Search

Accelerating global climate change drives new climate risks. People around the world are researching, designing, and implementing strategies to manage these risks. Identifying and implementing sound climate risk management strategies poses nontrivial challenges including (a) linking the required disciplines, (b) identifying relevant values and objectives, (c) identifying and quantifying important uncertainties, (d) resolving interactions between decision levers and the system dynamics, (e) quantifying the trade-offs between diverse values under deep and dynamic uncertainties, (f) communicating to inform decisions, and (g) learning from the decision-making needs to inform research design. Here we review these challenges and avenues to overcome them. People and institutions are confronted with emerging and dynamic climate risks. Stakeholder values are central to defining the decision problem. Mission-oriented basic research helps to improve the design of climate risk management strategies. [ABSTRACT FROM AUTHOR]

The decision on how to manage a forest under climate change is subject to deep and dynamic uncertainties. The classic approach to analyze this decision adopts a predefined strategy, tests its robustness to uncertainties, but neglects their dynamic nature (i.e., that decision-makers can learn and adjust the strategy). Accounting for learning through dynamic adaptive strategies (DAS) can drastically improve expected performance and robustness to deep uncertainties. The benefits of considering DAS hinge on identifying critical uncertainties and translating them to detectable signposts to signal when to change course. This study advances the DAS approach to forest management as a novel application domain by showcasing methods to identify potential signposts for adaptation on a case study of a classic European beech management strategy in South-West Germany. We analyze the strategy's robustness to uncertainties about model forcings and parameters. We then identify uncertainties that critically impact its economic and ecological performance by confronting a forest growth model with a large sample of time-varying scenarios. The case study results illustrate the potential of designing DAS for forest management and provide insights on key uncertainties and potential signposts. Specifically, economic uncertainties are the main driver of the strategy's robustness and impact the strategy's performance more critically than climate uncertainty. Besides economic metrics, the forest stand's past volume growth is a promising signpost metric. It mirrors the effect of both climatic and model parameter uncertainty. The regular forest inventory and planning cycle provides an ideal basis for adapting a strategy in response to these signposts. [ABSTRACT FROM AUTHOR]

Sea-level rise poses considerable risks to coastal communities, ecosystems, and infrastructure. Decision makers are faced with uncertain sea-level projections when designing a strategy for coastal adaptation. The traditional methods are often silent on tradeoffs as well as the effects of tail-area events and of potential future learning. Here we reformulate a simple sea-level rise adaptation model to address these concerns. We show that Direct Policy Search yields improved solution quality, with respect to Pareto-dominance in the objectives, over the traditional approach under uncertain sea-level rise projections and storm surge. Additionally, the new formulation produces high quality solutions with less computational demands than an intertemporal optimization approach. Our results illustrate the utility of multi-objective adaptive formulations for the example of coastal adaptation and point to wider-ranging application in climate change adaptation decision problems. [ABSTRACT FROM AUTHOR]

In tropical montane cloud forests, climate change can cause upslope shifts in the distribution ranges of species, leading to reductions in distributional range. Endemic species with small ranges are particularly vulnerable to such decreases in range size, as the population size may be reduced significantly. To ensure the survival of cloud forest species in the long term, it is crucial to quantify potential future shifts in their distribution ranges and the related changes in habitat availability in order to assure the long-term effectiveness of conservation measures. In this study, we assessed the influence of climate change on the availability of forested habitat for the endemic El Oro parakeet. We investigated the future range shift by modelling the climatic niche of the El Oro parakeets and projecting it to four different climate change scenarios. Depending on the intensity of climate change, the El Oro parakeets shift their range between 500 and 1700 m uphill by the year 2100. On average, the shift is accompanied by a reduction in range size to 15% and a reduction in forested habitat to only 10% of the original extent. Additionally, the connectivity between populations in different areas is decreasing in higher altitudes. To prevent a population decline due to habitat loss following an upslope range shift, it will be necessary to restore habitat across a large elevational span in order to allow for movement of El Oro parakeets into higher altitudes. [ABSTRACT FROM AUTHOR]

Background: Pediatric lifestyle interventions have positive short-term effects on obese patients. Studies on long-term effects are still scarce in Europe. We investigated long-term weight patterns and sociodemographic predictors of a weight change in a large Central European (Germany, Austria and Switzerland) overweight pediatric cohort. Methods: The APV (Adiposity Patients Verlaufsbeobachtung) database was retrospectively analyzed; 157 specialized childhood obesity centers contributed standardized data of 29,181 patients [body mass index (BMI) ≥90th percentile; 5-25 years old] presenting between 2000 and 2012. BMI standard deviation scores (BMI-SDS) were analyzed in a 2-year follow-up and grouped according to BMI-SDS changes. Multiple logistic regression analyses were conducted to assess associations between sociodemographic factors and weight patterns. Results: 2-year follow-up data were available in 3,135 patients (54.6% female). Five distinct weight trajectories 'rapid weight loss' (n = 735, 23.4%), 'delayed success' (n = 697, 22.2%), 'cycling weight' (n = 43, 1.4%), 'initial weight loss' and 'weight rebound' (n = 383, 12.2%) and 'no weight loss throughout' (n = 1,277, 40.7%) best characterized long-term BMI-SDS changes. Younger and male patients were more likely to reduce weight and maintain weight loss. Conclusions: Our results suggest that an intervention before the onset of puberty seems promising for long-term weight maintenance in overweight children. Thus, new concepts are needed to improve long-term treatment success in patients with lower success rates. © 2014 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

ABSTRACT How has the Atlantic Meridional Overturning Circulation (AMOC) varied over the past centuries and what is the risk of an anthropogenic AMOC collapse? We report probabilistic projections of the future climate which improve on previous AMOC projection studies by (i) greatly expanding the considered observational constraints and (ii) carefully sampling the tail areas of the parameter probability distribution function (pdf). We use a Bayesian inversion to constrain a simple model of the coupled climate, carbon cycle and AMOC systems using observations to derive multicentury hindcasts and projections. Our hindcasts show considerable skill in representing the observational constraints. We show that robust AMOC risk estimates can require carefully sampling the parameter pdfs. We find a low probability of experiencing an AMOC collapse within the 21st century for a business-as-usual emissions scenario. The probability of experiencing an AMOC collapse within two centuries is 1/10. The probability of crossing a forcing threshold and triggering a future AMOC collapse (by 2300) is approximately 1/30 in the 21st century and over 1/3 in the 22nd. Given the simplicity of the model structure and uncertainty in the forcing assumptions, our analysis should be considered a proof of concept and the quantitative conclusions subject to severe caveats. [ABSTRACT FROM AUTHOR]

