Where will species go? Incorporating new advances in climate modelling into projections of species distributions

Abstract

Bioclimatic models are the primary tools for simulating the impact of climate change on species distributions. Part of the uncertainty in the output of these models results from uncertainty in projections of future climates. To account for this, studies often simulate species responses to climates predicted by more than one climate model and/or emission scenario. One area of uncertainty, however, has remained unexplored: internal climate model variability. By running a single climate model multiple times, but each time perturbing the initial state of the model slightly, different but equally valid realizations of climate will be produced. In this paper, we identify how ongoing improvements in climate models can be used to provide guidance for impacts studies. In doing so we provide the first assessment of the extent to which this internal climate model variability generates uncertainty in projections of future species distributions, compared with variability between climate models. We obtained data on 13 realizations from three climate models (three from CSIRO Mark2 v3.0, four from GISS AOM, and six from MIROC v3.2) for two time periods: current (1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995) and future (2025)(2026)(2027)(2028)(2029)(2030)(2031)(2032)(2033)(2034)(2035). Initially, we compared the simulated values for each climate variable (P, T max , T min , and T mean ) for the current period to observed climate data. This showed that climates simulated by realizations from the same climate model were more similar to each other than to realizations from other models. However, when projected into the future, these realizations followed different trajectories and the values of climate variables differed considerably within and among climate models. These had pronounced effects on the projected distributions of nine Australian butterfly species when modelled using the BIOCLIM component of DIVA-GIS. Our results show that internal climate model variability can lead to substantial differences in the extent to which the future distributions of species are projected to change. These can be greater than differences resulting from between-climate model variability. Further, different conclusions regarding the vulnerability of species to climate change can be reached due to internal model variability. Clearly, several climate models, each represented by multiple realizations, are required if we are to adequately capture the range of uncertainty associated with projecting species distributions in the future.

Will climate change be beneficial or detrimental to the invasive swede midge in North America? Contrasting predictions using climate projections from different general circulation models

“…They concluded that 'internal climate model variability can lead to substantial differences in the extent to which the future distributions of species are projected to change. These can be greater than differences resulting from between-climate model variability' (Beaumont et al, 2007).…”

“…Unlike the physical models of climate, the scenario models attempt to represent social and political systems that are relatively poorly understood and predictable only with great uncertainty (IPCC, 2001). BEM modellers have clearly accepted that predictions depend on which scenario is used, and have, for quite some time, been using multiple scenarios to make predictions (Beaumont et al, 2007).…”

“…For example, recently Beaumont et al (2007) quantitatively compared 13 realizations from three climate models (three from CSIRO Mark2 v3.0, four from GISS AOM, and six from MIROC v3.2) for two time periods: current (1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995) and future (2025)(2026)(2027)(2028)(2029)(2030)(2031)(2032)(2033)(2034)(2035), for predicting bioclimatic envelopes for nine Australian butterflies. For the baseline period , they found that there was more variability of climate projections between GCMs than within an individual GCM.…”

An overview of recent remote sensing and GIS based research in ecological informatics

“…Such studies can provide a valuable initial assessment of likely climate change impacts, especially if used at coarse spatial scales where macro-climate variation has most impact on species distributions (Pearson and Dawson, 2003;Luoto et al, 2005;Heikkinen et al, 2006). There are, of course, limitations to such modeling (Pearson and Dawson, 2003;Beaumont et al, 2007;Brooker et al, 2007;Osborne et al, 2007a). For example, there is often a need to accommodate for negative impacts of spatial autocorrelation (Hampe, 2004;Dorman, 2007).…”

Predicting the future of forests in the Mediterranean under climate change, with niche- and process-based models: CO2 matters!

“…It is of great importance to develop several methods independently and to compare (for the same species and under the same scenarios) their predictions in order to identify both robust results and model inadequacies (Beaumont et al, 2007). Such cross comparisons may provide information on which policy makers and stakeholders can rely.…”