**Chapter V: Model simulation of major climate features**

**Mean surface temperature and precipitation**

**Simulations of** **monthly** **near-surface air temperature** **and
precipitation provide a standard starting point for model evaluation due
to the centrality of these fields** **to many applications.** **These
two fields also illustrate the difficulty of designing appropriate
metrics for measuring model quality.** **[(What I would like to get
across here is something to the effect that temperature is much easier
that precipitation, just because the temnperature field has a simpler
structure]{.mark}** **[-- if one displaces the ITCZ by a few degrees
latitude, the correlation and RMS detiorate rapidly, but there is no
corresponding challenge for temperature]{.mark}** [**- along the same
lines, if one normalized the variance by the interannual variability,
for example, the picture of which field one simulated the best might
shift somewhat.**]{.mark} **[I am not sure how to say this or where to
put it.)]{.mark}**

**[(I question whether the]{.mark}** **[Taylor diagram is appropriate
for this audience. If]{.mark}** **[we go with the Taylor diagram, I also
question the choice of including points refered to in ch. 6 in this
figure -- this creates more confusion in someone trying to understand
the figure . IH)]{.mark}**

The basic structure of monthly mean near-surface temperature is well
simulated by modern AOGCMs. This success occurs despite the fact that
nearly all models now allow the ocean and atmosphere to exchange heat
and water without explicitly forcing agreement with observation by
artificial adjustment to air-sea fluxes. Figure V A quantifies the
extent of agreement between simulations by several models [(what set of
models ?? --CMIP3?)]{.mark} and observations for both temperature and
precipitation [(the triangular points will be discussed in Chapter VI
below).]{.mark} Each model's temperature or precipitation simulation
produce a single point on the diagram, but in the figure the ranges of
results from all the models are shown as shaded areas.

![](media/image1.jpeg){width="4.520138888888889in"
height="2.540277777777778in"}

Figure V. A. Taylor Diagram of CMIP3 models

This type of diagram (Taylor 2001) displays the overall space-time
correlation between simulated and observed variables as an angular
coordinate. A 100% perfect correlation would place a point along the
horizontal direction to the right, while zero correlation would place a
point along the upward vertical direction. Looking at the red-shaded
area that depicts the range of near-surface temperature simulations, one
sees a 95--98% correlation with observations. The radial coordinate in
the diagram gives the ratio of simulated to observed amplitude for the
variations that are being correlated. A value of 1.0 indicates perfect
agreement of the amplitudes. In this coordinate system, complete
agreement between simulation and observation in both dimensions would
place a point where the dashed semicircle and the horizontal line
intersect. The distance from this point to the actual point for any
given model is proportional to the combined root-mean-square model error
in both space and time dimensions. Temperature points for all of the
models lie very close to complete agreement with observation---indeed
nearly within the uncertainty range of the observations themselves
(Covey *et al*., 2003). (I find this confusing -- since later on we say
that there regional errors that are clearly larger than observervational
error !!)

For monthly mean precipitation, AOGCM simulations are considerably less
precise than for temperature. The figure shows that overall space-time
correlation between models and observations is \~50--60%. [(This figure
is dominated by the tropics]{.mark} [--]{.mark} [could]{.mark} [we
generate the same number for, say, gthe NH north of 35N?)]{.mark}
Qualitative examination of latitude-longitude maps shows that AOGCMs
generally reproduce the observed broad patterns of precipitation amount
and year-to-year variability (A. Dai, 2006: Precipitation
characteristics in eighteen coupled climate models, J. Climate, in
press). The most prominent error is that models without flux adjustment
fail to simulate the observed northwest-to-southeast orientation of a
large region of particularly heavy cloudiness and precipitation in the
Southwest Pacific Ocean (the Southwest Pacific Convergence Zone or
SPCZ). Instead, these models produce an unrealistic set of
Inter-Tropical Convergence Zones in two parallel lines straddling the
Equator: a "double ITCZ" pattern. The double-ITCZ error has been
frustratingly persistent in climate models despite much effort to
correct it. The average day-night cycle of temperature and precipitation
in AOGCMs exhibits general agreement with observations, although
simulated cloud formation tends to start too early in the day. Another
discrepancy between models and observations appears upon sorting
precipitation into light, moderate and heavy categories. Models
reproduce the observed extent of moderate precipitation (10-20 mm/day)
but underestimate the extent of heavy precipitation and overestimate the
extent of light precipitation (Dai 2006). Additional model errors appear
when precipitation is studied in detail for particular regions, e.g.
within the US (Ruiz-Barradas, A., and S. Nigam, 2007).

Taking examples from two of the US model families discussed in Chapter
IV, one finds that AOGCM-simulated and observed maps of surface
temperature and even precipitation appear rather similar at first sight.
Constructing simulated-minus-observed "difference maps," however,
reveals monthly and seasonal mean temperature and precipitation errors
up to \~10°C and 7 mm / day respectively at some points (Figs V B, W.
Collins *et al*., 2006; and V C Delworth *et al*., 2006).

Figure V.B 1--4. CCSM3 annual mean simulated-minus-observed sea surface
temperature \[°C\]

![](media/image2.jpeg){width="4.8902777777777775in"
height="6.7347222222222225in"}

![](media/image3.jpeg){width="5.175in" height="2.0347222222222223in"}

The CCSM3 temperature difference maps exhibit the largest errors in the
Arctic (note scale change in last frame), where continental wintertime
near-surface temperature is overestimated. AOGCMs find this quantity
particularly difficult to simulate because, for land areas near the
poles in winter, models must resolve a strong temperature inversion
(warm air overlying cold air).

[What is the last panel]{.mark} -- this caption needs work.

![](media/image4.jpeg){width="5.995138888888889in"
height="4.602777777777778in"}

Figure V. C 1-5. **Observed and model-simulated precipitation
\[mm/day\]**

The GFDL precipitation difference maps reveal significant widespread
errors in the tropics, most notably in the ITCZ region discussed above
and in the Amazon River basin, where precipitation is underestimated by
several millimeters per day.

(The observed is Xie-Arkin here -- requires a reference)

Similar precipitation errors appear in the following table of CCSM3
results Table V 1.

Table V 1 CCSM3 Precipitation by region(Collins, et al, 2006)

  ------------------------------- ------------------------ --------------
  **Region**                      **CCSM3-simulated        **Error**
                                  precip**                 

  Southeast USA (30-40°N,         2.4 mm/day               −24%
  80-100°W)                                                

  Amazon basin (10°S-10°N,        4.5 mm/day               −28%
  60-80°W)                                                 

  Southeast Asia (10-30°N,        3.1 mm/day               −24%
  80-110°E)                                                
  ------------------------------- ------------------------ --------------

[This selection of regions and choice of just one model seems too
limited to me --IPCC Ch. 11]{.mark} [supplementary material]{.mark} [has
this kind of information for all CMIP3 models for all of the Giorgi
regions.]{.mark} [We could make a more interesting figure or table out
of those statistics.]{.mark} [(Are these annual means?) One could also
make the case]{.mark} [from these statistics for the quality of the
"mean"model, if desired.]{.mark}

AOGCM precipitation errors have serious implications for "Earth system"
models with interactive vegetation, because such models use the
simulated precipitation to calculate plant growth (see Chapter VI
below). Errors of the magnitude shown above would produce an unrealistic
distribution of vegetation in an Earth system model, e.g. by spuriously
deforesting the Amazon basin.

In summary, modern AOGCMs generally simulate large-scale mean climate
with considerable accuracy, but the models are not reliable for aspects
of mean climate in some regions, especially precipitation.

**20^th^ century trends**

Modern AOGCMs are able to simulate not only the time-average climate but
also changes (trends) of climate during over the past century or more.
For example, Figure V D shows results from the three US models and the
"average" CMIP3 model. All parts of the figure display the same curves
of annual mean globally averaged near-surface temperature as observed by
the UK Climatic Research Unit (CRU), as well as simulated by the average
over all CMIP3 models and the average over only those CMIP3 models that
included the effects of volcanic eruptions. Results from individual US
models are shown both for separate ensemble members (dotted lines) and
for the average over all ensemble members (continuous lines). The
individual members of a parituclar model ensemble differ from each other
because they were run from different initial conditions. The precise
initial conditions, especially deep ocean temperature and salinity, are
not known for 1860; the spread among the dotted-line curves thus
indicates uncertainty in model-simulated temperature arising from our
lack of knowledge of initial conditions.

These results demonstrate that modern climate models typically exhibit
good agreement with observed near-surface temperature trends for the
global mean (Min and Hense, 2006). Global warming during the past few
decades is successfully simulated by the models only if they include
anthropogenic emissions of greenhouse gases and aerosols. Min and Hense,
(in press) show that the same is true for most individual continents.
Observed trends in climate extremes such as heat-wave frequency and
frost-day occurrence are also simulated with basic reliability by the
latest generation of AOGCMs (Tebaldi et al., in press). [(Don't we need
some recognition here that, for example, GISS achieves this agreement
with the inclusion of the indirect effect, while GFDL and CCSM get it
without any indirect effect. Both Schwartz (2007 and Keihl (submitted to
JGR]{.mark} [--]{.mark} [status?) discuss this issue]{.mark} [--]{.mark}
[the]{.mark} [later pointing out that climate sensitivity and strength
of aerosol forcing seem to]{.mark} [be]{.mark} [inversely proportional
across the models. We need to say, in some way, that these]{.mark}
[experiments]{.mark} [show that the 20the century trends]{.mark} [can be
simulated with plausible]{.mark} [forcings, but that the precise mix of
forcing and sensitivity is still uncertain -- we also need to be
consistent with the discussion of aerosol forcing in Ch. 2. As is, there
are not]{.mark} [inconsistent]{.mark} [in tone.)]{.mark}

Figure V D 1. Twentieth century globally averaged surface temperature
simulation from GFDL CM2.1

![](media/image5.jpeg){width="5.997222222222222in"
height="4.634027777777778in"}

Figure V D 2 Twentieth century globally averaged surface temperature
simulation from GISS Model E-r These figures need to organized into a
single figure

![](media/image6.jpeg){width="5.997222222222222in"
height="4.634027777777778in"}

Figure V D 3 Twentieth century globally averaged surface temperature
simulation from CCSM3

![](media/image7.jpeg){width="5.997222222222222in"
height="4.634027777777778in"}

Figure V D 4 Twentieth century globally averaged surface temperature
simulation from the three US CMIP3 models and the average of all CMIP3
models that include4d volcanic effects

![](media/image8.jpeg){width="5.997222222222222in"
height="4.634027777777778in"}

At smaller scales the model simulation of trends can be less accurate.
For example, model-simulated trends do not consistently match the
observed lack of 20^th^ century warming in the Central US **(**Kunkel et
al., in press). The evolution of large-scale patterns, however, can be
simulated with fair detail by modern climate models. For example, the
longitude-latitude map of trends from GISS modelE agrees reasonably well
with the observed spatial distribution Fig V E (Hansen *et al*., 2006) .
See also Knutson et al, 2006).

![](media/image9.jpeg){width="6.0in" height="2.7in"}

**Figure V. E.** The figure shows general agreement between the model
and observations not only for the overall period 1880-2003, but also for
the segments 1880--1940 and 1979--2003, which encompass periods of early
and late 20^th^ century warming.

