Explore the vast range of titles available.

Satellite observations and their corresponding instrument simulators are used to document global cloud biases in the Community Atmosphere Model (CAM) versions 4 and 5. The model–observation comparisons show that, despite having nearly identical cloud radiative forcing, CAM5 has a much more realistic representation of cloud properties than CAM4. In particular, CAM5 exhibits substantial improvement in three long-standing climate model cloud biases: 1) the underestimation of total cloud, 2) the overestimation of optically thick cloud, and 3) the underestimation of midlevel cloud. While the increased total cloud and decreased optically thick cloud in CAM5 result from improved physical process representation, the increased midlevel cloud in CAM5 results from the addition of radiatively active snow. Despite these improvements, both CAM versions have cloud deficiencies. Of particular concern, both models exhibit large but differing biases in the subtropical marine boundary layer cloud regimes that are known to explain intermodel differences in cloud feedbacks and climate sensitivity. More generally, this study demonstrates that simulator-facilitated evaluation of cloud properties, such as amount by vertical level and optical depth, can robustly expose large and at times radiatively compensating climate model cloud biases.

This paper describes the first implementation of the Δx = 3.25 km version of the Energy Exascale Earth System Model (E3SM) global atmosphere model and its behavior in a 40‐day prescribed‐sea‐surface‐temperature simulation (January 20 through February 28, 2020). This simulation was performed as part of the DYnamics of the Atmospheric general circulation Modeled On Non‐hydrostatic Domains (DYAMOND) Phase 2 model intercomparison. Effective resolution is found to be ∼6× the horizontal dynamics grid resolution despite using a coarser grid for physical parameterizations. Despite this new model being in an immature and untuned state, moving to 3.25 km grid spacing solves several long‐standing problems with the E3SM model. In particular, Amazon precipitation is much more realistic, the frequency of light and heavy precipitation is improved, agreement between the simulated and observed diurnal cycle of tropical precipitation is excellent, and the vertical structure of tropical convection and coastal stratocumulus look good. In addition, the new model is able to capture the frequency and structure of important weather events (e.g., tropical cyclones, extratropical cyclones including atmospheric rivers, and cold air outbreaks). Interestingly, this model does not get rid of the erroneous southern branch of the intertropical convergence zone nor the tendency for strongest convection to occur over the Maritime Continent rather than the West Pacific, both of which are classic climate model biases. Several other problems with the simulation are identified, underscoring the fact that this model is a work in progress. Plain Language Summary This paper describes the new global 3.25 km version of the Energy Exascale Earth System Model (E3SM) atmosphere model and its behavior in a 40‐day northern‐hemisphere wintertime simulation. In exchange for huge computational expense, this high‐resolution model avoids many but not all biases common in lower‐resolution models. It also captures several types of extreme weather that would simply not be resolved in lower‐resolution models. Several opportunities for further development are identified. Key Points Describes the Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM) SCREAM performs well in a 40‐day boreal winter simulation at 3.25 km Δx Resolving deep convection solves many long‐standing climate model biases

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

Simulations of the recent historical period from 1950 to 2014 are conducted with E3SM‐MMF, which uses an embedded 2D cloud resolving model that runs efficiently on GPUs in place of traditional parameterizations for cloud and turbulence. Analysis of the climate and variability reveal several aspects where E3SM‐MMF produces smaller biases compared to E3SMv2, including better agreement with the observed evolution of global mean surface temperature, although the representation of ENSO is too weak and fast. Three idealized abrupt CO2 experiments were also conducted to assess climate sensitivity and feedbacks. These yield three estimates of effective climate sensitivity (4.38, 5.21, and 6.06 K), with a corresponding spread in the shortwave cloud feedbacks. These estimates are on the higher end of sensitivity estimates from CMIP ensembles, and the spread indicates substantial state‐dependent feedbacks. These results demonstrate how multiscale modeling framework (MMF) models can be used for climate relevant experiments and projections by leveraging modern GPU enabled computational platforms. The unique qualities of E3SM‐MMF shown in previous literature are largely still present, but various instances of reduced biases suggest that MMF models have utility in improving future projections. Plain Language Summary One of the largest source of uncertainty in climate projections comes from clouds, which are often represented with relatively crude parameterizations that fail to capture the rich complexity and scale interactions in the real atmosphere. The multiscale modeling framework (MMF) was designed to address the need for a model that could be used for climate scale experiments while explicitly representing clouds in a computationally efficient way. Here we present results from an Earth system model that uses this approach, along with active ocean and sea‐ice components to simulate the recent historical period and assess the biases relative to its traditionally parameterized counterpart. Many biases are improved with the MMF despite significantly less effort spent on tuning uncertain parameters, but there are also some aspects of the variability that are worse. Additional simulations with varying levels of CO2 are used to calculate effective climate sensitivity, which is higher than most traditional models. Key Points E3SM‐MMF performs well over the historical period of 1950–2014 with active ocean and sea‐ice components despite minimal tuning The representation of ENSO variability is generally too weak and too fast compared to both E3SMv2 and observations Abrupt CO2 experiments yield a wide range of effective climate sensitivity (4.38–6.06 K) indicating substantial state‐dependent feedbacks

