"Climate" in a location can be described as the average
(or central tendencies of) weather over specific periods of time.
Climate forecasts are developed by using Global Circulation Models
(GCMs), which are mathematical representations of the physics driving
climatic processes. Current GCMs typically link dynamic models of
the atmosphere and ocean so that the interactions between these two
aspects of the climate system can be captured. Before they are used to
predict future changes, models are carefully calibrated by tuning them
until they can accurately predict past climate conditions.
Most agree that GCMs are currently the best way to predict climate change and they are constantly being improved. However, there are two complications for decision makers to consider when using GCMs. First, irreducible uncertainties are an inherent element of climate change, regardless of the sophistication of modelling techniques. Some of the issues regarding uncertainty are discussed in the Irreducible Uncertainty section.
The second challenge concerns the resolution of these models. GCMs are built by creating a grid that covers the entire globe. Predictions are made by resolving the model equations for each square and then linking this to the surrounding squares. The complexity of the models limits the resolution at which predictions can be generated. Currently, most GCMs generate information for grid squares that are about 2° square, which means that in British Columbia, for example, grids are several hundred kilometres square.
Within the climate science community, significant resources have been invested into finding ways to downscale GCM data so that regional predictions can take local variations into account. This work is ongoing, and while specific downscaling has been conducted for some regions, the most commonly available information is directly available from GCMs. This information does indicate the potential general changes within a given region. However, it is unclear how much of an advantage increased resolutions of predictions will be for local decision makers. To some extent, the irreducible uncertainties may mean that the lower resolution GCMs are as useful as higher resolution downscaling in immediate planning activities.
Forecasts relevant to a particular region can be generated in two ways. First, data on the region can be extracted directly from a GCM. These data are fairly low resolution (large scale), but show general trends and expectations. Second, climate downscaling can be used to generate more specific forecasts for a particular region. These data will have higher resolution, but are more expensive and difficult to generate. Either way, developing regional scenarios and creating influence diagrams that correlate these scenarios to regional systems can help make climate data more useful for decision making.
The Pacific Climate Impacts Consortium (PCIC) has made their
Regional Analysis Tool publicly available at http://pacificclimate.org/tools/ regionalanalysis/. This tool has the
capacity to present regional data for North America from ten GCMs and
has global scale data from an additional fifteen GCMs. This tool does not use
regionally downscaled data, but shows the results from CGMs for the
particular region in question.
The PCIC tool allows one to use data from multiple GCMs under several emission scenarios to generate maps and basic statistics describing potential impacts on a range of variables. Variables that can be explored include mean temperature, mean precipitation, soil moisture, and wind speed. By considering these variables, planners can think about how regional systems might interact with these predicted changes.
Developing regionally downscaled climate predictions is more difficult than accessing regionally relevant GCM data. However, the Canadian Climate Change Scenarios Network provides some of the basic data required and two downscaling tools. These tools are the Statistical Downscaling Model (SDSM) and a weather generator (LARS-WG). The tools are available at http://www.ccsn.ca/ The_Network/The_Network-e.html.
The Canadian Climate Change Scenarios Network also has maps and data from Canada's Regional
Climate Model (CRCM). Additionally, the results of
downscaling predictions for particular regions are available. For
example, some work has been done linking GCM predictions to the effects
produced by altitude that can only be captured by more specific
Maps generated through partial downscaling conducted by the BC Royal Museum in conjunction with the PCIC are available at http://www.pacificclimate.org/resources/ climateimpacts/rbcmuseum.
More information on how downscaling works is provided in the Downscaling Climate Data section.
Two factors are particularly important in shaping regional climate forecasts:
1. The GCM used to generate the forecast; and
2. Assumptions about greenhouse gas (GHG) emission generation over time.
The number of relevant GCM forecasts available through the PCIC tool depends on the specific variables (e.g., temperature, precipitation, wind speed), region, and time of interest. Similarly, there are several possible GHG emission scenarios for which data can be accessed. When combined, these two factors create a wide range of possible results for the variables in question.
In order to make the diversity of results useable for decision makers, it makes sense to create a smaller set of regional scenarios. For example, based on differences in GCM and emissions assumptions, a set of 'high, medium and low' scenarios can be built to show the range of impacts on the selected variables of interest. This can help to identify regional vulnerabilities. A recent example of a regional scenario is found in work prepared for British Columbia's biodiversity plan at http://www.biodiversitybc.org/assets/Default/BBC Major Impact Climate Change.pdf.
Using the PCIC Regional Analysis Tool, the full range of GCM predictions
across all scenarios were collected. This collection gives the full
range of predictions for the province. To make this information more
useful, only three combinations of emission scenario and GCM were
selected. These three combinations represented a "high" range for
climate change, a "medium" and a "low" level. Decision makers could
start to predict what the broad range of impacts might be using
these combinations for seasonal and annual predictions for temperature
Why Would You Want to Explore Multiple GCMs?
Climate modelling is inherently uncertain, but this does not mean that forecasts do not have value. One way to make decisions despite this uncertainty is to consider the range of possible climate outcomes instead of relying on single forecasts.
Because each GCM incorporates slightly different assumptions about how the climate works, each generates different results. Decision makers can make more resilient decisions by incorporating a range of these results in their considerations.
The variation in GCM outcomes can also be combined with variation in greenhouse gas emission scenarios.