[The model being used needs to be mentioned in the caption.]{.mark}

Amplification of warming and cooling at high northern latitudes is the
most obvious feature in the observations. For the period 1940--1979,
this model simulates only a small change in global mean temperature, in
agreement with observations, but it fails to simulate the strong north
polar cooling observed for this period. As a result, the model-simulated
global mean temperature change (upper right corner of each frame) for
1940--1979 is slightly positive rather than slightly negative as
observed. For both 20^th^ century warming periods, the model simulates
but underestimates the high-latitude amplification of global warming.

[Sea--level is discussed in the ocean section, so is redundant
here.]{.mark}

A number of specific climate phenomena in addition to near-surface
temperature, precipitation and sea level are discussed in the following
sections. These are important for practical applications of climate
models because they directly affect near-surface temperature and
precipitation patterns, and thereby indirectly affect circulation and
cloud patterns that affect feedbacks and climate sensitivity as well as
the evolution of sea level, together with many other features of climate
([this could be stated more clearly]{.mark}).

***Annular Modes***

The primary mode of Arctic interannual variability is the Arctic
Oscillation (Thompson and Wallace 1998), which is also referred to as
the Northern Annual Mode (NAM) and which is related to the North
Atlantic Oscillation (Hurrell 1995). The primary mode of Antarctic
interannual variability is the Southern Annular mode (SAM) (Thompson and
Wallace 2000), also known as Antarctic Oscillation. These modes of
variability are of special importance for the attribution of climate
change and for climate projections, because observed changes in the
circulation in the past few decades (especially in the Southern
Hemisphere) as well as the changes in circulation projected by models in
the future, strongly resemble these structures.

Coupled global climate models have shown skill in simulating the NAM
(Fyfe et al. 1999, Shindell et al. 1999, Miller et al. 2006), although
in some cases too much of the variability in sea level pressure is
associated with the NAM in these models (Miller et al. 2006). Global
climate models also realistically simulate the SAM (Fyfe et al. 1999,
Cai et al. 2003, Miller et al. 2006), although some details of the SAM
(e.g. amplitude and zonal structure) show disagreement between global
climate model simulations and reanalysis data (Raphael and Holland 2006;
Miller et al. 2006).

In response to increasing concentrations of greenhouse gases and
tropospheric sulfate aerosols in the 20th century, the multi-model
average exhibits a positive annular trend in both hemispheres, with
decreasing sea-level pressure (SLP) over the poles and a compensating
increase in mid-latitudes (Miller et al. 2006). A variety of modeling
studies have also shown that trends in the stratospheric climate can
affect the annular modes in the troposphere. Indeed, an importance
result from atmospheric modeling in recent years is the realization that
the stratospheric ozone hole has contributed significantly to the
observed trends in the surface winds and sea-levewl pressure
distribution of the Southern Hemisphere. (refs) However, the models may
not be trustworthy in their simulation of the relative magnitude of the
GHG and stratospheric ozone effects on the annular mode (Miller et al.
2006; Arblaster and Meehl, 2006), and may underestimate the coupling of
stratospheric changes due to volcanic aerosols to annular variations at
the surface.

.

***Ocean structure and circulation***

A set of ocean characteristics or metrics (sea surface temperature,
ocean heat uptake, meridional overturning and ventilation, sea level
variability and global sea level rise) is used to describe the realism
of the ocean in the climate models.

*Sea surface temperature:* The sea surface temperature (SST) plays a
critical role in the determination of the climate and the predictability
of the changes. In general, when the simulated fields of SST are
compared to observational fields there is improvement (improvement
compared with what?) in the models\' representation of the mean SST
Figure V F(Delworth *et al*., 2006) compares the CM2.0 and CM2.1 mean
SST field averaged over a period of 100 years to the Reynolds SST
observational climatology. With an improved atmospheric core and with
modified treatment of subgrid scale oceanic mixing, the later version
(CM2.1) of the GFDL climate model produces a reduced cold bias in the
northern hemisphere.

Figure V F Maps of errors in simulation of annual mean sea-surface
temperature (SST). Units are K. The errors are computed as model minus
observations, where the observations are from the ReynoldsSST data
(provided by the NOAA-CIRES Climate Diagnostics Center, Boulder,
Colorado, USA, from their Web site at http://www.cdc.noaa.gov/). (a)
CM2.0 (using model years 101-200). (b) CM2.1 (using model years
101-200). Contour interval is 1K, except that there is no shading for
values between 1 K and +1 K.

![](media/image10.pct){width="3.558333333333333in" height="3.0625in"}

The CCSM3.0 model also has improved its simulation of SST primarily in
the handling of the processes associated with the mixed layer of the
upper ocean waters (Danabasoglu *et al*., 2005). The improvement in the
representation of the SST is apparent especially in the eastern tropical
Pacific (see Figure V G). An inter-model comparison of the 50 year
global SST trend for each model is shown in Figure V H. The SST trends
range from a low of 0.1°C/50yrs to a high of about 0.6°C/50yrs, with the
observational trend estimate given as about 0.43°C/50yrs. The figure
also shows that within a group, the estimates significantly vary. This
distribution of values in SST trends shows that improvements in any
model\'s representation of SST are dependent on both advances in the
ocean and atmospheric components.

Figure V G Differences in annual-mean surface temperature between CCSM2
and the HadISST data set (Rayner *et al*. 2003) (top); corresponding
differences for CCSM3 (bottom) (Collins, et al, 2005).

![](media/image11.pct){width="4.001388888888889in"
height="4.094444444444444in"}

Figure V H Scatter plot of the SST trends averaged in the central and
eastern tropical Pacific (9 S--9 N and 90--180 W), and global mean
surface temperature trends. Correlation of the model results is 0.58, of
higher magnitude than the 95% significance level of 0.46. The 1:1 line
is drawn for clarity. The red boxes denote US Climate models and the
black box is the relationship computed from observations. (**Zhang &
McPhaden, 2006)**

![](media/image12.pct){width="4.557638888888889in"
height="4.542361111111111in"}

*Meridional overturning circulation and ventilation:* The
planetary-scale circulation transporting heat and freshwater throughout
the global oceans is referred to as thermohaline circulation. The
Atlantic portion of this process is called the Atlantic meridional
overturning circulation (AMOC). Tropical and warm waters flow northward
via the Gulf Stream and North Atlantic Current. The southward flow
occurs when water is sub-ducted in the regions of the Labrador Sea and
Greenland Seas and occurs when the freshening of the surface waters
become denser and flow down the slope to deeper depths. Similar
processes occur at locations in the Southern Ocean. Ventilation is the
process by which these dense surface waters are carried into the
interior of the ocean. The important climate parameter is the rate at
which this process occurs, the so-called \"ventilation rate\". It has
been suggested that this pattern of circulation may weaken (i.e. less
warmer water flowing towards Europe) thereby impacting the climate. It
is thus important to understand how well the ocean component simulates
the observed estimates of these overturning processes.

*Schmittner et al*. \[2005\] examined the performance of the models in
reproducing the observed meridional overturning in 4 of the 5 US models.
The authors examined a small ensemble set of simulations to quantify the
uncertainty in the models\' representation of 20^th^ century AMOC
transports. To make their estimate, they evaluated the global
temperature (T), the global salinity (S), the pycnocline depth (D), the
surface temperature and surface salinity in the Atlantic (SST, SSS), and
calculations of the overturning at 3 locations \~in the Atlantic. Their
results suggest that temperature is simulated the most successfully on
the large scale and that the overturning transports at 24°N are close
(\~18Sv) to the observed measurements (\~15.8Sv). However, the maximum
mean overturning transports in these models are too high (23.2, 31.7,
27.7, and 30.9 Sv: *Schmittner et al*. \[2005\] and 21.2 Sv from *Bryan
et al.* \[2006\]) than the observed value (17.7 Sv). Table V 2 shows a
reduced version of Table 1 from *Schmittner et al*. \[2005\] that shows
the root mean errors (RMS) for the various quantities as compared to
observations. The authors do not attempt to explain why the models are
different from each other and from observations, rather, that there is a
broad range in the value of these metrics for a set of climate models.
(Can the results for the full CM IKP3 ensemble be presented first to
provide a context for the US model results.)

Table V 2 Model Errors

  ----------- ----------- ----------- ----------- ----------- ----------- ------ --------- -------- ---------------
  Model       T~global~   S~global~   D~global~   SST~NAtl~   SSS~NAtl~          D~NAtl~            AMOC~24N~
                                                                                                    (15.8)

                                                                                           (SV)     

  GFDL-2.0    0.20        0.43        0.57        0.34        0.53        0.75             0.16     
                                                                                           (18.3)   

  GISS-AOM    0.66        0.75        2.29        0.43        0.79        3.48             0.22     
                                                                                           (19.2)   

  GISS-EH     0.31        0.76        1.57        0.61        1.12        1.85             0.34     
                                                                                           (21.1)   

  GISS-ER     0.69        0.82        2.06        0.65        1.11        2.40             0.13     
                                                                                           (17.9)   
  ----------- ----------- ----------- ----------- ----------- ----------- ------ --------- -------- ---------------

[*From Schmittner et al*. \[2005\] Table 1]{.underline}. RMS Errors for
the Individual Models; RMS errors are normalized by the standard
deviation of the observations unless otherwise stated. *Schmittner et
al*. 2005; \"Observation-based estimates of the AMOC at 24 N from
*Ganachaud and Wunsch* \[2000\] *and Lumpkin and Speer* \[2003\], at 48
N from *Ganachaud* \[2003\], and its maximum value in the North Atlantic
from *Smethie and Fine* \[2001\] *and Talley et al.* \[2003\], as well
as temperature, salinity, and pycnocline depth observations from the
World Ocean Atlas 2001 \[*Conkright et al*., 2002\] are used to evaluate
the climate models.\"

Another aspect of the planetary-scale ocean circulation that is of
interest is transport of mass by the Antarctic Circumpoilar Current
through the Drake Passage. The passage, between the tip of South America
and the Antarctic Peninsula provides a constrained passage to measure
the flow between two large ocean basins. The observed mean transport is
around 135 Sv. *Russell et al.* \[2006\] estimate the flow in the
passage for a subset of the climate models (Table V 3). There is a wide
range in the simulated mean values. The interaction between the
atmospheric and ocean component models appears to be important in
reproducing the observed transport. The strength and location of the
zonal wind stress provided by the atmosphere correlates with how well
the transport reflects observed values. (Given the poot performance of
the GISS models here, why not drop this table and must mention that the
GFDL models, for example, simulate realistic values for this transport
-- put into CMIP3 context?)

