (for details see Sterl et al. [2008]) we
investigate for two selected seasons (DJF and JJA) changes
in the probability distributions of mean and maximum daily
precipitation. We demonstrate that the future behavior of
100 year return levels of seasonal precipitation maxima
differs significantly from that of the seasonal precipitation
means. We introduce a simple empirical definition for very
wet (dry) periods by considering joint events of high (low)
seasonal means and peaks. With this definition we show that
the occurrence of wet periods is increasing in wet areas and
decreasing in dry areas.
2. Data Set and Statistical Methods
2.1. The ESSENCE Data Set
[5] In the present study we use 24 h precipitation amounts
from the 17 runs of the Ensemble Simulations of Extreme
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Weather Events Under Nonlinear Climate Change (ESSENCE)
[Sterl et al., 2008] to detect changes in seasonal mean and
extremes. In the ESSENCE project a 17-member ensemble
of runs with a state-of-the-art climate model was generated
by perturbing the initial state of the atmosphere. Gaussian
noise with an amplitude of 0.1 K was added to the initial
temperature field. The initial ocean state was not perturbed.
The model used was the ECHAM5/MPI-OM coupled climate model developed at the Max Planck Institute for
Meteorology in Hamburg. It is based on version 5.2 of the
ECHAM model with a T63 spatial resolution, equivalent to a
1.875° resolution in latitude and longitude. The two component models, ECHAM5 for the atmosphere and MPI-OM
for the ocean, are well documented (ECHAM5 [Roeckner
et al., 2003] and MPI-OM [Marsland et al., 2003]), and a
special section of the Journal of Climate (19(16), 3769–
3987) devoted to the coupled model and its validation. Model
runs start in 1950 and end in 2100. For the historical part
(1950–2000) the concentrations of greenhouse gases (GHG)
and tropospheric sulfate aerosols are specified from observations, while for the future part (2001–2100) they follow
the SRES A1b scenario [Nakicenovic et al., 2000].
2.2. The GPCP Observations
[6] To evaluate our precipitation model output we use
daily Global Precipitation Climatology Project (GPCP) data
(http://www.ncdc.noaa.gov/oa/wmo/wdcamet-ncdc.html).
The GPCP One Degree Daily (1DD) Precipitation data set
provides daily, global 1° 1° gridded fields of precipitation
totals from October 1996 onward and is regularly updated.
The 1DD set combines several different data sources covering different areas of the globe. Every attempt has been
made to make the complete record homogeneous, given the
different available input sources (http://www.ncdc.noaa.gov/
oa/wmo/wdcamet-ncdc.html).
2.3. Test of Significance
[7] During 25 years the climate change signal is small.
Hence, with good approximation, we can treat 25 years as
stationary. Furthermore the 17 realizations are independent
as their pairwise correlations are small, and year-to-year
values are independent as the autocorrelation decay time is
shorter than 1 year. We have used the first period (1951–
1975) as the reference to measure the future seasonal precipitation changes. Each sample for both mean and extreme
precipitation is a set of 17 (ensembles) times 25 (seasonal
peaks or means) equals 425 events defined as follows:
Mi ¼
Ei ¼
17
[
y[
max;i
Smean ðe; yÞ;
ð1Þ
Sext ðe; yÞ;
ð2Þ
e¼1 y¼ymin;i
17
[
y[
max;i
e¼1 y¼ymin;i
for i = 1,…,6, with (ymin,1,ymax,1) = (1951, 1975),…,(ymin,6,
ymax,6) = (2076, 2100). Here ∪ denotes the union of sets,
e the model ensemble member, and Smean and Sext denote
the seasonal mean and maximum of daily precipitation,
respectively. The sets Mi and Ei are defined for each studied
season.
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[8] We will only show results for periods 3 (2001–2025),
5 (2051–2075) and 6 (2076–2100). Changes before the
year 2000 are small, and those in period 4 (2026–2050) look
like a linear interpolation between periods 3 and 5. From our
analysis we exclude the very dry regions, defined as the
areas where at least in one of the three selected periods
the seasonal mean and peaks are below 0.1 and 1 mm/d,
respectively. These areas are masked by dotted black patterns in Figures 4–9. The exclusion of the arid regions from
our analysis is because future precipitation changes there are
difficult to interpret. Models have difficulty in simulating
very dry conditions [Sterl et al., 2008]. As many authors
[Giannini, 2010; Biasutti and Giannini, 2006; Cook and
Vizy, 2006; Douville et al., 2006; Christensen et al., 2007]
have discussed, projections of 21st century change are very
uncertain in these regions, with equal numbers of models
predicting a significantly wetter or drier future, or no significant change with respect to present conditions at all.