Anthropogenic greenhouse gas emissions may trigger climate threshold responses, such as a collapse of the North Atlantic meridional overturning circulation (MOC). Climate threshold responses have been interpreted as an example of “dangerous anthropogenic interference with the climate system” in the sense of the United Nations Framework Convention on Climate Change (UNFCCC). One UNFCCC objective is to “prevent” such dangerous anthropogenic interference. The current uncertainty about important parameters of the coupled natural – human system implies, however, that this UNFCCC objective can only be achieved in a probabilistic sense. In other words, climate management can only reduce – but not entirely eliminate – the risk of crossing climate thresholds. Here we use an integrated assessment model of climate change to derive economically optimal risk-reduction strategies. We implement a stochastic version of the DICE model and account for uncertainty about four parameters that have been previously identified as dominant drivers of the uncertain system response. The resulting model is, of course, just a crude approximation as it neglects, for example, some structural uncertainty and focuses on a single threshold, out of many potential climate responses. Subject to this caveat, our analysis suggests five main conclusions. First, reducing the numerical artifacts due to sub-sampling the parameter probability density functions to reasonable levels requires sample sizes exceeding 103. Conclusions of previous studies that are based on much smaller sample sizes may hence need to be revisited. Second, following a business-as-usual (BAU) scenario results in odds for an MOC collapse in the next 150 years exceeding 1 in 3 in this model. Third, an economically “optimal” strategy (that maximizes the expected utility of the decision-maker) reduces carbon dioxide(CO2) emissions by approximately 25% at the end of this century, compared with BAU emissions. Perhaps surprisingly, this strategy leaves the odds of an MOC collapse virtually unchanged compared to a BAU strategy. Fourth, reducing the odds for an MOC collapse to 1 in 10 would require an almost complete decarbonization of the economy within a few decades. Finally, further risk reductions (e.g., to 1 in 100) are possible in the framework of the simple model, but would require faster and more expensive reductions in CO2 emissions. [ABSTRACT FROM AUTHOR]

The authors reflect on the risks of potential climate thresholds. They discuss the impact of human activities to the climate system and cite the uncertainties on the understanding of potential climate threshold responses. They call the need to enhance research strategies on possible climate thresholds and improve the understanding on the dangerous threats brought by anthropogenic interference with the climate system.

We analyze the ability of an oceanic monitoring array to detect potential changes in the North Atlantic meridional overturning circulation (MOC). The observing array is ‘deployed’ into a numerical model (ECHAM5/MPI-OM), and simulates the measurements of density and wind stress at 26°N in the Atlantic. The simulated array mimics the continuous monitoring system deployed in the framework of the UK Rapid Climate Change program. We analyze a set of three realizations of a climate change scenario (IPCC A1B), in which – within the considered time-horizon of 200 years – the MOC weakens, but does not collapse. For the detection analysis, we assume that the natural variability of the MOC is known from an independent source, the control run. Our detection approach accounts for the effects of observation errors, infrequent observations, autocorrelated internal variability, and uncertainty in the initial conditions. Continuous observation with the simulated array for approximately 60 years yields a statistically significant (p < 0.05) detection with 95 percent reliability assuming a random observation error of 1 Sv (1 Sv = 106 m3 s−1). Observing continuously with an observation error of 3 Sv yields a detection time of about 90 years (with 95 percent reliability). Repeated hydrographic transects every 5 years/ 20 years result in a detection time of about 90 years/120 years, with 95 percent reliability and an assumed observation error of 3 Sv. An observation error of 3 Sv (one standard deviation) is a plausible estimate of the observation error associated with the RAPID UK 26°N array. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one on the workshop hosted by the Aspen Global Change Institute on abrupt climate change and another on the implications for climate policy.

Abstract Carbon dioxide (CO2) sequestration has been proposed as a key component in technological portfolios for managing anthropogenic climate change, since it may provide a faster and cheaper route to significant reductions in atmospheric CO2 concentrations than abating CO2 production. However, CO2 sequestration is not a perfect substitute for CO2 abatement because CO2 may leak back into the atmosphere (thus imposing future climate change impacts) and because CO2 sequestration requires energy (thus producing more CO2 and depleting fossil fuel resources earlier). Here we use analytical and numerical models to assess the economic efficiency of CO2 sequestration and analyze the optimal timing and extent of CO2 sequestration. The economic efficiency factor of CO2 sequestration can be expressed as the ratio of the marginal net benefits of sequestering CO2 and avoiding CO2 emissions. We derive an analytical solution for this efficiency factor for a simplified case in which we account for CO2 leakage, discounting, the additional fossil fuel requirement of CO2 sequestration, and the growth rate of carbon taxes. In this analytical model, the economic efficiency of CO2 sequestration decreases as the CO2 tax growth rate, leakage rates and energy requirements for CO2 sequestration increase. Increasing discount rates increases the economic efficiency factor. In this simple model, short-term sequestration methods, such as afforestation, can even have negative economic efficiencies. We use a more realistic integrated-assessment model to additionally account for potentially important effects such as learning-by-doing and socio-economic inertia on optimal strategies. We measure the economic efficiency of CO2 sequestration by the ratio of the marginal costs of CO2 sequestration and CO2 abatement along optimal trajectories. We show that the positive impacts of investments in CO2 sequestration through the reduction of future marginal CO2 sequestration costs and the alleviation of future inertia constraints can initially exceed the marginal sequestration costs. As a result, the economic efficiencies of CO2 sequestration can exceed 100% and an optimal strategy will subsidize CO2 sequestration that is initially more expensive than CO2 abatement. The potential economic value of a feasible and acceptable CO2 sequestration technology is equivalent - in the adopted utilitarian model - to a one-time investment of several percent of present gross world product. It is optimal in the chosen economic framework to sequester substantial CO2 quantities into reservoirs with small or zero leakage, given published estimates of marginal costs and climate change impacts. The optimal CO2 trajectories in the case of sequestration from air can approach the pre-industrial level, constituting geoengineering. Our analysis is silent on important questions (e.g., the effects of model and parametric uncertainty, the potential learning about these uncertainties, or ethical dimension of such geoengineering strategies), which need to be addressed before our findings can be translated into policy-relevant recommendations. [ABSTRACT FROM AUTHOR]

