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Earth system models (ESMs) are state-of-the-art climate models that allow numerical simulations of the past, present-day, and future climate. To extend our understanding of the Earth system and improve climate change projections, the complexity of ESMs heavily increased over the last decades. As a consequence, the amount and volume of data provided by ESMs has increased considerably. Innovative tools for a comprehensive model evaluation and analysis are required to assess the performance of these increasingly complex ESMs against observations or reanalyses. One of these tools is the Earth System Model Evaluation Tool (ESMValTool), a community diagnostic and performance metrics tool for the evaluation of ESMs. Input data for ESMValTool needs to be formatted according to the CMOR (Climate Model Output Rewriter) standard, a process that is usually referred to as "CMORization". While this is a quasi-standard for large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP), this complicates the application of ESMValTool to non-CMOR-compliant climate model output. In this paper, we describe an extension of ESMValTool introduced in v2.6.0 that allows seamless reading and processing of "native" climate model output, i.e., operational output produced by running the climate model through the standard workflow of the corresponding modeling institute. This is achieved by an extension of ESMValTool's preprocessing pipeline that performs a CMOR-like reformatting of the native model output during runtime. Thus, the rich collection of diagnostics provided by ESMValTool is now fully available for these models. For models that use unstructured grids, a further preprocessing step required to apply many common diagnostics is regridding to a regular latitude–longitude grid. Extensions to ESMValTool's regridding functions described here allow for more flexible interpolation schemes that can be used on unstructured grids. Currently, ESMValTool supports nearest-neighbor, bilinear, and first-order conservative regridding from unstructured grids to regular grids. Example applications of this new native model support are the evaluation of new model setups against predecessor versions, assessing of the performance of different simulations against observations, CMORization of native model data for contributions to model intercomparison projects, and monitoring of running climate model simulations. For the latter, new general-purpose diagnostics have been added to ESMValTool that are able to plot a wide range of variable types. Currently, five climate models are supported: CESM2 (experimental; at the moment, only surface variables are available), EC-Earth3, EMAC, ICON, and IPSL-CM6. As the framework for the CMOR-like reformatting of native model output described here is implemented in a general way, support for other climate models can be easily added. [ABSTRACT FROM AUTHOR]

A common task in Earth Sciences is to infer climate information at local and regional scales from global climate models. Dynamical downscaling requires running expensive numerical models at high resolution, which can be prohibitive due to long model runtimes. On the other hand, statistical downscaling techniques present an alternative approach for learning links between the large- and local-scale climate in a more efficient way. A large number of deep neural networkbased approaches for statistical downscaling have been proposed in recent years, mostly based on convolutional architectures developed for computer vision and super-resolution tasks. This paper presents deep learning for empirical downscaling (DL4DS), a python library that implements a wide variety of state-of-the-art and novel algorithms for downscaling gridded Earth Science data with deep neural networks. DL4DS has been designed with the goal of providing a general framework for training convolutional neural networks with configurable architectures and learning strategies to facilitate the conduction of comparative and ablation studies in a robust way. We showcase the capabilities of DL4DS on air quality Copernicus Atmosphere Monitoring Service (CAMS) data over the western Mediterranean area. [ABSTRACT FROM AUTHOR]

The research paper entitled "Impact of Climate Change on Global Ecosystem and SDGs" is an attempt to define the climate change, its scope, adaptability, vulnerability and risk particularly in the context of its direct impact on cities globally. It is observed and learnt from various research studies and recent literature review on climate change impact and risks associated with it not only on the cities globally, but also its severe impact on the ecosystem due to its variability and forms as there are variations/changes/occurrences of climate hazards at different points of time including the uncertainty of climate change hazards and vulnerabilities associated with it. The onset of COVID-19 pandemic and its severity have also put tremendous pressure on the ecosystem and have resulted in climatic disorders thereby leading to serious imbalances in climate change across the ecosystem. A mention has also been made particularly with regards to the role of UN Security Council and the UNDRR agency in addressing climate change hazards, vulnerabilities, risks globally. The study has made a mention of the recent research studies/literature review on climate change and its impact on the ecosystem in the context of posing a threat to humanity and its wellbeing and health of the global ecosystem, integration of disaster risk reduction and climate change adaptation, impact of climate change on SIDS, adapting to new climate reality, nature-based solutions & natural variability, climate risk response, physical hazards and socioeconomic impacts and response, assessment and computation of climate change models capturing the related probabilistic functions, regressive behavior and non-linearity of climate change risk models etc., The paper also focuses on the quantitative assessment of climate change risk at an urban scale, impact on infrastructure services, process of decarbonization at scale including review of recent progress and outlook for future, research on downscaling of large-scale climate change to a local scale and its integration with the global scale and its effects, The other aspects covered in the study include major challenges in climate change, simulation of urban development and micro climate and vulnerability, risk identification in climate change, vulnerability of economy and wealth, ecosystem and biodiversity, resource availability, climate forcing among others. [ABSTRACT FROM AUTHOR]

Traditional approaches for comparing global climate models and observational data products typically fail to account for the geographic location of the underlying weather station data. For modern global highresolution models with a horizontal resolution of tens of kilometers, this is an oversight since there are likely grid cells where the physical output of a climate model is compared with a statistically interpolated quantity instead of actual measurements of the climate system. In this paper, we quantify the impact of geographic sampling on the relative performance of high-resolution climate model representations of precipitation extremes in boreal winter (December-January-February) over the contiguous United States (CONUS), comparing model output from five early submissions to the HighResMIP subproject of the CMIP6 experiment. We find that properly accounting for the geographic sampling of weather stations can significantly change the assessment of model performance. Across the models considered, failing to account for sampling impacts the different metrics (extreme bias, spatial pattern correlation, and spatial variability) in different ways (both increasing and decreasing). We argue that the geographic sampling of weather stations should be accounted for in order to yield a more straightforward and appropriate comparison between models and observational data sets, particularly for high-resolution models with a horizontal resolution of tens of kilometers. While we focus on the CONUS in this paper, our results have important implications for other global land regions where the sampling problem is more severe. [ABSTRACT FROM AUTHOR]

In the 1960s, theoretical biologist Richard Levins criticised modellers in his own discipline of population biology for pursuing the "brute force" strategy of building hyper-realistic models. Instead of exclusively chasing complexity, Levins advocated for the use of multiple different kinds of complementary models, including much simpler ones. In this paper, I argue that the epistemic challenges Levins attributed to the brute force strategy still apply to state-of-the-art climate models today: they have big appetites for unattainable data, they are limited by computational tractability, and they are incomprehensible to the human modeller. Along the lines Levins described, this uncertainty generates a trade-off between realistic, precise models with predictive power and simple, highly idealised models that facilitate understanding. In addition to building ensembles of highly complex dynamical models, climate modellers can address model uncertainty by comparing models of different types, such as dynamical and data-driven models, and by systematically comparing models at different levels of what climate modellers call the model hierarchy. Despite its age, Levins' paper remains incredibly insightful and should be considered an important entry into the philosophy of computational modelling. [ABSTRACT FROM AUTHOR]

Summary: We synthesise the findings from 10 years of ecological restoration in the Midlands of Tasmania, Australia, captured in the series of 14 papers in this special issue of Ecological Management and Restoration. The papers illustrate how expertise from disciplines as diverse as law, economics, social sciences, the arts, education, zoology, botany, genetics, climate modelling, agriculture, spatial sciences and fire ecology are necessary to address the complex social, ecological and financial questions that underpin restoration ecology. We highlight the complexity of the task, the multi‐disciplinary and collaborative approach needed, the importance of science to inform restoration practice and the problem of achieving functional connectivity. We also outline steps that need to be taken in the next 10 years. Together, the outcomes and recommendations from these studies provide a template for restoration in similar highly cleared and degraded agricultural landscapes affected by climate change in Australia and internationally. [ABSTRACT FROM AUTHOR]

One of the crucial branches of activity at the Łukasiewicz Research Network—Institute of Aviation is developing a suborbital rocket vehicle capable of launching small payloads beyond the Earth's atmosphere, reaching over 100 km in altitude. Ensuring safety is a primary concern, particularly given the finite flight zone and impact area. Crucial to safety analysis is the wind profile, especially in the very first seconds of a flight, when rocket velocity is of the same order as the wind speed. Traditional near-ground wind data sources, ranging from wind towers to numerical models of the atmosphere, have limitations. Wind towers are costly and unfeasible at many test ranges used for launches, while numerical modeling may not reflect the specific ground profile near the launcher due to their large cell size (2 to +10 km). Meteorological balloons are not favorable for such measurements as they aim to provide the launch operator with a wind profile at high altitudes, and are launched only 1–2 times per flight attempt. Our study sought to prototype a wind measurement system designed to acquire near-ground wind profile data. It focuses on measuring wind direction and speed at near-ground altitudes with higher flight frequency, offering data on demand shortly before launch to help ensure safety. This atmosphere sounding system consists of an Unmanned Aerial Vehicle (UAV) equipped with an onboard ultrasonic wind sensor. Some reports in the literature have discussed the possibility of using UAV-borne anemometers, but the topic of measurement errors introduced by placing the anemometer onboard an UAV remains under studied. Limited research in this area underlines the need for experimental validation of design choices–for specific types of UAVs, anemometers, and mounting. This paper presents a literature review, a detailed overview of the prototyped system, and flight test results in both natural (outdoor) and controlled (indoor, no wind) conditions. Data from the UAV system's anemometer was benchmarked against a stationary reference weather station, in order to examine the influence of the UAV's rotor on the anemometer readings. Our findings show a wind speed Root Mean Square Error (RMSE) of 5 m/s and a directional RMSE of below 5.3° (both averaged for 1 min). The results were also compared with similar UAV-based wind measurements. The prototyped system was successfully used in a suborbital rocket launch campaign, thus demonstrating the feasibility of integrating UAVs with dedicated sensors for performing regular meteorological measurements in automatic mode. [ABSTRACT FROM AUTHOR]

