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The randomness of sea conditions and loading sequences are among the key contributing factors that affect our ability to accurately predict the fatigue crack growth in ships. Climate change may alter the long-term characteristics of these factors along the service life of existing or newly constructed ships. This paper presents a framework for quantifying the impact of climate change on crack propagation in ship hulls. A probabilistic fatigue crack propagation approach is developed to account for uncertainties associated with material properties and loading conditions; specifically, those affected by climate change. Global Climate Models (GCMs) are used to quantify the long-term effects of climate change on the sea conditions and the resulting ship loading time histories. The proposed approach is applied to a tanker ship operating within predefined routes in the Atlantic Ocean. The results show that the effect of climate change on the crack propagation depends on the navigation route. [ABSTRACT FROM AUTHOR]

Warming climate and precipitation changes induce notable shifts in climate zones. In this study, the latest generation of global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the previous generation CMIP5 under high‐emission scenarios are used together with observations and applied to the Köppen‐Geiger climate classification. The aim is to shed light on how projected warming and changes in precipitation will influence future climate zones and their associated ecosystems while revealing differences between the two model generations. Compared to CMIP5 models, CMIP6 models exhibit slightly improved performance in replicating the observed Köppen‐Geiger map for the historical (1976–2005) period and similar inter‐model agreement for the future. The models show major changes in climate zones with a range of projections depending on which ensemble subset is used: 37.9%–48.1% of the global land area is projected to change climate zone by the end of the century, with the most pronounced changes expected over Europe (71.4%–88.6%) and North America (51.2%–65.8%). CMIP6 models project a higher rate of areal climate zone change (km2/year) throughout the 21st century, which is mainly driven by their greater global land warming rates. Using a likely equilibrium climate sensitivity subset of CMIP6 models that is consistent with the latest evidence constrains the climate zone shifts, and their projections better match the results of CMIP5 simulations. Although the high warming rates of some CMIP6 models are less credible, the risks associated with them are greater, and they heighten the need for urgent action to preserve terrestrial ecosystems. Plain Language Summary: Köppen‐Geiger climate classification is a tool to map regional climate zones based on a region's temperature and precipitation. In recent decades, increasing temperatures and changing precipitation trends have started to affect the distribution of those climate zones, and more extensive changes are expected throughout the 21st century. In this paper, we assess the observed and expected changes in the global distribution of Köppen‐Geiger climate zones using observations along with the latest and the previous generation of global climate model projections. We find that shifts in climate zones are more pronounced in the latest generation of models due to their questionable greater global warming rate projections. Depending on which subset of model projections is used, up to half of the Earth's land area faces the risk of shifting to a different climate zone by the end of the century, with the greatest changes expected in Europe and North America. The rate of change (affected area per year) is also projected to accelerate through the 21st century, suggesting that vulnerable species and agricultural practices might have less time to adapt to changes in climate zones than previously projected. Key Points: Coupled Model Intercomparison Project Phase 6 (CMIP6) exhibits slightly improved performance in simulating the globally observed historical Köppen‐Geiger climate zones compared to CMIP5By the end of the century, 38%–48% of the global land area is projected to be in a different climate zone than todayThe rate of climate zone change is projected to accelerate, with more pronounced rates in CMIP6 due to considerably higher warming rates [ABSTRACT FROM AUTHOR]

Even in the cool climate of Finland, severe heatwaves occur sporadically, having multiple implications on public health, forestry, fishery, agriculture, and reindeer husbandry, for instance. This study assesses the occurrence and severity of ≥ 3 -day heatwaves in Finland at the 0.5 °C, 1.0 °C, 1.5 °C, and 2.0 °C global warming levels above pre-industrial conditions, utilising bias-corrected daily-mean temperature data from 60 runs performed with 25 global climate models. The severity of a heatwave is measured by the heatwave extremity index, consisting of the sum of exceedances above a fixed threshold of daily mean temperature. Three alternative threshold temperatures, 20 °C, 24 °C and 28 °C, are considered. A shift from the 0.5 °C to 2.0 °C global warming level is projected to result in an increase in the mean annual number of heatwave days above 20 °C from 1 to 5 in central Lapland and from 5 to 20 in south-eastern Finland. Concurrently, the annual sum of the extremity index becomes 4 to 10 -fold. The higher the threshold temperature, the larger is the growth in relative terms. At the 2.0 °C global warming level, heatwaves above 20 °C are experienced in southern Finland nearly every year and in the majority of northern Lapland approximately every second year. Apart from Lapland, heatwaves occurring once in 10 (100) years at the 0.5 °C warming level will then have annual probabilities of 50% (> 10%). Even between the 1.5 °C and 2.0 °C global warming levels, projected changes in heatwave characteristics are substantial, especially for the most severe heatwaves. For example, in southern and central Finland, a heatwave with an annual probability of 12% to 13% at the 1.5 °C warming level is projected to substantially increase in likelihood under the 2.0 °C warming level, up to 19% to 21%. The paper includes a literature review on potential impacts of the intensifying heatwaves. [ABSTRACT FROM AUTHOR]

