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A well-mixed diluted sample is essential for unbiased measurement of cookstove emissions. Most cookstove testing labs employ a dilution tunnel, also referred to as a “duct,” to mix clean dilution air with cookstove emissions before sampling. It is important that the emissions be well-mixed and unbiased at the sampling port so that instruments can take representative samples of the emission plume. Some groups have employed mixing baffles to ensure the gaseous and aerosol emissions from cookstoves are well-mixed before reaching the sampling location [2, 4]. The goal of these baffles is to to dilute and mix the emissions stream with the room air entering the fume hood by creating a local zone of high turbulence. However, potential drawbacks of mixing baffles include increased flow resistance (larger blowers needed for the same exhaust flow), nuisance cleaning of baffles as soot collects, and, importantly, the potential for loss of PM2.5 particles on the baffles themselves,thus biasing results.A cookstove emission monitoring system with baffles will collect particles faster than the duct’s walls alone. This is mostly driven by the available surface area for deposition by processes of Brownian diffusion (through the boundary layer) and turbophoresis (i.e. impaction). The greater the surface area available for diffusive and advection-driven depositionto occur, the greater the particle loss will be at the sampling port. As a layer of larger particle “fuzz” builds on the mixing baffles, even greater PM2.5 loss could occur. The micro structure of the deposited aerosol will lead to increased rates of particle loss by interception and a tendency for smaller particles to deposit due to impaction on small features of themicro structure. If the flow stream could be well-mixed without the need for baffles, these drawbacks could be avoided and the cookstove emissions sampling system would be more robust.

In many regions, residential wood heaters are a leading source of harmful air pollution but only satisfy a small portion of local heating demands. In response, standardized laboratory test methods have been developed to characterize and limit wood heater emissions. While these test methods are a key tool for advancing both wood heater technology and environmental regulations, many of the experimental procedures are outdated and provide few actionable insights for improving heater performance. Furthermore, these test methods vary widely around the world and may not adequately capture the performance of wood heaters operating in residences. This study presents a comprehensive review of standardized wood heater test methods to identify fundamental experimental objectives and regulated performance metrics. Using the results of this review, recommendations are provided to make the test methods more accessible and representative of residential performance, while generating actionable data to motivate heater design improvements. This study elucidates the current state of standard test methods, and the developments needed to advance clean wood heater technologies and public policies.

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts aimed at better understanding the past, present, and future of climate changes in a multi-model context. A total of 21 model intercomparison projects (MIPs) were endorsed in its sixth phase (CMIP6), which included 190 different experiments that were used to simulate 40 000 years and produced around 40 PB of data in total. This paper presents the main findings obtained from the CPMIP (the Computational Performance Model Intercomparison Project), a collection of a common set of metrics, specifically designed for assessing climate model performance. These metrics were exclusively collected from the production runs of experiments used in CMIP6 and primarily from institutions within the IS-ENES3 consortium. The document presents the full set of CPMIP metrics per institution and experiment, including a detailed analysis and discussion of each of the measurements. During the analysis, we found a positive correlation between the core hours needed, the complexity of the models, and the resolution used. Likewise, we show that between 5 %–15 % of the execution cost is spent in the coupling between independent components, and it only gets worse by increasing the number of resources. From the data, it is clear that queue times have a great impact on the actual speed achieved and have a huge variability across different institutions, ranging from none to up to 78 % execution overhead. Furthermore, our evaluation shows that the estimated carbon footprint of running such big simulations within the IS-ENES3 consortium is 1692 t of CO2 equivalent. As a result of the collection, we contribute to the creation of a comprehensive database for future community reference, establishing a benchmark for evaluation and facilitating the multi-model, multi-platform comparisons crucial for understanding climate modelling performance. Given the diverse range of applications, configurations, and hardware utilised, further work is required for the standardisation and formulation of general rules. The paper concludes with recommendations for future exercises aimed at addressing the encountered challenges which will facilitate more collections of a similar nature.