Sporadic E (Es) layers are thin layers of enhanced electron density that commonly appear at altitudes of 90–130 km, often impacting radio communications and navigation systems. The wind shear theory posits that the vertical ion drift, influenced by atmospheric neutral winds and the magnetic field, serves as a significant dynamic driver for the formation and movement of Es layers. In current studies, both the heights of ion vertical velocity null (IVN) and the maximum vertical ion convergence (VICmax) have been proposed as the potential height of Es layer occurrence. In this study, utilizing the neutral atmospheric wind data derived from the WACCM-X (The Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension), we computed and compared these two parameters with the observed Es layer heights recorded by the FORMOSAT-3/COSMIC (FORMOsa SATellite-3/Constellation Observing System for Meteorology, Ionosphere, and Climate) radio occultation (RO) observations. The comparative analysis suggests that IVN is a more likely node for Es layer occurrence than VICmax. Subsequently, we examined the height–time distributions of IVN and Es layers, as well as their respective descent rates at different latitudes. These results demonstrated a notable agreement in height variations between IVN and Es layers. The collective results presented in this paper provide strong support that the ion vertical velocity null plays a crucial role in determining the height of Es layers. [ABSTRACT FROM AUTHOR]

Simple Summary: Ongoing climate change is causing temperatures to rise in both summer and winter, allowing insect pests to invade new areas and potentially causing economic and human health problems. Low winter temperatures are thought to be one of the main barriers to the colonization of higher latitudes. Climate models predict that winter temperatures will increase more than summer temperatures in temperate regions, which may allow insects from warmer regions to colonize the colder, higher latitudes in the future. Understanding how climate change will affect insect distributions is critical for many areas of human activity. This paper presents a method to assess the potential of insects to colonize colder regions under a warming winter scenario. The method is based on exposing insects to laboratory simulations of a warming winter climate. The applicability of the method is tested using the example of a Mediterranean pest, Sesamia nonagrioides, whose ability to colonize Central Europe is assessed. The results indicate that S. nonagrioides could survive Central European winters even under the current state of warming or under a warmer climate predicted for the near future. The presented method may be particularly useful in pest management to estimate overwinter survival and distribution of pests due to climate change. Ongoing climate change and anthropogenic pressure are having a profound influence on insects, causing species diversity to decline and populations to shrink. Insect pests invade new areas and cause economic and human health problems. Low temperatures in winter are thought to be one of the main barriers to the successful colonization of higher latitudes. Climate models predict that winter temperatures will increase more than summer temperatures in temperate and polar regions, potentially allowing species from warmer climates to colonize higher latitudes. Understanding how climate change will affect the distribution of insects is critical to many areas of human activity. One possible but seldom used way to predict likely range shifts of insects due to climate change is through simulation experiments. Here, I present and test a method to assess the potential of insect species from warmer regions to survive winters in colder regions under a warming winter scenario. The method is based on laboratory simulations of warming winters. The applicability of the method is demonstrated using the example of a Mediterranean pest, Sesamia nonagrioides, whose ability to survive Central European winters under a warming winter scenario is assessed. The method presented here is relatively simple, with potentially high accuracy of estimates. [ABSTRACT FROM AUTHOR]

In order to effectively model the potential impacts of future climate change, there is a requirement for climate data inputs which (a) are of high spatial and temporal resolution, (b) explore a range of future climate change scenarios, (c) are consistent with historical observations in the historical period, and (d) provide an exploration of climate model uncertainty. This paper presents a suite of climate projections for the United Kingdom that conform to these requirements: CHESS-SCAPE. CHESS-SCAPE is a 1 km resolution dataset containing 11 near-surface meteorological variables that can be used to as input to many different impact models. The variables are available at several time resolutions, from daily to decadal means, for the years 1980–2080. It was derived from the state-of-the art regional climate projections in the United Kingdom Climate Projections 2018 (UKCP18) regional climate model (RCM) 12 km ensemble, downscaled to 1 km using a combination of physical and empirical methods to account for local topographic effects. CHESS-SCAPE has four ensemble members, which were chosen to span the range of temperature and precipitation change in the UKCP18 ensemble, representing the ensemble climate model uncertainty. CHESS-SCAPE consists of projections for four emissions scenarios, given by the Representative Concentration Pathways 2.6, 4.5, 6.0 and 8.5, which were derived from the UKCP18 RCM RCP8.5 scenarios using time shifting and pattern scaling. These correspond to UK annual warming projections of between 0.9–1.9 K for RCP2.6 up to 2.8–4.3 K for RCP8.5 between 1980–2000 and 2060–2080. Little change in annual precipitation is projected, but larger changes in seasonal precipitation are seen with some scenarios projecting large increases in precipitation in the winter (up to 22 %) and large decreases in the summer (up to - 39 %). All four RCP scenarios and ensemble members are also provided with bias correction, using the CHESS-met historical gridded dataset as a baseline. With high spatial and temporal resolution, an extensive range of warming scenarios and multiple ensemble members, CHESS-SCAPE provides a comprehensive data resource for modellers of climate change impacts in the UK. The CHESS-SCAPE data are available for download from the NERC EDS Centre for Environmental Data Analysis: 10.5285/8194b416cbee482b89e0dfbe17c5786c. [ABSTRACT FROM AUTHOR]

The hydrological assessment of the Vistula River basin in the near future will be a key element in the development of strategies to adapt agriculture to climate change. The Vistula River basin covers 61% of Poland's area (190,062 km2) and is mainly used for agricultural production. The aim of this study is to assess the water balance of the Vistula River basin from the perspective of 2050 based on the analysis of two climate scenarios, RCP 4.5 and RCP 8.5, and the three climate models ICHEC-EC-EARTH_KNMI-RACMO22E (A), ICHEC-EC-EARTH_DMI-HIRHAM5 (B), and ICHEC-EC-EARTH_SMHI-RCA4 (C). This paper presents the steps in the development of the SWAT model and the results of the hydrological analysis of the Vistula catchment. Calibration and validation of the model were carried out using the SUFI-2 algorithm in the SWAT-CUP programme for 2013–2018. The data used to calibrate the SWAT model are monthly flow measurements [m3/s] from the measurement station in Tczew, located near the estuary of the Vistula basin to the Baltic Sea. The summary result of the work is the results of modelling the flow of the Vistula River catchment for different climate scenarios in the 2020–2050 perspective. The average annual precipitation for all projections in 2021–2030, 2031–2040, and 2041–2050 will be higher by up to 22% (763 mm) (RCP 8.5.C for 2041–2050) compared to the 2013–2018 simulation years (624 mm). The average annual temperature for most climate projections for 2021–2030 will fall to as low as 8.7 °C (RCP 4.5.B) compared to the 2013–2018 simulation period (9.2 °C). In contrast, for all projections in 2031–2040 and 2041–2050, the average annual temperature will increase to as much as 10.3 °C (RCP 8.5.C). The simulation results for the climate projections (2020–2050) indicate that there are no clear trends of change in the water management of the Vistula River basin for the coming decades. According to scenarios RCP 4.5.A, RCP 8.5.A, and RCP 8.5.B, the annual sums of potential evapotranspiration show a slight downward trend. On the other hand, for the RCP 8.5.C and RCP 4.5.C projections and the climate change scenario RCP 4.5.B, the results obtained show a slight upward trend in the annual sum of potential evapotranspiration. For the overall evapotranspiration and potential evapotranspiration assessment for all climate projections analysed, the annual evapotranspiration total shows a clear increase compared to the 2013–2018 baseline period. The average annual actual evapotranspiration for all projections in 2021–2030, 2031–2040, and 2041–2050 will increase up to 467 mm (RCP 4.5.A—2021–2030) compared to the 2013–2018 simulation period of 401 mm. The average annual potential evapotranspiration for all projections in 2021–2030, 2031–2040, and 2041–2050 will increase up to 755 mm (RCP 8.5.C—2031–2040) compared to the 2013–2018 simulation period—616 mm. The analysis of the total runoff in all climate models for the RCP 4.5 scenario shows that the annual average total runoff tends to decrease. The results of the simulations carried out for the RCP 8.5 scenario, which are generally characterised by an increase in total runoff in subsequent years, are different. When analysing annual total runoff on a regional basis, it appears that for most of the climate projections analysed (except for the RCP 8.5.A scenario), annual runoff will be lower, especially in the lowlands in the central part of the Vistula basin. In regions where the increase in precipitation is greatest in the north-western and southern basins, higher total runoff should be expected. The analysis of the total runoff in all climate models for the RCP 4.5 scenario shows that the annual average total runoff tends to decrease. The results of the simulations carried out for the RCP 8.5 scenario, which are generally characterised by an increase in total runoff in subsequent years, are different. When analysing annual total runoff on a regional basis, it appears that for most of the climate projections analysed (except for the RCP 8.5.A scenario), annual runoff will be lower, especially in the lowlands in the central part of the Vistula basin. In regions where the increase in precipitation is greatest in the north-western and southern basins, higher total runoff should be expected. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to predicting the distribution of harmful substances in the atmosphere based on the combined use of the parameter estimation technique and machine learning algorithms. The essence of the proposed approach is based on the assumption that the concentration values predicted by machine learning algorithms at observation points can be used to refine the pollutant concentration field when solving a differential equation of the convection-diffusion-reaction type. This approach reduces to minimizing an objective functional on some admissible set by choosing the atmospheric turbulence coefficient. We consider two atmospheric turbulence models and restore its unknown parameters by using the limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm. Three ensemble machine learning algorithms are analyzed for the prediction of concentration values at observation points, and comparison of the predicted values with the measurement results is presented. The proposed approach has been tested on an example of two cities in the Republic of Kazakhstan. In addition, due to the lack of data on pollution sources and their intensities, an approach for identifying this information is presented. [ABSTRACT FROM AUTHOR]

