Uncertainty In Climate Projections Research Articles

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.

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.

Process Modeling of Aerosol‐Cloud Interaction in Summertime Precipitating Shallow Cumulus Over the Western North Atlantic

AbstractProcess modeling of Aerosol‐cloud interaction (ACI) is essential to bridging gaps between observational analysis and climate modeling of aerosol effects in the Earth system and eventually reducing climate projection uncertainties. In this study, we examine ACI in summertime precipitating shallow cumuli observed during the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE). Aerosols and precipitating shallow cumuli were extensively observed with in‐situ and remote‐sensing instruments during two research flight cases on 02 June and 07 June, respectively, during the ACTIVATE summer 2021 deployment phase. We perform observational analysis and large‐eddy simulation (LES) of aerosol effect on precipitating cumulus in these two cases. Given the measured aerosol size distributions and meteorological conditions, LES is able to reproduce the observed cloud properties by aircraft such as liquid water content (LWC), cloud droplet number concentration (Nc) and effective radius reff. However, it produces smaller liquid water path (LWP) and larger Nc compared to the satellite retrievals. Both 02 and 07 June cases are over warm waters of the Gulf Stream and have a cloud top height over 3 km, but the 07 June case is more polluted and has larger LWC. We find that the Na‐induced LWP adjustment is dominated by precipitation feedback for the 2 June precipitating case and there is no clear entrainment feedback in both cases. An increase of cloud fraction due to a decrease of aerosol number concentration is also shown in the simulations for the 02 June case.

Evaluation of the climate models used by the IPCC based on the position of the ITCZ in the Equatorial South Atlantic

AbstractA number of the uncertainties in climate projections for the end of the century are due to the difficulty of general circulation models (GCMs) to represent large‐scale precipitating systems such as the Intertropical Convergence Zone (ITCZ) in the historical period. In this study, we evaluate the Coupled Model Intercomparison Project Phase 5 and 6 models based on the position of the ITCZ in the South Atlantic during the period in which it is climatologically further south (February–May). This period characterizes the rainy season of the Brazilian semiarid. The models were ranked according to two objective methods and validated through a qualitative analysis. The first method considered a success rate to evaluate the correct position of the areas with maximum precipitation and minimum pressure and meridional wind as representative of the mean position of the ITCZ. Among the 15 coupled GCMs analysed for each CMIP, those that best represented the position of the ITCZ were HADGEM.2‐AO, MIROC5 and CANESM2 from CMIP5 and CESM2 and MIROC6 from CMIP6. These models represented well the following characteristics of the ITCZ in February–May: maximum precipitation near the equator at the height of the coast of South America and with the wind convergence zone sloping north towards the African continent. The most common problem noticed in the other GCMs was to displace the ITCZ to the south of its observed mean position within the period February–May. Also, some GCMs presented a double ITCZ (GISS‐E2‐H, GDFL‐CM3 and IPSL‐CM5A‐LR from CMIP5 and INM‐CM5‐0 from CMIP6) following two high TSM cores. One GCM (ACCESS1‐3 from CMIP5) presented a relatively good performance, but the zone of greater precipitation was shifted to the east, being located in the centre of the Atlantic basin. Knowing how the GCMs represent the ITCZ is a criterion that must be considered when choosing the GCMs for climate change studies.

Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity

The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)-key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO[Formula: see text] forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections.

Amplified Extreme Floods and Shifting Flood Mechanisms in the Delaware River Basin in Future Climates