Climate classifications are useful to synthesize the physical state of the climate with a proxy that can be directly related to biota. However, they present a potential drawback, namely a strong sensitivity because of the use of hard thresholds (step functions). Thus, minor discrepancies in the base data produce large differences in the type of climate. However, such an a priori limitation is also a strength because such sensitivity can be used to better gauge model performance. Although previous attempts of classifying climates of the world using global climate model outputs were encouraging, the applicability of their classifications to impact studies has been limited by past model issues. Notwithstanding the persistence of certain imperfections and limitations in current models, the high‐quality physical simulations from phase six of the Coupled Intercomparison Project (CMIP6) has opened new possibilities in the field, thanks to their improved representation of atmospheric and oceanic physics. The purpose of this paper is twofold: to show that climate classifications from CMIP6 are sufficiently robust for use in impact studies, and to use those classifications for identifying error sources and potential issues that deserve further attention in models. Thus, 52 CMIP6 climate models were evaluated by using three climate classifications schemes, classical Köppen, extended‐Köppen, and modified Thornthwaite. We first assessed model ability to reproduce present climate types by comparing their outputs with observational data. Models performed best for the Köppen and extended‐Köppen classification methods (Cohen's kappa κ = 0.7), and had moderate scores for the Thornthwaite climate classification (κ = 0.4). By tracing back the observed biases, we were able to pinpoint the misrepresentation of dry climates as a major source of error. Another finding was that most models still had some difficulties in representing the seasonal variability of precipitation over several monsoonal regions, thereby yielding the wrong climate type there. Models were also evaluated for future climate. Substantial changes in climate types are projected in the SSP5‐8.5 scenario. These changes include a shrinkage of polar/frigid climates (22%) and an increase of dry climates (7%). [ABSTRACT FROM AUTHOR]

Extreme temperature events are becoming more frequent due to global warming, and have critical effects on natural ecosystems, social and economic spheres, human production and life. Predicting changes in temperature extremes and trends under future climate scenarios helps to assess the impact of climate change accurately. Based on climate observations from 54 meteorological stations in the North China Plain and the projection data from seven general circulation models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6), this paper researches nine representative extreme temperature indices under four typical climate scenarios. The aim is to reveal the temporal and spatial variations in extreme temperature indices in the North China Plain during the past (1971–2010) and the future (2061–2100). The results show that: using a support vector machine (SVM) to perform regression analysis on the multi-GCMs prediction results, the root mean square error (RMSE) and relative standard deviation (RSD) of the multi-model ensemble simulations obtained by the SVM method are lower than those of the arithmetic mean method and can better match the trend of the historical extreme temperature index; the extreme high temperature index is predicted to show a significant upward trend in the future, while the extreme low temperature index will decrease significantly; and there are significant spatial differences in the extreme temperature index in both historical and future periods, with the extreme temperature index under the high radiation forcing scenario (SSP585) showing the most considerable variation and the most significant spatial differences. [ABSTRACT FROM AUTHOR]

This paper compiles indices for the El Niño—Southern Oscillation (ENSO) and seven unforced decadal-to-multidecadal climate modes (Interdecadal Pacific Oscillation (IPO), Tripole Pacific Index (TPI), Pacific Decadal Oscillation focused over North Pacific (PDO) and South Pacific (SPDO), North Atlantic Multidecadal Oscillation (AMO), South Atlantic Multidecadal Oscillation (SAMO), and Indian Ocean basin (IOB)) in a 100-member ensemble of the Max Planck Institute Earth System Model (MPI-ESM1.1) and a 35-member ensemble of Community Earth System Model (CESM1). Comparison among various detrending approaches indicates that the best approach to remove the unforced component is by subtracting the ensemble average temperature at each grid point from the original model output. Similar characteristics are investigated in the indices of both MPI-ESM1.1 and CESM1 ensemble models. The results further indicate no statistically significant lead-lag correlations between the unforced multidecadal climate modes originating from North Pacific (e.g., PDO) and North Atlantic (e.g., AMO), suggesting that a high correlation found in previous observational studies may be due to biased detrending approaches. [ABSTRACT FROM AUTHOR]

The hydrological projections provided by the outputs of Global Climate Models (GCMs) combining hydrological models include multi‐source uncertainties, which may challenge the formulation of relevant adaption and mitigation policies. In this paper, the overall uncertainty and the relative contribution of each uncertainty component were investigated for hydrological projections over 408 watersheds in China by using 3 shared socioeconomic pathway emission scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5), 21 GCMs, 8 bias correction methods, 4 hydrological models, and 2 sets of optimized hydrological model parameters. The results show that the total uncertainty (T) is mainly contributed by uncertainty related to global climate models (G), with the mean percentage ranging from 60.4 to 64.1%, followed by the interaction uncertainties among all components, with the mean percentage ranging from 22.0 to 26.4%. The uncertainty contribution of hydrological models (H) (6.1–9.4%) ranks third, followed by emission scenarios (S) (2.9–5.9%) and bias correction methods (B) (0.2–1.1%). The uncertainty contribution of the optimized hydrological model parameters (P) (0.2–0.3%) is almost negligible. In terms of spatial variability, the relative contribution of uncertainty related to global climate models (G) is the highest in the near future for northern China (67.5–70.6%) and in the far future for southern China (66.1–66.7%). However, it was found to be lower for the Tibetan Plateau and northwestern China (45.3–57.9%) in the near and far future. The relative contribution of hydrological model uncertainty is higher for southwestern and northwestern China and the Tibetan Plateau (7.2–19.5%) and lower for northern, eastern, and southern China (2.5–6.6%). This study highlights the importance of including multiple GCMs and hydrological models in hydrological impact studies to consider their overall uncertainty. The development of global climate models and hydrological models is still the best way to reduce the uncertainty of climate change impact studies. [ABSTRACT FROM AUTHOR]

In this paper we consider some questions surrounding whether or not regional climate models "add value," a controversial issue in climate science today. We highlight some objections frequently made about regional climate models both within and outside the community of modelers, including several claims that regional climate models do not "add value." We show that there are a number of issues involved in the latter claims, the primary ones centering on the fact that different research questions are being pursued by the modelers making the complaints against regional climate models. Further issues focus on historical deficiencies of particular—but not generalizable—failures of individual regional models. We provide tools to sort out these different research questions and particular failures, and to improve communication and understanding surrounding added value in climate modeling and philosophy of climate science. [ABSTRACT FROM AUTHOR]

