Climate Impacts Research Paper

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1. Introduction

While there is scientific consensus that increased atmospheric concentrations of greenhouse gases will likely raise global temperatures, with associated increases in global precipitation and sea level, there is no consensus on how fast and how much the climate may change, on how regional climates may change, or on how climate variability may change. Climate change impact assessment for a country or region consists of a set of tasks beginning with problem definition and leading through sector analysis to analysis of adaptation methods and response policies.

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A broad understanding of the potential future with climate change demands multifaceted analyses, involving study of both biophysical and socioeconomic processes. A wide range of methods for climate change impact analysis has been developed, from simple regression models to complex integrated systems models. Techniques are becoming ever more complex as more interacting systems and the propagation of uncertainties are included in the analysis. The challenge is to simulate the biophysical and socioeconomic aspects of a system (such as agriculture, human health, urban areas) in a framework appropriate to regional, national, international, and global scales. Spatial analyses and first-order biophysical impacts are important, as well as assessment of vulnerability in the socioeconomic welfare of the different components of the system. Thus, biophysical scientists and social scientists must work together to provide realistic assessments of how climate change might affect a system in the future (see Fig. 1).

Climate Impacts Research Paper




Methodological issues to be resolved include how to generalize from the enormous heterogeneity of exposure units and systems and how to address spatial scales and units of analysis from field to region to nation and beyond. Models must be continually tested, calibrated, and validated, and improved for their use to be well-founded. The inclusion of the transient nature of climate change and its associated uncertainties in the modeling techniques is particularly important.

2. Approach

There are several approaches that serve as foundations to climate change impact studies. One approach is based on climate change scenarios, that is, projections of what future climate variables (and the characteristics of future impacts) may be like. Equilibrium climate change scenarios have been most often used in this approach, but recent more realistic studies and projections incorporate dynamic or ‘transient’ climate change scenarios. The climate change scenarios approach sometimes includes the study of responses of the system to past climatic variations, in order to allow comparison with future projections.

Another approach is threshold-based, and attempts to define the limits of sensitivity of a system as it is currently configured to changes in climatic variables. The first approach addresses the question, ‘What will the system be like in a given future changed climate?’ while the threshold approach asks, ‘What type, magnitude, and rate of climate change would seriously perturb the system as we know it?’ This approach most often applies transient scenarios of climate change. Both of the approaches construct a chain of causality from the biophysical responses at a small scale to socioeconomic effects at the regional, national, and international levels.

Several different techniques from the field of economics have been used in climate change impact analysis. One technique is the utilization of economic data to estimate the value of climate to the exposure unit (i.e., farmers) implicitly through regression equations. Linear programming models of the national sector (i.e., agriculture) are also used, as well as linked national and regional models.

Analysis of adaptive responses to climate change is an important part of climate change impacts research. The biophysical approaches described above allow the explicit examination of exposure unit adaptations, while the economic approach deals with adaptation implicitly.

3. Climate Change Scenarios

Climate change scenarios are defined as plausible combinations of climatic conditions that may be used to test possible impacts and to evaluate responses to them. Scenarios may be used to determine how vulnerable a sector is to climate change, to identify thresholds at which impacts become negative or severe, and compare the relative vulnerability among sectors in the same region or among similar sectors in different regions.

It is still difficult, if not impossible, to associate probabilities with any particular scenario of climate change, due to uncertainties in future emissions of radiatively active trace gases and in the response of the climate system to those emissions. Thus, impact studies based on climate change scenarios do not make actual predictions; rather, they are useful in defining for critical biophysical and socioeconomic systems directions of change, relative magnitudes of change, and potential critical thresholds of climate-sensitive processes. By conducting climate change impact analyses, researchers and resource managers are con-ducting ‘practice’ exercises, which help to engender flexibility in the systems’ responses to potentially changing conditions in the future.

3.1 Arbitrary Scenarios

The simplest scenario is the application of prescriptive changes, such as a 2°C increase in temperature and/or a 10 percent decrease in precipitation, to observed climate. Tests with such simple changes can help to identify the sensitivities of systems to changes in different variables. One can isolate the effects of one climate variable, for example, temperature, while holding other variables constant. However, such tests do not offer a consistent set of climate variables, since evaporation, precipitation, wind, and other variables are all likely to change with change in temperature. Arbitrary scenarios do, however, provide a set of responses to which other types of scenarios may be compared.