Anthropogenic greenhouse gas emissions may trigger threshold responses of the climate system. One relevant example of such a potential threshold response is a shutdown of the North Atlantic meridional overturning circulation (MOC). Numerous studies have analyzed the problem of early MOC change detection (i.e., detection before the forcing has committed the system to a threshold response). Here we analyze the early MOC prediction problem. To this end, we virtually deploy an MOC observation system into a simple model that mimics potential future MOC responses and analyze the timing of confident detection and prediction. Our analysis suggests that a confident prediction of a potential threshold response can require century time scales, considerably longer that the time required for confident detection. The signal enabling early prediction of an approaching MOC threshold in our model study is associated with the rate at which the MOC intensity decreases for a given forcing. A faster MOC weakening implies a higher MOC sensitivity to forcing. An MOC sensitivity exceeding a critical level results in a threshold response. Determining whether an observed MOC trend in our model differs in a statistically significant way from an unforced scenario (the detection problem) imposes lower requirements on an observation system than the determination whether the MOC will shut down in the future (the prediction problem). As a result, the virtual observation systems designed in our model for early detection of MOC changes might well fail at the task of early and confident prediction. Transferring this conclusion to the real world requires a considerably refined MOC model, as well as a more complete consideration of relevant observational constraints. [ABSTRACT FROM AUTHOR]

Many climate models predict that anthropogenic greenhouse gas emissions may cause a threshold response of the North Atlantic meridional overturning circulation (MOC). These model predictions are, however, uncertain. Reducing this uncertainty can have an economic value, because it would allow for the design of more efficient risk management strategies. Early information about the MOC sensitivity to anthropogenic forcing (i.e., information that arrives before the system is committed to a threshold response) could be especially valuable. Here the focus is on one particular kind of information: the detection of anthropogenic MOC changes. It is shown that an MOC observation system based on infrequent (decadal scale) hydrographic observations may well fail in the task of early MOC change detection. This is because this system observes too infrequently and the observation errors are too large. More frequent observations and reduced observation errors would result in earlier detection. It is also shown that the economic value of information associated with a confident and early prediction of an MOC threshold response could exceed the costs of typically implemented ocean observation systems by orders of magnitude. One open challenge is to identify a feasible observation system that would enable such a confident and early MOC prediction across the range of possible MOC responses. [ABSTRACT FROM AUTHOR]

We explore the combined effects of a climate threshold (a potential ocean thermohaline circulation collapse), parameter uncertainty, and learning in an optimal economic growth model. Our analysis shows that significantly reducing carbon dioxide (CO2) emissions may be justified to avoid or delay even small (and arguably realistic) damages from an uncertain and irreversible climate change—even when future learning about the system is considered. Parameter uncertainty about the threshold specific damages and the CO2 level triggering a threshold can act to decrease near-term CO2 abatements that maximize expected utility. [Copyright &y& Elsevier]

While the bulk of human exposure to mercury is through the consumption of marine fish, most of what we know about mercury methylation and bioaccumulation is from studies of freshwaters. We know Little of where and how mercury is methylated in the open oceans, and there is currently a debate whether methylmercury concentrations in marine fish have increased along with global anthropogenic mercury emissions. Measurements of mercury concentrations in Yellowfin tuna caught off Hawaii in 1998 show no increase compared to measurements of the same species caught in the same area in 1971. On the basis of the known increase in the global emissions of mercury over the past century and of a simple model of mercury biogeochemistry in the Equatorial and Subtropical Pacific ocean, we calculate that the methylmercury concentration in these surface waters should have increased between 9 and 26% over this 27 years span if methylation occurred in the mixed layer or in the thermocline. Such an increase is statistically inconsistent with the constant mercury concentrations measured in tuna. We conclude tentatively that mercury methylation in the oceans occurs in deep waters or in sediments. [ABSTRACT FROM AUTHOR]

Presents a study which examined the potential decadal trends of the marine biological carbon pump in the North Pacific Ocean. Analysis of the observations of the Geochemical Ocean Sections Study and the World Ocean Circulation Experiment; Examination of nutrient concentrations and oxygen utilization; Methods; Results and discussion.

Presents a study which investigated the optimal policy response to the risk of a reorganization of the North Atlantic's current pattern known as the thermohaline circulation collapse. Analysis of the link between atmospheric greenhouse gas concentrations and the ocean circulation; Integrated assessment model used in the study; Results and discussion.

Agent-based models (ABMs) are widely used to analyze coupled natural and human systems. Descriptive models require careful calibration with observed data. However, ABMs are often not calibrated in a formal sense. Here we examine the impact of data record size and aggregation on the calibration of an ABM for housing abandonment in the presence of flood risk. Using a perfect model experiment, we examine (i) model calibration and (ii) the ability to distinguish a model with inter-agent interactions from one without. We show how limited data sets may not adequately constrain a model with just four parameters and relatively minimal interactions. We also illustrate how limited data can be insufficient to identify the correct model structure. As a result, many ABM-based inferences and projections rely strongly on prior distributions. This emphasizes the need for utilizing independent lines of evidence to select sound and informative priors. • Adding one parameter can substantially increase the amount of data required to constrain an agent-based model. • Small amounts of data can make it difficult to identify the presence of an additional feedback mechanism. • Sound and informative priors are important for reliable inference for agent-based models. [ABSTRACT FROM AUTHOR]

Describes the acid-base properties of oxides, highlighting the surface complexation model. Presentation of a thermodynamic description of acid; Reference to the scientific contributions of Werner Stumm, environmental chemist; Details on the influence of hydrogen-ion concentration (pH) independent charges on acid base properties; Information on the central aspect of surface complexation models.

