Are long-term climate projections useful for on-farm adaptation decisions?

The current literature on climate services for farmers predominantly focuses on seasonal forecasts, with an assumption that longer-term climate projections may not be suitable for informing farming decisions. In this paper, we explore whether certain types of long-term climate projections may be useful for some specific types of farming decisions. Through interviews with almond tree crop farmers and farm advisors in California, we examine how farmers perceive the utility and accuracy levels of long-term climate projections and identify the types of projections that they may find useful. The interviews revealed that farmers often perceive long-term climate projections as an extension of weather forecasts, which can lead to their initial skepticism of the utility of such information. However, we also found that when farmers were presented with long-term trends or shifts in crop-specific agroclimatic metrics (such as chill hours or summer heat), they immediately perceived these as valuable for their decision-making. Hence, the manner in which long-term projections are framed, presented, and discussed with farmers can heavily influence their perception of the potential utility of such projections. The iterative conversations as part of the exploratory interview questions, served as a tool for “joint construction of meaning” of complex and ambiguous terms such as “long-term climate projections,” “long-term decisions” and “uncertainty.” This in-turn supported a joint identification (and understanding) of the types of information that can potentially be useful for on-farm adaptive decisions, where the farmer and the interviewer both improvise and iterate to find the best types of projections that fit specific decision-contexts. Overall, this research identifies both the types of long-term climate information that farmers may consider useful, and the engagement processes that are able to effectively elicit farmers' long-term information needs

Global Evidence of Constraints and Limits to Human Adaptation

Constraints and limits to adaptation are critical to understanding the extent to which human and natural systems can successfully adapt to climate change. We conduct a systematic review of 1,682 academic studies on human adaptation responses to identify patterns in constraints and limits to adaptation for different regions, sectors, hazards, adaptation response types, and actors. Using definitions of constraints and limits provided by the Intergovernmental Panel on Climate Change (IPCC), we find that most literature identifies constraints to adaptation but that there is limited literature focused on limits to adaptation. Central and South America and Small Islands generally report greater constraints and both hard and soft limits to adaptation. Technological, infrastructural, and ecosystem-based adaptation suggest more evidence of constraints and hard limits than other types of responses. Individuals and households face economic and socio-cultural constraints which also inhibit behavioral adaptation responses and may lead to limits. Finance, governance, institutional, and policy constraints are most prevalent globally. These findings provide early signposts for boundaries of human adaptation and are of high relevance for guiding proactive adaptation financing and governance from local to global scales

Supplementary Data for Manuscript

Supplementary data for the Araos Jagannathan et al Manuscript: Equity in human adaptation-related responses: A systematic global revie