A well-mixed diluted sample is essential for unbiased measurement of cookstove emissions. Most cookstove testing labs employ a dilution tunnel, also referred to as a “duct,” to mix clean dilution air with cookstove emissions before sampling. It is important that the emissions be well-mixed and unbiased at the sampling port so that instruments can take representative samples of the emission plume. Some groups have employed mixing baffles to ensure the gaseous and aerosol emissions from cookstoves are well-mixed before reaching the sampling location [2, 4]. The goal of these baffles is to to dilute and mix the emissions stream with the room air entering the fume hood by creating a local zone of high turbulence. However, potential drawbacks of mixing baffles include increased flow resistance (larger blowers needed for the same exhaust flow), nuisance cleaning of baffles as soot collects, and, importantly, the potential for loss of PM2.5 particles on the baffles themselves,thus biasing results.A cookstove emission monitoring system with baffles will collect particles faster than the duct’s walls alone. This is mostly driven by the available surface area for deposition by processes of Brownian diffusion (through the boundary layer) and turbophoresis (i.e. impaction). The greater the surface area available for diffusive and advection-driven depositionto occur, the greater the particle loss will be at the sampling port. As a layer of larger particle “fuzz” builds on the mixing baffles, even greater PM2.5 loss could occur. The micro structure of the deposited aerosol will lead to increased rates of particle loss by interception and a tendency for smaller particles to deposit due to impaction on small features of themicro structure. If the flow stream could be well-mixed without the need for baffles, these drawbacks could be avoided and the cookstove emissions sampling system would be more robust.

Billions of households worldwide cook using biomass fires and suffer from the toxic smoke emitted into their homes. Laboratory studies of wood-burning cookstoves demonstrate that secondary air injection can greatly reduce the emission of harmful air pollution, but these experimental advancements are not easily translated into practical cookstove designs that can be widely adopted. In this study, we use a modular cookstove platform to experimentally quantify the practical secondary air injection design requirements (e.g., flow rate, pressure, and temperature) to reduce mass emissions of particulate matter (PM), carbon monoxide (CO), and black carbon (BC) by at least 90% relative to a traditional cooking fire. Over the course of 111 experimental trials, we illuminate the physical mechanisms that drive emission reductions, and outline fundamental design principles to optimize cookstove performance. Using the experimental data, we demonstrate that low-cost (

Billions of households worldwide cook using biomass fires and suffer from the toxic smoke emitted into their homes. Laboratory studies of wood-burning cookstoves demonstrate that secondary air injection can greatly reduce the emission of harmful air pollution, but these experimental advancements are not easily translated into practical cookstove designs that can be widely adopted. In this study, we use a modular cookstove platform to experimentally quantify the practical secondary air injection design requirements (e.g., flow rate, pressure, and temperature) to reduce mass emissions of particulate matter (PM), carbon monoxide (CO), and black carbon (BC) by at least 90% relative to a traditional cooking fire. Over the course of 111 experimental trials, we illuminate the physical mechanisms that drive emission reductions, and outline fundamental design principles to optimize cookstove performance. Using the experimental data, we demonstrate that low-cost (

