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Climate models are developed based on well-established physical principles applied to past and recent climate changes. There is considerable confidence that the models can also provide estimates of some climate variables (i.e., surface temperature, CO2 levels, ocean heat content). Despite advanced mathematical developments in the field of climate modeling, the existing climate models suffer from the following major limitations: first, the models do not consider that their estimations will be highly unreliable when a tipping point is triggered; secondly, many of the environmental tipping points are already triggered, however their existence is overlooked; and third, the existing climate models do not consider the interrelations among the tipping points (i.e., one tipping point can trigger other tipping points to be tipped more rapidly). Our objective is to describe the importance of environmental “tipping points,” the importance of which is often ignored or downplayed in relevant literature. Our analysis, based on extensive multidisciplinary literature searches, reveals that there are many environmental tipping points which are overlooked in climate-modeling studies. We argue that climate modeling could be improved when the tipping points and their interrelations are all considered within the modeling process. We further discuss two other important issues regarding environmental tipping points: first, all tipping points might not be as impactful on the climate system, therefore their relative impacts should be ranked; second, it is in principle impossible to know the exact number of environmental tipping points, therefore even though it could be possible to devise improvements to the existing climate models with our suggestions, it may be impossible to achieve a perfect model to estimate the climate variables of the upcoming years. The remainder of this paper is structured as follows: In the background section, we introduce research on tipping points within commonly used climate models. We explain the aerosol masking effect and ocean dynamics with respect to their commonly overlooked roles as important contributors to environmental change. We introduce remote sensing and AI methods that serve as promising approaches for identification of currently unknown tipping points. We mention perturbation theory, a standard set of mathematical methods in physics that serves as a potentially systematic method to rank environmental tipping points according to their impact on extant climate models. In the discussion section, we make suggestions regarding further research on identifying the typically overlooked tipping points, and we make suggestions to improve climate models by considering additional information presented in the current paper. Finally, we conclude this article summarizing our chief methodological recommendations.

The deterioration of natural habitats for oak species has steadily occurred as a consequence of both climate change and human pressure. Therefore, detailed and reliable information about the geographic distribution of oak species under changing climate conditions is needed for diverse ecological and conservation practices. This study examined the habitat suitability of endemic Kasnak oak, Quercus vulcanica Boiss. & Heldr. ex Kotschy, an endemic that ranges across the Central Anatolia Region and surrounding mountains in Türkiye. The occurrence data were gathered through fieldwork, and new records were identified. Next, we applied ecological niche modeling to assess the past, present-day, and future potential geographic range of the species in Anatolia. Projections for the Last Glacial Maximum indicate that most of the suitable areas for Kasnak oak were in southern Anatolia. However, present-day estimates suggest projections estimate suitable habitats in northern Anatolia and around the Anatolian Diagonal. According to future projections, the distribution of the species seems to decrease by 2100, with habitat suitability reduction ranging from 3.27% to 7.88%. Projections suggest a decrease in habitat suitability for the species, particularly in the western and southern Türkiye in the future. Moreover, the projections indicated that suitable habitats for the northern range of the species would likely persist until 2100, although they would diminish towards the northeast. The results can be effectively applied to enhance biodiversity conservation planning and management, leading to the development of innovative strategies. [ABSTRACT FROM AUTHOR]

In Ethiopia, the impacts of climate change are expected to have significant consequences for agriculture and food security. This study investigates both past (1981–2010) and future (2041–2070) climate trends and their influence on the length of the growing season (LGS) in the North-Western Ethiopian highlands. Climate observations were obtained from the National Meteorological Agency of Ethiopia, while the best performing and highest resolution models from the CMIP5 experiment and RCP6 (Coupled Models Intercomparison Project and representative concentration pathway 6) were used for the analysis. Standard statistical methods were applied to compute soil water content, evaluate climate variability and trends, and assess their impact on the length of the growing season. Maximum temperature (tasmax) and minimum temperature (tasmin) inter-annual variability anomalies show that the region has experienced cooler years than hotter years in the past. However, in the future, the coolest years are expected to decrease by −1.2 °C, while the hottest years will increase by +1.3 °C. During the major rainfall season (JJAS), the area has received an adequate amount of rainfall in the past and is very likely to receive similar rainfall in the future. On the other hand, the rainfall amount in the season February to May (FMAM) is expected to assist only with early planting. For the season October to January (ONDJ), the rainfall amount may help lengthen the growing season of JJAS if properly utilized; otherwise, the season has the potential to destroy crops before and during the harvesting time. The soil water content changes in the future remain close to those of the past period. The length of growing seasons has less variable onset and cessation dates, while in the future, the length of the growing period (LGP) of 174 to 177 days will be suitable for short- and long-cycle crops, as well as double cropping, benefiting crop production yield in the North-Western Ethiopian highlands in the future. [ABSTRACT FROM AUTHOR]

