Uncertainty In Projections Research Articles

Observational Constraints on Basin‐Scale Runoff: A Request for Both Improved ESMs and Streamflow Reconstructions

AbstractEfforts to predict long‐term changes in continental runoff at both global and basin scales generally remain ambiguous. Here we use a global runoff reconstruction and a Bayesian statistical method to narrow uncertainties in runoff projections from the latest generation of global climate models. Three representative tropical river basins are used to illustrate the application and showcase the potential for substantial reduction in modeling uncertainty. Yet, results are fairly sensitive to the selected reconstruction thus highlighting the need for reliable and homogeneized gridded runoff data sets or river discharge measurements. Moreover, climate models do not account for water withdrawals, whose effect on observed runoff should also be removed in order to detect and attribute the hydrological effect of climate change. Finally, and more importantly, most models fail at capturing the observed recent decrease in runoff ratio, which may highlight either model deficiencies or increasing water derivation over the selected river basins.

Duration and resource constraint prediction models for construction projects using regression machine learning method

PurposeThe construction projects are highly subjected to uncertainties, which result in overruns in time and cost. Realistic estimates of workforce and duration are imperative for construction projects to attain their intended objectives. The aim of this study is to provide accurate labor and duration estimates for the construction projects, considering actual uncertainties.Design/methodology/approachThe dataset was formulated from the information collected from 186 construction projects through direct interviews, group discussions and questionnaire methods. The actual uncertainties and exposure conditions of construction activities were recorded. The data were verified with the standard guideline to remove the outliers. The prediction model was developed using support vector regression (SVR), a machine learning (ML) method. The performance was evaluated using the widely adopted regression metrics. Further, the cross validation was made with the visualization of residuals and predicted errors, ridge regression with transformed target distribution and a Gaussian Naive Bayes (NB) regressor.FindingsThe prediction models predicted the duration and labor requirements with the consideration of actual uncertainties. The residual plot indicated the appropriate use of SVR to develop the prediction model. The duration (DC) and resource constraint (RC) prediction models obtained 80 and 82% accuracy, respectively. Besides, the developed model obtained better accuracy for the training and test scores than the Gaussian NB regressor. Further, the range of the explained variance score and R2 was from 0.95 to 0.97, indicating better efficiency compared with other prediction models.Research limitations/implicationsThe researchers will utilize the research findings to estimate the duration and labor requirements under uncertain conditions and further improve the construction project management practices.Practical implicationsThe research findings will enable industry practitioners to accurately estimate the duration and labor requirements, considering historical uncertain conditions. A precise estimation of resources will ensure the attainment of the intended project outcomes.Social implicationsDelays in construction projects will be reduced by implementing the research findings, which significantly ensures the effective utilization of resources and attainment of other economic benefits. The policymakers will develop a guideline to develop a database to collect the uncertainties of the construction projects and relatively estimate the resource requirements.Originality/valueThis is the first study to consider the actual uncertainties of construction projects to develop RC and DC prediction models. The developed prediction models accurately estimate the duration and labor requirements with minimal computational time. The industry practitioners will be able to accurately estimate the duration and labor requirements using the developed models.

Impact of Asian aerosols on the summer monsoon strongly modulated by regional precipitation biases

