Understanding social resilience to climate variability in primary enterprises and industries

Resource-dependent industries are particularly vulnerable to climate change, and their ability to adapt will be as critical to society as to the natural systems upon which they rely. More than ever, resource-users will need to anticipate, and prepare for, climate-related changes, and institutions will need to be particularly supportive, if resource industries and the extended social systems dependent on them are to be sustained. I examine the capacity of cattle-graziers in Australia to cope and adapt to climate variability as a precursor for understanding their vulnerability to climate change by assessing: (i) their perception of risk, (ii) their capacity to plan, learn and reorganise, (iii) their proximity to the thresholds of coping, and (iv) their level of interest in adapting to change. Graziers perceived themselves to be resilient to climate variability in their perceptions of climate risk, reorganising capacity, coping, and interest in adapting. Their dependency on the grazing resource and use of seasonal climate forecasts were significant influences, suggesting that resilience could be enhanced. Facilitated collaborative learning amongst graziers and other stakeholders may assist to develop strategic skills, increasing climate awareness, developing financial security and adopt climate tools such as seasonal climate forecasts. Enhanced strategies for coping with climate variability will provide a way for encouraging gradual, incremental adjustments for climate adaptation.     

The reluctance of resource-users to adopt seasonal climate forecasts to enhance resilience to climate variability on the rangelands

As the impacts of climate-change on resource-dependent industries manifest, there is a commensurate effort to identify and implement strategies to reduce them. Yet, even when useful knowledge and tools exist, there can be poor adoption of adaptation strategies. We examine the reasons behind sub-optimal adoption of seasonal climate forecasts by graziers for managing climate variability. We surveyed 100 graziers in north-east Queensland, Australia and examined the influence of adaptive capacity, resource-dependency and forecast-perception on uptake. Technical perceptions were not important. Strategic skills, environmental awareness and social capital were. Results suggest that social factors (but not technical factors) are significant. These insights are important for adaptation planning and for maximising the resilience of communities and industries dependent on climate-sensitive resources.     

Identifying stakeholder-relevant climate change impacts: A case study in the Yakima River Basin,Washington, USA

Designing climate-related research so that study results will be useful to natural resource managers is a unique challenge. While decision makers increasingly recognize the need to consider climate change in their resource management plans, and climate scientists recognize the importance of providing locally-relevant climate data and projections, there often remains a gap between management needs and the information that is available or is being collected. We used decision analysis concepts to bring decision-maker and stakeholder perspectives into the applied research planning process. In 2009 we initiated a series of studies on the impacts of climate change in the Yakima River Basin (YRB) with a four-day stakeholder workshop, bringing together managers, stakeholders, and scientists to develop an integrated conceptual model of climate change and climate change impacts in the YRB. The conceptual model development highlighted areas of uncertainty that limit the understanding of the potential impacts of climate change and decision alternatives by those who will be most directly affected by those changes, and pointed to areas where additional study and engagement of stakeholders would be beneficial. The workshop and resulting conceptual model highlighted the importance of numerous different outcomes to stakeholders in the basin, including social and economic outcomes that go beyond the physical and biological outcomes typically reported in climate impacts studies. Subsequent studies addressed several of those areas of uncertainty, including changes in water temperatures, habitat quality, and bioenergetics of salmonid populations.     

Stakeholder Networks: Improving Seasonal Climate Forecasts

In order for a scientific innovation to reach a wide audience it needs to travel through diverse networks and be understandable to a variety of people. This paper focuses on networks of stakeholders involved in the diffusion of seasonal climate forecasts. It is argued that understanding stakeholder networks is key to determining the opportunities and barriers to the flow of forecast information, which could enable more focused forecast dissemination. Lesotho is used as a case study where Stakeholder Thematic Networks (STNs) are used as a novel method for investigating forecast dissemination. STNs enable qualitative information to be analysed through semi-quantitative mapping of relationships that enable the networks and scales of linkages to be visualised. This illustrates the types of nodes and channels of seasonal forecast information flow and so enables existing or emerging patterns of dissemination to be uncovered. Sub-networks that exist for purposes other than climate information dissemination are identified as salient sub-networks for focusing development of future forecast dissemination. These existing sub-networks enable stakeholder needs to be addressed and decrease the need for new networks to be established. By using these sub-networks, information relating to climate variability can be mainstreamed into existing development pathways. This is critical to recognise if innovations relating to climate information are to be used to improve climate change adaptation.     

Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 1: baseline climate

Assessing future climate and its potential implications on river flows is a key challenge facing water resource planners. Sound, scientifically-based advice to decision makers also needs to incorporate information on the uncertainty in the results. Moreover, existing bias in the reproduction of the ‘current’ (or baseline) river flow regime is likely to transfer to the simulations of flow in future time horizons, and it is thus critical to undertake baseline flow assessment while undertaking future impacts studies. This paper investigates the three main sources of uncertainty surrounding climate change impact studies on river flows: uncertainty in GCMs, in downscaling techniques and in hydrological modelling. The study looked at four British catchments’ flow series simulated by a lumped conceptual rainfall–runoff model with observed and GCM-derived rainfall series representative of the baseline time horizon (1961–1990). A block-resample technique was used to assess climate variability, either from observed records (natural variability) or reproduced by GCMs. Variations in mean monthly flows due to hydrological model uncertainty from different model structures or model parameters were also evaluated. Three GCMs (HadCM3, CCGCM2, and CSIRO-mk2) and two downscaling techniques (SDSM and HadRM3) were considered. Results showed that for all four catchments, GCM uncertainty is generally larger than downscaling uncertainty, and both are consistently greater than uncertainty from hydrological modelling or natural variability. No GCM or downscaling technique was found to be significantly better or to have a systematic bias smaller than the others. This highlights the need to consider more than one GCM and downscaling technique in impact studies, and to assess the bias they introduce when modelling river flows.     

On atmospheric radiative feedbacks associated with climate variability and change

Using the method of radiative ‘kernels’ an analysis is made of water vapour, lapse rate and ‘Planck’ (uniform vertical temperature) long wave feedbacks in models participating in the World Climate Research Program (WCRP) Coupled Model Intercomparison Project phase 3 (CMIP3). Feedbacks are calculated at climate change timescales from the A1B scenario, and at three ‘variability’ timescales from the corresponding preindustrial experiments: seasonal, interannual and decadal. Surface temperature responses show different meridional patterns for the different timescales, which are then manifest in the structures of the individual feedbacks. Despite these differences, mean water vapour feedback strength in models is positive for all models and timescales, and of comparable global magnitude across all timescales except for seasonal, where it is much weaker. Taking into consideration the strong positive lapse rate feedback at seasonal timescales, combined water vapour/lapse rate feedback is indeed similar across all timescales. To a good approximation, global water vapour feedback is found to be well represented by the temperature response along with an assumption of unchanged relative humidity under both variability and climate change. A comparison is also made of model feedbacks with reanalysis derived feedbacks for seasonal and interannual timescales. No strong relationships between individual modelled feedbacks at different timescales are evident: i.e., strong feedbacks in models at variability timescales do not in general predict strong climate change feedback, with the possible exception of seasonal timescales. There are caveats on this (and other) findings however, from uncertainties associated with the kernel technique and from, at times, very large uncertainties in estimating variability related feedbacks from temperature regressions.     

The role of culture and traditional knowledge in climate change adaptation: Insights from East Kimberley,Australia

Indigenous peoples offer alternative knowledge about climate variability and change based on their own locally developed knowledges and practices of resource use. In this article we discuss the role of traditional ecological knowledge in monitoring and adapting to changing environmental conditions. Our case study documents a project to record the seasonal knowledge of the Miriwoong people in northern Australia. The study demonstrates how indigenous groups’ accumulate detailed baseline information about their environment to guide their resource use and management, and develop worldviews and cultural values associated with this knowledge. We highlight how traditional ecological knowledge plays a critical role in mediating indigenous individuals and communities’ understandings of environmental changes in the East Kimberley region of north-west Australia, and how these beliefs may influence future decision-making about how to go about adapting to climate change at a local level.     

