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This study examined adaptive functioning, strategies used to develop adaptive functioning skills, and areas where additional services could benefit autistic young adults without intellectual disability. Participants were 21 autistic young adults and at least one parent of each young adult. Quantitative analyses replicated previous reports of an adaptive functioning disadvantage relative to intellectual functioning such that adaptive functioning standard scores were significantly lower than intelligence quotient scores. Qualitative analysis utilized grounded theory methodology and yielded a conceptual model describing the nature and development of adaptive functioning in this demographic. Together, findings provide a more nuanced understanding of the gap between intellectual and adaptive functioning in autistic young adults without intellectual disability.

For effective radiative forcing (ERF) to be an ideal metric for comparing the strength of different climate drivers (such as CO2 and aerosols), the ratio of radiative forcing to global-mean temperature change must be the same for each driver. Typically, this ratio is divided by the same ratio for CO2 and termed efficacy. Previously it has been shown that efficacy is close to unity in abrupt perturbation experiments for a range of climate drivers, but efficacy with respect to CO2 has not been investigated in transient realistic simulations. Here, we analyse transient simulations from CMIP6 experiments and show comparable results between transient and abrupt perturbation experiments. We demonstrate that aerosol efficacy is not significantly different from unity, however inter-model differences in aerosol experiments are notably large. [ABSTRACT FROM AUTHOR]

Evidence supporting the effectiveness of the PEERS® social skills intervention for adolescents with autism spectrum disorder is relatively strong. Less is known about whether the duration of the program impacts participant outcomes. The current study compared outcomes of participants randomly assigned to participate in an accelerated version of PEERS that met twice weekly for 7 weeks (n = 11) or the traditional PEERS program that met once weekly for 14 weeks (n = 10). The accelerated PEERS group demonstrated improvements consistent with previous research on the program, and treatment response did not differ significantly between the accelerated PEERS and traditional PEERS groups. Together, findings provide preliminary evidence that PEERS is effective when administered as a 7-week program.

Despite considerable research on school social experiences among younger children with autism spectrum disorder (ASD), little is known about the classroom social relationships of high school students with ASD without intellectual disability. Additional research in this area is necessary to refine and develop social skills interventions and outcome measures of social success for this age group. The current study compared peer nomination variables between 10 high school students with ASD and randomly selected gender-matched samples of their general education classmates at three time points over two academic years. All students attended schools in the Southwestern USA. Across all three time points, students with ASD were less accepted by their peers compared to their general education classmates. Among participants with ASD, social difficulties appeared to be most pervasive at the third time point. Findings suggest that some of the difficulties associated with establishing peer relationships documented among children and young adolescents with ASD are also present during high school. Feasibility of peer nomination methods in high school settings is discussed. Findings have implications for researchers, school psychologists, educators, and stakeholders, including individuals with ASD and their parents.

Despite efforts to detect autism spectrum disorder during toddlerhood, many children with autism spectrum disorder remain undiagnosed until school age. To identify characteristics of children whose autism spectrum disorder might not be diagnosed during toddlerhood, this study used archived Autism Diagnostic Interview-Revised records to examine the historical presentation of autism spectrum disorder symptoms in 48 school-age children with autism spectrum disorder. Children diagnosed after starting school (Late-Diagnosed; n = 24) were compared to age-matched children diagnosed before school age (Early-Diagnosed; n = 24). Symptom presentation was similar between groups, with the Late-Diagnosed group exhibiting only marginally fewer symptoms historically. The most commonly reported historical symptoms were "negative" symptoms, namely, deficits in social behaviors. "Positive" symptoms, such as unusual preoccupations, rituals, and mannerisms, were less commonly reported. These findings may aid earlier identification of autism spectrum disorder in children who would likely be diagnosed at school age.

Recent analyses show the importance of methane shortwave absorption, which many climate models lack. In particular, Allen et al. (2023) used idealized climate model simulations to show that methane shortwave absorption mutes up to 30 % of the surface warming and 60 % of the precipitation increase associated with its longwave radiative effects. Here, we explicitly quantify the radiative and climate impacts due to shortwave absorption of the present-day methane perturbation using the Community Earth System Model version 2. Our results corroborate that present-day methane shortwave absorption mutes the warming and wetting effects of longwave absorption. For example, the global mean cooling in response to the present-day methane shortwave absorption is -0.10 ± 0.04 K, which offsets 29 % of the surface warming associated with present-day methane longwave radiative effects. Similarly, we explicitly estimate 66 % of the precipitation increase associated with the longwave radiative effects of the present-day methane perturbation is offset by shortwave absorption. Unlike other solar absorbers (i.e., black carbon), the decrease in global mean precipitation under methane shortwave absorption is driven by both fast (atmospheric absorption) and slow (surface temperature cooling) responses. Finally, we show that the present-day methane shortwave radiative effects, relative to its longwave radiative effects, are about five times larger as compared to those under idealized carbon dioxide perturbations. The unique responses to methane shortwave absorption are related to its vertical atmospheric solar heating profile. Methane remains a potent greenhouse gas and continued endeavors to decrease methane emissions are necessary to stay below the 1.5 °C global warming threshold. [ABSTRACT FROM AUTHOR]

The climate science community aims to improve our understanding of climate change due to anthropogenic influences on atmospheric composition and the Earth's surface. Yet not all climate interactions are fully understood, and uncertainty in climate model results persists, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. We synthesize current challenges and emphasize opportunities for advancing our understanding of the interactions between atmospheric composition, air quality, and climate change, as well as for quantifying model diversity. Our perspective is based on expert views from three multi-model intercomparison projects (MIPs) – the Precipitation Driver Response MIP (PDRMIP), the Aerosol Chemistry MIP (AerChemMIP), and the Radiative Forcing MIP (RFMIP). While there are many shared interests and specializations across the MIPs, they have their own scientific foci and specific approaches. The partial overlap between the MIPs proved useful for advancing the understanding of the perturbation–response paradigm through multi-model ensembles of Earth system models of varying complexity. We discuss the challenges of gaining insights from Earth system models that face computational and process representation limits and provide guidance from our lessons learned. Promising ideas to overcome some long-standing challenges in the near future are kilometer-scale experiments to better simulate circulation-dependent processes where it is possible and machine learning approaches where they are needed, e.g., for faster and better subgrid-scale parameterizations and pattern recognition in big data. New model constraints can arise from augmented observational products that leverage multiple datasets with machine learning approaches. Future MIPs can develop smart experiment protocols that strive towards an optimal trade-off between the resolution, complexity, and number of simulations and their length and, thereby, help to advance the understanding of climate change and its impacts. [ABSTRACT FROM AUTHOR]