Abstract. Reliable attribution of Asian summer monsoon variations to aerosol forcing is critical to reducing uncertainties in future projections of regional water availability, which is of utmost importance for risk management and adaptation planning in this densely populated region. Yet, simulating the monsoon remains a challenge for climate models that suffer from long-standing biases, undermining their reliability in attributing anthropogenically forced changes. We analyze a suite of climate model experiments to identify a link between model biases and monsoon responses to Asian aerosols and associated physical mechanisms, including the role of large-scale circulation changes. The aerosol impact on monsoon precipitation and circulation is strongly influenced by a model's ability to simulate the spatio-temporal variability in the climatological monsoon winds, clouds, and precipitation across Asia, which modulates the magnitude and efficacy of aerosol–cloud–precipitation interactions, an important component of the total aerosol response. There is a strong interplay between South Asia and East Asia monsoon precipitation biases and their relative predominance in driving the overall monsoon response. We found a striking contrast between the early- and late-summer aerosol-driven changes ascribable to opposite signs and seasonal evolution of the biases in the two regions. A realistic simulation of the evolution of the large-scale atmospheric circulation is crucial to realize the full extent of the aerosol impact over Asia. These findings provide important implications for better understanding and constraining the diversity and inconsistencies of model responses to aerosol changes over Asia in historical simulations and future projections.

Seawater oxygen isotopes as a tool for monitoring future meltwater from the Antarctic ice-sheet

To reduce uncertainties in future sea level projections, it is necessary to closely monitor the evolution of the Antarctic ice-sheet. Here, we show that seawater oxygen isotopes are an effective tool to monitor ice-sheet freshwater discharge and its contributions to sea level rise. Using the isotope-enabled Community Earth System Model with imposed estimates of future meltwater fluxes, we find that the anthropogenic ice-sheet signal in water isotopes emerges above natural variability decades earlier than salinity-based estimates. The superiority of seawater isotopes over salinity in detecting the ice-sheet melting can be attributed to the higher signal-to-noise ratio of the former and the fact that future sea ice changes only contribute little to seawater isotopes but a lot to salinity. We conclude that in particular in the Ross Sea sector, continuous seawater oxygen isotope measurements could serve as an early warning system for rapid anthropogenic Antarctic ice-sheet mass loss.

Shallow cumulus cloud fields are optically thicker when they are more clustered

AbstractShallow trade cumuli over subtropical oceans are a persistent source of uncertainty in climate projections. Mesoscale organization of trade cumulus clouds has been shown to influence their cloud radiative effect (CRE) through cloud cover. We investigate whether organization can explain CRE variability independently of cloud‐cover variability. By analyzing satellite observations and high‐resolution simulations, we show that more clustered cloud fields feature geometrically thicker clouds with larger domain‐averaged liquid water paths, smaller cloud droplets, and consequently larger cloud optical depths. The relationships between these variables are shaped by the mixture of deep cloud cores and shallower interstitial clouds or anvils that characterize cloud organization. Eliminating cloud‐cover effects, more clustered clouds reflect up to 20 W/m more instantaneous shortwave radiation back to space.

Potential impacts of climate change on ecosystem services in Austria

Climate change is considered a major driver for environmental changes and impacts on human well-being. Understanding the relationships between climatic changes and ecosystem services (ES) is therefore crucial to develop effective adaption and mitigation measures. However, studies that comprehensively assess climate change impacts on ES, providing also spatially explicit information, are greatly lacking. To address this gap, this study aims at assessing and mapping potential impacts on multiple ES in Central Europe, using the example of Austria. Our analysis steps included (1) mapping provisioning, regulating, and cultural ES (n = 19) using a land use/cover-based approach, (2) deriving potential impacts on indicators (n = 58), used to assess ES, through a literature analysis, (3) mapping potential impacts on ES in qualitative terms, and (4) analysing spatial patterns across Austria. Our results indicate that, in particular, water-related ES and cultural ES will decline, while some provisioning and regulating ES will improve. The spatial analysis revealed that regions located in the south-eastern parts of Austria will be potentially affected the most by climate change impacts, while less ES will decline in western mountain regions. Our findings contribute to the knowledge base for decision-making at different governance levels, supporting the development of policies and management strategies at the national and international level, as well as fostering communication with stakeholders and the elaboration of targeted management plans at the local and regional level. Our proposed mapping approach is easily transferable to other regions, but future research should address current limitations related to uncertainties in climate projections, the clear distinction of climate-induced impacts, and the role of climate-related hazards.

