SimCLIM is a computer model system for examining the effects of climate variability and change over time and space. Its "open-framework" feature allows users to customise the model for their own geographical area and spatial resolution and to attach impact models.

SimCLIM is designed to support decision making and climate proofing in a wide range of situations where climate and climate change pose risk and uncertainty. A user customised SimCLIM Open Framework System software package has the capacity to assess baseline climates and current variability and extremes. Risks can be assessed both currently and in the future. Adaptation measures can be tested for present day conditions and under future scenarios of climate change and variability. With the program, users can conduct sensitivity analysis and examine sectoral impacts of climate change. SimCLIM supports integrated impact analysis at various scales.

• SimCLIM can be used to:
• Describe baseline climates
• Examine current climate variability and extremes
• Assess risks - present and future
• Investigate adaptation - present and future
• Create scenarios of climate and sea-level change
• Conduct sensitivity analyses
• Project sectoral impacts of climate and sea level change
• Examine risks and uncertainties; and
• Facilitate integrated impact analyses.

SimCLIM has many features which allow you to use your own data. We are always improving on this aspect of usability. Not only can you import your own data but CLIMsystems can work closely with clients to build a customised version of SimCLIM that suit their requirements. This requires clients to access local data and provide it to CLIMsystems so that the best possible customised version of SimCLIM can be delivered. Upon delivery, customisation and maintenance of the system can be done quickly and easily by the client, for example, adding additional shape or vector files or updating site specific time series climate data.

SimCLIM requires two types of climate data:

• long term monthly-mean to represent the baseline climatology, and
• observed daily time-series data.

Long-term, monthly mean values for temperature, rainfall and solar radiation (or sunshine hours) can be developed from site station data. Normally the values are derived from the 1961-1990 or 1970-2000 period of record (for rainfall and maximum, mean and minimum temperature), as a 30-year period is needed to conform to the WMO standard.

The monthly-mean values can then be used to develop spatially-interpolated climatologies at the user's defined resolution. Several methods can be used to interpolate the site data to cover the user's defined area. These include the simplest linear interpolation to more mathematically complicated spline interpolation methods. A common method used for this practice is ANUSPLIN (Hutchinson, 1989).

Daily time series of rainfall and temperature data are required as input to more complex impact models and the extreme event analysis tool. In a number of cases there may be significant amounts of missing data (particularly for solar radiation), so methodologies may need to be applied to provide estimates for identified gaps in the record, as reliable time series data is critical for future impact assessment.

For data to be used in SimCLIM, it needs to be organised in the right format so it can be imported using the SimCLIM data importing toolbox. The current version of SimCLIM can import various formats of site data and three formats of spatial data. CLIMsystems Ltd. can work with clients to rapidly and efficiently transform large data sets for easy incorporation into the SimCLIM model system.

Local Climate Data

In order to assess climate change impacts regionally, two types of local data are needed: site specific historical climate data and spatial climatologies. Hourly, daily, monthly time series observed data can be imported to the system, and the climate variability and extreme event analysis can then be carried out for a specific site. In order to produce future climate change scenarios, a set of baseline climatologies and GCM patterns are required. The baseline climatologies are created from interpolation of long term average (usually 1961 to 1990) of site data to the required spatial resolution. The GCM patterns are used for downscaling the global climate change scenario to the local spatial resolution, with projected time series from the baseline to 2100.

Past climate data can be queried through an extreme event analysis tool which can, among other things, determine the probability of a particular extreme event, such as heavy rainfall or extremely hot or cold temperatures. The probabilities and return periods for such extreme events can also be queried for the future using an array of future scenarios of climate change as released by the Intergovernmental Panel on Climate Change (IPCC). This type of information is vital to engineers designing infrastructure to withstand future climatic events and a whole host of other individuals and organisations, such as the insurance industry, that must factor in potential climatic risk when planning for developments that will persist into a climate changed future. Researchers can use the system for many types of climate-based assessments.

A total of six emission scenarios (SRES) are included in the software package and they can be queried for their associated changes in temperature, sea level (total and thermal expansion only) and CO2 concentration as produced by the Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC). For each SRES the software program produces a graph of projections from 1990 to 2100 with low, medium and high estimates. The latest Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report data comes pre-loaded in SimCLIM.

General Circulation Model (GCM) Patterns

General circulation models, or GCMs, are physically-based, complex, three-dimensional climate models that takes into account as many factors as possible that could influence climate and hence must be incorporated to simulate the global climate system. They are essentially the only viable tools for simulating regional patterns of climate change from increasing atmospheric concentrations of greenhouse gases. GCM outputs have thus been widely used to assess climate change impacts for various geographical regions of the world, and have been incorporated for this purpose. The advantages and disadvantages of using GCMs for impact assessments have been summarised by Carter and Hulme (1999). As the authors indicate, the main advantage is that GCMs provide the only tool that estimates changes in climate due to increased greenhouse gases for a large number of climate variables in a physically consistent manner. However, some disadvantages are:

• The model normally runs on a relatively coarse horizontal resolution (up to several kilometres in grid);
• Although most GCMs are quite accurate in their representation of global climate, their simulations of current regional climate can often be inaccurate;
• Despite the physical consistency in their estimation, the internal relationships between the model variables produced from GCMs may not always be the same as those found in observational data;
• Output from GCMs is usually produced at a much coarser temporal and spatial resolution than is required for impact studies.

To address the first point, the user could select those GCM results which have both validated well globally and in the user defined region, and import them into their system. In many cases, an impact model requires a fine spatial resolution as input. A downscaling technique may be needed to satisfy such a model requirement. Several downscaling methods have been developed. In New Zealand, a downscaling techniques that takes into account topographical and circulatory influences on climate at an appropriate spatial and temporal resolution.

Climate Sensitivity

The climate sensitivity defines the equilibrium response of global-mean surface air temperature to an instantaneous doubling of CO2 or CO2-equivalent concentration. The IPCC mid-range estimate of this parameter is 2.5°C, with a range from 1.5°C to 4.5 °C. These three runs define the low and the high ends of the range of global temperature projections displayed in the Temperature Graphs.