TABLE V 3

+----------------+-----+-----------+----------+-------------+---------+
| Model          | ACC | *dρ/dy*   | Total    | Max τ~x~ (N | Lat of  |
|                | (   |           | τ~x~     | m^-2^)      | max     |
|                | Sv) | (kg       | 10^12^ N |             | τ~x~    |
|                |     | m^-3^)    |          |             |         |
+----------------+-----+-----------+----------+-------------+---------+
| Observational  | 135 | 0.58      | 6.5      | 0.161       | 52.4    |
| estimate       |     |           |          |             |         |
+----------------+-----+-----------+----------+-------------+---------+
| GISS-ER        | 266 | 0.62      | 4.3      | 0.107       | 46.0    |
+----------------+-----+-----------+----------+-------------+---------+
| GISS-AOM       | 202 | 0.38      | 2.9      | 0.166       | 43.5    |
+----------------+-----+-----------+----------+-------------+---------+
| GFDL-CM2.1     | 135 | 0.58      | 6.1      | 0.162       | 51.0    |
+----------------+-----+-----------+----------+-------------+---------+
| GFDL-CM2.0     | 113 | 0.56      | 4.5      | 0.149       | 46.0    |
+----------------+-----+-----------+----------+-------------+---------+
| GISS-EH        | -6  | 0.43      | 3.6      | 0.096       | 46.0    |
+----------------+-----+-----------+----------+-------------+---------+

**[Reduced From Table 1 *Russell et al.* \[2006\]]{.underline}** Various
parameters related to the strength of the ACC. The ACC transport is the
integral of the zonal velocity across the Drake Passage at 69°W. The
density gradient (d*ρ*/dy) is the zonally averaged density difference
between 65° and 45°S. The total ACC-related wind stress (τ~x~ total) is
the integral of the zonal wind stress over the Drake Passage channel
(54°--64°S). The maximum westerly wind stress (τ max) is the maximum of
the zonally averaged wind stress that is located at the latitude given
by Lat τ max. The observed ACC strength is from *Cunningham et al.*
(2003). The observed density gradient is calculated from the World Ocean
Atlas 2001 (*Conkright et al.* 2002). The observed wind data are from
the NCEP long-term mean *(Kistler et al.* 2001). NA indicates data not
archived at PCMDI

*Northward Heat Transport*: A common metric used to quantify the realism
in ocean models is the northward transport of heat. This integrated
quantity (from top to bottom and across latitude bands) gives an
estimate of how heat moves within the ocean and is important in
balancing the overall heat budget of the tropics and of the
extratropical regions of the Earth. The calculations for the ocean\'s
northward heat transport in the current generation of climate models
show that the models reasonably represent the observations (*Delworth et
al*. 2006, *Collins et al*. 2006, and *Schmidt et al*. 2006). The
current models have significantly improved over the last generation in
the Northern Hemisphere. Comparisons of the simulated values to the
observed values for the North Atlantic are within the uncertainty of the
observations. In the Southern Hemisphere, the comparisons in all the
models are not as good, with the Indian Ocean transport estimates
contributing to a significant part of the mismatch.

*Heat Content:* The ocean heat content can be thought of approximately
as the mean temperature of the oceans. Its time evolution is of central
importance in determining how realistically the models reproduce the
uptake of heat. The seasonal cycle of heat content as well as longer
term trends provide useful metrics. An evaluation of the temporally
evolving ocean heat content in the suite of climate models for the AR4
shows the models abilities to simulate the zonally integrated annual and
semi-annual cycle in heat content. In the middle latitudes \[*Gleckler
et al.* 2006\], the models do a reasonable job while there is a broad
spread of values for the tropical and polar regions. This analysis
showed that the models replicate the dominant amplitude of the annual
cycle along with its phasing in the mid-latitudes **\[**Fig V I\]. At
high latitudes, the comparisons with observations are not as consistent.
While the annual cycle and global trend are reproduced, analyses of the
models \[e.g. *Hansen et al.* 2005\] show that they do not simulate the
decadal changes in estimates made from observations \[*Levitus et al.*
2001\]. Part of the difficulty of comparisons at high latitudes and at
long periods is the paucity of observational data \[*Gregory et al.*
2004\].

Figure V I 1

![](media/image13.pct){width="4.706944444444445in"
height="6.499305555555556in"}

**[From Glecker *et al*. 2006]{.underline}** .**Figure 1.** Observed
(WOA04) and simulated zonally integrated ocean heat content (0--250 m):
(a) annual cycle amplitude (108J/ m^2^) and (b) semiannual/annual
(A2/A1).

Figure V I 2

![](media/image14.pct){width="5.998611111111111in"
height="6.829166666666667in"}

**From Glecker *et al*., 2006**, **Figure 3**. Annual cycle of observed
(WOA04) and simulated basin average global ocean heat content

(0--250 m). Units are 1022J. Arctic Ocean is defined as north of 60 N,
and Southern Ocean is south of

60 S.

*Global mean sea level rise:* Two separate physical processes contribute
to the sea level rising: 1) the thermal expansion of the ocean from an
increase in the heat uptake by the ocean (steric component) and 2) the
addition of freshwater from precipitation, continental ice melt, and/or
river runoff (the eustatic component). The various ocean models handle
freshwater fluxes in different ways. With the addition of a free surface
in the current generation of ocean models, the freshwater flux into the
oceans can be included directly \[*Griffies et al*. 2001\]. The
freshwater contribution is computed by quantities estimated by
atmosphere/and/ice sheet components of the climate model \[e.g. *Church
et al.,* 2005, *Gregory et al.*, 2006\]. In general, the
state-of-the-art climate models underestimate the combined global mean
sea level rise as compared to tide gauge and satellite altimeter
estimates while the rise for each of the separate components is within
the uncertainty of the observed values. The reason for this is an open
research question and may relate to either observational sampling or not
correctly accounting for the all the eustatic contributions. The steric
component to the global mean sea level rise is estimated to be
0.40+/-0.05mm/yr from observations \[*Antonov et al.* 2005\]. The models
simulate a similar, but somewhat smaller rise \[*Gregory et al.*, 2006,
*Meehl et al.* 2005\]. There are also significant differences in the
magnitudes of the decadal variability between the observed and the
simulated sea level or SSH. It most be noted, however, that progress is
been made over the previous generation of climate models. When
atmospheric volcanic contributions are included, for example, ocean
models of the current generation capture the observed impact on the
ocean (a decrease in the global mean sea level). Figure V J from *Church
et al*. 2006 gives an example of a few models and their de-trended
estimate of the historic global mean sea level that shows the influence
of including the additional atmospheric forcing agents in changing the
steric height of the ocean.

Figure V J

![](media/image15.pct){width="4.282638888888889in"
height="5.874305555555556in"}

![](media/image16.pct){width="5.988888888888889in"
height="2.4138888888888888in"}

**C. Simulation of specific climate dynamical features**

***Extratropical storms***

Climate models have developed from numerical weather prediction models
whose performance has been primarily judged on their ability to forecast
mid-latitude weather. The success of these models in their simulation of
midlatitude cyclones and anticyclones has resulted in the continuous
growth in the value of numerical weather prediction. The ability of
general circulation models to generate realistic statistics of
midlatitude weather has also been central in the development of climate
modeling. This is not only because midlatitude weather is important in
its own right, but also because these storms are the primary mechanism
by which heat, momentum, and water vapor are transported by the
atmosphere, making their simulation crucial for the simulation of the
atmospheric climate.

Indeed, it can be thought of as the defining feature of Atmospheric
General Circulation Models (AGCMs) that they compute midlatitude eddy
statistics and the associated eddy fluxes through explicit computation
of the life cycles of individual weather systems and not through some
turbulence or closure theory. It may seem very inefficient to compute
the evolution of individual eddies when primarily interested in the long
term statistics of the eddies, but it is has been the clear judgment of
the community for decades that the explicit simulation of these eddies
in climate models is far superior to the attempts that have been made to
date in developing closure theories for the eddy statistics. The latter
theories typically form the basis for EMICs (Earth System Models of
Intermediate Complexity), which are far more efficient computationally
than GCMs, but provide less convincing simulations.

Two figures illustrate the quality of the simulations of midlatitude
eddy statistics that coupled AOGCMs of the horizontal resolution used in
AR4 are capable of generating. Shown for the GFDL CM2.1 in Fig. V K 1 is
the wintertime variance of the north-south component of velocity at 300
hPa (in the upper troposphere) and in Fig. V K 2 the wintertime poleward
eddy heat flux, or the covariance between temperature and north-south
velocity, at 850mb (in the lower troposphere). When analyzing eddy
statistics it is often useful to filter the flow fields to retain only
those time scales, roughly 2-10 days, associated with midlatitude
weather systems, but the two quantities chosen here are dominated by
these time scales to a sufficient degree that they are relatively
insensitive to filtering. Here we have simply removed the monthly means
before computing variances. In each case, the eddy statistics are
compared to the estimates of the observed statistics obtained from the
NCEP-NCAR reanalysis (B.Wyman, personal communication).

In winter, Northern Hemisphere storms are organized into two major
oceanic storm tracks over the Pacific and Atlantic oceans. Historically,
it has been found that atmospheric models of resolutions of about
200-300 kms are typically capable of simulating the midlatitude storm
tracks with comparable realism to that shown in the figure. The eddy
amplitudes are often a bit weak and often displaced slightly
equatorward, especially in Southern hemisphere summer (although the
model shown here has a weaker Southern hemisphere bias than most
models). In models with resolution coarser than 200-300kms, the
simulation of the midlatitude storm tracks typically deteriorates
significantly (see for example, Boyle 1993). It is thought to be
important for the general improvement in model simulations described in
Chapter 1 that most of the models in the CMIP3 database are now
utilizing this 200-300km resolution. While finer resolution results in
better simulations of the structure of midlatitude storms, including the
structure of warm and cold fronts as well as the interaction between
these storms and coastlines and mountain ranges, the improvements in the
midlatitude climate on large scales tend to be less dramatic and
systematic. Other factors besides horizontal resolution are considered
to be important for the details of storm track structure, including the
distribution of tropical rainfall, which is sensitive to the closure
schemes utilized for moist convection, interactions between the
stratosphere and the troposphere, which are sensitive to vertical
resolution. Roeckner *et al* (2006) illustrate the importance of
vertical resolution for the midlatitude circulation and storm track
simulation.

A more detailed look at the ability of the AR4 models to simulate the
space-time spectra of the observed eddy statistics is provided by
Lucarini, *et* al,(2006). These authors view the deficiencies noted,
which vary in detail from model to model, as serious limitations to the
credibility of the models. But, as indicated in Chapter 1, our ability
to translate measures of model biases into useful measures of model
credibility is limited, and the implications of these biases in the
space-time spectra of the eddies is not self-evident. Indeed, in the
context of the simulation of the eddy characteristics generated in
complex turbulent flows in the laboratory (e.g., Dimotakis , 2005) the
quality of these atmospheric simulations, closely based on fluid
dynamical first principles, should probably be thought of as one of the
most impressive characteristics of current models.

As an example of a significant model deficiency that can plausibly be
linked to limitations in the credibility of the climate projections,
note that the Atlantic storm track, as indicated by the maximum in
velocity variance in Fig 5.1, is too zonally oriented, the observed
stormtrack having more of an southwest-northeast tilt. This particular
deficiency is common in the CMIP-3 models (van Ulden and van Oldenborgh,
2006) and is related to the difficulty in simulating the phenomenon of
blocking in the North Atlantic with the correct frequency and amplitude.
Van Ulden and van Oldenborgh make the case that this bias is significant
for the quality of regional climate projections over Europe.

.

**Figure V K 1**: Top: variance of north-south velocity at 300hPa as
simulated by the GFDL CM2.1 model in years 1981-2000 of one realization
of the 20C3M simulation, as contributed to the CMIP3 database. Units are
m^2^/s^2^. Middle: The same quantity as obtained from the NCEP-NCAR
reanalysis (ref). Bottom: model minus observations.