Convergence research is driven by specific and compelling problems and requires deep integration across disciplines. The potential of convergence research is widely recognized, but questions remain about how to design, facilitate, and assess such research. Here we analyze a seven-year, twelve-million-dollar convergence project on sustainable climate risk management to answer two questions. First, what is the impact of a project-level emphasis on the values that motivate and tie convergence research to the compelling problems? Second, how does participation in convergence projects shape the research of postdoctoral scholars who are still in the process of establishing themselves professionally? We use an interview-based approach to characterize what the project specifically enabled in each participant’s research. We find that (a) the project pushed participants’ research into better alignment with the motivating concept of convergence research and that this effect was stronger for postdoctoral scholars than for more senior faculty. (b) Postdocs’ self-assessed understanding of key project themes, however, appears unconnected to metrics of project participation, raising questions about training and integration. Regarding values, (c) the project enabled heightened attention to values in the research of a large minority of participants. (d) Participants strongly believe in the importance of explicitly reflecting on values that motivate and pervade scientific research, but they question their own understanding of how to put value-focused science into practice. This mismatch of perceived importance with poor understanding highlights an unmet need in the practice of convergence science. [ABSTRACT FROM AUTHOR]

Climate change is predicted to impact corn yields. Previous studies analyzing these impacts differ in data and modeling approaches and, consequently, corn yield projections. We analyze the impacts of climate change on corn yields using two statistical models with different approaches for dealing with county-level effects. The first model, which is novel to modeling corn yields, uses a computationally efficient spatial basis function approach. We use a Bayesian framework to incorporate both parametric and climate model structural uncertainty. We find that the statistical models have similar predictive abilities, but the spatial basis function model is faster and hence potentially a useful tool for crop yield projections. We also explore how different gridded temperature datasets affect the statistical model fit and performance. Compared to the dataset with only weather station data, we find that the dataset composed of satellite and weather station data results in a model with a magnified relationship between temperature and corn yields. For all statistical models, we observe a relationship between temperature and corn yields that is broadly similar to previous studies. We use downscaled and bias-corrected CMIP5 climate model projections to obtain detrended corn yield projections for 2020–2049 and 2069–2098. In both periods, we project a decrease in the mean corn yield production, reinforcing the findings of other studies. However, the magnitude of the decrease and the associated uncertainties we obtain differ from previous studies. [ABSTRACT FROM AUTHOR]

Flood-related risks to people and property are expected to increase in the future due to environmental and demographic changes. It is important to quantify and effectively communicate flood hazards and exposure to inform the design and implementation of flood risk management strategies. Here we develop an integrated modeling framework to assess projected changes in regional riverine flood inundation risks. The framework samples climate model outputs to force a hydrologic model and generate streamflow projections. Together with a statistical and hydraulic model, we use the projected streamflow to map the uncertainty of flood inundation projections for extreme flood events. We implement the framework for rivers across the state of Pennsylvania, United States. Our projections suggest that flood hazards and exposure across Pennsylvania are overall increasing with future climate change. Specific regions, including the main stem Susquehanna River, lower portion of the Allegheny basin, and central portion of Delaware River basin, demonstrate higher flood inundation risks. In our analysis, the climate uncertainty dominates the overall uncertainty surrounding the flood inundation projection chain. The combined hydrologic and hydraulic uncertainties can account for as much as 37% of the total uncertainty. We discuss how this framework can provide regional and dynamic flood-risk assessments and help to inform the design of risk-management strategies. [ABSTRACT FROM AUTHOR]

For computer simulation models to usefully inform climate risk management, uncertainties in model projections must be explored and characterized. Because doing so requires running the model many times over, and because computing resources are finite, uncertainty assessment is more feasible using models that demand less computer processor time. Such models are generally simpler in the sense of being more idealized, or less realistic. So modelers face a trade-off between realism and uncertainty quantification. Seeing this trade-off for the important epistemic issue that it is requires a shift in perspective from the established simplicity literature in philosophy of science. [ABSTRACT FROM AUTHOR]

Addressing contemporary environmental and social crises requires solutions-based, systems-level changes. To achieve these changes, transdisciplinary research approaches are needed to align problem framing with solution deployment at landscape scales. However, practical frameworks to guide this work are lacking. Here we propose a new framework to help bridge this gap: regenerative landscape design (RLD). We define RLD as a process for finding pattern-based solutions, emphasizing cooperative, iterative, and facilitated engagement for the co-production of locally relevant knowledge for desirable landscape stewardship. To do so, we review how key components of RLD (e.g., landscapes, design thinking, and regenerative processes) have been differentially and unevenly applied in disciplines ranging from resilience, landscape ecology, geography, architecture, agriculture, sociology, tourism, and more. We then put forward research considerations of a RLD approach to enhance social and environmental well-being. We use two emerging case studies (i.e., Chesapeake Bay Watershed, Pennsylvania, USA and Narok County, Kenya) to put forward pathways for implementation of the RLD strategy. [ABSTRACT FROM AUTHOR]