Despite their essential role in the high‐latitude climate, the representation of mixed‐phase clouds is still a challenge for Global Climate Models (GCMs)'s cloud schemes. In this study we propose a methodology for robustly assessing Arctic mixed‐phase cloud properties in a climate model using airborne measurements. We leverage data collected during the RALI‐THINICE airborne campaign that took place near Svalbard in August 2022 to evaluate the simulation of mid‐level clouds associated with Arctic cyclones. Simulations are carried out with the new limited‐area configuration of the ICOLMDZ model which combines the recent icosahedral dynamical core DYNAMICO and the physics of LMDZ, the atmospheric component of the IPSL‐CM Earth System Model. Airborne radar and microphysical probes measurements are then used to evaluate the simulated clouds. A comparison method has been set‐up to guarantee as much as possible the spatiotemporal co‐location between observed and simulated cloud fields. We mostly focus on the representation of ice and liquid in‐cloud contents and on their vertical distribution. Results show that the model overestimates the amount of cloud condensates and exhibits a poor cloud phase spatial distribution, with too much liquid water far from cloud top and too much ice close to it. The downward gradual increase in snowfall flux is also not captured by the model. This in‐depth model evaluation thereby pinpoints priorities for further improvements in the ICOLMDZ cloud scheme. Plain Language Summary: Mixed‐phase clouds, where ice crystals and supercooled liquid droplets coexist at sub‐zero temperatures, are ubiquitous in the polar regions. Albeit essential for the high‐latitude climate, they are still poorly represented by climate models. In this study, original measurements from the RALI‐THINICE airborne campaign, which took place in the Svalbard region in August 2022, are used to assess how the ICOLMDZ atmospheric model represents Arctic mixed phase clouds. A methodology is developed to make robust and co‐located model‐aircraft observation comparisons. In situ cloud probes and onboard radars are then used to evaluate the simulated amounts of liquid water and ice in clouds as well as the snowfall flux in deep clouds associated with the transit of Arctic cyclones. The results of the model evaluation show an overall overestimation of cloud condensates with too much liquid water far from cloud top and too much ice close to it. Key Points: Airborne RALI‐THINICE observations are used to evaluate Arctic clouds in the new limited area version of the ICOLMDZ modelA methodology has been developed to make robust and co‐located model‐observation comparisonsICOLMDZ simulates overly high amounts of liquid and ice in clouds particularly near cloud top [ABSTRACT FROM AUTHOR]

Nearly 40% of the world's population regularly cooks on inefficient biomass stoves that emit harmful airborne pollutants, such as particulate matter (PM). Secondary air injection can significantly reduce PM mass emissions to mitigate the health and climate impacts associated with biomass cookstoves. However, secondary air injection can also increase the number of ultrafine particles emitted, which may be more harmful to health. This research investigates the effect of secondary air injection on the mass and size distribution of PM emitted during solid biomass combustion. An experimental wood-burning cookstove platform and parametric testing approach are presented to identify and optimize secondary air injection parameters that reduce PM and other harmful pollutants. Size-resolved measurements of PM emissions were collected and analyzed as a function of parametric stove design settings. The results show that PM emissions are highly sensitive to secondary air injection flow rate and velocity. Although increasing turbulent mixing (through increased velocity) can promote more complete combustion, increasing the total flow rate of secondary air may cause localized flame quenching that increases particle emissions. Therefore, biomass cookstoves that implement secondary air injection should be carefully optimized and validated to ensure that PM emission reductions are achieved throughout the particle size range.

Low-cost air pollution sensors are emerging and increasingly being deployed in densely distributed wireless networks that provide more spatial resolution than is typical in traditional monitoring of ambient air quality. However, a low-cost option to measure black carbon (BC)-a major component of particulate matter pollution associated with adverse human health risks-is missing. This paper presents a new BC sensor designed to fill this gap, the Aerosol Black Carbon Detector (ABCD), which incorporates a compact weatherproof enclosure, solar-powered rechargeable battery, and cellular communication to enable long-term, remote operation. This paper also demonstrates a data processing methodology that reduces the ABCD's sensitivity to ambient temperature fluctuations, and therefore improves measurement performance in unconditioned operating environments (e.g., outdoors). A fleet of over 100 ABCDs was operated outdoors in collocation with a commercial BC instrument (Magee Scientific, Model AE33) housed inside a regulatory air quality monitoring station. The measurement performance of the 105 ABCDs is comparable to the AE33. The fleet-average precision and accuracy, expressed in terms of mean absolute percentage error, are 9.2 ± 0.8% (relative to the fleet average data) and 24.6 ± 0.9% (relative to the AE33 data), respectively (fleet-average ± 90% confidence interval).