Selection of suitable Global Climate Models (GCMs) for average temperature (Tavg) simulation is assessed in this paper for Telangana region on the southern part of India covering 14 grid points. Five statistical performance metrics: Skill Score (SS), Correlation Coefficient (CC), Nash Sutcliffe Efficiency (NSE), Normalised Root Mean Square Deviation (NRMSD), and Absolute Normalised Mean Biased Deviation (ANMBD) are considered in this study to evaluate GCMs using historical observations. Three cases (i.e., case 1: CC-SS-NRMSD; case 2: CC-SS-ANMBD; case 3: CC-NSE-SS-NRMSD-ANMBD) were considered to evaluate the suitable models and selection of multi model ensemble for Telangana region which can be further used in climate impact assessment studies. Weights are determined for each performance metric using Entropy technique and sensitivity analysis. Compromise Programming (CP) is applied to rank GCMs based on the distance measure technique. Group decision making is employed to conclude collective ranking patterns of GCMs. The results of the study suggest that MIROC5, CNRM-CM5, Access 1.0 and BCC-CSM1.1(m) are suitable models for prediction of Tavg for the Telangana region under consideration. [ABSTRACT FROM AUTHOR]

The Equatorial Undercurrent (EUC) is a vital component of the coupled ocean‐atmosphere system in the tropical Pacific. The details of its termination near the Galápagos Islands in the eastern Pacific have an outsized importance to regional circulation and ecosystems. Subject to diverse physical processes, the EUC is also a rigorous benchmark for global climate models (GCMs). Simulations of the EUC in three generations of GCMs are evaluated relative to recent underwater glider observations along 93°W. Simulations of the EUC have improved, but a slow bias of ~36% remains in the eastern Pacific, along with a dependence on resolution. Additionally, the westward surface current is too slow, and stratification is too strong (weak) by ~50% above (within) the EUC. These biases have implications for mixing in the equatorial cold tongue. Downstream lies the Galápagos, now resolved to varying degrees by GCMs. Properly representing the Galápagos is necessary to avoid new biases as the EUC improves. Plain Language Summary: The Equatorial Undercurrent (EUC) is a swift current that flows eastward along the equator in the Pacific Ocean, about 100 m below the surface. This current is just as challenging to observe as it is to simulate with models—after all, it was only discovered in the 1950s. One of the interesting aspects of the Undercurrent is how it is diverted by the Galápagos Islands when it encounters them in the eastern Pacific. The resultant upwelling is responsible for the remarkably productive and diverse ecosystem of the Galápagos. This paper takes advantage of a unique set of observations from a recent, successful field campaign using underwater gliders to measure the Undercurrent just before it reaches the Galápagos, in order to evaluate how the latest generations of global climate models simulate this current and its neighboring features. Models have steadily improved, but they still struggle to capture the high speed of the EUC. Models are also being run at finer spatial resolution, which enables islands like the Galápagos to be included in some of the model grids. A sampling of islands in different models, and how they interact with the EUC, demonstrates the importance of a proper representation of the Galápagos in models. Key Points: A recent glider campaign offers a unique opportunity to evaluate model simulations of equatorial circulation in a key region for climateGlobal climate model simulations of the Equatorial Undercurrent have improved, but a slow bias of ~36% remains in the eastern PacificDetails of the encounter of the Equatorial Undercurrent with the Galápagos are impactful; resolving them well is demonstrably important [ABSTRACT FROM AUTHOR]

We document the development and simulation characteristics of the next generation modeling system for seasonal to decadal prediction and projection at the Geophysical Fluid Dynamics Laboratory (GFDL). SPEAR (Seamless System for Prediction and EArth System Research) is built from component models recently developed at GFDL—the AM4 atmosphere model, MOM6 ocean code, LM4 land model, and SIS2 sea ice model. The SPEAR models are specifically designed with attributes needed for a prediction model for seasonal to decadal time scales, including the ability to run large ensembles of simulations with available computational resources. For computational speed SPEAR uses a coarse ocean resolution of approximately 1.0° (with tropical refinement). SPEAR can use differing atmospheric horizontal resolutions ranging from 1° to 0.25°. The higher atmospheric resolution facilitates improved simulation of regional climate and extremes. SPEAR is built from the same components as the GFDL CM4 and ESM4 models but with design choices geared toward seasonal to multidecadal physical climate prediction and projection. We document simulation characteristics for the time mean climate, aspects of internal variability, and the response to both idealized and realistic radiative forcing change. We describe in greater detail one focus of the model development process that was motivated by the importance of the Southern Ocean to the global climate system. We present sensitivity tests that document the influence of the Antarctic surface heat budget on Southern Ocean ventilation and deep global ocean circulation. These findings were also useful in the development processes for the GFDL CM4 and ESM4 models. Plain Language Summary: In this paper we describe the development and simulation characteristics of a new climate model that will be used for seasonal to multidecadal climate prediction and projection. The model combines a set of newly developed components that simulate the ocean, atmosphere, land, and sea ice. We document this model by assessing its performance in simulating the current climate and by showing the model's response to changing greenhouse gases and aerosols over the 20th and 21st centuries. We also show results from a set of sensitivity experiments that were an important part of the model development process. These sensitivity tests explore connections between the surface energy balance over Antarctica and the circulation of the deep ocean. Key Points: Development and performance of the next generation GFDL seasonal to decadal prediction model is documentedThe response of this model to realistic radiative forcing changes is shown via a large ensemble of climate simulations for 1921–2100The influence of the Antarctic surface energy balance on the world ocean was crucial in model development as shown via sensitivity tests [ABSTRACT FROM AUTHOR]