The difference in the uncertainty sources between future projections of mean and extreme precipitation over East Asia

As the incidence of extreme precipitation events attributable to global climate change increases, providing policymakers with accurate model predictions is of the utmost importance. However, model projections have inherent uncertainties. The present study attempted to distinguish the sources of the uncertainty of the mean and extreme precipitation projections in the East Asia region using the mean boreal summer precipitation, simple precipitation intensity index (SDII), maximum cumulative 5 day precipitation, and annual maximum daily precipitation (Rx1d). The results show that while the mean precipitation was projected to change very little regardless of the scenario, more extreme indices were projected to increase considerably by the end of the century, particularly in the high-emissions scenarios. On average, model uncertainty accounted for the largest part of the uncertainty. However, for Rx1d in the 2030s, as well as mean and SDII in some regions until the 2060s, the internal variability was the largest contributor. In addition, whilst scenario uncertainty accounted for a negligible proportion of average precipitation variability, for the more extreme the precipitation indices, scenario uncertainty contribution to total variability by the end of the century was significant; namely, the scenario uncertainty contribution was overall highest for the maximum one-day precipitation. Additionally, comparatively wetter regions had greater overall projection uncertainties, especially uncertainty arising from internal variability, likely due to the influence of interannual variability from the EA summer monsoon.

Possible shift in controls of the tropical Pacific surface warming pattern.

Changes in the sea surface temperature (SST) pattern in the tropical Pacific modulate radiative feedbacks to greenhouse gas forcing, the pace of global warming and regional climate impacts. Therefore, elucidating the drivers of the pattern is critically important for reducing uncertainties in future projections. However, the causes of observed changes over recent decades, an enhancement of the zonal SST contrast coupled with a strengthening of the Walker circulation, are still debated. Here we focus on the role of external forcing and review existing mechanisms of the forced response categorized as either an energy perspective that adopts global and hemispheric energy budget constraints or a dynamical perspective that examines the atmosphere-ocean coupled processes. We then discuss their collective andrelative contributions to the past and future SST pattern changes and propose a narrative that reconciles them. Although definitive evidence is not yet available, our assessment suggests that the zonal SST contrast has been dominated by strengthening mechanisms in the past, but will shift towards being dominated byweakening mechanisms in the future. Finally, we present opportunities to resolve the model-observations discrepancy regarding the recent trends.

Breaking the Sisyphean loop: reconceptualizing the treatment of risk and uncertainty in transport projects

Worldwide transport projects repeatedly exceed their budgets. This problem has received widespread attention over several decades, with little progress made to improve project cost performance. Consequently, this has led to the emergence of the following recurring question: How can public agencies responsible for delivering transport projects improve the estimation accuracy of their actual construction costs? We use the metaphor of the Sisyphean task to assist us in suggesting that the conflation of risk and uncertainty results in an inability to estimate actual construction costs accurately. Ambiguity aversion, an illusion of certainty, and the repeated occurrence of cost deviations, as demonstrated by our illustrative example and analysis of the elemental cost models of 39 transport projects, collectively create a Sisyphean loop that is impossible to break. Thus, we question the prevailing conceptualisation and treatment of risk and uncertainty used to estimate the actual construction costs of projects. We offer a new theoretical lens framed around ecologically rational heuristics and provide suggestions to reconceptualize and recalibrate the treatment and assessment of uncertainty in cost estimates. We hope this paper provides a much-needed circuit breaker to public agencies’ prevailing treatment of risk and uncertainty and stimulates new lines of inquiry to improve the cost performance of transport projects.