Moreover in these areas, as detected by the goodness-of-fit
analysis described in section 2.4, the GEV model can only
be accepted at a lower confidence (level of significance
between 5% and 20%).
[9] To test whether precipitation in period i is significantly
different from that in the reference period (period 1) we
determine whether the two data sets M1 and Mi (and E1 and
Ei) are different, precisely, whether or not they are drawn
from the same distribution at a chosen level of significance
(5%). To do so we use the Kolmogorov-Smirnov (K-S) test
[e.g., Von Storch and Zwiers, 2003; Iacus and Masarotto,
2007], which has the advantage of making no assumptions
about the distribution of the data and is nonparametric. For
those grid points where we detect changes, i.e., the null
hypothesis that M1 and Mi (E1 and Ei) are drawn from the
same population is rejected at the 5% significance level,
we quantify changes in seasonal precipitation means and
extremes.
[10] For heavy-tailed distributions the K-S test may not be
optimal as it gives relatively little weight to the tail. There is
a large family of related tests, such as the Anderson-Darling
test [Anderson and Darling, 1954; Von Storch and Zwiers,
2003] that give more weight to the tails than does the K-S
test. It uses statistics that are more difficult to compute, but
that are also more powerful and more sensitive to departures
from the hypothesized distribution in the tails of the distribution. The K-S test is distribution-free in the sense that the
critical values do not depend on the specific distribution
being tested. The Anderson-Darling test makes use of the
specific distribution in calculating critical values. This has
the advantage of allowing a more sensitive test and the disadvantage that critical values must be calculated for each
distribution. We prefer to use the K-S test for both mean
and extremes.
2.4. Generalized Extreme Values and Empirical
Distributions
[11] To quantify changes in the mean precipitation we use
the 50th percentile (i.e., the median) of the empirical distribution for the set of 425 seasonal means. Given an ordered
sample of independent observations
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from a population with distribution function F, the empirical
distribution function is defined by [Coles, 2001]
~ ðx i Þ ¼
F
i
nþ1
for
xðiÞ ≤ x < xðiþ1Þ :
ð3Þ
[12] Changes of extremes are quantified by the difference
of the 100 year return value of precipitation between the two
periods. The return time T(x) for a value x is the average time
between two occurrences of x. To estimate the 100 year
return values we use the generalized extreme value (GEV)
distribution. According to the extremal types theorem [Coles,
2001] the GEV provides a model for the distribution of
block (annual or seasonal) maxima. It is given by
h
x mi1=x :
GðxÞ ¼ exp 1 þ x
s
ð4Þ
The parameter m describes the location of the distribution,
the scale parameter s its width, and the shape parameter
x its asymmetry, determining
the behavior of the tail. G(x) is
defined for {x : 1 + x xm
> 0} where the parameters satisfy
s
∞ < m < + ∞, s > 0 and ∞ < x < + ∞. The cases x = 0,
x > 0 and x < 0 define the widely known Gumbel, Frechét,
and Weibull distribution families, respectively.
[13] The GEV is fitted to the sets Ei of 425 block (seasonal)
maxima, using the maximum-likelihood fitting implemented
in the R package ismev (http://cran.r-project.org/web/
packages/ismev/index.html). The T-return value is given by
the 1 1/T(x) percentile of G, i.e.,
T ðxÞ ¼
1
:
1 GðxÞ
ð5Þ
Inserting (4) and inverting gives for the 100 year return value
(
T100 ¼
i
sh
for
1 ðln 0:99Þx
x
m slnðln 0:99Þ
for
m
x≠0
x¼0
ð6Þ
[14] This equation shows that the increase of any of the
three parameters leads to an increase of the 100 year return
value. An increasing location parameter describes a shift of
the whole distribution toward higher values, an increasing
scale parameter a widening, indicating higher variability,
and an increasing (decreasing) positive shape parameter a
lengthening (shortening) of the high-end tail, indicating a
tendency toward rare, but extreme events. This is again an
increasing variability, but only toward the high extremes.
The 100 year return level uncertainty was estimated as the
95% level of confidence by using the delta method as
described by Coles [2001].
