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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]

ABSTRACT Changes in climate in the South Atlantic region and adjacent regions have been described in numerous works using projections from global climate models from CMIP5 and CMIP6. This paper presents an evaluation of the ability of these models to reproduce the atmospheric circulation patterns (weather types) and their seasonal and inter‐annual variability. The analyses are performed based on the probability of occurrence of weather types in the historical period and in future projections. The scatter index and the relative entropy are the statistical parameters used to evaluate the models' performance in the historical period. Future projections consist of RCP2.6, 4.5 and 8.5 scenarios for the CMIP5 models and the SSP126, 245, 370 and 585 scenarios for the CMIP6 and are assessed at different time intervals: short term (2015–2039), mid‐term (2040–2069) and long term (2070–2100). The performance of projections is measured by analysing their consistency, that is, based on the similarity between projections of the same scenario in different models. The results show that the reproduction of the probability of occurrence of historical weather types and their seasonal and interannual variability was better performed by ACCESS1‐0, HadGEM2‐ES, HadGEM2‐CC, CMCC‐CM and MPI‐ESM‐P when assessing the models from CMIP5, and by HadGEM3‐GC31‐MM, ACCESS‐ESM1‐5, ACCESS‐ CM2 and MRI‐ESM‐P when assessing the models from CMIP6. As for future projections, only the BESM‐AO2‐5, GFDL‐ESM4 and HadGEM3‐GC31‐MM models showed inconsistency in one or more scenarios. [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]

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]

Polar sea ice is an important input to global climate models and is considered to be a sensitive indicator of climate change. While originally designed only for wind estimation, radar backscatter measurements collected by wind scatterometers have proven useful for estimating the extent of sea ice. During the Quick Scatterometer (QuikSCAT) mission, SeaWinds data were used to operationally map the sea ice extent. The resulting sea ice maps were used to mask near-surface winds to support SeaWinds' primary mission of measuring near-surface winds over the ocean. This paper describes the operational SeaWinds sea ice extent mapping algorithm, provides validation comparisons, and presents results from the ten-year data product. Starting with enhanced resolution horizontal polarization and vertical polarization backscatter images, the algorithm employs an iterative maximum-likelihood classifier with fixed thresholds to segment sea ice and open ocean pixels. Residual classification errors are reduced through binary image processing techniques and sea ice growth/retreat constraint methods. The algorithm results are compared with sea ice concentrations derived from Special Sensor Microwave/Imager data and with RADARSAT synthetic aperture radar imagery. The results suggest differences in the sensitivities of active and passive products given their channel sets and specific algorithms. Derived sea ice extents over the full decade-long QuikSCAT mission data set are analyzed to show important trends in sea ice extent for the Antarctic and Arctic regions. [ABSTRACT FROM PUBLISHER]

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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]

ABSTRACT The ability of numerical models in reproducing the observed statistical links between the sea surface temperature of central and eastern tropical Pacific Ocean is analysed in this paper. Significant links in the whole spectrum of frequency were studied for models of the World Climate Research Program (WCRP-CMIP3/IPCC-AR4) using the methodology of wavelet coherence. During the second half of the twentieth century, in-phase or quasi-in-phase links in oscillations of 2-6 years, a lack of relation in oscillations of 8-12 years and time lagged links in the longest periodicities are detected in the observed dataset. Some of the analysed models are able to reproduce the observed relationships in oscillations shorter than 6 years and few of them also reproduce the lacks of significant relation in the band of 8-12 years. On the other hand, all models have serious problems representing the observed time lagged links in the longest waves. The analysis shows that climate models are still unable to reproduce specific features of tropical Pacific interactions with potential effect on the Southern Hemisphere atmospheric circulation and, in particular, the South American climatic variability. [ABSTRACT FROM AUTHOR]

This study reflects a portion of the riverine analysis for a Federal Emergency Management Agency initiative to estimate the economic risks associated with climate and land use change to the U.S. National Flood Insurance Program. Specifically, this paper investigates how the 1% annual chance flood discharge, (equivalent to a 100-year return period flood), may change based on climate change and population projections through the year 2100. Watershed characteristics and observations of climate indicators at 2,357 U.S. Geological Survey gauging stations were used to develop regression relationships to estimate . Projections of the climate indicators that measure extremes in temperature and precipitation from a suite of global climate models were then used within a Monte Carlo sampling framework to estimate future changes to throughout the United States, while also translating the uncertainty resulting from multiple climate model projections into uncertainty in estimating the future . Population growth models consistent with climate model emission assumptions were used to estimate increases to impervious area over the next century, along with corresponding contributions to the estimates. The study provides a screening-level analysis of possible changes to flow and suggests spatial trends across the United States. The results suggest that may increase substantially over many areas of the United States over the next century, especially in the Pacific Northwest, the Northeast, and highly urbanized areas. [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]