Characterizing climate change impacts on water resources typically relies on Global Climate Model (GCM) outputs that are bias‐corrected using observational data sets. In this process, two pivotal decisions are (a) the Bias Correction Method (BCM) and (b) how to handle the historically observed time series, which can be used as a continuous whole (i.e., without dividing it into sub‐periods), or partitioned into monthly, seasonal (e.g., 3 months), or any other temporal stratification (TS). Here, we examine how the interplay between the choice of BCM, TS, and the raw GCM seasonality may affect historical portrayals and projected changes. To this end, we use outputs from 29 GCMs belonging to the CMIP6 under the Shared Socioeconomic Pathway 5–8.5 scenario, using seven BCMs and three TSs (entire period, seasonal, and monthly). The results show that the effectiveness of BCMs in removing biases can vary depending on the TS and climate indices analyzed. Further, the choice of BCM and TS may yield different projected change signals and seasonality (especially for precipitation), even for climate models with low bias and a reasonable representation of precipitation seasonality during a reference period. Because some BCMs may be computationally expensive, we recommend using the linear scaling method as a diagnostics tool to assess how the choice of TS may affect the projected precipitation seasonality of a specific GCM. More generally, the results presented here unveil trade‐offs in how BCMs are applied, regardless of the climate regime, urging the hydroclimate community to carefully implement these techniques. Plain Language Summary: Global Climate Models (GCMs) are useful tools to characterize the historical and future evolution of the Earth's climate and its impacts on water resources. Because these models contain errors and their horizontal resolution is too coarse for local impact assessments, spatial downscaling, and bias correction are required steps. In particular, bias correction methods can be trained and applied using all the available historical data or by splitting the time series (e.g., by season or months). Since there are no guidelines for selecting a temporal stratification (TS), we analyze bias‐corrected GCM outputs obtained using three types of strategies (entire period, seasons, and months) and seven bias‐correction techniques over continental Chile. We show that the choice of BCM and the TS applied can modify the projected precipitation signal and seasonality. We also propose using a simple statistical technique to identify if the TS may be a relevant decision for climate impact assessments for a given climate model. Key Points: The choice of temporal stratification (TS) for GCM bias correction is crucial for removing biases, even for GCMs with good raw seasonalityDifferent temporal stratifications used for GCM bias correction may yield different future seasonalities and signals in projected changesThe linear scaling approach can be used to easily identify GCMs whose projections are sensitive to the choice of TS [ABSTRACT FROM AUTHOR]

The enhancement of warming rates with elevation, so-called elevation-dependent warming (EDW), is one of the regional, still not completely understood, expressions of global warming. Sentinels of climate and environmental changes, mountains have experienced more rapid and intense warming trends in the recent decades, leading to serious impacts on mountain ecosystems and downstream. In this paper we use a state-of-the-art Global Climate Model (EC-Earth) to investigate the impact of model spatial resolution on the representation of this phenomenon and to highlight possible differences in EDW and its causes in different mountain regions of the Northern Hemisphere. To this end we use EC-Earth climate simulations at five different spatial resolutions, from ∼ 125 to ∼ 16 km, to explore the existence and the driving mechanisms of EDW in the Colorado Rocky Mountains, the Greater Alpine Region and the Tibetan Plateau-Himalayas. Our results show that the more frequent EDW drivers in all regions and seasons are the changes in albedo and in downward thermal radiation and this is reflected in both daytime and nighttime warming. In the Tibetan Plateau-Himalayas and in the Greater Alpine Region, an additional driver is the change in specific humidity. We also find that, while generally the model shows no clear resolution dependence in its ability to simulate the existence of EDW in the different regions, specific EDW characteristics such as its intensity and the relative role of different driving mechanisms may be different in simulations performed at different spatial resolutions. Moreover, we find that the role of internal climate variability can be significant in modulating the EDW signal, as suggested by the spread found in the multi-member ensemble of the EC-Earth experiments which we use. [ABSTRACT FROM AUTHOR]

The Andes Cordillera plays a role in driving moisture and heat from tropical onto subtropical South America. It forces the development of a lee-side trough that covers most of western Argentina and a low-level jet that maximizes over Paraguay, eastern Bolivia and northern Argentina and is tightly linked to precipitation variability over much of central and southeastern South America. Its steep slopes and the large zonal gradients in topography between the Equator and 40°S are misrepresented in climate simulations using Global Climate Models (GCM) with resolutions coarser than about 100 km, since they naturally have a poor representation of the Andes and related circulation features. This paper analyses the impact of varying artificially the altitude of the Andes Cordillera in a GCM as well as increasing the horizontal resolution to study how these variations determine moisture fluxes and precipitation over selected regions of South America. Results show that the height of the Andes is crucial in shaping moisture fluxes pathways onto subtropical South America all year long. In particular, the low-level jet is only simulated when the Andes heights are doubled. At the same time, the relationship between the Andes shape and the location of the Bolivian High in summer is also discussed. In terms of precipitation, the lowest bias in the simulations is achieved when the horizontal resolution is increased, while in particular near the Andes foothills the simulated annual rainfall is largely determined by the Mountains shape. [ABSTRACT FROM AUTHOR]

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CO2 absorbs and emits radiation, which allows it to act both as radiative forcing and feedback. Recent work has shown CO2's feedback effect becomes dominant in hothouse climates, giving rise to a non‐monotonic climate sensitivity around 310 K. However, CO2's feedback effect in colder climates is less clear. We use a line‐by‐line model to explore the CO2‐dependence of the longwave clear‐sky feedback and identify a dividing temperature. Above 290 K, feedback increases with CO2 concentration; below 290 K, feedback decreases with CO2 concentration. We explain this dependence in terms of spectral competition under CO2 increases. In hot climates, CO2's moderate feedback replaces near‐zero feedback from the H2O bands; in cold climates, CO2's moderate feedback replaces the large feedback from the surface. Given that global mean temperature is currently close to 290 K, our results suggest that feedback CO2‐dependence is weak at present but can be important in past and future climates. Plain Language Summary: CO2 traps heat, causing warming. But CO2 also emits heat to space, acting as radiative feedback. Recent work has shown CO2's feedback effect crucially helps to stabilize very hot climates, but how does it affect present‐day Earth? We show that in hot climates, more CO2 increases Earth's feedback, while in cold climates, more CO2 decreases it. To understand why, we explain that the surface is an effective emitter, CO2 is a moderate emitter, while H2O is a poor emitter. At high temperatures, adding CO2 to the atmosphere thus replaces feedback that would have otherwise come from H2O, increasing the overall feedback; at low temperatures, adding CO2 replaces feedback that would have otherwise come from the surface, decreasing the overall feedback. Currently, Earth's global‐mean temperature falls between these two temperature regimes, where CO2's effect on feedback is nearly zero. Our results explain why CO2's impact on feedback is small now but can be significant in past or future climates. Key Points: An increase in CO2 concentration strengthens Earth's feedback in hot climates, ∂λ/∂CO2 > 0, but weakens it in colder climates, ∂λ/∂CO2 < 0Whether feedback CO2‐dependence, ∂λ/∂CO2, is positive or negative primarily depends on the extent of the H2O windowFeedback CO2‐dependence and forcing temperature‐dependence, ∂λ/∂CO2 = −∂F2x/∂Ts, can be important for past or future climates [ABSTRACT FROM AUTHOR]

