Uncertainty In Climate Projections Research Articles

Climate change and flood risk in the U.S.: A mini review of models future scenarios and adaptation strategies

This paper systematically reviews the interplay between climate change, flood risk, and the adaptation strategies employed in the U.S., focusing on models and future scenarios. As climate change accelerates, the frequency and intensity of flood events have increased, posing significant threats to communities, infrastructure, and ecosystems. Advanced modeling techniques, including hydrological and climate models, provide critical insights into future flood scenarios by simulating precipitation patterns, storm surges, and sea-level rise under varying climate projections. These models are instrumental in understanding regional vulnerabilities and shaping adaptive responses. This review examines how these predictive models contribute to flood risk assessment and explores innovative adaptation strategies such as resilient infrastructure design, ecosystem-based solutions, and community engagement initiatives. Key challenges, including uncertainties in climate projections, funding limitations, and policy gaps, are addressed, with an emphasis on the need for cross-sectoral collaboration and robust governance frameworks. The paper also evaluates current U.S. flood mitigation policies and highlights emerging technologies like nature-based solutions and early warning systems as critical tools for enhancing resilience. By synthesizing recent research, this review offers a comprehensive understanding of the dynamic relationship between climate change and flood risk while providing actionable recommendations for adaptive planning. The study concludes that proactive measures integrating cutting-edge modeling with sustainable adaptation strategies are essential for reducing flood impacts and fostering climate resilience in the U.S.

The Net Radiative Effect of the Ill‐Defined Clear‐Sky in the Vicinity of Clouds

AbstractClouds play a crucial role in Earth's energy budget resulting in significant deviations from clear‐sky fluxes. This important role, combined with their complexity, makes them a major source of uncertainty in climate projections. Over the past two decades, the radiative signatures of clouds in their surroundings have been extensively studied, highlighting the challenges of defining clouds and distinguishing them from the aerosol embedded in clear‐sky. Accurately defining clouds and aerosol is essential for quantifying their radiative effects and for remote sensing of the surface and atmosphere under clear‐sky conditions. In this study, we combine two of the most mature and sophisticated space‐borne instruments: the MODerate resolution Imaging Spectroradiometer with its high‐resolution multispectral images and the Clouds and the Earth's Radiant Energy System broadband top‐of‐the‐atmosphere radiative flux data. This0 allows us to quantify the net RE of clouds within the clear‐sky around them for the first time. Our findings show that the local RE of clouds over the ocean around noon ranges from −7 to −10 W in the solar band and 1–1.5 W in the longwave infrared. These results suggest that clouds drive a significant portion of the aerosol direct RE. Additionally, the near‐cloud longwave infrared effect is equivalent to an effect of 90 ppm of . We propose to categorize the sky into three regimes—cloudy, cloud‐influenced‐clear‐sky, and far‐field clear‐sky and suggest a path to move beyond a discrete definition of the sky. This will improve our understanding of the role of clouds and aerosol in the climate system, ultimately reducing uncertainties in climate projections.

Exploring Optimal Complexity for Water Stress Representation in Terrestrial Carbon Models: A Hybrid‐Machine Learning Model Approach

AbstractTerrestrial biosphere models offer a comprehensive view of the global carbon cycle by integrating ecological processes across scales, yet they introduce significant uncertainties in climate and biogeochemical projections due to diverse process representations and parameter variations. For instance, different soil water limitation functions lead to wide productivity ranges across models. To address this, we propose the Differentiable Land Model (DifferLand), a novel hybrid machine learning approach replacing unknown water limitation functions in models with neural networks (NNs) to learn from data. Using automatic differentiation, we calibrated the embedded NN and the physical model parameters against daily observations of evapotranspiration, gross primary productivity, ecosystem respiration, and leaf area index across 16 FLUXNET sites. We evaluated six model configurations where NNs simulate increasingly complex soil water and photosynthesis interactions against test data sets to find the optimal structure‐performance tradeoff. Our findings show that a simple hybrid model with a univariate NN effectively captures site‐level water and carbon fluxes on a monthly timescale. Across a global aridity gradient, the magnitude of water stress limitation varies, but its functional form consistently converges to a piecewise linear relationship with saturation at high water levels. While models incorporating more interactions between soil water and meteorological drivers better fit observations at finer time scales, they risk overfitting and equifinality issues. Our study demonstrates that hybrid models have great potential in learning unknown parameterizations and testing ecological hypotheses. Nevertheless, careful structure‐performance tradeoffs are warranted in light of observational constraints to translate the retrieved relationships into robust process understanding.