Atmospheric inversions allow us to estimate the terrestrial carbon sink by combining atmospheric observations with atmospheric transport models. However, these inverse estimates remain highly uncertain. Here we quantify uncertainties in simulations of North American atmospheric CO2 concentrations using a probabilistic approach. We demonstrate that uncertainty in fossil fuel emissions is a key factor in the uncertainty surrounding biospheric flux estimates. We show that atmospheric transport uncertainties in state‐of‐the‐art numerical weather models diminish when averaged over time, while uncertainties in large‐scale CO2 boundary inflow considerably impair our ability to quantify regional fluxes. Current estimates of the North America land sink that neglect the uncertainties in CO2 boundary inflow and fossil fuel emissions are likely overconfident. Our findings suggest that targeted use of new atmospheric observations and improved quantification of uncertainty components are a promising avenue to improve atmospheric inversions with the goal to refine estimates of biospheric CO2 fluxes on regional and continental scales. Plain Language Summary: The uncertainty in biospheric carbon dioxide (CO2) flux estimates drives divergent projections of future climate and uncertainty in prescriptions for climate mitigation. The terrestrial carbon sink can be inferred from atmospheric CO2 observations with transport models via inversion methods. Regional CO2 flux estimates remain uncertain due to the mixture of uncertainties caused by transport models, prior estimates of biospheric fluxes, large‐scale CO2 boundary inflow, the assumptions in the inversion process, and the limited density of atmospheric CO2 observations. Understanding the characteristics of these uncertainties in space and time is essential for accurate biospheric CO2 flux estimates. Here we identify the terms that most confound biospheric flux estimates. Our results show that, over North America, (i) biospheric fluxes dominate the model uncertainty over all timescales. (ii) Contrary to expectation, fossil fuel emissions are the second largest source of uncertainty at all timescales. (iii) Transport uncertainties are large at short timescales but act like random errors decreasing with time averaging. (ix) Continental boundary inflow uncertainties are large near the boundaries and become significant at seasonal to annual timescales. We propose sampling and analysis strategies that can better quantify and reduce uncertainties in both fossil fuel emission and biospheric flux estimates. Key Points: We quantify biospheric flux, fossil fuel emission, atmospheric transport, and boundary inflow uncertainties in modeled atmospheric CO2Biospheric fluxes and fossil fuel emissions are the largest contributors to atmospheric CO2 uncertainty over North AmericaTransport uncertainties can be approximated by random errors, while boundary inflow uncertainties are persistent and can be sizeable [ABSTRACT FROM AUTHOR]

Climate change has increased the frequency and intensity of natural disasters. Does this translate into increased economic damages? To date, empirical assessments of damage trends have been inconclusive. Our study demonstrates a temporal increase in extreme damages, after controlling for a number of factors. We analyze event-level data using quantile regressions to capture patterns in the damage distribution (not just its mean) and find strong evidence of progressive rightward skewing and tailfattening over time. While the effect of time on averages is hard to detect, effects on extreme damages are large, statistically significant, and growing with increasing percentiles. Our results are consistent with an upwardly curved, convex damage function, which is commonly assumed in climate-economics models. They are also robust to different specifications of control variables and time range considered and indicate that the risk of extreme damages has increased more in temperate areas than in tropical ones. We use simulations to show that underreporting bias in the data does not weaken our inferences; in fact, it may make them overly conservative. [ABSTRACT FROM AUTHOR]

Managing coastal flood risks involves selecting a portfolio of different strategies. Analyzing this choice typically requires a model. State-of-the-art coastal risk models provide detailed regional information, but they can be difficult to implement, computationally challenging, and potentially inaccessible. Simple economic damage models are more accessible but may not incorporate important features and thus fail to model risks and trade offs with enough fidelity to support decision making. Here, we develop a new framework to analyze coastal flood risk management. The framework is computationally inexpensive yet incorporates common features of many coastal cities. We apply this framework to an idealized coastal city and assess and optimize two objectives using combinations of risk mitigation strategies against a wide range of future states of the world. We find that optimization using combinations of strategies allows for identification of Pareto optimal strategy combinations that outperform individual strategy options. • We develop the island City on a Wedge (iCOW) framework to analyze coastal flood risk. • iCOW is computationally inexpensive yet includes common features of coastal cities. • iCOW can find Pareto optimal combinations of storm surge mitigation strategies. • Optimal combinations outperform individual ones giving decisionmakers better options. [ABSTRACT FROM AUTHOR]

Presents a comment and a corresponding response to the report `Temporal Trends in Deep Ocean Redfield Ratios,' by Markus Pahlow and Ulf Riebesell. Discussion on how human activities changes the marine biodata; Ratio of nitrate to phosphate.

Designing decision analytical models requires making choices that can involve a range of trade-offs and interactions between epistemic and ethical considerations. Such choices include determining the complexity of a model and deciding what types of risk will be assessed. Here, we demonstrate how model design choices can involve trade-offs between the epistemic benefits of representational completeness and simplicity, which interact with ethical considerations about fairness and human life. We illustrate this point by focusing on modeling studies that assess flood risks in New Orleans, Louisiana. Addressing the ethical and epistemic implications of model design choices can help clarify the scope of factors necessary to inform ethically sound and economically efficient decision-making. [ABSTRACT FROM AUTHOR]

The response of the Antarctic ice sheet (AIS) to changing global temperatures is a key component of sea-level projections. Current projections of the AIS contribution to sea-level changes are deeply uncertain. This deep uncertainty stems, in part, from (i) the inability of current models to fully resolve key processes and scales, (ii) the relatively sparse available data, and (iii) divergent expert assessments. One promising approach to characterizing the deep uncertainty stemming from divergent expert assessments is to combine expert assessments, observations, and simple models by coupling probabilistic inversion and Bayesian inversion. Here, we present a proof-of-concept study that uses probabilistic inversion to fuse a simple AIS model and diverse expert assessments. We demonstrate the ability of probabilistic inversion to infer joint prior probability distributions of model parameters that are consistent with expert assessments. We then confront these inferred expert priors with instrumental and paleoclimatic observational data in a Bayesian inversion. These additional constraints yield tighter hindcasts and projections. We use this approach to quantify how the deep uncertainty surrounding expert assessments affects the joint probability distributions of model parameters and future projections. [ABSTRACT FROM AUTHOR]

In many coastal communities, the risks driven by storm surges are motivating substantial investments in flood risk management. The design of adaptive risk management strategies, however, hinges on the ability to detect future changes in storm surge statistics. Previous studies have used observations to identify changes in past storm surge statistics. Here, we focus on the simple and decision-relevant question: How fast can we learn from past and potential future storm surge observations about changes in future statistics? Using Observing System Simulation Experiments, we quantify the time required to detect changes in the probability of extreme storm surge events. We estimate low probabilities of detection when substantial but gradual changes to the 100-year storm surge occur. As a result, policy makers may underestimate considerable increases in storm surge risk over the typically long lifespans of major infrastructure projects. [ABSTRACT FROM AUTHOR]