In order to address the health risks and climate impacts associated with pollution from cooking on biomass fires, researchers have focused on designing new cookstoves that improve cooking performance and reduce harmful emissions, specifically particulate matter (PM). One method for improving cooking performance and reducing emissions is using air injection to increase turbulence of unburned gases in the combustion zone. Although air injection reduces total PM mass emissions, the effect on PM size distribution and number concentration has not been thoroughly investigated. Using two new wood-burning cookstove designs from Lawrence Berkeley National Laboratory, this research explores the effect of air injection on cooking performance, PM and gaseous emissions, and PM size distribution and number concentration. Both cookstoves were created using the Berkeley-Darfur Stove as the base platform to isolate the effects of air injection. The thermal performance, gaseous emissions, PM mass emissions, and particle concentrations (ranging from 5 nm to 10 μm in diameter) of the cookstoves were measured during multiple high-power cooking tests. The results indicate that air injection improves cookstove performance and reduces total PM mass but increases total ultrafine (less than 100 nm in diameter) PM concentration over the course of high-power cooking.

In order to address the health risks and climate impacts associated with pollution from cooking on biomass fires, researchers have focused on designing new cookstoves that improve cooking performance and reduce harmful emissions, specifically particulate matter (PM). One method for improving cooking performance and reducing emissions is using air injection to increase turbulence of unburned gases in the combustion zone. Although air injection reduces total PM mass emissions, the effect on PM size-distribution and number concentration has not been thoroughly investigated. Using two new wood-burning cookstove designs from Lawrence Berkeley National Laboratory, this research explores the effect of air injection on cooking performance, PM and gaseous emissions, and PM size distribution and number concentration. Both cookstoves were created using the Berkeley-Darfur Stove as the base platform to isolate the effects of air injection. The thermal performance, gaseous emissions, PM mass emissions, and particle concentrations (ranging from 5 nm to 10 μm in diameter) of the cookstoves were measured during multiple high-power cooking tests. The results indicate that air injection improves cookstove performance and reduces total PM mass but increases total ultrafine (less than 100 nm in diameter) PM concentration over the course of high-power cooking.

Based on the fifth phase of the Coupled Model Intercomparison Project (CMIP5)-generation previous Institut Pierre Simon Laplace (IPSL) Earth system model, we designed a new version, IPSL-CM5A2, aiming at running multi-millennial simulations typical of deep-time paleoclimate studies. Three priorities were followed during the setup of the model: (1) improving the overall model computing performance, (2) overcoming a persistent cold bias depicted in the previous model generation and (3) making the model able to handle the specific continental configurations of the geological past. These developments include the integration of hybrid parallelization Message Passing Interface – Open Multi-Processing (MPI-OpenMP) in the atmospheric model of the Laboratoire de Météorologie Dynamique (LMDZ), the use of a new library to perform parallel asynchronous input/output by using computing cores as “I/O servers” and the use of a parallel coupling library between the ocean and the atmospheric components. The model, which runs with an atmospheric resolution of 3.75∘×1.875∘ and 2 to 0.5∘ in the ocean, can now simulate ∼100 years per day, opening new possibilities towards the production of multi-millennial simulations with a full Earth system model. The tuning strategy employed to overcome a persistent cold bias is detailed. The confrontation of a historical simulation to climatological observations shows overall improved ocean meridional overturning circulation, marine productivity and latitudinal position of zonal wind patterns. We also present the numerous steps required to run IPSL-CM5A2 for deep-time paleoclimates through a preliminary case study for the Cretaceous. Namely, specific work on the ocean model grid was required to run the model for specific continental configurations in which continents are relocated according to past paleogeographic reconstructions. By briefly discussing the spin-up of such a simulation, we elaborate on the requirements and challenges awaiting paleoclimate modeling in the next years, namely finding the best trade-off between the level of description of the processes and the computing cost on supercomputers.

Abstract The assessment of current and future risks for natural and human systems associated with climate change largely relies on numerical simulations performed with state‐of‐the‐art climate models. Various steps are involved in the development of such models, from development of individual components of the climate system up to free parameter calibration of the fully coupled model. Here, we describe the final tuning phase for the IPSL‐CM6A‐LR climate model. This phase alone lasted more than 3 years and relied on several pillars: (i) the tuning against present‐day conditions given a small adjustment of the ocean surface albedo to compensate for the current oceanic heat uptake, (ii) the release of successive versions after adjustments of the individual components, implying a systematic and recurrent adjustment of the atmospheric energetics, and (iii) the use of a few metrics based on large scale variables such as near‐global mean temperature, summer Arctic sea‐ice extent, as targets for the tuning. Successes, lessons and prospects of this tuning strategy are discussed.