Over the last few decades, ensemble forecasts based on global climate models have become an important part of climate forecast due to the ability to reduce uncertainty in prediction. Moreover in ensemble forecast, assessing the prediction uncertainty is as important as estimating the optimal weights, and this is achieved through a probabilistic forecast which is based on the predictive distribution of future climate. The Bayesian model averaging has received much attention as a tool of probabilistic forecasting due to its simplicity and superior prediction. In this paper, we propose a new Bayesian model averaging method for probabilistic ensemble forecasting. The proposed method combines a deterministic ensemble forecast based on a multivariate regression approach with Bayesian model averaging. We demonstrate that the proposed method is better in prediction than the standard Bayesian model averaging approach by analyzing monthly average precipitations and temperatures for ten cities in Korea. [ABSTRACT FROM AUTHOR]

The influence of climate feedbacks on regional hydrological changes under warming is poorly understood. Here, a moist energy balance model (MEBM) with a Hadley Cell parameterization is used to isolate the influence of climate feedbacks on changes in zonal‐mean precipitation‐minus‐evaporation (P − E) under greenhouse‐gas forcing. It is shown that cloud feedbacks act to narrow bands of tropical P − E and increase P − E in the deep tropics. The surface‐albedo feedback shifts the location of maximum tropical P − E and increases P − E in the polar regions. The intermodel spread in the P − E changes associated with feedbacks arises mainly from cloud feedbacks, with the lapse‐rate and surface‐albedo feedbacks playing important roles in the polar regions. The P − E change associated with cloud feedback locking in the MEBM is similar to that of a climate model with inactive cloud feedbacks. This work highlights the unique role that climate feedbacks play in causing deviations from the "wet‐gets‐wetter, dry‐gets‐drier" paradigm. Plain Language Summary: Climate feedbacks, which act to amplify or dampen global warming, play an important role in shaping how the climate system responds to changes in greenhouse‐gas concentrations. Here, we use an idealized climate model, which makes a simplified assumption about how energy is transported in the atmosphere, to examine how climate feedbacks influence the patterns of precipitation and evaporation change under global warming. We find that the cloud feedback acts to narrow the band of rainfall on the equator known as the Intertropical Convergence Zone and that the surface‐albedo feedback acts to shift the location of maximum rainfall. We also find that the cloud feedback accounts for most of the uncertainty associated with feedbacks in regional hydrological change under warming. The idealized model with a locked cloud feedback also simulates a change in precipitation and evaporation that is similar to a comprehensive climate model with an inactive cloud feedback. Key Points: A moist energy balance model (MEBM) is used to investigate the influence of climate feedbacks on regional hydrological changes under warmingCloud feedbacks act to narrow and increase tropical P − E and are the dominant source of feedback uncertainty in regional hydrological changesThe MEBM with a locked cloud feedback largely replicates a climate model with an inactive cloud feedback [ABSTRACT FROM AUTHOR]

Mountain lee waves present significant hazards to aviation, often inducing turbulence and aircraft icing. The current study focuses on understanding the potential impact of global climate change on the precursor environments to mountain lee wave cloud episodes over central Iberia. We examine the suitability of several Global Climate Models (GCMs) from CMIP6 in predicting these environments using the ERA5 reanalysis as a benchmark for performance. The dataset is divided into two periods: historical data (2001–2014) and projections for the SSP5–8.5 future climate scenario (2015–2100). The variations and trends in precursor environments between historical data and future climate scenarios are exposed, with a particular focus on the expansion of the Azores High towards the Iberian Peninsula, resulting in increased zonal winds throughout the Iberian Peninsula in the future. However, the increase in zonal wind is insufficient to modify the wind pattern, so future mountain lee wave cloud events will not vary significantly. The relative humidity trends reveal no significant changes. Moreover, the risk of icing precursor environments connected with mountain lee wave clouds is expected to decrease in the future, due to rising temperatures. Our results highlight that the EC-EARTH3 GCM reveals the closest alignment with ERA5 data, and statistically significant differences between the historical and future climate scenario periods are presented, making EC-EARTH3 a robust candidate for conducting future studies on the precursor environments to mountain lee wave cloud events. [ABSTRACT FROM AUTHOR]

In most cases, climate change projections from General Circulation Models (GCM) and Regional Climate Models cannot be directly applied to climate change impact studies, and downscaling is, therefore, needed. A large number of statistical downscaling methods exist, but no clear recommendations exist of which methods are more appropriate, depending on the application. This paper compares two different statistical downscaling methods, Pre and Pre, using the Coupled Model Intercomparison Project Phase 5 (CMIP5) datasets and station observations. Both methods include two steps, but the major difference between them is how the CMIP5 dataset and the station data used. The downscaled precipitation data are validated with observations through China and Jiangxi province from 1976 to 2005. Results show that GCMs cannot be used directly in climate change impact studies. In China, the second method Pre, which establishes regression model based on the station data, has a tendency to overestimate or underestimate the real values. The accuracy of Pre is much better than Pre based on mean absolute error, mean relative error and root mean square error. Pre fuses the mode data and station data effectively. Results also show the importance of the meteorological station data in the process of residual modification. [ABSTRACT FROM AUTHOR]