Carbon monoxide (CO) concentrations in the troposphere are decreasing globally, with Africa as an exception. Yet, the region is understudied, with a deficit of ground-based observations and highly uncertain CO emission inventories. This paper reports multiyear observational CO data from the Mt. Kenya Global Atmosphere Watch (GAW) station, as well as summertime CO isotope observations from both Mt. Kenya and Nairobi, Kenya. The CO variability at Mt. Kenya is characterized by slightly increased concentrations during dry periods and a strong influence of short-term pollution events. While some data gaps and differences in instrumentation complicate decadal-scale trend analysis, a small long-term increase is resolved. High-pollution events are consistent with isotopic signal from downwind savanna fires. The isotope fingerprint of CO in Nairobi indicates an overwhelming dominance (near 100 %) of primary emissions from fossil fuel combustion with implications for air pollution policy. In contrast, the isotope signature of CO intercepted at the large-footprint Mt. Kenya region suggests that at least 70 % is primary sourced, with a predominance likely from savanna fires in Africa. Taken together, this study provides quantitative constraints of primary vs. secondary CO in the eastern Africa region and in urban Nairobi, with implications for satellite-based emission inventories as well as for chemical transport and climate modeling. [ABSTRACT FROM AUTHOR]

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A model‐circulation increment‐based dynamic statistical technique (MIDST) is proposed in this paper to improve the prediction of summer rainfall over southern China (SC) where quite low prediction skills have been found in the Beijing Climate Center Climate System Model version 1.1 with a moderate resolution (BCC_CSM1.1m). The results show that BCC_CSM1.1m can hardly represent the variability of the summer rainfall anomaly and its year‐to‐year increment over SC, and the skillful predictions are mostly confined over the middle reaches of the Yangtze River. Using the dynamic model output and statistical method, the MIDST is established to capture the coupled modes between the year‐to‐year increments of the summer rainfall anomaly and the associated simultaneous three‐dimensional coupled air‐sea circulation predictors. The cross‐validation indicates that the prediction skills of the MIDST are evidently improved for both the summer rainfall increment prediction and summer rainfall anomaly prediction compared with the direct BCC_CSM1.1m prediction. The skillful prediction can persist for long forecast leads over most regions except southwestern China. As the major predictability source of seasonal prediction, the intense response to changes in the circulation related to the El Niño‐Southern Oscillation (ENSO) is well captured, and thus, the performance improvement of the MIDST is primarily due to its more realistic representation of the incremental circulation related to the ENSO. [ABSTRACT FROM AUTHOR]

For this paper, a new global atmospheric model (Global-to-Regional Integrated forecast SysTem; GRIST) with improved sub-grid scale orographic parameterization was verified and assessed, with an emphasis on the precipitation caused by typhoons. Four typical typhoon cases were selected for the verification of the model. The results indicate that, compared to the control experiments, the sensitivity experiments consistently simulated the trends in the three-hour cumulative precipitation changes and the high-value regions of total precipitation better. However, the improved experiments only had an ameliorating effect on the cumulative precipitation modelling biases for Typhoon LEKIMA and Typhoon HAGUPIT, not all of them. Precipitation bias is smaller on flat land than that on mountainous land, but the precipitation bias on windward/leeward slopes depends on the typhoon case. Precipitation modelling accuracy varies considerably between flat and mountainous terrain but very little between windward and leeward slopes. The precipitation simulation is poor for all terrains, with large precipitation thresholds in three typhoon cases, but for Typhoon HOTA, after improving the terrain, the model has the ability to forecast the heavy rainfall scenarios of the mountainous terrain. [ABSTRACT FROM AUTHOR]

Following the projected increase in extreme precipitation, an increase in extreme snowfall may be expected in cold regions, e.g., for high latitudes or at high elevations. By contrast, in low- to medium-elevation areas, the probability of experiencing rainfall instead of snowfall is generally projected to increase due to warming conditions. Yet, in mountainous areas, despite the likely existence of these contrasted trends according to elevation, changes in extreme snowfall with warming remain poorly quantified. This paper assesses projected changes in heavy and extreme snowfall, i.e., in mean annual maxima and 100-year return levels, in the French Alps as a function of elevation and global warming level. We apply a recent methodology, based on the analysis of annual maxima with non-stationary extreme value models, to an ensemble of 20 adjusted general circulation model–regional climate model (GCM–RCM) pairs from the EURO-CORDEX experiment under the Representative Concentration Pathway 8.5 (RCP8.5) scenario. For each of the 23 massifs of the French Alps, maxima in the hydrological sense (1 August to 31 July) are provided from 1951 to 2100 and every 300 m of elevations between 900 and 3600 m. Results rely on relative or absolute changes computed with respect to current climate conditions (corresponding here to +1 ∘ C global warming level) at the massif scale and averaged over all massifs. Overall, daily mean annual maxima of snowfall are projected to decrease below 3000 m and increase above 3600 m, while 100-year return levels are projected to decrease below 2400 m and increase above 3300 m. At elevations in between, values are on average projected to increase until +3 ∘ C of global warming and then decrease. At +4 ∘ C, average relative changes in mean annual maxima and 100-year return levels, respectively, vary from -26% and -15% at 900 m to +3% and +8% at 3600 m. Finally, for each global warming level between +1.5 and +4 ∘ C, we compute the elevation threshold that separates contrasted trends, i.e., where the average relative change equals zero. This elevation threshold is shown to be lower for higher return periods, and it is projected to rise from 3000 m at +1.5 ∘ C to 3350 m at +4 ∘ C for mean annual maxima and from 2600 to 3000 m for 100-year return levels. These results have implications for the management of risks related to extreme snowfall. [ABSTRACT FROM AUTHOR]

Field research campaigns in 2019 and 2020 collected hourly atmospheric profiles via radiosonde surrounding the 2 July 2019 and 14 December 2020 total solar eclipses over South America from locations within the paths of eclipse totality. As part of these atmospheric data collection campaigns, the eclipse module of the Advanced Research Weather Research and Forecast (WRF-ARW) model was utilized to model meteorological conditions before, during, and after the eclipse events. The surface and upper-air observational datasets collected through these campaigns have enabled further assessment and validation of the WRF-ARW's eclipse module performance in simulating atmospheric responses to total solar eclipses. We provide descriptions of the field campaigns for both 2019 and 2020 and present results from comparisons of meteorological variables both at the surface and aloft using observational datasets obtained through the campaigns. The paper concludes by recommending further scientific analyses to be explored utilizing the unique datasets presented. [ABSTRACT FROM AUTHOR]

The National Center for Atmospheric Research released a global atmosphere model named Community Atmosphere Model version 5.0 (CAM5), which aimed to provide a global climate simulation for meteorological research. Among them, the cloud microphysics scheme is extremely time-consuming, so developing efficient parallel algorithms faces large-scale and chronic simulation challenges. Due to the wide application of GPU in the fields of science and engineering and the NVIDIA's mature and stable CUDA platform, we ported the code to GPU to accelerate computing. In this paper, by analyzing the parallelism of CAM5 cloud microphysical schemes (CAM5 CMS) in different dimensions, corresponding GPU-based one-dimensional (1D) and two-dimensional (2D) parallel acceleration algorithms are proposed. Among them, the 2D parallel algorithm exploits finer-grained parallelism. In addition, we present a data transfer optimization method between the CPU and GPU to further improve the overall performance. Finally, GPU version of the CAM5 CMS (GPU-CMS) was implemented. The GPU-CMS can obtain a speedup of 141.69 × on a single NVIDIA A100 GPU with I/O transfer. In the case without I/O transfer, compared to the baseline performance on a single Intel Xeon E5-2680 CPU core, the 2D acceleration algorithm obtained a speedup of 48.75 × , 280.11 × , and 507.18 × on a single NVIDIA K20, P100, and A100 GPU, respectively. [ABSTRACT FROM AUTHOR]

This paper investigates the process behind climate model biases, especially in rainfall, during the southwest (June to August; JJA) and northeast monsoon (December to February; DJF) seasons over the Philippines using the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment-Southeast Asia (SEACLID/CORDEX-SEA). Biases in the surface and near-surface climate are assessed over land in eastern (EastPH) and western (WestPH) Philippines using observation and reanalysis datasets. Results show that models with well-represented sea surface temperature (SST) provided better added values after downscaling. Although the CMIP5-driven models tend to overestimate both surface energy and water partitioning, simulations with well-represented SST have better surface energy partitions. In contrast to the reference simulation driven by ERA-Interim reanalysis, the CMIP5-driven simulations tend to underestimate (overestimate) moisture flux convergence during the DJF (JJA) season, which might have contributed to the overall underestimation (overestimation) of rainfall in this season. Further investigation along a vertical transect shows the differences in governing mechanisms for each season. During DJF (JJA) season, the simulation with underestimated (overestimated) SST, near-surface air temperature, and mixing ratio results in underestimated (overestimated) rainfall over EastPH (WestPH). In addition, the CMIP5-driven models were found to overestimate low-level winds, which may have induced the deep convection regardless of season, suggesting the role of low-level winds in the convection scheme used in RegCM4 aside from topography and land cover. [ABSTRACT FROM AUTHOR]

