Trade-offs associated with different modeling approaches for assessment of fish and shellfish responses to climate change

Considerable progress has been made in integrating carbon, nutrient, phytoplankton and zooplankton dynamics into global-scale physical climate models. Scientists are exploring ways to extend the resolution of the biosphere within these Earth system models (ESMs) to include impacts on global distribution and abundance of commercially exploited fish and shellfish. This paper compares different methods for modeling fish and shellfish responses to climate change on global and regional scales. Several different modeling approaches are considered including: direct applications of ESM’s, use of ESM output for estimation of shifts in bioclimatic windows, using ESM outputs to force single- and multi-species stock projection models, and using ESM and physical climate model outputs to force regional bio-physical models of varying complexity and mechanistic resolution. We evaluate the utility of each of these modeling approaches in addressing nine key questions relevant to climate change impacts on living marine resources. No single modeling approach was capable of fully addressing each question. A blend of highly mechanistic and less computationally intensive methods is recommended to gain mechanistic insights and to identify model uncertainties.     

Climate sensitivity estimated from ensemble simulations of glacial climate

The concentration of greenhouse gases (GHGs) in the atmosphere continues to rise, hence estimating the climate system’s sensitivity to changes in GHG concentration is of vital importance. Uncertainty in climate sensitivity is a main source of uncertainty in projections of future climate change. Here we present a new approach for constraining this key uncertainty by combining ensemble simulations of the last glacial maximum (LGM) with paleo-data. For this purpose we used a climate model of intermediate complexity to perform a large set of equilibrium runs for (1) pre-industrial boundary conditions, (2) doubled CO₂ concentrations, and (3) a complete set of glacial forcings (including dust and vegetation changes). Using proxy-data from the LGM at low and high latitudes we constrain the set of realistic model versions and thus climate sensitivity. We show that irrespective of uncertainties in model parameters and feedback strengths, in our model a close link exists between the simulated warming due to a doubling of CO₂, and the cooling obtained for the LGM. Our results agree with recent studies that annual mean data-constraints from present day climate prove to not rule out climate sensitivities above the widely assumed sensitivity range of 1.5–4.5°C (Houghton et al. 2001). Based on our inferred close relationship between past and future temperature evolution, our study suggests that paleo-climatic data can help to reduce uncertainty in future climate projections. Our inferred uncertainty range for climate sensitivity, constrained by paleo-data, is 1.2–4.3°C and thus almost identical to the IPCC estimate. When additionally accounting for potential structural uncertainties inferred from other models the upper limit increases by about 1°C.     

Governing epidemics in an age of complexity: Narratives,politics and pathways to sustainability

This paper elaborates a ‘pathways approach’ to addressing the governance challenges posed by the dynamics of complex, coupled, multi-scale systems, while incorporating explicit concern for equity, social justice and the wellbeing of poor and marginalised groups. It illustrates the approach in relation to current policy challenges of dealing with epidemics and so-called ‘emerging infectious diseases’ such as avian influenza and haemorrhagic fevers, which involve highly dynamic, cross-scale, often-surprising viral–social–political–ecological interactions. Amidst complexity, we show how different actors in the epidemics field produce particular narratives which frame systems and their dynamics in different ways, promote particular goals and values, and justify particular pathways of disease response. These range from ‘outbreak narratives’ emphasising threat to global populations, to alternative but often marginalised narratives variously emphasising long-term structural, land use and environmental change, local knowledge and livelihood goals. We highlight tendencies – supported by cognitive, institutional and political pressures – for powerful actors and institutions to ‘close down’ around narratives that emphasise stability, underplaying longer term, less controllable dynamics. Arguing that governance approaches need to ‘open up’ to embrace strategies for resilience and robustness in relation to epidemics, we outline what some of the routes towards this might involve, and what the resulting governance models might look like. Key are practices and arrangements that involve flexibility, diversity, adaptation, learning and reflexivity, as well as highlighting and supporting alternative pathways within a progressive politics of sustainability.     

