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Global and regional sources and sinks of carbon across the earth's surface have been studied extensively using atmospheric carbon dioxide (CO2) observations and atmospheric chemistry-transport model (ACTM) simulations (top-down/inversion method). However, the uncertainties in the regional flux distributions remain unconstrained due to the lack of high-quality measurements, uncertainties in model simulations, and representation of data and flux errors in the inversion systems. Here, we assess the representation of data and flux errors using a suite of 16 inversion cases derived from a single transport model (MIROC4-ACTM) but different sets of a priori (bottom-up) terrestrial biosphere and oceanic fluxes, as well as prior flux and observational data uncertainties (50 sites) to estimate CO2 fluxes for 84 regions over the period 2000–2020. The inversion ensembles provide a mean flux field that is consistent with the global CO2 growth rate, land and ocean sink partitioning of-2.9 ± 0.3 (± 1σ uncertainty on the ensemble mean) and-1.6 ± 0.2 PgC yr-1, respectively, for the period 2011–2020 (without riverine export correction), offsetting about 22 %–33 % and 16 %–18 % of global fossil fuel CO2 emissions. The rivers carry about 0.6 PgC yr-1 of land sink into the deep ocean, and thus the effective land and ocean partitioning is -2.3 ± 0.3 and -2.2 ± 0.3, respectively. Aggregated fluxes for 15 land regions compare reasonably well with the best estimations for the 2000s (∼ 2000–2009), given by the REgional Carbon Cycle Assessment and Processes (RECCAP), and all regions appeared as a carbon sink over 2011–2020. Interannual variability and seasonal cycle in CO2 fluxes are more consistently derived for two distinct prior fluxes when a greater degree of freedom (increased prior flux uncertainty) is given to the inversion system. We have further evaluated the inversion fluxes using meridional CO2 distributions from independent (not used in the inversions) aircraft and surface measurements, suggesting that the ensemble mean flux (model–observation mean ± 1σ standard deviation = -0.3 ± 3 ppm) is best suited for global and regional CO2 flux budgets than an individual inversion (model–observation 1σ standard deviation = -0.35 ± 3.3 ppm). Using the ensemble mean fluxes and uncertainties for 15 land and 11 ocean regions at 5-year intervals, we show promise in the capability to track flux changes toward supporting the ongoing and future CO2 emission mitigation policies.

This case report demonstrates the use of a video laryngoscope to aid in the incision and drainage of a peritonsillar abscess in a 30-year-old male. This technique, which has not been widely discussed in the literature, provided enhanced visualization, overcoming challenges like poor access due to trismus and poor lighting. The video laryngoscope improved safety, offered clearer guidance, and provided valuable teaching opportunities, particularly for less experienced physicians and residents. This case contributes to the literature by illustrating how the video laryngoscope can function as both a therapeutic tool and an educational resource, improving the management of peritonsillar abscesses and potentially reducing complications. Peritonsillar abscess, peritonsillar aspiration, peritonsillar incision, video laryngoscope.

This study investigates how subgrid cloud water inhomogeneity within a grid spacing of a general circulation model (GCM) links to the global climate through precipitation processes. The effect of the cloud inhomogeneity on autoconversion rate is incorporated into the GCM as an enhancement factor using a prognostic cloud water probability density function (PDF), which is assumed to be a truncated skewed-triangle distribution based on the total water PDF originally implemented. The PDF assumption and the factor are evaluated against those obtained by global satellite observations and simulated by a global cloud-system-resolving model (GCRM). Results show that the factor implemented exerts latitudinal variations, with higher values at low latitudes, qualitatively consistent with satellite observations and the GCRM. The GCM thus validated for the subgrid cloud inhomogeneity is then used to investigate how the characteristics of the enhancement factor affect global climate through sensitivity experiments with and without the factor incorporated. The latitudinal variation of the factor is found to have a systematic impact that reduces the cloud water and the solar reflection at low latitudes in the manner that helps mitigate the too-reflective cloud bias common among GCMs over the tropical oceans. Due to the limitation of the factor arising from the PDF assumption, however, no significant impact is found in the warm rain formation process. Finally, it is shown that the functional form for the PDF in a GCM is crucial to properly characterize the observed cloud water inhomogeneity and its relationship with precipitation.

