3.5.3. Baseline Climatologies
184.108.40.206. Baseline Period
Any climate scenario must adopt a reference baseline period from which to calculate
changes in climate. This baseline data set serves to characterize the sensitivity
of the exposure unit to present-day climate and usually serves as the base on
which data sets that represent climate change are constructed. Among the possible
criteria for selecting the baseline period (IPCC, 1994), it should be representative
of the present-day or recent average climate in the study region and of a sufficient
duration to encompass a range of climatic variations, including several significant
weather anomalies (e.g., severe droughts or cool seasons).
A popular climatological baseline period is a 30-year "normal" period,
as defined by the WMO. The current WMO normal period is 1961-1990, which
provides a standard reference for many impact studies. Note, however, that in
some regions, observations during this time period may exhibit anthropogenic
climate changes relative to earlier periods.
220.127.116.11. Sources and Characteristics of Data
Sources of baseline data include a wide variety of observed data, reanalysis
data (a combination of observed and model-simulated data), control runs of GCM
simulations, and time series generated by stochastic weather generators. Different
impact assessments require different types and resolutions of baseline climatological
data. These can range from globally gridded baseline data sets at a monthly
time scale to single-site data at a daily or hourly time scale. The variables
most often required are temperature and precipitation, but incident solar radiation,
relative humidity, windspeed, and even more exotic variables sometimes may be
Two important issues in the development of baseline data sets are their spatial
and temporal resolution and uncertainties related to their accuracy (New, 1999)
(see TAR WGI Section 13.3.2 for
further details). Evaluation of the differences between baseline data sets recently
has become an important step in scenario development because these differences
can have an important bearing on the results obtained in an impact assessment
(Arnell, 1995; Pan et al., 1996).
3.5.4. Construction of Scenarios
Techniques for constructing climate scenarios (i.e., scenario information that
is directly usable in impact studies) have evolved very slowly during the past
2 decades. However, in the past few years several new developments in climate
modeling and scenario development have expanded the array of techniques for
scenario formation. The following subsections discuss some of these issues and
present some background illustrative material.
18.104.22.168. Choosing Variables of Interest
In principle, GCM-based scenarios can be constructed for a wide range of variables
at time resolutions down to subdaily time steps. In practice, however, not all
data are available at the desired temporal and spatial resolutions. Most scenarios
are conventionally based on changes in monthly mean climate, although with greater
quantities of model output now being saved operationally, daily output and information
on certain types of extreme events (e.g., mid-latitude cyclone intensities)
can be accessed readily. However, consideration must be given to whether model
output regarding a particular phenomenon is deemed "meaningful." For
example, although information on changes in the frequency and intensity of El
Niño-Southern Oscillation (ENSO) events may be desirable from an impacts
point of view, analyses of possible future changes in this oscillation still
are very preliminary (see TAR WGI Chapter
22.214.171.124. Selecting GCM Outputs
Many equilibrium and transient climate change experiments have been performed
with GCMs (Kattenberg et al., 1996; TAR WGI Chapter
9). Several research centers now serve as repositories of GCM information
(see, e.g., Hulme et al., 1995; CSIRO, 1997). The IPCC Data Distribution
Centre (IPCC-DDC, 1999) complements these existing sources. Table
3-5 lists GCM experiments that have been used to develop scenarios for impacts
studies evaluated in this report.
Four criteria for selecting GCM outputs from such a large sample of experiments
are suggested by Smith and Hulme (1998):
- Vintage: Recent model simulations are likely (though by no means
certain) to be more reliable than those of an earlier vintage since they are
based on recent knowledge and incorporate more processes and feedbacks.
- Resolution: In general, increased spatial resolution of models has
led to better representation of climate.
- Validation: Selection of GCMs that simulate the present-day climate
most faithfully is preferred, on the premise that these GCMs are more likely
(though not guaranteed) to yield a reliable representation of future climate.
- Representativeness of results: Alternative GCMs can display large
differences in estimates of regional climate change, especially for variables
such as precipitation. One option is to choose models that show a range of
changes in a key variable in the study region.