Internal variability of the winter North Atlantic Oscillation longitudinal displacements

The winter North Atlantic Oscillation (NAO), one of the leading modes of atmospheric variability in the Northern Hemisphere and key driver of surface climate anomalies, was long considered to be spatially stable. Yet, its northern center-of-action – the Icelandic Low (IL) – shifted eastward in the late 1970s compared to the preceding decades of the mid-20th century. The responsible processes are still uncertain, particularly after the decline of the positive NAO trend in the 21st century. Here, we present observational and model evidence that the NAO-IL moves naturally alternating between two preferential locations, west/east of Iceland, with no need for changes in anthropogenic forcing or low-frequency oceanic variability. These recurrent longitudinal displacements of the NAO pattern appear linked to zonal changes in the fluctuations (not mean-state) of transient-eddy activity, emphasizing the relevance of internal atmospheric variability, and could represent a major source of uncertainty in regional climate prediction and projection.

Milk contamination in Europe under anticipated climate change scenarios

Transforming the food system while addressing climate change requires proactive measures based on quantitative projections of anticipated future conditions. A key component of the food system that must be considered during this transformation is food safety, which is the focus of this paper. Milk safety has been selected as a case study. Future milk contamination levels in Europe, in terms of total bacterial counts, are evaluated under various climate change scenarios. Projections from multiple climate models are integrated into a data-driven milk contamination model, validated using data from Malta, Spain, and Belgium. The modeling framework accounts for variability among dairy farms and the inherent uncertainties in climate projections. Results are presented through geographical heatmaps, highlighting coastal and southern areas such as Portugal, Western Spain, Southern Italy, and Western France as regions expected to face the highest bacterial counts. The analysis underlines the significant roles of humidity and wind speed, alongside temperature. It also examines compliance with the regulatory threshold for raw milk, revealing an increased frequency of summer weeks exceeding the threshold of 100,000 colony-forming units. Based on this analysis, regions are classified into low-risk, high-risk, and emerging-risk categories. This classification can guide the selection of farm strategies aimed at meeting future food safety standards. By informing these decisions with the anticipated impacts of climate change, the food system can be future-proofed.

Discrepancies in precipitation changes over the Southwest River Basin of China based on ISIMIP3b

Selecting appropriate global climate models (GCMs) is crucial for minimizing uncertainty in regional climate projections under future scenarios. Previous studies have predominantly assessed the modeling capability of GCMs for regional precipitation climatology and its long-term patterns based on annual and seasonal precipitation data. Building upon these, we primally evaluated the performance of five GCMs from phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b) in simulating precipitation concentration and its variations in the Southwest River Basin (SWRB) of China using the precipitation concentration index (PCI). The results indicate that: (1) The 5 GCMs generally capture the spatial distribution of annual average precipitation in the SWRB but significantly overestimate its magnitude, with a maximum regional average deviation of 207.80 mm. Furthermore, all models tend to overestimate the overall drying trend in the SWRB and show limited capability in simulating interdecadal variations of annual precipitation. (2) While the 5 GCMs reasonably simulate the spatial distribution of annual average PCI in the SWRB, they tend to overestimate its values, with a maximum regional average deviation of 1.54. Additionally, their simulation performance in capturing PCI trends and interdecadal variations is also limited. (3) The 5 GCMs tend to overestimate seasonal precipitation in the SWRB, with the best simulation performance for the distribution of autumn precipitation, followed by spring and summer, and the poorest for winter. Significant differences exist in the simulation performance of the models for seasonal precipitation proportions, which result in discrepancies in the models’ representation of PCI. Moreover, the models’ poor simulation performance of PCI trends is partly due to their inadequate modeling of trends in seasonal precipitation proportions. The findings will contribute to laying the foundation for meteorological hydrological research and water resource management in the SWRB.