2.5. Extreme Periods
[15] To define very wet (dry) periods we use the 90th
(10th) percentile of data sets M1 and E1 (equations (1) and
(2)) from the reference period 1951–1975. For each studied
season (DJF and JJA) we define two bivariate thresholds,
Cwet and Cdry. Cwet is that point in the (mean, extreme) plane
corresponding to the 90th percentiles of the sets M1 and E1,
whereas Cdry corresponds to the 10th percentiles. Figure 1
shows a graphical example of our calculation for the grid
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point corresponding to the location of De Bilt, Netherlands
(5.6°E, 51.3°N). A season (DJF or JJA) is a wet (dry) event
if both its seasonal mean and peak are above (below) the
(mean, extreme) coordinates of Cwet (Cdry). We count the
number of such wet (dry) events for each 25 year period and
estimate their future behavior. Summarizing, a wet (dry)
event is a season (DJF or JJA) in some year in which both
mean and maximum precipitation are extraordinarily high
(low). A wet (dry) period is a 25 year period in which the
number of wet (dry) events is higher (lower) than the number of wet (dry) events in the reference period.
3. Results
3.1. Model Output Evaluation
[16] Comparisons of seasonal precipitation means and
extremes as derived from ECHAM5 model output and
GPCP data are shown in Figures 2 and 3. In order to calculate the ESSENCE-GPCP differences the GPCP data were
interpolated onto the ECHAM5 grid (T63). The time period
used for model output evaluation is between 1997 and 2009,
when it is possible to get daily GPCP observations (see
section 2.2). For each of the 13 years of the evaluation
period we calculate the mean and maximum for each studied
season (DJF and JJA) from both ECHAM5 and GPCP and
compare their medians (Figures 2 and 3). Qualitatively,
observation and model data compare very well. The dry and
wet patterns, for both seasonal mean and extremes, are well
reproduced (Figures 2a–2d and 3a–3d). Quantitatively, the
absolute differences (Figures 2e, 2f, 3e, and 3f) over 80%–
90% of the global area do not exceed 2 mm/d and 10 mm/d
for seasonal means and extremes, respectively. The relative
differences (Figures 2g, 2h,
3g, and 3h)
between model and
100, are estimated to
observations, defined as EssenceGPCP
GPCP
be around 30% in the extratopics with higher values in dry
areas/seasons. Over very dry regions (e.g., Sahel) where we
compare numbers very close to zero, Figures 2e–2h and 3e–
3h show very small ( 0) absolute, but very large (between
80% and 160%) relative differences. Such differences are
well within the range of published model/data discrepancies
[Kharin et al., 2007, Sun et al., 2007, Min et al., 2011, Allan
and Soden, 2008]. Sun et al. [2007] found that the ECHAM5
model underestimates the observed heavy precipitation
(>20 mm/d) and overestimates the intensity and amount of
light to moderate precipitation (<20 mm/d). This is confirmed here. For instance, we find the model to overestimate
mean summer precipitation (Figure 2) over much of the
continental landmasses north of about 40°N, where seasonal
means are moderate, while the heavy winter extremes in the
same areas are underestimated (Figure 3), as are the tropical
maxima.
[17] In general, the model shows a fair degree of correspondence with the observations. Therefore we have some
confidence that the model output can be used to infer precipitation changes in a future climate.
3.2. GEV Goodness of Fit
[18] To test the goodness of fit a K-S test was carried out
between the empirical distribution function of a sample Ei
(i = 1,…,6, see equation (2)) and the fitted GEV distribution
function defined by the parameters m, s and x. The K-S test is
a popular goodness-of-fit statistic that measures the distance
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Figure 1. The calculation method used to define a dry or a wet period. The example is for the winter
(DJF) season at the location of De Bilt, Netherlands (5.6°E, 51.3°N). On the x and y axis we report the
seasonal mean and extreme values, respectively, expressed in mm/d. The two black open circles are the
points corresponding to the Cdry and Cwet bivariate thresholds. The open gray circles and gray pluses
are the precipitation values within reference period 1 (1951–1975) and period 6 (2076–2100), respectively. The dashed black lines define areas with seasonal mean and maximum precipitation values below
the Cdry or above the Cwet thresholds. The black pluses represent the occurrence of dry (values below the
Cdry point) and wet periods (values above the Cwet point) counted in period 6. The number of black pluses
above Cwet is 48 (11.3%), while the number of gray open circles above Cwet is equal to 12 (2.8%), meaning that in De Bilt in the future the probability of having a wet DJF period is 4 times larger than during the
reference period. A similar discussion applies for the dry periods.
between the empirical distribution function and the specified distribution [Von Storch and Zwiers, 2003]. The p
values of the goodness of the GEV fit for the last studied
period (2075–2100) are shown in Figure 4. In the other
periods the results look quite similar.
