Current Global Climate Model Results Southern Africa
By: Nicholas Geyer
GEOG 412W
1. Introduction (All tables and figures are appended at the end of the paper)
1a. What are models?
In the expanding realm of climate research and legislation, the ability to accurately
describe the Earth’s climate is more important than ever. Scientists use models to examine the
possible outcomes of such events as Global Warming, volcanic activity, and day-to-day climate
variations. One may ask: what is a model? According to the Intergovernmental Panel on
Climate Change (IPCC), a model is defined as mathematical representations of the climate
system expressed as computer codes and run on powerful computers (Randall et al., 2007).
Models come in two types: numerical weather prediction (NWP) models and climate models.
NWP models are designed with a set of physical equations aimed at determining the
weather forecast. Examples of NWP models include the Weather Research and Forecasting
model and the MM5. These models typically require less computing power, are very fast, and
can only produce accurate forecasts 3 or 4 days in advance because of inherent system chaos.
Climate models come in a vast array of shapes and sizes. Their size can be anywhere
from a local to a global scale climate model. These models are much larger and more powerful
than the NWP models and try to account for most natural action and phenomenon to produce
results about the climate. They run on massive supercomputers like the Jaguar Cray XT-5, seen
in figure 1, and run for days, weeks, or months to produce projected data for the future. This
paper will deal with these global scale models otherwise known as global climate models
(GCMs).
1b. Reliability of models
When examining the reliability of a model to produce accurate data for a future climate
scenario, there must be ways to determine the model’s validity. There are three main ways to
determine whether or not a GCM can accurately produce results. The first way is to trust in the
equations and natural laws of the system. Every model used in climate research is based upon
fundamental laws of nature. These include such laws as the conservation of mass, momentum,
and energy (Randall et al., 2007). The idea is that if the equations in the model can conserve and
follow all natural laws, then it should behave in a natural way and can be trusted. Although this
can be quite simple, it can also be misleading and there are other, more concrete alternatives.
The second test of reliability is the model’s ability to simulate past climate events and
climate change. According to the IPCC, certain practical choices must be made by scientists to
observe if their models are accurately describing the events of interest like climate change
(Randall et al., 2007). Thanks to ice core and sediment data, scientists have a decent
understanding of past global climate. Models that can accurately reproduce such major events
like the Holocene, and most recent glaciations are considered accurate enough to attempt runs of
current and future climate scenarios (Randall et al., 2007). According to the IPCC, if a model has
the following characteristics, its output is deemed insignificant:
1. Unpredictable internal variability (e.g., the observational
period contained an unusual number of El Niño events);
2. Expected differences in forcing (e.g., observations for the
1990s compared with a ‘pre-industrial’ model control run);
3. Uncertainties in the observed fields (Randall et al. 2007).
The third method of reliability is a process known as an intercomparison. This is a
process of taking a certain climate scenario with measurable observations like the Madden-Julian
Oscillation (MJO) or the El Nino Southern Oscillation and seeing how accurate a model is at
reproducing the observations and how it compares to other similar models. Intercomparison
projets, such as CMIP3, CMIP5, AMIP, and TWP-ICE, have been used since the late 1980s
(Randall et al., 2007). The data gathered from these intercomparison projects produce ensembles
of a variety of system biases and trends. This paper will show an example of an intercomparison
of three different models and how good each is at examining the present climate of southern
Africa.
1c. Models used in this paper
To keep with the validity of the results of the IPCC reports, this investigation will involve
several models used in the IPCC fourth assessment report (IPCC AR4).
The IPCC AR4 used
many different “coupled” climate models from all over the world to produce an ensemble picture
of the current and future climate (Hudson & Jones, 2004). A “coupled” climate model is a GCM
that utilizes at least both an atmospheric and ocean model. For this assessment, three GCM have
been chosen from the IPCC AR4:
1. Geophysical Fluid Dynamics Laboratory Climate Model 2.0 (GFDL CM 2.0)
2. National Center for Atmospheric Research Community Climate System Model 3
(NCAR CCSM 3)
3. UK Met Office Hadley Centre Climate Model 3 (UKMO-HadCM3)
The statistics on each of these models are located in tables 1 through 3. By using a brief
analysis of actual data produced for the IPCC AR4, previous and current research, as well as
climatological observations, we can draw a picture of how GCMs are producing results for the
present-day climate of southern Africa.