Wood-Based Bioenergy in North America: An Overview of Current Knowledge

Policy priorities for wood-based bioenergy in North America have undergone fluctuations over time, influenced significantly by the dynamic interplay of sociopolitical factors. Recent years, however, have seen a renewed public interest in wood-based bioenergy in the United States, Canada, and Mexico. This resurgence is driven by fluctuating energy prices and growing concerns about climate change. This review provides an overview of current energy production and consumption scenarios, and highlights critical issues related to the sustainability of bioenergy feedstocks and their economic potential across the three North American countries. Different cross-cutting issues related to public health, climate change, and social acceptance of wood-based bioenergy are thoroughly examined. Within this context, several challenges have been identified, including uncertainties in climate projections, inadequate tree inventories beyond forestlands, deforestation concerns, technological shifts in wood processing, fluctuations in bioenergy demand, and the imperative need for access to reliable markets. Addressing these challenges requires increased research and investment in wood-based energy to enhance energy security, reduce greenhouse gas emissions, and improve economic and social viability in bioenergy production. This proactive approach is vital for fostering a sustainable and resilient wood-based bioenergy sector in North America.

Recent improvements and maximum covariance analysis of aerosol and cloud properties in the EC-Earth3-AerChem model

Abstract. Given the importance of aerosols and clouds and their interactions in the climate system, it is imperative that the global Earth system models accurately represent processes associated with them. This is an important prerequisite if we are to narrow the uncertainties in future climate projections. In practice, this means that continuous model evaluations and improvements grounded in observations are necessary. Numerous studies in the past few decades have shown both the usability and the limitations of utilizing satellite-based observations in understanding and evaluating aerosol–cloud interactions, particularly under varying meteorological and satellite sensor sensitivity paradigms. Furthermore, the vast range of spatio-temporal scales at which aerosol and cloud processes occur adds another dimension to the challenges faced when evaluating climate models. In this context, the aim of this study is two-fold. (1) We evaluate the most recent, significant changes in the representation of aerosol and cloud processes implemented in the EC-Earth3-AerChem model in the framework of the EU project FORCeS compared with its previous CMIP6 version (Coupled Model Intercomparison Project Phase 6; https://pcmdi.llnl.gov/CMIP6/, last access: 13 February 2019). We focus particularly on evaluating cloud physical properties and radiative effects, wherever possible, using a satellite simulator. We report on the overall improvements in the EC-Earth3-AerChem model. In particular, the strong warm bias chronically seen over the Southern Ocean is reduced significantly. (2) A statistical, maximum covariance analysis is carried out between aerosol optical depth (AOD) and cloud droplet (CD) effective radius based on the recent EC-Earth3-AerChem/FORCeS simulation to understand to what extent the Twomey effect can manifest itself in the larger spatio-temporal scales. We focus on the three oceanic low-level cloud regimes that are important due to their strong net cooling effect and where pollution outflow from the nearby continent is simultaneously pervasive. We report that the statistical covariability between AOD and CD effective radius is indeed dominantly visible even at the climate scale when the aerosol amount and composition are favourably preconditioned to allow for aerosol–cloud interactions. Despite this strong covariability, our analysis shows a strong cooling/warming in shortwave cloud radiative effects at the top of the atmosphere in our study regions associated with an increase/decrease in CD effective radius. This cooling/warming can be attributed to the increase/decrease in low cloud fraction, in line with previous observational studies.