Ready-to-use? - Bridging the climate science-usability gap for adaptation

As societies across the world increasingly experience the devastating impacts of climate change, there is an urgent need to implement and accelerate adaptation actions. Effective adaptation decisions need to be grounded in, and supported by robust science on expected climate change and its impacts. Despite several advances in climate science and modeling, the use of science in adaptation decisions is still very limited. This suggests that there is a gap between the production of climate science, and its use in adaptation i.e. there exists a climate science-usability gap for adaptation. My dissertation deconstructs the various aspects of this gap, by identifying the types of scientific knowledge and the processes of knowledge production that can lead to increased uptake of climate information in adaptation decisions. The overall aim of my research is to bridge the usability gap, by providing scientists with a better understanding of decision-makers’ climate information needs, providing decision-makers with an improved understanding of how science may be useful in their adaptation contexts, and recommending broader systemic changes that can sustain the development of usable science. Finally, this dissertation argues for and promotes ‘engaged’ models of research, where scientists and decision-makers jointly develop climate science that achieves more benefit to society.Chapter 1 of this dissertation reviews on-the-ground adaptation projects from across the world to understand how adaptation is conceptualized and promoted by the international community, and the types of scientific information that are used in the planning and design of these projects. Using the case of ecosystems-based adaptation, we find that 65% of adaptation projects either did not use any information on expected climate change, or just used broad macro-scale climate projections that were not specific to local sectoral contexts. A majority of projects did not address uncertainty in future climate change or in adaptation benefits, nor did they track adaptation outcomes. This pervasive lack of use of climate science to inform adaptation actions is concerning, as it is difficult to ascertain the level of climate impacts that these adaptation projects can cope with. Chapters 2.1 and 2.2 identify decision-makers’ climate information needs for undertaking adaptation action. Through semi-structured, exploratory interviews with perennial tree crop growers in California, Chapter 2.1 finds that farmers perceive long-term climate projections as an extension of weather forecasts, which can often lead to their skepticism of the utility of such information. Hence, the manner in which long-term projections are framed, presented, and discussed with farmers is critical to how they may perceive the potential utility of such information. We find that in-depth iterative conversations were essential to effectively understand the types of climate projections that are useful to farmers, and recognize that projections of crop-specific agro-climatic metrics are often more useful to farmers than projections of physical climatic metrics. Such conversations help in joint construction of meaning, or in this case joint understanding of useful climate information, where the farmer and the interviewer both improvise and iterate to find the best types of projections that fit specific decision-contexts. Chapter 2.2 presents a case of co-production (Project Hyperion) wherein scientists and water managers jointly developed decision-relevant metrics for adaptive water management. We find that arriving at these actionable metrics is more complicated and iterative than is generally acknowledged in the literature. We identify engagement strategies that target both direct and indirect knowledge elicitation as effective approaches for translating managers’ needs into quantitative metrics. These strategies, along with the list of metrics we develop, provide tangible recommendations to both researchers and practitioners seeking to develop usable climate information. Chapter 3 evaluates the skill of different Global Circulation Models (GCMs) in predicting decision-relevant climatic metrics (using the case of chill hours in California), and examines how differences in model choice may impact future projections. We find that the multi-model mean of GCMs is not the best predictor of this specific metric. Additionally, downscaled LOCA projections, which is a dataset recommended by the State of California, systematically underestimate the negative trend observed in historical chill hours. Further, we also find that good skill in predicting broader physical climate metrics does not guarantee skill in prediction of specific decision-relevant metrics such as chill hours. Since many decision-relevant metrics are non-linear derivations of primary physical quantities, approaching model evaluations through the lens of decision-relevant metrics can provide critical insights on model choice for adaptation decisions.Finally, Chapter 4 reviews recent co-produced climate change adaptation projects alongside the theoretical scholarship about co-production – to compare the expected theoretical outcomes of co-production with those that are reported in practice. In bringing these two streams of thought and action together, we find that the practice of knowledge co-production is realizing improvements in knowledge utilization, but is not reporting on the more radical or transformational outcomes that the theory expects from co-production (such as changing power dynamics, transforming dominant knowledge paradigms and bringing institutional change). We identify five key reasons for why co-production practice may be falling short of its expectations. We propose that to address these issues and unleash the full potential of co-production, a more transparent, conversant, and interactive research agenda and discourse is required