The climate changes occurring in the Arctic are expressed in an increase in extreme values, as well as in a wide range of permafrost degradation processes, the state of which, being sensitive to climate change, causes the activation of rapidly proceeding exogenous processes. To predict the climate in the Yamal–Nenets Autonomous Okrug for the mid-twenty-first century, data from 42 models of the CMIP6 were used. The temperature in January is expected to increase by about 3.5°C (on the coast of the Kara Sea even by 4.5°C), and in July, by ~2.5°C everywhere. The 5 and 95% quantiles will increase by 3 and 4.5°C in January and by 2 and 3°C in July, respectively. The amount of monthly precipitation will increase by an average of 10% in January and will not change in July. The 95% quantile values will increase by 30 and 10% in January and July, respectively. The number of frost days will be reduced. The warming in combination with an increase in precipitation will lead to an increase in the activity of avalanches and slush flows on the eastern slopes of the Urals and tell on the characteristics of landslides, abrasion, suffusion, erosion, thermokarst, and solifluction. Climate-dependent branches of the economy should be ready for projected changes in the natural environment. [ABSTRACT FROM AUTHOR]

Rise in mean temperature put a great deal of uncertainty about how weather and climate extremes may play out, particularly in India's varied climatic zones. Consequently, it is important to understand the possible changes in both magnitude and direction of weather and climate extremes like rainfall for different warming levels of 1.5 and 2°C scenarios concerning preindustrial and present levels. Hence in the present study, the precipitation behavior of seven North Indian states that is, Haryana, Himachal Pradesh, J&K, Punjab, Rajasthan, Uttar Pradesh, and Uttarakhand carefully studied using CMIP5 models. Future projections of precipitation has been done for the Paris Agreement global warming level of 1.5 and 2°C scenarios. Along with model validation and future projections of precipitation, the return period of extreme rainfall is also discussed to understand the behavior of the occurrence of extreme precipitation. Statistical analysis shows that the ensemble means have the least error as compared to the other six CMIP5 models. Therefore, future analysis has been done with the ensemble mean. Our findings show that the precipitation is likely to decrease in the 1.5°C scenarios, while it is likely to increase in the 2°C scenarios. The occurrence and intensity of extreme rainfall events are likely to be more frequent in all the models. The return period of the extreme rainfall events is likely to increase in all the states in both the warming scenarios. A three‐fold rise is likely to increase extreme rainfall events in the 2°C scenario. Key Points: Study performs a detailed analysis of rainfall behavior of the northern states India during preindustrial, present, 1.5 and 2° scenariosSix climate models were selected out of 26 CMIP5 climate models based on the capability of representing the monsoon rainfall over IndiaIn the 2°C scenario, the recurrence period for intense rainfall events will likely to increase by a factor of 3 [ABSTRACT FROM AUTHOR]

