Search

Emerging fishing activity due to melting ice and poleward species distribution shifts in the rapidly‐warming Arctic Ocean challenges transboundary management and requires proactive governance. A 2021 moratorium on commercial fishing in the Arctic high seas provides a 16‐year runway for improved scientific understanding. Given substantial knowledge gaps, characterizing areas of highest uncertainty is a key first step. Marine ecosystem model ensembles that project future fish distributions could inform management of future Arctic fisheries, but Arctic‐specific variation has not yet been examined for global ensembles. We use the Fisheries and Marine Ecosystem Intercomparison Project ensemble driven by two Earth System Models (ESMs) under two Shared Socioeconomic Pathways (SSP1‐2.6 and SSP5‐8.5) to illustrate the current state of and uncertainty among biomass projections for the Arctic Ocean over the duration of the moratorium. The models generally project biomass increases in more northern Arctic ecosystems and decreases in southern ecosystems, but wide intra‐model variation exceeds projection means in most cases. The two ESMs show opposite trends for the main environmental drivers. Therefore, these projections are currently insufficient to inform policy actions. Investment in sustained monitoring and improving modeling capacity, especially for sea ice dynamics, is urgently needed. Concurrently, it will be necessary to develop frameworks for making precautionary decisions under continued uncertainty. We conclude that researchers should be transparent about uncertainty, presenting these model projections not as a source of scientific "answers," but as bounding for plausible, policy‐relevant questions to assess trade‐offs and mitigate risks. Plain Language Summary: As the Arctic Ocean gets warmer, melting ice is opening up new opportunities for fishing. However, we don't know where fish will go and how they can be managed sustainably. An important first step is to figure out which unknowns we can solve quickly with more research, and what is so uncertain that we will have to make decisions without ideal information. In this paper, we looked at uncertainty in a set of global models that predict how fish populations might shift in the next 10–25 years. Overall, these models show that fish populations might increase in the northern parts of the Arctic while decreasing in the south. But the models make very different predictions, and some disagree on whether fish populations will increase or decrease in certain areas. A major source of uncertainty is how sea ice will change, and how ocean life will respond. Therefore, this is a priority area to invest in long‐term research and better models. Overall, these models are too uncertain to rely on for specific management decisions about Arctic fishing. Instead, scientists and decision makers can use them to shape more informed discussions about potential trade‐offs and risks of future fishing in the Arctic. Key Points: Variation and disagreement in marine ecosystem model projections are too high to be informative for near‐term Arctic fisheries managementInsufficient inclusion and knowledge of sea ice cover and sea ice productivity dynamics are major drivers of uncertaintyResearchers should be transparent about uncertainty and risk; present model projections as the basis for hypotheses and scenario planning [ABSTRACT FROM AUTHOR]

In recent years considerable interest has been generated in a new approach known as resist–accept–direct, or RAD, for managing ecosystems in the face of climate change. Under RAD, strategic responses to climate change are described in terms of three broad categories: resisting climate transformation, accepting the transformation and continuing to manage as best one can, and directing the transformed system toward novel ecological conditions. In particular, the potential for integrating RAD and adaptive management has been broadly considered, though absent a decision‐making framework needed for implementation.We propose a hierarchical decision scheme for RAD that accounts for strategy selection among the three RAD options, as well as adaptive decision making within each option. We use stochastic models and uncertainties about ecosystem processes to account for the dynamics of climate‐transformed ecosystems, and show how these features can be used to inform RAD strategies. Operationally, the approach involves decisions at two levels: one level involves choosing a policy for each strategy, and the second level involves deciding which strategy has the greatest policy value.The structure described here extends recent work in climate change adaptation, by including Markovian decisions under climate change, strategy‐specific policies, and value functions for assessing and selecting RAD strategies. We provide a hierarchical accounting of decisions and responses, and develop rules for the timing of those decisions.Combining RAD and adaptive management can help to organize thinking about ecological conservation under climate change, and focus attention on mechanisms for making decisions. We believe the structure presented here can facilitate conservation efforts under the non‐stationary climate conditions we are sure to face for the foreseeable future. [ABSTRACT FROM AUTHOR]