Adapting water resources management to global climate change

This paper provides an overview of the impact of global climate change on water resources management. Changes in precipitation and temperature of the scale predicted by General Circulation Models for a doubled CO₂ level will significantly affect annual runoff, runoff variability, and seasonal runoff. These in turn will affect water supply, flood protection, hydropower generation, and environmental resources. In addition, climate change will significantly affect the geomorphic response of the watershed, increasing soil erosion and altering the hydrologic response of the watershed. These geomorphic changes will in turn affect water supply, flood hazard, and riparian ecosystems.Possible water resources management responses are identified. This includes reallocation of water supply from less valuable irrigated agriculture to municipal uses; changes in agricultural methods; increasing incentives for integrated flood management; increasing incentives for watershed management; integration of ecosystem needs in water resources planning; and the need to redesign the operation of existing water projects.     

Scale and Modeling Issues in Water Resources Planning

Resource planners and managers interested in utilizing climate model output as part of their operational activities immediately confront the dilemma of scale discordance. Their functional responsibilities cover relatively small geographical areas and necessarily require data of relatively high spatial resolution. Climate models cover a large geographical, i.e. global, domain and produce data at comparatively low spatial resolution. Although the scale differences between model output and planning input are large, several techniques have been developed for disaggregating climate model output to a scale appropriate for use in water resource planning and management applications. With techniques in hand to reduce the limitations imposed by scale discordance, water resource professionals must now confront a more fundamental constraint on the use of climate models—the inability to produce accurate representations and forecasts of regional climate. Given the current capabilities of climate models, and the likelihood that the uncertainty associated with long-term climate model forecasts will remain high for some years to come, the water resources planning community may find it impractical to utilize such forecasts operationally.     

Identifying climate services needs for national planning: insights from Malawi

The importance of climate services, i.e. providing targeted, tailored, and timely weather and climate information, has gained momentum, but requires improved understanding of user needs. This article identifies the opportunities and barriers to the use of climate services for planning in Malawi, to identify the types of information that can better inform future adaptation decisions in sub-Saharan Africa. From policy analysis, stakeholder interviews, and a national workshop utilizing serious games, it is determined that only 5–10 day and seasonal forecasts are currently being used in government decision making. Impediments to greater integration of climate services include spatial and temporal scale, accessibility, timing, credibility and the mismatch in timeframes between planning cycles (1–5 years) and climate projections (over 20 years). Information that could more usefully inform planning decisions includes rainfall distribution within a season, forecasts with 2–3 week lead times, likely timing and location of extreme events in the short term (1–5 years), and projections (e.g. rainfall and temperature change) in the medium term (6–20 years). Development of a national set of scenarios would also make climate information more accessible to decision makers, and capacity building around such scenarios would enable its improved use in short- to medium-term planning. Improved climate science and its integration with impact models offer exciting opportunities for integrated climate-resilient planning across sub-Saharan Africa. Accrual of positive impacts requires enhanced national capacity to interpret climate information and implement communication strategies across sectors.

Policy relevance

For climate services to achieve their goal of improving adaptation decision making, it is necessary to understand the decision making process and how and when various types of weather and climate information can be incorporated. Through a case study of public sector planning in Malawi, this article highlights relevant planning and policy-making processes. The current use of weather and climate information and needs, over various timescales – sub-annual to short term (1–5 years) to medium term (6–20 years) – is outlined. If climate scientists working with boundary organizations are able to address these issues in a more targeted, sector-facing manner they will improve the uptake of climate services and the likelihood of climate-resilient decisions across sub-Saharan Africa.     

Climate Variability, Climate Change and Water Resource Management in the Great Lakes

Water managers always have had to cope with climate variability. All water management practices are, to some extent, a response to natural hydrologic variability. Climate change poses a different kind of problem. Adaptation to climate change in water resource management will involve using the kinds of practices and activities currently being used. However, it remains unclear whether or not practices and activities designed with historical climate variability will be able to cope with future variability caused by atmospheric warming. This paper examines the question of adaptation to climate change in the context of Canadian water resources management, emphasizing issues in the context of the Great Lakes, an important binational water resource.     

Seasonal Climate Forecasts – Potential Agricultural-Risk Management Tools?

