Search

Species distribution models (SDMs) have been used to forecast the impact of climate change on species' potential distribution, with results that might support decisions for conservation and biodiversity management. Despite their vulnerability to parameterization and data quality input, SDM use has been increasing in the last decades. In fact, inappropriate inputs and the lack of awareness about the effects of methodological decisions on results can lead to potential unreliability in results, a problem that might gain relevance when SDMs are used to predict climate change impacts on species-suitable areas. Aiming to assess how far such a topic is considered, an analysis of the calibration data and methodological decisions was conducted for recent publications (2018 to 2022) that include SDMs in this context, aiming to identify putative deviations from the consensual best practices. Results show that the parameters presented more consistently are the algorithm in use (MaxEnt was used in 98% of the studies), the accuracy measures, and the time windows. But many papers fail to specify other parameters, limiting the reproducibility of the studies. Some papers fail to provide information about calibration procedures, others consider only a fraction of the species' range, and others provide no justification for including specific variables in the model. These options can decrease reliability in predictions under future scenarios, since data provided to the model are inaccurate from the start or there is insufficient information for output discussion. [ABSTRACT FROM AUTHOR]

The effect of climate change on forest dynamics is likely to increase in importance in the forthcoming decades. For this reason, it is essential to predict the extent to which changes in temperature, precipitation, and atmospheric CO2 might affect the development of forest ecosystems and successional pathways. The gap model ZELIG-CFS was used to simulate the potential long-term effects of climate change on species-specific annual change in mean basal area and stand density under two scenarios of representative concentration pathways (RCP), 4.5 and 8.5, for the boreal forest region of Ontario, Canada, where mean temperature, precipitation, and atmospheric CO2 are expected to increase. Forest ecosystems in this boreal region included pure and mixed stands of black spruce (Picea mariana [Mill.] B.S.P.), paper birch (Betula papyrifera Marsh.), balsam fir (Abies balsamea [L.] Mill.), jack pine (Pinus banksiana Lamb.), trembling aspen (Populus tremuloides Michx.), white spruce (Picea glauca [Moench] Voss), northern white cedar (Thuja occidentalis L.), American larch (Larix laricina [Du Roi] K. Koch), and balsam poplar (Populus balsamifera L.). Simulation results under climate change generally predicted a decline in the basal area and stand density for black spruce, balsam fir, jack pine, and white spruce, but an increase for paper birch, trembling aspen, American larch, and balsam poplar. However, the extent of change differed regionally among species. Forest composition is expected to change over the long term. Simulation results indicated that shade-intolerant deciduous and conifer species will increase their dominance over the 100-year time horizon. This transition toward the increasing presence of deciduous forests is likely explained by more favorable temperature conditions for their growth and development. [ABSTRACT FROM AUTHOR]

Energy modelling plays a crucial role in assisting governmental and policymaking bodies to strategise long-term investments within the context of energy transition. Among the well-established open-source optimisation models, OSeMOSYS—the Open-Source Energy Modelling System—stands out. This paper introduces clicSAND, a novel user interface designed for OSeMOSYS, aimed at reducing the learning curve and supporting novice energy modelers in efficiently conducting long-term investment analyses. clicSAND, freely available and open-source, features a user-friendly Excel interface for data input, integrated solvers, and a visualisation dashboard for result interpretation. The outcomes, projected up to 2070, hold the potential to inform policy decisions and mobilise financial resources for sustainable development endeavors, such as ensuring affordable and secure energy supply and mitigating climate change impacts. This advancement not only democratises access to energy modelling tools but also empowers policymakers and stakeholders to conduct thorough long-term investment analyses with ease. This paper elaborates on clicSAND's key advantages, architecture, and functionalities. Additionally, it discusses the evolutionary journey from clicSAND 1.0 to 3.0, emphasising a commitment to continuous improvement and user-centric adaptation, thereby enhancing its utility and relevance. The inclusion of a South African case study, conducted during the EMP-A (Energy Modelling Platform for Africa) 2021 international capacity-building event, showcases clicSAND's efficacy in facilitating knowledge transfer and skill development among inexperienced users, while providing a tangible example of its application in addressing specific regional energy challenges and policy contexts. Finally, current applications and future extensions of the software are also presented. [ABSTRACT FROM AUTHOR]

Projecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM‐LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM‐LIN method improved R2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM‐LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation. Plain Language Summary: Natural hazards, such as flooding and extreme heat, can damage our infrastructure and local communities. These hazards often stem from extreme weather conditions such as heavy precipitation and hot temperatures. Global climate change is expected to affect precipitation and air temperature, leading to more extreme weather events and hazards in the future. Predicting frequency and severity of these future events is key to protect our infrastructure and communities. Climate models have been widely used to project statistics of future extremes. However, they come with uncertainties due to numerical approximations, low spatial resolutions etc. Consequently, predictions generated by these models are inherently uncertain. This paper used a new approach to improve future projections of extreme climatic events, focusing on South Florida. Our findings revealed substantial improvements in projecting future events, particularly for extreme precipitation. We also showed that nationally and globally derived simulations may not be suitable for accurately projecting heavy precipitation, but can suffice for air temperature and low/medium precipitation rates. This paper offers promising avenues for refining projections of future weather events. By enhancing our ability to anticipate upcoming weather events more accurately, we can better protect our infrastructure and communities against the challenges posed by climate change. Key Points: A hybrid statistical approach to improve bias correction of GCM precipitation and air temperature simulationsMajor improvements in both climatic variables, especially extreme precipitation, using our hybrid statistical approach in South FloridaCoarse resolution downscaled data may be insufficient for deriving high quantile precipitation at the regional scale [ABSTRACT FROM AUTHOR]

The new generation of heterogeneous CPU/GPU computer systems offer much greater computational performance but are not yet widely used for climate modeling. One reason for this is that traditional climate models were written before GPUs were available and would require an extensive overhaul to run on these new machines. In addition, even conventional "high–resolution" simulations don't currently provide enough parallel work to keep GPUs busy, so the benefits of such overhaul would be limited for the types of simulations climate scientists are accustomed to. The vision of the Simple Cloud‐Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) project is to create a global atmospheric model with the architecture to efficiently use GPUs and horizontal resolution sufficient to fully take advantage of GPU parallelism. After 5 years of model development, SCREAM is finally ready for use. In this paper, we describe the design of this new code, its performance on both CPU and heterogeneous machines, and its ability to simulate real‐world climate via a set of four 40 day simulations covering all 4 seasons of the year. Plain Language Summary: This paper describes the design and development of a 3 km version of the Energy Exascale Earth System Model (E3SM) atmosphere model, which has been fully rewritten in C++ using the Kokkos library for performance portability. This newly rewritten model is able to take advantage of the state–of–the–science high performance computing systems which use graphical processor units (GPUs) to mitigate much of the computational expense which typically plagues high–resolution global modeling. Taking advantage of this high–performance we are able to run four seasons of simulations at 3 km global resolution. We discuss the biases, including the diurnal cycle, by comparing model results with satellite and Atmospheric Radiation Measurement ground‐based site data. Key Points: Describes the C++/Kokkos implementation of the Simple Cloud–Resolving E3SM Atmosphere Model (SCREAMv1)SCREAMv1 leverages GPUs to surpass one simulated year per compute day at global 3 km resolutionHigh resolution improves some meso‐scale features and the diurnal cycle but large‐scale biases require improvement across all four seasons [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are among the most destructive natural hazards and yet, quantifying their financial impacts remains a significant methodological challenge. It is therefore of high societal value to synthetically simulate TC tracks and winds to assess potential impacts along with their probability distributions for example, land use planning and financial risk management. A common approach to generate TC tracks is to apply storm detection methodologies to climate model output, but such an approach is sensitive to the method and parameterization used and tends to underestimate intense TCs. We present a global TC model (the UQAM‐TCW model thereafter) that melds statistical modeling, to capture historical risk features, with a climate model large ensemble, to generate large samples of physically coherent TC seasons. Integrating statistical and physical methods, the model is probabilistic and consistent with the physics of how TCs develop. The model includes frequency and location of cyclogenesis, full trajectories with maximum sustained winds and the entire wind structure along each track for the six typical cyclogenesis basins from IBTrACS. Being an important driver of TCs globally, we also integrate ENSO effects in key components of the model. The global TC model thus belongs to a recent strand of literature that combines probabilistic and physical approaches to TC track generation. As an application of the model, we show global hazard maps for direct and indirect hits expressed in terms of return periods. The global TC model can be of interest to climate and environmental scientists, economists and financial risk managers. Plain Language Summary: Tropical cyclones (TCs) are among the most destructive natural hazards and yet, quantifying their financial impacts remains a difficult task. Being able to randomly simulate TCs and their features (such as wind speed) with mathematical models is therefore critical to build scenarios (and their corresponding probability) for land use planning and financial risk management. A common approach is to simulate TCs by tracking them directly in climate model outputs but this often underestimates the frequency of intense TCs while being computationally costly overall to generate a large number of events. For these reasons, many authors have looked into alternative approaches that replicate key physical features of TCs but rather using statistical models that are much less computationally demanding. This paper therefore presents a global TC model that leverages the strengths of both statistical and climate models to simulate a large number of TCs whose features are consistent with the physics and observations. As an important global phenomenon that affects TCs globally, we also integrate in our model the effects of El Niño. The paper focuses on the methodology and validation of each model component and concludes with global hazard maps for direct and indirect hits. Key Points: We present a global tropical cyclone (TC) wind model built upon a climate model large ensemble that can be used for risk analysisWe integrate ENSO into our model since it is a strong driver of storm annual frequency, cyclogenesis, trajectories, and intensityWe present global hazard maps consistent with statistical features of TC components and coherent with a global climate model [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is crucial for the global water balance, plant growth, and freshwater availability. It connects the surface water balance with surface energy fluxes, making its accurate representation vital for climate projections. However, global climate models (GCMs) struggle with ET representation due to resolution limitations and simplified depictions of soil, plant, and atmosphere interactions. Simulated future changes in ET are uncertain, and the role of driving processes remain unclear. Here, we explore the utility of a simple and interpretable method to disentangle these varying drivers. We investigate the sensitivity of JJA ET to different atmospheric variables through simple linear models predicting ET from atmospheric variables only. The model consistently yields good results across GCMs or forcing scenarios. We find that GCMs have shown strong decreases and subsequent increases in ET over the historical period, related to changes in net surface radiation. For future climate projections, decreases in water availability compete with higher available surface radiation, making future projections uncertain. Single forcing GCM realizations show that historical ET trends in densely populated regions have been more influenced by aerosol emissions than greenhouse gases. Finally, we investigate which atmospheric variables explain most short‐term (year‐to‐year) and long‐term (decadal) changes. While water availability may be the most important driver of short‐term variability, for certain regions, radiation trends dominate long‐term forcing. This paper leverages a simple approach to provide a comprehensive and understandable view into recent and future changes in ET, reconciling the evidence provided by more complex case studies. Plain Language Summary: Evapotranspiration (ET) is essential for the global water cycle, plant growth, and the availability of freshwater. It links the surface water balance with surface energy exchanges, making its accurate representation important for predicting climate change. However, climate models have difficulty accurately representing ET due to their coarse resolution and simplified interactions between soil, plants, and the atmosphere. This leads to uncertainty in predicted future changes in ET and the underlying processes driving these changes. We use a simple method to analyze the various factors influencing ET. We focus on the sensitivity of boreal summer (June, July, August) ET to different atmospheric variables. Our findings indicate that climate models show significant fluctuations in ET over the historical period, influenced by changes in radiation. For future projections, reduced water availability competes with increased radiation, creating uncertainty. Additionally, climate model experiments reveal that historical ET trends in populated areas have been more affected by aerosol emissions than by greenhouse gases. We also explore which atmospheric variables are most influential for short‐term (year‐to‐year) and long‐term (decadal) changes. Water availability seems to be the main driver of short‐term variability, while radiation trends are more important for long‐term changes in certain regions. Key Points: Historical boreal summer evapotranspiration (ET) trends over populated regions have been influenced more by anthropogenic aerosol emissions than greenhouse gasesThe sensitivity of long‐term ET changes to energy limitation is not as apparent when considering short‐term variabilityWhile radiation drives recent historical change, future changes appear equally influenced by water limitation as well [ABSTRACT FROM AUTHOR]