This paper describes the global eddying ocean–sea ice simulation produced at the Euro-Mediterranean Center on Climate Change (CMCC) obtained following the experimental design of the Ocean Model Intercomparison Project phase 2 (OMIP2). The eddy-rich model (GLOB16) is based on the NEMOv3.6 framework, with a global horizontal resolution of 1/16 ∘ and 98 vertical levels and was originally designed for an operational short-term ocean forecasting system. Here, it is driven by one multi-decadal cycle of the prescribed JRA55-do atmospheric reanalysis and runoff dataset in order to perform a long-term benchmarking experiment. To assess the accuracy of simulated 3D ocean fields and highlight the relative benefits of resolving mesoscale processes, the GLOB16 performances are evaluated via a selection of key climate metrics against observational datasets and two other NEMO configurations at lower resolutions: an eddy-permitting resolution (ORCA025) and a non-eddying resolution (ORCA1) designed to form the ocean–sea ice component of the fully coupled CMCC climate model. The well-known biases in the low-resolution simulations are significantly improved in the high-resolution model. The evolution and spatial pattern of large-scale features (such as sea surface temperature biases and winter mixed-layer structure) in GLOB16 are generally better reproduced, and the large-scale circulation is remarkably improved compared to the low-resolution oceans. We find that eddying resolution is an advantage in resolving the structure of western boundary currents, the overturning cells, and flow through key passages. GLOB16 might be an appropriate tool for ocean climate modeling efforts, even though the benefit of eddying resolution does not provide unambiguous advances for all ocean variables in all regions. [ABSTRACT FROM AUTHOR]

The destructive Sir Ivan Dougherty fire burned 55,000 hectares around 250 km northwest of Sydney in New South Wales on 12 February 2017. Record hot temperatures were recorded in the area during the lead-in days and the fire conditions at the time were described as the 'worst ever seen in NSW'. The observed weather conditions were hot, dry and very windy ahead of a synoptic frontal wind change during the afternoon. 'Extreme' to 'catastrophic' fire weather was predicted, and the potential for extreme fire behaviour was identified several days in advance. The Australian coupled fire–atmosphere model ACCESS-Fire has been run to explore the characteristics of the Sir Ivan fire. Several features resulting from fire–atmosphere interaction are produced in the simulations. Simulated heat flux along the fire perimeter shows increased intensity on the northern fire flank in response to gradual backing winds ahead of the main frontal wind change. Temporal and spatial variability in fire activity, seen as pulses in fire intensity and fireline wind speed, develop in response to boundary layer rolls in the wind fields. Deep moist convection consistent with the observed pyrocumulonimbus (pyroCb) cloud is simulated over the fire at around the time of the frontal wind change, and matches guidance from the 'PyroCb Firepower Threshold' tool, which showed transient favourable conditions. After the wind change, short-lived near-surface and elevated vortices suggest organised rotating features on the northern flank of the fire. The coupled model captures processes that cannot be produced in uncoupled fire predictions and that are not captured in current operational meteorological forecast products provided to Australian fire agencies. This paper links the features from coupled simulations to available observations and suggests pathways to embed the learnings in operational practice. [ABSTRACT FROM AUTHOR]

A mass‐conserving method to downscale precipitation from global climate models (GCMs) using sub‐grid‐scale topography and modeled 700‐mb wind direction is presented. Runoff is simulated using a stand‐alone hydrological model, with this and several other methods as inputs, and compared to runoff simulated using historical observations over the western contiguous United States. Results suggest the mitigation of grid‐scale biases is more critical than downscaling for some regions with large wet biases (e.g., the Great Basin and Upper Colorado). In other regions (e.g., the Pacific Northwest) the new method produces more realistic sub‐grid‐scale variability in runoff compared to unadjusted GCM output and a simpler downscaling method. The presented method also brings the runoff centroid timing closer to that simulated with observations for all subregions examined. Plain Language Summary: Due to limitations in computing power which necessitates coarse spatial resolution, climate models do not include many details on mountains and their impact on precipitation. For this reason, it is difficult to estimate the impact of climate change on the availability of water for human consumption in places like the western United States, where mountain snowpack is an important source of water. This paper presents a way to adjust precipitation estimates from climate models by using some simple statistics about nearby mountains and valleys. The adjusted precipitation is then used in a hydrologic model to calculate the runoff that is simulated with different precipitation inputs. Results show that in most cases, the precipitation adjustment improves estimates of the resultant runoff relative to simulations with observed precipitation. The main exceptions are in very dry areas where the climate model produces far too much precipitation. With further work, the proposed adjustment may be integrated into the climate model itself to make it easier for those managing water resources (e.g., controlling reservoir levels) to use the model output to plan and adapt to climate change. Key Points: A mass‐conserving method for downscaling orographic precipitation improves modeled runoff from the CESM2Considering upwind topography further improves modeled runoff compared to simpler adjustmentsNot tuning to individual model grid points makes this method more generalizable than many existing statistical downscaling methods [ABSTRACT FROM AUTHOR]

In recent years, the concept of a Fiducial Reference Measurement (FRM) has been developed to highlight the need for precise and well-characterised measurements tailored explicitly to the post-launch calibration and validation (Cal/Val) of Earth observation satellite missions. The confidence that stems from robust, unambiguous uncertainty assessment of space observations is fundamental to assessing the changes in the Earth system and climate model prediction and delivering the essential evidence-based input for policy makers and society striving to mitigate and adapt to climate change. The underlying concept of an FRM has long been a core element of a Cal/Val program, providing a 'trustable' reference against which performance can be anchored or assessed. The 'FRM' label was created to embody into such a reference a set of key criteria. These criteria included the establishment of documented evidence of uncertainty with respect to a community-agreed reference (ideally SI-traceable) and specific tailoring to the needs of a satellite mission. It therefore facilitates comparison and interoperability between products and missions in a cost-efficient manner. Committee on Earth Observation Satellites (CEOS) Working Group Cal/Val (WGCV) is now putting in place a framework to assess the maturity and compliance of a 'Cal/Val reference measurement' in terms of a set of community-agreed criteria which define it to be of CEOS-FRM quality. The assessment process is based on a maturity matrix that provides a visual assessment of the state of any FRM against each of a set of given criteria, making visible where it is mature and where evolution and effort are still needed. This paper provides the overarching definition of what constitutes an FRM and introduces the new CEOS-FRM assessment framework. [ABSTRACT FROM AUTHOR]

This paper introduces a fast urban microscale meteorological model with a horizontal resolution of O(10) m, named URBAN (Urban Rapid & Building-Aware Neighborhood), which is capable of rapid assessment of meteorological fields over key urban areas, including wind speed, air temperature, humidity and thermal comfort index, with the execution time less than 10 minutes consuming 1 CPU core. URBAN uses a fast wind diagnostic method to construct three-dimensional (3-D) wind fields surrounding complex building clusters with their geometry resolved explicitly To enhance the accuracy of wind reconstruction and the continuity of the initial wind field around irregular buildings, we propose a new parameterization method based on stream functions, which can accurately characterize the influences of complex urban building clusters on the three-dimensional wind field The model can provide various results for the meteorological service of large outdoor activities, including conventional meteorological elements (wind, temperature, humidity, radiation, etc.) and the Universal Thermal Comfort Index, which is derived from the relationship between physiological processes and environmental meteorological conditions. In this paper, URBAN is applied to develop an automatic analysis and forecast system of microscale meteorological elements over the central Beijing region in summer during a large outdoor event. By comparing with the half-hourly observations from three auto weather stations (AWSs) in the region, the root-mean-square errors (RMSEs) of the modeled 10-meter-height wind speed, 2-meter-height air temperature and humidity are 0.98 m s−1,1.37 °C and 7.37%, respectively. [ABSTRACT FROM AUTHOR]

In the complex landscape of cost estimation for large-scale oil and gas projects, this study addresses the critical need for a precise and reliable model to estimate the cost of atmospheric API 650 storage tanks within a National Oil Company. The urgency of this issue is further emphasized in light of Hubbert's peak theory, which predicts a peak in oil production followed by a decline. The paper introduces an innovative methodology, developing Company Developed Complex Models (CDCMs) using Solver for Excel. This approach is rigorously validated through statistical measures, including R-square, Mean Squared Error (MSE), F, and Significant F values, ensuring the model's accuracy and reliability. An essential contribution of this research is the development of a Log-Linear Model, identified as the preferred solution among the CDCMs. This model is presented as a practical and robust tool for cost estimation, designed to adapt to the unique challenges of large-scale projects in the oil and gas sector. Acknowledging the dynamic nature of cost estimation, the paper outlines a systematic approach for recalibrating the model, ensuring its responsiveness to changing conditions. In conclusion, this paper contributes significantly to parametric cost estimation, presenting a comprehensive, validated, and adaptable model designed to meet the demanding standards of large-scale oil and gas projects, especially for Atmospheric API 650 storage tanks cost estimation. [ABSTRACT FROM AUTHOR]