Abstract Emerging fishing activity due to melting ice and poleward species distribution shifts in the rapidly‐warming Arctic Ocean challenges transboundary management and requires proactive governance. A 2021 moratorium on commercial fishing in the Arctic high seas provides a 16‐year runway for improved scientific understanding. Given substantial knowledge gaps, characterizing areas of highest uncertainty is a key first step. Marine ecosystem model ensembles that project future fish distributions could inform management of future Arctic fisheries, but Arctic‐specific variation has not yet been examined for global ensembles. We use the Fisheries and Marine Ecosystem Intercomparison Project ensemble driven by two Earth System Models (ESMs) under two Shared Socioeconomic Pathways (SSP1‐2.6 and SSP5‐8.5) to illustrate the current state of and uncertainty among biomass projections for the Arctic Ocean over the duration of the moratorium. The models generally project biomass increases in more northern Arctic ecosystems and decreases in southern ecosystems, but wide intra‐model variation exceeds projection means in most cases. The two ESMs show opposite trends for the main environmental drivers. Therefore, these projections are currently insufficient to inform policy actions. Investment in sustained monitoring and improving modeling capacity, especially for sea ice dynamics, is urgently needed. Concurrently, it will be necessary to develop frameworks for making precautionary decisions under continued uncertainty. We conclude that researchers should be transparent about uncertainty, presenting these model projections not as a source of scientific “answers,” but as bounding for plausible, policy‐relevant questions to assess trade‐offs and mitigate risks.

Moving to a low carbon society requires pro-active decisions to transform social and physical systems and their supporting infrastructure. However, the inherent complexity of these systems leads to uncertainty in their responses to interventions, and their critical societal role means that stakes are high. Techniques for decision making under deep uncertainty (DMDU) have recently begun to be applied in the context of transformation to a low carbon society. Applying DMDU to support transformation necessitates careful attention to uncertainty in system relationships (structural uncertainty), and to actions targeting deep leverage points to transform system relationships. This paper presents outcomes of a structured literature review of 44 case studies in which DMDU is applied to infrastructure decisions. Around half of these studies are found to neglect structural uncertainty entirely, and no study explicitly considers alternative system conceptions. Three quarters of studies consider actions targeting only parameters, a shallow leverage point for system transformation. Where actions targeting deeper leverage points are included, models of system relationships are unable to represent the transformative change these interventions could effect. The lack of attention to structural uncertainty in these studies could lead to misleading results in complex and poorly understood systems. The lack of interventions targeting deep leverage points could lead to neglect of some of the most effective routes to achieving transformative change. This review recommends greater attention to deeper leverage points and structural uncertainty in applications of DMDU targeting transformative change.

Emerging insights into factors responsible for soil organic matter stabilization and decomposition are being applied in a variety of contexts, but new tools are needed to facilitate the understanding, evaluation, and improvement of soil biogeochemical theory and models at regional to global scales. To isolate the effects of model structural uncertainty on the global distribution of soil carbon stocks and turnover times we developed a soil biogeochemical testbed that forces three different soil models with consistent climate and plant productivity inputs. The models tested here include a first-order, microbial implicit approach (CASA-CNP), and two recently developed microbially explicit models that can be run at global scales (MIMICS and CORPSE). When forced with common environmental drivers, the soil models generated similar estimates of initial soil carbon stocks (roughly 1,400 Pg C globally, 0-100 cm), but each model shows a different functional relationship between mean annual temperature and inferred turnover times. Subsequently, the models made divergent projections about the fate of these soil carbon stocks over the 20th century, with models either gaining or losing over 20 Pg C globally between 1901 and 2010. Single-forcing experiments with changed inputs, temperature, and moisture suggest that uncertainty associated with freeze-thaw processes as well as soil textural effects on soil carbon stabilization were larger than direct temperature uncertainties among models. Finally, the models generated distinct projections about the timing and magnitude of seasonal heterotrophic respiration rates, again reflecting structural uncertainties that were related to environmental sensitivities and assumptions about physicochemical stabilization of soil organic matter. By providing a computationally tractable and numerically consistent framework to evaluate models we aim to better understand uncertainties among models and generate insights about factors regulating the turnover of soil organic matter.