3.2 Historical Analogs

Another type of climate change scenario is based on the historical record. Observations from cool or warm, wet or dry historical periods are used to construct scenarios for use in modeling studies of climate change impacts. Such periods are also useful for the insights provided by studying the responses of any given system to periods of climatic extremes. The Dust Bowl of the 1930s in the Southern Great Plains is a well-known example (see e.g., Warrick 1984), but past freeze events, aquifer depletion, and lake-level changes have also been used to study societal responses to regional climate change (Glantz 1988).

A difficulty with either of these scenario approaches as proxies for the global warming currently predicted for increasing CO and other trace gases is that the patterns of climate warming may be different de-pending on the nature of the atmospheric forcing mechanisms.

3.3 GCM-Based Scenarios

Climate change scenarios are also derived from global climate model (GCM) experiments with specified forcing mechanisms (e.g., 1 percent annual increase in greenhouse gas concentrations in the atmosphere). Current GCM model experiments are conducted to produce transient climate projections. The advantages of GCM scenarios are their internal consistency and global extent. GCMs estimate how regional and global climates may change in response to increased concentrations of trace gases. Thus, regional and global climate responses are internally consistent. The climate variables are also physically consistent, as heat, moisture, and energy processes are calculated from a consistent set of equations representing physical processes.

At present, GCMs represent current climate at global and zonal (latitudinal) scales, but do not do particularly well at simulating regional current cli-mate. Differences in climate projections among GCMs increase as scale decreases from the global to the regional and gridbox levels. GCM simulation of current temperature regimes is better than simulation of current hydrological regimes. A range of GCM scenarios should be included in the design of impact studies in order to incorporate a range of climate sensitivities to greenhouse gas forcing, and it is very important to consider GCM regional climate change projections as examples of possible future climates, rather than actual predictions.

Because GCM simulation of current climates is often inaccurate, direct projections of GCM-generated future climates is seldom used. Changes in climate variables in the perturbed simulations relative to the control run are often applied to historical observed weather data to create the climate change scenarios used in impact studies. Absolute model biases are omitted by using the relative changes. Thirty years of current climate data are often used to develop the baseline climate scenario to which the GCM changes are applied. A 30-year period is considered long enough to represent ‘normal’ climate variability. Recent periods, e.g., 1951–80, or 1961–90, are often selected, representing current climate and having ac-curate data most easily available. The latter period contains some of the warmest years on record that may have been caused by the enhanced greenhouse effect.

The use of GCM transient scenarios (i.e., time dependent) in climate change impact studies is growing, since they provide a much more realistic picture of the projected warming from current conditions to some point in the future.

4. Integrated Global Change Scenarios

Climate is not the only factor that will be changing as the twenty-first century unfolds. Population growth and changing economic and technological conditions are likely to affect world society and the environment even more than changes in climate. It is important to take such changes into account in climate change impact analyses: first, because climate change will occur not in the present but in the future, and second, because such changes may affect the sensitivity of a system or sector to climate. However, predicting population growth rates and future economic conditions is equally if not more uncertain than predicting the future climate. Therefore, future scenarios need to be designed carefully to address a range of possible conditions. One approach is to contrast ‘optimistic’ and ‘pessimistic’ views of the future. In the optimistic scenario, population growth rates are low, economic growth rates and incomes rise, environmental pollution decreases, and land degradation abates. In more pessimistic scenarios, population growth rates are high, economic growth rates and incomes are low, environmental pollution increases, and land degradation accelerates. A scenario of no change (i.e., present conditions) should also be included. The differential effects of climate change on current conditions, and on these two alternative scenarios of the future may then be evaluated.

In order to place possible changes in climate in the context of potential socioeconomic changes, estimates of population, economic growth, and technological change are needed. These estimates will also affect future rates of CO and other greenhouse gas emissions. Economic projections beyond the next 10 to 20 years generally are unreliable. Furthermore, socioeconomic factors are not unrelated, since changes in population are likely to affect national and per-capita income. Recent IPCC scenarios include estimates of population and economic growth rates for a set of possible futures (IPCC 2000).

Socioeconomic factors that are often considered in future scenarios include population, income, productivity, and technology levels. Environmental factors may include stratospheric and tropospheric ozone levels and changes in land use. Institutions and legal structures may change as well, but these evolutions are very hard to predict. The World Bank (1994) and the United Nations (1999) have published population estimates by country through 2100 for a range of scenarios. The World Bank (1993) has published estimates of changes in income. Various economic models are used to project such productivity factors as gross domestic product (GDP) into the future. Population and economic growth may bring increases in urbanization, expansion of agriculture and mining of natural resources, and accelerating rates of deforestation, habitat fragmentation, desertification, and water and air pollution (FAO 1993, Dregne and Chou 1992).

