Transcript Document

Regional Climate Change
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Summary of TAR Findings
How well do the Models Work at Regional
Scales?
Some Preliminary Simulation Results
Understanding Climate Variability Versus
Scale
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Figure 10.1: Regions used for the analysis presented in Figures
10.2 to 10.5 (from Giorgi and Francisco, 2000b).
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Simulations of present day climate
Coarse resolution AOGCMs simulate atmospheric general circulation features
well in general. At the regional scale the models display area-average biases
that are highly variable from region-to-region and among models, with subcontinental area-averaged seasonal temperature biases typically within 4ºC
and precipitation biases mostly between -40 and +80% of observations. In
most cases, these represent an improvement compared to the AOGCM results
evaluated in the SAR.
Regional CMs consistently improve the spatial detail of simulated climate
compared to General Circulation Models (GCMs). RCMs driven by observed
boundary conditions show area-averaged temperature biases (regional scales
of 105 to 106 km2) generally within 2ºC and precipitation biases within 50%
of observations. Statistical downscaling demonstrates similar performance,
although greatly depending on the methodological implementation and
application.
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Figure 10.2: Surface temperature
biases (in °C) for 1961 to 1990 for
experiments using the AOGCMs of
CSIRO Mk2, CCSR/NIES,
ECHAM/OPYC, CGCM1 (a threemember ensemble) and HadCM2 (a
four-member ensemble) with
historical forcing including sulfates
(further experimental details are in
Table 9.1). Regions are as indicated
in Figure 10.1 and observations are
from New et al. (1999a,b). (a)
surface air temperature, (b)
precipitation (from Giorgi and
Francisco, 2000b).
Model Bias 1961-1990
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Precipitation
Model Bias 1961-1990
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Spatial Resolution
of Global Models
1980s AGCM
1990s AOGCM
2000s AOGCM
www.gfdl.noaa.gov/~kd/ClimateDynamics
www.gfdl.noaa.gov/~kd/ClimateDynamics
Simulation of climate change for the late decades of the
21st century
Climate means
*It is very likely that: nearly all land areas will warm more rapidly than the global
average, particularly those at high latitudes in the cold season; in Alaska, northern
Canada, Greenland, northern Asia, and Tibet in winter and central Asia and Tibet in
summer the warming will exceed the global mean warming in each model by more
than 40% (1.3 to 6.9°C for the range of models and scenarios considered). In
contrast, the warming will be less than the global mean in south and Southeast Asia
in June-July-August (JJA), and in southern South America in winter.
*It is likely that: precipitation will increase over northern mid-latitude regions in
winter and over northern high latitude regions and Antarctica in both summer and
winter. In December-January-February (DJF), rainfall will increase in tropical
Africa, show little change in Southeast Asia and decrease in central America. There
will be increase or little change in JJA over South Asia. Precipitation will decrease
over Australia in winter and over the Mediterranean region in summer. Change of
precipitation will be largest over the high northern latitudes.
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Figure 10.3: Simulated
temperature
changes in °C (mean for 2071 to 2100 minus
1990) conditions of 1%/yr increasing CO2
without and with sulphate forcing using
experiments undertaken with the AOGCMs of
CSIRO Mk2, CCSR/NIES, ECHAM/OPYC,
CGCM1 and Hadley Centre (further
experimental details are in Table 9.1). Under
both forcing scenarios a four-member ensemble
is included of the Hadley Centre model, and
under the CO2 plus sulphate scenario a threemember ensemble is included for the CGCM1
model. (a) increased CO2 only (GG), (b)
increased CO2 and sulphate aerosols (GS).
Global model warming values in the CO2
increase-only experiments are 3.07°C for
HadCM2 (ensemble average), 3.06°C for CSIRO
Mk2, 4.91°C for CGCM1, 3.00°C for
CCSR/NIES and 3.02°C for ECHAM/OPYC.
Global model warming values for the
experiments including sulphate forcing are
2.52°C for HadCM2 (ensemble average), 2.72°C
for CSIRO Mk2, 3.80°C for CGCM1 (ensemble
average) and 2.64°C for CCSR/NIES (from
Giorgi and Francisco, 2000b).
Temperature Changes
2071-2100 from 1990
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Figure 10.4: Analysis of inter-model consistency in regional warming relative to each model’s global warming, based on the
results presented in Figure 10.3. Regions are classified as showing either agreement on warming in excess of 40% above the
global average (“Much greater than average warming”), agreement on warming greater than the global average (“Greater
than average warming”), agreement on warming less than the global average (“Less than average warming”), or
disagreement amongst models on the magnitude of regional relative warming (“Inconsistent magnitude of warming”). There is
also a category for agreement on cooling (which is not used). GG is the greenhouse gas only case (see Figure 10.3a), and, GS,
the greenhouse gas with increased sulphate case (see Figure 10.3b). In constructing the figure, ensemble results were averaged
to a single case, and “agreement” was defined as having at least four of the five GG models agreeing or three of the four GS
models agreeing. The global annual average warming of the models used span 3.0 to 4.9°C for GG and 2.5 to 3.8°C for GS,
and therefore a regional 40% amplification represents warming ranges of 4.2 to 6.9°C for GG and 3.5 to 5.3°C for GS.
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Figure 10.6: Analysis of inter-model consistency in regional precipitation change based on the
results presented in Figure 10.5. Regions are classified as showing either agreement on increase
with an average change of greater than 20% (“Large increase”), agreement on increase with an
average change between 5 and 20% (“Small increase”), agreement on a change between –5 and
+5% or agreement with an average change between –5 and 5% (“No change”), agreement on
decrease with an average change between –5 and -20% (“Small decrease”), agreement on
decrease with an average change of less than -20% (“Large decrease”), or disagreement
(“Inconsistent sign”). GG is the greenhouse gas only case (see Figure 10.5a), and, GS, the
greenhouse gas with increased sulphate case (see Figure 10.5b). In constructing the figure,
ensemble results were averaged to a single case, and “agreement” was defined as having at least
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of four the five GG models agreeing or three of the four GS models agreeing.
Figure 10.7: For the European
region, simulated change in annual
precipitation, averaged by latitude
and normalized to % change per
°C of global warming. Results are
given for twenty-three enhanced
GHG simulations (forced by CO2
change only) produced between the
years 1983 and 1998. The earlier
experiments are those used in the
SCENGEN climate scenario
generator (Hulme et al., 1995) and
include some mixed-layer 1x and
2xCO2 equilibrium experiments;
the later ones are the AOGCM
experiments available through the
DDC. From Hulme et al. (2000).
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Climate variability and extremes (1)
*Daily to interannual variability of temperature will likely decrease in winter
and increase in summer in mid-latitude Northern Hemisphere land areas.
*Daily high temperature extremes will likely increase in frequency as a
function of the increase in mean temperature, but this increase is modified by
changes in daily variability of temperature. There is a corresponding decrease
in the frequency of daily low temperature extremes.
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Climate variability and extremes (2)
*There is a strong correlation between precipitation interannual variability and mean
precipitation. Future increases in mean precipitation will very likely lead to increases
in variability. Conversely, precipitation variability will likely decrease only in areas of
reduced mean precipitation.
*For regions where daily precipitation intensities have been analyzed (e.g., Europe,
North America, South Asia, Sahel, southern Africa, Australia and the South Pacific)
extreme precipitation intensity may increase.
*Increases in the occurrence of drought or dry spells are indicated in studies for
Europe, North America and Australia.
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Tropical cyclones
Despite no clear trends in the observations, a series of theoretical
and model-based studies, including the use of a high resolution
hurricane prediction model, suggest:
*It is likely that peak wind intensities will increase by 5 to 10% and mean and
peak precipitation intensities by 20 to 30% in some regions;
*There is no direct evidence of changes in the frequency or areas of formation.
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Just how good is regional modeling?
The following simple analysis shows how much of the variance
for a small patch on the Earth comes from contributions from
very large scales.
We use as an example a small patch on a latitude circle. We
make a model of fluctuations on the circle and then find how an
average on a small patch varies. Then we see how the variability
of this patch-average is decomposed into contributions from
larger scales on the circle. The finding is that the large scales
contribute greatly to the variability of the patch-average.
The moral is that it is exceedingly difficult to estimate the
variability of a patch-average if we do not have accurate larger
scale information.
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Consider a Loop such as a Latitude Circle. We can expand
the Temperature Anomaly Field into a Fourier Series:
T ( )   a n cos n   b n sin n 
n0
a n and b n are random variables with mean
 . The
2
n
zero and the same variance