Improving the way we think about projecting future energy use and emissions of carbon dioxide

A variety of decision makers need projections of future energy demand, CO₂ emissions and similar factors that extend many decades into the future. The past performance of such projections has been systematically overconfident. Analysts have often used scenarios based on detailed story lines that spell out “plausible alternative futures” as a central tool for evaluating uncertainty. No probabilities are typically assigned to such scenarios. We argue that this practice is often ineffective. Rather than expanding people’s judgment about the range of uncertainty about the future, scenario-based analysis is more likely to lead to systematic overconfidence, to an underestimate of the range of possible future outcomes. We review relevant findings from the literature on human judgment under uncertainty and discuss their relevance to the task of making probabilistic projections. The more detail that one adds to the story line of a scenario, the more probable it will appear to most people, and the greater the difficulty they likely will have in imagining other, equally or more likely, ways in which the same outcome could be reached. We suggest that scenario based approaches make analysts particularly prone to such cognitive biases, and then outline a strategy by which improved projections, tailored to the needs of specific decision makers, might be developed.     

Deep uncertainty in long-term hurricane risk: Scenario generation and implications for future climate experiments

Current projections of long-term trends in Atlantic hurricane activity due to climate change are deeply uncertain, both in magnitude and sign. This creates challenges for adaptation planning in exposed coastal communities. We present a framework to support the interpretation of current long-term tropical cyclone projections, which accommodates the nature of the uncertainty and aims to facilitate robust decision making using the information that is available today. The framework is populated with projections taken from the recent literature to develop a set of scenarios of long-term hurricane hazard. Hazard scenarios are then used to generate risk scenarios for Florida using a coupled climate–catastrophe modeling approach. The scenarios represent a broad range of plausible futures; from wind-related hurricane losses in Florida halving by the end of the century to more than a four-fold increase due to climate change alone. We suggest that it is not possible, based on current evidence, to meaningfully quantify the relative confidence of each scenario. The analyses also suggest that natural variability is likely to be the dominant driver of the level and volatility of wind-related risk over the coming decade; however, under the highest scenario, the superposition of this natural variability and anthropogenic climate change could mean notably increased levels of risk within the decade. Finally, we present a series of analyses to better understand the relative adequacy of the different models that underpin the scenarios and draw conclusions for the design of future climate science and modeling experiments to be most informative for adaptation.     

Portfolio screening to support the mainstreaming of adaptation to climate change into development assistance

The need to mainstream adaptation to climate change into development planning and ongoing sectoral decision-making is increasingly recognised, and several bilateral and multilateral development agencies are starting to take an interest. Over the past years at least six development agencies have screened their project portfolios, generally with two goals in mind: (1) to ascertain the extent to which existing development projects already consider climate risks or address vulnerability to climate variability and change, and (2) to identify opportunities for incorporating climate change explicitly into future projects. As each portfolio screening was conducted independently, the broader lessons emerging from the screenings have not been systematically analysed. In this paper we assess the screening activities to date, focusing on both the results and the methods applied. Based on this assessment we identify opportunities for development agencies to expand their current focus on the links between climate and development. Most agencies already consider climate change as a real but uncertain threat to future development, but they have given less thought to how different development patterns might affect vulnerability to climate change. The screenings undertaken have shown the need to take a comprehensive approach to adaptation and its integration into development planning and sectoral decision-making, and a number of policy initiatives have been taken to promote such integration. We provide some initial guidance as to how portfolio screening can be carried out in a way that would allow agencies to assess systematically the relevance of climate change to their ongoing and planned development projects.     

Linking global and local scales: designing dynamic assessment and management processes

This paper identifies challenges inherent in addressing multi-scale environmental problems, and outlines tentative guidelines for addressing such challenges and linking science and policy across scales. The study and practice of environmental assessment and management increasingly recognize the importance of scale and cross-scale dynamics in understanding and addressing global environmental change. These ongoing efforts, however, lack a systematic way of thinking about and addressing the challenges involved in integrating science and policy across multiple scales, for example, in the design of policy-relevant, scientific assessments of problems such as climate change. These challenges include matching scales of biogeophysical systems with scales of management systems, avoiding scale discordance (matching the scale of the assessment with the scale of management), and accounting for cross-scale dynamics. In this paper we propose tentative guidelines for meeting such challenges for both assessors and decision-makers: (1) utilize boundary organizations — institutions which serve to mediate between scientists and decision-makers, and between these actors at different scales; (2) utilize scale-dependent comparative advantages — coordinating the allocation of resources, technical expertise, and decision-making authority to best capitalize on scale-specific capabilities; and (3) employ adaptive assessment and management strategies — constructing long-term, iterative, experiment-based processes of integrated assessment and management.     