Decomposing the uncertainty of global climate models is highly instructive in understanding climate change. However, it remains unclear whether sources of uncertainty have changed as the models have evolved and the extents to which uncertainty in temperature and precipitation are narrowed after bias correction (BC). We quantified uncertainty in temperature and precipitation projections over global land from three sources—model uncertainty, scenario uncertainty, and internal variability—and compared results from the models participating in the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). In addition, we investigated the potential of four BC methods for narrowing uncertainty in temperature and precipitation over the globe and individual continents. Raw projections of temperature and precipitation have greater uncertainty and lower fractional uncertainty relative to their anomalies. The largest temperature uncertainties appear in high‐latitude and high‐altitude regions, and the largest precipitation uncertainties are in low‐latitude regions and mountainous and coastal areas. For uncertainties in CMIP6 temperatures, the contribution from model uncertainty decreases with time (from 99% to 39%), while the contribution from scenario uncertainty increases with time (from 0.01% to 61%). For precipitation projections, the contribution from model uncertainty predominates (98%), while the contributions from scenario uncertainty (1.8%) and internal variability (0.2%) are extremely low. Four BC methods have exhibited excellent ability to reduce uncertainty, and among them, BC and spatial disaggregation has the best performance. These findings can help us better understand the characteristics of the models, while also providing decision makers with more accurate information to address climate mitigation and adaptation measures. Plain Language Summary: Global climate models (GCMs) have a powerful ability to reproduce past climate characteristics and project future climate evolution, and they are currently one of the most effective tools for climate change research. However, quantitative climate projections from GCMs are subject to high uncertainty due to our incomplete knowledge of climate, insufficient representation of climate system, and limited computer resources. Clarifying uncertainty sources can provide important scientific support for enhancing the credibility of future projection results and resolve relevant scientific questions for subsequent modeling applications. In this study, we tried to quantify the uncertainty in temperature and precipitation projections from three sources‐model uncertainty, scenario uncertainty, and internal variability‐arising from model outputs of 21 phase 5 of the Coupled Model Intercomparison Project and 26 phase 6 of the Coupled Model Intercomparison Project GCMs. We also investigated the potential of four bias correction methods for narrowing uncertainty in temperature and precipitation projections over the globe and continents. Key Points: For uncertainties in phase 6 of the Coupled Model Intercomparison Project temperatures, the contribution of model uncertainty decreases with time, while the contribution of scenario uncertainty increases with timeFor precipitation projections, the model uncertainty predominates (98%) the total uncertaintyFour bias correction (BC) methods have exhibited excellent ability to reduce uncertainty, and BC and spatial disaggregation has the best performance [ABSTRACT FROM AUTHOR]

The future climate for central zone of Kerala was estimated with climate change projections generated using ECHAM model and GFDL-CM3 model based on RCP 4.5 and RCP 8.5. The CERES Rice model was used to evaluate the influence of climate change on the rice variety Jyothi during near (2030), mid (2050) and end of the century (2080) under 5 dates of planting. Results showed that the performance of rice variety Jyothi was better on October 1st planting under projected climate. Under RCP 4.5 the decrease in yield was 13.2, 21.3 and 31.9 per cent and under RCP 8.5 scenario, the decrease was 12.7, 24.8 and 36.0 per cent respectively during near, mid and end centuries. The yield reduction in almost all the planting dates in RCP 4.5 and RCP 8.5 scenarios may be due to increased minimum and maximum temperature and increased rainfall during anthesis. [ABSTRACT FROM AUTHOR]

The intensification of heat stress in a changing climate poses great threats to both human health and labor productivity. It is of great practical importance to assess the impacts of climate-induced heat stress on labor productivity and to develop effective adaptation strategies. In this paper, an integrated optimization-based productivity restoration modeling framework is proposed for the first time to develop the optimal policies for adaptation to climate change. To address underlying uncertainties associated with climate and labor management systems, we take into account ensemble projections from five global climate models (GCMs) under two Representative Concentration Pathways (RCP2.6 and RCP8.5) and inexact system costs. The system costs, including direct and indirect costs such as management costs, energy costs, and labor costs, are presented as interval numbers due to inherent uncertainty caused by population growth, technology development, and other social-economic factors. Uncertain information can be effectively communicated into the optimization processes in this study to generate optimal and reliable decision alternatives. We find that the increased Wet-Bulb Globe Temperature (WBGT) will lead to a large reduction in labor capacities over China except for the Tibetan Plateau under both RCPs by the end of the 21st century. The less developed regions tend to achieve the minimum system cost by having labor productivity recovered through working overtime due to the relatively low cost of overtime. This could result in more heat-related work injuries in the less developed regions. Since the less developed regions are not heat-prone areas in China, the changing climate would be a more dangerous threat and cause more damages to these regions where the residents are less acclimatized to heat stress. Moreover, we obtain a range of minimum system costs from 1.86 to 8.97 billion dollars under RCP2.6 and from 9.42 to 32.31 billion dollars under RCP8.5 (about 0.2% of China's GDP in 2019, 0.01% of China's GDP projected in 2100 under a sustainable socio-economic development scenario) for the restoration of labor productivity in a warming climate. We argue that urgent actions are needed to mitigate global warming impacts on labor productivity. • An integrated optimization modeling framework is developed to achieve optimal labor productivity restoration in a warming climate. • Various uncertainties are addressed through ensemble climate projections and interval programming models. • Intensification of heat stress leads to a large decrease in labor capacity over China except for the Tibetan Plateau. • More losses in working hours are projected for moderate labor activities compared to light and heavy labor activities. • Less developed regions tend to have more working hours recovered by working overtime than air conditioning. [ABSTRACT FROM AUTHOR]