Verification, validation, and uncertainty quantification for electron-density diagnostics of polarimeter/interferometer system on experimental advanced superconducting tokamak

The verification, validation, and uncertainty quantification (VVUQ) analysis is a systematic method for identifying and quantifying uncertainties in complex engineering projects. This method has been used by the International Atomic Energy Agency as a critical assessment method in the field of high-precision plasma diagnostics in tokamaks. Starting from the basic theory to the final results, it assesses every process to obtain the uncertainties, system confidence level, and similarity between the model and experimental assessments. In this study, we evaluated the electron-density diagnosis of the polarimeter/interferometer system on an experimental advanced superconducting tokamak using a cylindrical simulation model. This study applied the VVUQ to an entire plasma diagnostic system for the first time, coupled with the statistical concepts of confidence and similarity. Through the VVUQ process, the uncertainty from the simulation code, diagnostic code, and engineering errors were identified, quantified, and minimized.

Navigating Uncertainties: A Comprehensive Review on Change Factors in Healthcare Construction Projects

Healthcare construction projects are complex and considered as dynamic environment that are constantly changing, reinvented, and reworked. These occurrences continue throughout the entire project lifecycle. Each healthcare project aims to deliver high-quality of healthcare services. Yet uncertainties are inherent in these projects which significantly impact their outcomes. In a recent report published by the Public Work Department, more than 90% of hospital projects across Malaysia are categorized as underperforming. The report mentioned that the scenario was due to ineffective project management, which ultimately led to extreme delay considered futile. This study explores the multifaceted nature of uncertainties in healthcare construction projects that shape the trajectory of change within these projects, which ultimately determining their success. A systematic literature review was conducted on relevant previous research works in the field of change management in healthcare projects. The analysis of these studies leads to the identification of the factors affecting change in a project setting. The finding reveals that the five main factors affecting change order in healthcare projects are medical technology, design and scope, operational, external environmental, and contractor. In addition, several future-proofing approaches were adopted as a mitigating strategy towards improved change management. This study underscores the significance of recognizing and addressing uncertainties in healthcare construction projects. It provides insightful contributions for project professionals in navigating change management while balancing the force of project uncertainties and maintaining performance that meets the project’s intended functionality and success.

Sequential learning of climate change via a physical-parameter-based state-space model and Bayesian inference

Flexible decision-making strategies provide an alternative option for climate adaptation by considering future learning of climate change. A physical-parameter-based state-space model (SSM) with Bayesian inference is developed in this work to investigate reduction of uncertainty from more observations and facilitate flexible adaptation strategies. This SSM method integrates a two-layer, energy-balance model to describe global mean temperature response, models multiple sources of uncertainty such as climate sensitivity and aerosol forcing, and uses the informative priors from processing Global Climate Model simulations. Focusing on global mean temperature anomaly, which has important implications on policies and related impacts, the SSM is assessed by applying it to both historical and pseudo-observations (i.e., model simulations used as observations), assessing the posterior probabilities of physical parameters, and evaluating reduction of projection uncertainty. Some limitations of the method are observed, such as the sensitivity related to the adopted forcing time series. Comparing the end-of-the-century projections of global mean temperature sequentially made at year 2020, 2050, and 2080 using pseudo-observations, the reduction of uncertainty from the SSM is evident: the range of 95% prediction intervals on average decreases from 1.9°C in 2020 to 1.0°C in 2050, and to 0.6°C in 2080 under the Shared Socioeconomic Pathway (SSP) 2–4.5 (or from 2.7°C, to 1.2°C and to 0.7°C under SSP5-8.5). These results illustrate how the SSM framework provides probabilistic projections of climate change that can be sequentially updated with more observations, and this process can facilitate flexible adaptation strategies.