![](media/image17.png){width="3.9590277777777776in"
height="5.129166666666666in"}

**Figure V K 2:** Top: covariance of north-south velocity and
temperature at 850hPa as simulated by the GFDL CM2.1 model in years
1981-2000 of one realization of the 20C3M simulation, as contributed to
the CMIP3 database. Units are Km/s. Middle: The same quantity as
obtained from the NCEP-NCAR reanalysis (ref). Bottom: model minus
observations.

![](media/image18.png){width="3.8569444444444443in"
height="4.997222222222222in"}

***Monsoons***

A monsoonal circulation is distinguished by its seasonal reversal after\
the sun crosses the equator into the new summer hemisphere.  Rain is\
largest, if not entirely restricted, to the summer within monsoonal\
climates, when continental rainfall is supplied mainly by evaporation\
from the nearby ocean.  This limits the reach of monsoon rains to the\
distance over which moisture can be transported onshore (Prive and\
Plumb 2007).  Variations in the spatial extent of the monsoon from\
year to year determine which inland regions experience a drought.\
\
\
While the monsoon takes its name from a language spoken by traders\
around the Arabian Sea, this circulation reaches far beyond the\
periphery of the Indian Ocean, and local cultures have their own words\
for the monsoon: for example, Mei-yu in China, Chang-ma in Korea, and\
Bai-yu in Japan.  Over a billion people are dependent upon the arrival\
of the monsoon rains for water and irrigation for agriculture.  The\
Asian monsoon during NH summer is the most prominent example of the\
monsoon circulation, dominating global rainfall during this\
season. However, the seasonal reversal of winds and summer rainfall\
maximum also indicate monsoon circulations in West Africa and the\
Amazon basin. In addition, during NH summer, air flows off the eastern\
Pacific Ocean toward Mexico and the American southwest, while over the\
Great Plains of the United States, moisture from the Gulf of Mexico\
brings an annual peak in rainfall. Thus, the climate in these regions\
is also described as monsoonal.\
\
Because of the geographic extent of the Asian monsoon, and the
differences in detailed structure in different sub-regions, measures of
the fidelity of Asian monsoonal simulations can differ depending on the
specific regional focus on the metrics being used.. Kripalani et al.
(2007) judged that three-quartersof the eighteen analyzed coupled models
match the timing and magnitude of the summertime peak in precipitation
over East Asia between 100 and 145E and 20 to 40N that is evident in the
NOAANCEP Climate Prediction Center Merged Analysis of Precipitation
(CMAP,\
Xie and Arkin 1997). However, only half of these models were able to
reproduce the gross observed spatial distribution of monsoon rainfall,
and its extension along the coast of China toward the Korean peninsula
and Japan.  Considering a broader range of longitude (40-180E) that
includes the Indian subcontinent,Annamalai et al. (2007) found that only
six of eighteen CGCMs\
significantly correlated with the observed spatial pattern of CMAP
precipitation during June through September. (These six models also
produced relatively realistic simulation of ENSO variability, which is
known to influence interannual variations in the Asian summer monsoon.)
Kitoh and Uchiyama (2007) computed the spatial correlation and
root-mean-square error of simulated precipitation over a similar region
and found the GFDL models in the top tercile with a spatial correlation
exceeding 0.8, while the GISS\
modelE-R correlation was just under 0.5.\
\
During NH winter, the Asian surface winds are directed offshore: from\
the northeast over India, and the northwest over East Asia. The two\
American models included in the comparison of the simulated East Asian\
winter monsoon by Hori and Ueda (2006), GFDL CM2.0 and GISS modelE-R,\
generally reproduce the observed spatial distribution of sea level\
pressure and 850 mb zonal wind.\
\
(I HAVE DELETED A SECTION TRHAT DISCUSSES PROJECTIONS, AS THIS IS NOT
THE SUBJECT OF THIS REPORT.)\
 \
Observed variability of the West African monsoon is known to be related
to remote ocean temperatures in the North and South Atlantic, and Indian
Oceans (Rowell, et al; Latif, Zhang and Delworth) as well as to
temperatures in the nearby Gulf of Guinea. Cook and Vizy (2006) found
that slightly over half of the 18 analyzed coupled models reproduced the
observed maximum in\
precipitation over land during June through August. Of these models,
only six reproduced the observed anti-correlation between Gulf of Guinea
ocean temperature and Sahel rainfall. The GISS modelE-H and both GFDL
models were among the more realistic in this regard..\
\
The late 20^th^ century Sahel drought was a dramatic changes in the
Earth's hydrological cycle that plausibly must be simulated by climate
models if we are to have any confidence in their ability to project
climate in this region into the future. Atmospheric models when run over
observed oceanic temperatures simulate this drought reasonably well
(Hoerling, et al. , 2006). In these models, the drought is at least
partly forced by a warming of the Northern Hemisphere oceans,
particularly the North Atlantic, with respect to the Southern Hemisphere
oceans, especially the Indian Ocean and Gulf of Guinea. While there is a
consensus that these variations in ocean temperature gradients are at
least part due to natural variability, it is also possible that they
were in part anthropogfentically forced. The analysis of the CMIP3
simulations of the 20^th^ century by Biasutti and Giannini (2006)
suggests that aerosol forcing in these models played a part in
generating this drought by cooling the North Atlantic with respect to
the other ocean basins. A small number of the coupled models simulate
droughts of the observed magnitide, including the GFDL models (Held ety
al.,2006) but it is not understood why some models are more realistic in
this regard than others.\
\
([I have again removed the Amazon section that focused on projections.
We probably need something on the Amazon here that focused instead on
the simulations.]{.mark} )

The ability of climate models to simulate NH summer rainfall over the\
US Great Plains and Mexico was summarized by Ruiz-Barradas and Nigam\
(2006). As an example, GISS modelE-H matches the annual cycle of
precipitation over the Great Plains and Mexico closely, and is one of
two models to simulate interannual\
variations in precipitation that are significantly correlated with\
observed variability during the second half of the 20th century.\
\
Initial evaluations of the monsoon simulated by the most recent\
generation of climate models have emphasized the seasonal time scale.\
However, subseasonal variations, such as break periods when the\
monsoon rains are temporarily interrupted, are crucial to forecasts\
and the impact of the monsoon upon water supply.  Simulation of the\
diurnal cycle, and the local hour of rainfall, is also important to\
the partitioning of rainfall between runoff and transpiration, and\
these are important topics for future model evaluation.  Transports of\
moisture by regional circulations beneath the resolution of the model\
(such as low-level jets along the Rockies and Andes and tropical\
cyclones) contribute to the onshore transport of moisture.  In\
general, the models show success at simulating the gross seasonal\
features of the various monsoon circulations, but studies of variations
on\
the smaller spatial and time scales that are important to specific\
watersheds and hydrological projections are limited.

***Tropical storms***

Tropical storms (hurricanes in the Atlantic and typhoons in the Pacific
and Indian Oceans) are of too small a scale to be reliably simulated in
the class of global climate models currently used for climate
projections. There is hope for qualitatively useful simulations of the
climatology of incipient tropical depressions, however. The work of
Vitart and Anderson (2001) is an example of evidence for signficant
information content concerning tropical storm-like vortices in
simulations with models of this type, using the model's ability to
simulate the effects of El Nino on Atlantic storm frequency as a guide.

The recent 20km resolution simulation with an atmospheric model over
prescribed ocean temperatures by Oouchi et al (2006) is indicative of
the kinds of modeling that will be brought to bear on this problem in
the next few years. Experience with tropical storm forecasting suggests
that this resolution should be adequate for describing many aspects of
the evolution of nature tropical storms, and possibly the generation of
storms from incipient disturbances, but probably not tropical storm
intensity.

An alternative promising approach is described by Knutson et al (2007),
in which a regional model of comparable resolution (18 km) is used in a
downscaling framework to simulate the Atlantic hurricane season. Given
the observed year-to year variations in the large-scale structure of the
atmosphere over the Atlantic ocean, the model is capable of simulating
the year-to-year variations in hurricane frequency over a 30-year period
with a correlation of 0.7-0.8 and also captures the observed trend
towards greater hurricane frequency over this period in the Atlantic.
These results suggest that models of this resolution may be able to
provide a convincing downscaling capability for tropical storm frequency
projections into the future, although these projections will still rely
on the quality of the global model projections for changes in sea
surface temperature, atmospheric stability, and vertical shear. The
behavior of the El Nino Southern Oscillation into the future will be a
key element affecting changes in those aspects of the large-scale
structure of the atmosphere over the Atlantic that control tropical
storm formation and tracks.

***Polar climates***

Changes in polar snow and ice cover affect the Earth's albedo and thus
the amount of insolation heating the planet (e.g., Holland and Bitz
2003, Hall 2004, Dethloff *et al*. 2006). Concern has also emerged about
potential melting of glaciers and ice sheets in Greenland and Antarctica
that could produce substantial sea-level rise (Arendt *et al*. 2002,
Braithwaite and Raper 2002, Alley *et al*. 2005). Polar regions thus
require accurate simulation for projecting future climate change and its
impacts.

Polar regions present unique environments and, consequently, challenges
for climate modeling. Key processes include sea ice, seasonally frozen
ground and permafrost (Lawrence and Slater 2005, Yamaguchi *et* al.
2005) and seasonal snow cover (Slater *et al*. 2001), which can have
significant sub-grid heterogeneity (Liston 2004), and clear-sky
precipitation, especially in the Antarctic (King and Turner 1997, Guo
*et al*. 2003). Polar radiation also has important characteristics that
test the ability of models to handle extreme geophysical behavior, such
as longwave radiation in clear, cold environments (Hines *et al*. 1999,
Chiacchio *et al*. 2002, Pavolonis *et al*. 2004) and cloud microphysics
in the relatively clean polar atmosphere (Curry *et al*. 1996, Pinto *et
al*. 2001, Morrison and Pinto 2005). In addition, polar atmospheric
boundary layers can be very stable (Duynkerke and de Roode 2001,
Tjernström *et al*. 2004, Mirocha *et al*. 2005), and stable boundary
layers remain an important area for model improvement.

Confidence in climate model projections of future climate is greatly
increased if it can be shown that climate models can accurately simulate
the current climate state, and much effort has gone into this type of
analysis (e.g. Collins et al. 2006, Delworth et al. 2006). In particular
climate models should be able to reproduce both long-term and short-term
variations in climate including daily, seasonal, interannual, and
decadal variability. For polar regions, much of the assessment of
simulated interannual variability has focused on the primary modes of
polar interannual variability, the Northern and Southern Annular Modes.
Assessment of simulated annular modes appears in Section B. of this
chapter.

Less attention has been given to the ability of global climate system
models to simulate shorter-duration climate and weather variability in
polar regions. Uotila *et al*. (2007) and Cassano *et al*. (2007)
evaluated the ability of an ensemble of 15 global climate-system models
to simulate the daily variability in sea level pressure in the Antarctic
and Arctic. In both polar regions, it was found that the 15-model
ensemble was not able to reproduce many feastures of the daily synoptic
climatology, with only a small subset of the models accurately
simulating the frequency of the primary synoptic weather patterns
identified in global reanalysis data sets. The U.S. models discussed in
detail in Chapter 2 of this report spanned the same range of accuracy as
non-U.S. models, with GFDL and CCSM models part of the small, accurate
subset. More encouraging results were obtained by Vavrus *et al*. (2006)
who assessed the ability of seven global climate models to simulate
extreme cold-air outbreaks in the Northern Hemisphere.