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts aimed at better understanding the past, present, and future of climate changes in a multi-model context. A total of 21 model intercomparison projects (MIPs) were endorsed in its sixth phase (CMIP6), which included 190 different experiments that were used to simulate 40 000 years and produced around 40 PB of data in total. This paper presents the main findings obtained from the CPMIP (the Computational Performance Model Intercomparison Project), a collection of a common set of metrics, specifically designed for assessing climate model performance. These metrics were exclusively collected from the production runs of experiments used in CMIP6 and primarily from institutions within the IS-ENES3 consortium. The document presents the full set of CPMIP metrics per institution and experiment, including a detailed analysis and discussion of each of the measurements. During the analysis, we found a positive correlation between the core hours needed, the complexity of the models, and the resolution used. Likewise, we show that between 5 %–15 % of the execution cost is spent in the coupling between independent components, and it only gets worse by increasing the number of resources. From the data, it is clear that queue times have a great impact on the actual speed achieved and have a huge variability across different institutions, ranging from none to up to 78 % execution overhead. Furthermore, our evaluation shows that the estimated carbon footprint of running such big simulations within the IS-ENES3 consortium is 1692 t of CO 2 equivalent. As a result of the collection, we contribute to the creation of a comprehensive database for future community reference, establishing a benchmark for evaluation and facilitating the multi-model, multi-platform comparisons crucial for understanding climate modelling performance. Given the diverse range of applications, configurations, and hardware utilised, further work is required for the standardisation and formulation of general rules. The paper concludes with recommendations for future exercises aimed at addressing the encountered challenges which will facilitate more collections of a similar nature. [ABSTRACT FROM AUTHOR]

Objective: To characterize the electro-clinical presentation of patients with pyridoxine-dependent epilepsy (PDE) and pyridoxal phosphate (PLP)-dependent epilepsy in order to determine whether some of them could be diagnosed as de novo West syndrome, i. e., West syndrome that starts after the age of 2 months without other types of seizures (focal seizures for instance) before the onset of epileptic spasms.Methods: We analyzed data from an unpublished cohort of 28 genetically confirmed cases of PDE with antiquitine (ATQ) deficiency and performed a review of the literature looking for description of West syndrome in patients with either PDE with ATQ deficiency or PLP-dependent epilepsy with Pyridox(am)ine phosphate oxidase (PNPO) deficiency.Results: Of the 28 cases from the ATQ deficiency French cohort, 5 had spasms. In four cases, spasms were associated with other types of seizures (myoclonus, focal seizures). In the last case, seizures started on the day of birth. None of these cases corresponded to de novo West syndrome. The review of the literature found only one case of PNPO deficiency presenting as de novo West syndrome and no case of ATQ deficiency.Significance: The presentation of PDE- and PLP-dependent epilepsy as de novo West syndrome is so exceptional that it probably does not justify a systematic trial of pyridoxine or PLP. We propose considering a therapeutic trial with these vitamins in West syndrome if spasms are associated with other seizure types or start before the age of 2 months.

Abstract This study presents the version of the LMDZ global atmospheric model used as the atmospheric component of the Institut Pierre Simon Laplace coupled model (IPSL‐CM6A‐LR) to contribute to the 6th phase of the international Coupled Model Intercomparison Project (CMIP6). This LMDZ6A version includes original convective parameterizations that define the LMDZ “New Physics”: a mass flux parameterization of the organized structures of the convective boundary layer, the “thermal plume model,” and a parameterization of the cold pools created by reevaporation of convective rainfall. The vertical velocity associated with thermal plumes and gust fronts of cold pools are used to control the triggering and intensity of deep convection. Because of several shortcomings, the early version 5B of this New Physics was worse than the previous “Standard Physics” version 5A regarding several classical climate metrics. To overcome these deficiencies, version 6A includes new developments: a stochastic triggering of deep convection, a modification of the thermal plume model that allows the representation of stratocumulus and cumulus clouds in a unified framework, an improved parameterization of very stable boundary layers, and the modification of the gravity waves scheme targeting the quasi‐biennal oscillation in the stratosphere. These improvements to the physical content and a more well‐defined tuning strategy led to major improvements in the LMDZ6A version model climatology. Beyond the presentation of this particular model version and documentation of its climatology, the present paper underlines possible methodological pathways toward model improvement that can be shared across modeling groups.