Storm surges are key drivers of coastal flooding, which generate considerable risks. Strategies to manage these risks can hinge on the ability to (i) project the return periods of extreme storm surges and (ii) detect potential changes in their statistical properties. There are several lines of evidence linking rising global average temperatures and increasingly frequent extreme storm surges. This conclusion is, however, subject to considerable structural uncertainty. This leads to two main questions: What are projections under various plausible statistical models? How long would it take to distinguish among these plausible statistical models? We address these questions by analyzing observed and simulated storm surge data. We find that (1) there is a positive correlation between global mean temperature rise and increasing frequencies of extreme storm surges; (2) there is considerable uncertainty underlying the strength of this relationship; and (3) if the frequency of storm surges is increasing, this increase can be detected within a multidecadal timescale (≈20 years from now). [ABSTRACT FROM AUTHOR]

Strategies to manage the risks posed by future sea-level rise hinge on a sound characterization of the inherent uncertainties. One of the major uncertainties is the possible rapid disintegration of large fractions of the Antarctic ice sheet in response to rising global temperatures. This could potentially lead to several meters of sea-level rise during the next few centuries. Previous studies have typically been silent on two coupled questions: (i) What are probabilistic estimates of this 'fast dynamic' contribution to sea-level rise? (ii) What are the implications for strategies to manage coastal flooding risks? Here, we present probabilistic hindcasts and projections of sea-level rise to 2100. The fast dynamic mechanism is approximated by a simple parameterization, designed to allow for a careful quantification of the uncertainty in its contribution to sea-level rise. We estimate that global temperature increases ranging from 1.9 to 3.1 °C coincide with fast Antarctic disintegration, and these contributions account for sea-level rise of 21-74 cm this century (5-95% range, Representative Concentration Pathway 8.5). We use a simple cost-benefit analysis of coastal defense to demonstrate in a didactic exercise how neglecting this mechanism and associated uncertainty can (i) lead to strategies which fall sizably short of protection targets and (ii) increase the expected net costs. [ABSTRACT FROM AUTHOR]

Managing socio-ecological systems is a challenge wrought by competing societal objectives, deep uncertainties, and potentially irreversible tipping points. A classic, didactic example is the shallow lake problem in which a hypothetical town situated on a lake must develop pollution control strategies to maximize its economic benefits while minimizing the probability of the lake crossing a critical phosphorus (P) threshold, above which it irreversibly transitions into a eutrophic state. Here, we explore the use of direct policy search (DPS) to design robust pollution control rules for the town that account for deeply uncertain system characteristics and conflicting objectives. The closed loop control formulation of DPS improves the quality and robustness of key management tradeoffs, while dramatically reducing the computational complexity of solving the multi-objective pollution control problem relative to open loop control strategies. These insights suggest DPS is a promising tool for managing socio-ecological systems with deeply uncertain tipping points. [ABSTRACT FROM AUTHOR]

Rising sea levels increase the probability of future coastal flooding. Many decision-makers use risk analyses to inform the design of sea-level rise (SLR) adaptation strategies. These analyses are often silent on potentially relevant uncertainties. For example, some previous risk analyses use the expected, best, or large quantile (i.e., 90%) estimate of future SLR. Here, we use a case study to quantify and illustrate how neglecting SLR uncertainties can bias risk projections. Specifically, we focus on the future 100-yr (1% annual exceedance probability) coastal flood height (storm surge including SLR) in the year 2100 in the San Francisco Bay area. We find that accounting for uncertainty in future SLR increases the return level (the height associated with a probability of occurrence) by half a meter from roughly 2.2 to 2.7 m, compared to using the mean sea-level projection. Accounting for this uncertainty also changes the shape of the relationship between the return period (the inverse probability that an event of interest will occur) and the return level. For instance, incorporating uncertainties shortens the return period associated with the 2.2 m return level from a 100-yr to roughly a 7-yr return period (∼15% probability). Additionally, accounting for this uncertainty doubles the area at risk of flooding (the area to be flooded under a certain height; e.g., the 100-yr flood height) in San Francisco. These results indicate that the method of accounting for future SLR can have considerable impacts on the design of flood risk management strategies. [ABSTRACT FROM AUTHOR]

Long-term flood risk management often relies on future sea-level projections. Projected uncertainty ranges are however widely divergent as a result of different methodological choices. The IPCC has condensed this deep uncertainty into a single uncertainty range covering 66% probability or more. Alternatively, structured expert judgment summarizes divergent expert opinions in a single distribution. Recently published uncertainty ranges that are derived from these 'consensus' assessments appear to differ by up to a factor four. This might result in overconfidence or overinvestment in strategies to cope with sea-level change. Here we explore possible reasons for these different interpretations. This is important for (i) the design of robust strategies and (ii) the exploration of pathways that may eventually lead to some kind of consensus distributions that are relatively straightforward to interpret. [ABSTRACT FROM AUTHOR]

Sea level could rise by several meters over the next centuries. The Greenland ice sheet could be an important contributor to future sea-level rise, because of its large volume and its high sensitivity to surface air temperature increases. Frameworks for the integrated climate risk management often require fast, simplified treatments of sea-level rise, in particular for estimating the risks associated with low probabilities but potentially high impacts. State-of-the-art ice sheet models provide important insights, but are often computationally too demanding to evaluate tail-area risks. Here we present SIMPLE, a physically motivated model of the Greenland ice sheet in response to temperature changes. SIMPLE can skillfully reproduce the results from a three-dimensional ice sheet model and outperforms existing simple models, after similar calibration. We anticipate that SIMPLE will be calibrated to other ice sheet models and can provide a fast approximation (emulator) for such models in studies that require many model evaluations. [ABSTRACT FROM AUTHOR]

Presents a response to the comment on sources and variations of mercury in Tuna.

The article presents the author's comment on a review made on a study concerning the effectiveness of famotidine, pizotifen, cognitive behavioural therapy, and peppermint in treating recurrent abdominal pain in children.