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]

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]

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]

European temperatures and their projected changes under the 8.5 W/m 2 Representative Concentration Pathway scenario are evaluated in an ensemble of 33 global climate models participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Respective contributions of large-scale dynamics and local processes to both biases and changes in temperatures, and to the inter-model spread, are then investigated from a recently proposed methodology based on weather regimes. On average, CMIP5 models exhibit a cold bias in winter, especially in Northern Europe. They overestimate summer temperatures in Central Europe, in association with a greater diurnal range than observed. The projected temperature increase is stronger in summer than in winter, with the highest summer warming occurring over Mediterranean regions. Links between biases and sensitivities are evidenced in winter, suggesting a potential influence of snow cover biases on the projected surface warming. A brief analysis of daily temperature extremes suggests that the intra-seasonal variability is projected to decrease (slightly increase) in winter (summer). Then, in order to understand model discrepancies in both present-day and future climates, we disentangle effects of large-scale atmospheric dynamics and regional physical processes. In particular, in winter, CMIP5 models simulate a stronger North-Atlantic jet stream than observed and, in contrast with CMIP3 results, the majority of them suggests an increased frequency of the negative phase of the North-Atlantic Oscillation under future warming. While large-scale circulation only has a minor contribution to ensemble-mean biases or changes, which are primarily dominated by non-dynamical processes, it substantially affects the inter-model spread. Finally, other sources of uncertainties, including the North-Atlantic warming and local radiative feedbacks related to snow cover and clouds, are briefly discussed. [ABSTRACT FROM AUTHOR]

This paper is designed to present the likely impact of climate change on groundwater resources in general and Libya in particular. State of the art reviews on recent research studies, and methodology to assess the impact of climate change on groundwater resources shows that climate change poses uncertainties to the supply and management of water resources. It outlines to demonstrate that how climate change impact assessment plays a vital role in forming the sensitive water balance rarely achieved in most area owing to precipitation variability's and seasonality. It demonstrates that how large increases in water demand with very little recharge from precipitation have strained Libya's groundwater resources resulting in declines of groundwater levels and its quality, especially on Libyan coastal areas where most of the agriculture, domestic and industrial activities are concentrated. Based on several research studies it demonstrates that how policy and decision making process using best practices for monitoring, analyzing and forecasting variation of climate is a way forward to cope with the impact of sea level rise, and combat some water supplies in vulnerable areas that are becoming unusable due to the penetration of salt water into coastal aquifers (Jifara Plain, Sirt, Jebal El-Akhdar). Finally, a number of Global Climate Models (GCM) are reviewed to demonstrate that how better understanding of climate and climate change forecasting helps in devising appropriate adaptation strategies due to the impact of climate change. [ABSTRACT FROM AUTHOR]

Antarctic sea ice and its snow cover are integral components of the global climate system, yet many aspects of their vertical dimensions are poorly understood, making their representation in global climate models poor. Remote sensing is the key to monitoring the dynamic nature of sea ice and its snow cover. Reliable and accurate snow thickness data are currently a highly sought after data product. Remotely sensed snow thickness measurements can provide an indication of precipitation levels, predicted to increase with effects of climate change in the polar regions. Airborne techniques provide a means for regional-scale estimation of snow depth and distribution. Accurate regional-scale snow thickness data will also facilitate an increase in the accuracy of sea ice thickness retrieval from satellite altimeter freeboard estimates. The airborne data sets are easier to validate with in situ measurements and are better suited to validating satellite algorithms when compared with in situ techniques. This is primarily due to two factors: better chance of getting coincident in situ and airborne data sets and the tractability of comparison between an in situ data set and the airborne data set averaged over the footprint of the antennas. A 2-8-GHz frequency modulated continuous wave (FMCW) radar loaned by the Center for Remote Sensing of Ice Sheets to the Australian Antarctic Division is used to measure snow thickness over sea ice in East Antarctica. Provided with the radar design parameters, the expected performance parameters of the radar are summarized. The necessary conditions for unambiguous identification of the air/snow and snow/ice layers for the radar are presented. Roughnesses of the snow and ice surfaces are found to be dominant determinants in the effectiveness of layer identification for this radar. Finally, this paper presents the first in situ validated snow thickness estimates over sea ice in Antarctica derived from an FMCW radar on a helicopterborne platform. [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]

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]