Attention has also been given to the ability of regional climate models
to simulate polar climate. In particular, the Arctic Regional Climate
Model Intercomparison Project (ARCMIP) (Curry and Lynch 2002) engaged a
suite of Arctic regional atmospheric models to simulate a common domain
and period over the western Arctic. Rinke *et al*. (2006) evaluated the
spatial and temporal patterns simulated by 8 ARCMIP models, and found
that the model ensemble agreed well with global reanalyses, despite some
large errors for individual models. Tjernstrom *et al*. (2005) evaluated
near-surface properties simulated by 6 ARCMP models. In general surface
pressure, air temperature, humidity, and wind speed were all well
simulated, as were radiative fluxes and turbulent momentum flux.
Tjernstrom *et al*. (2005) found that turbulent heat flux was poorly
simulated, and that over an entire annual cycle the accumulated
turbulent heat flux simulated by the models was an order of magnitude
larger than the observed turbulent heat flux (Fig. PA-1).

In global models, polar simulation may be affected by errors in
simulating other regions of the planet, but much of the difference from
observations and uncertainty about projected climate change stems from
current limitations in polar simulation. These limitations include
missing or incompletely represented processes and poor resolution of
spatial distributions.

As with other regions, model resolution affects simulation of important
processes. In the polar regions, surface distributions of snow depth
vary markedly, especially when snow drifting occurs. Improved snow
models are needed to represent such spatial heterogeneity (e.g., Liston
2004), which will continue to involve scales smaller than resolved for
the foreseeable future. Frozen ground, whether seasonally frozen or
occurring as permafrost, presents additional challenges. Models for
permafrost and seasonal freezing and thawing of soil are being
implemented in land surface models (see Chapter 2, Land Surface Models).
Modeling soil freeze and thaw continues to be a challenging problem as
characteristics of energy and water flow through the soil affect
temperature changes, and such fluxes are poorly understood (Yamaguchi
*et al*. 2005**)**.

Frozen soil affects surface and subsurface hydrology, which influences
the spatial distribution of surface water with attendant effects on
other parts of the polar climate system such as carbon cycling (e.g.,
Gorham 1991, Aurela *et al*. 2004), surface temperature (Krinner 2003),
and atmospheric circulation (Gutowski *et al*. 2007). The flow of fresh
water into polar oceans potentially alters their circulation, too.
Surface hydrology modeling typically includes limited, at best,
representation of subsurface water reservoirs (aquifers) and horizontal
flow of water at both the surface and below surface. These features
limit the ability of climate models to represent changes in polar
hydrology, especially in the Arctic.

Vegetation has been changing in the Arctic (Callaghan *et al*. 2004) and
projected warming, which may be largest in regions where snow and ice
cover retreat, may produce further changes in vegetation (e.g., Lawrence
and Slater 2005). Current models use static distributions of vegetation,
but dynamic vegetation models will be needed to account for changes in
land-atmosphere interactions influenced by vegetation.

A key concern in climate simulations is how projected anthropogenic
warming may alter ice sheets on land, whose melting could raise sea
levels substantially. At present, climate models do not include
ice-sheet dynamics and thus cannot account directly for how ice sheets
might change, possibly changing heat absorption from the sun and
atmospheric circulation in the vicinity of the ice sheets.

Distributions of snow, ice sheets, surface water, frozen ground and
vegetation have important spatial variation on scales smaller than the
resolutions of typical contemporary climate models. This need for finer
resolution may be satisfied by regional models simulating just a polar
region. Because both the northern and southern polar regions are within
circumpolar atmospheric circulations, their coupling with other regions
is more limited than is the case with midlatitude regions, where the
westerlies rapidly move synoptic systems in and out of a region (e.g.,
Wei *et al*. 2002), which could allow polar-specific models that focus
on antarctic/arctic processes, in part to improve modeling of
surface-atmosphere exchange processes (Fig. V L ). While each of the
above processes have been simulated in finer scale, stand-alone models,
their interactions as part of a climate system also need to be simulated
and understood.

Fig. V L. Cumulative fluxes of surface sensible heat (top panel) and
latent heat (bottom) at the SHEBA site from six models simulating a
western Arctic domain for Sept. 1997 -- Sept. 1998 for ARCMIP. SHEBA
observations are the gray vertical bars; model identifications are given
by the key in the upper panel. Adapted from Tjernstrom *et al*. (2005).
![](media/image19.png){width="4.922916666666667in"
height="7.751388888888889in"}

**[Sea ice]{.underline}** plays a critical role in the exchange of heat,
mass, and momentum between the ocean and atmosphere and any errors in
the sea-ice system will contribute to errors in the other components.
Two recent papers \[*Holland and Raphael* 2006 and *Parkinson et al.*
2006\] quantify how the current models simulate the sea-ice process of
the climate system. Very limited observations make any evaluation of sea
ice difficult. The primary observation available is sea ice
concentration. In some comparisons, sea ice extent (ice concentration
greater than 15%), is used. Satellites have made it possible for a more
complete data set of observations for the past few decades. Prior to
satellite measurements becoming available, observations of ice extent
were fewer. Other quantities that might be evaluated include ice
thickness, but due to limited observations of thickness comparisons with
models are difficult and will not be discussed further here.

*Ice Concentration and extent:* The seasonal pattern in ice growth and
decay in the polar regions for all the models is reasonable \[*Holland
and Raphael* 2006\] (**Figure V M**). However, there is a large amount
of variability between the models in their representation of the sea ice
extent in both the northern and southern hemispheres. Generally, the
models do better in simulating the Arctic than the Antarctic region, as
shown with **Figure V N\].** An example of the complex nature of
reproducing the ice field is given in *Parkinson et al.* \[2006\]. They
found that all the models showed an ice-free region in winter to the
west of Norway, as seen in observational data, but all the models also
produced too much ice north of Norway. They suggest that this is because
the North Atlantic Current is not being simulated correctly. In a
qualitative comparison, Hudson Bay is ice covered in winter in all the
models correctly reproducing the observations. The set of models that
have the most fidelity in Arctic is not the same as the set having the
most fidelity in the Antarctic, which may be do to differences in the
sea ice models that make themselves felt differently due to the
distinctive ice regimes in the north and south, or may be do to
differences in the simulations of aspects of oceanic or meteorological
circulations in those regions.

*Holland and Raphael* \[2006\] examined the variability in the Southern
Ocean sea ice extent extensively. As an indicator of the ice response to
large scale atmospheric events, they compared a set of IPCC AR4 climate
models sea ice response to the atmospheric index, the Southern Annular
Mode (SAM) for the April-June (AMJ) period (Table V 4). The models show
that the ice variability does respond modestly to the large scale
atmosphere forcing but less than limited observations show. Table V4
uses the U.S. models to examine whether models exhibit the observed
out-of-phase buildup of ice between the Atlantic and Pacific sectors
(refered to as the Antarctic Dipole).

**Table V 4**

**MODIFIED FROM Table 1 *Holland and Raphael* \[2006\]** Correlations of
the leading mode of sea ice variability and the southern annular mode
(SAM) for the observations and model simulations

  ----------------------- ----------------------- -----------------------
                          AMJ SAM and high-pass   AMJ SAM and detrended
                          filtered fields         fields

  Observations            **0.47**                **0.47**

  CCSM3                   **0.40**                **0.44**

  GFDL-CM2.1              **0.39**                0.19

  GISS-ER                 0.30                    0.20
  ----------------------- ----------------------- -----------------------

Bold values are significant at the 95% level accounting for the
autocorrelation of the timeseries

Figure V M

![](media/image20.pct){width="4.555555555555555in"
height="3.5430555555555556in"}

**From *Holland and Raphael* 2006**. Fig. 1 The annual cycle of southern
hemisphere ice extent defined to be the area of ice with concentrations
greater than 15%

Figure V N

![](media/image21.pct){width="3.73125in" height="4.128472222222222in"}

**From Parkinson *et al*. 2006:** **Figure 4.** Difference between the
modeled 1979--2004 monthly average sea ice extents and the
satellite-based observations (modeled minus observed), for each of 11
major GCMs, for both the (a) Northern Hemisphere and (b) Southern
Hemisphere.

***Modes of variability***

**[The Madden-Julian Oscillation]{.underline}**: (MJO) is a
characteristic pattern in the tropical atmosphere. It has taken on
special prominence in research on simulating the tropical atmosphere.
This phenomenon consists of large-scale eastward propagating patterns in
humidity, temperature, and atmospheric circulation which strengthen and
weaken tropical rainfall as they propagate around the Earth in roughly
30-60 days. This pattern often dominates intraseasonal (within season)
variability of tropical precipitation on time scales longer than a few
days, creating such phenomena as 1-2 week breaks in Asian monsoonal
rainfall and weeks with enhanced hurricane activity in the Eastern North
Pacific and the Gulf of Mexico. Inadequate prediction of the evolution
of these propagating structures is considered one the main impediments
to more useful extended-range weather forecasts in the tropics, and
improved simulation of this phenomenon is considered by some a litmus
test for the credibility of climate models in the tropics

Recent surveys of model performance indicate that simulations of the MJO
remain inadequate. For example, Lin *et al* (2006), in a study of many
of the models in the CMIP-3 models, conclude that "... current GCMs
still have significant problems and display a wide range of skill in
simulating the tropical intraseasonal variability", while Zhang *et al*.
(2005) in another multi-model comparison study, state that "...
commendable progress has been made in MJO simulations in the past
decade, but the models still suffer from severe deficiencies ..." Nearly
all models do capture the essential feature of the pattern, with
large-scale eastward propagation and with roughly the correct vertical
structure. But the propagation is often too rapid and the amplitudes too
weak. As an example of recent work, Klein (2007?) studies whether two of
the US IPCC models can maintain a pre-existing strong MJO pattern when
initialized with observations (from the TOGA-COARE field experiment),
with limited success. Controlled experiments have suggested that for
models to simulate MJO, the instability of the atmospehre must be
allowed to accumulate to a certain amount before convective storms are
triggered, and sufficient mesoscale statiform heating from convective
systems should exist in the upper troposphere (Wang and Schlesinger
1999). These processes are however poorly understood in current climate
models.

The difficulty in simulation of the MJO is related to the multi-scale
nature of the phenomenon: the propagating pattern is itself of large
enough scale that it should be resolvable by climate models, but the
convection and rainfall modulated by this pattern, and feeding back and
energizing it, occur on much smaller, unresolved, scales. In addition to
this dependence on the parameterization of tropical convection, a long
list of other effects has been shown by models and/or observational
studies to be important for the MJO. These include the pattern of
evaporation generated as the MJO propagates through convecting regions,
feedback from cloud-radiative interactions, intraseasonal ocean
temperature changes, the diurnal cycle of convection over the ocean, as
well as the vertical structure of the latent heating, including
especially the proportion of shallow cumulus congestus clouds and deep
convective cores in the different phases of the oscillation (Lin *et
al*. 2004).

A picture seems to be emerging that the difficulty in simulation may not
be due to a single model deficiency but a result of the complexity of
the phenomenon, given this long list of factors thought to be
significant. In several of the multi-model studies, such as Lin *et al*
(2006) a few of the models do perform well, but without a clearer
understanding of how these factors combine to generate the observed
characteristics of the MJO, it is difficult to maintain a good
simulation as the model is modified for other reasons, and it is
difficult to transfer one model's successful simulation to other models.
It also remains unclear whether the models with superior MJO simulations
should be given extra weight in multi-model studies of climate change in
the tropics.

