Matthew Livermore

Climate change and world food security: a new assessment

Building on previous work quantitative estimates of climate change impacts on global food production have been made for the UK Hadley Centres HadCM2 greenhouse gas only ensemble experiment and the more recent HadCM3 experiment (Hulme et al., 1999). The consequences for world food prices and the number of people at risk of hunger as defined by the Food and Agriculture Organisation (FAO, 1988) have also been assessed. Climate change is expected to increase yields at high and mid-latitudes, and lead to decreases at lower latitudes. This pattern becomes more pronounced as time progresses. The food system may be expected to accommodate such regional variations at the global level, with production, prices and the risk of hunger being relatively unaffected by the additional stress of climate change. By the 2080s the additional number of people at risk of hunger due to climate change is about 80 million people (±10 million depending on which of the four HadCM2 ensemble members is selected). However, some regions (particularly the arid and sub-humid tropics) will be adversely affected. A particular example is Africa, which is expected to experience marked reductions in yield, decreases in production, and increases in the risk of hunger as a result of climate change. The continent can expect to have between 55 and 65 million extra people at risk of hunger by the 2080s under the HadCM2 climate scenario. Under the HadCM3 climate scenario the effect is even more severe, producing an estimated additional 70+ million people at risk of hunger in Africa.

Climate change, global food supply and risk of hunger

This paper reports the results of a series of research projects which have aimed to evaluate the implications of climate change for food production and risk of hunger. There are three sets of results: (a) for IS92a (previously described as a ‘business-as-usual’ climate scenario); (b) for stabilization scenarios at 550 and 750 ppm and (c) for Special Report on Emissions Scenarios (SRES). The main conclusions are: (i) the region of greatest risk is Africa; (ii) stabilization at 750 ppm avoids some but not most of the risk, while stabilization at 550 ppm avoids most of the risk and (iii) the impact of climate change on risk of hunger is influenced greatly by pathways of development. For example, a SRES B2 development pathway is characterized by much lower levels of risk than A2; and this is largely explained by differing levels of income and technology not by differing amounts of climate forcing.