An Analysis of Drilling Projection Uncertainty and Implications for Collision Avoidance Management Systems

Summary During well planning, specifying the separation that must be maintained from offset wellbores is critical to ensuring safe operations. Recent efforts on API RP 78, an upcoming American Petroleum Institute recommended practice for wellbore placement, have provided guidelines for calculating safe separation distances and engineering considerations for those calculations. A major milestone was the publication of a separation rule (Sawaryn et al. 2019) standardizing a formula for separation factor (SF), which in turn defines the minimum allowable separation distance (MASD) between two wellbores and the allowable deviation from plan (ADP) for a wellbore being drilled. One element of this formula, σpa, describes the accuracy with which a future wellpath can be predicted. Prior guidance provides a suggested value for σpa (1.6 ft or 0.5 m at 1-sigma) based on heuristics. A more rigorous approach for estimating this value is presented, where the design of the drilling program can be considered. A framework is proposed for modeling deviation from a projected path using a planned trajectory, bottomhole assembly (BHA) properties, and survey practices. The method identifies where a deviation is detected through survey measurements and estimates a planned recovery operation. Equations are provided for estimating the distance from plan at deviation detection along with the maximum expected deviation during recovery. Common drilling scenarios are analyzed for sensitivity to operational parameters such as survey course length, sensor offset, tool face control, and BHA performance. The impact of varying σpa is explored across prototypical collision avoidance cases. A discussion of the relationships between the σpa value and the common collision avoidance calculations, such as SF, MASD, and ADP, is included along with considerations for the design of a risk management policy. Survey course length, survey sensor offset, and uncertainty in directional performance are all shown to have a significant impact on the potential maximum deviation from plan. An additional factor considered is the aggressiveness expected when performing recovery operations. An analysis of common drilling scenarios suggests the previously provided guidance of 1.6 ft at 1-sigma appears suitable for cases where the directional behavior of the BHA is well characterized and the combination of course length and survey sensor offset is kept to 150 ft or less. Longer survey course lengths, larger sensor offsets, or uncertainty in directional performance of the BHA can produce larger σpa values, in some scenarios causing a significant impact on the MASD and ADP. Previous work has called attention to the uncertainty in drilling to a projection, but up to now, there has not been a rigorous method for estimating that quantity. With the methods outlined in this paper, well planners and drilling engineers can make more informed decisions on how to ensure safe separation practices in their own operations.

Evaluating the contribution of methanotrophy kinetics to uncertainty in the soil methane sink

The oxidation of atmospheric methane by soil microbes is an important natural sink for a potent greenhouse gas. However, estimates of the current and future soil methane sink are highly uncertain. Here we assessed the extent to which methanotrophy enzyme kinetics contribute to uncertainty in projections of the soil methane sink. We generated a comprehensive compilation of methanotrophy kinetic data from modern environments and assessed the patterns in kinetic parameters present in natural samples. Our compiled data enabled us to quantify the global soil methane sink through two idealized calculations comparing first-order and Michaelis–Menten models of kinetics. We show that these two kinetic models diverge only under high atmospheric CH4 scenarios, where first-order rate constants slightly overestimate the soil methane sink size, but produce similar predictions at modern atmospheric concentrations. Our compilation also shows that the kinetics of methanotrophy in natural soil samples is highly variable—both the V max (oxidation rate at saturation) and KM (half-saturation constant) in natural samples span over six orders of magnitude. However, accounting for the correlation we observe between V max and KM reduces the range of calculated uptake rates by as much as 96%. Additionally, our results indicate that variation in enzyme kinetics introduces a similar magnitude of variation in the calculated soil methane sink as temperature sensitivity. Systematic sampling of methanotroph kinetic parameters at multiple spatial scales should therefore be a key objective for closing the budget on the global soil methane sink.

Hydrometeorological response to afforestation in the UK: findings from a kilometer-scale climate model

