Accounting for Climate Change

Box 11.3 of the most recent IPCC report on regional climate projections provides a brief overview of expected climate change in the mountains (Christensen et al., 2007). One of the most challenging questions to be answered during the design process, and one that has rarely been carried out in mountainous permafrost environments is how to account for climate change. Good climate records for a specific site are imperative for the best possible projection of future conditions. The site must be compared with available data from nearby long-term stations to obtain past average conditions. Offsets can be determined based on comparisons between local records and data from such a long-term station. Generally, conditions from the past 30 years provide an average that is utilised for modelling initial conditions. During the calculation of the past average conditions, extreme events, such as temperature anomalies caused by the El Niño Southern Oscillation (most recently 2006-07, or a major event in 199798), must be judged accordingly.

When selecting a climate scenario for predicting future climate conditions, the IPCC suggests five criteria that should be met if they are to be useful for impacting on researchers and policy makers (IPCC, 2007):

o Criterion 1: Consistency with global projections. They should be consistent with a broad range of global warming projections based on increased concentrations of greenhouse gases. This range is variously cited as 1.4°C to 5.8°C by 2100, or 1.5°C to 4.5°C for a doubling of atmospheric CO2 concentration.

o Criterion 2: Physical plausibility. They should be physically plausible; that is, they should not violate the basic laws of physics. Hence, changes in one region should be physically consistent with those in another region and globally. In addition, the combination of changes in different variables (which are often correlated with each other) should be physically consistent.

o Criterion 3: Applicability in impact assessments. They should describe changes in a sufficient number of variables on a spatial and temporal scale that allows for impact assessment.

o Criterion 4: Representative. They should be representative of the potential range of future regional climate change. Only in this way can a realistic range of possible impacts be estimated.

o Criterion 5: Accessibility. They should be straightforward to obtain, interpret and apply for impact assessment.

General Circulation Models (GCM) represent physical processes in the atmosphere, ocean, cryosphere and land surface. They are the most advanced tools available to date for simulating the response of the global climate system to increasing greenhouse gas concentrations. GCMs, possibly in conjunction with nested regional models (fine-resolution regional climate models - RCM), have the potential to provide geographically and physically consistent estimates of regional climate change, which are required in the impact analysis. However, even the selection of all available GCM experiments would not guarantee a representative range, due to other uncertainties that GCMs do not fully address, especially the range in estimates of future atmospheric composition. The GCMs are to be used to calculate median values describing base case climate change scenario as well as a high case scenario, which could be median plus one standard deviation. Since climate change does not only affect air temperatures, changes in precipitation, vegetation cover and wind speed should also be considered to the best possible degree, since they have an effect on the surface energy balance that can not be ignored. Perhaps the most important factor influencing permafrost in mountain areas is the snow cover, which is present for long periods of the year and can have a cooling or insulating effect, depending on the timing and duration of the snow cover. Long-term snow data in the Swiss Alps show a distinct step-like reduction in the number of snow days at altitudes below 1800 m asl since the mid 1980's (Marty, 2008), yet no long-term trends can be distinguished at altitudes above 2100 m asl, where permafrost is found. It is therefore difficult to predict future snow scenarios at high altitudes. Potential changes and trends should, however, be taken into account for structure design.

Finally, seasonal variations in the warming trend also must be incorporated in long-term predictions. As indicated in the IPCC reports (IPCC, 2007) and modeled by GCMs, warming during the winter months is higher than average warming, whereas air temperatures during the summer months increase at a rate lower than average. Therefore heat extraction during the cold winter months is lower than an average would predict. Because several non-linearity processes are involved, e.g. surface energy balance, not considering seasonal trends in the long-term models would not be adequate.

A deterministic / probabilistic approach is recommended due to all these uncertainties, variabilities and sensitivities when accounting for climate change, similar to seismic designs for earthquakes (e.g. Klügel, 2008).

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