Sustainable human exploitation of living marine resources stems from a delicate balance between yield stability and population persistence to achieve socioeconomic and conservation goals. But our imperfect knowledge of how oceanic oscillations regulate temporal variation in an exploited species can obscure the risk of missing management targets. We illustrate how applying a management policy to suppress fluctuations in fishery yield in variable environments (prey density and regional climate) can present unintended outcomes in harvested predators and the sustainability of harvesting. Using Atlantic cod (Gadus morhua, an apex predatory fish) in the Barents Sea as a case study we simulate age‐structured population and harvest dynamics through time‐varying, density‐dependent and density‐independent processes with a stochastic, process‐based model informed by 27‐year monitoring data. In this model, capelin (Mallotus villosus, a pelagic forage fish), a primary prey of cod, fluctuations modulate the strength of density‐dependent regulation primarily through cannibalistic pressure on juvenile cod survival; sea temperature fluctuations modulate thermal regulation of cod feeding, growth, maturation, and reproduction. We first explore how capelin and temperature fluctuations filtered through cod intrinsic dynamics modify catch stability and then evaluate how management to suppress short‐term variability in catch targets alters overharvest risk. Analyses revealed that suppressing year‐to‐year catch variability impedes management responses to adjust fishing pressure, which becomes progressively out of sync with variations in cod abundance. This asynchrony becomes amplified in fluctuating environments, magnifying the amplitudes of both fishing pressure and cod abundance and then intensifying the density‐dependent regulation of juvenile survival through cannibalism. Although these transient dynamics theoretically give higher average catches, emergent, quasicyclic behaviors of the population would increase long‐term yield variability and elevate overharvest risk. Management strategies that overlook the interplay of extrinsic (fishing and environment) and intrinsic (life history and demography) fluctuations thus can inadvertently destabilize fish stocks, thereby jeopardizing the sustainability of harvesting. These policy implications underscore the value of ecosystem approaches to designing management measures to sustainably harvest ecologically connected resources while achieving socioeconomic security. [ABSTRACT FROM AUTHOR]

Numerical simulations of the global climate system provide inputs to integrated assessment modeling for estimating the impacts of greenhouse gas mitigation and other policies to address global climate change. While essential tools for this purpose, computational climate models are subject to considerable uncertainty, including intermodel "structural" uncertainty. Structural uncertainty analysis has emphasized simple or weighted averaging of the outputs of multimodel ensembles, sometimes with subjective Bayesian assignment of probabilities across models. However, choosing appropriate weights is problematic. To use climate simulations in integrated assessment, we propose, instead, framing climate model uncertainty as a problem of partial identification, or "deep" uncertainty. This terminology refers to situations in which the underlying mechanisms, dynamics, or laws governing a system are not completely known and cannot be credibly modeled definitively even in the absence of data limitations in a statistical sense. We propose the min-max regret (MMR) decision criterion to account for deep climate uncertainty in integrated assessment without weighting climate model forecasts. We develop a theoretical framework for cost-benefit analysis of climate policy based on MMR, and apply it computationally with a simple integrated assessment model. We suggest avenues for further research. [ABSTRACT FROM AUTHOR]

Climate projections are uncertain; this uncertainty is costly and impedes progress on climate policy. This uncertainty is primarily parametric (what numbers do we plug into our equations?), structural (what equations do we use in the first place?), and due to internal variability (natural variability intrinsic to the climate system). The former and latter are straightforward to characterise in principle, though may be computationally intensive for complex climate models. The second is more challenging to characterise and is therefore often ignored. We developed a Bayesian approach to quantify structural uncertainty in climate projections, using the idealised energy-balance model representations of climate physics that underpin many economists’ integrated assessment models (IAMs) (and therefore their policy recommendations). We define a model selection parameter, which switches on one of a suite of proposed climate nonlinearities and multidecadal climate feedbacks. We find that a model with a temperature-dependent climate feedback is most consistent with global mean surface temperature observations, but that the sign of the temperature-dependence is opposite of what Earth system models suggest. This difference of sign is likely due to the assumption tha the recent pattern effect can be represented as a temperature dependence. Moreover, models other than the most likely one contain a majority of the posterior probability, indicating that structural uncertainty is important for climate projections. Indeed, in projections using shared socioeconomic pathways similar to current emissions reductions targets, structural uncertainty dwarfs parametric uncertainty in temperature. Consequently, structural uncertainty dominates overall non-socioeconomic uncertainty in economic projections of climate change damages, as estimated from a simple temperature-to-damages calculation. These results indicate that considering structural uncertainty is crucial for IAMs in particular, and for climate projections in general.