An enhanced engineering perspective of global climate systems and statistical formulation of terrestrial CO2 exchanges

This paper designs a comprehensive approach based on the engineering machine/system concept, to model, analyze, and assess the level of carbon dioxide (CO₂) exchange between the atmosphere and terrestrial ecosystems, which is an important factor in understanding changes in global climate. The focus of this article is on spatial patterns and on the correlation between levels of CO₂ fluxes and a variety of influencing factors in eco-environments. The engineering/machine concept used is a system protocol that includes the sequential activities of design, test, observe, and model. This concept is applied to explicitly include various influencing factors and interactions associated with CO₂ fluxes. To formulate effective models of a large and complex climate system, this article introduces a modeling technique that will be referred to as stochastic filtering analysis of variance (SF-ANOVA). The CO₂ flux data observed from some sites of AmeriFlux are used to illustrate and validate the analysis, prediction, and globalization capabilities of the proposed engineering approach and the SF-ANOVA technique. The SF-ANOVA modeling approach was compared to stepwise regression, ridge regression, and neural networks. The comparison indicated that the proposed approach is a valid and effective tool with similar accuracy and less complexity than the other procedures.     

Characterizing climate change risks by linking robust decision frameworks and uncertain probabilistic projections

There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning.     

Systems thinking as a paradigm shift for sustainability transformation

The Sustainable Development Goals (SDGs), adopted as reference and universal guidepost for transitioning to Sustainable Development by the United Nations in 2015 as part of the 2030 Agenda for Sustainable Development, are intended to be used as a set of interconnected goals and global targets for ‘Transforming our world’, as the 2030 Agenda is titled. This is a far more challenging task than business as usual; it requires systems thinking for understanding the conditions that generate and propagate sustainability challenges, moving away from the reductionist and anthropocentric thinking that created them in the first place. Taking a systems approach to addressing these challenges has been gaining currency with academics and policymakers alike, and here we make the case for holistic, integrated, and interdisciplinary thinking that challenges assumptions and worldviews, crucially based on public participation and engagement, to create the enabling conditions for sustainability to emerge. System transformations require interconnected changes to technologies, social practices, business models, regulations and societal norms, an intentional process designed to fundamentally alter the components and structures that cause the system to behave in its current unsustainable ways, a paradigm shift enabling the transition to sustainability.     

Variances in the projections,resulting from CLIMEX,Boosted Regression Trees and Random Forests techniques

The aim of this study was to have a comparative investigation and evaluation of the capabilities of correlative and mechanistic modeling processes, applied to the projection of future distributions of date palm in novel environments and to establish a method of minimizing uncertainty in the projections of differing techniques. The location of this study on a global scale is in Middle Eastern Countries. We compared the mechanistic model CLIMEX (CL) with the correlative models MaxEnt (MX), Boosted Regression Trees (BRT), and Random Forests (RF) to project current and future distributions of date palm (Phoenix dactylifera L.). The Global Climate Model (GCM), the CSIRO-Mk3.0 (CS) using the A2 emissions scenario, was selected for making projections. Both indigenous and alien distribution data of the species were utilized in the modeling process. The common areas predicted by MX, BRT, RF, and CL from the CS GCM were extracted and compared to ascertain projection uncertainty levels of each individual technique. The common areas identified by all four modeling techniques were used to produce a map indicating suitable and unsuitable areas for date palm cultivation for Middle Eastern countries, for the present and the year 2100. The four different modeling approaches predict fairly different distributions. Projections from CL were more conservative than from MX. The BRT and RF were the most conservative methods in terms of projections for the current time. The combination of the final CL and MX projections for the present and 2100 provide higher certainty concerning those areas that will become highly suitable for future date palm cultivation. According to the four models, cold, hot, and wet stress, with differences on a regional basis, appears to be the major restrictions on future date palm distribution. The results demonstrate variances in the projections, resulting from different techniques. The assessment and interpretation of model projections requires reservations, especially in correlative models such as MX, BRT, and RF. Intersections between different techniques may decrease uncertainty in future distribution projections. However, readers should not miss the fact that the uncertainties are mostly because the future GHG emission scenarios are unknowable with sufficient precision. Suggestions towards methodology and processing for improving projections are included.     