Repurposing weather modification for cloud research showcased by ice crystal growth.

The representation of cloud processes in models is one of the largest sources of uncertainty in weather forecast and climate projections. While laboratory settings offer controlled conditions for studying cloud processes, they cannot reproduce the full range of conditions and interactions present in natural cloud systems. To bridge this gap, here we leverage weather modification, specifically glaciogenic cloud seeding, to investigate ice growth rates within natural clouds. Seeding experiments were conducted in supercooled stratus clouds (at to C) using an uncrewed aerial vehicle, and the created ice crystals were measured 4-10 min downwind by in situ and ground-based remote sensing instrumentation. We observed substantial variability in ice crystal growth rates within natural clouds, attributed to variations in ice crystal number concentrations and in the supersaturation, which is difficult to reproduce in the laboratory and which implies faster precipitation initiation than previously thought. We found that for the experiments conducted at C, the ice crystal populations grew nearly linearly during the time interval from 6 to 10 min. Our results demonstrate that the targeted use of weather modification techniques can be employed for fundamental cloud research (e.g. ice growth processes, aerosol-cloud interactions), helping to advance cloud microphysics parameterizations and to improve weather forecasts and climate projections.

Agriculture development and its Impact: A Comprehensive Time seriesanalysis of Climate Variables

Climate change poses significant challenges that necessitate the development of policies to manage aggregate input and social costs. To formulate such policies, an analysis of the factors and their current trends must be conducted. This study explores the factors influencing climate change and provides insights into their impacts through changes in arable land and greenhouse gas (GHG) emissions in India from 1990 to 2020. Utilizing time series analysis, this study examined trends in GHG emissions from agriculture and developed a simulation model to estimate overall GHG emissions through methane and nitrous oxide emissions. The results indicate that enteric fermentation and agricultural soil are major contributors to methane and nitrous oxide emissions, respectively, with enteric fermentation contributing approximately 69.33% and agricultural soil contributing approximately 97.66% to methane and nitrous oxide emissions, respectively. Additionally, a higher growth rate was observed for nitrous oxide emissions than for methane emissions, with nitrous oxide emissions showing a 161% increase from 1960 to 2010. Furthermore, a positive correlation (r=0.587) between GHG emissions and changes in the annual mean temperature underscores the direct impact of agricultural emissions on climate dynamics in India, with a regression coefficient factor of 0.176. It is estimated that the overall GHG emissions from agriculture through methane and nitrous oxide emissions will be approximately 695.87 to 818.73 MMTCDE in the year 2030, while the change in annual mean temperature is estimated to be approximately 1.65 ± 0.58o C from 1990 to 2030 in India. This study faces challenges such as uncertainties in long-term climate projections and emission estimates, variability in regional agricultural practices, and the need for more granular data. These findings highlight the urgent need for effective mitigation strategies within the agricultural sector to address the growing threat of climate change.

Tropical Pacific trends explain the discrepancy between observed and modelled rainfall change over the Americas

Understanding the causes for discrepancies between modelled and observed regional climate trends is important for improving present-day climate simulation and reducing uncertainties in future climate projections. Here, we analyse the performance of coupled climate models in reproducing regional precipitation trends during the satellite era. We find statistically significant observed drying in southwestern North America and wetting in the Amazon during the period 1979–2014. Historical climate model simulations do not capture these observed precipitation trends. We trace this discrepancy to the inability of coupled simulations to capture the observed Pacific trade wind intensification over this period. A linear adjustment of free running historical simulations, based on the observed strengthening of the Pacific trade winds and modeled ENSO teleconnections, explains the discrepancy in precipitation trends. Furthermore, both the Pacific trade wind trends and regional precipitation trends are reproduced in climate simulations with prescribed observed sea surface temperatures (SST), underscoring the role of the tropical Pacific SST patterns.

Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model

Abstract Ice formation remains one of the most poorly represented microphysical processes in climate models. While primary ice production (PIP) parameterizations are known to have a large influence on the modeled cloud properties, the representation of secondary ice production (SIP) is incomplete and its corresponding impact is therefore largely unquantified. Furthermore, ice aggregation is another important process for the total cloud ice budget, which also remains largely unconstrained. In this study, we examine the impact of PIP, SIP, and ice aggregation on Arctic clouds, using the Norwegian Earth System Model, version 2 (NorESM2). Simulations with both prognostic and diagnostic PIP show that heterogeneous freezing alone cannot reproduce the observed cloud ice content. The implementation of missing SIP mechanisms (collisional breakup, drop shattering, and sublimation breakup) in NorESM2 improves the modeled ice properties, while improvements in liquid content occur only in simulations with prognostic PIP. However, results are sensitive to the description of collisional breakup. This mechanism, which dominates SIP in the examined conditions, is very sensitive to the treatment of the sublimation correction factor, a poorly constrained parameter that is included in the utilized parameterization. Finally, variations in ice aggregation treatment can also significantly impact cloud properties, mainly through their impact on collisional breakup efficiency. Overall, enhancement in ice production through the addition of SIP mechanisms and the reduction in ice aggregation (in line with radar observations of shallow Arctic clouds) result in enhanced cloud cover and decreased TOA radiation biases, compared to satellite measurements, especially during the cold months. Significance Statement Arctic clouds remain a large source of uncertainty in projections of the future climate due to the poor representation of the microphysical processes that govern their life cycle. Ice formation is among the least understood processes. While it is widely recognized that better constraints on primary ice production (PIP) are needed to improve existing parameterizations, we show that secondary ice production (SIP) and ice aggregation can have also a significant impact on ice number concentrations. Constraining ice formation through the addition of missing SIP mechanisms and reducing ice aggregation can improve the representation of the cloud macrophysical properties and enhance total cloud cover in the Arctic region, which in turn contributes to decreased TOA radiation biases in the cold months.

A method to assess the cloud-aerosol transition zone from ceilometer measurements

Cloud and aerosol contribution to the Earth's radiative budget constitutes one of the most significant uncertainties in future climate projections. To distinguish between clouds and cloud-free air, atmospheric scientists have been using a wide range of instruments, techniques, and algorithms with various detection thresholds. However, since the change from a cloudy to a cloud-free atmosphere may be gradual and, in some cases, far from obvious, recent research is questioning where the threshold between both phases should be established. These considerations lead to contemplate a transition zone (TZ) between cloud and cloud-free conditions. In the present study, backscatter profiles obtained by a Vaisala CL31 ceilometer were processed to assess the transition zone. First, two widely used cloud detection algorithms were applied and compared: the method provided by the ceilometer manufacturer (Vaisala) and Cloudnetpy, the algorithm from ACTRIS Cloudnet, a project devoted to aerosol, clouds, and trace gases research. Second, a sensitivity analysis was applied to the backscatter and signal-to-noise ratio thresholds used for cloud detection in Cloudnetpy. This methodology has allowed us to assess the vertical distribution of clouds, aerosols, the TZ, and its frequency of occurrence. Results indicate a gradual transition in backscatter retrievals from cloud to cloud-free, where particles detected near cloud boundaries induced higher backscatter values than those found further away. Depending on the thresholds used, we observed a 9.3% (with an estimated range of uncertainty of 5.420%) variation in cloud occurrence which can be attributed to TZ conditions. Analysing the whole backscatter profile, we found as many TZ conditions as cloudy values, which emphasises the importance of studying the vertical distribution of the TZ. Moreover, the analysis of TZ occurrence in height and time revealed that such conditions concentrate below 800m during night periods, although annual height-hour distributions involve a remarkable variability among seasons. These findings highlight the importance of either including an additional phase between ‘pure clouds’ and ‘pure aerosols’ or treating them as a continuum of suspended particles in the atmosphere.