4.1 CO2 And Greenhouse Gas Emission Scenarios

CO2 and greenhouse gas emission scenarios are needed, especially for agriculture because of the need to estimate crop responses to the CO2 fertilization effect, as well as projections of sea-level rise. Climate modelers need estimates of future levels of atmospheric CO2 and other trace gases in order to prepare transient scenarios of future climate. Crop and forest modelers also need such estimates in order to take fertilization effects into account in their impact analyses. Global emissions of CO2, the most important greenhouse gas, depend primarily on fossil fuel use in three major sectors—electrical generation, industry, and transportation. A growing world economy consists of growth in industrial production, consumption of goods, and travel and concomitant increases in energy use. Deforestation also contributes to CO2 emissions and is linked to economic growth as land is converted from natural ecosystems to agriculture and other uses.

The IPCC (2000) and others estimate growth rates of world carbon emissions from fossil fuels and de-forestation in order to calculate atmospheric CO2 levels for climate projections. Such calculations are also important in international negotiations that consider limiting CO2 emissions. Since only a portion (about one-half ) of the carbon added to the atmosphere remains, carbon cycle models are used to translate carbon emissions into atmospheric levels of CO2. Models that include the effects of CO2 fertilization, feedback from stratospheric ozone depletion, and the radiative effects of sulfate aerosols have been combined to project radiative forcing of climate, changes in global-mean temperature, and sea level (Wigley and Raper 1992). Recent projections have tended to reduce the projected rates of warming and sea-level rise, but they are still four to five times the rates observed over the twentieth century.

5. Modeling Techniques: A Case Study Of The Agricultural Sector

Modeling techniques of several kinds are used to study potential impacts and responses of agriculture to changing climate and atmospheric composition. The agricultural sector is chosen to illustrate the range of modeling techniques, because agriculture is a key socioeconomic sector for development in many regions, agricultural land use is a primary driver of land-use change, and agriculture is a sector vulnerable to global environmental change. Choice of technique depends on the sphere of analysis considered and the research questions posed.

5.1 Biophysical Modeling

5.1.1 Crop Suitability. Spatial analysis consists of identification of critical environmental limits (primarily climate, soil and water resources) of specific crops or agricultural systems, applications of climate change scenarios, and calculation of resulting spatial shifts in crop or agricultural regions. This agro climatic method provides an approximation of possible changes in crop areas from a biological perspective, but does not address potential changes in either yield or production.

5.1.2 Potential Production. Potential production may be estimated from climatic variables or indices such as length of growing season, precipitation, evapotranspiration, solar radiation, and temperature. A prime example of this technique is found in the Agro-Ecological Zone Project of the FAO (FAO 1978). The FAO Agro-Ecological Zone modeling technique simulates both crop zonation and potential production (Leemans and Solomon 1993, Cramer and Solomon 1993, Fischer et al. 2001).

5.1.3 Statistical Regression Models. Multiple regression models have been developed from the statistical relationships between historical crop yields and climatic variables in specific locations (i.e., Waggoner 1983). The use of regression models is limited by their lack of explanatory power, since the techniques rely on statistical coefficients rather than on descriptions of the underlying biophysical relationships.

5.1.4 Dynamic Crop Models. Dynamic crop growth models formulate the principal physiological, morphological, and physical processes involving the transfers of energy and mass within the crop and between the crop and its environment. Such models have been developed for most of the major crops, with the aim of predicting their responses to specified climatic, edaphic, and management factors governing production. Dynamic models capable of simulating the response of crops to climatic variables may be used in conjunction with GCM climate change scenarios to explore the consequences of increased atmospheric CO and climate change on yields and phenology or to determine thresholds of crop growth sensitivity to changing climate variables. They are also useful for testing possible adaptations to climate change, such as altered planting dates, irrigation scheduling, or crop variety (see Rosenzweig and Iglesias (1994) for applications of crop models to climate change impact evaluation).

5.2 Economic Techniques

Economic measures are an important component of the information that policymakers need to evaluate the climate change issue. Economic analyses are concerned with the reciprocal relations between physical and biological changes on the one hand and the economic responses of individuals and institutions on the other. Once crop yield impacts are estimated, it is useful to translate such biophysical responses into economic measures of human welfare. While bio-physical analyses focus primarily on the production of agricultural crops, economic analyses consider both producers and consumers of agricultural goods. Economic measures of interest include the responses of input and output market prices to yield changes and the responses in terms of inputs and outputs that affected individuals make to minimize losses or maximize gains, based on the changes in production and consumption opportunities and in price. If climate change causes substantial changes in outputs, price and quality changes can result, which, in turn, can lead to further market-induced output changes. Even if prices remain constant, accurate indications of output changes are needed if production practices and types of outputs may change.