In this study, we examine sequential landfalling tropical cyclones (TCs) along U.S. East and Gulf Coasts. We find that Florida and Louisiana are most prone to sequential landfall risk. The minimal time between sequential landfalling TC has decreased for most regions since 1979, although the trend is not statistically significant given limited data. A climate projection indicates a significant increase in sequential landfalls over the 21st century under the SSP5 8.5 scenario, with the chance of a location experiencing a less‐than‐10‐day break between two TC impacts being doubled for most regions. The increases in sequential landfalls in the historical period and projected future climate are both related to increased landfall frequency, even though the storm season has been slightly expanding and may continue to expand. This study highlights a new type of TC hazard resulting from the temporal compounding of landfalls and urges the improvement of coastal resilience. Plain Language Summary: Sequential landfalling tropical cyclones (TCs), which make landfall near a location within a short period of time, can be hazardous for coastal communities. Analyzing the historical records for the U.S. East and Gulf Coasts, we find that the states of Florida and Louisiana are most prone to sequential TC hazard, and this hazard has had an increasing potential for most coastal regions since 1979. Performing future projections of TCs under the effect of climate change, we find that the sequential TC hazard may increase even more significantly in the future. The increased sequential TC hazard in both the historical period and a projected future climate is related to the increased annual frequency of landfalling TCs. This study highlights a new type of TC hazard resulting from the temporal compounding of landfalling TCs and urges the improvement of coastal resilience, e.g., shortening the time scales for infrastructure recovery after TC landfalls. Key Points: Time intervals between sequential landfalling tropical cyclones (TCs) has decreased for most US regions, although the trend is not statistically significantA climate downscaling projection indicates that intervals between sequential US landfalling TCs may significantly decrease in the futureThe decreased intervals and increased chances of sequential landfalling TCs are mainly driven by increases in storm landfall frequency [ABSTRACT FROM AUTHOR]

We evaluate the Oyashio and Kuroshio fronts latitudinal transition under the projected climate change scenarios using eddy resolving regional ocean climate projection products from 1981 to 2100. The regional ocean climate projections are produced based on dynamical downscaling of four CMIP5 models and two RCP experiments RCP2.6 and RCP8.5. Two approaches of the fronts detection methods are compared. One is conventional approach based on subsurface specific isotherm and another is newly proposed approach based on the Oyashio and Kuroshio water temperature and salinity (TS) profiles that may change as the global warming progresses. It is found that the Oyashio TS profile rapidly shift to be high both in temperature and salinity in RCP8.5 cases while the Kuroshio TS profile shows small change toward the end of the twenty-first century in all cases. Northward shift of the Oyashio and Kuroshio fronts reaches 2° northward by the late twenty-first century in extreme case of RCP8.5 cases from the temperature-based definition. On the other hand, the northward shifts of fronts are less than 1° when we use the TS-based definition. The comparison of both fronts positions with the surface velocity distributions suggest that the TS-based definition captures transitions of the fronts better than the temperature-based definition. [ABSTRACT FROM AUTHOR]

This study assessed the impact of climate change on water availability and variability in two subbasins in the upper Blue Nile basin of Ethiopia. Downscaled future climate data from HadCM3 of A2 (medium-high) and B2 (medium-low) emission scenarios were compared to the observed climate data for a baseline period (1961-1990). The emission scenario representing the baseline period was used to predict future climate and as input to a hydrologic model to estimate the impact of future climate on the streamflow at three future time horizons: 2020-2045, 2045-2070 and 2070-2100. Results suggest that medium-high emission scenario best represents the local rainfall and temperature pattern. With A2 scenario, daily maximum/minimum temperature will increase throughout the future time horizons. The minimum and maximum temperature will increase by 3.6 °C and 2.4 °C, respectively, towards the end of the 21st century. Consequently, potential evapotranspiration is expected to increase by 7.8%, although trends in annual rainfall do not show statistically meaningful trends between years. A notable seasonality was found in the rainfall pattern, such that dry season rainfall amounts are likely to increase and wet season rainfall to decrease. The hydrological model indicated that the local hydrology of the study watersheds will be significantly influenced by climate change. Overall, at the end of the century, streamflow will increase in both rivers by up to 64% in dry seasons and decrease by 19% in wet seasons. [ABSTRACT FROM AUTHOR]