Climate projections are being used for decision‐making related to climate mitigation and adaptation and as inputs for impacts modeling related to climate change. The plethora of available projections presents end users with the challenge of how to select climate projections, known as the "practitioner's dilemma." In addition, if an end‐user determines that existing projections cannot be used, then they face the additional challenge of producing climate projections for their region that are useful for their needs. We present a methodology with novel features to address the "practitioner's dilemma" for generating downscaled climate projections for specific applications. We use the Edwards Aquifer region (EAR) in south‐central Texas to demonstrate a process to select a subset of global climate models from both the CMIP5 and CMIP6 ensembles, followed by downscaling and verification of the accuracy of downscaled data against historical data. The results show that average precipitation changes range from a decrease of 10.4 mm to an increase of 25.6 mm, average temperature increases from 2.0°C to 4.3°C, and the number of days exceeding 37.8°C (100°F) increase by 35–70 days annually by the end of century. The findings enhance our understanding of the potential impacts of climate change on the EAR, essential for developing effective regional management strategies. Additionally, the results provide valuable scenario‐based projected data to be used for groundwater and spring flow modeling and present a clearly documented example addressing the "practitioner's dilemma" in the EAR. Plain Language Summary: Groundwater, constituting over one‐third of global water resources, is crucial for sustaining ecosystems, agriculture, and drinking water supplies. In the face of climate change, rising temperatures and shifting precipitation patterns are anticipated to diminish the availability of groundwater for both societal and ecological requirements. Regional managers, in preparing for these changes, need localized climate projections for effective planning. However, the abundance of available climate projections poses a significant challenge for decision‐makers in climate adaptation, known as the "practitioner's dilemma." This dilemma, though widely acknowledged, lacks a standardized solution. Our paper introduces a methodology to navigate this challenge, specifically tailored to the needs of the Edwards Aquifer Authority. This authority is actively engaged in implementing protection and habitat conservation plans to alleviate stress on groundwater and major springs in the Edwards Aquifer Region, located in south‐central Texas. Our projections indicate that rising temperatures are likely to increase evapotranspiration, thereby exacerbating the strain on groundwater resources in this region as climate conditions evolve. Furthermore, our approach offers a customizable approach to "the practitioner's dilemma," potentially serving as a model for other decision‐makers in the United States to effectively utilize climate projections in their strategic planning. Key Points: This study presents a flexible approach to the challenge of selecting climate projections for decision‐makingWe find projected temperature and precipitation changes will stress groundwater resources in the Edwards Aquifer using this approach [ABSTRACT FROM AUTHOR]

Climate change is expected to alter both forest stand- and landscape-level dynamics through a change in environmental characteristics. While numerous studies have employed models to assess the ecological and/or economic impacts of such changes on forests throughout the world, there is need to further refine such analyses. In this paper, we contribute to this literature by coupling an ecological (PICUS–LANDIS II) modeling framework with an economic (computable general equilibrium) model to better account for the economic impact associated with climate-induced impacts on forest stand- and landscape-level structure and composition dynamics. Applying this framework to a case-study region of New Brunswick, Canada, we estimate that climate change will reduce softwood supply by 16%–73% and impact hardwood supply in the range of −2% to +4% by 2150. The change in wood supply is estimated to reduce the value of the softwood and hardwood forestry and logging sector output by up to 51% and 17%, respectively, by 2150. These sector-level impacts may lead to a 0.08%–0.88% reduction in annual gross domestic product by 2150. The methodological advances established in this study can be used to better inform future forest management and economic plans that aim to lessen both the ecological and economic impact of climate change. [ABSTRACT FROM AUTHOR]

To help with preparedness efforts of Canadian public health and safety systems for adaptation to climate change, the humidity index (humidex) and three threshold‐based humidex indices (annual number of days with humidex greater than 30, 35 and 40) were computed for a multi‐model ensemble of climate change projections, over Canada. The ensemble consists of one run from each 19 Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models and offers historical simulations starting in 1950 and future projections out to 2100 following Shared Socioeconomic Pathways (SSPs): SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5. Each ensemble member was bias‐adjusted and statistically downscaled using the Multivariate bias correction—N‐dimensional probability density function transform (MBCn) with hourly data from ERA5‐Land as the target dataset and following a method proposed by Diaconescu et al. (2023; International Journal of Climatology, 43, 837) to calculate humidex from daily climate model outputs. This paper details the steps for data production including evaluation of the target historical gridded data and selection of downscaling method and presents some of the resulting humidex projections at the end of the century. [ABSTRACT FROM AUTHOR]

This paper applied the fuzzy function approach, combined with the ridge regression model, to produce daily rainfall projections from large-scale climate variables. This study developed a statistical downscaling model based on principal components, c-means fuzzy clustering, Volterra series, and ridge regression. The model is known, hereafter as SDC2R2. In the developed downscaling model, the use of ridge regression, instead of multiple linear regression, is proposed to downscale daily rainfall with wide range (WR) predictors. The WR predictors were applied to sufficiently incorporate climate change signals. The developed model also captured the non-linear interactions of the climate variables by applying the transformation of Volterra series realization over WR predictors. This transformation was performed by applying principal components as orthogonal filters. Further, these principal components were clustered by using c-means clustering and non-linear transformations were applied on these membership functions, to improve the prediction ability of the model. The reanalysis of climate data from the National Centres for Environmental Prediction (NCEP) was used to develop the model and was validated by using the Global Climate Model (GCM) for four locations in the Manawatu River basin. The developed model was used to obtain future daily rainfall projections from three Representative Concentrative Pathways (RCP 2.6, RCP 4.5, and RCP 8.5) scenarios from the Canadian Earth System Model (CanESM2) GCM. The performance of the model was compared with a widely used statistical downscaling model (SDSM). It was observed that the model performed better than SDSM in downscaling rainfall on a daily basis. Every scenario indicated that there is a probability of obtaining high future rainfall frequency. The results of this study provide valuable information for decision-makers since climate change may potentially impact the Manawatu basin. [ABSTRACT FROM AUTHOR]