Millions at risk: defining critical climate change threats and targets

Agreements to mitigate climate change have been hampered by several things, not least their cost. But the cost might well be more acceptable if we had a clear picture of what damages would be avoided by different levels of emissions reductions, in other words, a clear idea of the pay off. The problem is that we do not. The Third Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) published this year (IPCC (2001a) and IPCC (2001b)) lists a wide range of potential impacts but has difficulty in discriminating between those that are critical in their nature and magnitude from those that are less important. Yet, the identification of critical impacts (e.g. ones that should be avoided at any reasonable cost) is obviously a key to addressing targets for mitigating climate change. Indeed, a central objective of the UN Framework Objective on Climate Change (UNFCCC) is to avoid “dangerous levels” of climate change that could threaten food security, ecosystems and sustainable development (areas of risk that are specifically mentioned in UNFCCC Article 2). For several years, we have been researching impacts in key areas of risk: hunger, water shortage, exposure to malaria transmission, and coastal flooding, as part of a global fast-track assessment (Parry and Livermore, 1999). 1 The results of our work have been reported widely and form a significant part of the IPCCs assessment of likely impacts (IPCC (2001a) and IPCC (2001b)). But they are scattered through different parts of the IPCC report and other literature and, before now, we have not brought them together. For this review, we have graphed our estimates of effects as a single measure: the additional millions of people who could be placed at risk as a result of different amounts of global warming ( Fig. 1). Full-size image (36K) - Opens new window Full-size image (36K) Fig. 1. Additional millions at risk due to climate change in 2050s and 2080s for hunger, coastal flooding, water shortage and malaria. The width of the curve indicates one standard deviation of variance around the mean, based on results from four HadCM2 experiments (Parry and Livermore, 1999; IPCC, 2000). Solid lines indicate model-based estimates. Dotted lines are inferred ( IPCC (2001a) and IPCC, 2001b. Climate change 2001: The Scientific Basis. Technical Summary of the Working Group I Report, Geneva, 2001.IPCC (2001b)) and intended as schematic. Stab. 450 (etc.)=stabilisation@450 ppmv (etc.). View Within Article The figure shows the increase in millions at risk due to higher temperatures for two time periods—2050s and 2080s. The analysis takes into account likely non-climate developments such as growth in population, and income and developments of technology, and these become important assumptions behind future trends in, for example, increases in crop yield and the building of coastal defences. These developments themselves have very great effects on the numbers at risk and represent a (non-climate change) reference case. The graph thus shows the additional millions at risk due specifically to estimated future changes in climate. But now for the caveats: the reference case is only for one future world (what the IPCC used to call a best estimate or “business-as-usual” future, now referred to as IS92a). More recently, the IPCC has explored a set of six different developmental pathways that the world may follow (IPCC, 2000), and the millions at risk in these alternative futures will certainly differ. Our work on these is in hand but will probably take a year to complete. We need also to emphasise that the graph is a global estimate which hides important regional variations and, so far, it is based on one model of future climate patterns (the UKs Hadley Centre second generation global climate model) ( Johns et al., 1997). While these are the only global impact estimates currently available, we need urgently to complete similar ones for different climate models and for a variety of development pathways. Five important points emerge from this figure. First, the curves of additional millions at risk generally become steeper over time. Less obviously, this results as much from a larger and more vulnerable exposed population in 2080 than in 2050, as from increases in temperature or inferred changes in precipitation and sea-level rise. For example, the remarkable steepness of the water shortage curve in 2080 is the outcome of very large city populations in China and India becoming newly at risk. In the case of hunger, however, the rising curve in 2080 stems from widespread heat stress of crops, while up to about 2050 lesser amounts of warming lead to yield gains in temperate regions that balance losses elsewhere and lead to only small net increases in hunger (Parry and Livermore, 1999). These complex interactions between exposure and climate change tell a clear story: there will be more millions at risk as time progresses. Secondly, the figure indicates how much we need to reduce emissions in order to draw-down significantly the numbers at risk. We have estimated effects assuming that atmospheric concentrations of CO2 are stabilised at 750 parts per million (ppmv) by 2250 and at 550 ppmv by 2150 (Arnell, in press). These are approximately equivalent, respectively, to 10 times and 20 times the reduction in emissions assumed in the Kyoto Protocol. The 750 ppmv target delays the damage but does not avoid it. By 2080, it would halve the number at risk from hunger and flooding, reduce the population at risk of malaria by perhaps a third and water shortage by about a quarter. But to bring risk levels down from hundreds to tens of millions would require a stabilisation target of about 550 ppmv. We have also indicated on the graph, but only in a schematic form, the approximate locations of 450, 650 and 1000 ppmv stabilisation pathways and their effect on millions at risk (IPCC (2001a) and IPCC (2001b)). Although impact analyses have not yet been conducted for these stabilisation levels, it appears that the 450 ppmv pathway would achieve very great reductions in millions at risk, although very high costs of mitigation would be incurred. It is precisely this kind of pay-off that needs to be analysed properly. A third conclusion is that information is now available that can help inform the selection of climate change targets. Thus far these targets, such as Kyoto, have been chosen in broadly a top–down manner, without clear knowledge of the impacts that would be avoided, and that has been partly their weakness. Now we may argue, for example, that in order to keep damages below an agreed tolerable level (for example, a given number of additional people at risk) global temperature increases would need to be kept below a given amount; and emissions targets could then be developed to achieve that objective. Fourthly, it is clear that mitigation alone will not solve the problem of climate change. Adaptation will be necessary to avoid, or at least reduce, much of the possible damage, and since we need many of the benefits of adaptation today, regardless of climate change in the future (e.g. increased drought protection of agriculture, improved flood defences, more efficient use of water, better malaria control), many of the adaptive strategies for climate change can be “win–win”. We need to find a blend of mitigation and adaptation to meet the challenge of climate change. Mitigation can buy time for adaptation (for example, delaying impacts until improved technology and management can handle them), and adaptation can raise thresholds of tolerance that need to be avoided by mitigation (for example, by increasing drought tolerance of crops). Considered separately, they appear inadequate to meet such a challenge, but combined they would make a powerful response.