Afforestation is of international interest for its positive benefits on carbon storage, ecology, and society, but its impacts on terrestrial and atmospheric processes are still poorly understood. This study presents the first use of a coupled land surface and convection permitting atmospheric model (CPM) to quantify hydrometeorological effects of afforestation across the United Kingdom, focusing on atmospheric processes often missing in hydrological models. Generating a scenario of 93 000 km2 (40%) additional woodland across the UK, the periods of 2042–2052 and 2062–2072 are analysed. Simulated afforestation alters seasonal and regional UK hydrometeorology. Countrywide runoff increases in all seasons (between 5.4–11 mm and 4.3–8.6% per season) due to elevated subsurface flows from greater soil moisture. Evaporation decreases in summer (−20.6 mm, −10%) but increases in winter (8.1 mm, 15%) whereas rainfall increases throughout all seasons (between 2.2–6.86 mm and 0.9%–2.2% per season). Greater winter rainfall is detected along Great Britain’s west coastline as increased surface roughness produces prolonged and heavier rainfall. In the summer, lower albedo increases potential evapotranspiration and reduces near surface specific humidity: water is locked in deeper soil layers as transpiration diminishes and the topsoil dries out. However, the magnitude of hydrometeorological change due to altered land cover is smaller than the uncertainty in local climate change projections. This work sets a precedent in illustrating the impacts of afforestation on hydrology using a high-resolution CPM and highlights the importance of coupled hydrometeorological processes when investigating land cover impacts on hydrological processes.

Risk mitigation in urban bus concession contracts: Overcoming uncertainties with a real options model

In this paper, we analyzed a risk-sharing mechanism that can be used in urban bus public service contracts (PSC) to deal with demand uncertainty in the upcoming years. This mechanism is similar to a collar option: an MRG (Minimum Revenue Guarantee) combined with a revenue cap. We applied this mechanism to a case study, the Lisbon Metro Area bus contracts. We compared the chosen model (a gross cost contract) with a simulated scenario modeled as a collar option. To develop this scenario, we model the flexibilities applying Real options Analysis and the project uncertainty as a Geometric Brownian Motion (GBM). The calculations were performed using Monte Carlo simulation. The results show that, compared to the gross cost, the collar option model allows the private operator to benefit from a potential demand increase. In the case of a downturn, the loss is shared between the two sides, reducing the contract cost borne by the government. Furthermore, from a public policy perspective, contrary to the gross cost model, this model may incentivize the private partner to improve service quality as a strategy to increase demand and, consequently, revenues. In addition, if demand evolves in a different trend, the financial rebalance is made automatically, reducing litigation costs for both sides.

Regulatory Frameworks for Decentralized Finance (DeFi): Challenges and opportunities

Decentralized Finance (DeFi) has emerged as a disruptive force in the financial industry, offering innovative solutions such as smart contracts, decentralized exchanges, and lending protocols. However, the regulatory landscape for DeFi remains uncertain and fragmented, posing challenges and opportunities for its development and adoption. This abstract provides an overview of the regulatory frameworks for DeFi, highlighting key challenges and opportunities. The regulatory landscape for DeFi is complex and varies significantly across jurisdictions. While some countries have embraced DeFi and blockchain technology, others have adopted a cautious approach, citing concerns such as money laundering, consumer protection, and financial stability. The lack of a harmonized regulatory framework has created uncertainty for DeFi projects and users, hindering mainstream adoption. One of the key challenges facing DeFi is the lack of clarity regarding regulatory compliance. DeFi projects often operate in a decentralized and borderless manner, making it difficult to determine which regulations apply. This ambiguity has led to regulatory scrutiny and enforcement actions in some cases, highlighting the need for clear and comprehensive regulatory guidelines. Despite these challenges, there are also significant opportunities for DeFi to thrive within a regulated framework. Regulatory clarity can provide legitimacy and credibility to the DeFi industry, attracting institutional investors and mainstream users. Moreover, regulations can help protect consumers and ensure the integrity of DeFi protocols, fostering trust and confidence in the ecosystem. To address the challenges and leverage the opportunities, stakeholders in the DeFi ecosystem, including regulators, policymakers, developers, and users, must collaborate to develop a balanced regulatory framework. This framework should prioritize innovation while addressing concerns related to security, privacy, and financial stability. By working together, the DeFi industry can navigate the regulatory landscape and unlock the full potential of decentralized finance for global financial inclusion and economic empowerment.