Strategies for managing climate-change risks impact diverse stakeholder groups that possess potentially conflicting preferences. Basic physics and economics suggest that reconciling all of these preference conflicts may not be possible. Moreover, different climate risk management strategies can yield diverse and potentially severe impacts across different global stakeholders. These preference conflicts and their uncertain impacts require an explicit understanding of the trade-offs that emerge across different risk management strategies. Traditionally, integrated assessment models (IAMs) typically aggregate the stakeholders' preferences across the entire globe into a single, a priori defined utility function. This framing hides climate risk management trade-offs as well as the inherent stakeholder compromises implicit to the resulting single 'optimal' expected utility solution. Here, we analyze a simple IAM to quantify and visualize the multidimensional trade-offs among four objectives representing global concerns: (i) global economic productivity, (ii) reliable temperature stabilization, (iii) climate damages, and (iv) abatement costs. We quantify and visualize the trade-offs across these objectives and demonstrate how a traditional optimal expected utility policy implicitly eliminates many relevant policy pathways. Explicit trade-off analysis provides a richer context for exploring conflicting global policy preferences and clarifies the implications of alternative climate risk mitigation policies to better inform negotiated compromises. [ABSTRACT FROM AUTHOR]

Managing ecosystems with deeply uncertain threshold responses and multiple decision makers poses nontrivial decision analytical challenges. The problem is imbued with deep uncertainties because decision makers do not know or cannot converge on a single probability density function for each key parameter, a perfect model structure, or a single adequate objective. The existing literature on managing multistate ecosystems has generally followed a normative decision-making approach based on expected utility maximization (MEU). This approach has simple and intuitive axiomatic foundations, but faces at least two limitations. First, a prespecified utility function is often unable to capture the preferences of diverse decision makers. Second, decision makers' preferences depart from MEU in the presence of deep uncertainty. Here, we introduce a framework that allows decision makers to pose multiple objectives, explore the trade-offs between potentially conflicting preferences of diverse decision makers, and to identify strategies that are robust to deep uncertainties. The framework, referred to as many-objective robust decision making (MORDM), employs multiobjective evolutionary search to identify trade-offs between strategies, re-evaluates their performance under deep uncertainty, and uses interactive visual analytics to support the selection of robust management strategies. We demonstrate MORDM on a stylized decision problem posed by the management of a lake in which surpassing a pollution threshold causes eutrophication. Our results illustrate how framing the lake problem in terms of MEU can fail to represent key trade-offs between phosphorus levels in the lake and expected economic benefits. Moreover, the MEU strategy deteriorates severely in performance for all objectives under deep uncertainties. Alternatively, the MORDM framework enables the discovery of strategies that balance multiple preferences and perform well under deep uncertainty. This decision analytic framework allows the decision makers to select strategies with a better understanding of their expected trade-offs (traditional uncertainty) as well as their robustness (deep uncertainty). [ABSTRACT FROM AUTHOR]

• Complex interactions among hazards, exposures, and vulnerabilities drive flood risks. • We use a global sensitivity analysis to identify key drivers of the uncertainties surrounding the projections of riverine flood risks. • In the considered case study, uncertainties surrounding flood hazards are the primary driver of uncertainties about projected flood risks. Designing strategies to manage flood risks is complicated by the often large uncertainties surrounding flood risk projections. Uncertainty surrounding riverine flood risks can stem from choices regarding boundary and initial conditions, model structures, and parameters as well as interactions among hazards, exposures, and vulnerabilities. Here we analyze a case study to rank the drivers of uncertainties surrounding riverine flood hazards and risks. Using Sobol' sensitivity analysis with a large number of simulations, we thoroughly explore the interactions among different sources of uncertainty. We find that the projected flood risk is most sensitive to factors associated with flood hazards, rather than exposure and vulnerability: upstream discharge, river bed elevation, channel roughness, and the digital elevation model resolution. Our results highlight the importance of uncertainty quantification in enhancing the reliability of flood models and risk assessments. [ABSTRACT FROM AUTHOR]

Probabilistic projections of baseline (with no additional mitigation policies) future carbon emissions are important for sound climate risk assessments. Deep uncertainty surrounds many drivers of projected emissions. Here, we use a simple integrated assessment model, calibrated to century-scale data and expert assessments of baseline emissions, global economic growth, and population growth, to make probabilistic projections of carbon emissions through 2100. Under a variety of assumptions about fossil fuel resource levels and decarbonization rates, our projections largely agree with several emissions projections under current policy conditions. Our global sensitivity analysis identifies several key economic drivers of uncertainty in future emissions and shows important higher-level interactions between economic and technological parameters, while population uncertainties are less important. Our analysis also projects relatively low global economic growth rates over the remainder of the century. This illustrates the importance of additional research into economic growth dynamics for climate risk assessment, especially if pledged and future climate mitigation policies are weakened or have delayed implementations. These results showcase the power of using a simple, transparent, and calibrated model. While the simple model structure has several advantages, it also creates caveats for our results which are related to important areas for further research. [ABSTRACT FROM AUTHOR]

Crop yields are sensitive to extreme weather events. Improving the understanding of the mechanisms and the drivers of the projection uncertainties can help to improve decisions. Previous studies have provided important insights, but often sample only a small subset of potentially important uncertainties. Here we expand on a previous statistical modeling approach by refining the analyses of two uncertainty sources. Specifically, we assess the effects of uncertainties surrounding crop-yield model parameters and climate forcings on projected crop yield. We focus on maize yield projections in the eastern U.S.in this century. We quantify how considering more uncertainties expands the lower tail of yield projections. We characterized the relative importance of each uncertainty source and show that the uncertainty surrounding yield model parameters is the main driver of yield projection uncertainty. [ABSTRACT FROM AUTHOR]

Biases in the depth estimation of expendable bathythermograph (XBT) measurements cause considerable errors in oceanic estimates of climate variables. Efforts are currently underway to improve XBT probes by including pressure switches. Information from these pressure measurements can be used to minimize errors in the XBT depth estimation. This paper presents a simple method to correct the XBT depth biases using a number of discrete pressure measurements. A blend of controlled simulations of XBT measurements and collocated XBT/CTD data is used along with statistical methods to estimate error parameters, and to optimize the use of pressure switches in terms of number of switches, optimal depth detection, and errors in the pressure switch measurements to most efficiently correct XBT profiles. The results show that given the typical XBT depth biases, using just two pressure switches is a reliable strategy for reducing depth errors, as it uses the least number of switches for an improved accuracy and reduces the variance of the resulting correction. Using only one pressure switch efficiently corrects XBT depth errors when the surface depth offset is small, its optimal location is at middepth (around or below 300 m), and the pressure switch measurement errors are insignificant. If two pressure switches are used, then results indicate that the measurements should be taken in the lower thermocline and deeper in the profile, at approximately 80 and 600 m, respectively, with an RMSE of approximately 1.6 m for pressure errors of 1 m. [ABSTRACT FROM AUTHOR]