Highlights • Comparison of two hydrological modeling studies at the local scale in the Everest region. • At high altitudes, snow-pack is the main driver of the discrepancy between the two models. • The time step impacts the snow-pack simulation, in particular during the summer season. • Estimations of soil processes are associated with high uncertainties due to models structure. Abstract This paper compares the hydrological responses at the local scale of two models using different degrees of refinement to represent physical processes in sparsely instrumented mountainous Himalayan catchments. This work presents the novelty of applying, at a small spatio-temporal scale and under the same forcing conditions, a fully distributed surface scheme based on mass and energy balance equations (ISBA surface scheme), and a semi-distributed calibrated model (J2000 hydrological model). A new conceptual module coupled to the ISBA surface scheme for flow routing is presented. Two small catchments located in mid- and high- mountain environments were chosen to represent the very different climatic and physiographic characteristics of the Central Himalayas in the Everest region of eastern Nepal. The results show that both models globally represent the dynamic of the processes for evaporation, quick runoff and discharge in a similar way. The differences in the model structures and results mainly concern the snow processes and the soil processes. In particular for the high-mountain catchment, the snow-pack simulation is shown to be the main driver of the discrepancy between the two models. The sub-daily variations of snow processes are shown to significantly influence the estimation of the snow-melt contribution to discharge. [ABSTRACT FROM AUTHOR]

Benefit-cost integrated assessment models (BC-IAMs) inform climate policy debates by quantifying the trade-offs between alternative greenhouse gas abatement options. They achieve this by coupling simplified models of the climate system to models of the global economy and the costs and benefits of climate policy. Although these models have provided valuable qualitative insights into the sensitivity of policy trade-offs to different ethical and empirical assumptions, they are increasingly being used to inform the selection of policies in the real world. To the extent that BC-IAMs are used as inputs to policy selection, our confidence in their quantitative outputs must depend on the empirical validity of their modeling assumptions. We have a degree of confidence in climate models both because they have been tested on historical data in hindcasting experiments and because the physical principles they are based on have been empirically confirmed in closely related applications. By contrast, the economic components of BC-IAMs often rely on untestable scenarios, or on structural models that are comparatively untested on relevant time scales. Where possible, an approach to model confirmation similar to that used in climate science could help to build confidence in the economic components of BC-IAMs, or focus attention on which components might need refinement for policy applications. We illustrate the potential benefits of model confirmation exercises by performing a long-run hindcasting experiment with one of the leading BC-IAMs.We show that its model of long-run economic growth--one of its most important economic components--had questionable predictive power over the 20th century. [ABSTRACT FROM AUTHOR]

Projections of the impacts of climate change on marine ecosystems are a key prerequisite for the planning of adaptation strategies, yet they are inevitably associated with uncertainty. Identifying, quantifying, and communicating this uncertainty is key to both evaluating the risk associated with a projection and building confidence in its robustness. We review how uncertainties in such projections are handled in marine science. We employ an approach developed in climate modelling by breaking uncertainty down into (i) structural (model) uncertainty, (ii) initialization and internal variability uncertainty, (iii) parametric uncertainty, and (iv) scenario uncertainty. For each uncertainty type, we then examine the current state-of-the-art in assessing and quantifying its relative importance. We consider whether the marine scientific community has addressed these types of uncertainty sufficiently and highlight the opportunities and challenges associated with doing a better job. We find that even within a relatively small field such as marine science, there are substantial differences between subdisciplines in the degree of attention given to each type of uncertainty. We find that initialization uncertainty is rarely treated explicitly and reducing this type of uncertainty may deliver gains on the seasonal-to-decadal time-scale.Weconclude that all parts of marine science could benefit from a greater exchange of ideas, particularly concerning such a universal problem such as the treatment of uncertainty. Finally, marine science should strive to reach the point where scenario uncertainty is the dominant uncertainty in our projections. [ABSTRACT FROM AUTHOR]

This study investigates the performance of two planetary boundary layer (PBL) parameterisations in the regional climate model RegCM4.2 with specific focus on the recently implemented prognostic turbulent kinetic energy parameterisation scheme: the University of Washington (UW) scheme. When compared with the default Holtslag scheme, the UW scheme, in the 10-year experiments over the European domain, shows a substantial cooling. It reduces winter warm bias over the north-eastern Europe by 2 °C and reduces summer warm bias over central Europe by 3 °C. A part of the detected cooling is ascribed to a general reduction in lower tropospheric eddy heat diffusivity with the UW scheme. While differences in temperature tendency due to PBL schemes are mostly localized to the lower troposphere, the schemes show a much higher diversity in how vertical turbulent mixing of the water vapour mixing ratio is governed. Differences in the water vapour mixing ratio tendency due to the PBL scheme are present almost throughout the troposphere. However, they alone cannot explain the overall water vapour mixing ratio profiles, suggesting strong interaction between the PBL and other model parameterisations. An additional 18-member ensemble with the UW scheme is made, where two formulations of the master turbulent length scale in unstable conditions are tested and unconstrained parameters associated with (a) the evaporative enhancement of the cloud-top entrainment and (b) the formulation of the master turbulent length scale in stable conditions are systematically perturbed. These experiments suggest that the master turbulent length scale in the UW scheme could be further refined in the current implementation in the RegCM model. It was also found that the UW scheme is less sensitive to the variations of the other two selected unconstrained parameters, supporting the choice of these parameters in the default formulation of the UW scheme. [ABSTRACT FROM AUTHOR]

