Download Dynamic and thermodynamic changes in mean and extreme

GEOPHYSICAL RESEARCH LETTERS, VOL. 32, L17706, doi:10.1029/2005GL023272, 2005
Dynamic and thermodynamic changes in mean and extreme
precipitation under changed climate
S. Emori
National Institute for Environmental Studies, Tsukuba, Japan
Frontier Research Center for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
S. J. Brown
Hadley Centre for Climate Prediction and Research, Met Office, Exeter, UK
Received 19 April 2005; revised 21 June 2005; accepted 26 July 2005; published 13 September 2005.
[1] Extreme precipitation has been projected to increase
more than the mean under future changed climate, but its
mechanism is not clear. We have separated the ‘dynamic’
and ‘thermodynamic’ components of the mean and extreme
precipitation changes projected in 6 climate model
experiments. The dynamic change is due to the change in
atmospheric motion, while the thermodynamic change is
due to the change in atmospheric moisture content. The
model results consistently show that there are areas with
small change or decreases in the thermodynamic change for
mean precipitation mainly over subtropics, while the
thermodynamic change for extreme precipitation is an
overall increase as a result of increased atmospheric
moisture. The dynamic changes play a secondary role in
the difference between mean and extreme and are limited to
lower latitudes. Over many parts of mid- to high latitudes,
mean and extreme precipitation increase in comparable
magnitude due to a comparable thermodynamic increase.
Citation: Emori, S., and S. J. Brown (2005), Dynamic and
thermodynamic changes in mean and extreme precipitation under
changed climate, Geophys. Res. Lett., 32, L17706, doi:10.1029/
2005GL023272.
1. Introduction
[2] It has been projected that precipitation extremes could
increase by more than the (annual or seasonal) mean and may
cause more frequent and severe floods in a future warmed
climate [e.g., Cubasch et al., 2001]. Trenberth [1999] argued
that enhancement of extreme precipitation is principally
caused by enhancement of atmospheric moisture content,
which feeds increased moisture to all weather systems. On a
global mean basis, annual mean precipitation is constrained
by the energy balance between atmospheric radiative cooling
and latent heating, which is expected to limit mean precipitation increase to be lower than the rate of atmospheric
moisture increase [Allen and Ingram, 2002]. On a regional
basis, however, Emori et al. [2005] showed that areas where
the changes in extremes are larger than those of the mean are
limited to a few regions, rather than the whole globe.
[3 ] The mechanism that causes larger increases in
extremes than in the mean is not clear. For example,
simplifying the argument by Trenberth [1999] to an idealized case by assuming that precipitation increases by the
same percentage across all severity of events (following the
’fixed fractional uplift’ case of Jones et al. [1997]), the
percentage change in extremes and the mean should be
the same. To further clarify this relationship between the
changes in mean and extreme precipitation under changed
climate, we attempt to answer the question, to what degree
we can attribute atmospheric moisture increase to the
changes in precipitation, both for the mean and extremes.
2. Method
[4] Daily mean 500 hPa vertical velocity (w) is taken as a
proxy of the strength of ’dynamic disturbance’ at each grid
point on each day. For example, in mid-latitudes winter, it is
expected to correspond to the phase and strength of extratropical cyclones and anticyclones passing over the grid
point of interest. For computational purposes, w is divided
into bins with a uniform width of 50 hPa/day. Figure 1a
shows an example of the relative frequency of occurrence
for each w bin, regarded as probability density function
(PDF) of w and denoted by Prw, averaged over a particular
region (equatorial central Pacific) obtained from the control
run of CCSR/NIES/FRCGC AGCM. It has a peak around w =
0 and tails for both stronger upward and downward motion
regimes. This shape of PDF is qualitatively the same for
different regions from different models. Note that the positive
w represents upward motion throughout this study, unlike the
usual definition of pressure velocity, since positively correlated precipitation with upward vertical motion, as will be
shown below, is to be preferred. Daily mean precipitation is
then composited for each w bin to obtain the expected values
of daily precipitation as a function of w at each grid point (Pw).
Figure 1b shows an example of Pw, for the same region and
model as in Figure 1a. Precipitation is expected to be small
over the downward motion regime (w < 0), while it is expected
to be larger with stronger upward velocity over the upward
motion regime (w > 0). A similar relationship is obtained for
different regions from different model results, though the
slope in the upward motion regime is dependent on the
regions and also on the model used. Although Prw and Pw
are dependent on seasons, the annual relationships are used in
this initial study.
for a grid point can
[5] The annual mean precipitation P
be represented by
¼
P
Published in 2005 by the American Geophysical Union.