Single‐model initial‐condition large ensembles are powerful tools to quantify the forced response, internal climate variability, and their evolution under global warming. Here, we present the CMIP6 version of the Max Planck Institute Grand Ensemble (MPI‐GE CMIP6) with currently 30 realizations for the historical period and five emission scenarios. The power of MPI‐GE CMIP6 goes beyond its predecessor ensemble MPI‐GE by providing high‐frequency output, the full range of emission scenarios including the highly policy‐relevant low emission scenarios SSP1‐1.9 and SSP1‐2.6, and the opportunity to compare the ensemble to complementary high‐resolution simulations. First, we describe MPI‐GE CMIP6, evaluate it with observations and reanalyzes and compare it to MPI‐GE. Then, we demonstrate with six application examples how to use the power of the ensemble to better quantify and understand present and future climate extremes, to inform about uncertainty in approaching Paris Agreement global warming limits, and to combine large ensembles and artificial intelligence. For instance, MPI‐GE CMIP6 allows us to show that the recently observed Siberian and Pacific North American heatwaves would only avoid reaching 1–2 years return periods in 2071–2100 with low emission scenarios, that recently observed European precipitation extremes are captured only by complementary high‐resolution simulations, and that 3‐hourly output projects a decreasing activity of storms in mid‐latitude oceans. Further, the ensemble is ideal for estimates of probabilities of crossing global warming limits and the irreducible uncertainty introduced by internal variability, and is sufficiently large to be used for infilling surface temperature observations with artificial intelligence. Plain Language Summary: Climate model simulations that start from different initial states and differ only due to the chaos in the climate system are used extensively to quantify the forced climate response, variability intrinsic to the climate system, and their change under global warming. Here, we present a new version of the Max Planck Institute Grand Ensemble (MPI‐GE CMIP6) that is run as part of the latest generation of climate models. This single‐model ensemble currently consists of 30 realizations for the historical period 1850–2014 and for five scenarios of possible future climates until 2100. The power of MPI‐GE CMIP6 goes beyond its predecessor by not only providing monthly mean but also 3‐hourly to daily model output, the full range of future scenarios including the two highly policy‐relevant scenarios that were designed to match the Paris Agreement global warming limits of 1.5 and 2°C, and the opportunity to compare the low‐resolution ensemble to simulations of the same model version with higher horizontal resolution. In this paper, we describe the new ensemble and demonstrate with application examples how to use its power. Key Points: The Max Planck Institute Grand Ensemble in its CMIP6 version (MPI‐GE CMIP6) is a 30‐member initial‐condition large ensemble with up to 3‐hourly model output and five emission scenariosThe ensemble is specifically suited to investigate climate extremes and Paris Agreement global warming limitsMPI‐GE CMIP6 adequately represents heat extremes, while precipitation extremes are captured by complementary high‐resolution simulations [ABSTRACT FROM AUTHOR]

Clouds, especially low clouds, are crucial for regulating Earth's energy balance and mediating the response of the climate system to changes in greenhouse gas concentrations. Despite their importance for climate, they remain relatively poorly understood and are inaccurately represented in climate models. A principal reason is that the high computational expense of simulating them with large‐eddy simulations (LES) has inhibited broad and systematic numerical experimentation and the generation of large data sets for training parametrization schemes for climate models. Here we demonstrate LES of low clouds on tensor processing units (TPUs), application‐specific integrated circuits that were originally developed for machine learning applications. We show that TPUs in conjunction with tailored software implementations can be used to simulate computationally challenging stratocumulus clouds in conditions observed during the Dynamics and Chemistry of Marine Stratocumulus (DYCOMS) field study. The TPU‐based LES code successfully reproduces clouds during DYCOMS and opens up the large computational resources available on TPUs to cloud simulations. The code enables unprecedented weak and strong scaling of LES, making it possible, for example, to simulate stratocumulus with 10× speedup over real‐time evolution in domains with a 34.7 km × 53.8 km horizontal cross section. The results open up new avenues for computational experiments and for substantially enlarging the sample of LES available to train parameterizations of low clouds. Plain Language Summary: The study of clouds has been impeded by, among other factors, limitations in our ability to simulate them rapidly and on sufficiently large domains. In particular, computational limitations in simulating low clouds are among the reasons for the difficulties of representing them accurately in climate models; this is one of the dominant uncertainties in climate predictions. This paper demonstrates how the large computing power available on tensor processing units (TPUs) (integrated circuits originally designed for machine learning applications) can be harnessed for simulating low clouds. We demonstrate the largest simulations of low clouds to date, with hundreds of billions of variables, and we document their fidelity to aircraft observations. The results open up the large computational resources available on TPUs, hitherto primarily used for machine learning, to the study of clouds in the climate system. Key Points: We introduce a large‐eddy simulation (LES) framework that runs on tensor processing units (TPUs, accelerators designed for machine learning)The fidelity of the LES is established by reproducing aircraft observations of nocturnal stratocumulus clouds over the PacificThe LES exhibit unprecedented scalability on TPUs, enabling the large‐scale generation of training data for cloud parameterizations [ABSTRACT FROM AUTHOR]

An idealized aquaplanet moist global atmospheric model with realistic radiative transfer but no clouds and no convective parameterization is found to possess multiple climate equilibria. When forced symmetrically about the equator, in some cases the Inter Tropical Convergence Zone (ITCZ) migrates to an off‐equatorial equilibrium position. Mechanism denial experiments prescribing relative humidity imply that radiation‐circulation coupling is essential to this instability. The cross‐equatorial asymmetry occurs only when the underlying slab ocean is sufficiently deep and the atmosphere's spectral dynamical core is sufficiently coarse (∼T170 or less with our control parameters). At higher resolutions, initializing with an asymmetric state indicates metastability with very slow (thousands of days) return to hemispheric symmetry. There is some sensitivity to the model timestep, which affects the time required to transition to the asymmetric state, with little effect on the equilibrium climate. The instability is enhanced when the planetary boundary layer scheme favors deeper layers or by a prescribed meridional heat transport away from the equator within the slab. The instability is not present when the model is run with a convective parameterization scheme commonly utilized in idealized moist models. We argue that the instability occurs when the asymmetric heating associated with a spontaneous ITCZ shift drives a circulation that rises poleward of the perturbed ITCZ. These results serve as a warning of the potential for instability and non‐uniqueness of climate that may complicate studies with idealized models of the tropical response to perturbations in forcing. Plain Language Summary: Much of the rainfall in the tropics occurs in the Intertropical Convergence Zones (ITCZs). These regions are associated with the convergence of surface winds, which produces the rising air required to feed the Hadley cells. The ITCZ location is known to be determined by the spatial distribution of atmospheric heating as the Hadley cells act to erase thermal imbalance within the tropics. With hemispherically symmetric heating, a symmetric equatorial ITCZ is expected. However, it is shown in this paper that changes in the ITCZ location affect the distribution of the radiative cooling of the atmosphere through changes in the water vapor distribution, which may make the equatorial ITCZ unstable under some circumstances. In this situation, two alternative climates with the ITCZ shifted into either one of the hemispheres exist. This instability may play an important role for understanding the distribution of tropical precipitation and the potential for the existence of multiple climates for the same set of external conditions. Key Points: The equatorial ITCZ can be destabilized by radiation‐circulation coupling in a hemispherically symmetric aquaplanet modelAs the ITCZ drifts off the equator two different asymmetric equilibria may exist under symmetric forcing and boundary conditionsA theory is proposed based on the energy flux equator sensitivity to the perturbed asymmetric heating when the ITCZ moves off the equator [ABSTRACT FROM AUTHOR]

Parameterization of mesoscale eddies in coarse resolution ocean models is necessary to include the effect of eddies on the large‐scale oceanic circulation. We propose to use a multiple‐scale Quasi‐Geostrophic (MSQG) model to capture the eddy dynamics that develop in response to a prescribed large‐scale flow. The MSQG model consists in extending the traditional quasi geostrophic (QG) dynamics to include the effects of a variable Coriolis parameter and variable background stratification. Solutions to this MSQG equation are computed numerically and compared to a full primitive equation model. The large‐scale flow field permits baroclinically unstable QG waves to grow. These instabilities saturate due to non‐linearities and a filtering method is applied to remove large‐scale structures that develop due to the upscale cascade. The resulting eddy field represents a dynamically consistent response to the prescribed background flow, and can be used to rectify the large‐scale dynamics. Comparisons between Gent‐McWilliams eddy parameterization and the present solutions show large regions of agreement, while also indicating areas where the eddies feed back onto the large scale in a manner that the Gent‐McWilliams parameterization cannot capture. Also of interest is the time variability of the eddy feedback which can be used to build stochastic eddy parameterizations. Plain Language Summary: Climate models are running at horizontal spatial resolutions that capture only the basin‐scale dynamics. This is a strong limitation because the missing mesoscale eddies have a non‐negligible impact on the large‐scale flow. The problem of including the effect of eddies in climate models is a long standing issue in the ocean modeling community. In this work, we decompose the ocean dynamics into stationary large‐scale dynamics and turbulent small‐scale dynamics. One can thus write a system of equations for the large‐scale dynamics—which can be interpreted as the ocean dynamics given by climate models—and another system of equations for the small‐scale dynamics. The numerical implementation of this model reveals that this reduced system of equations faithfully reproduces the dynamics of a high resolution ocean model. This paper is a first step that will eventually guide us to use this model as a parameterization of unresolved scales in low resolution ocean models. Key Points: We implement a numerical version of a multiple‐scale Quasi‐Geostrophic (MSQG) modelMultiple Scale Quasi Geostrophy allows for variable stratification and variable Coriolis parameterThe MSQG model reproduces the dynamics of a high resolution primitive equation model [ABSTRACT FROM AUTHOR]