Abstract This study presents the global climate model IPSL‐CM6A‐LR developed at Institut Pierre‐Simon Laplace (IPSL) to study natural climate variability and climate response to natural and anthropogenic forcings as part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). This article describes the different model components, their coupling, and the simulated climate in comparison to previous model versions. We focus here on the representation of the physical climate along with the main characteristics of the global carbon cycle. The model's climatology, as assessed from a range of metrics (related in particular to radiation, temperature, precipitation, and wind), is strongly improved in comparison to previous model versions. Although they are reduced, a number of known biases and shortcomings (e.g., double Intertropical Convergence Zone [ITCZ], frequency of midlatitude wintertime blockings, and El Niño–Southern Oscillation [ENSO] dynamics) persist. The equilibrium climate sensitivity and transient climate response have both increased from the previous climate model IPSL‐CM5A‐LR used in CMIP5. A large ensemble of more than 30 members for the historical period (1850–2018) and a smaller ensemble for a range of emissions scenarios (until 2100 and 2300) are also presented and discussed.

Many poor households cannot meet the requirements of automotive mobility. They suffer great inequalities in their daily mobility, especially when they live away from urban centers, in areas where the provision of public transport and local amenities are reduced. Nevertheless, a normative interpretation in terms of shortages, based on the observation of low mobility capacity, underestimates the concrete mobility practices of poor households, and secondly how these households bypass the injunction of mobility required of them. This set of alternative practices – be they tactics, strategies, and projects – can substitute expensive automotive mobility with a set of resources drawn fundamentally from spatial proximity. This article aims to analyze the diversity of these practices, their quantitative importance and nesting, for the case of Paris suburban and periurban areas. To conclude, it draws how the variety of adjustments exhibited by poor households could be taken into account by public action.keywords : daily mobility, poverty, spatial inequality, social inequality, stakeholder, territorial policies

A climate model represents a multitude of processes on a variety of timescales and space scales: a canonical example of multi-physics multi-scale modeling. The underlying climate system is physically characterized by sensitive dependence on initial conditions, and natural stochastic variability, so very long integrations are needed to extract signals of climate change. Algorithms generally possess weak scaling and can be I/O and/or memory-bound. Such weak-scaling, I/O, and memory-bound multi-physics codes present particular challenges to computational performance. Traditional metrics of computational efficiency such as performance counters and scaling curves do not tell us enough about real sustained performance from climate models on different machines. They also do not provide a satisfactory basis for comparative information across models. codes present particular challenges to computational performance. We introduce a set of metrics that can be used for the study of computational performance of climate (and Earth system) models. These measures do not require specialized software or specific hardware counters, and should be accessible to anyone. They are independent of platform and underlying parallel programming models. We show how these metrics can be used to measure actually attained performance of Earth system models on different machines, and identify the most fruitful areas of research and development for performance engineering. codes present particular challenges to computational performance. We present results for these measures for a diverse suite of models from several modeling centers, and propose to use these measures as a basis for a CPMIP, a computational performance model intercomparison project (MIP).

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts to better understand past, present and future climate changes in a multi-model context. A total of 21 Model Intercomparison Projects (MIPs) were endorsed in its 6th phase (CMIP6), which included 190 different experiments that were used to simulate 40000 years and produced around 40 PB of data in total. This paper shows the main results obtained from the collection of performance metrics done for CMIP6 (CPMIP). The document provides the list of partners involved, the CPMIP metrics per institution/model, and the approach used for the collection and the coordination behind this process. Furthermore, a section has been included to analyze the results and prove the usefulness of the metrics for the community. Moreover, we describe the main difficulties faced during the collection and propose recommendations for future exercises. [ABSTRACT FROM AUTHOR]