Inland waters are among the most threatened biodiversity hotspots. Ponds located in alpine areas are experiencing more rapid and dramatic water temperature increases than any other biome. Despite their prevalence, alpine ponds and their biodiversity responses to climate change have been poorly explored, reflecting their small size and difficult access. To understand the effects of climate change on alpine pond biodiversity, we performed a comprehensive literature review for papers published since 1955. Through analysis of their geographic distribution, environmental features, and biodiversity values, we identified which environmental factors related to climate change would have direct or indirect effects on alpine pond biodiversity. We then synthesized this information to produce a conceptual model of the effects of climate change on alpine pond biodiversity. Increased water temperature, reduced hydroperiod, and loss of connectivity between alpine ponds were the main drivers of biodiversity geographic distribution, leading to predictable changes in spatial patterns of biodiversity. We identified three major research gaps that, if addressed, can guide conservation and restoration strategies for alpine ponds biodiversity in an uncertain future. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change is changing the Earth system processes that control the characteristics of natural hazards both globally and across Australia. Model projections of hazards under future climate change are necessary for effective adaptation. This paper presents BARPA-R (the Bureau of Meteorology Atmospheric Regional Projections for Australia), a regional climate model designed to downscale climate projections over the Australasian region with the purpose of investigating future hazards. BARPA-R, a limited-area model, has a 17 km horizontal grid spacing and makes use of the Met Office Unified Model (MetUM) atmospheric model and the Joint UK Land Environment Simulator (JULES) land surface model. To establish credibility and in compliance with the Coordinated Regional Climate Downscaling Experiment (CORDEX) experiment design, the BARPA-R framework has been used to downscale ERA5 reanalysis. Here, an assessment of this evaluation experiment is provided. Performance-based evaluation results are benchmarked against ERA5, with comparable performance between the free-running BARPA-R simulations and observationally constrained reanalysis interpreted as a good result. First, an examination of BARPA-R's representation of Australia's surface air temperature, precipitation, and 10 m winds finds good performance overall, with biases including a 1 ∘ C cold bias in daily maximum temperatures, reduced diurnal temperature range, and wet biases up to 25 mm per month in inland Australia. Recent trends in daily maximum temperatures are consistent with observational products, while trends in minimum temperatures show overestimated warming and trends in precipitation show underestimated wetting in northern Australia. Precipitation and temperature teleconnections are effectively represented in BARPA-R when present in the driving boundary conditions, while 10 m winds are improved over ERA5 in six out of eight of the Australian regions considered. Secondly, the paper considers the representation of large-scale atmospheric circulation features and weather systems. While generally well represented, convection-related features such as tropical cyclones, the South Pacific Convergence Zone (SPCZ), the Northwest Cloudband, and the monsoon westerlies show more divergence from observations and internal interannual variability than mid-latitude phenomena such as the westerly jets and extratropical cyclones. Having simulated a realistic Australasian climate, the BARPA-R framework will be used to downscale two climate change scenarios from seven CMIP6 global climate models (GCMs). [ABSTRACT FROM AUTHOR]

Climate models help us simulate and predict how the Earth's climate is going to change in the future. Wind speed data is critical for developing and validating such models. However, in the real world, often owing to many factors such as station maintenance and sensor failures, a considerable amount of wind data goes missing. The Generative Adversarial Network (GAN) has been used to impute missing wind data, but the handling of unrealistic GAN output has remained largely unstudied. In this paper, we propose a novel hybrid approach that combines both the GAN and dual annealing algorithms to not only impute missing wind speed data but also counter unrealistic GAN outcomes. The hourly mean wind data has been collected from the National Centers for Environmental Information for four Indian stations, viz. Ahmedabad, Indore, Mangaluru and Mumbai. We compared the performance of the proposed approach with those of k-nn, soft imputation, and plain GAN-based approaches on mean, variance, standard deviation, kurtosis, skewness, and R-square. We found that our approach ranks number one based on the R-square value for all the considered stations. Our model consistently produces realistic results, unlike plain GAN. We observed that Mumbai has the lowest percentage of missing data (13.14%) and the highest R-square value (0.9999186451). However, Indore has the highest percentage of missing data (46.6463%) and the lowest R-square value (0.9046885604). [ABSTRACT FROM AUTHOR]

The stochastic synthesis of extreme, rare climate scenarios is vital for risk and resilience models aware of climate change, directly impacting society in different sectors. However, creating high-quality variations of under-represented samples remains a challenge for several generative models. This paper investigates quantizing reconstruction losses for helping variational autoencoders (VAE) better synthesize extreme weather fields from conventional historical training sets. Building on the classical VAE formulation using reconstruction and latent space regularization losses, we propose various histogram-based penalties to the reconstruction loss that explicitly reinforces the model to synthesize under-represented values better. We evaluate our work using precipitation weather fields, where models usually strive to synthesize well extreme precipitation samples. We demonstrate that bringing histogram awareness to the reconstruction loss improves standard VAE performance substantially, especially for extreme weather events. [ABSTRACT FROM AUTHOR]

Large‐scale dynamical and thermodynamical processes are common environmental drivers of high‐impact weather systems causing extreme weather events. However, such large‐scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating high‐impact weather systems and extreme weather events. In this paper, a machine learning (ML) approach was employed to bias correct the large‐scale wind, temperature, and humidity simulated by the atmospheric component of the Energy Exascale Earth System Model (E3SM) at ∼1° resolution. The usefulness of the ML approach for extreme weather analysis was demonstrated with a focus on three high‐impact weather systems, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large‐scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve water vapor transport associated with ARs, and representations of thermodynamical flows associated with ETCs. When the bias‐corrected large‐scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large‐scale storm environments as well as the occurrence and intensity of three weather systems. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large‐scale storm environments simulated by low‐resolution climate models. Plain Language Summary: A machine learning model is employed to bias correct the large‐scale dynamical and thermodynamical fields simulated by a low‐resolution global climate model. The impact of the machine learning model on the large‐scale storm environment associated with tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs) was evaluated. It is found that the ML bias correction can effectively reduce the mean climate biases in large‐scale wind, temperature, and humidity fields associated with the three types of high‐impact weather systems. For storms such as ETCs and ARs that can be partly resolved by the low‐resolution climate models, the machine learning bias correction shows skills in improving the long‐term statistics of these weather events. For storms such as TCs that can not be well resolved in the low‐resolution climate models, the machine learning approach produces more realistic statistics of the tropical cyclone tracks by providing more realistic large‐scale steering winds for downscaling approaches. By reducing model biases without affecting the climate change signals in large‐scale storm environments derived from the low‐resolution climate model simulations, machine learning bias correction has the potential to provide more reliable projections for assessing future changes in extreme weather events. Key Points: A machine learning approach with a long short‐term memory network is used to bias correct climate simulations from the E3SM atmosphere modelThe approach effectively reduces biases in the simulated large‐scale model states while preserving their responses to climate changeImproving large‐scale storm environment with bias correction facilitates modeling of high‐impact weather systems and their future changes [ABSTRACT FROM AUTHOR]

Over‐estimation of summer precipitation over the Tibetan Plateau (TP) is a well‐known and persistent problem in most climate models. This study demonstrates the impact of a Gaussian Probability Density Function cloud fraction scheme on rainfall simulations using the Weather Research and Forecasting model. It is found that this scheme in both 0.1° and 0.05° resolutions significantly reduces the wet bias through both local feedbacks and large‐scale dynamic process. Specifically, increased cloud water/ice content with this scheme reduces surface shortwave radiation, and consequently surface heat fluxes and evapotranspiration. This, in turn, dampens the large‐scale thermal effect of the TP and weakens the exaggerated monsoon circulation and low‐level moisture convergence. It is this large‐scale dynamic process that contributes the most (∼70%) to the wet bias reduction. Although this paper presents a modeling study, it highlights the cloud radiative feedback to the large‐scale dynamics and precipitation over the TP. Plain Language Summary: Despite numerous attempts to correct the overestimation of summer precipitation over the Tibetan Plateau (TP) in current global and regional climate models, the issue persists. This study applies the Gaussian Probability Density Function (GPDF) cloud fraction scheme in the Weather Research and Forecasting model at two different resolutions (0.1° and 0.05°) during a summer over the TP. The results show that the GPDF scheme significantly mitigates the precipitation overestimation, particularly in the high‐resolution modeling. We explored the physical processes, both local and remote, that contribute to this improvement. Specifically, an increase in cloudiness reduces the amount of radiation reaching the land surface. This decrease in surface radiative heating not only reduces local evaporation but also weakens the thermal effect of the TP. The latter is a major driver of the South Asian monsoon that conveys moisture to the TP, and its weakening reduces moisture convergence over the TP. Both the decreases in local evaporation and remote moisture convergence contribute to the alleviation of the precipitation overestimation, and the latter plays a dominant role. These findings provide a unique perspective for reducing the wet bias over the TP, focusing on the surface available energy and associated remote moisture processes. Key Points: The use of the Gaussian Probability Density Function cloud fraction scheme in high resolution greatly reduces wet bias over the Tibetan Plateau (TP) during summerMore cloud water/ice with the scheme lessens TP's thermal effect, causing a weaker South Asian monsoon and moisture convergenceWet bias reduction is mainly governed by the decrease in remote moisture rather than local evapotranspiration [ABSTRACT FROM AUTHOR]