A three-tiered approach to participatory vulnerability assessment in the Solomon Islands

Greater recognition of the seriousness of global environmental change has led to an increase in research that assesses the vulnerability of households, communities and regions to changing environmental or economic conditions. So far, however, there has been relatively little attention given to how assessments can be conducted in ways that help build capacity for local communities to understand and find their own solutions to their problems. This paper reports on an approach that was designed and used to work with a local grass roots organization in the Solomon Islands to promote inclusivity and participation in decision-making and to build the capacity of the organization to reduce the vulnerability of communities to drivers of change. The process involved working collaboratively with the organization and training its members to conduct vulnerability assessments with communities using participatory and deliberative methods. To make best use of the learning opportunities provided by the research process, specific periods for formal reflection were incorporated for the three key stakeholders involved: the primary researchers; research assistants; and community members. Overall, the approach: (1) promoted learning about the current situation in Kahua and encouraged deeper analysis of problems; (2) built capacity for communities to manage the challenges they were facing; and (3) fostered local ownership and responsibility for problems and set precedents for future participation in decision-making. While the local organization and the communities it serves still face significant challenges, the research approach set the scene for greater local participation and effort to maintain and enhance livelihoods and wellbeing. The outcomes highlight the need for greater emphasis on embedding participatory approaches in vulnerability assessments for communities to benefit fully from the process.     

High-resolution streamflow trend analysis applicable to annual decision calendars: a western United States case study

Changes in the seasonality of streamflow in the western United States have important implications for water resources management and the wellbeing of coupled human-natural systems. An assessment of changes in the timing and magnitude of streamflow resolved at fine time scales (days to weeks and seasons) is highly relevant to adaptive management strategies that are responsive to changing hydrologic baselines. In this paper, we present a regional analysis of the changes in streamflow seasonality through a broad classification of streams and quantification of increases and decreases in flow, based on a quantile regression methodology. This analysis affords a useful research product to examine the diversity of trends across seasons for individual streams. The trend analysis methodology can identify windows of change, thus revealing vulnerabilities within decision calendars and species lifecycles, an important consideration for adaptation and mitigation efforts.     

Can we learn from the past? Towards better analogies and historical inference in society-environmental change research

In light of the challenges posed by contemporary environmental changes, interest in past environmental impacts and societies’ responses to them is burgeoning. The main strength of such research lies in its ability to analyze completed society-environment interactions. Scholars have argued that such analyses can improve our understanding of present challenges and offer useful lessons to guide adaptation responses. Yet despite considerable differences between past and present societies, our inherently limited knowledge of the past and our changing understanding of it, much of this research uses historical antecedents uncritically, assuming that past societal impacts and responses are directly analogous to contemporary ones. We argue that this approach is unsound both methodologically and theoretically, thus drawing insights that might offer an erroneous course of action.To illustrate the challenges in drawing historical analogies, we outline several fundamental differences between past and present societies as well as broader limitations of historical research. Based on these points, we argue that scholars who apply historical inference in their work should do so critically, while reflecting on the objectives of learning from the past and the limitations of this process. We suggest a number of ways to improve past-present analogies, such as defining more explicitly what we can learn from the past, clarifying the rationale for using the analogy, and reducing the number of variables compared between past and present.     

From MONEX to the global monsoon: A review of monsoon system research

Substantial progress has been made over the past three decades since the Monsoon Experiments(MONEX) of 1978–79. Here, we review these achievements by highlighting four breakthroughs in monsoon research:(1) The identification of the coupled ocean–land–atmosphere nature of the monsoon in the process of the annual cycle of solar heating;(2) new understanding of the changes in the driving forces of monsoon systems, with anthropogenic factors(climate effects of increased greenhouse gas and aerosol emissions) playing an important role in the regulation of monsoons;(3) detection of the interdecadal- and centennial-scale variability of monsoon systems, and its attribution to the combined impact of global warming and natural(especially oceanic) effects; and(4) the emerging concept of the global monsoon and its long-term variation under the impact of global climate change. All the observational and model-derived evidence demonstrates that the monsoon system, as an important component of the global climate system, has already changed and will continue to change in the future. This picture of an evolving monsoon system poses great challenges for near-term prediction and long-term projection.     

