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

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220 R. Warren global-scale estimates. Such models typically represent climate change impacts as a combination of market and non-market economic damages, not in physical metrics [6], and do not consider the interactions listed earlier. Arguably, the most useful application of such models is in probabilistic analyses [7,8]. Such models use simple equations to detail the relationship between monetized impacts and temperature. The damage function shape used is theoretical and is often based on an arbitrary choice of function. Specifically, market damages are quadratic in DICE/RICE [9] and MERGE [10], between linear and cubic in a probabilistic fashion in PAGE, and take a variety of theoretical forms in FUND. Only FUND’s damage functions take into account the rate of temperature change as well as its magnitude (in the agricultural and ecosystem sectors only). However, the representation of the climate system in some of these simple models often is not consistent [11] with that of the Intergovernmental Panel of Climate Change (IPCC) [12]; for example, FUND shows smaller temperature responses to reducing emissions than IPCC simulations, and PAGE assumes strong carbon cycle feedbacks. Modellers have updated their code to remove these inconsistencies [13,14]. While only FUND is sector-specific [7], interactions between sectors are not considered. Non-market damages are estimated through a willingness-to-pay approach, while only PAGE explicitly simulates adaptation [8]. Damage functions tend to be calibrated using studies of climate change impacts in the USA, scaled to represent impacts in other regions. In FUND, impacts cause only instantaneous damage, hence ignoring permanent loss of ecosystem services. Mastrandrea [6] discusses these issues in further detail. Although some key insights about some interaction processes have been obtained from biophysically based IAMs (and these are highlighted in the review), only a small number of processes has so far been studied. This paper addresses this gap by considering a much wider range of potential interactions and their possible consequences. 2. Types of interactions (a) How impacts in one sector could affect another sector in the same region While some of the interactions between impacts in different sectors in the same location are commonly considered, others are not. Models simulating changes in crop yields generally take into account changes in precipitation and soil moisture, thus linking change in the agricultural and hydrological sectors. However, other interactions, such as the ways in which loss of ecosystem services affect human systems, are rarely considered. I attempt to divide interactions into those generally included in physically based modelling approaches and those rarely included. Table 2 summarizes the interactions discussed. (i) Some interactions between sectors affected by climate change that are generally covered in physically based impact models Hydrology has a strong interaction with agriculture through water availability. However, the relationship between (daily) precipitation and soil moisture is not constant and, therefore, hydrological processes need to be, and often are, incorporated into process-based models designed to simulate climate Phil. Trans. R. Soc. A (2011) Downloaded from rsta.royalsocietypublishing.org on November 30, 2010
Review. Interactions in a 4 ◦ C world 221 change impacts upon agriculture. Models such as CERESWheat [15] simulate physiological processes in plant development, growth and evapotranspiration, using detailed soil composition data to derive water availability. They underlie prominent analyses of global and regional climate change impacts on agriculture [1,16]. Other models relying on the hedonic method [17,18] assume that projected future regional precipitation and temperature may be used as proxies for agro-economical output. Here, the relationship between precipitation and soil moisture is assumed constant and hence hydrological processes are ignored [19]. Impacts on agriculture affect human health, largely through the potential for malnutrition, and hence a few studies have estimated millions at risk of hunger resulting from climate change impacts on crops [1,4,20]. Here, changes in yields produced by CERES crop models are aggregated to generate national cereal production estimates for input to a world food trade model that balances demand and supply of food according to per capita gross domestic product (GDP). However, most studies only provide estimates of yield reductions, without analysing changes in production or trade [21]. Changes in hydrology can also directly affect human health, through mortality (which tends to fall with rising GDP [22], water stress or loss of livelihoods), and hydrological changes are used in estimating potential future millions of people at risk of water stress [23,24]. Similarly persons at risk from fluvial and coastal flooding have been estimated using detailed spatially downscaled projections of future populations [1,25]. The impacts of sea-level rise upon coasts will interact with coastal ecosystems causing globally significant losses of coastal wetlands, saltmarsh and mangroves (e.g. [26,27]). It will also interact with the agricultural sector through inundation, with case studies illustrating effects in a number of vulnerable regions (e.g. [25,28,29]) and for the globe as a whole [30]. However, standard approaches to the simulation of agriculture impacts (e.g. [1,20]) do not include losses owing to salinization or inundation. (ii) Some interactions between sectors affected by climate change that are generally not included in physically based impact models Loss or disruption of natural ecosystems can lead to a breakdown of ecosystem functioning, leading to a loss of ecosystem services [32]. The large proportion of species is at risk of extinction from climate change (e.g. 40% of species studied in a 4◦C world; table 3); together with the effects of increased extreme events, such as drought and forest losses due to fire, this means that such services are at risk. These include the water purification provided by wetlands, the purification of air provided by forests, the protection of coastal areas from storm surges by mangroves and coral reefs, the regulation of pests and disease, the recycling of waste nutrients and the removal of carbon from the atmosphere [32,33]. In the USA, at least half of the medicines used today derive from natural sources and 116 out of 158 new drugs licensed between 1998 and 2002 were derived from natural origins [34]. However, only 1 per cent of known plants have been analysed for their potential use in medicine. Animals and microbes also make vital contributions, and the exploration of the potential of marine organisms is 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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52 N. H. A. Bowerman et al. In most
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emissions (GtC) Review. Global warm
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global surface warming (°C) 6 5 4
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temperature (relative to 1861-1890
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°C above pre-industrial 8 7 6 5 4
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Regional temperature and precipitat
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Water availability in +2 ◦ C and
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(ii) Water stress index Water avail
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(b) (c) (d) (a) Water availability
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(a) number of models, black line nu
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change in run-off (%) change in run
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Agriculture and food systems in sub
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118 P. K. Thornton et al. 1. Introd
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120 P. K. Thornton et al. increases
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122 P. K. Thornton et al. Table 1.
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124 P. K. Thornton et al. century.
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Humid tropical forests on a warmer
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0 retraction risk 17 120 80 40 (a)
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0 retraction risk 17 120 80 40 0
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162 R. J. Nicholls et al. expected.
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