Aedes sp. mosquitoes are changing their geographic range in response to climate change. This is of concern because these mosquitoes can carry dengue fever and other viral diseases. Changing weather patterns can also increase the numbers of Aedes mosquitoes, leading to greater human exposure and enhancing population health risks. We project the geographic distribution of Aedes and associated changes in populations exposed to dengue in Asian metropolitan areas under warming scenarios from 1.5°C to 5.0°C above pre‐industrial temperatures, using multi‐model ensembles. With global warming, the southern part of the Arabian Peninsula, the coast of the Arabian Sea in southern Iran, southern Pakistan in West Asia, the Korean Peninsula, most of the Japanese islands, and parts of North China in East Asia are projected to become suitable for dengue transmission. The numbers of metropolitan areas exposed to dengue is projected to change from 142 (48%) in the reference period (1995–2014) to 211 (71%) at 5.0°C warming. With the combined impact of socioeconomic and climate change, population exposure to dengue in Asian metropolitan areas is projected to increase from 263 (multi‐model range 252–268) million in 1995–2014 to 411 (394–432) million, 446 (420–490) million, 509 (475–601), 558 (493–685) and 587 (529–773) million, respectively, at 1.5°C, 2.0°C, 3.0°C, 4.0°C and 5°C warming, with an average of 2.9 million new people exposed to dengue fever in metropolitan areas each year. Plain Language Summary: Dengue fever, spread by Aedes aegypti and Aedes albopictus mosquitoes, is the most serious arbovirus disease affecting Asia. With global warming, mosquitoes that may carry dengue virus continue to expand in geographic range and vectorial capacity. Continued growth of mosquito populations can lead to a considerable increase in dengue exposure, especially in urban areas, increasing public health risks. Based on the occurrence data of the two mosquito species and climate observations, we explored the correlation between different climate elements and the spread of the mosquitoes. Using the outputs of 34 global climate models, we project the probability of occurrence and mosquito vectorial capacity under different warming levels in Asia. With global warming, much of Asia's area is projected to become suitable for dengue transmission. We have also estimated the future population changes in Asian metropolitan areas, to explore the combined effects of socioeconomy and climate change on exposure to dengue. As population density continues to increase and dengue epidemic potential rises, the population exposed to potential dengue fever transmission in Asian metropolitan areas is projected to surge in the future. Information conveyed in our paper can help increase preparedness for dengue outbreaks. Key Points: With global warming, a large number of areas in Asia are projected to become emerging regions suitable for dengue transmissionThe combined effects of climate and socioeconomic changes are projected to jointly lead to a surge in the population exposed to dengueAn average of 2.9 million new people are projected to be exposed to dengue fever in metropolitan areas each year in the future [ABSTRACT FROM AUTHOR]

Far infrared (FIR) emission from the Earth's polar regions has become an area of increasing scientific interest and value. FIR emission is important for understanding Earth's radiative balance and improving global climate models, especially in rapidly changing Arctic conditions. Far‐infrared emission from Earth is not currently being monitored from space, except as part of broadband emission channels of Earth radiation budget measurements like those from the CERES project, and only limited measurements in the FIR spectrum exist. The Absolute Radiance Interferometer (ARI), developed as a prototype of the infrared spectrometer for CLARREO at the University of Wisconsin‐Madison, Space Science and Engineering Center, measures absolute spectrally resolved infrared (IR) radiance from 200 to 2,000 cm−1 (or 5–50 μm) at 0.5 cm−1 resolution with high accuracy (<0.1 K 3‐sigma brightness temperature at scene temperature). This instrument was taken into the field in Madison, Wisconsin, USA, during the winters of 2021 and 2022, where the weather can reach polar‐like conditions to measure high spectral resolution radiances of various sample types. Sample materials included water, snow, ice, evergreen leaves, dry grass, and sand, all characteristic of high latitude regions. Radiances collected from both a sky view and the sample view in clear‐sky conditions were used to retrieve FIR emissivity. This paper describes the ARI instrument configuration and capability for ground‐based measurements in the FIR region, and documents retrieved emissivities of various analyzed samples. The retrieved emissivity results are publicly available, and comparisons are made to simulated emissivity estimates. Key Points: Absolute Radiance Interferometer used to obtain high spectral resolution far infrared radiation measurements in semi‐controlled environmentsRadiation measurements of the sky and samples, including snow, water, ice, sand, and vegetation, allowed for retrieval of sample emissivityThe retrieved far infrared emissivity spectra and radiance measurements are available for model intercomparisons and other uses [ABSTRACT FROM AUTHOR]

In the age of climate change, the Instagram influencer is still a popular role model for ways to navigate the modern world, and they present us with their visual practices of self-presentation as well as their activism, lifestyle, and business model. However, conspicuous consumption – once a hallmark of success – is now being challenged, and perhaps a paradigm shift is building with the new breed of climate influencers with engaged expertise and ethical vigilance. The aim of this paper is first to identify climate celebrities and then to focus specifically on macro influencers, such as activists or actors, and on micro-influencers, such as entrepreneurs. The article examines how they communicate climate change applying different strategies on Instagram while remaining authentic, using concepts from research on social media within studies of celebrity culture and in climate celebrity communication Second, we demonstrate a shift taking place in the way climate change is communicated: from traditional legacy-media advocacy by celebrities to activists and entrepreneurs performing sustainable everyday practices on social media. This work will contribute directly to how we analyse both the macro and micro-climate influencer as a role model in the age of climate change, providing solutions for coping, engaging and taking action. [ABSTRACT FROM AUTHOR]

The editorial discusses the impact of climate change on forest plants, focusing on adaptation, plasticity, and extinction mechanisms. It highlights the importance of forests in mitigating climate change by absorbing carbon dioxide and addresses the challenges posed by changing climate conditions on forest management. The research covers various topics such as phenology, morphology, tree-ring growth, and wood anatomical traits in different forest ecosystems, providing insights into how different species respond to climate change. The papers aim to enhance understanding of how diverse species adapt to climate change across different continents and climates. [Extracted from the article]

The cloud feedback may result in amplification or damping of Arctic warming. Two common techniques used to diagnose the top‐of‐the‐atmosphere cloud feedback are the Adjusted Cloud Radiative Effect (AdjCRE) method and the Cloud Radiative Kernel (CRK) method. We apply both to CMIP5 and CMIP6 model data, finding that the AdjCRE calculated Arctic shortwave cloud feedback is twice as correlated with sea ice loss in CMIP5, and four times in CMIP6, as the CRK method. We find that the CRK method produces Arctic all‐sky residual percentages exceeding 20% in 15 of 18 models. We use the CRK method to decompose the feedback in CMIP5 and CMIP6 finding that its median value changed from negative to positive driven by a less‐negative cloud optical depth feedback. Despite its lack of closure, we conclude that the CRK method is better suited for Arctic SW feedbacks as it is less impacted by surface albedo changes. Plain Language Summary: The cloud feedback is the process by which cloud property changes in a warming climate can either further enhance warming or damp it. The Arctic is warming faster than the rest of the globe, and one of the largest sources of uncertainty in its climate projections is the cloud feedback. There are two popular methods to calculate the cloud feedback: the Adjusted Cloud Radiative Effect technique, and the Cloud Radiative Kernel technique. In this paper we compare the two methods in a suite of climate models by considering the extent to which changes in Arctic sea ice impact the cloud feedbacks. From this analysis we conclude that the Cloud Radiative Kernel method is less affected by sea ice loss. We then apply the Cloud Radiative Kernel technique to data from the two most recent generations of global climate models to investigate how polar day Arctic cloud feedbacks have changed between these generations. We find that the median value of these Arctic feedbacks is slightly positive in the newest generation of models, a change from slightly negative in the previous generation that is largely fueled by a weakening of the feedback associated with changes in cloud optical depth. Key Points: The Cloud Radiative Kernel method is less sensitive to surface albedo changes than the Adjusted Cloud Radiative Effect techniqueThe Cloud Radiative Kernel method provides poor radiative closure in a suite of global climate modelsThe median shortwave Arctic cloud feedback in recent climate models is slightly positive due to a weakened cloud optical depth feedback [ABSTRACT FROM AUTHOR]

