Utilizing the Koeppen climate classification to assess the future climate change

It is suggested that global warming due to anthropogenic greenhouse gasses will cause a large change in the mean temperature and precipitation patterns of the future. One way to quantify the impact of this change is to use the climate classification method. Classifying the climate into regions with distinct properties instead of using only physical properties such as temperature and precipitation helps to give an objective view of how climate change affects the environment such as the land-surface types and vegetation. TheKoeppen climate classification has a long history of application and modification and is known to give a robust classification of the mean climate that closely follows the distribution of vegetation types. In this study, we apply theKoeppen climate classification on the result of 19 Atmosphere-Ocean GCM results provided by the PCMDI for the upcoming IPCC - AR4. By applying this method to the long-term future projection of climate models, instability of a particular climate region and its expected change in the longer timescales are quantified. The classification is performed on the 20th century simulation (20C3M) and the SRES-A1B / A2 scenario based on the long-term monthly climatology. The overall changes in classifications as well as inter-model distribution is calculated for all each model and the skill weighted ensemble mean. Results show that due to warmer climate and increase in moisture, large area of western Russian region and north America experience a shift from aDf (snow / fully moist) climate to Cf (Warm temperate / fully moist) classification which is in good agreement with the stronger NAO/AO phase in the north Atlantic. On the other hand, coastal Greenland region changes from a Ef (Polar frost) classification to Ef (Polar tundra) classification, which is in good agreement with the SST and sea-ice distribution. In contrast, northern China undergoes a change from Cf classification to Cw (Warm temperate / winter dry) classification which marks a drying of this region. Weakening of the Aleutian low and a strongerENSO signal among models may have contributed to this result. In the presentation, major changed in classification and its physical background is highlighted.