Joseph Daron

Interrogating empirical-statistical downscaling

The delivery of downscaled climate information is increasingly seen as a vehicle of climate services, a driver for impacts studies and adaptation decisions, and for informing policy development. Empirical-statistical downscaling (ESD) is widely used; however, the accompanying responsibility is significant, and predicated on effective understanding of the limitations and capabilities of ESD methods. There remain substantial contradictions, uncertainties, and sensitivity to assumptions between the different methods commonly used. Yet providing decision-relevant downscaled climate projections to help support national and local adaptation is core to the growing global momentum seeking to operationalize what is, in effect, still foundational research. We argue that any downscaled climate information must address the criteria of being plausible, defensible and actionable. Climate scientists cannot absolve themselves of their ethical responsibility when informing adaptation and must, therefore, be diligent in ensuring any information provided adequately addresses these three criteria. Frameworks for supporting such assessment are not well developed. We interrogate the conceptual foundations of statistical downscaling methodologies and their assumptions, and articulate a framework for evaluating and integrating downscaling output into the wider landscape of climate information. For ESD there are key criteria that need to be satisfied to underpin the credibility of the derived product. Assessing these criteria requires the use of appropriate metrics to test the comprehensive treatment of local climate response to large-scale forcing, and to compare across methods. We illustrate the potential consequences of methodological choices on the interpretation of downscaling results and explore the purposes, benefits and limitations of using statistical downscaling.

The role of regional climate projections in managing complex socio-ecological systems

Climate is one of many factors to be considered in adapting systems to environmental and societal change and often it is not the most important factor. Moreover, given considerable model inadequacies, irreducible uncertainties, and poor accessibility to model output, we may legitimately ask whether or not regional climate projections ought to have a central role in guiding climate change adaptation decisions. This question is addressed by analysing the value of regional downscaled climate model output in the management of complex socio-ecological systems (SESs) vulnerable to climate change. We demonstrate, using the example of the Dwesa–Cwebe region in South Africa, that the management of such systems under changing environmental and socio-economic conditions requires a nuanced and holistic approach that addresses cross-scale system interdependencies and incorporates “complexity thinking”. We argue that the persistent focus on increasing precision and skill in regional climate projections is misguided and does not adequately address the needs of society. However, this does not imply that decision makers should exclude current and future generations of regional climate projections in their management processes. On the contrary, ignoring such information, however uncertain and incomplete, risks the implementation of maladaptive policies and practices. By using regional climate projections to further explore uncertainties and investigate cross-scale system dependencies, such information can be used to aid understanding of how SESs might evolve under alternative future societal and environmental scenarios.

On predicting climate under climate change

Can today’s global climate model ensembles characterize the 21st century climate in their own ‘model-worlds’? This question is at the heart of how we design and interpret climate model experiments for both science and policy support. Using a low-dimensional nonlinear system that exhibits behaviour similar to that of the atmosphere and ocean, we explore the implications of ensemble size and two methods of constructing climatic distributions, for the quantification of a model’s climate. Small ensembles are shown to be misleading in non-stationary conditions analogous to externally forced climate change, and sometimes also in stationary conditions which reflect the case of an unforced climate. These results show that ensembles of several hundred members may be required to characterize a model’s climate and inform robust statements about the relative roles of different sources of climate prediction uncertainty.

Climate adaptation research for the next generation

The last decade has seen a rapid proliferation of climate change adaptation research resulting in a broad theoretical and conceptual understanding of adaptation. However, significant gaps still exist in applying these theoretical frameworks and tools in policy and practice. There is also little agreement on which methods and frameworks are truly robust, while many developing countries lack access to key literature and data. Several issues are especially relevant for early career researchers and practitioners. These include working in an area of science that crosses disciplinary boundaries, improving the quality of and capacity to undertake adaptation research, and equity and ethics. We elaborate on these themes based on our experiences as early career adaptation researchers working in developed and developing countries. We also identify several support mechanisms required to enable early career researchers to advance their engagement with the climate change adaptation agenda.

Challenges in using a Robust Decision Making approach to guide climate change adaptation in South Africa

Conventional forecast driven approaches to climate change adaptation create a cascade of uncertainties that can overwhelm decision makers and delay proactive adaptation responses. Robust Decision Making inverts the analytical steps associated with forecast-led methodologies, reframing adaptation in the context of a specific decision maker’s capacities and vulnerabilities. In adopting this bottom-up approach, the aim is to determine adaptation solutions which are insensitive to uncertainty. Yet despite the increased use of the approach in large-scale adaptation projects in developed countries, there is little empirical evidence to test whether or not it can be successfully applied in developing countries. The complex realities of decision making processes, the need to combine quantitative data with qualitative understanding and competing environmental, socio-economic and political factors all pose significant obstacles to adaptation. In developing countries, these considerations are particularly relevant and additional pressures exist which may limit the uptake and utility of the Robust Decision Making approach. In this paper, we investigate the claim that the approach can be deemed valuable in developing countries. Challenges and opportunities associated with Robust Decision Making, as a heuristic decision framework, are discussed with insights from a case study of adapting coastal infrastructure to changing environmental risks in South Africa. Lessons are extracted about the ability of this framework to improve the handling of uncertainty in adaptation decisions in developing countries.

