IPCC Fourth Assessment Report: Climate Change 2007
Climate Change 2007: Working Group II: Impacts, Adaptation and Vulnerability Joint attribution using climate model studies

Several studies have linked the observed responses in some biological and physical systems to regional-scale warming due to anthropogenic climate change using climate models.

One study demonstrated joint attribution by considering changes in wild animals and plants (Root et al., 2005). They found spring phenological data for 145 Northern Hemisphere species from 31 studies. The changes in the timing of these species’ spring events (e.g., blooming) are significantly associated with the changes in the actual temperatures recorded as near to the study site as possible and for the same years that the species were observed. If the temperature was warming and the species phenology was getting earlier in the year, then the expected association would be negative, which is what was found for the correlations between the species data and the actual temperatures (Figure 1.7).



Temperature data from the HadCM3 climate model were used to determine whether the changes in the actual temperatures with which the phenological changes in species were associated were due to human or natural causes. Modelled temperature data were derived for each species, over the same years a species was studied and for the grid box within which the study area was located. Three different forcings were used when calculating the modelled values: natural only, anthropogenic only, and combined natural and anthropogenic. Each species’ long-term phenological record was correlated with the three differently forced temperatures derived for the location where the species was recorded. The agreement is quite poor between the phenological changes in species and modelled temperatures derived using only natural climatic forcing (K = 60.16, P > 0.05; Figure 1.7a). A stronger agreement occurs between the same phenological changes in species and temperatures modelled using only anthropogenic forcing (K = 35.15, P > 0.05; Figure 1.7b). As expected, the strongest agreement is with the modelled temperatures derived using both natural and anthropogenic (combined) forcings (K = 3.65, P < 0.01; Figure 1.7c). While there is uncertainty in downscaling the model-simulated temperature changes to the areas that would affect the species being examined, these results demonstrate some residual skills, thereby allowing joint attribution to be shown.

Other similar studies have shown that the retreat of two glaciers in Switzerland and Norway cannot be explained by natural variability of climate and the glaciers alone (Reichert et al., 2002), that observed global patterns of changes in streamflow are consistent with the response to anthropogenic climate change (Milly et al., 2005), and that the observed increase in the area of forests burned in Canada over the last four decades is consistent with the response due to anthropogenic climate change (Gillett et al., 2004). Each of these studies has its limitations for joint attribution. For example, the analysis by Reichert used a climate model linked to a local glacier mass balance model through downscaling and showed that the observed glacier retreat over the 20th century could not be explained by natural climate variability. However, they did not show that the observed retreat was consistent with the response to anthropogenic climate change, nor did they eliminate other possible factors, such as changes in dust affecting the albedo of the glacier. Similarly, Gillett and colleagues showed that the observed increases in area of forests burned was consistent with the response to anthropogenic forcing and not consistent with natural climate variability. However, they did not consider changes in forest management as a factor, nor did they consider the climate response to other external forcing factors.

Taken together, these studies show a discernible influence of anthropogenic climate change on specific physical (cryosphere, hydrology) and biological (forestry and terrestrial biology) systems.