2. South African Intercomparison
2a. Overview
The present-day climate simulations of southern Africa can involve hundreds of different
variables and rates. The assessment of the validity behind most IPCC models demanded a 30
year climatology of variables such as rainfall rates, radiation fluxes, surface temperatures, and
prevailing winds. In addition, the IPCC also drew upon the observational data from the last one
hundred years from all over the globe to reproduce the current global climate in 30 year monthly
increments. (IPCC DDC, 2010)
Because of the wide range of variables for the given time periods, the IPCC required each
model to reproduce several of the most common fields. This paper will focus upon three of these
fields: temperature, outgoing shortwave radiation, and precipitation. A model’s ability to
reproduce the proper magnitude, spatial distribution, and time variation of observed quantities
will give a feel for how each GCM is performing in southern Africa. Using data from the IPCC
Data Distribution Center (IPCC-DDC), we can reproduce the actual model results in a visual
format and locate seasonal means and extremes as well as spatial patterns in comparison with
observations.
2b. Basic southern African physical geography and climate
The domain and topography of southern Africa is displayed in figure 2. Figure 3 displays
a feel for what the climate should be around the region. On the southeastern coast there are the
Drakensberg Mountains, which provide a rain shadow for the Kalahari Desert located to the
northwest. Along the southwestern coastline, there is an arid climate created from the Benguela
current flowing from the Antarctic (figure 4). Also in figure 4, the Agulhas current funnels
warm moist air down from the equatorial Indian Ocean, which provides east and southeast
southern Africa with a sub-tropical climate. As can be seen, the variety of physical geographies
and microclimates in southern Africa present a challenge for any GCM to properly reproduce the
local climate.
2c. Temperature
One of the most important variables to accurately reproduce is temperature. The IPCCDDC’s most recent and complete observational data record is the 30 year period from 1961 to
1990. (IPCC DDC, 2010) Using this data, a 30-year temperature climatology plot has been
produced and shown in figures 5a through 5d for the months of March, June, September, and
November. The predominant temperature trend is still a hot land producing values of 10 to 18
o
C. The Drakensberg Mountains contain a minimum of temperature near 2 oC over the eastern
part of South Africa (figure 5a). There is little semblance of a pattern to the observations
because the temperature difference is not very large in the region. The onset of winter is nearing,
as the Drakensberg Mountains are the coldest area in the region (figure 5b). The cold pool of
temperature extends toward the western coast because of the seasonal variation and the Benguela
current. (Hulme et al., 2001) A March-like scenario is seen during the month of September
(figure 5c). The coldest temperatures still reside in the Drakensberg Mountains, and elsewhere,
temperatures have risen to nearly 20 oC in the north with a strong north to south temperature
gradient. During the summertime, pictured in figure 5d, the hottest temperatures occur. The
local temperature minimum still resides in the Drakensberg Mountains and a maximum of
approximately 30 oC resides along the eastern coastline. The temperature pattern during the
summer shows little signs of having any significant temperature differential.
Figures 6a through 8d show the model outputs of the 20C3M scenario, which is a climate
scenario of the past 100 years using observed rises in aerosols and other known climatic events
(IPCC DDC, 2010). Most models predict the magnitude of the temperature within 2 to 3 oC.
The temperature minimum expected over the Drakensberg Mountains is replicated fairly well
during the months of March, June, and September. The prediction from the HadCM3 model is
skewed from the observations nearly 7 oC during the month of September (figure 8c). The
biggest error occurs during November, where all model runs overestimate the temperature
minima. The condensed November minimum is very expansive and overestimated by nearly 8
o
C (figures 6d, 7d, and 8d).
When looking at the spatial pattern of temperature, these models do a good job in
replicating observations, except in the southern portion of the region. During March, the cold
pool that is fairly isolated in the observations is much more expansive in the model runs,
extending all the way along the eastern coastline. This is further worsened during June as the
cold pool in the models extends far into the western portions of southern Africa and into the
Kalahari Desert (figures 6b, 7b, and 8b). The minima are contracted back into the Drakensberg
Mountains as the errors in spatial patterns are dampened during the change into spring and
summer (figures 6c, 6d, 7c, 7d, 8c, and 8d).
Clearly, the best model in this circumstance is the CCSM3. The 1km by 1km grid
resolution allowed for the most accurate temperature results as well as the most condensed
spatial patterns. The CM 2.0’s lower grid resolution still allowed the model to retain the
temperature magnitudes, but sacrificed spatial accuracy (figures 7a-7d). As seen in figures 8a to
8d, HadCM3 produced the most divergent temperature magnitudes as well as the poorest spatial
pattern of any model during any month.