AbstractHistorical records in the Delaware River Basin reveal complex and spatially diverse flood generating mechanisms influenced by the region's mountains‐to‐plains gradients. This study focuses on predicting future flood hazards and understanding the underlying drivers of changes across the region. Using a process‐based hydrological model, we analyzed the hydrometeorological condition of each historical and future flood event. For each event, at the subbasin scale, we identified the dominant flood generating mechanism, including snowmelt, rain‐on‐snow, short‐duration rain, and long‐duration rain. The rain‐induced floods are further categorized based on the soil's Antecedent Moisture Condition (AMC) before the event, whether dry, normal, or wet. Our historical analysis suggests that rain‐on‐snow is the primary flood mechanism of the Upper Basin. Although most frequent, the magnitude of rain‐on‐snow floods is often less severe than short rain floods. In contrast, historical floods in the Lower Basin are primarily caused by short rain under normal AMC. Given the uncertainties in climate projections, we used an ensemble of future climate scenarios for flood projections. Despite variations in regional climate projections, coherent perspectives emerge: the region will shift toward a warmer, wetter climate, with a projected intensification of extreme floods. The Upper Basin is projected to experience a marked decrease in rain‐on‐snow floods, but a substantial increase in short rain floods with wet AMC. The largest increase in flood magnitude will be driven by short rains with wet AMC in the Upper Basin and by short rains with normal AMC in the Lower Basin.

Biodiversity losses associated with global warming of 1.5 to 4 °C above pre-industrial levels in six countries

We quantify the projected impacts of alternative levels of global warming upon the climatically determined geographic ranges of plants and vertebrates in six countries (China, Brazil, Egypt, Ethiopia, Ghana and India), accounting for uncertainties in regional climate projection. We quantify in a spatially explicit fashion the species richness remaining or lost, allowing the identification of climate refugia which we define as areas where > 75% of the species currently present remain in a world with a particular level of global warming above pre-industrial levels. In all countries and in both taxa, species richness declines with warming, as does the proportion of each country remaining a climate refugium for plants or vertebrates. In percentage terms, refugia loss relative to a 1961–1990 baseline period is greatest in India and Brazil, and least in Ghana and Ethiopia for the same level of warming, and is greater for plants than for vertebrates. Taking account of present land uses (i.e. area still considered natural), and using species richness of plants as a proxy to indicate biodiversity more generally, the proportion of land acting as climate refugia for biodiversity in five of the countries variously declines from 32–75% of a country in the 1961–1990 baseline period to 20–64% for 1.5 °C global warming, 11–53% for 2 °C, 3–33% for 3 °C and 2–24% for 4 °C warming. In Ethiopia, India, Brazil and China, climate refugia decline rapidly with warming while in Ghana and China some refugia persist even with 3–4 °C of warming. Only small percentages of Brazil, India and China are both climate refugia and lie within protected areas; hence, an expansion of the protected area networks in these countries would be required to deliver climate resilient biodiversity conservation. These percentages are larger in Ethiopia and Ghana and, in some areas of Ghana, the only remaining refugia are in protected areas, the remaining landscape converted to other uses.

Climate change impact on sub-tropical lakes – Lake Kinneret as a case study

Climate change is anticipated to alter lake ecosystems by affecting water quality, potentially resulting in loss of ecosystem services. Subtropical lakes have high temperatures to begin with and are expected to exhibit higher temperatures all year round which might affect the thermal structure and ecological processes in a different manner than lakes in temperate zones.In this study the ecosystem response of the sub-tropical Lake Kinneret to climate change was explored using lake ecosystem models. Projection reliability was increased by using a weather generator and ensemble modelling, confronting uncertainty of both climate projections and lake models. The study included running two 1D hydrodynamic-biogeochemical models over one thousand realizations of two gradual temperature increase scenarios that span over 49 years.Our predictions show that an increase in air temperature would have subtle effects on stratification properties but may result in considerable changes to biogeochemical processes. Water temperature rise would cause a reduction in dissolved oxygen. Both of these changes would produce elevated phosphate and lowered ammonium concentrations. In turn, these changes are predicted to modify the phytoplankton community, expressed chiefly in increased cyanobacteria blooms at the expense of green phytoplankton and dinoflagellates; these changes may culminate in overall reduction of primary production. Identification of these trends would not be possible without the use of many realizations of climate scenarios. The use of ensemble modelling increased prediction reliability and highlighted elements of uncertainty. Though we use Lake Kinneret, the patterns identified most likely indicate processes that are expected in sub-tropical lakes in general.