Potential Changes in Climate Due to Greenhouse Gases

The impacts of climatic change on agriculture will depend on the ultimate form of climatic change. The geographical pattern of temperature and precipitation changes and any change in the variability of weather could be quite important, but there is little confidence in such details of climatic change scenarios produced by general circulation models (GCMs) because of the coarse resolution of these models. The analyses presented in subsequent sections of this chapter generally rely on climatic projections generated by GCMs based on equilibrium 2 x CO2 simulations. The typical approach is to compute the average difference between 5-10 years of simulated 1 x CO2 and 2 x CO2 data of monthly means for temperature and precipitation. These calculated differences are then applied to actual climatic data for the specific location or regions being investigated. In the structural modelling approach, this involves applying the monthly changes to a detailed weather record. As a result, the pattern of temperature and precipitation over the month as displayed in the historical record is preserved in the climatic change scenario. Impact models are typically run over a period of 10-30 years, with the average impact reported, to avoid generating results that are due to choice of a single year that may not be representative of the site's climate.

In some of the studies reported, other climatic scenarios have been used. A unique approach used by Kaiser et al. (1993) is to construct a simple statistical weather generator. This allows construction of many different weather scenarios that show gradual warming over time consistent with a predetermined final temperature. While this approach is limited to the sites for which it was developed, it provides a way to generate time paths of climatic change in the absence of such data directly from GCM runs.

While equilibrium 2 x CO2 scenarios have been standard model experiments reported from GCMs, these experiments do not provide information on when these changes will occur. The timing of climatic change depends on the specific path of CO2 concentration increase and climatic system interactions with the ocean. Figure 21.1 indicates how the global mean temperature changes in these scenarios compare with the time path presented by IPCC (Houghton et al., 1996). These scenarios generally represent global temperature increases beyond what is expected by 2100. New transient climatic scenarios (GCM scenarios that trace climatic change over time) have been developed recently but have generally not yet been used in impact analysis.

Regional changes in mean surface temperature and precipitation differ from the global means and there are large differences in the pattern of change among the different GCM scenarios used in these impact analyses. There is some agreement that higher-latitude areas will warm more than the global average, that high-latitude areas will receive proportionately more precipitation and that mid-continental mid-latitude areas will become drier, in terms of reduced soil moisture (Houghton et al., 1996).

The four GCM scenarios presented above are representative of possible climatic changes under a 2 x CO2 climate, but should not be considered 'predictions' of what will happen under climatic change. In general, a number of limitations of these and most available GCM climatic scenarios exist. These include:

• The global time path and local rate of global change. Localized changes may be more rapid, slower, or in a different direction than the global average because geographical patterns can change while the global mean

Fig. 21.1. Global mean temperature rise. Projections of global mean temperature from 1990 to 2100 for three climate sensitivities (4.5, 2.5 and 1.5°C) and a median emissions scenario including uncertainty in future aerosol concentrations. Increasing aerosols (e.g. sulphur, also from coal-burning) are estimated to have a cooling effect (after Houghton et al., 1996). See Table 21.1 for identification of GCMs.

Fig. 21.1. Global mean temperature rise. Projections of global mean temperature from 1990 to 2100 for three climate sensitivities (4.5, 2.5 and 1.5°C) and a median emissions scenario including uncertainty in future aerosol concentrations. Increasing aerosols (e.g. sulphur, also from coal-burning) are estimated to have a cooling effect (after Houghton et al., 1996). See Table 21.1 for identification of GCMs.

is changing. Changes in regular storm tracks could, over the course of a few years, lead to greatly reduced rainfall in one area and increased rainfall in a new area.

• Changes in the daily and seasonal pattern of climatic change. Daily, monthly and seasonal patterns of temperature and precipitation will likely be affected. Recent history shows an upward trend in night-time low temperatures in the northern hemisphere but little or no change in daytime high temperature (Kukla and Karl, 1993). Results from GCMs at this level are generally considered to provide little real information, because of coarse resolution of the models and inability to simulate phenomena such as ENSO (El Niño Southern Oscillation). Schimmelpfennig and Yohe (1994) estimate an index of crop vulnerability that provides a first step in understanding how changes in variability of climate affect production.

• Changes in the intensity of weather events. Heavy rain and high winds damage crops and cause soil erosion. Some scientific findings suggest that rainfall could become more intense (Pittock et al., 1991). The frequency and strength of regular weather cycles such as ENSO and the strength of the jet stream may alter and thus change weather patterns. These factors and others leading to the occurrence of hurricanes, tornadoes, hail and wind storms are not adequately modelled by coarse-resolution GCM simulations.

• Sulphate aerosols. Sulphur, emitted as a result of burning coal, is thought to have a significant cooling effect but is only now being introduced into climatic models (Houghton et al., 1995). Sulphur emissions remain in the atmosphere only a few days and hence their cooling effect is confined to areas downwind from where significant amounts are released.

Given these and other limitations of GCM scenarios, impact analyses derived from them are illustrative of what might happen rather than a prediction for which confidence bounds can be specified. It may be possible to improve the realism of climatic scenarios by addressing the limitations identified above but firm 'predictions' of climate and its impact on agriculture decades into the future may never be possible. Hence, agricultural impact methods need to be improved to take advantage of the improving realism of climatic scenarios. It is important to understand what farmers, agricultural policy-makers, agricultural researchers and others can do to prepare for climatic changes that cannot be precisely predicted.

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