Z
1
Pw Prw dw:
1
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EMORI AND BROWN: MEAN AND EXTREME PRECIPITATION CHANGES
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generally different from the 99th percentile value of w. The
change in 99th percentile precipitation between changed
and control climate can be expressed by
dP99 ¼
Figure 1. (a) Relative frequency of daily upward vertical
motion over equatorial central Pacific (10S–10N, 180E–
210E) in the control run of CCSR/NIES/FRCGC AGCM.
(b) Composited daily precipitation for each daily vertical
motion bin for the same region and the model as in (a); error
bars denote one standard deviation of composited data.
The change in mean precipitation between the changed and
can be expressed by
control climate, dP,
¼
dP
Z
Z
1
1
Z
1
Pw dPrw dw þ
1
dPw Prw dw þ
1
dPw dPrw dw: ð1Þ
1
The first term of the r.h.s is the change in mean precipitation
due to the change in the PDF of w, that is, due to the change
in the strength and/or frequency of dynamic disturbances.
Hence, we call this term ‘‘dynamic change’’. The second
term is due to the change in the expected precipitation for
given w. This term can be called non-dynamic or
‘‘thermodynamic change’’ and is taken to represent the
change in atmospheric moisture content. The third represents the covariation term. This approach is basically that of
Bony et al. [2004] for cloud-radiation analysis over the
tropics. Note, however, that we use daily data and averages
are taken only over time, while Bony et al. [2004] used
monthly data and spatial as well as temporal averaging over
the tropics.
[6] A similar expression can be defined for the change in
extreme precipitation. Here, we take multi-year mean of
yearly 4th largest (approximately 99th percentile) value at
each grid point as an index of extreme precipitation.
Hereafter, we call this the 99th percentile precipitation and
denote it by P99. The corresponding w value (w*99) can be
obtained by inverting the relationship of Pw , that is,
w*99 ¼ Pw1 ðP99 Þ:
For inverting Pw , a linear-interpolation is applied to the
values between the representative values of the bins to
obtain a continuous function of Pw. Note that this value is
@Pw
@P
dw*99 þ dPðw*99 Þ þ d
dw*99 :
@w
@w
ð2Þ
The first term of the r.h.s is due to the change in extreme
vertical velocity and is called dynamic change. The second
term is due to the change in expected precipitation for
extreme w fixed at the control value and is called
thermodynamic change. The third represents the covariation
term. Though the first and third terms are expressed with a
linear approximation, they are actually evaluated with a
finite difference approximation in the following analysis so
that the equation holds precisely.
3. Models and Experiments
[7] The models and experiments used in this study are
summarized in Table 1. The coupled ocean-atmosphere
climate models were obtained from the Program for Climate
Model Diagnosis and Intercomparison (PCMDI) data archive established for the Intergovernmental Panel on Climate Change (IPCC) 4th Assessment Report. In addition,
time-slice climate change experiments by two atmosphereonly models, CCSR/NIES/FRCGC AGCM [Emori et al.,
2005] and HadAM3P [Rowell, 2005] are also used. The
daily 500 hPa vertical velocity data for the coupled models
are estimated from daily three-dimensional horizontal velocity data using the continuity equation, while those for the
atmospheric models are direct output from the models.
4. Results
[8] The temporal correlation at each grid point between
daily precipitation and daily 500 hPa vertical velocity
(positive upward) is higher than 0.5 over most parts of the
globe in all the models examined. This fact supports the
validity of the method described in Section 2. The correlation is relatively low over some subtropical regions with
strong subsidence and over polar regions, where precipitation from clouds shallower than 500 hPa is expected to be
dominant. In the following analysis, the areas where the
correlation of at least one model is lower than 0.2 are
masked out (shaded gray in Figures 2 and 3).