2c. Southern African Precipitation
According to the IPCC, 90 % of models are overestimating precipitation of southern
Africa by more than 20% on average (Randall et al., 2007). With this in mind, confidence in
simulated rainfall is less than the confidence in temperature measurements because of criticism
due to three factors. First, the geographical location of southern Africa between the tropics and
the mid-latitudes lead to many different climate types. Thus, current models have difficulty in
simulating rainfall distribution and seasonality. Secondly, greenhouse gas forcing can notably
change the temperature and energy balance. This, in turn, will move the location of rainfall from
its expected locations. Finally, at 30-year seasonal time-scales, southern Africa as a whole does
not experience a definitive trend toward more dry or moist conditions (Fauchereau et al., 2003).
Observations from 1961-1990 of overall precipitation for the southern African climate
are shown during June and November, which are indicative of both the dry and rainy seasons,
respectively (figures 9a and 9b). In June, the entire landscape of southern Africa is very dry.
The average precipitation value is near 0 kg/m^2, which means there is nearly no rainfall. This
is intuitive because June is part of the southern African dry season. During the wet season in
November, the landscape is shown to be much wetter. The maximum in precipitation is located
toward the northwest, and the precipitation gradient is from northwest to southwest.
Additionally in November, the western coast is notably very dry, which is attributed to the dry
air transported from the Benguela current.
The model rainfall presented here is given in kg m-2 s-1 which is a measure of rainfall
intensity. In figures 10a, 11a and 12a, which represent June, there is a good indication that the
region is very dry. In all model outputs, an abnormally strong precipitation result appears to the
northwest during June on the order of .0002 kg m-2 s-1. In November, there is a shift in the
spatial distribution of rainfall (figures 10b, 11b, and 12b). In both the CCSM3 and CM 2.0
model outputs, the maximum shifted toward the north instead of the expected northwest location.
In the HadCM3 model, the proper location of the maximum is in the northwestern part of the
region. In terms of dry areas, the expanse of the dry western coast is observed in all three model
outputs. All models show the northeast with a dry swath along the coastline, which conflicts
with the observations during November.
When comparing the models, the display shows that all three models are good at
predicting intensity. HadCM3, despite its low grid resolution, is the most accurate portrayal of
the rainfall maximum location. The grid size inhibits the HadCM3 from properly producing
November’s precipitation along the southeastern coast, showing another large maximum just
along the coastline. The fantastic grid resolution of the CCSM3 results in the most accurate
portrayal of the size and shape of the wet and dry areas. CM 2.0’s best feature was the portrayal
of the western coastline’s dry area as it was almost the same size and shape as the real feature in
the observations.
2d. Southern African Outgoing Shortwave Radiation
The radiation balance in a climate model is very important to understanding the overall
structure of the climate balance. One important observation is the amount of outgoing shortwave
radiation (OSR) from the planet. Since Earth radiates in the longwave spectrum, OSR can be
considered analogous to the albedo (Power & Mills, 2005).
In figure 13, a rough estimate of the globally averaged OSR can be observed. Over
southern Africa, the western coastline has OSR values from 100 to 120 W/m^2, and in the
interior the value jumps to about 210 W/m^2. This makes sense because cloud and ground
albedo is greater than ocean albedo. Furthermore, using Google Earth and OSR data, figures 14
and 15 represent the OSR for June and November, respectively, in southern Africa
(Christopherson et al., 2005). In June, the OSR values range from 50 to 150 W/m^2 and during
November, they range from 150 to 260 W/m^2.
The models, by comparison, show many flaws. During June, all of the models exhibit
magnitude errors (figures 16a, 17a, and 20a). The three models have an OSR range around 50
W/m^2 to just over 200 W/m^2. The range’s upper value is too high during this time of year
(Power & Mills, 2005). During November, all models exhibit the OSR range to be from 100
W/m^2 to over 350 W/m^2 (figures 16b, 17b, and 20b). These values are 50 to 100 W/m^2 off
from what is seen in the observations.
The model’s spatial distribution of the OSR is also questionable. During June, the
models produce a north to south OSR gradient, with higher values toward the north. According
to the observations, there should be a pattern extending from the northeastern coastline to the
southwestern coastline with low OSR pools on either side. During November, the models match
fairly well until the western coastline. In each model, the western coastline’s OSR value is
above 300 W/m^2, which poses a positive bias. With the western coastline bias aside, each of
the models does produce a proper OSR spatial pattern and falls within the range of the
observations.
