The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
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 (1985–1995) and future (2025–2035). Initially, we compared the simulated values for each climate variable (P, Tmax, Tmin, and Tmean) 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.
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Citations
Cites
A globally coherent fingerprint of climate change impacts across natural systems
Parmesan, C., & Yohe, G. (2003) · Nature · doi:10.1038/nature01286 · Cited by 11,265
Fingerprints of global warming on wild animals and plants
Root, T. L., Price, J. T., Hall, K. R., Schneider, S. H., Rosenzweig, C., & Pounds, J. A. (2003) · Nature · doi:10.1038/nature01333 · Cited by 4,774
Pearson, R. G., & Dawson, T. P. (2003) · Global Ecology and Biogeography · doi:10.1046/j.1466-822x.2003.00042.x · Cited by 4,252
Quantification of modelling uncertainties in a large ensemble of climate change simulations.
Murphy, J. M., Sexton, D. M. H., Barnett, D. N., Jones, G. S., Webb, M. J., Collins, M., & Stainforth, D. A. (2004) · Nature · doi:10.1038/nature02771 · Cited by 1,655
Cited by
Accelerating extinction risk from climate change
Urban, M. C. (2015) · Science · doi:10.1126/science.aaa4984 · Cited by 2,436
Do weak AR4 models bias projections of future climate changes over Australia?
Perkins, S. E., & Pitman, A. J. (2009) · Climatic Change · doi:10.1007/s10584-008-9502-1 · Cited by 48
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Metadata
- Authors
- BEAUMONT, L. J., PITMAN, A. J., POULSEN, M., & HUGHES, L.
- Venue
- Global Change Biology
- Publisher
- Wiley
- Published
- 2007-04-10
- Volume
- 13
- Issue
- 7
- Pages
- 1368-1385
- ISSN
- 1354-1013 (ISSN-L)
- 1365-2486
- OpenAlex
- W2132105901
- Cited by
- 190
- References
- 4
- FWCI
- 11.12
- Type
- journal article
- Language
- en
- Open access
- closed
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- BEAUMONT, L. J., PITMAN, A. J., POULSEN, M., & HUGHES, L. (2007). Where will species go? Incorporating new advances in climate modelling into projections of species distributions. Global Change Biology. https://doi.org/10.1111/j.1365-2486.2007.01357.x
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.