[19] Except for the very dry regions, where the p values
range between 0.1 and 0.15, the GEV model fits the
ESSENCE precipitation data with high confidence, showing
a level of significance of at least 5%. The grid points where
the GEV model fits with a lower confidence (p values
between 0.1 and 0.15) are localized in the surrounding of the
very arid regions masked out from our analysis (represented
in Figure 4 as the dotted black area). In these regions also the
100 year return level uncertainty, estimated by using the
95% level of confidence (see section 2.4) and expressed as
percent error of the 100 year return value, is higher (up to
10%) than in the extratropical and subtropical regions, where
it does not exceed 6% (not shown). In general we can say
that due to the large number of samples per time slice (425)
the resulting estimates of the distribution parameters have
small error bars.
3.3. Mean and Extremes Intensity Changes
[20] Figures 5 (left) and 6 (left) show relative changes of
the median of seasonal mean, and Figures 5 (right) and
6 (right) show 100 year return values for periods 3 (2001–
2025), 5 (2051–2075), and 6 (2076–2100), expressed as
percentage of the reference period (1951–1975).
[21] Away from the very dry regions the percentage of
global area showing significant seasonal precipitation
changes increases from ≈40% in period 3 (2001–2026) to
90% in period 6 (2076–2100). The extremes increase nearly
everywhere. Decreases occur mainly in JJA and in regions
that are much smaller than those for which mean precipitation is projected to decrease. Similar results were obtained
by Kharin et al. [2007] for the annual maximum precipitation. Changes in the period 2001–2025 have the same patterns as those in the later periods, but lower magnitude, a
result that was also found for temperature [Sterl et al., 2008;
Russo and Sterl, 2011]. Over the wet extratropical areas the
largest changes occur in winter (DJF). The dry tropicalsubtropical areas show the same intensity of changes during
both seasons, but in summer (JJA) the dry pattern is more
extended than in winter (DJF) (Figures 5 (left) and 6 (left)).
[22] During both winter and summer seasons, seasonal
mean precipitation change is generally negative between 45°
S and 45°N, while the magnitude of extreme precipitation is
mainly increasing in the same zonal band. An exception to
this general rule is the Intertropical Convergence Zone
(ITCZ), where both mean and extreme precipitation increase.
These results are consistent with those of Kharin et al. [2007]
and Sun et al. [2007]. Using a multimodel ensemble, Kharin
et al. [2007] calculated the median of yearly mean precipitation and 20 year return values of yearly maximum precipitation. They found that 20 year return values are increasing
nearly everywhere, while mean precipitation increases in
the tropics and in the midlatitudes and high latitudes, but
decreases in the subtropics. Sun et al. [2007] showed that
light to moderate precipitation increases over most of the
globe, except for subtropical regions, where it decreases
significantly. As nearly all precipitation in these areas is
light to moderate, this fits with our results.
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Figure 2. Median of seasonal mean precipitations (mm/d) in (left) DJF and (right) JJA for (a and b)
Essence and (c and d) GPCP data. Regions where the median of seasonal precipitation means is less than
0.1 mm/d are masked out in white. ESSENCE-GPCP (e and f) absolute and (g and h) relative differences
100).
(EssenceGPCP
GPCP
[23] In our study the band with decreasing mean and
increasing extremes is zonally moving by about 10° during
the annual cycle. This is the same movement as the present
climatological seasonal cycle. To summarize, the dry (subtropical) areas will become even drier and may also expand
poleward.
[24] As an example (Figure 6) we focus on a particularly
vulnerable (highly populated) area that is affected by this
drying, namely southern Europe. While the mean precipitation on the north shore of the Mediterranean seems to remain
fairly constant during DJF, the southern shore experiences
strong drying. During summer, the drying area moves
northward as far as southern Scandinavia. At the same time,
extreme rainfall tends to increase, with a larger increase in
winter (DJF) than in summer (JJA). Other areas around
40°N/S experience similar changes.
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Figure 3. As Figure 2 but for the median of the seasonal maxima (in mm/d). In Figures 3a–3d the regions
where the median of seasonal precipitation maxima is less than 1 mm/d are masked out in white.
[25] Outside of the 45°S–45°N geographical band, both
mean and extreme precipitation increase throughout each
season. This corroborates results for annual values as found
by Kharin et al. [2007], Sun et al. [2007] and Min et al.