Aerosols affect climate by modifying cloud properties through their role as cloud condensation nuclei or ice nuclei, called aerosol–cloud interactions. In most global climate models (GCMs), the aerosol–cloud interactions are represented by empirical parameterisations, in which the mass of cloud liquid water (LWP) is assumed to increase monotonically with increasing aerosol loading. Recent satellite observations, however, have yielded contradictory results: LWP can decrease with increasing aerosol loading. This difference implies that GCMs overestimate the aerosol effect, but the reasons for the difference are not obvious. Here, we reproduce satellite-observed LWP responses using a global simulation with explicit representations of cloud microphysics, instead of the parameterisations. Our analyses reveal that the decrease in LWP originates from the response of evaporation and condensation processes to aerosol perturbations, which are not represented in GCMs. The explicit representation of cloud microphysics in global scale modelling reduces the uncertainty of climate prediction. Most global climate models overestimate the aerosol effect on cloud properties, but the reason for this is unclear. Here the authors show that using explicit representation of cloud microphysics, in global scale modelling, rather than parameterisations, reduces the overestimation.

The dust direct radiative effect (DRE) depends strongly on the dust particle size distribution (PSD) and complex refractive index (CRI). Although recent studies constrained the dust PSD in the models, its CRI uncertainties are still large. As a result, whether dust warms or cools the climate system remains unclear. Here, we estimate the dust DRE by employing the regionally‐dependent dust CRI based on global measurements. We find that new dust CRI significantly enhances the scattering of dust in the shortwave while reduces its absorption in the longwave, which is opposite to that caused by increasing the coarse and giant dust fraction via constraining the PSD. Constraining both PSD and CRI ultimately leads to a net dust DRE of −0.68 W m−2, a cooling stronger than current model estimates. Plain Language Summary Impacts of dust on the Earth’s climate are sensitive to the size and composition of dust particles. Previous research found that dust composition varies among its source regions. Using a single dust complex refractive index by assuming a uniform dust particle composition is inadequate for accurate dust modeling. In this study, we develop a regionally‐dependent dust refractive index scheme based on global observations to represent the differences in dust composition among its source regions. We find that the optical and radiative properties of the modeled dust are much improved when compared with observations. Our results show an enhanced dust cooling effect when accounting for regional differences in the dust complex refractive index, which is opposite to that when increasing more large dust particles. As a result, the combined effect leads to a stronger dust cooling than our previous model estimate. This study emphasizes the need to constrain the dust size distribution and the refractive index in the model to more accurately quantify the impacts of dust on climate. Key Points New dust simulations are constrained by a combination of observed dust size distributions and regionally‐dependent dust refractive indices New dust refractive indices increase dust scattering in the shortwave and reduce dust absorption in the longwave New dust refractive indices greatly enhance dust cooling and change the sign of the net dust direct radiative effect in its source regions

Introduction Eudaimonic well‐being (EWB), which refers to optimal human functioning, is associated with psychophysiological outcomes, such as reduced inflammation and a lower risk of depression. Although physical activity (PA) and mindfulness have been shown to be predictive factors for EWB, potential mediators of the relationships of PA or mindfulness with EWB have yet to be identified. PA, mindfulness‐related psychophysiological factors (including serotonin [5‐HT], oxytocin [OXT]), and dopamine [DA] levels), and heart rate variability (HRV) have been shown to be associated with mental disorders or emotion regulation capacity. Purpose The purpose of this study is to explore the potential psychophysiological factors associated with PA, mindfulness and EWB. Methods A total of 49 young adults (25 males, 24 females) were included with an average age of 25 years (± 5). Plasma 5‐HT, OXT, and DA levels were obtained via blood samples from the brachial vein and were analyzed with enzyme‐linked immunosorbent assays (ELISAs), and HRV was obtained via 5‐min electrocardiograms (ECGs), with participants in the supine position. Spearman's correlation analyses were performed, followed by partial correlation analyses controlling for age, sex, and social status (i.e., student or working professional). Results HRV was found to be positively correlated with both moderate‐intensity PA (r = 0.47, p = 0.04) and EWB (purpose in life; r = 0.50, p = 0.03), even after controlling for relevant variables. On the other hand, neither 5‐HT, OXT, nor DA was correlated with PA, mindfulness, and EWB. Conclusion These results suggest that HRV may mediate the relationship between PA and EWB. Additional intervention studies are needed to elucidate the causal relationships among PA, HRV, and EWB.