Previous work on the economics of environmental stresses on agriculture has resulted in a number of general findings (Rosenzweig and Hillel 1998). Important points are that both producers and consumers are included in the domain, that economic activities constitute a type of societal adaptation to environ-mental stresses, leading in the most part to mitigation of negative effects, and that environmental stresses have differential effects on the comparative advantage of regions and countries.

Economic models calculate estimates of the potential impacts of climate change on measurable economic quantities, including production, consumption, income, gross domestic product (GDP), and employment. It is important to remember, however, that these may be only partial indicators of social welfare. Different social systems, households, and individuals may not be represented in models that are based on producer and consumer theory. Furthermore, many of the economic models do not account for climate-change induced alterations in land availability and water for irrigation; these nonmarket aspects of a changing climate may be critical.

As a starting point, the gathering of available information about production, consumption, and policies provides a framework for determining the existence and possible magnitude of economic vulnerability in the agricultural sector (US Country Studies Program 1994). Microeconomic farm-level models are designed to simulate the decision-making process of a representative farmer in regard to methods of production and allocation of capital, labor, and land and infrastructure. Such models are based on the goal of maximizing economic returns to inputs. Some farm-level models include a range of farmer behavior in regard to risk, for example risk-averse or risk-neutral.

Macroeconomic equilibrium models of the agricultural sector include price-responsive behavior for both consumers and producers. Equations for these relationships are developed based on economic principles that consumers will maximize the utility of their food-buying and that producers (farmers) will minimize their costs of production. Such models usually are calibrated for a given reference year; for climate change purposes, the models solve for the reference year given perturbations in crop production and water supply and demand for irrigation derived from bio-physical techniques (see e.g., Adams et al. 1990). Population growth and improvements in technology are set exogenously (i.e., not computed dynamically in the model). Model results include equilibrium prices and quantities.

General equilibrium economic models are useful because they measure the potential magnitude of climate change impacts on the economic welfare of both producers and consumers of agricultural goods. They do not, however, provide a detailed picture of how the economy will respond over time. These models may overestimate the adjustment of the agricultural economy to climate change. Results of changes in production and prices from agricultural sectoral models can then be used in general equilibrium models of the larger economy.

Regression models have been developed that test for statistical relationships between climate variables and economic indicators such as farm values. Some recent studies utilize these methods known as the ‘Ricardian’ approach (e.g., Mendelsohn et al. 1994, Polsky and Easterling 2001). The behavior of consumers is not included in this approach and world food prices, domestic farm output prices, and thus farm revenues that are dependent on changes in agricultural production inside and outside of the US are assumed to be held constant.

6. Integrating Across Sectors

Integrated studies link the biophysical and economic realms, and ideally may extend to interactions both within and across sectors such as agriculture and its competing demands for water by irrigators or urban users, or shifting patterns of land use between agricultural and forest (or other natural) ecosystems. This is a more realistic, but more complicated approach, because individual biophysical and socioeconomic sectors will not be affected by climate change in isolation. For example, agricultural responses will be sensitive not only to changes in crop yields, but also to alterations in water supplies, demand for water from other sectors, and to the inundation and salinization of arable land by rising seas. The following are some examples of integration in agricultural impact studies:

(a) Parry et al. (1988) report on integrated agricultural sector studies in high-latitude regions in Canada, Iceland, Finland, USSR, and Japan, that involved teams of meteorologists, agronomists, and economists. The general conclusions of the studies were that warmer temperatures may aid crop production by lengthening the growing season at high latitudes, but that potential for higher evapotranspiration and drought conditions may counteract the positive effects and may even be detrimental to productivity.

(b) Adams et al. (1990) conducted an integrated study for the US, linking models from atmospheric science, plant science, and agricultural economics. While the outcomes for US agriculture in the study depended on the severity of climate change and the compensating effects of carbon dioxide on crop yields, the simulations suggest that irrigated acreage will expand and that regional patterns of US agriculture will shift with predicted global warming. With the more severe climate change scenario tested, the movement of US production into export markets was reduced substantially.

(c) The Missouri, Iowa, Nebraska, and Kansas (MINK) study integrated potential biophysical and economic effects of climate change on agriculture and other sectors (Rosenberg 1993). The study incorporated the physiological effects of CO2 and adaptation by farmers to the climatic conditions of the 1930s. Even with the relatively mild warming (1.1°C) of the 1930s and with farmer adaptation and CO2 effects taken into account, regional production declined by 3.3 percent. Given the estimate of 2.5°C warming for doubled CO2 conditions, the results of the MINK study imply agricultural losses of about 10 percent (Cline 1992).