The reproducibility and future changes of the onset of the Asian summer monsoon were analyzed based on the simulations and projections under the Representative Concentration Pathways (RCP) scenario in which anthropogenic emissions continue to rise throughout the 21st century (i.e. RCP8.5) by all realizations from four Chinese models that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Delayed onset of the monsoon over the Arabian Sea was evident in all simulations for present-day climate, which was associated with a too weak simulation of the low-level Somali jet in May. A consistent advanced onset of the monsoon was found only over the Arabian Sea in the projections, where the advanced onset of the monsoon was accompanied by an increase of rainfall and an anomalous anticyclone over the northern Indian Ocean. In all the models except FGOALS-g2, the enhanced low-level Somali jet transported more water vapor to the Arabian Sea, whereas in FGOALS-g2 the enhanced rainfall was determined more by the increased wind convergence. Furthermore, and again in all models except FGOALS-g2, the equatorial SST warming, with maximum increase over the eastern Pacific, enhanced convection in the central West Pacific and reduced convection over the eastern Indian Ocean and Maritime Continent region, which drove the anomalous anticyclonic circulation over the western Indian Ocean. In contrast, in FGOALS-g2, there was minimal (near-zero) warming of projected SST in the central equatorial Pacific, with decreased convection in the central West Pacific and enhanced convection over the Maritime Continent. The broader-scale differences among the models across the Pacific were related to both the differences in the projected SST pattern and in the present-day simulations. [ABSTRACT FROM AUTHOR]

Due to climate change, the Iberian Peninsula is suffering an increasing trend of extreme temperature events. The main objective of this study is to analyse this trends in frequency, duration and intensity of warm events and heat waves. The datasets used are 14 different regionalized dynamic climate projections. We choose the projections that present a spatial dependence similar to that of observed data. The spatial dependence is calculated by adjusting the data to max-stable processes. The observed data belong to the SPAIN02 grid for the period 1961–2000. We apply the Mann-Kendall test and the Theil-Sen estimator to calculate model trends in the future period (2011–2099). We have studied future extreme temperature events using two different definitions. One varying the threshold for each period and the other keeping it constant. The results show that the variability of maximum temperatures is decreasing for the western region of the Peninsula, while the Mediterranean area will see an increase in this variability. There will be an increase in the frequency of warm events for the southwestern corner of the Peninsula. Also, maximum temperatures will be higher in this area at the end of the century. However, in the Mediterranean region the warm events will last longer. Heat waves will be more frequent throughout the territory and more lasting in the Mediterranean area. We also found that studying extreme events using a varying threshold allows these events to be studied from the point of view of the variability of maximum temperatures, while if the study is carried out maintaining the threshold constant the results will be more direct. [ABSTRACT FROM AUTHOR]

Future projections of anthropogenic climate change play a pivotal role in devising viable countermeasures to address climate-related risks. This study strove to construct future daily rainfall and maximum and minimum temperature scenarios in Vu Gia Thu Bon river basin by employing the Statistical DownScaling Model (SDSM). The model performance was evaluated by utilizing a Taylor diagram with dimensioned and dimensionless statistics. During validation, all model-performance measures show good ability in simulating extreme temperatures and reasonable ability for rainfall. Subsequently, a set of predictors derived from HadCM3 and CanESM2 was selected to generate ensembles of each climatic variables up to the end of 21st century. The generated outcomes exhibit a consistent increase in both extreme temperatures under all emission scenarios. The greatest changes in maximum and minimum temperature were predicted to increase by 2.67–3.9 °C and 1.24–1.96 °C between the 2080s and reference period for the worst-case scenarios. Conversely, there are several discrepancies in the projections of rainfall under different emission scenarios as well as among considered stations. The predicted outcomes indicate a significant decrease in rainfall by approximately 11.57%–17.68% at most stations by 2099. Moreover, all ensemble means were subjected to the overall and partial trend analysis by applying the Innovative-Şen trend analysis method. The results exhibit similar trend patterns, thereby indicating high stability and applicability of the SDSM. Generally, it is expected that these findings will contribute numerous valuable foundations to establish a framework for the assessment of climate change impacts at the river basin scale. [ABSTRACT FROM AUTHOR]