The Nonhydrostatic ICosahedral Atmospheric Model (NICAM), a global model with an icosahedral grid system, has been under development for nearly two decades. This paper describes NICAM16-S, the latest stable version of NICAM (NICAM.16), modified for the Coupled Model Intercomparison Project Phase 6, High Resolution Model Intercomparison Project (HighResMIP). Major updates of NICAM.12, a previous version used for climate simulations, included updates of the cloud microphysics scheme and land surface model, introduction of natural and anthropogenic aerosols and a subgrid-scale orographic gravity wave drag scheme, and improvement of the coupling between the cloud microphysics and the radiation schemes. External forcings were updated to follow the protocol of the HighResMIP. A series of short-term sensitivity experiments were performed to determine and understand the impacts of these various model updates on the simulated mean states. The NICAM16-S simulations demonstrated improvements in the ice water content, high cloud amount, surface air temperature over the Arctic region, location and strength of zonal mean subtropical jet, and shortwave radiation over Africa and South Asia. Some long-standing biases, such as the double intertropical convergence zone and smaller low cloud amount, still exist or are even worse in some cases, suggesting further necessity for understanding their mechanisms, upgrading schemes and parameter settings, and enhancing horizontal and vertical resolutions.

Purpose While there is growing scientific evidence for and significant advances in the use of genomic technologies in medicine, there is a significant lag in the clinical adoption and sustainability of genomic medicine. Here we describe the findings from the National Human Genome Research Institute’s (NHGRI) Implementing GeNomics In pracTicE (IGNITE) Network in identifying key constructs, opportunities, and challenges associated with driving sustainability of genomic medicine in clinical practice. Methods Network members and affiliates were surveyed to identify key drivers associated with implementing and sustaining a genomic medicine program. Tallied results were used to develop and weigh key constructs/drivers required to support sustainability of genomic medicine programs. Results The top three driver–stakeholder dyads were (1) genomic training for providers, (2) genomic clinical decision support (CDS) tools embedded in the electronic health record (EHR), and (3) third party reimbursement for genomic testing. Conclusion Priorities may differ depending on healthcare systems when comparing the current state of key drivers versus projected needs for supporting genomic medicine sustainability. Thus we provide gap-filling guidance based on IGNITE members’ experiences. Although results are limited to findings from the IGNITE network, their implementation, scientific, and clinical experience may be used as a road map by others considering implementing genomic medicine programs.

Methane (CH 4 ) emission reduction to limit warming to 1.5 °C can be tracked by analyzing CH 4 concentration and its isotopic composition ( δ 13 C, δ D) simultaneously. Based on reconstructions of the temporal trends, latitudinal, and vertical gradient of CH 4 and δ 13 C from 1985 to 2020 using an atmospheric chemistry transport model, we show (1) emission reductions from oil and gas exploitation (ONG) since the 1990s stabilized the atmospheric CH 4 growth rate in the late 1990s and early 2000s, and (2) emissions from farmed animals, waste management, and coal mining contributed to the increase in CH 4 since 2006. Our findings support neither the increasing ONG emissions reported by the EDGARv6 inventory during 1990–2020 nor the large unconventional emissions increase reported by the GAINSv4 inventory since 2006. Total fossil fuel emissions remained stable from 2000 to 2020, most likely because the decrease in ONG emissions in some regions offset the increase in coal mining emissions in China.

This article reviews the development of a global non-hydrostatic model, focusing on the pioneering research of the Non-hydrostatic Icosahedral Atmospheric Model (NICAM). Very high resolution global atmospheric circulation simulations with horizontal mesh spacing of approximately O (km) were conducted using recently developed supercomputers. These types of simulations were conducted with a specifically designed atmospheric global model based on a quasi-uniform grid mesh structure and a non-hydrostatic equation system. This review describes the development of each dynamical and physical component of NICAM, the assimilation strategy and its related models, and provides a scientific overview of NICAM studies conducted to date.