Paleoclimate data assimilation (DA) is a tool for reconstructing past climates that directly integrates proxy records with climate model output. Despite the potential for DA to expand the scope of quantitative paleoclimatology, these methods remain difficult to implement in practice due to the multi-faceted requirements and data handling necessary for DA reconstructions, the diversity of DA methods, and the need for computationally efficient algorithms. Here, we present DASH, a MATLAB toolbox designed to facilitate paleoclimate DA analyses. DASH provides command line and scripting tools that implement common tasks in DA workflows. The toolbox is highly modular and is not built around any specific analysis, and thus DASH supports paleoclimate DA for a wide variety of time periods, spatial regions, proxy networks, and algorithms. DASH includes tools for integrating and cataloguing data stored in disparate formats, building state vector ensembles, and running proxy (system) forward models. The toolbox also provides optimized algorithms for implementing ensemble Kalman filters, particle filters, and optimal sensor analyses with variable and modular parameters. This paper reviews the key components of the DASH toolbox and presents examples illustrating DASH's use for paleoclimate DA applications. [ABSTRACT FROM AUTHOR]

Generating plausible future climate time series is needed for bottom-up climate impact modeling, as well as downscaling climate model output for hydrological applications. A novel method for generating multisite daily stochastic climate series is developed based on 1) linear regression between climate teleconnection time series (e.g., IPO/SOI) and annual rainfall, 2) clustered method of fragments for subannual disaggregation, and 3) a regression-based approach to daily potential evapotranspiration (PET) for hydrological modeling. We demonstrate that bias (i.e., oversampling) occurs with the standard method of fragments disaggregation in the multisite context and show that selection of an analog year from clustered rainfall amounts provides better sampling properties than the standard method of fragments. Using hydrological data for southeastern Australia, we model runoff from observed and simulated rainfall and PET using the GR4J (Génie Rural à 4 paramètres Journalier) model. Simulated annual and daily rainfall and runoff characteristics from the new method are similar to existing methods, with improvements demonstrated in wet–wet transition probabilities and spatial (between-site) correlations. Significance Statement: In this paper we develop a novel method for generating multisite daily stochastic climate series regressing climate teleconnections (e.g., ENSO, IPO) to annual site rainfall, and disaggregation using a clustered version of the method of fragments. The modular nature of the method also allows for the possibility of the generation of replicates from GCM output of climate teleconnections, as well as perturbed climate futures for scenario-neutral modeling. Results demonstrate that rainfall and runoff metrics of interest for water resource modeling are reproduced well using the model. [ABSTRACT FROM AUTHOR]

Since the beginning of the altimeter mission TOPEX/Poseidon (T/P), followed by Jason-1 and Jason-2 on similar orbits, and many other missions on different orbits (ERS, EnviSat, etc.), mean sea level (MSL) products became essential for the comprehension of global ocean circulation. Since early in the T/P mission, a suspicious signal, having a period of near 59 days and amplitude of roughly 5 mm, was apparent in the Global MSL record. Compared with the 4–5-mm amplitude of the annual signal, the 59-day signal has understandably attracted attention. Moreover, the same signal has been subsequently detected in Jason-1 and later in Jason-2 MSLs. In 2010, the Ocean Surface Topography Science Team (OSTST) concluded this signal as the aliasing of a higher frequency error inherited from the tide model correction: the semi-diurnal wave S2. The source of this error was mainly attributed to T/P measurements, which were assimilated in ocean tide models. When these models are used in the computation of T/P MSL, most of the error cancels. However, this error is communicated to Jason-1 and Jason-2 MSLs. In order to gather and publish the OSTST analyses on this matter, this paper first attempts to list the myriad possibilities for the puzzling 59-day error in MSL. Then, this paper goes deeper into the description of the main contributor to this list: the tide models error. Indeed, since 2010, considerable efforts have been undertaken within the ocean tide community in order to correct ocean tide S2-waves from this error, particularly in the Goddard Ocean Tide (GOT) and finite element solution (FES) latest versions. Comparing several GOT and FES versions and a pure hydrodynamic tide model, this paper assesses, quantifies, and describes a reduction of the MSL 59-day error thanks to the latest releases. These analyses also confirm that a large part of this error has its origins in the T/P mission and has contaminated ocean tide solutions and Jason-1 and Jason-2 MSLs. They also suggest that ocean tide is not the only possible vector. Jason-1 and Jason-2 MSLs contain additional 59-day error—though to a lesser extent—that may either come from the measurements themselves or from another vector. [ABSTRACT FROM AUTHOR]

This paper investigates the identification and modeling of a greenhouse's climate using real climate data from a greenhouse installed in the LAPER laboratory in Tunisia. The objective of this paper is to propose a solution to the problem of nonlinear time-variant inputs and outputs of greenhouse internal climate. Combining fuzzy logic technique with Least Mean Squares (LMS), a robust greenhouse climate model for internal temperature prediction is proposed. The simulation results demonstrate the effectiveness of the identification approach and the power of the implemented Takagi-Sugeno Fuzzy model-based algorithm. [ABSTRACT FROM AUTHOR]

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

Empirical evidence continues to show that climate change remains a threat to the stability of the hydrologic system. As the climate system interacts with the hydrologic cycle, one significant repercussion of global warming includes changes in water availability at both regional and local scales. Climate change adaptation is intrinsically difficult to attain due to the dynamic earth system and lack of a comprehensive understanding of future climate and its associated uncertainties. Mostly in developing countries, climate adaptation is hampered by scarcity of good quality and adequate hydro-meteorological data. This article provides a synopsis of the modelling chain applied to investigate the response of the hydrologic system under changing climate, which includes choosing the appropriate global climate models, downscaling techniques, emission scenarios, and the approach to be used in hydrologic modelling. The conventional criteria for choosing a suitable hydrological model are discussed. The advancement of emission scenarios including the latest Shared Socioeconomic Pathways and their role in climate modelling, impact assessment, and adaptation, are also highlighted. This paper also discusses the uncertainties associated with modelling the hydrological impacts of climate change and the plausible approaches for reducing such uncertainties. Among the outcomes of this review include highlights of studies on the commonly used hydrological models for assessing the impact of climate change particularly in the sub-Saharan Africa region and some specific reviews in southern Africa. Further, the reviews show that as human systems keep on dominating within the earth system in several ways, effective modelling should involve coupling earth and human systems models as these may truly represent the bidirectional feedback experienced in the modern world. The paper concludes that adequate hydro-meteorological data is key to having a robust model and effective climate adaptation measures, hence in poorly gauged basins use of artificial neural networks and satellite datasets have shown to be successful tools, including for model calibration and validation. [ABSTRACT FROM AUTHOR]

The greenhouse climate is a non-linear system that contains multiple inputs (predictors) and multiple outputs (responses). This project aimed to provide a solution, aided by artificial intelligence, to the issue of variations in time, input and output factors in a greenhouse internal climate that can adversely affect tomato seedlings. Machine learning Methodologies such as fuzzy inference and neural networks have been applied to mimic idealistic behavior. This paper proposes an adaptive system based on artificial neural networks technique embedded with fuzzy logic technique calls Adaptive Neuro Fuzzy Inference System (ANFIS) to predict air humidity, air temperature, internal radiation, and CO2 concentration while the seeds grow, in order to produce favorable greenhouse climate conditions. The input parameters include ten meteorological and control actuators that majorly influence tomato plants during their growth process in the greenhouse climate. This discussion revolves around a linguistic ANFIS model that will operate during the 48 days that it takes for the seedlings to grow. It will provide estimates of the greenhouse climate using meteorological data along with control actuators rooted in trained neural networks with back propagation optimization algorithm, and 500 iterations of the least square algorithm. Simulations have revealed the efficiency of this model. [ABSTRACT FROM AUTHOR]

Rainfall prediction is vital for the management of available water resources. Accordingly, this study used large lagged climate indices to predict rainfall in Iran's Sefidrood basin. A radial basis function neural network (RBFNN) and a multilayer perceptron (MLP) network were used to predict monthly rainfall. The models were trained using the naked mole rat (NMR) algorithm, firefly algorithm (FFA), genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. Large lagged climate indices, as well as three hybrid models, i.e., inclusive multiple model (IMM)-MLP, IMM-RBFNN, and the simple average method (SAM), were then employed to predict rainfall. This paper aims to predict rainfall using large climate indices, ensemble models, and optimized artificial neural network models. Also, the paper considers the uncertainty resources in the modeling process. The inputs were selected using a new input selection method, namely a hybrid gamma test (GT). The GT was integrated with the NMR algorithm to create a new test for determining the best input scenario. Therefore, the main innovations of this study were the introduction of the new ensemble and the new hybrid GT, as well as the new MLP and RBFNN models. The introduced ensemble models of the current study are not only useful for rainfall prediction but also can be used to predict other metrological parameters. The uncertainty of the model parameters and input data were also analysed. It was found that the IMM-MLP model reduced the root mean square error (RMSE) of the IMM-RBFNN, SAM, MLP-NMR, RBFNN-NMR, MLP-FFA, RBFNN-FFA, MLP-PSO, RBFNN-PSO, MLP-GA, and RBFNN-GA, MLP, and RBFNN models by 12%, 25%, 31%, 55%, 60%, 62%, 66%, 69%, 70%, 71%, 72%, and 72%, respectively. The IMMs, such as the IMM-MLP, IMM-RBFNN, and SAM, outperformed standalone models. The uncertainty bound of the multiple inclusive models was narrower than that of the standalone MLP and RBFNN models. The MLP-NMR model decreased the RMSE of the RBFNN-NMR, RBFNN-FFA, RBFNN-PSO, and RBFNN models by 15%, 26%, 37%, 42%, and 45%, respectively. The proposed ensemble models were robust tools for combining standalone models to predict hydrological variables. [ABSTRACT FROM AUTHOR]