**[The El Nino -- Southern Oscillation (ENSO)]{.underline}** By the mid
20^th^ century, scientists recognized that a local anomaly in rainfall
and oceanic upwelling in Peru and the nearby Pacific ocean was in fact
part of a disruption to the atmospheric and ocean circulations across
the entire Pacific basin. During El Niño, atmospheric mass migrates west
of the dateline as part of the Southern Oscillation, reducing surface
pressure and drawing rainfall into the central and eastern Pacific
(Rasmussen and Wallace 1983). Together, El Niño and the Southern
Oscillation, often abbreviated in combination as ENSO, are the largest
source of tropical variability observed during recent decades.\
\
Changes along the equatorial Pacific have been linked to global
disruptions of climate (Ropelewski and Halpert 1987). During an El Niño
event, the Asian monsoon is typically weakened, along with rainfall over
eastern Africa, while precipitation increases over the American
southwest. El Niño raises the surface temperature as far poleward as
Canada, while changes in the North Pacific Ocean are linked to decadal
variations in ENSO (Trenberth and Hurrell 1994). In many regions far
from the eastern equatorial Pacific, accurate projections of climate
change in the twenty-first century depend upon the accurate projection
of changes to El Niño. Moreover, the demonstration that ENSO alters
climate across the globe indicates that even changes to the
*time-averaged* equatorial Pacific during the 21st century will
influence climate far beyond the tropical ocean. For example, a
long-term warming of the eastern equatorial Pacific relative to the
surrounding ocean will favor a weaker Asian monsoon, even in the absence
of changes to the size and frequency of El Niño events.\
\
Because of the Earth\'s rotation, easterly winds along the equator cool
the surface by raising cold water from below, which offsets heating by
the absorption of sunlight (e.g. Clement *et al* 1996). Cold water is
especially close to the surface in the East Pacific, while warm water
extends deeper in the west so upwelling has little effect upon the
surface temperature of the West Pacific. The westward increase of
temperature along the equator is associated with a decrease in
atmospheric pressure, reinforcing the easterly Trade winds.\
\
[(I have deleted a paragraph discussing projections)]{.mark}

In general, coupled models developed for the CMIP3 are far more
realistic than those of a decade ago, when ENSO variability was
comparatively weak, and some models lapsed into permanent El Niño states
(Neelin *et al.,* 1992). Even compared to the models assessed more
recently by ENSIP and CMIP2 (Latif *et al.,* 2001; AchutaRao and Sperber
2002), ENSO variability of ocean surface temperature is more realistic,
although sea level pressure and precipitation anomalies show little
recent improvement (AchutaRao and Sperber 2006). Part of this progress
is the result of increased resolution of the equatorial ocean
circulation that has accompanied inevitable increases in computing
speed. Table V 5 shows the horizontal and vertical resolution near the
equator in the oceanic components of the seven American coupled models
whose output was submitted to AR4.\
\
Table V 5

Spacing of grid points at the equator in the American coupled models
developed for AR4. Except for the GISS models, spacing of grid points
generally increases away from the equator outside of the domain of ENSO,
so that resolution is highest on the equator.\
\
Model: Longitude Latitude Vertical Levels\
\
GFDL CM2.0 1 1/3 50

GFDL CM2.1 1 1/3 50

GISS AOM 5 4 13

GISS modelE-H 2 2 16

GISS modelE-R 5 4 13

NCAR CCSM3 1.125 0.27 27

NCAR PCM 0.94 0.5 32\
\
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\
\
Along the equator, oceanic waves that adjust the equatorial temperature
and currents to changes in the wind are tightly confined to within a few
degrees of latitude. To simulate this adjustment, the ocean state is
calculated at points as closely spaced as 0.27 degrees of latitude in
the NCAR CCSM3. NCAR PCM has half degree resolution, while both GFDL
models have equatorial resolution of one-third of a degree. This degree
of detail is a substantial improvement compared to previous generations
of models. In contrast, the GISS AOM and modelE-R calculate equatorial
temperatures at grid points separated by four degrees of latitude. This
is broad compared to the latitudinal extent of cold temperatures
observed within the eastern PacificThe cooling effect of this rising
water is spread over a larger area, so that the amplitude of the
resulting temperature fluctuation at the surface is weakened. In fact,
both the GISS AOM and modelE-R models have unrealistic ENSO variations
that are much smaller than observed (Hansen *et al* 2007). This
minimizes the influence of their simulated El Nino and La Nina events on
climate outside the equatorial Pacific, and we will not discuss these
two models further in this section.\
\
In comparison to previous generations of global models, where ENSO
variability was typically weak, the AR4 coupled models generally
simulate El Nino near the observed amplitude, or even above (Neelin *et
al* 1992; AchutaRao and Sperber 2006). The latter study compared sea
surface temperature (SST) variability within the tropical Pacific
calculated under pre-industrial conditions. Despite its comparatively
low two-degree latitudinal grid spacing, the GISS modelE-H among the
American models most closely matches observed SST variability since the
mid-19^th^ century, according to the HadISST v1.1 data set (Rayner *et
al* 2003). The NCAR PCM also exhibits El Niño warming close to the
observed magnitude. This comparison is based upon spatial averages
within three longitudinal bands, and GISS modelE-H along with the NCAR
models exhibit their largest variability in the eastern band as
observed. However, GISS modelE-H underestimates variability since 1950,
when the NCAR CCSM3 is closest to observations (Joseph and Nigam 2006).
While the fidelity of each model\'s ENSO variability depends upon the
specific data set and period of comparison (c.f. Capotondi *et al.,*
2006; Merryfield 2006, van Oldenborgh *et al.,* 2005), the general
consensus is that the GISS modelE-H, both NCAR models, and GFDL CM2.0
have roughly the correct amplitude, while variability is too large by
roughly one-third in the GFDL CM2.1. While most models (including GISS
modelE-H and both NCAR models, but excluding the GFDL models) exhibit
the largest variability in the eastern band of longitude, none of the
AR4 models match the observed variability at the South American coast,
where El Nino was originally identified (AchutaRao and Sperber 2006;
Capotondi *et al.,* 2006). This is possibly because the longitudinal
spacing of the model grids is too large to resolve coastal upwelling,
and its interruption during El Niño (Philander and Pacanowski 1981).
Biases in the atmospheric model, including underestimate of the
persistent stratus cloud decks along the coast, may also contribute
(Mechoso *et al.,* 1995).\
\
El Niño occurs every few years, albeit irregularly. The spectrum of
anomalous ocean temperature shows a broad peak between two and seven
years, and there are multi-decadal variations in event frequency and
amplitude. Almost all of the AR4 models have spectral peaks within this
range of time scales. Interannual power is broadly distributed within
the American models, as observed, with the exception of the NCAR CCSM3
which exhibits strong biennial oscillations.\
\
While the models generally simulate the observed magnitude and frequency
of events, reproduction of their seasonality is more elusive. Anomalous
warming typically peaks late in the calendar year, as originally noted
by South American fisherman. Among American models, this seasonal
dependence is simulated only by the NCAR CCSM3 (Joseph and Nigam 2006).
Warming in the GFDL CM2.1 and GISS modelE-H are nearly uniform
throughout the year, while warming in the NCAR PCM is largest in
December but exhibits a secondary peak in early summer. The mean
seasonal cycle along the equatorial Pacific also remains a challenge for
the models. Each year, the east Pacific cold tongue is observed to warm
during NH spring and cool again late in the calendar year. The GFDL
CM2.1 and NCAR PCM1 have the weakest seasonal cycle among the American
models, while GISS modelE-H, GFDL 2.0 and NCAR CCSM3 are closest to the
observed amplitude (Guilyardi 2006). Among the worldwide suite of AR4
models, the amplitude of the seasonal cycle of equatorial ocean
temperature generally varies inversely with the strength of the ENSO
cycle.

Several studies have assessed the mechanisms contributing to variability
among the AR4 models. Confidence in the models\' projection of climate
within the tropical Pacific during the twenty-first century depends upon
accurate simulation of mechanisms of variability observed at present. El
Niño occurs when the upwelling of cold water to the surface is
interrupted within the equatorial eastern Pacific and South American
coast. This can occur because the rate of upwelling decreases, or
alternatively because the temperature of the upwelling water increases.
This subsurface temperature is related to the depth of the thermocline,
within which the water temperature falls off sharply with depth. During
El Niño, the thermocline deepens, so that upwelling water originating in
the cold water below now begins its rise within the relatively warm
layer above (Wyrtki 1975). In addition, the slowing of the easterly
Trade winds reduces the rate of upwelling (Bjerknes 1969), which at the
surface reduces the export of water from the cold tongue toward the West
Pacific. Within the weaker surface current, water has more time to be
warmed by the sun and overlying atmosphere. El Niño is a coupled
phenomenon because the winds that change the upwelling of cold water to
the surface depend upon the ocean temperature itself. Because the
easterlies are driven partly by the temperature contrast between the
cold east Pacific and the warmer ocean west of the dateline, warming in
the east reinforces the slackening of the easterly Trade winds.\
\
The coupling between ocean temperature and equatorial winds is typically
inferred by regressing wind stress upon temperature averaged within the
ENSO domain. The observed wind anomaly is westerly and strongest
slightly to the west of a warm ocean anomaly, as expected based upon
simple theoretical models (Gill 1980; Lindzen and Nigam 1987, Yu and
Neelin 1997). The model wind anomalies are typically displaced farther
west than observed, and are excessively confined to the equator
(Capotondi *et al.,* 1987). The NCAR PCM regression is roughly half the
observed strength, while among the American models, the NCAR CCSM3 and
GFDL CM2.1 come closest to observations (Van Oldenborgh *et al.,* 2005).
The GISS modelE-H exhibits reasonable coupling in the Central Pacific,
but almost no coupling toward South America.\
\
The converse response of SST to wind anomalies is diagnosed by
evaluating various terms in the equation for the evolution of ocean
temperature (van Oldenborgh 2005; Capotondi *et al.,* 2006). Changes in
the temperature of upwelling water are observed to be important in the
eastern Pacific (Capotondi *et al.,* 2006). This feedback is reproduced
by the GFDL CM2.0 and NCAR CCSM3 models, although with somewhat low
amplitude, possibly because the climatological upwelling is weak. (The
model output necessary for this diagnosis was not available for the GFDL
CM2.1, NCAR PCM, and GISS modelE-H.) While a decrease in the rate of
upwelling is crucial to observed warming in the Central Pacific, this
feedback is weak in the GFDL CM2.0, and absent in the NCAR CCSM3. The
ocean feedback to wind anomalies is also diagnosed by regressing the
evolution of ocean temperature upon various mechanisms represented
within the ocean heat budget (van Oldenborgh *et al.,*2005). The NCAR
PCM has very strong feedbacks of upwelling rate and temperature in
response to wind anomalies, which compensate for its weak wind response
to anomalous SST. The GFDL CM2.1 generally reproduces the observed
regression relations. In contrast, van Oldenborgh *et al.* (2005) note
that regression analysis of GISS modelE-H is noisy and difficult to
interpret. It is not clear at this point how GISS modelE-H compensates
for its weak wind response to ocean temperature anomalies in order to
create ENSO temperature variability near the observed magnitude and
location.\
\
In general, GFDL2.1 is consistently ranked among American models as the
most realistic simulation of El Nino (van Oldenborgh *et al.,* 2005;
Guilyardi 2006; Merryfield 2006). This is based not only on its surface
temperature variability (which in fact is slightly too high), but on its
faithful simulation of the observed relationship between ocean
temperature and surface wind, along with the wind-driven ocean response.
While SST in many models is consistently dominated either by anomalies
of upwelling strength or else temperature, these processes alternate in
importance over several decades within the GFDL CM2.1 as observed
(Guilyardi 2006). Since the 1970\'s, the upwelling temperature has been
the predominant feedback (Wang 1995).\
\
While GFDL CM2.1 predicts a reduced ENSO amplitude in response to
increased greenhouse forcing, there is no consensus even among the most
highly regarded models. Philip and Van Oldenborgh (2006) find that while
both upwelling feedbacks amplify as the greenhouse gas concentration
increases, damping processes (due to cloud radiation, for example) also
become more effective. A robust prediction of future El Niño amplitudes
requires both the upwelling feedback and damping along with their
relative amplitude to be simulated consistently, which remains a
challenge.