Demographic but not competitive time lags can transiently amplify climate-induced changes in vegetation carbon storage.

How terrestrial ecosystems will accumulate carbon as the climate continues to change is a major source of uncertainty in projections of future climate. Under growth-stimulating environmental change, time lags inherent in population and community dynamic processes have been posed to dampen, or alternatively amplify, short-term carbon gain in terrestrial vegetation, but these outcomes can be difficult to predict. To theoretically frame this problem, we developed a simple model of vegetation dynamics that identifies the stage-structured demographic and competitive processes that could govern the timescales of carbon storage and loss. We show that demographic lags associated with growth-stimulating environmental change can allow a rapid increase in population-level carbon storage that is lost back to the atmosphere in later years. However, this transient carbon storage only emerges when environmental change increases the transition of adult individuals into a larger size class that suffers markedly higher mortality. Otherwise, demographic lags simply slow carbon accumulation. Counterintuitively, an analogous tradeoff between maximum adult size and survivorship in two-species models, coupled with environmental change-driven replacement, does not generate the transient carbon gain seen in the single-species models. Instead lags in competitive replacement slow the approach to the eventual carbon trajectory. Together, our results suggest that time lags inherent in demographic and compositional turnover tend to slow carbon accumulation in systems responding to growth-stimulating environmental change. Only under specific conditions will lagged demographic processes in such systems drive transient carbon accumulation, conditions that investigators can examine in nature to help project future carbon trajectories.

A systematic evaluation of high-cloud controlling factors

Abstract. Clouds strongly modulate the top-of-the-atmosphere energy budget and are a major source of uncertainty in climate projections. “Cloud controlling factor” (CCF) analysis derives relationships between large-scale meteorological drivers and cloud radiative anomalies, which can be used to constrain cloud feedback. However, the choice of meteorological CCFs is crucial for a meaningful constraint. While there is rich literature investigating ideal CCF setups for low-level clouds, there is a lack of analogous research explicitly targeting high clouds. Here, we use ridge regression to systematically evaluate the addition of five candidate CCFs to previously established core CCFs within large spatial domains to predict longwave high-cloud radiative anomalies: upper-tropospheric static stability (SUT), sub-cloud moist static energy, convective available potential energy, convective inhibition, and upper-tropospheric wind shear (ΔU300). We identify an optimal configuration for predicting high-cloud radiative anomalies that includes SUT and ΔU300 and show that spatial domain size is more important than the selection of CCFs for predictive skill. We also find an important discrepancy between the optimal domain sizes required for predicting locally and globally aggregated radiative anomalies. Finally, we scientifically interpret the ridge regression coefficients, where we show that SUT captures physical drivers of known high-cloud feedbacks and deduce that the inclusion of SUT into observational constraint frameworks may reduce uncertainty associated with changes in anvil cloud amount as a function of climate change. Therefore, we highlight SUT as an important CCF for high clouds and longwave cloud feedback.

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.

Contributions From Cloud Morphological Changes to the Interannual Shortwave Cloud Feedback Based on MODIS and ISCCP Satellite Observations

AbstractThe surface temperature‐mediated change in cloud properties, referred to as the cloud feedback, continues to dominate the uncertainty in climate projections. A larger number of contemporary global climate models (GCMs) project a higher degree of warming than the previous generation of GCMs. This greater projected warming has been attributed to a less negative cloud feedback in the Southern Ocean. Here, we apply a novel “double decomposition method” that employs the “cloud radiative kernel” and “cloud regime” concepts, to two data sets of satellite observations to decompose the interannual cloud feedback into contributions arising from changes within and shifts between cloud morphologies. Our results show that contributions from the latter to the cloud feedback are large for certain regimes. We then focus on interpreting how both changes within and between cloud morphologies impact the shortwave cloud optical depth feedback over the Southern Ocean in light of additional observations. Results from the former cloud morphological changes reveal the importance of the wind response to warming increases low‐ and mid‐level cloud optical thickness in the same region. Results from the latter cloud morphological changes reveal that a general shift from thick storm‐track clouds to thinner oceanic low‐level clouds contributes to a positive feedback over the Southern Ocean that is offset by shifts from thinner broken clouds to thicker mid‐ and low‐level clouds. Our novel analysis can be applied to evaluate GCMs and potentially diagnose shortcomings pertaining to their physical parameterizations of particular cloud morphologies.