[9] Figure 2 shows the multi-model ensemble mean of
the total, dynamic and thermodynamic changes in annual
mean precipitation defined by (1). They are shown in
percentage change relative to the annual mean precipitation
in the control experiments. Before the ensemble mean is
taken, results from each model are scaled by the values of
Table 1. Models and Experiments
Model
MIROC3.2(hires)
MIROC3.2(medres)
GFDL-CM2.1
MRI-CGCM2.3.2a
CCSR/NIES/FRCGC AGCM
HadAM3P
Resolution
Global Mean Precipitation Change
6.6%
5.0%
2.8%
5.0%
4.3%
5.3%
1.125
2.81
2.50
2.81
1.125
1.875
1.125
2.81
2.00
2.81
1.125
1.250
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Control Experiment
20C3M
20C3M
20C3M
20C3M
Control
Control
(1981 – 2000)
(1981 – 2000)
(1981 – 2000)
(1981 – 2000)
(1979 – 1998)
(1960 – 1990)
Climate Change Experiment
A1B (2081 – 2100)
A1B (2081 – 2100)
A1B (2081 – 2100)
A1B (2081 – 2100)
2 CO2 (20 years)
A2 (2070 – 2100)
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EMORI AND BROWN: MEAN AND EXTREME PRECIPITATION CHANGES
Figure 2. Percentage change in annual mean precipitation due to climate change and its components: (a) total,
(b) dynamic and (c) thermodynamic.
global mean precipitation change listed in Table 1 to
exclude the effects of different climate (hydrological) sensitivity of the models and of different scenarios in the case
of time-slice runs. Also, all data is spatially interpolated to a
T42 (2.81) grid before averaging. The total change
(Figure 2a) shows general increase over tropics and midto high latitudes and decrease over some subtropical
regions, a pattern commonly seen in previous studies [e.g.,
Cubasch et al., 2001]. The dynamic change (Figure 2b) partly
explains the tropical Pacific increase and most of the subtropical decrease, while it is virtually zero over mid- to high
latitudes (outside of 40S to 40N). The thermodynamic
change (Figure 2c) explains almost all the mid- to high
latitudes increase and part of the tropical increase. The
covariation (figure not shown) is negligible except for an
increase of up to 40% over equatorial central Pacific. The
dominance of thermodynamic changes in extratropics and
dynamic changes in lower latitudes is consistent with previous studies where the dynamic and thermodynamic changes
of moisture transport and its convergence are discussed [e.g.,
Watterson, 1998].
[10] Figure 3 shows multi-model ensemble mean of the
total, dynamic and thermodynamic changes in extreme
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Figure 3. Percentage change in the annual 4th largest
daily precipitation (approximately 99th percentile) due to
climate change and its components: (a) total, (b) dynamic
and (c) thermodynamic.
(99th percentile) daily precipitation defined by (2), in
percentage relative to control values. The same scaling
and interpolation procedures as for the mean precipitation
have been applied before the ensemble mean is taken.
Because of insufficient sampling of Pw for extreme cases,
the separated dynamic and thermodynamic changes contain
noise. To reduce this, we have applied a 1-2-1 spatial filter
once (Figures 3b and 3c), assuming the noise as random.
The comparison between the total changes in mean and
extreme precipitation (Figures 2a and 3a) is basically the
same as was found by Emori et al. [2005] for the results of
CCSR/NIES/FRCGC AGCM. That is, though their overall
patterns are similar, there are some areas where the increase
in the extreme is larger than that in the mean (or the mean is
decreased). The global mean of the total changes in mean
and extreme precipitation are 6.0% and 13.0%, respectively
(note that data over masked areas are excluded). The pattern
of dynamic change in the extreme is quite similar to the
dynamic change in the mean (global mean of 4.3%,
4.4%, respectively, Figures 2b and 3b) though some
regional difference can be identified. The thermodynamic
change in the extreme shows overall increase, which is in
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remarkable contrast with the corresponding small changes
or decreases in the mean particularly over the subtropics
(global mean of 17.8%, 9.4%, respectively, Figures 2c and
3c). The difference in the total changes in mean and extreme
precipitation can therefore be attributed mainly to the
different thermodynamic change. The covariation for
extremes is negligible (not shown).
[11] The results of individual models are similar to the
ensemble means described above. The largest model difference is over lower latitudes (30S – 30N), where models
respond differently to the various projected (or prescribed,
in time-slice runs) sea surface temperature (SST) changes.
The inter-model standard deviation of the total change either
in the mean or in extreme is typically 10– 30% (relative to
control values) over the lower latitudes. Over the mid- to
high latitudes, the inter-model standard deviation is typically smaller than 10%, suggesting that the present results
are more robust for higher latitudes.
5. Concluding Discussion
[12] The thermodynamic change in extreme precipitation
has shown an overall increase (Figure 3c), which is due to
the expected precipitation for given vertical motion, Pw ,
increasing in the strong upward motion regime, regardless
of geographical location. As suggested in Section 2, we
consider that this is caused by increased atmospheric
moisture content. This relation is supported by the fact
that models with larger increases in global mean precipitable water (column integrated water vapor) tend to give
larger global mean thermodynamic increases in extreme
precipitation (the inter-model correlation coefficient is
0.85, which is significant at the 5% level). It is also
interesting to note that increased Pw at a given w means
increased latent heating for a given upward motion. We
found that, at least over the lower latitudes, this is
balanced by increased adiabatic cooling due to enhanced
dry static stability.