El Niño events are related to climate anomalies throughout the globe.
Models with more realistic ENSO variability generally exhibit an
anti-correlation with the strength of the Asian summer monsoon (e.g.
Annamalai *et al.,* 2006), while 21^st^ century changes to Amazon
rainfall have been shown to depend upon projected trends in the tropical
Pacific (Li *et al.,* 2006). El Niño has a long-established relation to
North American climate (Horel and Wallace 1981), assessed in the AR4
models by Joseph and Nigam (2006). This relation is strongest during NH
winter, when the tropical anomalies are largest. Anomalous circulations
driven by rainfall over the warming equatorial Central Pacific radiate
atmospheric disturbances into mid-latitudes that are amplified within
the North Pacific storm track (Sardeshmukh and Hoskins 1988; Held *et
al.,* 1989; Trenberth *et al.,* 1998). To simulate the influence of ENSO
upon North America, the models must simulate realistic rainfall
anomalies and in the correct season in order that the connection is
amplified by the wintertime storm tracks. The connection between
equatorial Pacific and North American climate is simulated most
accurately by the NCAR PCM model (Joseph and Nigam 2006). In the GFDL
CM2.1, North American anomalies are too large, consistent with the
model\'s excessive El Niño variability within the equatorial Pacific.
The connection between the two regions is realistic if the model\'s
tropical amplitude is accounted for. In the GISS model, anomalous
rainfall during ENSO is small, consistent with the weak tropical wind
stress anomaly cited above. The influence of El Nino over North America
is nearly negligible in this model. The weak rainfall anomaly is
presumably a result of unrealistic coupling between the atmospheric and
ocean physics. When SST is instead prescribed in this model, rainfall
calculated by the GISS modelE AGCM over the American southwest is
significantly correlated with El Niño as observed.\
\
Realistic simulation of El Niño, and its global influence, remains a
challenge for coupled models, because of the myriad processes
contributing and their changing importance in the observational record.
Key aspects of the coupling between the ocean and atmosphere, the
relation between SST and wind stress anomalies, for example, are the
result of complicated interactions between the resolved model
circulations, along with parameterizations of the ocean and atmospheric
boundary layers and moist convection. Simple models identify parameters
controlling the magnitude and frequency of El Niño, such as the wind
anomaly resulting from a change in SST (e.g., Zebiak and Cane 1987;
Fedorov and Philander 2000), offering guidance to improve the realism of
fully coupled GCM\'s. However, in a GCM, the coupling strength is
emergent rather than prescribed, and it is often unclear *a priori* how
to change the coupling. Nonetheless, the improved simulations of the
ENSO cycle compared to previous generations (AchutaRao and Sperber 2006)
suggest that additional realism can be expected in the future. This
optimism arises in part from the extensive and unprecedented model
comparisons carried out as part of the AR4, where the flaws identified
in current models may point toward future solutions.

### *Multi-decadal variability*

The Earth\'s climate varies naturally on multi-decadal scales due to the
internal dynamics of the system. These changes are apparent from
accurate measurements taken over decades to centuries. From the 1950s
onward, an unprecedented volume of observations has been collected that
contributes to the understanding of the changes to our climate. The
satellite era, beginning in the late 1960\'s has further expanded the
available data and contributed greatly to the set of measurements that
are used in this area of research. Further, retrospective research
efforts are able to deduce earlier changes to the climate through the
analyses of climate indicators such as tree rings and ice cores.

To understand the long period changes in the Earth\'s climate system,
scientists primarily use a set of indices that reduce a large amount of
data to a small set of time series. For example, in the tropical
Pacific, an index referred to as \"Nino 3\" is the average sea surface
temperature (SST) between 5°N-5°S and150°W-90°W, and indicates
variations associated with El Nino and the climate of the tropical
Pacific. Other indices, such as the North American Oscillation Index,
use sea surface pressure differences at two locations, one in Iceland
and one near the Azores (Jones et al. 1997, Hurrell 1995) to examine
large-scale shifts in atmospheric pressure systems. Long period
measurements of precipitation, such as over the Sahel (20°N-10°N,
20°W-10°E) (Janowiak 1988) also are used understand decadal variability.
These analyses can be used to assess the realism of internal or natural
variability of the climate models. In addition to whether actual events
have been modeled correctly, the climate models are evaluated also in
terms of whether the statistical properties of the observed variability
are well represented. Previous sections have described some of the low
frequency behavior of the climate models (e.g. ENSO, annular modes,
polar climates, ice models).

All the models have their own unique intrinsic or natural variability
due to the various model design decisions that have been made. The
models also tend to differ regionally in their simulation skill. For
example, some are better at simulating the North Atlantic, while others
have more skill in the tropics. Often, this regional skill is
serendipitous and emerges unexpectedly from attempts to improve
simulation of processes that operate globally. A set of examples are
given to provide an overview of the general abilities of the current
climate models to reproduce decadal and longer variability.

In the Arctic, during the last century, there have been two long period
warm events, one between 1920 and 1950 and another beginning after 1979.
Wang et al. (2007) evaluated a set of IPCC Fourth Assessment models as
to the models\' ability are to reproduce the amplitudes of air
temperature variability of the mid-century. As examples, the CCSM3 and
GFDL-CM2 models contain similar variance with the observational variance
in the Arctic region.

Multi-decadal variability in the North Atlantic is characterized by the
Atlantic Multidecadal Oscillation (AMO) index which represents a spatial
average of SST (Enfield et al. 2001). Kravtsov and Spannagle (2007)
analyzed SST from a set of current generation climate models. Their
analysis attempts to separate the variability that is associated with
internal fluctuations of the ocean from that associated with changes in
the atmospheric component due to anthropogenic contributions. By
isolating the multi-decadal period of several regions in the ensemble
SST series through statistical methods, they found that models, on
average, correlate well with the AMO (Figure 7, 8 from Kravtsov and
Spannagle, 2007).

In the mid-latitude Pacific region, the decadal variability is generally
under-represented in the ocean (e.g. volume transports as described by
Zhang and McPhaden, 2006, Figure 3), with some of the models approaching
the amplitudes seen in the observations. Examination of complicated
feedbacks between the atmosphere and ocean at the decadal and longer
scales show that the while the climate models generally reproduce the
pattern in SST related to the Pacific Decadal Oscillation (PDO),
observed correlations between the PDO and tropical SST are not seen in
the models (e.g. Alexander et al. 2006).

One of the most difficult areas to simulate is the Indian Ocean, because
of competing effects of warm water inflow through the Indonesian
archipelago, ENSO, monsoons, etc). The processes interact to varying
degrees, challenging a model\'s ability to simulate all aspects of the
system with the observed relative emphasis. An index used to understand
the Indian Ocean\'s variability is the Indian Ocean Dipole pattern that
combines information about the SST and wind stress fields of the Indian
Ocean (Saji et al., 1999). While most of the models evaluated by Saji et
al. (2006) were able to simulate the Indian\'s Ocean response to local
atmospheric forcing on short time periods (semi-annual), the longer
period events such as the ocean\'s response to ENSO changes in the
Pacific, were not simulated well.

###  *Extreme events*

Flood-producing precipitation, drought, heat waves, and cold waves have
severe impacts on North America. Flooding resulted in average annual
losses of \$3.7 billion during 1983-2003
(<http://www.flooddamagedata.org/>). Losses from the 1988 drought were
estimated at \$40 billion and the 2002 drought at \$11 billion. The heat
waves in 1995 resulted in 739 excess deaths in Chicago alone (Whitman
*et al*., 1997). It is probable that a large component of the overall
impacts of climate change will arise from changes in the intensity and
frequency of extreme events.

The modeling of extreme events poses special challenges since they are,
by definition, rare in nature. Although the intensity and frequency of
occurrence of extreme events are modulated by the state of the ocean and
land surface and by trends in the mean climate state, internal
variability of the atmosphere plays a very large role and the most
extreme events arise from the chance confluence of unlikely conditions.
Their very rarity makes statistical evaluation of model performance less
robust than for the mean climate. For example, if one wanted to evaluate
the ability of a model to simulate heat waves as intense as the 1995
event in Chicago, there are only a few episodes in the entire 20^th^
century that approach or exceed that intensity (Kunkel *et al*., 1996).
For such rare events, there is substantial uncertainty in the real risk,
varying from once every 30 years to once every 100 years or more. Thus,
a model that simulates such events at a frequency of once every 30 years
may be performing adequately, but it cannot be distinguished in its
performance from a model simulating such an event at a frequency of once
every 100 years.

Although one might expect that a change in mean climate conditions will
apply equally to changes in the extremes, this is not necessarily the
case. Using as an example the 50 state record low temperatures, the
decade with the largest number of records is the 1930's, yet winters
during this decade averaged as the third warmest since 1890; in fact,
there is no significant correlation between the number of records and
U.S. wintertime temperature (Vavrus *et al*., 2006). Thus, the severest
cold air outbreaks in the past have not necessarily been coincident with
cold winters. Another examination of model data showed that the future
changes in extreme temperatures differed from changes in the mean
temperature in many regions (Hegerl *et al*., 2004). This means that
climate model output must be analyzed explicitly for extremes by
examining daily (or even finer) resolution data, a resource-intensive
effort.

The evaluation of model performance with respect to extremes is hampered
by incomplete data on the historical frequency and severity of extremes.
A study by Frich *et al*., (2002) described a set of indices suitable
for performing global analyses of extremes and presented global results.
However, many areas were missing due to lack of suitable station data,
particularly in the tropics. It has become common to use some of these
indices for comparisons between models and observations. Another
challenge for model evaluation is the spatially-averaged nature of model
data, representing an entire grid cell, while station data represent
point observations. For some comparisons, it is necessary to average the
station data over areas representing a grid cell.

There are several approaches toward the evaluation of model performance
of simulation of extremes. One approach examines whether a model
reproduces the magnitude of extremes. For example, a daily rainfall
amount of 100 mm or more is expected to occur about once every year in
Miami, once every 6 years in New York City, once every 13 years in
Chicago, and once every 200 years in Phoenix. To what extent is a model
able to reproduce the absolute magnitudes and spatial variations of such
extremes? A second approach examines whether a model reproduces observed
trends in extremes. Perhaps the most prominent observed trend in the
U.S. is an increase in the frequency and intensity of heavy
precipitation, particularly during the last 20--30 years of the 20^th^
century. Another notable observed trend is an increase in the length of
the frost-free season.