Observations have revealed that the deadly "hot eastern‐pluvial western" climate pattern in Asia in summer 2022 is by no means an exception. In recent decade this pattern is becoming much more frequent owing to the notable westward extension and enhancement of the western Pacific subtropical high (WPSH). The anomalously enhanced WPSH fills the gap in geopotential heights between the Arabian Sea‐Iranian Plateau and the western Pacific, produces a heat dome over eastern Asia and intensifies the moisture convergence over western Asia by changing the water vapor transportation path. Projections of 19 CMIP6 models show that both the temperature in eastern Asia and precipitation in western Asia will remarkably increase under Shared Socioeconomic Pathway (SSP) 2‐4.5, SSP 3‐7.0, and SSP 5‐8.5 scenarios, with temperature anomalies 0.8°C, 1.5°C, and 2.4°C higher and the percentages of precipitation anomaly 6.3%, 9.4%, and 12.5% larger than the reference period in the near, middle, and long terms under the moderate emission scenario (SSP2‐4.5). Therefore, the "hot eastern‐pluvial western" Asia is becoming a new normal and exacerbating the risk of compound disasters. Plain Language Summary: In summer 2022, eastern Asia experienced the most extensive and long‐lasting heatwave since 1951, while exceptional floods hit western Asia during the same period. It is found that this "hot eastern‐pluvial western" pattern has occurred more and more frequently since 1981. This could be attributed to the linear trend of the westward extension and enhancement of the western Pacific subtropical high. In this paper, we assess the expected changes by using the latest generation of global climate model projections. The "hot eastern‐pluvial western" climate pattern and the associated atmospheric circulations are projected to increase from 2020s to the end of the 21st century under different greenhouse gas emission scenarios, suggesting more fatal heatwaves in eastern Asia and floods in western Asia in the future. Key Points: The catastrophic climate pattern of "hot eastern‐pluvial western" Asia occurred frequently in recent decades especially in summer 2022The increasing trend of this pattern could be attributed to the westward extension and enhancement of the western Pacific subtropical highProjections of CMIP6 models show that the "hot eastern‐pluvial western" will occur normally in future [ABSTRACT FROM AUTHOR]

Understanding projected changes in flooding across the contiguous United States (CONUS) helps increase our capability to adapt to and mitigate against this hazard. Here, we assess future changes in flooding across CONUS using outputs from 28 global climate models and four scenarios of the Coupled Model Intercomparison Project Phase 6. We find that CONUS is projected to experience an overall increase in flooding, especially under higher emission scenarios; there are subregional differences, with the Northeast and Southeast (Great Plains of the North and Southwest) showing higher tendency towards increasing (decreasing) flooding due to changes in flood processes at the seasonal scale. Moreover, even though trends may not be detected in the historical period, these projected future trends highlight the current needs for incorporating climate change in the future infrastructure designs and management of the water resources. This paper assesses future changes in flood magnitude across the conterminous United States based on multiple climate change scenarios. The results suggest that annual maximum peak discharge is projected to become more extreme under higher emission scenarios. [ABSTRACT FROM AUTHOR]

Scientists have proposed many "Beyond-GDP" indicators to replace the Gross Domestic Product (GDP) in order to quantify genuine societal development. While GDP is regularly projected, research on future trajectories of Beyond-GDP indicators is lacking, failing to meet policymakers' needs. Focusing on the Human Development Index (HDI), this paper attempts to calculate one of the first global well-being projections for 161 countries by 2100 using the shared socioeconomic pathways (SSPs), the socioeconomic inputs underlying global climate change scenarios. The results indicate a potential global well-being improvement from medium to very high level, depending on the SSPs, with most countries reaching high human development under SSPs 1&5. While serving as an initial step in well-being projection, the results highlight a crucial gap in existing climate change models which are used by the IPCC—they inadequately account for the feedback effects of climate change on well-being. This oversight results in counterintuitive or potentially misleading well-being projections. Therefore, we propose steps to improve this situation. By synthesizing climate change feedback effects on HDI determinants, this assessment delves into their implications for well-being and further underscores the necessity for interdisciplinary collaborations among well-being researchers, climate scientists and policy modelers to achieve sound integral well-being projections. [ABSTRACT FROM AUTHOR]

The present paper aims to study the Wachau Valley in Austria as a representative Cultural Landscape under threat from extreme hydrometeorological hazards linked to climate change. The primary objective is to investigate the impacts and assess the vulnerability associated with the events of heavy rain and flooding. The methodology employed consists of an investigation of recorded past events impacting the Wachau; a vulnerability ranking system; a climate time series analysis based on earth observation products; and future hazard maps at territorial level, developed with outputs from regional and global climate models. The investigation we carried out provides a vulnerability assessment of two terraced areas with a surface of about 10,000 m2 in total, characterized by the presence of dry stone walls, with different state of conservation in the Municipality of Krems (Wachau). In addition, climate projections at territorial level for the extreme climate indices R20mm, R95pTOT, and R×5day—selected for investigating the likelihood of increases/decreases in events of heavy rain and large basin flooding—are provided, with a spatial resolution of ~12 km for the near and far future (2021–2050; 2071–2100) under stabilizing (RCP 4.5) and pessimistic (RCP 8.5) scenarios. The results indicate a general increase for the three indices in the studied areas during the far future under the pessimistic scenario, suggesting a heightened risk of heavy rain and flooding. These findings aim to inform policymakers and decision-makers in their development of strategies for safeguarding cultural heritage. Furthermore, they serve to assist local stakeholders in enhancing their understanding of prioritizing interventions related to preparedness, emergency response, and recovery. [ABSTRACT FROM AUTHOR]

The relationship between trade and climate change is not a simple linear relationship. In this paper, using the threshold regression model, we estimated the effect of trade on climate change in South Africa. The paper applied the LM test to examine the nonlinear inference approach to test whether nonlinearity existed and if the threshold model was relevant to the study. The results show that when energy use is set as the threshold variable, the relationship between trade and climate change measured as methane is U-shaped. Also, in other models of GHG as climate change indicators, the results show that the effect of trade on climate change is not dynamic. This result supports the idea that high and low trade effects may have different impacts on climate change indicators. It is, therefore, recommended that all exporters in South Africa resort to more innovative environmental mechanisms to reduce the contribution to climate. The suggestion for future studies is to consider exports of different sectors to climate change. This approach will avoid the generalization of exporting firms as the worst emitters. [ABSTRACT FROM AUTHOR]

Dams have proliferated along the Mekong, spurred by energy demands from economic development and capital from private companies. Swift dam evolution has rendered many databases outdated, in which mismatches arise from differing compilation methods. Without a comprehensive database, up-to-date spatial assessment of dam growth is unavailable. Looking at future development, hydropower potential specifically within the Mekong remains to be systematically evaluated. In this paper, we offer (1) an open-access and unified database of 1055 dams, (2) a spatiotemporal analysis of dams on a sub-basin and country level from the 1980s to the post-2020s, and (3) a grid-based assessment of the theoretical basin-wide hydropower potential using present-day discharge from the CaMa-Flood model (2011–2015, 0.05°) and future discharge from the WaterGAP2 model used for ISIMIP2b (2021–2040, 0.5°). The dam count of 1055 is more than twice the largest existing database, with 608 hydropower dams generating a boom in hydropower capacity from 1242 MW in the 1980s to 69 199 MW post-2020s. While China had the largest capacity increase from the 2000s to the 2010s (+ 16 854 MW), Laos has the most planned dams and the highest projected growth post-2020s (+ 18 223 MW). Based on present-day discharge, we estimate a basin-wide hydropower potential of 1 334 683 MW , where Laos is the highest at 514 887 MW. Based on future discharge modeled with climate change, hydropower potential could grow to over 2 000 000 MW. Laos and China are the highest at around 900 000 MW each, together forming over 80 % of the total potential. Our database facilitates research on dam-induced hydrological and ecological alterations, while spatiotemporal analysis of hydropower capacity could illuminate the complex transboundary electricity trade. Through both spatiotemporal and hydropower potential evaluation, we address the current and future vulnerability of countries to dam construction, highlighting the need for better planning and management in the future hydropower hotspot Laos. The Mekong dam database is publicly available at 10.21979/N9/ACZIJN (Ang et al., 2023). [ABSTRACT FROM AUTHOR]

Desert dust is an important atmospheric aerosol that affects the Earth's climate, biogeochemistry, and air quality. However, current Earth system models (ESMs) struggle to accurately capture the impact of dust on the Earth's climate and ecosystems, in part because these models lack several essential aeolian processes that couple dust with climate and land surface processes. In this study, we address this issue by implementing several new parameterizations of aeolian processes detailed in our companion paper in the Community Earth System Model version 2 (CESM2). These processes include (1) incorporating a simplified soil particle size representation to calculate the dust emission threshold friction velocity, (2) accounting for the drag partition effect of rocks and vegetation in reducing wind stress on erodible soils, (3) accounting for the intermittency of dust emissions due to unresolved turbulent wind fluctuations, and (4) correcting the spatial variability of simulated dust emissions from native to higher spatial resolutions on spatiotemporal dust variability. Our results show that the modified dust emission scheme significantly reduces the model bias against observations compared with the default scheme and improves the correlation against observations of multiple key dust variables such as dust aerosol optical depth (DAOD), surface particulate matter (PM) concentration, and deposition flux. Our scheme's dust also correlates strongly with various meteorological and land surface variables, implying higher sensitivity of dust to future climate change than other schemes' dust. These findings highlight the importance of including additional aeolian processes for improving the performance of ESM aerosol simulations and potentially enhancing model assessments of how dust impacts climate and ecosystem changes. [ABSTRACT FROM AUTHOR]