This paper presents the spatial and temporal structures of drought regimes in Southern Africa and evaluates the capability of ten global climate models (GCMs) in simulating the regimes. The study uses a multi-scaled standardized index (called standardized precipitation evapo-transpiration index, SPEI) in characterizing droughts over Southern Africa at 3- and 12-month scales. The spatial patterns of the drought regimes are identified using the rotated principal component analysis (PCA) on the SPEI, while the temporal characteristics of the drought regimes are studied using wavelet analysis. The relationship between each drought regime and global SSTs (and climate indices) is quantified using correlation analysis and wavelet coherence analysis. The study also quantifies the capability of the GCMs in simulating the drought regimes. The PCA results show four main drought regimes that jointly explain about 50 % SPEI variance over South Africa. The drought regimes (hereafter PF1, PF2, PF3 and PF4) centre over the south-western part of Southern Africa (i.e. South Africa, Botswana and Namibia common border), Zimbabwe, Tanzania, and Angola, respectively. PF1, PF2 and PF4 are strongly correlated with SST over the South Atlantic, Tropical Pacific and Indian Oceans, while PF3 is strongly correlated with the SST over the Tropical Pacific, Atlantic and Indian Oceans. The drought regimes (except PF4) have significant coherence with some atmospheric teleconnection, but the strength, duration, and phase of the coherence vary with time. All the GCMs simulate the drought regimes better at a 3-month scale than at a 12-month scale. At a 3-month scale, 70 % of the GCMs simulate all the drought regimes with a high correlation coefficient (r > 0.6), but at a 12-month scale only 60 % of the models simulate at least three of the drought regimes with a high correlation coefficient (r > 0.6). The results of this study have applications in using GCMs to study the underlying atmospheric dynamics that control droughts and to understand the impacts of global warming on droughts. [ABSTRACT FROM AUTHOR]

This paper analyzes the diurnal temperature range (DTR) over land in simulations of the recent past and in future projections by 20 models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5). The annually averaged DTR is evaluated for the present-day climate using two gridded datasets (HadGHCND and CRU). The DTR varies substantially between different CMIP5 models, particularly in the subtropics, and is generally underestimated. In future projections of the high emission scenario RCP8.5, the models disagree on both the sign and the magnitude of the change in DTR. Still, a majority of the models project a globally averaged reduction in the DTR, with an increase over Europe, a decrease over the Sahara desert and a substantial decrease in DTR at high latitudes in winter. The general DTR reduction is partly linked to the enhancement of the downwelling clear sky longwave radiation due to greenhouse gases. At high latitudes in winter, the decrease in DTR seems to be enforced by an increase in cloudiness, but in most other regions counteracted by decreases in cloud fraction. Changes in the hydrological cycle and in the clear sky shortwave radiation also impact the DTR. The DTR integrates many processes and neither the model differences in the DTR nor in the change in DTR can be attributed to a single parameter. Which variables that impact the model discrepancies vary both regionally and seasonally. However, clouds seem to matter in most regions and seasons and the evaporative fraction is important in summer. [ABSTRACT FROM AUTHOR]

Climate classifications based on temperature and precipitation measurements are increasingly being used for environmental and climate change studies. Using three classification methods (Köppen, Extended Köppen, and Holdridge) and one observational dataset for present climate (CRU, Climate Research Unit), we show that GCMs have bridged the gap that led to the emergence of RCMs thirty years ago, as GCMs can now provide global climate classifications whose accuracy and precision are comparable to those of regional outputs of the RCMs. Projections of high-resolution GCMs for future climates under the assumptions of three Representative Concentration Pathways (RCP26, RCP45 and RCP85) can therefore be used as a primary source for climate change and global warming studies at high resolution. This paper provides comprehensive, model-derived climate classifications for the entire planet, using RCMs and two GCMs for present and future climate-change scenarios, and discusses how well the models actually represent the climates of the world when compared with reference, ground validation data. It turns out that both GCMs and RCMs appear still limited to provide practical estimates of the world climates even for present climate conditions. The modeling of precipitation remains the Achilles' heel of models and thus of multidimensional indices, which are very sensitive to this variable. The conclusion is that model outputs at regional scale need to be taken with extreme caution without venturing into informing policies presenting potentially large societal impacts. Nonetheless, the role of models as privileged tools to advance our scientific knowledge of the Earth's system remains undisputed. • A systematic analysis of climate classifications with RCMs and two high resolution GCMs is presented. • Three classification methods are used to illustrate the differences between models and observations. • Current high-resolution GCMs can provide climate classifications with the same accuracy as those from RCMs. • Uncertainties in modeling precipitation persist for large parts of the world. [ABSTRACT FROM AUTHOR]

• We develop shallow and deep neural networks for the Short-Term Load Forecasting problem. • Different neural networks architectures are tested, including uni- and bi-directional structures. • Global climate models' information is used as input of the neural networks. • We present a real study case of time series forecasting for the Brazilian power system. • Relevant results are presented and systematically compared using Diebold-Mariano test. This paper focuses on the development of shallow and deep neural networks in the form of multi-layer perceptron, long-short term memory, and gated recurrent unit to model the short-term load forecasting problem. Different model architectures are tested, and global climate model information is used as input to generate more accurate forecasts. A real study case is presented for the Brazilian interconnected power system and the results generated are compared with the forecasts from the Brazilian Independent System Operator model. In general terms, results show that the bidirectional versions of long-short term memory and gated recurrent unit produce better and more reliable predictions than the other models. From the obtained results, the recurrent neural networks reach Nash-Sutcliffe values up to 0.98, and mean absolute percentile error values of 1.18%, superior than the results obtained by the Independent System Operator models (0.94 and 2.01% respectively). The better performance of the neural network models is confirmed under the Diebold-Mariano pairwise comparison test. [ABSTRACT FROM AUTHOR]