Results from the new Department of Energy super‐parameterized (SP) Energy Exascale Earth System Model (SP‐E3SM) are analyzed and compared to the traditionally parameterized E3SMv1 and previous studies using SP models. SP‐E3SM is unique in that it utilizes Graphics Processing Unit hardware acceleration, cloud resolving model mean‐state acceleration, and reduced radiation to dramatically increase the model throughput and allow decadal experiments at 100‐km external resolution. It also differs from other SP models by using a spectral element dynamical core on a cubed‐sphere grid and a finer vertical grid with a higher model top. Despite these differences, SP‐E3SM generally reproduces the behavior of other SP models. Tropical wave variability is improved relative to E3SM, including the emergence of a Madden‐Julian Oscillation and a realistic slowdown of Moist Kelvin Waves. However, the distribution of precipitation exhibits indicates an overly frequent occurrence of rain rates less than 1 mm day −1, and while the timing of diurnal rainfall shows modest improvements the signal is not as coherent as observations. A notable grid imprinting bias is identified in the precipitation field and attributed to a unique feedback associated with the interactions between the explicit cloud resolving model convection and the spectral element grid structure. Spurious zonal mean column water tendencies due to grid imprinting are quantified—while negligible for the conventionally parameterized E3SM, they become large with super‐parameterization, approaching 10% of the physical tendencies. The implication is that finding a remedy to grid imprinting will become especially important as spectral element dynamical cores begin to be combined with explicitly resolved convection. Key Points SP‐E3SM improves tropical variability like previous super‐parameterized models but shows modest improvements in the diurnal precipitation The spectral element grid leads to an imprinting bias when used with super‐parameterization and has nonnegligible effects on the climate The throughput of SP‐E3SM was increased to roughly 1.2–1.4 simulated years per day through hardware (GPU) and algorithmic acceleration

Objective: To determine the reliability of an objective measure of pain, agitation and sedation using the Neonatal Pain, Agitation and Sedation Scale (N-PASS) compared with nursing bedside assessment. Study Design: Neonates admitted in neonatal intensive care unit over a 6-month period were eligible. Pain and sedation were assessed with N-PASS, and a subjective questionnaire was administered to the bedside nurse. Result: A total of 218 neonates were eligible (median: gestational age 34.6 weeks, age at assessment 7 days). N-PASS pain score correlated significantly with both nurses’ pain score (Spearman coefficient ( r )=0.37; P <0.001) and agitation score ( r =0.56; P <0.001). N-PASS sedation score correlated with nurses’ sedation score ( r =−0.39; P <0.001). Adjusting for gestational age, day of life, intrauterine drug exposure and use of high frequency ventilation only slightly attenuated the correlations ( r =0.36, 0.55 and −0.31, respectively). Conclusion: The N-PASS captures nursing assessment of pain, agitation and sedation in this broad population and provides a quantitative assessment of subjective descriptions that often drives patient therapy.

Bile acid diarrhoea (BAD) is a widespread gastrointestinal disease that is often misdiagnosed as irritable bowel syndrome and is estimated to affect 1% of the United Kingdom (UK) population alone. BAD is associated with excessive bile acid synthesis secondary to a gastrointestinal or idiopathic disorder (also known as primary BAD). Current licensed treatment in the UK has undesirable effects and has been the same since BAD was first discovered in the 1960s. Bacteria are essential in transforming primary bile acids into secondary bile acids. The profile of an individual’s bile acid pool is central in bile acid homeostasis as bile acids regulate their own synthesis. Therefore, microbiome dysbiosis incurred through changes in diet, stress levels and the introduction of antibiotics may contribute to or be the cause of primary BAD. This literature review focuses on primary BAD, providing an overview of bile acid metabolism, the role of the human gut microbiome in BAD and the potential options for therapeutic intervention in primary BAD through manipulation of the microbiome.