[2011]. The magnitude of the increase grows with time.
The increase of the maxima (100 year return values) is up to
ten times higher than that of the seasonal means.
3.4. Correlation Between Means and Extremes
[26] In section 3.3 we showed that in some regions mean
and extreme precipitation both increase (decrease), while in
others they change in opposite direction. We now assess the
significance of this result by using the Kendall rank correlation coefficient, commonly referred to as Kendall’s tau (t)
coefficient [Ruppert, 2011]. It is a statistic used to measure
the association between two data sets even if the number of
data points is small. In our case we have six mean and peak
values in each grid point, one for each of the six 25 year
periods.
[27] In this analysis we only consider correlations and
corresponding p values at every grid point. This is called the
local test approach because a local null hypothesis is tested
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Figure 4. The p values of the Kolmogorov-Smirnov test performed to evaluate the goodness of GEV fit
for period 6 (2076–2100). The areas marked by pluses and crosses are very dry regions where precipitation
changes are not investigated (see section 2.3).
at each grid point [Von Storch and Zwiers, 2003]. As explained
by, e.g., Von Storch and Zwiers [2003], Ventura et al.
[2004], Wilks [2006], and Sterl et al. [2007], the local test
decision cannot be used at a regional or global scale when
a univariate hypothesis test is used at multiple locations. This
means that we cannot average locally calculated p values
over a region, and when we talk about changes in a region,
we effectively look at a composite of single points. To calculate the level of significance ofa region or globally we
should apply multivariate tests as explained by Von Storch
and Zwiers [2003].
[28] Kendall’s tau correlation values are shown in Figure 7
(top left, winter (DJF) and bottom left, summer (JJA)). The
corresponding p values, expressed as percentages, are shown
Figure 5. Relative change in DJF of (left) the median of seasonal means and of (right) the 100 year return
period of seasonal maxima for (top) period 3 (2001–2025), (middle) period 5 (2051–2075), and (bottom)
period 6 (2076–2100). The values are expressed as percentage of relative change with respect to period 1
(1951–1976). The color bar is in percent, and changes that, according to the results of the KolmogorovSmirnov test applied to both seasonal precipitation mean and maximum values, are not statistically significant at the 5% level are masked out in white. The areas marked by pluses and crosses are very dry regions
where precipitation changes are not investigated (see section 2.3).
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Figure 6. As Figure 5 but for JJA.
in Figure 7 (right). Over the extratropical areas we see a
strong correlation signal. It is largest in Eurasia and North
America during winter (DJF), when Kendall’s tau values
exceed 0.8 with a significance level of 5% (Figure 7, top). In
summer the correlation values are lower (Figure 7, left), and
the area where they are significant is smaller (Figure 7,
right). High positive correlations with corresponding high
p values are also found near the equator. Here the signal is
Figure 7. (left) Kendall’s tau correlation between (top) winter (DJF) and (bottom) summer (JJA) median
and 100 year return level of seasonal maxima and (right) corresponding p values.
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Figure 8. Relative change (in percent) of occurrence of wet periods with respect to reference period
1 (1951–1976) for (top) period 3 (2001–2025), (middle) period 5 (2051–2075), and (bottom) period
6 (2076–2100). The grid points where, according to the results of the Kolmogorov-Smirnov test applied
to both seasonal precipitation mean and maximum values, precipitation changes are not statistically significant at the 5% level are masked out in white.
most pronounced in JJA, indicating a strengthening and/or
slight meridional shift of the ITCZ at its most northerly
position. In the dry subtropical areas the mean-extreme
correlation values differ from those in the rest of the world.
They are generally negative, but with a low confidence
during both seasons, reflecting the fact that the percentages
of change for both mean and extreme precipitation are
lower in these areas than elsewhere.
[29] Figure 7 confirms the findings from section 3.3. In the
extratropics and along the equator both seasonal means and
extremes increase, and in the subtropical areas the means are
decreasing while the extremes increase. The negative correlation shows high significant values only in very localized
areas (for example southern Australia and northwestern
Pacific in DJF). In areas where the p value does not exceed
0.1 (Figure 7, left) the correlation coefficient has only positive values. Together this means that there are only very
small regions on the globe with significant opposite changes
between seasonal means and extremes.