Adapting to the inevitable

Greenhouse-gas emissions targets to be discussed in Buenos Aires next month will have little effect on the potential impacts of climate change. We should be exploring ways of adapting to impacts, some of which are inevitable.

The consequences of CO2 stabilisation for the impacts of climate change

This paper reports the main results of an assessment of the global-scale implications of the stabilisation of atmospheric CO2 concentrations at 750 ppm (by 2250) and 550 ppm (by 2150), in relationto a scenario of unmitigated emissions. The climate change scenarios were derived from simulation experiments conducted with the HadCM2 global climate model and forced with the IPCC IS92a, S750 and S550 emissions scenarios. The simulated changes in climate were applied to an observed global baseline climatology, and applied with impacts models to estimate impacts on natural vegetation, water resources, coastal flood risk and wetland loss, crop yield and food security, and malaria. The studies used a single set of population and socio-economic scenarios about the future that are similar to those adopted in the IS92a emissions scenario.An emissions pathway which stabilises CO2 concentrations at 750 ppmby the 2230s delays the 2050 temperature increase under unmitigated emissions by around 50 years. The loss of tropical forest and grassland which occurs by the 2050s under unmitigated emissions is delayed to the 22nd century, and the switch from carbon sink to carbon source is delayed from the 2050s to the 2170s. Coastal wetland loss is slowed. Stabilisation at 750 ppm generally has relatively little effect on the impacts of climate change on water resource stress, and populations at risk of hunger or falciparum malaria until the 2080s.A pathway which stabilises CO2 concentrations at 550 ppm by the 2170s delays the 2050 temperature increase under unmitigated emissions by around 100 years. There is no substantial loss of tropical forest or grassland, even by the 2230s, although the terrestrial carbon store ceases to act as a net carbon sink by around 2170 (this time because the vegetation has reached a new equilibrium with the atmosphere). Coastal wetland loss is slowed considerably, and the increase in coastal flood risk is considerably lower than under unmitigated emissions. CO2 stabilisation at 550 ppm reduces substantially water resource stress, relative to unmitigated emissions, but has relatively little impact on populations at risk of falciparum malaria, and may even cause more people to be at risk of hunger. While this study shows that mitigation avoids many impacts, particularly in the longer-term (beyond the 2080s), stabilisation at 550 ppm appears to be necessary to avoid or significantly reduce most of the projected impacts in the unmitigated case.

Buenos Aires and Kyoto targets do little to reduce climate change impacts

Sodium caseinate hydrolysates were generated at laboratory-scale using 28 commercial protease preparations of bacterial, fungal, plant and animal origin. Caseinophosphopeptides (CPPs) were enriched from these hydrolysates by calcium chloride aggregation at pH 7.5 followed by ethanol precipitation of the aggregates. CPP yield ranged from 3.4 to 16.0% (w/w) of the original protein. The calcium binding and solubilising abilities of the enriched CPPs ranged from 0.40 to 0.61 and 7.4 to 24.0mg Ca2+mg-1CPP, respectively. Hydrolysis of sodium caseinate with Bioprotease N100L resulted in a 16.0% yield of CPPs which could solubilise 19.1mgCa2+mg-1 CPP. Significant differences in the gel permeation and reversed-phase chromatography profiles for the various enriched CPPs were evident. In general, no apparent relationship was observed between hydrolysate degree of hydrolysis (DH%), CPP yield, CPP calcium binding and solubilising abilities, and CPP apparent molecular mass distribution and hydrophobic peptide profiles