The entire cascade of scenario generation, global and regional climate modeling, as well as concrete measures towards climate adaptation are subject to uncertainties. An exact prediction of how the climate will change in the coming years, and how it will affect land use, is not possible. There is thus a perceived need to identify ways via which uncertainties can be addressed. Based on the need to address the research gap in this area, this paper reports the findings of a study on uncertainty in a climate change adaptation context, and how it is perceived. It consists of a multi-stakeholder survey among climate change professionals, including academic staff at universities, representatives from international agencies, members of NGOs, policymakers, and representatives of industry from 50 countries, including a balanced representation of industrialized and developing nations. The results obtained suggest that uncertainties are often a hindrance to engagement in climate change adaptation efforts, and to land management. Furthermore, there is a range of tools to reduce climate change adaptation uncertainties, whose deployment may help to address them. The paper concludes by providing a list of lessons learned and suggestions as to how uncertainty can be better communicated, and by doing so, how a reduction in the levels of climate change vulnerability may be achieved, and how land management may be fostered. [ABSTRACT FROM AUTHOR]

To understand the activity of gravity waves (GWs) over the Tibetan Plateau (TP) is of great significance for improving global climate models. Considering that the lower stratosphere is the main level of GWs activity, this paper first established a 14-year 2° × 2° longitude–latitude monthly mean GWs model in the lower stratosphere (18~20 km) of the TP by combining post-processed dry temperature profiles provided by the multi-Global Navigation Satellite System (GNSS) radio occultation (RO) missions: The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) and the Meteorological Operational (METOP) series polar-orbiting meteorological satellites (METOP-A, METOP-B, and METOP-C) from August 2006 to September 2020. Based on this model, this paper analyzed the characteristics of GWs activity around TP and the effects of topography, background wind, and zonal wind on GWs activity and summarized the general process of topographic wave excitation and upward propagation around TP. The spatial distribution of the lower stratospheric GW Ep is highly correlated with the spatial distribution of background wind and the topography of TP during GWs excitation. The GW Ep is obviously filtered by the zero-speed wind. The change in GW Ep is strongly correlated with the change in topography. These phenomena indicate that the GWs of TP are mainly topographic waves. Moreover, the lower stratospheric GW Ep of TP shows that periodic changes are mainly affected by the periodic background wind, and the GW Ep value is larger in February and smaller in August. The large GW Ep in the lower stratosphere of TP is not only related to the GWs strongly generated by the interaction between the strong background wind and the large elevation or large topographic changes but also related to the strong zonal westerly winds that promote the propagation of GWs upward. Multivariable linear regression models were used to reconstruct the lower stratospheric GW Ep over TP based on the background wind and the zonal wind and a goodness of fit of 81.1% was achieved. It indicates that the GW Ep is dominated by the topographic wave over TP in the lower stratosphere and the background wind has a greater influence on the GWs than the zonal wind. [ABSTRACT FROM AUTHOR]

High-frequency precipitation variance is calculated in 12 different free-running (non-data-assimilative) coupled high resolution atmosphere–ocean model simulations, an assimilative coupled atmosphere–ocean weather forecast model, and an assimilative reanalysis. The results are compared with results from satellite estimates of precipitation and rain gauge observations. An analysis of irregular sub-daily fluctuations, which was applied by Covey et al. (Geophys Res Lett 45:12514–12522, 2018. https://doi.org/10.1029/2018GL078926) to satellite products and low-resolution climate models, is applied here to rain gauges and higher-resolution models. In contrast to lower-resolution climate simulations, which Covey et al. (2018) found to be lacking with respect to variance in irregular sub-daily fluctuations, the highest-resolution simulations examined here display an irregular sub-daily fluctuation variance that lies closer to that found in satellite products. Most of the simulations used here cannot be analyzed via the Covey et al. (2018) technique, because they do not output precipitation at sub-daily intervals. Thus the remainder of the paper focuses on frequency power spectral density of precipitation and on cumulative distribution functions over time scales (2–100 days) that are still relatively "high-frequency" in the context of climate modeling. Refined atmospheric or oceanic model grid spacing is generally found to increase high-frequency precipitation variance in simulations, approaching the values derived from observations. Mesoscale-eddy-rich ocean simulations significantly increase precipitation variance only when the atmosphere grid spacing is sufficiently fine (< 0.5°). Despite the improvements noted above, all of the simulations examined here suffer from the "drizzle effect", in which precipitation is not temporally intermittent to the extent found in observations. [ABSTRACT FROM AUTHOR]

Air pollution as one of the major environmental issues in modern society, has already brought severe impact to human life and production, thus it is an urgent task for studying environmental and ecological science to solve this issue that has bothered human society for the last centuries. Scientists have endeavored to figure out the laws of atmospheric pollutant diffusion using various mathematical models and statistical models, and drawn some precious conclusions. This paper explored the basic model of atmospheric diffusion--modeling and solution of Gaussian Diffusion Model, clarify its principles and operation forms, then applied the model into the PM2.5 atmospheric diffusion cases, make the program planning base on the results of model calculation, and get the final conclusion. [ABSTRACT FROM AUTHOR]

One of the most important components of an atmospheric radiation scheme is its treatment of gas optical properties, which determines not only the accuracy of its radiative forcing calculations fundamental to climate prediction, but also its computational cost. This paper describes a free software tool "ecCKD" for generating fast gas‐optics models by optimally dividing the spectrum into pseudo‐monochromatic spectral intervals (known as k‐terms) according to a user‐specified error tolerance and the range of greenhouse‐gas concentrations that needs to be simulated. The models generated use the correlated k‐distribution method in user‐specified bands, but can also generate accurate "full‐spectrum correlated‐k" models that operate on the entire longwave or near‐infrared (NIR) parts of the spectrum. In the NIR, the large spectral variation in cloud absorption is represented by partitioning the parts of the spectrum where gases are optically thin into 2–6 sub‐bands, while allowing k‐terms for the optically thicker parts of the spectrum (where clouds and surface reflectance are less important) to span the entire NIR spectrum. Candidate models using only 16 and 32 k‐terms in each of the shortwave and longwave are evaluated against line‐by‐line calculations on clear and cloudy profiles. The 32‐term models are able to accurately capture the radiative forcing of varying greenhouse gases including CO2 concentrations spanning a factor of 12, and heating rates at pressures down to 1 Pa. Plain Language Summary: A crucial component of atmospheric computer models used to make climate projections and weather forecasts is the "gas optics scheme," which represents the interaction of sunlight and infrared radiation with greenhouse gases. This paper describes a free software tool "ecCKD" that uses a number of novel techniques to generate new gas optics schemes that are computationally faster than most existing schemes while still being very accurate. For example, the schemes are able to simulate variations in carbon dioxide concentration spanning a factor of 12 and methane concentration spanning a factor of 10. Users of ecCKD can generate schemes that are optimized for specific applications, such as short‐term weather forecasting or simulating past climates. A special focus has been placed on the near‐infrared part of the solar spectrum to ensure that the schemes work well when computing the interactions of sunlight simultaneously with gases and clouds, important to ensure that the impact of clouds on weather and climate is well simulated. Key Points: We describe a free software tool for generating accurate gas optics models for radiation schemes and test with line‐by‐line calculationsExtra efficiency is achieved via the use of the full‐spectrum correlated‐k method in the thermal‐ and near‐infrared (NIR)The spectral properties of clouds are treated accurately via the use of sub‐bands in the NIR and several other techniques [ABSTRACT FROM AUTHOR]