In some key respects, it is likely that the model simulation of
temperature extremes is less challenging than of precipitation extremes,
in large part due to the scales of these phenomena. The typical heat
wave or cold wave covers a relatively large region, of the order of
several hundred miles or more, or a number of grid cells in a modern
climate model. By contrast, heavy precipitation can be much more
localized, often extending over regions of much less than 150 km, or
less than the size of a grid cell. Thus, the modern climate model can
directly simulate the major processes causing temperature extremes while
heavy precipitation is sensitive to the parameterization of subgrid
scale processes, particularly convection (Chapter 2; Emori *et al*.,
2005; Iorio *et al*., 2004).

##### Droughts, particularly over North America and Africa 

##### 

##### Recent analysis indicates that there has been a globally-averaged trend toward greater areal coverage of drought since 1972 (Dai et al., 2004). A simulation by the HadCM3 model reproduces this dry trend (Burke et al., 2006) only if anthropogenic forcing is included. A control simulation indicates that the observed drying trend is outside the range of natural variability. The model, however, does not always correctly simulate the regional distributions of areas of increasing wetness and dryness. 

The simulation of specific regional features remains a major challenge
for models. Globally, one of the most significant observed changes is
the shift to more frequent and more severe droughts in the Sahel region
of Africa since about 1970. Lau *et al*., (2006) find that only eight
CGCMs produce a reasonable Sahel drought signal, while seven CGCMs
produce excessive rainfall over the Sahel during the observed drought
period. Even the model with the highest prediction skill of the Sahel
drought could only predict the increasing trend of severe drought events
but not the beginning and duration of the events. Hoerling *et al*.
(2006) also finds that the AR4 models fail to simulate the drying and
furthermore uses the model results to suggest that the observed drying
was not due to anthropogenic forcing. However, two GFDL models are
successful in reproducing the drying and analysis of those models
suggests that the drying is of anthropogenic origin (Held *et al*.
2005). Biasutti and Giannini (2006) interpret these results as an
indication that the drying was a combination of decadal-scale internal
variability superimposed on longer timescale changes associated with
anthropogenic forcing. The differences between modeled and observed
regional patterns may then be due to the randomness of natural
variability, but may also result from inadequate representation of
regional processes and feedbacks.

##### 

##### [Excessive rainfall leading to floods]{.underline} Several different measures of excessive rainfall have been used in analyses of model simulations. A common one is the annual maximum 5-day precipitation amount, one of the Frich et al. (2002) indices. This has been analyzed in several recent studies (Kiktev et al. 2003; Hegerl et al. 2004; Tebaldi et al. 2006). Other analyses have examined thresholds of daily precipitation, either absolute (e.g. 50 mm per day in Dai 2006) or percentile (e.g. 4^th^ largest precipitation event equivalent to 99^th^ percentile of the 365 daily values as in Emori et al. 2005). Recent studies of model simulations produced for the IPCC AR4 provide information on the performance of the latest generation of models.

There is a general tendency for models to underestimate very heavy
precipitation. This is shown in a comparison between satellite (TRMM)
estimates of daily precipitation and model-simulated values within the
50S-50N latitude belt (Dai 2006). The TRMM observations derive 7% of the
total precipitation from very heavy rainfall of 50 mm or more per day,
in contrast to only 0-2% for the models. For the frequency of very heavy
precipitation of 50 or more mm per day, the TRMM data show a frequency
of 0.35% (about once every 300 days), whereas it is 0.02-0.11% (once
every 900 to 5000 days) for the models. A global analysis of model
simulations showed that the models produced too little precipitation in
events exceeding 10 mm per day (Sun *et al*. 2006). Examining how many
days it takes to accumulate 2/3's of the annual precipitation, the
models generally show too many days compared to observations over North
America, although a few models are close to reality. In contrast to the
general finding of a tendency toward underestimation, a study (Hegerl
*et al*. 2004) of two models (HadCM3 and CGCM2) indicates generally good
agreement with the observed annual maximum 5-day precipitation amount
over North America for HadCM3 and even somewhat of an overestimation for
CGCM2.

This model tendency to produce rainfall events less intense than
observed appears to be due in part to the low spatial resolution of
global models. Experiments with individual models show that increasing
the resolution improves the simulation of heavy events. For example, the
4^th^ largest precipitation event in a model simulation with a
resolution of approximately 300 km averaged 40 mm over the conterminous
U.S., compared to an observed value of about 80 mm. When the resolution
was increased to 75 km and 50 km, the 4^th^ largest event was still
smaller than observed, but by a much smaller amount (Iorio *et al*.
2004). A second factor that is important is the parameterization of
convection. Thunderstorms are responsible for many intense events, but
their scale is smaller than the size of model grids and thus they must
be indirectly represented in models (Chapter 2). One experiment showed
that changes to this representation improves model performance and, when
combined with high resolution of about 1.1 deg latitude, can produce
quite accurate simulations of the 4^th^ largest precipitation event on a
globally-averaged basis (Emori 2005). Another experiment found that the
use of a cloud-resolving model imbedded in a global model eliminated the
underestimation of heavy events (Iorio *et al*. 2004). A cloud-resolving
model eliminates the need for a parameterization of convection, but is
very expensive to run. These sets of experiments indicate that the
problem of heavy event underestimation may be significantly reduced in
the future as increases in the computer power allows simulations at
higher spatial resolution and perhaps eventually the use of
cloud-resolving models.

The improved model performance at higher spatial resolutions provide
motivation for use of regional climate models when only a limited area
is of interest, such as North America. The spatial resolution of these
models is sufficient to resolve the major mountain chains; some of these
models thus display considerable skill in areas where topography plays a
major role in the spatial patterns. For example, they are able to
reproduce rather well the spatial distribution of the magnitude of the
95^th^ percentile of precipitation (Leung *et al*. 2003), the frequency
of days with more than 50 mm and 100 mm (Kim and Lee 2003), the
frequency of days over 25 mm (Bell *et al*. 2004), and the annual
maximum daily precipitation amount (Bell *et al*. 2004) over the western
U.S. Kunkel *et al*. (2002) found that an RCM's simulation of the
magnitude of extreme events over the U.S. varied spatially and depended
on the duration of the event being examined; there was a tendency for
overestimation in the western U.S. and good agreement or underestimation
in the central and eastern U.S.

Most studies of observed precipitation extremes suggest that such
extremes have increased in frequency and intensity during the latter
half of the 20^th^ century. A study by Tebaldi *et al*., (2006)
indicates that models generally simulate a trend towards a world
characterized by intensified precipitation, with a greater frequency of
heavy-precipitation and high-quantile events, although with substantial
geographical variability. This is in agreement with observations. Wang
and Lau (2006) find that the CGCMs simulate an increasing trend in heavy
rain over the tropical ocean.

#### [Heat and cold waves]{.underline}

Analysis of simulations produced for the IPCC AR4 by seven climate
models indicates that they reproduce the primary features of cold air
outbreaks (CAOs), with respect to location and magnitude (Vavrus *et
al*., 2006). In their analysis, a CAO is an episode of at least 2 days
duration during which the daily mean winter (December-January-February)
surface temperature at a gridpoint is 2 standard deviations below the
gridpoint's winter mean temperature. Maximum frequencies of about four
CAO days/winter are simulated over western North America and Europe,
while minimal occurrences of less than one day/winter exist over the
Arctic, northern Africa, and parts of the North Pacific. The GCMs are
generally accurate in their simulation of primary features, with a high
pattern correlation with observations and the maximum number of days
meeting the CAO criteria around 4 per winter. One favored region for
CAOs is in western North America, extending from southern Alaska into
the upper Midwest. Here, the models simulate a frequency of about 4 CAO
days per year, in general agreement with the observed values of 3-4
days. The models underestimate the frequency in the southeastern United
States: mean simulated values range from 0.5 to 2 days *versus* 2 to 2.5
days in observations. This regional bias occurs in all the models and
reflects the inability of GCMs to penetrate Arctic air masses far enough
southeastward over North America.

The IPCC AR4 model simulations show a positive trend for growing season,
heat waves and warm nights and a negative trend for frost days and daily
temperature range (maximum minus minimum) (Tebaldi *et al*. 2006). They
indicate that this is in general agreement with observations, except
that there is no observed trend in heat waves. The modeled spatial
patterns have generally larger positive trends in western North America
than in eastern sections. For the U.S., this is in qualitative agreement
with observations which show that the decreases in frost-free season and
frost days are largest in the western U.S. (Kunkel *et al*. 2004;
Easterling *et al*. 2002).

Analysis of individual models provides a more detailed picture of model
performance. In a simulation from the PCM (Meehl *et al*. 2004), the
largest trends for decreasing frost days occurs in the western and
southwestern USA (values greater than --2 days per decade), and trends
near zero in the upper Midwest and northeastern USA, in good agreement
with observations. The biggest discrepancy between model and
observations is over parts of the southeastern USA where the model shows
trends for decreasing frost days and the observations show slight
increases. This is thought to be a partial consequence of the two large
El Nino events in the observations during this time period (1982--83 and
1997--98) where anomalously cool and wet conditions occurred over the
southeastern USA and contributed to slight increases of frost days. The
ensemble mean from the model averages out effects from individual El
Nino events, and thus the frost day trends reflect a more general
response to the forcings that occurred during the latter part of the
*20^th^* century. An analysis of short-duration heat waves simulated by
the PCM (Meehl and Tebaldi, 2004) indicates good agreement with observed
heat waves for North America. In that study, heat waves were defined by
daily minimum temperature. The most intense events occurred in the
southeast U.S. for both the model simulation and observations. The
overall spatial pattern of heat wave intensity in the model matched
closely with the observed pattern. In a four-member ensemble of
simulations from the HadCM3 (Christidis *et al*. 2005), the model shows
a rather uniform pattern of increases in the warmest night for
1950-1999. The observations also show a global mean increase, but with
considerable regional variations. In North America, the observed trends
in the warmest night vary from negative in the south-central sections to
strongly positive in Alaska and western Canada, compared to a rather
uniform pattern in the model. However, this discrepancy might be
expected, since the observations probably reflect a strong imprint of
internal climate variability that is reduced by ensemble averaging of
the model simulations.

An analysis of the magnitude of temperature extremes for California in a
regional climate model simulation (Bell *et al*. 2004) show mixed
results. The hottest maximum in model is 4°C less than observations,
while coldest min is 2.3°C warmer. The number of days \>32°C is 44 in
the model compared to an observed value of 71. This could result from
the lower diurnal temperature range in the model (15.4°C observed vs.
9.7°C simulated). While these results are better than the driving GCM,
the RCM results are still somewhat deficient, perhaps reflecting the
very complex topography of the region of study.

Models display some capability to simulate extreme temperature and
precipitation events, but there are differences from observed
characteristics. They typically produce global increases in extreme
precipitation and severe drought, and decreases in extreme minimum
temperatures and frost days, in general agreement with observations.
There is a general, though not universal, tendency to underestimate the
magnitude of heavy precipitation events. Regional trend features are not
always captured. Since the causes of observed regional trend variations
are not known in general and such trends could be due in part to
stochastic variability of the climate system, it is difficult to assess
the significance of these discrepancies.