The importance of anchoring seasonal climate forecasts to user needs is examined in this paper. Although it is generally accepted that seasonal climate forecasts have potential value, many constraints preclude the optimal use of these forecasts, including the way forecasts are produced, interpreted and applied in a variety of decision-making processes. In South Africa, a variety of agricultural users exists, ranging from the small-scale farmer to larger commercial farming entities. Useful seasonal are those produced and disseminated with the end user in mind. A retroactive test period during the 1990s, evaluates the perceived impact of incorporating seasonal rainfall forecasts into decisions made by commercial crop farmers in the central parts of South Africa. Although a small sample of commercial farmers was interviewed, the results show some benefits to commercial agriculture if seasonal climate forecast information is continuously and effectively applied over the long-term.     

IPCC-AR4 climate simulations for the Southwestern US: the importance of future ENSO projections

Future climate trends for the Southwestern US, based on the climate models included in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report, project a more arid climate in the region during the 21st century. However, future climate variability associated with El Niño Southern Oscillation (ENSO)—an important driver for winter climate variability in the region—have not been addressed. In this work we evaluate future winter ENSO projections derived from two selected IPCC models, and their effect on Southwestern US climate. We first evaluate the ability of the IPCC coupled models to represent the climate of the Southwest, selecting the two models that best capture seasonal precipitation and temperature over the region and realistically represent ENSO variability (Max Planck Institute’s ECHAM5 and the UK Met Office HadCM3). Our work shows that the projected future aridity of the region will be dramatically amplified during La Niña conditions, as anomalies over a drier mean state, and will be characterized by higher temperatures (~0.5°C) and lower precipitation (~3 mm/mnt) than the projected trends. These results have important implications for water managers in the Southwest who must prepare for more intense winter aridity associated with future ENSO conditions.     

Understanding and enhancing climate information use in water management

This paper expands our understanding of water manager's climate information (CI) use and of the effectiveness of interactive research efforts in improving use by quantitatively measuring usability both within and outside the interactive research model. Using a mixed method approach (i.e., interviews and surveys), data was collected across five states and hundreds of water managers to understand the production of CI by scientists at two Regional Integrated Sciences and Assessments (RISAs) employing an interactive approach and the use of that information by water managers in the corresponding RISA regions. This study finds that RISAs are effective in three important ways: first, in co-producing usable information and achieving a high rate of information use among RISA clients; second, in overcoming barriers to information use arising from negative perceptions about the usability and reliability of CI; and, finally, in fostering innovation. RISA information use is contingent on sustained scientist-client interaction and is enabled by users' willingness and capacity making RISAs most effective in reaching the largest, most capable users. These users and those who use CI from other sources do so as a strategy to manage risk. This research suggests areas for enhancing RISA CI uptake: structuring RISAs as consortia, cultivating relationships with knowledge brokers and capitalizing on existing knowledge networks, and increasing public education and outreach. Beyond the interactive research models, findings suggest CI uptake may be enhanced by building capabilities for long-term water planning at water systems and bolstering public science citizenship and climate literacy.     

Value of perfect ENSO phase predictions for agriculture: evaluating the impact of land tenure and decision objectives

In many places, predictions of regional climate variability associated with the El Niño–Southern Oscillation phenomenon offer the potential to improve farmers’ decision outcomes, by mitigating the negative impacts of adverse conditions or by taking advantage of favorable conditions. While the notion that climate forecasts are potentially valuable has been established, questions of when they may be more or less valuable have proven harder to resolve. Using simulations, we estimate the expected value of seasonal climate information under alternative assumptions about (a) land tenure (ownership vs. short-term leases) and (b) the decision maker’s objective function (expected utility vs. prospect theory value function maximization), employing a full range of plausible parameter values for each objective function. This allows us to show the extent to which the value of information depends on risk preferences, loss aversion, wealth levels and expectations, as well as situational constraints. Our results demonstrate in a non-laboratory decision context that, in some cases, psychologically plausible deviations from expected utility maximization can lead to substantial differences in estimates of the expected value of climate forecasts. Efforts to foster effective use of climate information and forecasts in agriculture must be grounded in a firm understanding of the goals, objectives and constraints of decision makers.     

No increase in multi-day temperature variability in Austria following climate warming

This paper assesses the extent to which temperature variability has increased in Austria since the late 19th century using a novel objective approach. The approach focuses on multi-day temperature episodes and isolates variability from changes in the long-term mean and seasonal variation. We define and compute three different indices of temperature variability, and find—based on 140?years of data—that temperature variability has evolved independently of mean temperature but with no long-term trend. Early 21st century’s relatively raised temperature variability level is known from late 19th century’s pre-greenhouse climate state.     