Improving the prediction of clouds in shallow-cumulus regimes via turbulence parameterization in the planetary boundary layer (PBL) will likely increase the global skill of global climate models (GCMs) because this cloud regime is common over tropical oceans where low-cloud fraction has a large impact on Earth's radiative budget. This study attempts to improve the prediction of PBL structure in tropical trade wind regimes in the Community Atmosphere Model (CAM) by updating its formulation of momentum flux in CLUBB (Cloud Layers Unified by Binormals), which currently does not by default allow for upgradient momentum fluxes. Hindcast CAM output from custom CLUBB configurations which permit countergradient momentum fluxes are compared to in situ observations from weather balloons collected during the ElUcidating the RolE of Cloud–Circulation Coupling in ClimAte and Atlantic Tradewind Ocean–Atmosphere Mesoscale Interaction Campaign (EUREC 4 A/ATOMIC) field campaign in the tropical Atlantic in early 2020. Comparing a version with CAM–CLUBB with a prognostic treatment of momentum fluxes results in vertical profiles that better match large-eddy simulation results. Countergradient fluxes are frequently simulated between 950 and 850 hPa over the EUREC 4 A/ATOMIC period in CAM–CLUBB. Further modification to the planetary boundary layer (PBL) parameterization by implementing a more generalized calculation of the turbulent length scale reduces model bias and root mean squared error (RMSE) relative to sounding data when coupled with the prognostic momentum configuration. Benefits are also seen in the diurnal cycle, although more systematic model errors persist. A cursory budget analysis suggests the buoyant production of momentum fluxes, both above and below the jet maximum, significantly contributes to the frequency and depth of countergradient vertical momentum fluxes in the study region. This paper provides evidence that higher-order turbulence parameterizations may offer pathways for improving the simulation of trade wind regimes in global models, particularly when evaluated in a process study framework. [ABSTRACT FROM AUTHOR]

Rainfall erosivity, which signifies the inherent susceptibility of soil erosion induced by precipitation, plays a fundamental role in formulating a comprehensive soil loss equation (RUSLE). It stands as a crucial determinant among the foundational factors considered in a comprehensive soil loss equation's establishment. Nonetheless, the prediction and quantification of future alterations in rainfall erosivity under the influence of global warming have been relatively limited. In this study, climate change was widely evaluated and 10 preferred global climate models in the Loess Plateau were selected by using the data sets of 27 models simulating climate change and the CN05.1 data set provided by the latest CMIP6. The monthly precipitation forecast data were obtained by using the delta downscaling method. Combined with trend analysis, significance test, and coefficient of variation, the annual rainfall erosivity during 1961–2100 under four SSP scenarios was analyzed and predicted. Among the 27 GCM models used in this paper, the most suitable climate models for simulating monthly precipitation in the Loess Plateau were CMCC-CM2-SR5, CMCC-ESM2, TaiESM1, EC-Earth3, EC-Earth-Veg-LR, INM-CM4-8, CAS-ESM2-0, EC-Earth-Veg, ACCESS-ESM1-5, and IPSL-CM6A-LR. In comparison to the base period (1961–1990), during the historical period (1961–2014), the average annual rainfall erosivity on the Loess Plateau amounted to 1259.64 MJ·mm·hm−2·h−1·a−1, showing an insignificant downward trend. In the northwest of Ningxia, Yulin City and Yanan City showed a significant upward trend. In the future period (2015–2100), the annual rainfall erosivity continues to constantly change and increase. The potential average increase in rainfall erosivity is about 13.48–25.86%. In terms of spatial distribution, most areas showed an increasing trend. Among these regions, the majority of encompassed areas within Shanxi Province, central Shaanxi, and Inner Mongolia increased greatly, which was not conducive to soil and water conservation and ecological environment construction. This study offers a scientific reference for the projected future erosivity characteristics of the Loess Plateau. [ABSTRACT FROM AUTHOR]

Big climate change data have become a pressing issue that organizations face with methods to analyze data generated from various data types. Moreover, storage, processing, and analysis of data generated from climate change activities are becoming very massive, and are challenging for the current algorithms to handle. Therefore, big data analytics methods are designed for significantly large amounts of data required to enhance seasonal change monitoring and understand and ascertain the health risks of climate change. In addition, analysis of climate change data would improve the allocation, and utilisation of natural resources. This paper provides an extensive discussion of big data analytic methods for climate data analysis and investigates how climate change and sustainability issues can be analyzed through these approaches. We further present the big data analytic methods, strengths, and weaknesses, and the essence of analyzing big climate change using these methods. The common datasets, implementation frameworks for climate change modeling, and future research directions were also presented to enhance the clarity of these compelling climate change analysis challenges. This big data analytics method is well-timed to solve the inherent issues of data analysis and easy realization of sustainable development goals. [ABSTRACT FROM AUTHOR]

This paper presents a hydrological assessment of the 113,981 km2 Meta River basin in Colombia using 13 global climate models to predict water yield for 2050 under two CMIP6 scenarios, SSP 4.5 and SSP 8.5. Despite mixed performance across subbasins, the model was notably effective in the upper Meta River subbasin. This study predicts an overall increase in the basin's annual water yield due to increased precipitation, especially in flatter regions. Under the SSP 4.5, the Meta River basin's water flow is expected to rise from 5141.6 m3/s to 6397.5 m3/s, and to 6101.5 m3/s under the SSP 8.5 scenario, marking 24% and 19% increases in water yield, respectively. Conversely, the upper Meta River subbasin may experience a slight decrease in water yield, while the upper Casanare River subbasin is predicted to see significant increases. The South Cravo River subbasin, however, is expected to face a considerable decline in water yield, indicating potential water scarcity. This study represents a pioneering large-scale application of the InVEST–AWY model in Colombia using CMIP6 global climate models with an integrated approach to produce predictions of future water yields. [ABSTRACT FROM AUTHOR]

This paper critically addresses the need for a unified global climate policy, as opposed to region-specific emission trading systems, with a primary aim to contribute valuable insights to the ongoing discourse. Focused on the aluminum, cement, and iron and steel industries outlined in the EU's Carbon Border Adjustment Mechanism (CBAM) proposal, our comprehensive analysis using gravity model for trade, centers on testing the validity of the Pollution Haven Hypothesis and Porter Hypothesis. Drawing on data from 10 major EU economies and 19 OECD partners across continents, our study demonstrates that carbon leakage predominantly occurs through trade channels, wherein countries import carbon-intensive products from less regulated nations. Our findings substantiate the Pollution Haven Hypothesis, revealing unintended pollution havens resulting from stringent environmental regulations, leading to carbon leakage through trade or production relocation. In contrast, supporting the Porter Hypothesis, our research underscores how stringent environmental policies can drive innovation within polluting countries, obviating the need for relocation or product imports. By substantiating both hypotheses, our paper advocates for a globally uniform climate policy and emphasizes the potential drawbacks of asymmetrical approaches. The central aim is to contribute to the understanding of how such policies may inadvertently contribute to trade-induced leakage, undermining the positive impact of local systems on a global scale. In light of ambitious climate targets, our study underscores the urgency for synchronized global efforts, reinforcing the call for consistent policies to effectively address the challenges of climate change. [ABSTRACT FROM AUTHOR]

In recent decades, climate change has become a critical global issue with far-reaching consequences for regional climates and ecosystems. While regional climate models provide valuable information, there is a growing need for high-resolution simulations to assess local impacts. This paper addresses this gap by presenting the first simulation of a 3 km convection-permitting (CP) scenario simulation for Bulgaria. The main aim of this study is to assess different precipitation indices and their future changes for Bulgaria under the Representative Concentration Pathway 8.5 (RCP8.5) scenario following the Coordinated Regional Climate Downscaling Experiment Flagship Pilot Study protocol. The simulations are evaluated against high-resolution observations. We downscale Coupled Model Intercomparison Project 5 Global Climate Model (CMIP5 GCM) data for historical (1995–2004) and future (2089–2098) periods using a regional climate model (RCM) at 15 km grid spacing and parametrized convection. We use these fields as initial and boundary conditions for convection-permitting kilometer-scale simulations. The 15 km grid spacing driving model is used as a reference to assess the added value of the kilometer-scale simulation. Additionally, the 3 km seasonal mean and projected 2 m temperature and the winter snow water equivalent are presented. The results show that the kilometer-scale simulation shows better performance of wet-hour intensity in all seasons, wet-hour frequency in the spring, fall, and winter, and extreme precipitation (99.9th percentile of all precipitation events, p99.9) in the winter and fall. The kilometer-scale simulation improves the projected precipitation distribution and modifies the signal of the precipitation frequency, intensity, and heavy precipitation change over some areas. A positive projected change in the wet-hour intensity is expected in all seasons (13.86% in spring, MAM, 17.48% in summer, JJA, 1.97% in fall, SON, and 17.43% in winter, DJF) and in the heavy precipitation in the spring (13.14%) and winter (31.19%) in the kilometer-scale experiment. The projected increase in mean winter precipitation is accompanied by a significant decrease in mean winter snowfall over lowlands (50−70%). The convection-permitting Regional Climate Model, version 4.7.1 (RegCM4.7.1) suggests an increase in winter snowfall over the highest parts of the country, but a significant increase in the 2 m temperatures there. The results of this study are encouraging and may be of interest to the community of climate scientists and users of climate data for making reliable estimates of the local impacts of future climate change. [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]