Uncertainties in terrestrial carbon (C) cycle projections increase uncertainty of potential climate feedbacks. Efforts to improve model performance often include increased representation of biogeochemical processes, such as coupled carbon–nitrogen (N) cycles. In doing so, models are becoming more complex, generating structural uncertainties in model form that reflect incomplete knowledge of how to represent underlying processes. Here, we explore structural uncertainties associated with biological nitrogen fixation (BNF) and quantify their effects on C cycle projections. We find that alternative plausible structures to represent BNF result in nearly equivalent terrestrial C fluxes and pools through the twentieth century, but the strength of the terrestrial C sink varies by nearly a third (50 Pg C) by the end of the twenty-first century under a business-as-usual climate change scenario representative concentration pathway 8.5. These results indicate that actual uncertainty in future C cycle projections may be larger than previously estimated, and this uncertainty will limit C cycle projections until model structures can be evaluated and refined.

• Experimental study identifying uncertainty sources in FishMIP global model ensemble. • Warming and lower trophic level (LTL) impacts on model predictions isolated. • Coupling of lower and higher trophic levels a key driver of model warming response. • LTL impacts driven primarily by each model's choice of LTL driver. • Overall climate projections mostly a linear combination of warming and LTL impacts. Climate change is warming the ocean and impacting lower trophic level (LTL) organisms. Marine ecosystem models can provide estimates of how these changes will propagate to larger animals and impact societal services such as fisheries, but at present these estimates vary widely. A better understanding of what drives this inter-model variation will improve our ability to project fisheries and other ecosystem services into the future, while also helping to identify uncertainties in process understanding. Here, we explore the mechanisms that underlie the diversity of responses to changes in temperature and LTLs in eight global marine ecosystem models from the Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP). Temperature and LTL impacts on total consumer biomass and ecosystem structure (defined as the relative change of small and large organism biomass) were isolated using a comparative experimental protocol. Total model biomass varied between −35% to +3% in response to warming, and -17% to +15% in response to LTL changes. There was little consensus about the spatial redistribution of biomass or changes in the balance between small and large organisms (ecosystem structure) in response to warming, an LTL impacts on total consumer biomass varied depending on the choice of LTL forcing terms. Overall, climate change impacts on consumer biomass and ecosystem structure are well approximated by the sum of temperature and LTL impacts, indicating an absence of nonlinear interaction between the models' drivers. Our results highlight a lack of theoretical clarity about how to represent fundamental ecological mechanisms, most importantly how temperature impacts scale from individual to ecosystem level, and the need to better understand the two-way coupling between LTL organisms and consumers. We finish by identifying future research needs to strengthen global marine ecosystem modelling and improve projections of climate change impacts. [ABSTRACT FROM AUTHOR]

Integrated assessment (IA) modelling can be an effective tool to gain insight into the dynamics of coupled earth system (land use, climate etc.) and socio-economic components. Quantifying and communicating uncertainties is a challenge of any scientific assessment, but is here magnified by the complex and boundary-crossing nature of IA models. Understanding the dynamics of coupled earth and socio-economic systems require data and methods from multiple disciplines, each with its own perspective on epistemological uncertainties (parametric and structural uncertainties), and its own protocols for assessing uncertainty. During the Paris Agreement, the lack of uncertainty analyses (UA) in IAs was risen (Rogelj et al. 2017) and calls for close collaboration of scientists coming from different fields. In this study, we review how uncertainties are tackled in a range of science disciplines that are related to global change including climate, hydrology, energy and land use, and which contribute to IA modelling. We conducted a meta-analysis to identify the contributing disciplines, and review which type of uncertainties are assessed. We then describe sources of uncertainty (e.g. parameter values, model structure), and present opportunities for improved assessment and communication of uncertainties in IA modelling. We show in our meta-analysis that parametric uncertainty is the uncertainty analysis that has been applied the most, while structural uncertainty is less commonly applied, with the exception of the energy scientific discipline. We finish our study with key recommendations to improve uncertainty analysis such as including risk analysis. By embracing uncertainties, resilient and effective solutions for climate change mitigation and adaptation could be better communicated, identified and implemented. [ABSTRACT FROM AUTHOR]