Southern Ocean phytoplankton under climate change: a shifting balance of bottom-up and top-down control

Abstract. Phytoplankton form the base of the marine food web by transforming CO2 into organic carbon via photosynthesis. Despite the importance of phytoplankton for marine ecosystems and global carbon cycling, projections of phytoplankton biomass in response to climate change differ strongly across Earth system models, illustrating uncertainty in our understanding of the underlying processes. Differences are especially large in the Southern Ocean, a region that is notoriously difficult to represent in models. Here, we argue that total (depth-integrated) phytoplankton biomass in the Southern Ocean is projected to largely remain unchanged under climate change by the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble because of a shifting balance of bottom-up and top-down processes driven by a shoaling mixed-layer depth. A shallower mixed layer is projected on average to improve growth conditions, consequently weaken bottom-up control, and confine phytoplankton closer to the surface. An increase in the phytoplankton concentration promotes zooplankton grazing efficiency, thus intensifying top-down control. However, large differences across the model ensemble exist, with some models simulating a decrease in surface phytoplankton concentrations. To reduce uncertainties in projections of surface phytoplankton concentrations, we employ an emergent constraint approach using the observed sensitivity of surface chlorophyll concentration, taken as an observable proxy for phytoplankton, to seasonal changes in the mixed-layer depth as an indicator for future changes in surface phytoplankton concentrations. The emergent constraint reduces uncertainties in surface phytoplankton concentration projections by around one-third and increases confidence that surface phytoplankton concentrations will indeed rise due to shoaling mixed layers under global warming, thus favouring intensified top-down control. Overall, our results suggest that while changes in bottom-up conditions stimulate enhanced growth, intensified top-down control opposes an increase in phytoplankton and becomes increasingly important for the phytoplankton response to climate change in the Southern Ocean.

Coastal Zone Management in Response to Sea Level Rise in the 21st Century: A Study from Hurst Spit to Lymington, South England

This study examines the coastal region spanning from Hurst Spit to the mouth of the Lymington River along the Western Solent coast in Hampshire, South England. The area comprises critical habitats for international bird species on the seaward side and is protected by a seawall on the landward side. Rising sea levels pose a significant threat to this area, including habitat loss and potential seawall breaches. To address the uncertainty in sea level rise projections for the 21st century, this study utilises data processing in ArcGIS, including LiDAR data, tidal data, and sea level rise projections. It adopts contingency allowances for sea level rise, resulting in a projected 0.89 metre by 2100. Analysis reveals a significant transformation in habitat distribution, with standing water and mudflats expanding while salt marshes and dry land areas diminish. Salt marsh areas are projected to contract by 64.6%, with the pioneer salt marsh facing the greatest loss. The report recommends a proactive approach, including realigning the seawall in vulnerable areas, allowing for the creation of new salt marshes. This managed intervention strategy can reduce habitat loss to approximately 17.43%, mitigating the potential ecological and human habitat impacts of rising sea levels in that region.

Steady but model dependent Arctic amplification of the forced temperature response in 21st century CMIP6 projections

We examine sources of uncertainty in projections of Arctic amplification (AA) using the CMIP6 multi-model (MM) ensemble and single model initial-condition large ensembles of historical and future scenario simulations. In the CMIP6 MM mean, the annual mean AA ratio is steady at approximately 2.5, both in time and across scenarios, resulting in negligibly small scenario uncertainty in the magnitude of AA. Deviations from the steady value can be found at the low and high emission scenarios due to different root causes, with the latter being mostly evident in the summer and autumn seasons. Best estimates of model uncertainty are at least an order of magnitude larger than scenario uncertainty in CMIP6. The large ensembles reveal that irreducible internal variability has a similar magnitude to model uncertainty for most of the 21st century, except in the lowest emission scenario at the end of the 21st century when it could be twice as large.