The projected timing of climate departure from recent variability

Abstract

Ecological and societal disruptions by modern climate change are critically determined by the time frame over which climates shift beyond historical analogues. Here we present a new index of the year when the projected mean climate of a given location moves to a state continuously outside the bounds of historical variability under alternative greenhouse gas emissions scenarios. Using 1860 to 2005 as the historical period, this index has a global mean of 2069 (±18 years s.d.) for near-surface air temperature under an emissions stabilization scenario and 2047 (±14 years s.d.) under a ‘business-as-usual’ scenario. Unprecedented climates will occur earliest in the tropics and among low-income countries, highlighting the vulnerability of global biodiversity and the limited governmental capacity to respond to the impacts of climate change. Our findings shed light on the urgency of mitigating greenhouse gas emissions if climates potentially harmful to biodiversity and society are to be prevented.

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Figure 1: Estimating the projected timing of climate departure from recent variability.
Figure 2: The projected timing of climate departure from recent variability.
Figure 3: The projected timing of climate departure from recent variability in global biodiversity hotspots.
Figure 4: Biodiversity hotspots: exposure to climate departures, and economic capacity to respond.
Figure 5: Susceptibility of societies to climate departures, and economic capacity to respond.

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Acknowledgements

We thank D. Beilman for commenting on the paper; E. Wingert for help on the figures; D. Olsen for technical support; H. Kreft and the International Union for Conservation of Nature, BirdLife International, the Food and Agriculture Organization of the United Nations, the World Bank Database, the National Centers for Environmental Prediction, the World Database of Protected Areas, and the Gridded Human Population of the World Database for making their data openly available. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP5, and we thank the climate modelling groups (listed in Extended Data Table 1) for producing and making available their model outputs. This work was made possible by funding from the University of Hawai‘i Sea Grant to C.M. The paper was developed as part of the graduate course on ‘Methods for Large Scale Analyses’ in the Department of Geography, University of Hawai‘i at Mānoa. A.G.F. and T.W.G were supported by Pacific Islands Climate Change Cooperative (PICCC) award F10AC00077 and National Science Foundation Hawai‘i EPSCoR grant no. EPS-0903833. R.J.L. was supported by the Pacific Islands Climate Science Center and PICCC award F10A00079. R.S.D. and E.J.T. were supported by National Science Foundation Graduate Fellowships, and I.F.-S. by a postdoctoral fellowship from the Japanese Society for the Promotion of Science.

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All authors contributed equally to conceive the study, compile the data, conduct analyses, and write the manuscript.

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Correspondence to Camilo Mora.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Evaluating robustness of Earth System Models.

ac, Analysis of near-surface air temperature. df, Analysis of sea surface temperature. a, d, Normalized Taylor diagrams. The Taylor diagrams compare actual observations with CMIP5 model simulations, and summarize three different metrics of similitude: the correlation (curved axis), the ratio of the standard deviations (x and y axes) and the root mean squared error (blue arcs). Blue points indicate perfect fit, red points the multi-model average, and black points the comparison of each Earth System Model to actual observations. The closer a red or black point is to the blue point, the better the fit between actual and simulated data. b, c, e, f, Comparison between actual and multi-model minimum (b, e) and maximum (c, f) temperatures for the 20-year period 1986–2005 (the time frame for which actual observations were mostly available). Dashed lines indicate the 1:1 relationship.

Extended Data Figure 2 Multi-model uncertainty in the projected timing of climate departure.

Results are shown for near-surface air temperature (a), sea surface temperature (b), evaporation (c), sensible heat flux (d), ocean surface pH (e), precipitation (f) and transpiration (g). Maps on the left show the mean year of climate departure under RCP85, and maps on the right illustrate the spatial patterns of inter-model standard error of the mean for RCP85. The histograms on the right indicate the frequency of grid cells by multi-model standard error of the mean according to each emissions scenario (blue, RCP45; red, RCP85).

Extended Data Figure 3 Sea surface temperature and projected timing of climate departure.

a, b, Projected year when annual (a) or monthly (b) sea surface temperature means move to a state continuously outside annual or monthly historical bounds, respectively. c, Absolute change in mean annual sea surface temperature. (Results in ac are based on RCP85.) d, Cumulative frequency of 100-km resolution grid cells according to the year of climate departure under the two emissions scenarios and for mean annual and monthly sea surface temperature. e, Scatter plot relating the grid cells from the absolute change map (c) to the same grid cells from the projected timing of climate departure map (a).

Extended Data Figure 4 Projected timing of climate departure for different climate variables.

We calculated the year of climate departure for five variables in addition to temperature. We considered the year of climate departure as the year at which the first variable exceeded its historical bounds of variability. The plots show the year of climate departure (left), the absolute change (middle) and the relation between the departure year and absolute change (right) under RCP85. The plot at the bottom right compares the global average year using temperature alone with the year when considering additional climate variables. Vertical lines indicate s.d.

Extended Data Figure 5 Biodiversity hotspots.

Global patterns of species richness were mapped for 13 marine and terrestrial taxa. For each taxon, we outlined biodiversity hotspots as the top 10% most species-rich places on Earth where the given taxon occurred (bold black lines). For mammals, birds, reptiles, amphibians, marine fishes, cephalopods, corals, mangroves and seagrasses (ah, jm), we used expert-verified geographical ranges to map patterns of species richness by counting the number of species whose ranges overlapped with an equal-area grid with a resolution of 100 km. i, For terrestrial vascular plants we used the number of species in different regions (data from ref. 51) and calculated species richness as the highest number of species occurring in the regions intersecting each 100-km resolution grid cell. The number of species or species richness used for each taxonomic group is indicated in parentheses.

Extended Data Figure 6 Average gross domestic product (US$) per person for countries where the world’s biodiversity hotspots are located.

Horizontal bars represent the average GDP per person for the countries containing the hotspots for the 13 taxa examined.

Extended Data Table 1 Earth System Models analysed
Extended Data Table 2 Data sources

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Mora, C., Frazier, A., Longman, R. et al. The projected timing of climate departure from recent variability. Nature 502, 183–187 (2013). https://doi.org/10.1038/nature12540

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