Modelling climate change

Figure 1.3. General scheme of the Grande Dixence hydropower complex.

In addition, GCMs also provide initial and lateral boundary conditions to drive Regional Climate Models (RCMs). RCMs constitute dynamical downscaling tools that can increase the resolution of GCMs over a limited area of interest, with more detailed physical formulations, and indeed are sometimes referred to as “intelligent interpolators”. Hence, complex topographic features which strongly modulate the climate can be better taken into account by such models (Déqué, 2004; CCSP, 2008). The range of spatial resolution of RCMs is generally from 10 to 50 km and simulations are undertaken for several decades, mostly 20 to 30 years. RCMs are not an alternative to global high resolution modelling but a necessary complement (Giorgi, 2006).

At the other end of the model resolution spectrum, simulations of the climate dynamics can be generated by local models. Such models allow one to explore the potential sensitivity of the climate to a particular process over a wide range of parameters and a limited spatial

Without the natural greenhouse effect, life as we know would not exist as the mean global temperature would approach -18 °C. Instead of that, the average surface temperature is currently around 15 °C. This is due to the presence of t he atmosphere that traps a part of

the solar radiant energy. As explained by Schneider (1992) and the IPCC (2007) for instance, particles and gases in the Earth’s atmosphere absorb about 25% of this incoming energy, Earth’s surface about 45% and the remaining part is reflected directly back to space. To maintain the energetic equilibrium, the solar radiation that is absorbed by the Earth-atmosphere system must be balanced by an equal amount of outgoing radiant energy, without which the temperature of the planet would be modified. However, according to Wien’s Law, since the Earth is much colder than the Sun, it emits radiation at longer wavelengths, primarily in the infrared region of the spectrum. The consequence is that the Earth’s surface emits thermal radiation which are absorbed by a number of trace gases within the atmosphere and reradiated both up to space and back down to the Earth. The largest contributor to the natural greenhouse forcing in the atmosphere is water vapour, followed by carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). Should the amount of a GHG be increased, it would then intercept a larger fraction of the infrared energy coming upward from the surface and thus enhance the natural greenhouse forcing.

Since the pre-industrial era, a large alteration of the energy balance of the climate system has been actually observed, induced primarily by enhanced anthropogenic CO2 emissions caused by the burning of fossil fuels. The presence of water vapor in the atmosphere is indeed not believed to be affected by human activity by far in the same proportion as CO2

(Willett et al., 2007). As a result, global average CO2 concentrations have increased from 280 ppm in 1750 to 388.5 ppm in 2010 (Conway and Tans, 2010). At the same time, the observed global average temperatures have increased by about 0.74 °C between 1906 and 2005 (IPCC, 2007). The increasing concentration of other human-induced GHGs and pollutants in the atmosphere also contribute to global warming but to a lesser extent.

1.3.2.2. Emissions scenarios

The build-up of natural and anthropogenic GHGs in the atmosphere is the primary cause for concern related to global warming over coming decades. There are, however, large uncertainties inherent to the future course of climate, linked to insufficient understanding of complex, non-linear mechanisms between elements of the climate system, data gaps and insufficient data (Nakicenovic, 2000). Moreover, in order to anticipate changes in climate, it is necessary to take into account not only the physics of the system but also a wide range of the numerous driving forces of future emissions, that range from demographic to societal systems, including technological change and economic growth (IPCC, 2000). In this perspective, the Intergovernmental Panel on Climate Change published a Special Report on Emissions Scenario (SRES, IPCC 2000) where a set of alternative emissions projections are developed, each of which can be considered as representative of possible emission futures. Thus, 40 different SRES scenarios encompass most of the GHG emissions range in the published scenario literature and are intended to serve the scientific community in assessing climate change impacts. It should be noted however that they do not include any explicit policies directed at reducing GHG sources or enhancing sinks. The set of scenarios is rather inclined at providing a benchmark for the evaluation of mitigation and adaptation measures, as well as climate policy interventions.

The 40 SRES scenarios are grouped into four alternative scenario families or “storylines”

(A1, A2, B1 and B2) in an attempt to define a manageable and relative small number of projections. They represent nevertheless a wide range of possible futures and yet avoid the

impression that there is a central or most likely case. Each of the four storylines includes a demographic, politico-economic, societal and technological future associated with global and regional developments and their implications for GHG emissions. Within each family one or several particular scenarios were selected to serve as “prototypes” that can particularly well illustrate the entire set. In this study, only the SRES A2 scenario is applied on snow and runoff data. This A2 experiment gives a relatively strong climate change signal compared to the other scenarios. According to the IPCC (2000), it describes a very heterogeneous world. The underlying theme is the strengthening of regionally oriented economic growth with an emphasis on family values and local traditions, a continuously increasing global population, less concern for rapid economic development and low priorities on GHG abatement strategies. Concerning the repercussions on emissions values, this scenario projects a high level of CO2 concentrations of about 800 ppm by 2100, i.e.

about three times their pre-industrial values. In the following chapters the selection of a unique scenario should not be considered as limitative since the overall goal of the present study is to establish methodologies on how snow and runoff may be modified in an Prediction of Regional scenarios and Uncertainties for Defining European Climate change risks and Effects (PRUDENCE) project was carried out from 2001 to 2005 (Christensen, 2005). It involved more than 20 European research groups with the common objective of assessing general trends of climate change scenarios for Europe at the end of the 21^st century with dynamically downscaled high-resolution models. A further objective of PRUDENCE was to explore the uncertainty in these projections (Déqué, 2004; IPCC, 2007).

The results of such analyses have the advantage of justifying the use of simulations from one RCM experiment only. Indeed, the outputs of the HIRHAM RCM of the Danish Meteorological Institute (Christensen et al., 1998) correspond well with those of the other RCMs used in PRUDENCE and are located mostly in the middle of the range of simulations for most geographical regions, climate variables, and seasons (Christensen and Christensen, 2007). The HIRHAM model has therefore been chosen in this study in order to evaluate the physical impacts of climate change on snow and runoff. This RCM has a horizontal resolution of 50 km, in which Switzerland is covered by 21 grid points. Among the many fields archived in HIRHAM, only daily values of air temperature, precipitation and dew point temperature were used in this research. During the duration of the PRUDENCE project, this RCM completed both a control simulation to reproduce the baseline climate period from 1961 to 1990, and a future scenario simulation – the greenhouse climate – representing the period from 2071 to 2100. For the A2 scenario, simulations have used the boundary data from the Hadley Centre Atmospheric Model HadAM3H GCM to provide a detailed understanding of the regional model uncertainty (Pope et al., 2000; IPCC, 2007).

Again, the use of one rather than an ensemble of RCM outputs should not be viewed as restrictive, as the HIRHAM has in the past proven to be reasonably accurate in mountain

regions (Beniston et al., 2010). To apply the climate projection of the selected RCM to the physical features of snow and runoff, two different approaches of the so-called “delta downscaling method” were used. These are described in detail in chapters 2 and 5 and comparison of both methods is to be found in Appendix C.

1.3.3. Modelling snow