European heatwaves at the 0.5, 1.0, 1.5 and 2.0 ∘ C global warming levels above the pre-industrial temperature are examined using bias-corrected daily-mean temperature data from 60 simulations performed with 25 global climate models (GCMs). A heatwave event is defined here to consist of at least three consecutive days above the 90th percentile of summer daily-mean temperatures and a break of 1 day is allowed. At the 2.0 ∘ C global warming level compared with 0.5 ∘ C, the multi-GCM mean annual count of heatwave days is projected to be three to fourfold in northern and more than sixfold in southern Europe. The total annual heatwave extremity index, or the sum of exceedings above the threshold temperature over all the heatwave days, becomes approximatively fourfold in the north and tenfold in the south. In central Europe, the date of the strongest heatwave of year is delayed by about 1 week. By concatenating the bias-corrected output data of all the GCM runs, 1200-year samples were created from which probabilities of occurrence of the strongest heatwaves can be determined very robustly under all four warming levels. An intense heatwave occurring once in 10 years at the 0.5 ∘ C warming level has an annual probability of about 50% in northern and 80–90% in southern Europe under 2.0 ∘ C. Corresponding probabilities for 100-year heatwaves would be nearly 20% in the north and about 60% in the south. Finally, we discuss factors that explain the stronger increase in heatwave duration and extremity in the south than north. [ABSTRACT FROM AUTHOR]

Clear-air turbulence (CAT) has a large impact on the aviation sector. Our current understanding of how CAT may increase with climate change in future is largely based on simulations from CMIP3 and CMIP5 global climate models (GCMs). However, these models have now been superseded by high-resolution CMIP6 GCMs, which for the first time have grid lengths at which individual turbulence patches may start to be resolved. Here we use a multi-model approach to quantify projected moderate CAT changes over the North Atlantic using CMIP6 models. The influence of the model resolution on CAT projections is analysed. Twenty-one CAT diagnostics are used, in order to represent uncertainties in CAT production mechanisms. Each diagnostic responds differently in time, but the majority display an increase in moderate CAT between 1950 and 2050. Although winter is historically the most turbulent season, there is strong multi-model agreement that autumn and summer will have the greatest overall relative increase in CAT frequency. By 2050, summers are projected to become as turbulent as 1950 winters and autumns. The global-mean seasonal near-surface temperature is used as a comparative metric. For every 1 °C of global near-surface warming, autumn, winter, spring, and summer are projected to have an average of 14%, 9%, 9%, and 14% more moderate CAT, respectively. Our results confirm that the aviation sector should prepare for a more turbulent future. [ABSTRACT FROM AUTHOR]

Abstract Climate classifications of climate models' outputs have been used to assess environmental changes but systematic analyses of the differences between models, scenarios and classification methods are scarce. Here, the results of applying the most commonly used climate classifications to the outputs of 47 Global Climate Models (GCM) of different physical parameterizations and varied grid size are presented. The extent and intensity of changes for present climate, three different Representative Pathways Scenarios (RCP26, RCP45 and RCP85) and three increasingly-fine classification methods show that there is a consensus between models, and that climate classifications are indeed useful tools to translate physical climatology variables into environmental changes. The main conclusions are that climate classifications can indeed be used to gauge model performance at several grid sizes and that the classification method does not decisively affects the potential global changes in future climates under increasing greenhouse gas emissions. The analyses also reveal that there are several uncertainties that are not attributable to model grid size or to limitations in the reference datasets but more likely to deficiencies in the physics of the models. Highlights • This paper provides a systematic analysis of climate classifications for present and future climates. • Several classifications methods are used to illustrate the consensus between models and observations. • Overall, global models show consistent changes but uncertainties persist with large differences in individual model skill. [ABSTRACT FROM AUTHOR]

Abstract Most estimates of Chinese regional Surface Air Temperatures since the late-19th century have identified two relatively warm periods – 1920s–40s and 1990s–present. However, there is considerable debate over how the two periods compare to each other. Some argue the current warm period is much warmer than the earlier warm period. Others argue the earlier warm period was comparable to the present. In this collaborative paper, including authors from both camps, the reasons for this ongoing debate are discussed. Several different estimates of Chinese temperature trends, both new and previously published, are considered. A study of the effects of urbanization bias on Chinese temperature trends was carried out using the new updated version of the Global Historical Climatology Network (GHCN) – version 4 (currently in beta production). It is shown that there are relatively few rural stations with long records, but urbanization bias artificially makes the early warm period seem colder and the recent warm period seem warmer. However, current homogenization approaches (which attempt to reduce non-climatic biases) also tend to have similar effects, making it unclear whether reducing or increasing the relative warmth of each period is most appropriate. A sample of 17 Chinese temperature proxy series (12 regional and 5 national) is compared and contrasted specifically for the period since the 19th century. Most proxy series imply a warm early-20th century period and a warm recent period, but the relative warmth of these two periods differs between proxies. Also, with some proxies, one or other of the warm periods is absent. [ABSTRACT FROM AUTHOR]

Hydrological modeling relies on the inputs provided by General Circulation Model (GCM) data, as this allows researchers to investigate the effects of climate change on water resources. But there is high uncertainty in the climate projections with various ensembles and variables. Therefore, it is very important to carry out bias correction in order to analyze the impacts of climate change at a regional level. The performance evaluation of bias correction methods for precipitation, maximum temperature, and minimum temperature in the Upper Bhima sub-basin has been investigated. Four bias correction methods are applied for precipitation viz. linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Three bias correction methods are applied for temperature viz. linear scaling (LS), variance scaling (VS), and distribution mapping (DM). The evaluation of the results from these bias correction methods is performed using the Kolmogorov–Smirnov non-parametric test. The results indicate that bias correction methods are useful in reducing biases in model-simulated data, which improves their reliability. The results of the distribution mapping bias correction method have been proven to be more effective for precipitation, maximum temperature, and minimum temperature data from CMIP5-simulated data. [ABSTRACT FROM AUTHOR]