Unexpected sources of uncertainty in projecting habitat shifts for Arctic shorebirds under climate change

AbstractAimThe rapidly changing Arctic is ideal for investigating uncertainties in climate projections. Despite the challenges of collecting data in this region, an unprecedented large‐scale survey of shorebirds has been conducted over the last 30 years. Our study aimed to (1) develop probabilistic estimates for the change in suitable habitat for 10 Arctic shorebird species in Canada by 2075 and (2) assess the contribution of modelling decisions to the uncertainty in these estimates.LocationArctic Canada.MethodsTo evaluate uncertainty, we considered six classes of modelling decisions, yielding 216 unique projections for each species. We tested three decisions that are less commonly explored − the pool of candidate variables, a method for selecting variables, and the maximum distance of tree line dispersal, as well as the modelling algorithm, carbon emissions scenario, and global circulation model. We used a bootstrapping approach, creating a probability distribution for the proportional change in suitable habitat for each species.ResultsOur findings indicated a substantial risk for 8/10 species to lose over half of their suitable breeding habitat, but this projection is much less certain than has been described previously. While much uncertainty is unexplained, we were surprised that the largest source of uncertainty among our modelling decisions was from our choice of methods for variable selection, that the other modelling decisions were relatively small sources of uncertainty, overshadowing other modelling decisions.Main ConclusionsWhile most scenarios predict a northward shift and significant habitat loss for Arctic‐breeding shorebirds, the Arctic Archipelago of Canada will remain an important refuge because in many other Arctic regions, there is no land farther north for these species to shift into. A comprehensive understanding of uncertainty is important for deciding if future projections can or should be used when planning climate‐resilient protected area networks.

Future changes in the influence of the NAO on Mediterranean winter precipitation extremes in the EC-Earth3 large Ensemble: The prominent role of internal variability

One of the largest uncertainties in future climate projections is the interplay between internally generated and externally forced changes. This study investigates the changes in the link between the North Atlantic Oscillation (NAO) and Mediterranean winter extreme rainfall and dry days by the end of the 21st century compared to present day. We compare two different future pathways and estimate the extent to which the NAO imprint is affected by the global warming level using the latest EC-Earth3 large ensemble historical and future experiments. It is shown that the expected range of winter extremes changes due to internal and unpredictable fluctuations of the NAO largely overcomes the signal associated with externally-forced NAO variations. The NAO is found to exert a similar control on European climate variability, regardless of the amount of warming. For most of the Mediterranean region, magnitude and even sign of projected changes in the NAO-congruent precipitation indices vary substantially across the individual ensemble members according to the corresponding evolution of the NAO. Internal variability provides an average basin-wide contribution of up to 90% or more to the total NAO-driven variability in SSP1–1.9, and of about 80% in SSP5–8.5. Sub-regionally, the anthropogenic component of the NAO link is more evident over the Iberian Peninsula and parts of the central Mediterranean. This emphasises the role of internal variability and related uncertainty in determining the future impact of the NAO via the large spread in the circulation responses. However, the NAO is found to exert a weaker influence on the extreme precipitation total variability in both future scenarios given their future marked increase in total intensity and variance as opposed to the negligible NAO-related trends. Opposite conclusions are drawn for dry days, which are projected to decrease in the future, especially in the northern Mediterranean. Thus, this study also highlights how the variability of future extreme precipitation intensity in the Mediterranean basin will be less dependent on the principal mode of internal climate variability, posing further challenges for prediction and adaptation to weather-related hazards.