Unlike other natural disasters, drought is one of the most severe threats to all living beings globally. Due to global climate change, the frequency and duration of droughts have increased in many parts of the world. Therefore, accurate prediction and forecasting of droughts are essential for effective mitigation policies and sustainable research. In recent research, the use of ensemble global climate models (GCMs) for simulating precipitation data is common. The objective of this research is to enhance the multi-model ensemble (MME) for improving future drought characterizations. In this research, we propose the use of relative importance metric (RIM) to address collinearity effects and point-wise discrepancy weights (PWDW) in GCMs. Consequently, this paper introduces a new statistical framework for weighted ensembles called the discrepancy-enhanced beta weighting ensemble (DEBWE). DEBWE enhances the weighted ensemble data of precipitation simulated by multiple GCMs. In DEBWE, we addressed uncertainties in GCMs arising from collinearity and outliers. To evaluate the effectiveness of the proposed weighting framework, we compared its performance with the simple average multi-model ensemble (SAMME), Taylor skill score ensemble (TSSE), and mutual information ensemble (MIE). Based on the Kling–Gupta efficiency (KGE) metric, DEBWE outperforms all competitors across all evaluation criteria. These inferences are based on the analysis of historical simulated data from 22 GCMs in the CMIP6 project. The quantitative performance indicators strongly support the superiority of DEBWE. The median and mean KGE values for DEBWE are 0.2650 and 0.2429, compared to SAMME (0.1000, 0.0991), TSSE (0.2600, 0.2397), and MIE (0.1550, 0.1511). For drought assessment, we computed the adaptive standardized precipitation index (SPI) for three future scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. The steady-state probabilities suggest that normal drought (ND) is the most frequent condition, with extreme events (dry or wet) being less probable. [ABSTRACT FROM AUTHOR]

This paper highlights how technology can contribute to reaching the 2015 Paris Agreement goals of net zero carbon dioxide (CO2) emissions and global warming below 2°C in 2100. It uses the Advanced Climate Change Long-term model (ACCL), particularly adapted to quantify the consequences of energy price and technology shocks on CO2 emissions, temperature, climate damage and Gross Domestic Product (GDP). The simulations show that without climate policies the warming may be +5°C in 2100, with considerable climate damage. An acceleration in 'usual' technical progress not targeted at reducing CO2- even worsens global warming and climate damage. According to our estimates, the world does not achieve climate goals in 2100 without 'green' technologies. Intervening only via energy prices, e.g. a carbon tax, requires challenging hypotheses of international coordination and price increase for polluting energies. We assess a multi-lever climate strategy combining energy efficiency gains, carbon sequestration, and a decrease of 3% per year in the relative price of 'clean' electricity with a 1 to 1.5% annual rise in the relative price of polluting energy sources. None of these components alone is sufficient to reach climate objectives. Our last and most important finding is that our composite scenario achieves the climate goals. [ABSTRACT FROM AUTHOR]

The Antarctic sea ice, which undergoes annual freezing and melting, plays a significant role in the global climate cycle. Since satellite observations in the Antarctic region began, 2023 saw a historically unprecedented decrease in the extent of sea ice. Further ocean warming and future environmental conditions in the Southern Ocean will influence the extent and amount of ice in the Marginal Ice Zones (MIZ), the BioGeoChemical (BGC) cycles, and their interconnected relationships. The so‐called pancake floes are a composition of a porous sea ice matrix with interstitial brine, nutrients, and biological communities inside the pores. The ice formation and salinity are both dependent on the ambient temperature. To realistically model these multiphasic and multicomponent coupled processes, the extended Theory of Porous Media (eTPM) is used to develop Partial Differential Equations (PDEs) based high‐fidelity models capable of simulating the different seasonal variations in the region. All critical variables like salinity, ice volume fraction, and temperature, among others, are considered and have their equations of state. The phase transition phenomenon is approached through a micro‐macro linking scheme. In this paper, a phase‐field solidification model [4] coupled with salinity is used to model the microscale freezing processes and up‐scaled to the macroscale eTPM model. The evolution equations for the phase field model are derived following Landau‐Ginzburg order parameter gradient dynamics and mass conservation of salt allowing to model the salt trapped inside the pores. A BGC flux model for sea ice is set up to simulate the algal species present in the sea ice matrix. Ordinary differential equations (ODE) are employed to represent the diverse environmental factors involved in the growth and loss of distinct BGC components. Processes like photosynthesis are dependent on temperature and salinity, which are derived through an ODE‐PDE coupling with the eTPM model. Academic simulations and results are presented as validation for the mathematical model. These high‐fidelity models eventually lead to their incorporation into large‐scale global climate models. [ABSTRACT FROM AUTHOR]

Accurately and reliably predicting droughts under multiple models of Global Climate Models (GCMs) is a challenging task. To address this challenge, the Multimodel Ensemble (MME) method has become a valuable tool for merging multiple models and producing more accurate forecasts. This paper aims to enhance drought monitoring modules for the twenty-first century using multiple GCMs. To achieve this goal, the research introduces a new weighing paradigm called the Multimodel Homo-min Pertinence-max Hybrid Weighted Average (MHmPmHWAR) for the accurate aggregation of multiple GCMs. Secondly, the research proposes a new drought index called the Condensed Multimodal Multi-Scalar Standardized Drought Index (CMMSDI). To assess the effectiveness of MHmPmHWAR, the research compared its findings with the Simple Model Average (SMA). In the application, eighteen different GCM models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) were considered at thirty-two grid points of the Tibet Plateau region. Mann–Kendall (MK) test statistics and Steady States Probabilities (SSPs) of Markov chain were used to assess the long-term trend in drought and its classes. The analysis of trends indicated that the number of grid points demonstrating an upward trend was significantly greater than those displaying a downward trend in terms of spatial coverage, at a significance level of 0.05. When examining scenario SSP1-2.6, the probability of moderate wet and normal drought was greater in nearly all temporal scales than other categories. The outcomes of SSP2-4.5 demonstrated that the likelihoods of moderate drought and normal drought were higher than other classifications. Additionally, the results of SSP5-8.5 were comparable to those of SSP2-4.5, underscoring the importance of taking effective actions to alleviate drought impacts in the future. The results demonstrate the effectiveness of the MHmPmHWAR and CMMSDI approaches in predicting droughts under multiple GCMs, which can contribute to effective drought monitoring and management. [ABSTRACT FROM AUTHOR]

Inherent biases in numerical simulation models of global climate models (GCMs) reduce the scope for accurately assessing future droughts under multimodel ensemble. The aim of this research is to increase the efficiency of multimodel ensembles. Consequently, this paper introduces a two-way hybrid weighting scheme for ensembling multiple GCMs. The proposed weighting scheme enhances the coherence of the multimodel ensemble of climate model simulations with real observed data and minimizes the impact of non-identical seasonal behavior in climate simulations within the multimodel ensemble. In the application, we consider precipitation data from 18 GCMs of CMIP6 from the Tibet Plateau region. To evaluate the effectiveness of the proposed scheme, we compared the proposed ensemble data with simple model averaged (SMA) ensemble using the Pearson product-moment correlation coefficient (r), root mean squared error (RMSE), and mean absolute error (MAE). Furthermore, we assessed the future characteristics of various drought categories using the steady-state probabilities of a Markov Chain. Small values of errors metircs and high correlation indicate that the proposed hybrid weighting scheme exhibits better performance than the SMA. Under the proposed index, the long term probabilities of various drought classe show variations across different time scales of drought. These results provide perspectives on how the the long term probabilities of various drought categories evolve over time. [ABSTRACT FROM AUTHOR]

The accurate identification of extreme weather events (EWEs), particularly cyclones, has become increasingly crucial due to the intensifying impacts of climate change. In the Indian subcontinent, the frequency and severity of cyclones have demonstrably risen, highlighting the need for reliable detection methods to minimize casualties and economic losses. However, the inherent limitations of low-resolution data pose significant challenges to traditional detection methods. Deep learning models offer a promising solution, enabling the precise identification of cyclone boundaries crucial for assessing regional impacts using global climate models data. By leveraging the power of deep learning, we can significantly enhance our capabilities for cyclone detection and contribute to improved risk mitigation strategies in the vulnerable Indian subcontinent. Therefore, this paper introduces an edge-enhanced super-resolution GAN (EESRGAN) leveraging an end-to-end detector network. The proposed approach comprised of a generator network equipped by residual-in-residual dense block (RRDB) and discriminator containing Faster RCNN detector. The precise patterns of cyclone had been effectively extracted to help boundary detection. Extensive experiments have been conducted on Community Atmospheric Model (CAM5.1) data taken into account only seven variables. Four matrices including precision, recall, intersection over union, and mean average precision have been considered to assess the proposed approach. The results have been found very effective while achieving accuracy up to 86.3% and average precision (AP) of 88.63%. Moreover, the proposed method demonstrates its superiority while compared with benchmarks object detectors methods. Thus, the proposed method can be employed in the area of extreme climate detection and could enrich the climate research domain. [ABSTRACT FROM AUTHOR]