spectrum of the temperature
a n  bn
2
Sn 
T (  ) is
2
2
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A typical spectrum (so
looks like:
Sn 
- called red noise )
A
c n
2
2
, ( c, A constants)
A=100, c =10
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
A few properties:
T ( )
2

0
2
 Variance of
T at 

T ( ) d    S n
2
n0
S n  variance contribution of scale
n

n  10
20
0
2
0.50
Let’s look at the variance of the average of T from 0 to 0.5
 

0.5
g
 T ( )d  ,
g 0
0
0.5 0.5
g
2

 
0
g

2
T ( )T ( ' ) d  d  '
0
  Fn S n , Fn is a filter
n0
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Variance of a Small
Area Average involves
Sum over all scale
variances of larger scale
I calculated the Fn
for this case
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Product of Filter times Spectrum
To get the variance of the regional average, sum up the points
g
2
  Fn S n
n0
Note how much variance comes from the larger scales (smaller 23n)
The Moral:
In looking at the variance of a small region. All the variances of
Larger scales contribute according to the variance spectrum.
The Variance spectrum in geosciences usually is large for large
scales (small n) and tapers off for small scales.
This makes it very difficult to examine the variability of small
scales, because all the larger scales (and their errors) contribute.
In terms of regional climate modeling, the implication is that one
must have rather accurate large scale information to feed into a high
resolution sub-model. (Physically, these are the fluxes in/out)
So-called ‘downscaling’ is very difficult.
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