Quantifying sources of climate uncertainty to inform risk analysis for climate change decision-making

Quantitative estimates of future climate change and its various impacts are often based on complex climate models which incorporate a number of physical processes. As these models continue to become more sophisticated, it is commonly assumed that the latest generation of climate models will provide us with better estimates of climate change. Here, we quantify the uncertainty in future climate change projections using two multi-model ensembles of climate model simulations and divide it into different components: internal, scenario and model. The contributions of these sources of uncertainty changes as a function of variable, temporal and spatial scale and especially lead time in the future. In the new models, uncertainty intervals for each of the components have increased. For temperature, importance of scenario uncertainty is the largest over low latitudes and increases nonlinearly after the mid-century. It has a small importance for precipitation simulations on all time scales, which hampers estimating the effect which any mitigation efforts might have. In line with current state-of-the-art adaptation approaches, we argue that despite these uncertainties climate models can provide useful information to support adaptation decision-making. Moreover, adaptation decisions should not be postponed in the hope that future improved scientific understanding will result in more accurate predictions of future climate change. Such simulations might not become available. On the contrary, while planning adaptation initiatives, a rational framework for decision-making under uncertainty should be employed. We suggest that there is an urgent need for continued development and use of improved risk analysis methods for climate change adaptation.

On quantifying the climate of the nonautonomous Lorenz-63 model

The Lorenz-63 model has been frequently used to inform our understanding of the Earths climate and provide insight for numerical weather and climate prediction. Most studies have focused on the autonomous (time invariant) model behaviour in which the models parameters are constants. Here, we investigate the properties of the model under time-varying parameters, providing a closer parallel to the challenges of climate prediction, in which climate forcing varies with time. Initial condition (IC) ensembles are used to construct frequency distributions of model variables, and we interpret these distributions as the time-dependent climate of the model. Results are presented that demonstrate the impact of ICs on the transient behaviour of the model climate. The location in state space from which an IC ensemble is initiated is shown to significantly impact the time it takes for ensembles to converge. The implication for climate prediction is that the climate may-in parallel with weather forecasting-have states from which its future behaviour is more, or less, predictable in distribution. Evidence of resonant behaviour and path dependence is found in model distributions under time varying parameters, demonstrating that prediction in nonautonomous nonlinear systems can be sensitive to the details of time-dependent forcing/parameter variations. Single model realisations are shown to be unable to reliably represent the models climate; a result which has implications for how real-world climatic timeseries from observation are interpreted. The results have significant implications for the design and interpretation of Global Climate Model experiments.

The utility of weather and climate information for adaptation decision-making: current uses and future prospects in Africa and India

Developing countries share many common challenges in addressing current and future climate risks. A key barrier to managing these risks is the limited availability of accessible, reliable and relevant weather and climate information. Despite continued investments in Earth System Modelling, and the growing provision of climate services across Africa and India, there often remains a mismatch between available information and what is needed to support on-the-ground decision-making. In this paper, we outline the range of currently available information and present examples from Africa and India to demonstrate the challenges in meeting information needs in different contexts. A review of literature supplemented by interviews with experts suggests that externally provided weather and climate information has an important role in building on local knowledge to shape understanding of climate risks and guide decision-making across scales. Moreover, case studies demonstrate that successful decision-making can be achieved with currently available information. However, these successful examples predominantly use daily, weekly and seasonal climate information for decision-making over short time horizons. Despite an increasing volume of global and regional climate model simulations, there are very few clear examples of long-term climate information being used to inform decisions at sub-national scales. We argue that this is largely because the information produced and disseminated is often ill-suited to inform decision-making at the local scale, particularly for farmers, pastoralists and sub-national governments. Even decision-makers involved in long-term planning, such as national government officials, find it difficult to plan using decadal and multi-decadal climate projections because of issues around uncertainty, risk averseness and constraints in justifying funding allocations on prospective risks. Drawing on lessons learnt from recent successes and failures, a framework is proposed to help increase the utility and uptake of both current and future climate information across Africa and India.

A critical investigation of evaluation matrices to inform coastal adaptation and planning decisions at the local scale

Local governments are under pressure to tackle an increasing spectrum of complex contemporary problems, such as climate change, while ensuring multiple stakeholder interests are incorporated into decision processes. Multi-criteria decision tools can assist, but challenges remain in creating an enabling environment for incorporating and balancing different stakeholder perspectives. Here, we draw on interview data and a sensitivity analysis to investigate the use of an evaluation matrix to guide local coastal adaptation decision-making in South Africa. We adopt a participatory action research framework and find that decision-making is influenced by individual, departmental and institutional values that are not adequately captured in the matrix approach. Our study reveals the compromise between achieving broad stakeholder representation and utilising technical expertise, and that altering matrix assumptions can imply different decision outcomes. Suggestions are made to improve multi-criteria decision approaches to better facilitate integrated coastal management in responding to local coastal adaptation challenges.