In comparison with each other, the models all present roughly the same patterns. A
model’s ability to obtain better OSR values and spatial patterns rely upon its ability to accurately
model the albedo (Power & Mills, 2005). The CCSM3 has the most appropriate spatial patterns
and nearest range to that of the observations. The increased grid resolution allows the cloud
cover and OSR to be more accurately estimated. The CM 2.0’s coarser resolution produces
poorer results, but still maintains the relative OSR range and spatial patterns as the CCSM3. The
HadCM3’s low resolution results in the poorest OSR measurements in both range and spatial
accuracy. Clearly, the better the model’s grid resolution, the more refined the OSR will be.
3. Variability
3a. Problems and how to adjust them
As seen, the models do produce fairly accurate results, but there are many things that
modelers and scientists still must learn and understand before we get an extremely accurate result
comparable to that of actual observations. In regards to temperature, the biases caused are a
result of cloud and circulation problems (Hudson et al., 2002). During the CM 2.0’s model
analysis, it was noted that errors in temperature occur in models for four reasons:
1) Errors or omissions in the specified forcing agents
2) Errors in the simulated response to forcing agents
3) Errors in the simulation of internal climate variability
4) Errors in observed temperature data (Knutson et al., 2006).
One suggestion to make the models better at predicting temperature was to include
indirect aerosol forcing on temperature in a regional location like Southern Africa. By doing
this, models will be able to simulate aerosol effects on plant life, ocean surface, and a variety of
other features that can affect the temperature in an area (Joubert et al., 1997; Wittenberg et al.,
2006).
To improve our current GCM’s ability to predict OSR and other radiation quantities, the
focus should be placed upon aerosols. One of the key points to understanding the radiation
balance of the atmosphere is to have a firm understanding of the atmospheric and aerosol
composition (Powers & Mills, 2005). The aerosols in the atmosphere can cause cloud coverage
to become much higher due to the increased presence of cloud condensation nuclei. The
presence of higher aerosol concentrations could have caused thick clouds in the models, which
may have resulted in the miscalculation of the OSR fluxes (Hudson et al, 2002; Martin et al.
2005). Another reason could be an ocean model’s inability to produce the correct amount of
absorbed shortwave radiation (Wittenberg et al., 2006). Clearly, the issue with radiation is not
something purely based on a region, but on the globe as a whole. In order to get a region’s
radiation correct, the model must be able to produce an accurate global radiation balance.
With respect to precipitation, southern Africa is dominated by the Intertropical
Convergence Zone (ITCZ) and El Nino Southern Oscillation (ENSO). The rainfall in southern
Africa is heavily influenced by ENSO events. If ENSO is at a warm phase, then southern Africa
is marked by low rainfall trends, and vice versa during a cold phase (Hulme et al., 2001). The
problems with rainfall stem from greenhouse gas induced rainfall changes. In addition, coarse
resolution models like GCMs need to be downscaled to properly assess regional rainfall rates
(Fauchereau et al., 2003). The HadCM3’s atmospheric model, HadAM3, has many rainfall
biases. During UK-MET’s own simulations of southern Africa climate, they found that
HadAM3 had a bias with the ITCZ and the Zaire Air Boundary (ZAB), which caused rainfall
biases and spatial patterns observed in the IPCC reports (Hudson et al., 2002). This would
induce lower and heavier precipitation in southern Africa during both November and June which
could explain the shifting maximum in rainfall intensity for the models.
4. Conclusion
GCMs are doing a fairly good job of representing the current climate of Southern Africa.
In comparison to temperature observations, the models are doing a great job. The ability for all
three models, regardless of grid resolution, to contain the temperature to within a 2 to 3 K bias is
very good. The location of local minima was excellent; however, the expanse of these minima is
a few grid points too wide, which can skew the results when compared to the observations. The
extended cold pools over the western coastline of southern Africa present another model bias that
must be addressed by the modelers to make future GCM data more reliable.
Precipitation also has fairly accurate spatial patterns in the models. The models can
accurately reproduce the dry and wet seasons, but have a tough time with the shape of the spatial
location of rainfall intensity. This can be averted by correcting a double ITCZ problem as well
as understanding the aerosol composition in the region. In addition, a tendency to have a higher
resolution positively benefitted a model’s ability to match precipitation.
The model simulations of OSR observations are the most unreliable of these variables.