The increased frequency of extreme weather events associated with climate change is a growing concern for road authorities, consultants, contractors and end users. Climate change adaptation is not yet incorporated into South Africa’s transport design and planning. Current design methods for asphalt pavements include recently introduced performance-graded (PG) specifications for bitumen. Although the need exists to use future-projected temperature maps in the road industry, the hindrance to this adoption has been extracting the appropriate rising air temperatures from climate model projections. This study focuses on the integration of biasadjusted historical and projected climate model temperature outputs between 1980 and 2060 to estimate changes in pavement temperatures throughout South Africa to inform a strategy for adaptive material selection. Differences in bitumen selection are observed for the PG 58 and PG 52 maximum temperature grade, and an introduction of PG 70 regions, as derived from climate model data in comparison with the SATS 3208 technical standard. The general trend is a gradual increase in maximum pavement design temperatures, mostly affecting the country’s northern and central regions and corresponding road networks. This paper concludes that adjustments are required regarding climate projections for use in bituminous binder selection based on the current specification. [ABSTRACT FROM AUTHOR]

The paper investigates the effects of climate change on riverine ecosystems in the Upper Arno River basin, adopting a hybrid methodology relating the changes in streamflow to its related water ecosystem services. Twenty hydrological models were run using the Soil and Water Assessment Tool (SWAT), under their respective climate model and emission scenario. The streamflow variable obtained was analysed according to the method of Indicators of Hydrologic Alteration (IHA). The development of spatial maps of IHA allowed us to represent the spatial of the results according to the three river subbasins studied, with a focus on the presence of hotspots. Using IHA allowed to capture of the streamflow quantity and dynamics, through groups of magnitude, timing and rates, where the potential effect could be then linked to the concept of water ecosystem services. Finally, the results obtained were presented as a proxy that can be integrated into the existing Driver, Pressure, State, Impact, Response (DPSIR) management framework to eventually move towards an ecosystem‐based management methodology. The main results, induced from the Mann–Kendall trend analysis of the IHA, suggested that provisioning and regulating services are to be affected by climate change where the quantity of flows shows a decreasing trend under several emission scenarios, in addition to an intensification of the extreme events. This shift is accompanied by a change in the dynamics of flow, which holds the potential of affecting habitat suitability. With the emergence of studies directed towards ecosystem‐based management, the study refers to well‐established hydrological modelling to evaluate climate change as a driver and provide screening results to ensure a proactive response planning that converges with the idea of ecosystems. [ABSTRACT FROM AUTHOR]

Sources and fluxes of methane to the atmosphere from permafrost are significant but poorly constrained in global climate models. We present data collected from the variable permafrost setting of the outer Mackenzie River Delta, including observations of aquatic methane seepage, core determinations of in situ methane occurrence and seep gas isotope geochemistry. The sources and locations of in situ geologic methane occurrence and aquatic and atmospheric gas release appear to be controlled by the regional geology and permafrost conditions. Where permafrost is >250 m thick, thermogenic gas deposits at depth are isolated by laterally continuous, low permeability ice‐bearing sediments with few through‐going thawed taliks. Thus, the observed in situ methane and aquatic gas seepage appears to be dominated by microbial methane. In contrast, where permafrost is <80 m thick, taliks are more likely to be through‐going, providing permeable conduits from depth and migration pathways for both thermogenic and biogenic gas. Continuous annual fluid sampling of two lakes and a river channel documents aquatic methane flux from microbial sources, more deeply buried thermogenic sources, and mixtures of both. Using estimates of in situ methane concentration from deep core samples and observations of in situ free gas occurrences, we conclude that the reservoir of in situ geologic methane within ice bonded permafrost is substantial and that this methane is presently migrating with ongoing atmospheric release. It is our assessment that the permafrost setting, and processes described are sensitive to future climate change as the permafrost warms. Plain Language Summary: Methane is released from permafrost, yet the amount and sources of the methane are poorly understood. This paper presents data on methane released from water environments and stored in soil from the permafrost areas of the outer Mackenzie River Delta, NWT. It also uses isotope geochemistry to determine the source (type) of the methane ‐ either biogenic (produced by living organisms) or thermogenic (produced by breaking down of organic matter by heat). The sources and locations of the methane gas release appear to be controlled by the regional geology and permafrost conditions. We conclude that the methane stored within and beneath the ice bonded permafrost is substantial and that this methane is presently migrating to aquatic systems with ongoing atmospheric release. It is our assessment that this permafrost setting and the potential for increased methane release are sensitive to future climate change as the permafrost warms. Key Points: A unique annual record of dissolved methane geochemistry documents active microbial and geologic methane sources in a delta lake and channelSeep gas chemistry, geophysical, and core data allow appraisal of permafrost controls on subsurface methane dynamics in the Mackenzie DeltaThe large inventory of stored methane and the dynamic permafrost processes described are potentially responsive to future climate change [ABSTRACT FROM AUTHOR]

As a percentage of the total global energy supply, wind energy facilities could provide 10% of the total global energy supply by 2050 as reported in IEA World Energy Outlook (2022). Considering this, a just transition to renewable and sustainable energy in South Africa is a genuine possibility if steps are taken immediately to achieve this. The Eastern Cape Province exhibits a strong wind resource which can be exploited towards expediting such a just energy transition. No research and related modelling have, to date, been undertaken in quantifying and relating the detailed P50 energy yield analyses of representative wind energy facilities in temporal and spatial dimensions to the occurrence of specific synoptic types in South Africa. To quantify this energy meteorology climatology for a suitably sized geospatial area in the Eastern Cape Province of South Africa (spatial focus area, latitude −30 to −35, longitude 20 to 30), the approach of using self-organising maps is proposed. These maps are used to identify the most common synoptic circulation types occurring in the Eastern Cape and can subsequently be mapped onto an equivalent time resolution wind energy production timeseries calculated based on probable wind energy facility sites. This paper describes comprehensive methodologies used to model the wind energy facilities, calculate with high confidence the P50 energy production, and then identify the predominant synoptic weather types responsible for the wind energy production in this spatial focus area. After quantifying the energy production, running a self-organising map software generates a purposely selected 35 node map that characterises archetypal synoptic patterns over the 10-year period. The synoptic types can be ranked by the highest energy production. It is shown that in this spatial area, monthly wind energy production is higher during the winter months. When the well-established high-pressure cells move northward, synoptic types associated with higher energy production are frequent and include tropical and temperate disturbances across South Africa, patterns resembling a ridging anticyclone off the west coast of South Africa and low-pressure cells occurring to the north and south. Low energy producing patterns show characteristics of the high-pressure cells moving southwards producing fine weather and mildly disturbed conditions. The purpose of this methodology is that it provides the foundation required to derive long-term frequency changes of these synoptic weather systems using global climate model ensembles and thus changes in wind energy production. [ABSTRACT FROM AUTHOR]

Groundwater is an important resource for a multitude of purposes, and change in groundwater levels is an important factor in determining the increase and decrease of groundwater quantity in aquifers. This study has been carried out for Gomti River basin, where groundwater is the major drinking water source for the entire region. This paper evaluates the behavior of the groundwater levels using Visual MODFLOW under RCP 4.5 and RCP 8.5 scenarios. For modeling of groundwater flow, the entire study area was discretized into 108480 (X-direction) and 207090 (Y-direction) gridal network with each cell size of 1000 m × 1000 m. The model was calibrated for the period 2002 to 2008 and validated for the period 2009 to 2013 using data of 24 observation wells. During the calibration period of Visual MODFLOW, residual mean ranged between 0.658 and 0.268 m, and absolute residual mean ranged between 2.035 and 2.174 m. The calibrated Visual MODFLOW model was employed to evaluate the influence of climate change on future groundwater levels. Future groundwater recharge (2020–2030), obtained after the application of four (MIROC-ESM, MIROC-ESM-CHEM, MIROC MIROC5, and MOHC-HADGEM2-ES) global climate model (GCM) data into prior SWAT model, was applied to Visual MODFLOW. After applying, the projected future (2020–2030) trend in the groundwater levels was found continuously decreasing due to low groundwater recharge and excessive groundwater withdrawal in the basin in future. [ABSTRACT FROM AUTHOR]

The 55th session of the UN Statistical Commission recently took place, with representatives from 129 countries and various agencies in attendance. Important decisions were made regarding inclusivity, methodological enhancements, and the establishment of working groups for new statistical standards. The session also celebrated the 30th anniversary of the UN Fundamental Principles of Official Statistics. However, controversy arose over the establishment of an Independent Advisory Board on the FPOS, with some countries opposing external oversight. The session covered topics such as sustainable development, environmental-economic accounting, food security, and tourism statistics. Concerns were raised about the lack of a monitoring mechanism for political interference in statistical production. The SJIAOS includes an interview with the director of the UN Statistics Division and a special section on agricultural statistics. Other papers cover topics such as machine learning algorithms in agricultural surveys, household consumption surveys for food security analysis, and methodologies for identifying small-scale food producers in Italy. The document also summarizes articles in the Statistical Journal of the IAOS, which discuss a new methodology for data collection, surveys conducted during the COVID-19 pandemic, time series analysis, and the importance of data utilization. [Extracted from the article]