The climate scenarios that form the basis for current climate risk assessments have no assigned probabilities, and this impedes the analysis of future climate risks. This paper proposes an approach to estimate the probability of carbon dioxide (CO2) concentration scenarios used in key climate change modeling experiments. It computes the CO2 emissions compatible with the concentrations prescribed by Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 experiments. The distribution of these compatible cumulative emissions is interpreted as the likelihood of future emissions given a concentration pathway. Using Bayesian analysis, the probability of each pathway can be estimated from a probabilistic sample of future emissions. The approach is demonstrated with five probabilistic CO2 emission simulation ensembles from four Integrated Assessment Models (IAM), leading to independent estimates of the likelihood of the CO2 concentration of Representative Concentration Pathways (RCP) and Shared Socioeconomic Pathways (SSP). Results suggest that SSP5‐8.5 is unlikely for the second half of the 21st century, but offer no clear consensus on which of the remaining scenarios is most likely. Estimates of likelihoods of CO2 concentrations associated with RCP and SSP scenarios are affected by sampling errors, differences in emission sources simulated by the IAMs, and a lack of a common experimental framework for IAM simulations. These shortcomings, along with a small IAM ensemble size, limit the applicability of the results presented here. Novel joint IAM and the Earth System Model experiments are needed to deliver actionable probabilistic climate risk assessments. Plain Language Summary: Climate model simulations are increasingly used to understand future trends in the severity and frequency of impactful climate hazards and associated physical and socioeconomic risks. Such simulations describe physical climate change in response to scenarios of greenhouse gases and land use change. However, the scenarios most widely used today as the basis for adaptation planning have no assigned probabilities; rather, they are explicitly intended to span a range of arbitrary climate futures. This reduces the applicability of resulting hazards projections for cost/benefit analysis of adaptation investments. To support risk‐based adaptation decision‐making, this study analyzes five sets of probabilistic carbon dioxide emissions simulated by four Integrated Assessment Models (IAM), along with Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 climate model ensemble results, to estimate the probability of future greenhouse gases concentration scenarios. The results of the five IAMs simulations collated in this work vary, and although they might not be representative of the wider literature, most of them suggest that the high‐emission scenario SSP5‐8.5 becomes unlikely as we reach the second half of the century. Based on lessons learned in this exercise, we propose that new sets of IAM and climate model experiments be jointly designed to better support probabilistic climate change risk assessments. Key Points: Probabilistic Integrated Assessment Models (IAM) simulations of future carbon dioxide emissions are used to evaluate the relative probability of greenhouse gases concentration scenariosJoint coordinated IAM and climate modeling experiments should be designed to enable probabilistic climate change risk assessments [ABSTRACT FROM AUTHOR]

With the rapid development of meteorological models, numerical weather prediction is increasingly used in flood forecasting and reservoir regulation, but its forecasting ability is limited by the large amount of uncertainty from meteorological systems. In this paper, a new, hybrid framework is developed to improve numerical precipitation forecasting by combining the multimodel ensemble and probabilistic postprocessing methods. The results show that the multimodel ensemble method used in this paper is an efficient way to reduce prediction errors, especially missing alarm errors. In a comparison of the probabilistic postprocessors based the generalized Bayesian model (GBM) and bivariate probabilistic model (BPM), the GBM shows better performance from the aspects of indicators and is more suitable for real-time applications. Meanwhile, the assessment of probabilistic results shows that the skill of probabilistic precipitation forecasts is related to the quality of their inputs. According to these results, a new hybrid framework is proposed by taking the results from multimodel ensemble as the input of probabilistic postprocessor. Compared to using the raw numerical in GBM, the hybrid framework improves the accuracy, sharpness, reliability, and resolution ability from different lead times by 2–13%, 1–22%, and 0–12% respectively, especially when the lead time is less than 4 d, the improvement can reach 9–13%, 10–22%, and 5–12% respectively. In conclusion, the hybrid two-step framework can provide a more skillful precipitation forecast, which can be useful for flood forecasting and reservoir regulation. [ABSTRACT FROM AUTHOR]

10.3390/rs13132644 6 Li S., Xu T., Jiang N., Yang H., Wang S., Zhang Z. Regional Zenith Tropospheric Delay Modeling Based on Least Squares Support Vector Machine Using GNSS and ERA5 Data. Several papers [[1], [3]] discuss different aspects in dealing with the estimation of long-term GNSS-derived water vapor trends and intercomparisons with external sources and NWP models. 10.3390/rs12244101 10 Li Y., Yuan Y., Wang X. Assessments of the Retrieval of Atmospheric Profiles from GNSS Radio Occultation Data in Moist Tropospheric Conditions Using Radiosonde Data. [Extracted from the article]

In recent years, the remarkable progress in exoplanet science has unveiled a stunning diversity of exoplanets. Technological advancements have significantly expanded our capacity to investigate planetary objects beyond our solar system, which exhibit diverse properties including their sizes, compositions, and orbital characteristics. Although understanding the complex chemical-climate responses occurring in planetary-body atmospheres plays a pivotal role in determining potential habitability, much work is still required in this field. To interpret forthcoming spectral measurements obtained from exoplanetary atmospheres, there is a concerted effort to model these worlds. However, model parameters are often inferred from limited data sets. Consequently, laboratory-based atmospheric simulations are necessary for understanding complex gas-phase interactions and to complement atmospheric models through experimental examination of input parameters. In this paper we introduce the new Berlin Atmospheric Simulation Experimental chamber (BASE), present its current capabilities and provide an overview of experimental scenarios with applications ranging from planetary objects in the solar system (e.g. Mars, Venus, Titan) to Earth-like exoplanets. • Exoplanetary science demands experimental validation of atmospheric processes. • Laboratory simulations are fine tuned to a specific set of atmospheric parameters. • UV photons and electrons initiate important chemical reactions in the gas phase. • BASE simulates pressure, composition and radiation levels of planetary atmospheres. • BASE will provide important laboratory data to improve current atmospheric models. [ABSTRACT FROM AUTHOR]

Nitrogen is one of the most abundant chemical elements on the Earth and plays an important role in global environmental change. Leading Earth system models include coupled carbon and nitrogen cycle modules of varying complexity, but the INM RAS climate model family has not yet included an explicit N-cycle description. This paper presents a parameterization of the terrestrial N-cycle based on a simplification of the JULES-CN model, adapted for coupled use with the INM-CM land C-cycle module. Numerical simulations were carried out with a standalone carbon cycle model with nitrogen feedback disabled and enabled versions for the period 1850–2100. The simulated global pools show good agreement with results of other models with an implemented N-cycle. Taking into account the N-limitation of the C-cycle, the modelled dynamics of total carbon storage in terrestrial ecosystems from 1850 to the mid-20th century is specified. [ABSTRACT FROM AUTHOR]

Rainfall prediction, based on meteorological data and models, forecasts the possible rainfall conditions for a period in the future. It is one of the important issues in meteorology and hydrology, and holds significant scientific and social value for enhancing human society's adaptive capacity, reducing the risk of natural disasters, promoting sustainable development, and protecting the environment. This study proposes a rainfall prediction model based on CEEMDAN-VMD-BiLSTM, which couples CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise), VMD (Variational Mode Decomposition), and BiLSTM (Bidirectional Long Short-Term Memory). The model first employs CEEMDAN and VMD, two decomposition algorithms, for a secondary decomposition of the original data, followed by prediction using the BiLSTM network. The study integrates the characteristics of CEEMDAN, which include adaptability, completeness, denoising capability, and high precision, the characteristic of VMD in extracting trend information, and the ability of the BiLSTM model to better capture contextual information in sequence data and solve long-term dependency issues, thereby increasing the accuracy of rainfall prediction. The research selected Zhongwei City in the Ningxia Hui Autonomous Region as the study object and used 20 years of monthly rainfall data from 2001 to 2020 as the research data. The model was compared with standalone BiLSTM models, CEEMDAN-BiLSTM coupled models, and VMD-BiLSTM coupled models. The model was validated using four indicators: RMSE, MARE, MAE, and NSE. The results showed that the maximum relative error of the CEEMDAN-VMD-BiLSTM neural network rainfall prediction coupled model was 7.22%, and the minimum relative error was -7.03%. The prediction qualification rate was 100%. The overall NSE value of the model ranged from 0.63 to 0.97, with most values between 0.86 and 0.97. The excellent rate was about 84.6%, and the good and above rate was 92.3%. In the rainfall prediction for Zhongwei City, the prediction accuracy of this coupled model was better than the other three models. In summary, the CEEMDAN-VMD-BiLSTM rainfall prediction model proposed in this paper combines the advantages of various methods and has shown good predictive effects in experiments, providing an effective prediction method for rainfall. [ABSTRACT FROM AUTHOR]

Context. Modeling the convection process is a long-standing problem in stellar physics. To date, all ad hoc models have relied on a free parameter, α, (among others) that has no real physical justification and is therefore poorly constrained. However, a link exists between this free parameter and the entropy of the stellar adiabat. There are existing prescriptions, derived from 3D stellar atmospheric models, that treat entropy as a function of stellar atmospheric parameters (effective temperature, surface gravity, and chemical composition). This can offer sufficient constraints on α through the development of entropy-calibrated models. However, several questions have arisen as these models are increasingly used with respect to which prescription should be used and whether it ought to be used in its original form, along with the impacts of uncertainties on entropy-calibrated models. Aims. We aim to study the three existing prescriptions in detail and determine which of them demonstrate the most optimal performance and how it should be applied. Methods. We implemented the entropy-calibration method into the stellar evolution code (Cesam2k20) and performed comparisons with the Sun and the α Cen system. In addition, we used data from the CIFIST grid of 3D atmosphere models to evaluate the accuracy of the prescriptions. Results. Of the three entropy prescriptions currently available, we determined the one that has the best functional form for reproducing the entropies of the 3D models. However, the coefficients involved in this formulation must not be taken from the original paper because they were calibrated against a flawed set of entropies. We also demonstrate that the entropy obtained from this prescription should be corrected for the evolving chemical composition and for an entropy offset different between various EoS tables. This must be done following a precise procedure to ensure that the classical parameters obtained from the models are not strongly biased. Finally, we provide a data table with entropy of the adiabat of the CIFIST grid, along with the fits for these entropies. Conclusions. Thanks to a precise examination of entropy-calibrated modeling, we are able to offer our recommendations with respect to which adiabatic entropy prescription to use, how to correct it, and how to implement the method into a stellar evolution code. [ABSTRACT FROM AUTHOR]