Their problems included having flux values that were too high for observations as well as poor
spatial patterns. This could have been caused because of the climate and circulation outside of
southern Africa or because of thick cloud patterns that tend to appear in the models.
Even though the results for this generation of models are good, the next generation of
models, such as the CCSM4 and the Vector Vorticity Model, should take the errors found and
correct them. They have the capacity to simulate more than any of their predecessors could have
and in greater detail. Perhaps one day, we will be able to create a GCM that can match the
observations. Although it seems impossible now, we can continue to build on our errors and
research, and one day, it just may be possible.
5. References
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6. Tables
Table 1. Basic information for the GFDL CM 2.0 model (IPCC DDC, 2010)
Table 2. Basic information for the UKMO HadCM3 model (IPCC DDC, 2010)
Table 3. Basic information for the NCAR CCSM3 model. (IPCC DDC, 2010)
7. Figures
Figure 1. A frontal view of the Jaguar Cray XT-5 supercomputer. (Jaguar XT-5, 2008)
Figure 2. A view of the topography of Southern Africa. (Overview, 2006)
Figure 3. A view of Southern Africa’s climate zones. (Climate, 2005)
Figure 4. A basic illustration of the Benguela and Agulhas currents affecting Southern Africa.
(Cape Point)
Figures 5 a (top left), b (top right), c (bottom left) and d (bottom right). Observational 30 year
average temperature climatology of Southern Africa during the months of March (a), June (b),
September(c), and November (d). Warmer colors represent hotter temperatures (in oC). (IPCC
DDC, 2010)
Figures 6 a (top left), b (top right), c (bottom left) and d (bottom right). NCAR CCSM3’s 30 year
average temperature climatology of Southern Africa during the months of March (a), June (b),
September(c), and November (d). Warmer colors represent hotter temperatures (in K). (IPCC
DDC, 2010)
Figures 7 a (top left), b (top right), c (bottom left) and d (bottom right). GFDL CM 2.0’s 30 year
average temperature climatology of Southern Africa during the months of March (a), June (b),
September(c), and November (d). Warmer colors represent hotter temperatures (in K). (IPCC
DDC, 2010)
Figures 8 a (top left), b (top right), c (bottom left) and d (bottom right). HadCM3’s 30 year
average temperature climatology of Southern Africa during the months of March (a), June (b),
September(c), and November (d). Warmer colors represent hotter temperatures (in K). (IPCC
DDC, 2010)
Figures 9 a (left) and b (right). Observational 30 year average precipitation climatology of
Southern Africa during the months of June (a) and November (b). Cooler colors represent lower
rainfall. (IPCC DDC, 2010)
Figures 10 a (left) and b (right). NCAR CCSM3’s 30 year average precipitation intensity
climatology of Southern Africa during the months of June (a) and November (b). Cooler colors
represent lower rainfall. (IPCC DDC, 2010)
Figures 11 a (left) and b (right). GFDL CM 2.0’s 30 year average precipitation intensity
climatology of Southern Africa during the months of June (a) and November (b). Cooler colors
represent lower rainfall. (IPCC DDC, 2010)
Figures 12 a (left) and b (right). HadCM3’s 30 year average precipitation intensity climatology
of Southern Africa during the months of June (a) and November (b). Cooler colors represent
lower rainfall. (IPCC DDC, 2010)
Figure 13. A visualization of the global shortwave flux (W/m^2) as taken by the CERES
satellite. (Ceres, 2005)
Figure 14. An observational average of the total-sky outgoing shortwave flux (W/m^2) during
June. Measured by the CERES satellite and produced with Google Earth. (Christopherson et al.,
2005)
Figure 15. An observational average of the total-sky outgoing shortwave flux (W/m^2) during
June. Measured by the CERES satellite and produced with Google Earth. (Christopherson et al.,
2005)
Figure 16 a (left) and b (right). NCAR CCSM3’s 30 year outward shortwave radiation
climatology of Southern Africa during the months of June (a) and November (b). Warmer colors
represent more radiation, or flux. (IPCC DDC, 2010)
Figure 17 a (left) and b (right). GFDL CM 2.0 30 year outward shortwave radiation climatology
of Southern Africa during the months of June (a) and November (b). Warmer colors represent
more radiation, or flux. (IPCC DDC, 2010)
Figure 18 a (left) and b (right). GFDL CM 2.0 30 year outward shortwave radiation climatology
of Southern Africa during the months of June (a) and November (b). Warmer colors represent
more radiation, or flux. (IPCC DDC, 2010)