In his first term (2012-2017), Xi Jinping's signature domestic policy was an anticorruption campaign that targeted political enemies and venality in public office. The anti-corruption work has continued in his second term while being superseded in domestic political importance by a campaign to ＂Sweep Away Black and Eliminate Evil (2018-2020).＂ On the surface, the campaign to Sweep Away Black and Eliminate Evil is an anti-crime campaign that focuses on the ＂black and evil forces＂ of organized crime and their official protectors, but its scope extends well beyond the ganglands to target a wide range of social and political threats to the Chinese Communist Party (CCP). Drawing on interviews with government officials, police and citizens as well as analysis of policy documents, this paper argues that the campaign is a populist initiative designed to bolster CCP legitimacy and serve as a mechanism of social control. Like the Chongqing prototype that inspired it, however, the campaign harbors a dark side that could undermine the contemporary Chinese social contract in which people are willing to sacrifice personal freedoms in exchange for security and material benefits.

Machine learning (ML) models are commonly used to generate predictions, but these models can also support the discovery of new science. Generating accurate predictions necessitates that a model captures the structure of the underlying data. If the structure is properly extracted, ML could be a useful exploratory and evidential tool. In this paper, we present a case study that demonstrates the use of ML for exploratory data analysis (EDA) in the climate space. We apply the ML explainability method of spatiotemporal zeroed feature importance (stZFI) to understand how climate-variable associations evolve over space and time. Our analyses focus on data from ensembles of Earth system models (ESMs) which provide data on different climate states and conditions. We elect to work with ESM ensembles since they allow us to compare feature importance across alternative scenarios not available with observed data. The ensembles also account for natural variability so that we can distinguish between signal and noise due to natural climate variability when computing feature importance. The use of perturbed initial condition ensembles introduces variability mimicking the natural variability in the atmosphere; thus the signals emerging using feature importance (FI) can be evaluated against the natural variability in the climate system. For our analyses, we consider the 1991 volcanic eruption of Mount Pinatubo, which was a large stratospheric aerosol injection. We explore the climate pathway associated with the eruption from aerosols to radiation to temperature at both the near-surface and stratospheric levels. In addition to applying the method to data generated from two different ESMs, we apply stZFI to reanalysis data to compare the associations identified by stZFI. We show how stZFI tracks the importance of aerosol optical depth over time on forecasting temperatures. This case study illustrates usefulness of an ML tool (stZFI) for EDA on a well-studied climate exemplar.

Following tropical volcanic eruptions, westerly zonal-wind accelerations have been observed in the winter hemisphere polar vortex region. This wind response has been reproduced in some (but not all) simulated eruption studies. As the primary effect of volcanic aerosols during the initial post-eruption period is to heat the tropical stratosphere, the midlatitude zonal-wind response is often explained as a thermal wind effect. Several studies have shown that this explanation is insufficient in understanding the relative significance of the aerosol direct effect and indirect dynamical feedbacks. In this work, we use a Transformed Eulerian Mean (TEM) framework to identify the dynamical origins of stratospheric wind anomalies following the simulated 1991 eruption of Mt. Pinatubo. A paired set of volcanic and non-volcanic 15-member ensembles is used to isolate the volcanic impact. A TEM decomposition of the net zonal-wind forcing is then performed to close the differenced momentum budget between the two ensembles. Zonal-wind accelerations near 30–40° N and 3–30 hPa are identified with significance in the Northern Hemisphere (NH) during both the summer and winter. We find each of these seasonal acceleration episodes to have distinct dynamical drivers. In the summertime, the response is primarily governed by an accelerated meridional residual circulation. In the wintertime, the response is eddy-driven, where an equatorward deflection of planetary waves was robustly identified near 30N and 30 hPa. We additionally identified that a deficit of wave forcing in the tropical stratosphere dampens the amplitude of the quasi-biennial oscillation (QBO) for at least 2 years following the eruption.