Four degrees and beyond: the potential for a global ... - Amper

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102 F. Fung et al. hydrological model, only precipitation and temperature are available, as decadal seasonal means and at the model native resolution of 3.75 ◦ longitude by 2.5 ◦ latitude. Climate models are well known to contain biases in their climatology; for precipitation especially, any large biases can force the hydrological model to operate outside the range of the parametrizations that are used to simulate surface run-off. To correct for these biases, we use the change factor method whereby absolute changes in temperature and percentage changes in precipitation fields from a GCM for a 30 year period (e.g. 2000–2029) are calculated relative to a 1961–1990 baseline. The seasonal change factors were then disaggregated linearly to produce monthly factors, and interpolated to a 1 ◦ latitude/longitude resolution, as required by the hydrological model. To calculate future climate, precipitation and temperature change factors are then applied to the CRU TS2.1 baseline climate; other climate variables required by the hydrological model, for which we do not have GCM data, were kept the same as observed climate. One of the main criticisms of this bias-correction method is that it only adjusts the long-term mean and does not take into account additional changes in climate variability [21]. However, given that only decadal seasonal means are available from CPDN, a more sophisticated method of downscaling would not be warranted. The CMIP3 GCMs were processed in an identical manner to allow direct comparisons in the analysis. (c) Water availability (i) Global hydrological model We employ the MacPDM global hydrological model at a 1◦ spatial resolution. MacPDM is a semi-distributed daily water balance model that has been used in previous assessments of global water resources [7–9,22,23], and at its heart is a simplified version of the probability distribution model by Moore [24]. The model contains modules that simulate snowpack, lakes and wetlands but does not include a glacier component. As well as climate variables, other inputs include land cover, which has been derived from De Fries et al. [25], and soil textures, which have been taken from the FAO Digital Soil Map of the World. MacPDM internally disaggregates monthly precipitation to daily rates by using information on the number of rain days in each month and a random precipitation generator. The model is then run 10 times and the mean of the model outputs used. MacPDM outputs surface run-off at the monthly time step that, for this study, has been aggregated to mean seasonal values to reflect the temporal resolution of the CPDN climate model ensemble. There are a large number of global hydrological models, including those currently involved in the WATCH project [26], some of which treat the hydrological system in a more sophisticated manner than MacPDM. However, MacPDM is used in this investigation owing to its relative simplicity and therefore amenability for running large ensembles. The spatial resolution used in this study is also not as fine as those used by others [5–11] but does compare well with recently published work [27–29]. Despite infrastructure and flow routing not being included in the model, the compromise in the spatial resolution should not hinder our analysis of impacts on a larger, more synoptic scale. Phil. Trans. R. Soc. A (2011) Downloaded from rsta.royalsocietypublishing.org on November 30, 2010
(ii) Water stress index Water availability in +2 ◦ C and +4 ◦ C worlds 103 Water stress or scarcity can be defined as the balance between water supply (potential or realized) and water demand. A large number of water stress indices have been devised, incorporating information on factors such as accessibility of water under low flow conditions (e.g. [30]), water demands with the inclusion of national water withdrawal information on various water users (e.g. [11,31]), economic and technological advancement (e.g. [6,27]) as well as access to water sources (e.g. [32]). On the whole, these methodologies require a large amount of input data, much of which can be very uncertain. Given that the main purpose of this paper is to look at the impacts of two global temperature regimes by using large climate model ensembles, we have chosen to analyse the water resources per capita calculated as the ratio of annual surface run-off to population, which we call here the water stress index (WSI). Although simple and rather limited in its reflection of true water stress [33], it provides a less complicated view rather than having to attempt to tease information out from a more sophisticated WSI. The annual surface run-off required for the baseline WSI was obtained by driving MacPDM using the observed climate dataset for 1961–1990. The baseline population was obtained by using the Gridded Population of the World version 3 (GPWv3) dataset for 1990 [34] ata1 ◦ resolution. To calculate future WSI, we develop scenarios for future population growth for the 2030s and 2060s based on UN Population Division projections [35]. These projections are available at the national scale and provide estimates for urban and rural population growth up to 2050. As the GPWv3 is only available up to 2015, we have used a 30 ′′ dataset that defines urban areas (also included in the GPWv3 [36]) to determine the spatial distribution of rural and urban populations. The UN rural and urban growth projections were applied to the baseline population according to country and whether they are rural or urban cells (a procedure similar to that of Gaffin et al. [37]). Finally, the populations were aggregated to a1 ◦ spatial resolution. We have used the medium variant only for population growth and, as UN population growth rate projections are only available up to 2050, linear extrapolation was used to calculate the population for the 2060s for each country. 3. Results (a) Present-day water availability The baseline climatology of 1961–1990, representing the present-day climate, was used to drive MacPDM, producing monthly surface run-off values which were then aggregated to mean annual and seasonal values. The results of the baseline run are presented in figure 1b, representing the mean annual surface runoff (MAR) for the baseline time horizon. At this scale, a qualitative assessment of the spatial distribution of MAR reveals a similar pattern to that of mean annual precipitation (MAP; figure 1a), reflecting the strong control of precipitation on the water balance. Greenland has been excluded from this analysis as simple gridded hydrological models are notoriously bad at being able to capture the hydrology of the area. Further, from a water resource perspective, Greenland is not of particular significance. Phil. Trans. R. Soc. A (2011) Downloaded from rsta.royalsocietypublishing.org on November 30, 2010
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ISSN 1364-503X volume 369 number 19
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Phil. Trans. R. Soc. A (2011) 369,
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Editorial David Garner Phil. Trans.
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Four degrees and beyond: the potent
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Preface 5 commitments might give us
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8 M. New et al. In the final plenar
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10 M. New et al. supports the messa
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12 M. New et al. Table 1. Impacts a
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14 M. New et al. actors (NNSAs)—r
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16 M. New et al. as permanent crop
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18 M. New et al. 24 Boe, J. L., Hal
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Beyond 'dangerous' climate change:
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Beyond dangerous climate change 21
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(i) Energy and industrial process e
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(a) 50 (b) GtCO 2e yr -1 40 30 20 1
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Table 1. Summary of scenario pathwa
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Cumulative carbon emissions, emissi
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46 N. H. A. Bowerman et al. althoug
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48 N. H. A. Bowerman et al. a likel
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52 N. H. A. Bowerman et al. In most
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Humid tropical forests on a warmer
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162 R. J. Nicholls et al. expected.
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REVIEW Phil. Trans. R. Soc. A (2011
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Review. Interactions in a 4 ◦ C w
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spread in mosquitoborne disease may
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[89,94,95] further acidification by
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Editorial Editorial 3 D. Garner Pre
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