The Use of Seasonal Climate Forecasting in Policymaking: Lessons from Northeast Brazil

This article examines the use of seasonal climate forecasting in public and private efforts to mitigate the impacts of drought in Ceará, Northeast Brazil. Here, forecasts have been directed towards small scale, rainfed agriculturalists as well as state and local level policymakers in the areas of agriculture, water management, and emergency drought relief. In assessing possibilities and constraints of forecast application in Ceará, the present analysis takes into account three types of variables: (a)characteristics of the forecasts; (b) policymaking systems; and (c)institutional environments. We conclude that, on the one hand, several factors in the Ceará case have limited the effectiveness of seasonal climate forecast use. First, the current level of skill of the forecasts is inadequate for the needs of policy development and farmer decisionmaking. Second, forecast information application has been subject to distortion, misinterpretation and political manipulation. Third, focus on the forecast as a product until recently neglected to take into account end users' needs and decisionmaking behavior. On the other hand, climate forecasting has the potential to offer a dramatic opportunity for state and local level bureaucracies to embark on a path of proactive drought planning.     

Yield estimation and sowing date optimization based on seasonal climate information in the three CLARIS sites

The present article is a contribution to the CLARIS WorkPackage “Climate and Agriculture”, and aims at testing whether it is possible to predict yields and optimal sowing dates using seasonal climate information at three sites (Pergamino, Marcos Juarez and Anguil) which are representative of different climate and soil conditions in Argentina. Considering that we focus on the use of climate information only, and that official long time yield series are not always reliable and often influenced by both climate and technology changes, we decided to build a dataset with yields simulated by the DSSAT (Decision Support System for Agrotechnology Transfer) crop model, already calibrated in the selected three sites and for the two crops of interest (maize and soybean). We simulated yields for three different sowing dates for each crop in each of the three sites. Also considering that seasonal forecasts have a higher skill when using the 3-month average precipitation and temperature forecasts, and that regional climate change scenarios present less uncertainty at similar temporal scales, we decided to focus our analysis on the use of quarterly precipitation and temperature averages, measured at the three sites during the crop cycle. This type of information is used as input (predictand) for non-linear statistical methods (Multivariate Adaptive Regression Splines, MARS; and classification trees) in order to predict yields and their dependency to the chosen sowing date. MARS models show that the most valuable information to predict yield amplitude is the 3-month average precipitation around flowering. Classification trees are used to estimate whether climate information can be used to infer an optimal sowing date in order to optimize yields. In order to simplify the problem, we set a default sowing date (the most representative for the crop and the site) and compare the yield amplitudes between such a default date and possible alternative dates sometimes used by farmers. Above normal average temperatures at the beginning and the end of the crop cycle lead to respectively later and earlier optimal sowing. Using this classification, yields can be potentially improved by changing sowing date for maize but it is more limited for soybean. More generally, the sites and crops which have more variable yields are also the ones for which the proposed methodology is the most efficient. However, a full evaluation of the accuracy of seasonal forecasts should be the next step before confirming the reliability of this methodology under real conditions.     

Role of Education and Training in Agricultural Meteorology to Reduce Vulnerability to Climate Variability

Agricultural meteorologists are concerned with many operational aspects of the effects of climate on crop production, livestock, and natural resource management. For them to continue to make a contribution to the economy of a country they must continually sharpen their skills and remain updated on the latest available information. Training should include a variety of skills, including transferable skills (e.g. communication, numeracy), professional skills (including cognitive skills) and information technology skills. Problem-based learning can be used to promote critical thinking, decision making and analytical skills. More use should be made of computer-aided learning for agricultural meteorologists’ in-service training. In particular, the Internet or CDs could be used to disseminate specific recently developed techniques and applications to improve the understanding of the variability in climate and its effect on agricultural production and natural resource management. Examples that can address the vulnerability of farmers include crop–climate matching, the use of indices, crop modelling and risk assessment together with seasonal outlooks. A strategy needs to be formulated to address these needs and implement changes in the education and training of agricultural meteorologists. These training needs must be constantly updated to meet the changing demands of new technology to cope with climate change and climate variability.