Boundary-Layer Entrainment Estimation Through Assimilation of Radiosonde and Micrometeorological Data into a Mixed-Layer Model

In this study we estimate boundary-layer growth and entrainment bycombining radiosonde and micrometeorological observations with asimple coupled boundary-layer and land surface model. A variational(smoothing) approach is used to find the optimal estimate ofentrainment over the daytime window. This method is appealingbecause it accounts for the uncertainty in the various data streams,while enforcing the dynamics of the model (i.e., water and energybudgets in the boundary layer, mixed-layer growth, etc.). Thetraditional variational framework was modified in this study toinclude an ensemble approach, which not only yields a (mean) estimateof entrainment, but a measure of its uncertainty as well. Themethodology is applied to a field experiment site in Kansas. Resultsfrom this study indicate a much larger ratio of entrainment to surfacefluxes compared to early literature values from other sites. However,our results are consistent with recent estimates at the site usingindependent estimation methods. In tests where radiosonde data werewithheld, reasonable skill in entrainment estimation was still shown,suggesting the potential for more widespread applications where onlymicrometeorological data are available. Finally, the data assimilationframework presented here has not traditionally been used inatmospheric boundary-layer studies, and may provide a useful approachfor studying other aspects of the boundary layer in the future.     

Past,present and future vegetation-cloud feedbacks in the Amazon Basin

To begin exploring the underlying mechanisms that couple vegetation to cloud formation processes, we derive the lifting condensation level (LCL) to estimate cumulus cloud base height. Using a fully coupled land–ocean–atmosphere general circulation model (HadCM3LC), we investigate Amazonian forest feedbacks on cloud formation over three geological periods; modern-day (a.d. 1970–1990), the last glacial maximum (LGM; 21 kya), and under a future climate scenario (IS92a; a.d. 2070–2090). Results indicate that for both past and future climate scenarios, LCL is higher relative to modern-day. Statistical analyses indicate that the 800 m increase in LCL during the LGM is related primarily to the drier atmosphere promoted by lower tropical sea surface temperatures. In contrast, the predicted 1,000 m increase in LCL in the future scenario is the result of a large increase in surface temperature and reduced vegetation cover.     

Seasonal and Inter-Annual Climate Forecasting: The New Tool for Increasing Preparedness to Climate Variability and Change In Agricultural Planning And Operations

Climate variability and change affects individuals and societies. Within agricultural systems, seasonal climate forecasting can increase preparedness and lead to better social, economic and environmental outcomes. However, climate forecasting is not the panacea to all our problems in agriculture. Instead, it is one of many risk management tools that sometimes play an important role in decision-making. Understanding when, where and how to use this tool is a complex and multi-dimensional problem. To do this effectively, we suggest a participatory, cross-disciplinary research approach that brings together institutions (partnerships), disciplines (e.g., climate science, agricultural systems science, rural sociology and many other disciplines) and people (scientist, policy makers and direct beneficiaries) as equal partners to reap the benefits from climate knowledge. Climate science can provide insights into climatic processes, agricultural systems science can translate these insights into management options and rural sociology can help determine the options that are most feasible or desirable from a socio-economic perspective. Any scientific breakthroughs in climate forecasting capabilities are much more likely to have an immediate and positive impact if they are conducted and delivered within such a framework. While knowledge and understanding of the socio-economic circumstances is important and must be taken into account, the general approach of integrated systems science is generic and applicable in developed as well as in developing countries. Examples of decisions aided by simulation output ranges from tactical crop management options, commodity marketing to policy decisions about future land use. We also highlight the need to better understand temporal- and spatial-scale variability and argue that only a probabilistic approach to outcome dissemination should be considered. We demonstrated how knowledge of climatic variability (CV), can lead to better decisions in agriculture, regardless of geographical location and socio-economic conditions.     

Psychohistory revisited: fundamental issues in forecasting climate futures

Uncertainty in the trajectories of the global energy and economic systems vexes the climate science community. While it is tempting to reduce uncertainty by searching for deterministic rules governing the link between energy consumption and economic output, this article discusses some of the problems that follow from such an approach. We argue that the theoretical and empirical evidence supports the view that energy and economic systems are dynamic, and unlikely to be predictable via the application of simple rules. Encouraging more research seeking to reduce uncertainty in forecasting would likely be valuable, but any results should reflect the tentative and exploratory nature of the subject matter.