Central America Climate Change Modeling
For more information please contact:
Dr. Robert Ogelsby (Robert.J.Oglesby@nasa.gov )
Jayanthi Srikshen (Jayanthi.Srikishen@msfc.nasa.gov)
En Espanol
Joel Perez (joel.perez@cathalac.org)
Purpose:
The purpose of these climate model runs is to estimate possible future climatic changes
over Central America resulting from increases in atmospheric greenhouse gases. These
model runs are based on IPCC-mandated ‘21st century’ simulations. In order to evaluate
short-term changes, models were run for 2010, 2015, and 2025. In order to evaluate
longer-term changes, models were run for 2050 and 2099. (Model runs for 2005 serve as
a present-day control.)
Methodology:
The IPCC ‘business as usual’ (BAU) scenario was chosen. This scenario assumes no
attempts are made to limit greenhouse gas emissions, and therefore has the largest
increase occurring over the 21st century. The BAU was chosen specifically because it is
the ‘worst case’ scenario, that is, will provide the largest possible climate changes that
could be expected. Use or consideration of the IPCC ‘weak stabilization’ or ‘strong
stabilization’ scenarios should produce smaller climatic changes.
The basic IPCC-mandated climate model runs are made with global general circulation
models, or GCMs (also known as ‘global climate models’). These runs by themselves are
not, however, sufficient or suitable for an in-depth study of Central America. This is
because their spatial horizontal resolution is too coarse. The most recent suite of IPCC
runs (which we used) had a horizontal resolution of approximately 140 km latitude and
140 km longitude (at the latitudes of Central America). Prior IPCC runs had resolutions
that were even coarser than this. At 140 km resolution, the mountainous terrain of Central
America is simply not resolved at all. Since the mountains of Central America provide
probably the second-largest climatic control (after latitude), especially for precipitation,
use of GCM results ‘as-is’ is simply pointless; worse, as described later, it can lead to
conclusions diametrically opposed to those produced when the mountains are resolved.
To overcome this resolution issue, we employed a regional climate model (RCM). The
advantage to the RCM is that it has much higher horizontal resolution than the GCM. The
inherent disadvantage to the RCM is that because of computational limitations imposed
by the higher resolution and much shorter time steps therefore required, they can only be
run for a limited area domain (hence the ‘regional’ portion of their generic name). This
means that large-scale forcing must be applied at the lateral boundaries of the limited
domain. This forcing can be obtained from a quasi-observational operational analysis or
reanalysis for present-day studies. For future climate change studies, however, the lateral
forcing must be obtained from a model such as a GCM. Using the RCM can then be
considered as a method for a physically-based downscaling of the GCM results.
The regional model we chose was the widely used NCAR/PSU MM5, coupled to the
NOAH land surface model. We used a nested domain approach; that is, we had a large
outer domain at a fairly coarse resolution of 36 km that ranged from the central Pacific to
the Central Atlantic, and from the southern United States to south of the equator. Inside
this larger domain we nested a domain at a horizontal resolution of 12 km that
encompassed all of Central America (and southern Mexico) and adjacent ocean regions.
While this inner domain resolves the mountainous terrain fairly well, even it is too coarse
to capture all meaningful physiographic and surface features. It would, however, have
been prohibitively expensive in terms of computational resources to have made runs at a
higher resolution than 12 km. To provide the lateral forcing, we used output from a
widely used and well-regarded GCM, the NCAR CCSM3. We used a CCSM3 BAU
simulation that had output 4 times daily (that is, the lateral forcing for MM5 was updated
every 6 hours).
The MM5 was run for 2005, 2010, 2015, 2025, 2050, and 2099. For each of these years,
the wet season months (defined as June through September) and dry season months
(defined as January and February) were all run. For many, but not all, of the years the
remaining months were also run.
Analysis:
The basic MM5 output consists of over 50 fields that represent virtually every important
meteorological and climatic parameter; these fields are saved and written at 3-hourly
intervals (allowing a reasonable sampling of the diurnal cycle). For most purposes, this is
far more data than required. We selected 11 of the most commonly used fields, and made
monthly averages (based on an arithmetic mean) for each month of each year for which a
simulation was made. These fields and their units are:
tg – the ground (or surface) temperature in K
sm1-sm4 – volumetric soil moisture in m**3/m**3 for each of the four soil layers; sm1 is
the uppermost layer and sm4 the deepest layer
t2m – the 2 meter (above the surface) air temperature in K
q2m – the 2 meter (above the surface) mixing ratio in g/kg
u10 – the 10 meter (above the surface) zonal (east-west) wind component in m/s. Wind
from the west is positive; from the east negative
v10 – the 10 meter (above the surface) meridional (north-south) wind component in m/s.
Wind from the north is positive; from the south negative
pslv – surface pressure corrected to sea level in hPa (equivalent to mb)
prec – total precipitation in cm/month
Difference plots are made relative to the control year of 2005, and are always of the form
20XX – 2005.
All of these plots, and the data used in their making, are downloadable from this website.
The original raw model output is archived on the mass storage system at Oak Ridge
National Laboratory, and can be made available for reasonable requests.
Key Results:
A brief synopsis of the results is that as greenhouse gases increase, everywhere gets
warmer and more humid; the temperature increases are greatest over large land masses
and the farther out in the 21st century one goes. By more humid it is meant that the
amount of moisture in the air increases, as measured by the mixing ratio or dewpoint
temperature. This does not imply that the relative humidity necessarily increases, as the
increase in temperature can be proportionally larger than the increase in moisture.
Precipitation tends to decrease along the Atlantic coasts; this is because of a slackening of
the trade winds and implies somewhat less rainfall in an already wet region. The Pacific
coasts tend to have an increase in precipitation, meaning this generally dry region
becomes wetter. The signal is more subtle and complex in interior regions, but the overall
tendency is for a slight increase in rainfall. These results for precipitation also
demonstrate the advantage of an RCM relative to a GCM. Most GCM simulations tend to
have decreased precipitation over all of Central America; this is because these models
simulate the large-scale decrease in the trade winds. They cannot, however, resolve the
small-scale features and forcing mechanisms that actually cause precipitation away from
the Atlantic coast and windward mountains. The RCM can do this, which accounts for its
very different behavior over the Pacific coast and the interior.
Disclaimer:
These results are based on just one model combination (MM5 forced by CCSM3); all
climate models are known to be imperfect. Furthermore, each simulated year consists of
just one realization, that is, there is no way to directly account for natural variability in
the form of year-to-year variations. Therefore, while we have tried to do the best job
possible with the computational and human resources at our disposal, use these model
results at your own risk.