The High‐Resolution Model Intercomparison Project (HighResMIP) experiments from the Coupled Model Intercomparison Phase 6 represent a broad effort to improve the resolution, and performance of climate models. The HighResMIP suite provides high spatial resolution (i.e., 25‐ and 50‐km) forcings that have been shown to improve the representation of climate processes. However, little is known about their suitability for hydrologic applications. We use outputs from the HighResMIP suite to simulate annual maximum discharge with the Hillslope‐Link Model (HLM) at ∼1,000 river communities across Iowa. First, we assess whether the runoff from the climate models can be directly routed through the river network model in HLM to estimate annual maximum discharge. Runoff‐based simulations can capture the empirical distribution of flood peaks in five of the 10 models/members assessed. Next, we force the HLM with precipitation, temperature, and potential evapotranspiration from HighResMIP models to simulate flood peaks, finding all models/members produce empirical distributions similar to our reference. However, significant biases exist in the model/member forcings as correct flood response is being generated for the wrong reason. To improve their suitability for community‐level assessment, we use nine statistical approaches to bias‐correct and downscale HighResMIP precipitation to a 4‐km resolution. The bias‐correction and downscaling of climate model precipitation performs well for all models/members. Furthermore, we do not find significant changes in the magnitude flood peak projections for Iowa based on the HLM forced with HighResMIP outputs, or based on routed runoff, while there are indications that the variability in flood peaks is projected to increase across the state. Key Points: High‐Resolution Model Intercomparison Project (HighResMIP) runoff routed through a hydrologic model generates realistic distributions of annual maximum discharge for 5 of 11 realizations assessedHighResMIP precipitation forcings generate realistic flood peaks for all realizations assessed after bias‐correction and statistical downscalingChanges in flood peaks are not detected for the future but there is an overall increase in variance at 24% of the location in Iowa [ABSTRACT FROM AUTHOR]

Marine heatwaves in the summertime when temperatures may exceed organisms' thermal thresholds ("warm‐season marine heatwaves (MHWs)") have huge impacts on the health and function of ecosystems like kelp forests and coral reefs. While previous studies showed that MHWs are likely to become more frequent and severe under future climate change, there has been less analysis of the thermal properties of warm‐season MHWs or on the effects of climate model biases on these projections. In this study, we examine CMIP6 model ability to simulate five key thermal properties of warm‐season MHWs, and evaluate the global pattern of future projections for coral reef and kelp systems. The results show that the duration, accumulated heat stress and peak intensity are projected to increase by >60 days, 160°C·day and 1°C, respectively, across most of the ocean by the end of the 21st century. In contrast, the duration of "priming" (a period of sub‐lethal heat stress prior to substantial MHW heat stress) is projected to decrease by >30 days in the tropics, potentially reducing organisms' ability to acclimate to heat stress. The projected increases in MHW duration and accumulated heat stress in some coral reef and kelp forest locations, however, are likely overestimated due to model limitations in simulating surface winds, deep convection and some other processes that influence MHW evolution. The findings point to some possible processes to target in model development and regional biases to be considered when projecting the impacts of MHWs on marine ecosystems. Plain Language Summary: Periods of extremely high ocean temperatures that persist for days to months, known as marine heatwaves (MHWs), can cause the loss of marine life and impact coastal communities and economies. Climate change is expected to drive substantial increases in the length, strength and frequency of MHWs this century. There has been less analysis, however, of the characteristics of individual MHWs, like the rate at which they develop. In this research, we examine how well climate models can simulate these characteristics and the implication for future projections. We find considerable biases in the simulation of some key MHW characteristics in parts of the ocean due to model limitations in capturing physical processes like surface winds along the equator. Most MHW characteristics like duration and total heat stress are projected to increase sharply this century, particularly for coral reefs and kelp forests, although the increases in some regions are likely overestimated due to model biases. Conversely, we project decreases in "priming"—periods of sub‐lethal heat stress that help marine life prepare for heatwaves. These findings identify regional errors to consider when interpreting MHW projections and can help researchers identify areas for improving model performance. Key Points: Examination of three CMIP6 models show considerable regional biases in the thermal properties of warm‐season marine heatwavesUnlike marine heatwave duration and peak intensity, periods of "priming" heat stress that could train ecosystems are projected to declineIn some coral reef and kelp locations, projected increases in marine heatwave duration and accumulated heat stress are likely overestimated [ABSTRACT FROM AUTHOR]

Severe thunderstorms lead to multiple hazards including torrential precipitation, flash flood, hail, lightning, and wind gusts. The meso- to micro-scale nature of thunderstorms impose great challenges from understanding individual storm dynamics, storm climatology as well as projecting their future activities. High-resolution regional climate models can resolve the convective environments better than global models. Australia, especially the east and southeast parts of the continent, is a global hot spot for severe thunderstorms. This study evaluates the simulated convective environments from the New South Wales (NSW) and Australian Regional Climate Modelling (NARCliM) project based on the parameters of CAPE, CIN, 0–6-km vertical wind shear and storm severity. The ensemble regional downscaling is compared against the fifth-generation European Centre for Medium-range Weather Forecast Reanalysis (ERA5). The results show that although there are apparent biases (generally positive for CAPE and negative for CIN, and slightly overestimated vertical wind shear) in the downscaled storm parameters, the climatology of measures of storm severity over land, including their spatial patterns and seasonality, agree well with ERA5. These results have strong implication on the application of the climate projection to assess future convective environments in the region. [ABSTRACT FROM AUTHOR]