 (d) Strzepek et al. (1996) linked climate change impacts in Egypt on agriculture, water, and the coastal zone in an economic model. This integrated study demonstrates that the sectors directly affected by climate change need to be analyzed in concert with the other sectors of the economy in sufficient detail so that feedback can be part of the analysis. Egypt was found to be highly vulnerable to the warming as well as to changes in precipitation and river runoff that are forecast to accompany greenhouse-gas-induced climate change.

In its fullest sense, integrated assessment attempts to close the loop by linking the greenhouse gas emissions caused by human activities, the climatic consequences of the emissions, the impacts of the climate changes on important systems, and the feed-back of the impacts back to the generation of greenhouse gas emissions. Modeling frameworks have been devised to integrate the causes, impacts, feed-backs, and policy implications of global climate change (Nordhaus 1992, Manne et al. 1993, Hulme and Raper 1993, Alcamo et al. 1993, Edmonds et al. 1993). An example of a feedback in such models is the pathway leading from energy consumption, to green-house gas emissions, to climate warming, to changes in demand for energy (e.g., decreases in demand for energy for heating and increases in demand for energy for air conditioning), and thus back to changes in energy consumption. The models may be used, for example, to explore the effects of policies limiting greenhouse gas emissions, the ensuing reduction in global warming, and the alteration of potential climate change impacts, for example, on agriculture.

7. Thresholds, Risk, And Surprises

The identification of thresholds in climate change impacts research involves the analysis of the effects of different levels of climate forcing on a system or activity and the identification of possible discontinuities in response. The determination of critical levels of climate change for any given system may be separated into biophysical and socioeconomic realms. In the biophysical realm, although the thermal regimes and responses of managed and unmanaged ecosystems and water resource availability are complex, critical temperatures (minimum, optimum, and maximum) have been defined for many individual processes. In the socioeconomic realm, defining critical levels of warming is more challenging, due, at least in part, to the interplay of supply, demand and prices, and to the adaptability of the system. Here, determining critical levels of warming involves defining relative impacts on actors from diverse geographic and social groups.

For example, global effects of climate change in agriculture measured with current economic valuation techniques generally are predicted to be small to moderate. This occurs because the economic system is, in general, effective in fostering adaptation to the projected biophysical changes. However, the global perspective masks differences in levels of effects, regionally and socially. Studies done to date concur that there will be significant change in global agricultural patterns. All regions are likely to be affected, but large differences occur among regions. While changes in global production with climate change may be small, the potential remains for regional vulnerability to food deficits due to distributional problems of getting food to specific regions and groups of people. For subsistence farmers and people lacking entitlement to food, lower yields may result not only in measurable economic losses, but possibly malnutrition and starvation. Several studies have addressed vulnerability to food deficits explicitly and found potential increases (e.g., Rosenzweig and Parry 1994, Fischer et al. 2001).

Risk can be evaluated when the probability of occurrence of an event is known, but in impact evaluation, the associated probabilities to a particular scenario are generally not known. Therefore, the inclusion of uncertainty (i.e., when the event is known but the probabilities that it will occur are not known) into climate change impact methods is very important and recent studies are now beginning to include explicit methods to deal with it. Earlier studies have often used ‘best estimate’ scenarios that represent the mid-point of predictions. The inclusion of a range of scenarios representing upper and lower bounds of the predicted effects is more realistic and allows for the propagation of uncertainty throughout a model sys-tem. Further, probability distributions of different events may be defined, with contrasts between low probability catastrophic events (surprises) and higher probability gradual changes in climate trends.

One ‘surprise’ (i.e., when reality departs qualitatively from expectations) may lead to another in a cascade, since subsystems are connected. Complex systems and chaos theory provide conceptual and analytical tools for anticipating and preparing for surprises. Identification of potential surprises and communication of them to the public and policy-makers should allow improvements in environmental and societal resilience to surprise. Surprises related to global climate change may be either scientific or societal in nature. The anticipation of surprises in the science of global climate change may be encouraged by efforts to integrate across disciplines, to support a multiplicity of research approaches, and to focus on outlier outcomes and unconventional views. Beyond the anticipation of scientific surprise, it seems worth-while to increase the resilience and adaptability of social structures, so that the sensitivity to impacts of unexpected or uncertain perturbations is decreased. Such societal preparedness might include the diversification of economic, productive, and technological systems; the establishment of disaster, coping, and entitlement systems; and the creation of adaptive management systems capable of learning from surprises.

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