We use an integrated assessment model of climate change to analyze how alternative decision-making criteria affect preferred investments into greenhouse gas mitigation, the distribution of outcomes, the robustness of the strategies, and the economic value of information. We define robustness as trading a small decrease in a strategy's expected performance for a significant increase in a strategy's performance in the worst cases. Specifically, we modify the Dynamic Integrated model of Climate and the Economy (DICE-07) to include a simple representation of a climate threshold response, parametric uncertainty, structural uncertainty, learning, and different decision-making criteria. Economic analyses of climate change strategies typically adopt the expected utility maximization (EUM) framework. We compare EUM with two decision criteria adopted from the finance literature, namely Limited Degree of Confidence (LDC) and Safety First (SF). Both criteria increase the relative weight of the performance under the worst-case scenarios compared to EUM. We show that the LDC and SF criteria provide a computationally feasible foundation for identifying greenhouse gas mitigation strategies that may prove more robust than those identified by the EUM criterion. More robust strategies show higher near-term investments in emissions abatement. Reducing uncertainty has a higher economic value of information for the LDC and SF decision criteria than for EUM. [ABSTRACT FROM AUTHOR]

Abstract: The statistical distributions of cosmogenic nuclide measurements from moraine boulders contain previously unused information on moraine ages, and they help determine whether moraine degradation or inheritance is more important on individual moraines. Here, we present a method for extracting this information by fitting geomorphic process models to observed exposure ages from single moraines. We also apply this method to 94 10Be apparent exposure ages from 11 moraines reported in four published studies. Our models represent 10Be accumulation in boulders that are exhumed over time by slope processes (moraine degradation), and the delivery of boulders with preexisting 10Be inventories to moraines (inheritance). For now, we neglect boulder erosion and snow cover, which are likely second-order processes. Given a highly scattered data set, we establish which model yields the better fit to the data, and estimate the age of the moraine from the better model fit. The process represented by the better-fitting model is probably responsible for most of the scatter among the apparent ages. Our methods should help resolve controversies in exposure dating; we reexamine the conclusions from two published studies based on our model fits. [Copyright &y& Elsevier]

The authors examine recent changes in three agro-climate indices (frost days, thermal time, and heat stress index) in North America (centered around the continental United States) using observations from a historical climate network and an ensemble of 17 global climate models (GCMs) from the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Agro-climate indices provide the basis for analyzing agricultural time series that are unbiased by long-term technological intervention. Observations from the last 60 years (1951-2010) confirm conclusions of previous studies showing continuing declines in the number of frost days and increases in thermal time. Increases in heat stress are largely confined to the western half of the continent. The authors do not observe accelerating agro-climate warming trends in the most recent decade of observations. The spatial variability of the temporal trends in GCMs is lower compared to the observed patterns, which still show some regional cooling trends. GCM skill, defined as the ability to reproduce observed patterns (i.e., correlation and error) and variability, is highest for frost days and lowest for heat stress patterns. Individual GCM skill is incorporated into two model weighting schemes to gauge their ability to reduce predictive uncertainty for agro-climate indices. The two weighted GCM ensembles do not substantially improve results compared to the unweighted ensemble mean. The lack of agreement between simulated and observed heat stress is relatively robust with respect to how the heuristic is defined and appears to reflect a weakness in the ability of this last generation of GCMs to reproduce this impact-relevant aspect of the climate system. However, it remains a question for future work as to whether the discrepancies between observed and simulated trends primarily reflect fundamental errors in model physics or an incomplete treatment of relevant regional climate forcings. [ABSTRACT FROM AUTHOR]

Anthropogenic greenhouse gas emissions are changing the Earth's climate and impose substantial risks for current and future generations. What are scientifically sound, economically viable, and ethically defendable strategies to manage these climate risks? Ratified international agreements call for a reduction of greenhouse gas emissions to avoid dangerous anthropogenic interference with the climate system. Recent proposals, however, call for a different approach: to geoengineer climate by injecting aerosol precursors into the stratosphere. Published economic studies typically neglect the risks of aerosol geoengineering due to (i) the potential for a failure to sustain the aerosol forcing and (ii) the negative impacts associated with the aerosol forcing. Here we use a simple integrated assessment model of climate change to analyze potential economic impacts of aerosol geoengineering strategies over a wide range of uncertain parameters such as climate sensitivity, the economic damages due to climate change, and the economic damages due to aerosol geoengineering forcing. The simplicity of the model provides the advantages of parsimony and transparency, but it also imposes severe caveats on the interpretation of the results. For example, the analysis is based on a globally aggregated model and is hence silent on intragenerational distribution of costs and benefits. In addition, the analysis neglects the effects of learning and has a very simplistic representation of climate change impacts. Our analysis suggests three main conclusions. First, substituting aerosol geoengineering for CO abatement can be an economically ineffective strategy. One key to this finding is that a failure to sustain the aerosol forcing can lead to sizeable and abrupt climatic changes. The monetary damages due to such a discontinuous aerosol geoengineering can dominate the cost-benefit analysis because the monetary damages of climate change are expected to increase with the rate of change. Second, the relative contribution of aerosol geoengineering to an economically optimal portfolio hinges critically on, thus far, deeply uncertain estimates of the damages due to aerosol forcing. Even if we assume that aerosol forcing could be deployed continuously, the aerosol geoengineering does not considerably displace CO abatement in the simple economic optimal growth model until the damages due to the aerosol forcing are rather low. Third, substituting aerosol geoengineering for greenhouse gas emission abatement can fail an ethical test regarding intergenerational justice. Substituting aerosol geoengineering for greenhouse gas emissions abatements constitutes a conscious risk transfer to future generations, in violation of principles of intergenerational justice which demands that present generations should not create benefits for themselves in exchange for burdens on future generations. [ABSTRACT FROM AUTHOR]