3.5. Occurrence of Wet and Dry Periods
[30] We have seen that in many extratropical regions,
mainly during boreal winter, the intensities of both seasonal
means and peaks are strongly increasing. We now describe
present and future changes in the occurrence of wet and dry
periods as defined in section 2.5. For this study we consider
only those grid points where seasonal means and peaks are at
the same time changing at the 5% significance level as
determined by the Kolmogorov-Smirnov test (see section 2.3).
Results for wet and dry periods are shown in Figures 8 and 9,
respectively.
[31] Over the northern extratropical areas the occurrence
of wet periods is increasing more during DJF (Figure 8, left)
than during JJA (Figure 8, right). This corroborates the
findings from the mean and extreme intensity changes in
section 3.3.
[32] In the same zonal band the occurrence of dry periods
is decreasing (the changes are negative) by between 1% and
10% during both seasons. Thus the wet regions are becoming less dry, and in the future the probability to have a season with seasonal precipitation mean and maximum below
the values given by Cdry of the reference period (1951–1975)
is decreasing. So we can say that the 25 year bivariate distributions of seasonal mean and peaks are shifting in the
direction of wettening. Furthermore, the area showing significant changes is larger in DJF than in JJA.
[33] Over the tropics and subtropics areas where the
occurrence of dry periods increases are mixed with areas
where it decreases. This again is in accordance with corresponding results from section 3.3. While the decrease of the
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D01108
Figure 9. As Figure 8 but for dry periods.
number of wet seasons is generally less than 5%, the
number of dry periods is increasing in some (sub)tropical
areas by up to 30%.
4. Discussion and Conclusion
[34] In this study we used a large ensemble from one
model, driven by one emission scenario. Unfortunately,
available computing resources did not allow us to perform a
second ensemble using another emission scenario. However,
as can be seen from Figures 5 and 6, the response of the
model to increasing GHG levels is roughly linear suggesting
that a more rapid increase of GHGs would produce the
period 6 results earlier in time. The use of only one model is
a more serious limitation. Precipitation, and especially heavy
precipitation, is strongly dependent on details of the parametrization [e.g., Emori et al., 2005]. A multimodel ensemble
or a perturbed physics ensemble might be preferable and
yield different results. However, our results are in line with
those of Kharin et al. [2007], who use a multimodel ensemble, and with those of Sun et al. [2007], who use 14 climate
models with three emissions scenarios (SRES B1, A1B and
A2). Complementing these studies our use of a large singlemodel ensemble has the advantage of strongly reducing the
statistical uncertainty.
[35] Summarizing our results, in all seasons the mean
precipitation is increasing poleward of ≈40° and decreasing
equatorward of these latitudes, except for the ITZC, where it
increases. Dry areas become drier and wet areas become
wetter. The position of the boundary between areas becoming
drier/wetter moves zonally by about 10° during the annual
cycle. While the mean winter (DJF) precipitation over
southern Europe experiences only small changes over the
simulation period, summer (JJA) precipitation declines as
far northward as southern Scandinavia.
[36] Precipitation peaks behave differently. They are
increasing over most of the globe and in all seasons. They
decrease, or increase only slowly, only in a small fraction of the
tropical-subtropical area, where seasonal mean precipitation
decreases. Increases in the intensity of precipitation extremes
exceed those of mean precipitation nearly everywhere.
[37] The magnitude of the precipitation changes increases
with time. Maximum changes are found in the respective
winter hemisphere. Throughout the simulation period the
spatial patterns of change are the same with dry areas
becoming drier and wet areas becoming wetter.
[38] Acknowledgments. This work was funded by the EU (contract
MRTN-CT-2005-019374) as part of the SEAMOCS research training network. The ESSENCE project, led by Wilco Hazeleger (KNMI) and Henk
Dijkstra (UU/IMAU), was carried out with support of DEISA, HLRS,
SARA, and NCF (through NCF projects NRG-2006.06, CAVE-06-023,
and SG-06-267). We thank the DEISA Consortium (cofunded by the EU,
FP6 projects 508830 and 031513) for support within the DEISA Extreme
Computing Initiative (www.deisa.org). The authors thank Camiel Severijns
(KNMI) and HLRS and SARA staff for technical support. The plots have
been produced using the free Ferret software developed at NOAA/PMEL
(www.ferret.noaa.gov).
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S. Russo, Institute for Environmental Protection and Research, Via
Curtatone 3, Rome I-00185, Italy. ([email protected])
A. Sterl, Royal Netherlands Meteorological Institute, PO Box 201,
NL-3730 AE De Bilt, Netherlands. ([email protected])
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