[13] The thermodynamic change in mean precipitation
(Figure 2c) has areas of modest change or decreasing
values, mainly over the subtropics, unlike what was found
for the extreme precipitation. This is because Pw decreases
or changes little in the downward and/or weak upward
motion regime over these areas, in spite of the increased
atmospheric moisture content. Although Pw is small over
the weak/downward motion regime and its change is also
small, this is not negligible when integrated over whole w
range (i.e., annual mean), because of the large statistical
weight for this regime. It is this mechanism that keeps
the percentage increase in global annual mean precipitation lower than that in global mean extreme precipitation.
The decrease can be partly understood by considering the
moisture budget of atmospheric column. When atmospheric moisture is increased, moisture divergence will
be increased for a given lower tropospheric wind divergence, which would act to reduce Pw in the downward
motion regime, unless surface evaporation increases in
compensation.
[14] Over many parts of the mid- to high latitudes and
tropics, the thermodynamic increase in the mean is as
high as that in the extreme, resulting in the total increase
in the both being comparable. These areas roughly
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correspond to areas with a mean upward motion in the
control climate. Important exceptions are some tropical
land areas (e.g., Amazon) and northern North Atlantic
including the UK/Europe area. Further analysis is needed
to clarify these regional details.
[15] The dynamic terms play a secondary role in making
difference between mean and extreme precipitation changes.
The dynamic changes suggest that the frequency of strong
upward motion is decreased over many parts of subtropics
and is increased over the equatorial Pacific. Though this
seems to be related to the changes in SST and atmospheric
stability, more work is needed to clarify this.
[16] The seasonal breakdown of the present analysis is
desired for future work. The spread of composited data in
constructing Pw (error bars in Figure 1b) should be smaller
in a seasonal relationship than in the annual, especially for
higher latitudes, where Pw can be considerably seasonally
dependent. However, we have confirmed that seasonality
does not seriously affect the annual results of the present
study. For the extreme precipitation, it is because extremely
strong upward motions (w*99) occur mostly accompanying
extreme precipitation events (P99) in wet seasons and
seldom occur in dry seasons. That is, for an extremely
strong upward motion, the annual Pw approximately represents that of the wet seasons alone.
[17] The present analysis relies on the modeled Pw and PDF
of w. At present, there seems to be no good way to validate
these functions, since daily vertical velocities of re-analysis
data seem strongly dependent on the models used in the reanalysis and does not always correspond well to the observed
daily precipitation. The quantitative validation of this analysis
remains for future work. Nevertheless, the result of this study
is qualitatively robust among all the models examined.
[18] Acknowledgments. This work was done whilst the first author
(SE) was at the Hadley Centre as a visiting scientist. We thank people at the
Hadley Centre as well as the K-1 Japan project members for support and
discussion. Thanks are extended to Ian Watterson, Isaac Held, Julia Slingo,
Chris Ferro, Richard Jones, Myles Allen, William Ingram, Pardeep Pall and
an anonymous reviewer for helpful comments and discussion. We also
acknowledge the international modeling groups for providing their data for
analysis, the PCMDI for collecting and archiving the model data, the JSC/
CLIVAR Working Group on Coupled Modelling (WGCM) and their
Coupled Model Intercomparison Project (CMIP) and Climate Simulation
Panel for organizing the model data analysis activity, and the IPCC WG1
TSU for technical support. The IPCC Data Archive at Lawrence Livermore
National Laboratory is supported by the Office of Science, U.S. Department
of Energy. This work was partially supported by the Research Revolution
2002 (RR2002) of the Ministry of Education, Sports, Culture, Science and
Technology of Japan, by the Global Environment Research Fund (GERF)
of the Ministry of the Environment of Japan, and by the U.K. Department
of the Environment, Food and Rural Affairs (Contract PECD/7/12/37). The
model calculations of MIROC3.2 and CCSR/NIES/FRCGC AGCM were
made on the Earth Simulator. The GFD-DENNOU Library was used for the
drawings.
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S. J. Brown, Hadley Centre, Met Office, FitzRoy Road, Exeter, EX1 3PB,
UK. ([email protected])
S. Emori, National Institute for Environmental Studies, 16-2 Onogawa,
Tsukuba, Ibaraki 305-8506, Japan. ([email protected])
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