Figure 1.7. Plotted are the frequencies of the correlation coefficients (associations) between the timing of changes in traits (e.g., earlier egg-laying) of 145 species and modelled (HadCM3) spring temperatures for the grid-boxes in which each species was examined. At each location, all of which are in the Northern Hemisphere, the changing trait is compared with modelled temperatures driven by: (a) natural forcings (purple bars), (b) anthropogenic (i.e., human) forcings (orange bars), and (c) combined natural and anthropogenic forcings (yellow bars). In addition, on each panel the frequencies of the correlation coefficients between the actual temperatures recorded during each study and changes in the traits of 83 species, the only ones of the 145 with reported local-temperature trends, are shown (dark blue bars). On average the number of years that species were examined is about 28, with average starting and ending years of 1960 and 1998. Note that the agreement: (a) between the natural and actual plots is weaker (K = 60.16) than (b) between the anthropogenic and actual (K = 35.15), which in turn is weaker than( c) the agreement between combined and actual (K = 3.65). Taken together, these plots show that a measurable portion of the warming regional temperatures to which species are reacting can be attributed to humans, therefore showing joint attribution (after Root et al., 2005).
related to temperature or other climate change variable as described by the authors, for the period 1970 to 2004 (study periods may be extended later), with at least 20 years of data. Observations in the studies are characterised as 'change consistent with warming' and 'change not consistent with warming'.
Figure 1.8 shows the warming trends over the period 1970 to 2004 (from the GHCN-ERSST dataset; Smith and Reynolds,
2005) and the geographical locations of significant observed changes. A statistical comparison shows that the agreement between the regions of significant and regional warming across the globe and the locations of significant observed changes in systems consistent with warming is very unlikely to be due to natural variability in temperatures or natural variability in the systems (Table 1.12) (see also Supplementary Material).
For regions where there are both significant warming and observed changes in systems, there is a much greater probability of finding coincident significant warming and observed responses in the expected direction. The statistical agreement between the patterns of observed significant changes in systems and the patterns of observed significant warming across the globe very likely cannot be explained by natural climate variability.
Uncertainties in observed change studies at the regional level relate to potential mismatches between climate and system data in temporal and spatial scales and lack of time-series of sufficient length to determine whether the changes are outside normal ranges of variability. The issue of non-climate driving forces is also important. Land-use change, changes in human management practices, pollution and demography shifts are all, along with climate, drivers of environmental change. More explicit consideration of these factors in observed change studies will strengthen the robustness of the conclusions. However, these factors are very unlikely to explain the coherent responses that have been found across the diverse range of systems and across the broad geographical regions considered (Figure 1.9).
Since systems respond to an integrated climate signal, precise assignment of the proportions of natural and anthropogenic forcings in their responses in a specific grid cell is difficult. The observed continent-averaged warming in all continents except Antarctica over the last 50 years has been attributed to anthropogenic causes (IPCC, 2007, Summary for Policy Makers). The prevalence of observed changes in physical and biological systems in expected directions consistent with anthropogenic warming on every continent and in some oceans means that anthropogenic climate change is having a discernible effect on physical and biological systems at the global scale.
Was this article helpful?