Ready-to-use? - Bridging the Climate Science-usability Gap for Adaptation

As societies across the world increasingly experience the devastating impacts of climate change, there is an urgent need to implement and accelerate adaptation actions. Effective adaptation decisions need to be grounded in, and supported by robust science on expected climate change and its impacts. Despite several advances in climate science and modeling, the use of science in adaptation decisions is still very limited. This suggests that there is a gap between the production of climate science, and its use in adaptation i.e. there exists a climate science-usability gap for adaptation. My dissertation deconstructs the various aspects of this gap, by identifying the types of scientific knowledge and the processes of knowledge production that can lead to increased uptake of climate information in adaptation decisions. The overall aim of my research is to bridge the usability gap, by providing scientists with a better understanding of decision-makers’ climate information needs, providing decision-makers with an improved understanding of how science may be useful in their adaptation contexts, and recommending broader systemic changes that can sustain the development of usable science. Finally, this dissertation argues for and promotes ‘engaged’ models of research, where scientists and decision-makers jointly develop climate science that achieves more benefit to society. Chapter 1 of this dissertation reviews on-the-ground adaptation projects from across the world to understand how adaptation is conceptualized and promoted by the international community, and the types of scientific information that are used in the planning and design of these projects. Using the case of ecosystems-based adaptation, we find that 65% of adaptation projects either did not use any information on expected climate change, or just used broad macro-scale climate projections that were not specific to local sectoral contexts. A majority of projects did not address uncertainty in future climate change or in adaptation benefits, nor did they track adaptation outcomes. This pervasive lack of use of climate science to inform adaptation actions is concerning, as it is difficult to ascertain the level of climate impacts that these adaptation projects can cope with. Chapters 2.1 and 2.2 identify decision-makers’ climate information needs for undertaking adaptation action. Through semi-structured, exploratory interviews with perennial tree crop growers in California, Chapter 2.1 finds that farmers perceive long-term climate projections as an extension of weather forecasts, which can often lead to their skepticism of the utility of such information. Hence, the manner in which long-term projections are framed, presented, and discussed with farmers is critical to how they may perceive the potential utility of such information. We find that in-depth iterative conversations were essential to effectively understand the types of climate projections that are useful to farmers, and recognize that projections of crop-specific agro-climatic metrics are often more useful to farmers than projections of physical climatic metrics. Such conversations help in joint construction of meaning, or in this case joint understanding of useful climate information, where the farmer and the interviewer both improvise and iterate to find the best types of projections that fit specific decision-contexts. Chapter 2.2 presents a case of co-production (Project Hyperion) wherein scientists and water managers jointly developed decision-relevant metrics for adaptive water management. We find that arriving at these actionable metrics is more complicated and iterative than is generally acknowledged in the literature. We identify engagement strategies that target both direct and indirect knowledge elicitation as effective approaches for translating managers’ needs into quantitative metrics. These strategies, along with the list of metrics we develop, provide tangible recommendations to both researchers and practitioners seeking to develop usable climate information. Chapter 3 evaluates the skill of different Global Circulation Models (GCMs) in predicting decision-relevant climatic metrics (using the case of chill hours in California), and examines how differences in model choice may impact future projections. We find that the multi-model mean of GCMs is not the best predictor of this specific metric. Additionally, downscaled LOCA projections, which is a dataset recommended by the State of California, systematically underestimate the negative trend observed in historical chill hours. Further, we also find that good skill in predicting broader physical climate metrics does not guarantee skill in prediction of specific decision-relevant metrics such as chill hours. Since many decision-relevant metrics are non-linear derivations of primary physical quantities, approaching model evaluations through the lens of decision-relevant metrics can provide critical insights on model choice for adaptation decisions. Finally, Chapter 4 reviews recent co-produced climate change adaptation projects alongside the theoretical scholarship about co-production – to compare the expected theoretical outcomes of co-production with those that are reported in practice. In bringing these two streams of thought and action together, we find that the practice of knowledge co-production is realizing improvements in knowledge utilization, but is not reporting on the more radical or transformational outcomes that the theory expects from co-production (such as changing power dynamics, transforming dominant knowledge paradigms and bringing institutional change). We identify five key reasons for why co-production practice may be falling short of its expectations. We propose that to address these issues and unleash the full potential of co-production, a more transparent, conversant, and interactive research agenda and discourse is required

The Making of a Metric: Co-Producing Decision-Relevant Climate Science

Developing decision-relevant science for adaptation requires the identification of climatic parameters that are both actionable for practitioners as well as tractable for modelers. In many sectors, these decision-relevant climatic metrics and the approaches that enable their identification remain largely unknown. “Co-production” of science with scientists and decision-makers is one potential way to identify these metrics, but there is little research describing specific and successful co-production approaches. This paper examines the negotiations and outcomes from Project Hyperion, wherein scientists and water managers jointly developed decision-relevant climatic metrics for adaptive water management. We identify successful co-production strategies by analyzing the project's numerous back-and-forth engagements and tracing the evolution of the science during these engagements. We found that effective mediation between scientists and managers needed dedicated “boundary spanners” with significant modeling expertise. Translating practitioners' information needs into tractable climatic metrics required direct and indirect methods of eliciting knowledge. We identified four indirect methods that were particularly salient for extracting tacitly held knowledge and enabling shared learning: developing a hierarchical framework linking management issues with metrics, starting discussions from the planning challenges, collaboratively exploring the planning relevance of new scientific capabilities, and using analogies of other “good” metrics. The decision-relevant metrics we developed provide insights into advancing adaptation-relevant climate science in the water sector. The co-production strategies we identified can be used to design and implement productive scientist-decision-maker interactions. Overall, the approaches and metrics we developed can help climate science to expand in new and more use-inspired directions