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Nearly a decade ago, the "Framework for K-12 Science Education" argued for the need to intertwine science and engineering practices, disciplinary core ideas, and crosscutting concepts in performance expectations. However, there are few empirical examples for how intertwining three dimensions facilitates learning. In this study, we used a learning progressions approach to examine how student engagement in computational thinking (science and engineering practice) intertwines with learning about the flow of water through environmental systems (disciplinary core ideas) and understanding of systems and system models (crosscutting concept). We developed three secondary-level curriculum units situated in current groundwater contamination and urban flooding contexts. Units included specially designed NetLogo computational models. Post-assessments measured student performances in computational thinking processes and understanding of hydrologic systems. Using item response theory in our analysis, we identified distinct levels of performance on a learning progression. At the lower end, literal model users interacted with models and manipulated model interfaces to achieve a specified goal. In the middle, Model Technicians used computational models to solve real-world problems. At the upper end, principle-based model users used computational thinking processes and principles related to systems modeling and hydrology to explain how the models worked to predict water flow. Differences between performances of literal model users, model technicians, and principle-based model users reflected shifts in how students made sense of the systems and system models crosscutting concept. These shifts in performances aligned with progress in computational thinking practices and finally with use of hydrology disciplinary core ideas. These findings contribute to understanding of how science and engineering practices, disciplinary core ideas, and crosscutting concepts intertwine during learning; how computational thinking practices develop; and how computational thinking about system models facilitates learning for environmental science literacy.

Diana Wall, a respected environmental and sustainability scientist, has passed away at the age of 80. She was a University Distinguished Professor at Colorado State University and made significant contributions to the fields of soil ecology and sustainability science. Diana was a strong advocate for soil biodiversity and emphasized the crucial role of soil organisms in ecosystem processes. Her work has been recognized with numerous awards and honors, and her legacy will continue to inspire and impact the scientific community. Additionally, the given text references several sources that provide valuable insights into the formation of soil and the significance of worms in this process, including Charles Darwin's book "The Formation of Vegetable Mould, Through the Action of Worms, with Observations on Their Habits" published in 1881, a review of this book by W. Gifford et al. in 1882, H. Jenny's book "Factors of Soil Formation – A System of Quantitative Pedology" published in 1941, and a global soil biodiversity atlas by A. Orgiazzi et al. published in 2016. [Extracted from the article]

Beijing is undergoing multiple challenges including urbanization, warming and aging. The Beijing megalopolis of 20 million people now suffers more cold‐related than heat‐related deaths. Stratospheric aerosol injection (SAI) geoengineering is designed to lower surface temperatures, so if SAI were ever done, it may reduce future heat‐related mortality, while also increasing cold‐related mortality. Here we use four Earth System Models (ESM) downscaled to 10 km resolution with the Weather Research and Forecasting (WRF) system to capture urban temperature, humidity and wind speeds. Temperature‐related mortality risk were calculated using a distributed lag nonlinear model (DLNM) of the elderly (over 65s) under the dynamically downscaled moderate (RCP4.5) and extreme (RCP8.5) greenhouse gas, and the G4 SAI scenarios. We used population demographics for all five shared socioeconomic pathways (SSP) and various adaptation measures. Heat‐related excess deaths under G4 are 630∼3,160 per year fewer than RCP4.5, while cold‐related deaths are 370∼1,990 more than RCP4.5 during 2060–2069, with a marginally significant net reduction. G4 significantly reduces the excess deaths relative to RCP8.5. Both heat‐related and cold‐related mortality will increase by 240∼490% when the aging population is accounted for, and decrease by 11%, 23% and 44% under low, medium and high adaptation relative to a no adaptation scenario. Dynamical downscaling produces better quality climate simulations than commonly used statistical approaches, and in the case of Beijing, significantly fewer heat‐related deaths. The marginal health benefits of modest future SAI in Beijing may be representative of the population impacts in the extra‐tropics where deaths due to cold are more than those caused by heat. Plain Language Summary: Climate warming has increased the intensity and frequency of extreme weather has consequences for human health in the 21st century. Stratospheric aerosol injection (SAI) geoengineering can, to some extent, slow down global warming, but its impact on human health is unclear. Beijing is one of the most populous and developed cities in China and is undergoing dual challenges of warming and aging which amplify the health risks to the elderly population. It is therefore of both local significance and wider relevance for developing world cities to explore the impact of SAI geoengineering on the mortality of elderly people in Beijing. We project heat‐related and cold‐related excess deaths under different climate, population and adaptation scenarios in Beijing during the 2060s. SAI geoengineering could reduce approximately 630∼3,160 heat‐related excess deaths, but it could also increase 370∼1,990 cold‐related excess deaths. There are large uncertainties in projection of excess deaths under different shared socioeconomic pathways. Key Points: Stratospheric aerosol injection (SAI) geoengineering on both heat‐related and cold‐related mortality for the elderly in Beijing was simulatedGeoMIP G4 SAI reduces heat‐related excess deaths, but also increases cold‐related excess deaths, with little net negative differences from RCP4.5Dynamical downscaling significantly reduces projecting future heat‐related excess deaths relative to statistical methods [ABSTRACT FROM AUTHOR]

We conducted a model-based assessment of changes in permafrost area and carbon storage for simulations driven by RCP4.5 and RCP8.5 projections between 2010 and 2299 for the northern permafrost region. All models simulating carbon represented soil with depth, a critical structural feature needed to represent the permafrost carbon-climate feedback, but that is not a universal feature of all climate models. Between 2010 and 2299, simulations indicated losses of permafrost between 3 and 5 million km2 for the RCP4.5 climate and between 6 and 16 million km2 for the RCP8.5 climate. For the RCP4.5 projection, cumulative change in soil carbon varied between 66-Pg C (1015-g carbon) loss to 70-Pg C gain. For the RCP8.5 projection, losses in soil carbon varied between 74 and 652 Pg C (mean loss, 341 Pg C). For the RCP4.5 projection, gains in vegetation carbon were largely responsible for the overall projected net gains in ecosystem carbon by 2299 (8- to 244-Pg C gains). In contrast, for the RCP8.5 projection, gains in vegetation carbon were not great enough to compensate for the losses of carbon projected by four of the five models; changes in ecosystem carbon ranged from a 641-Pg C loss to a 167-Pg C gain (mean, 208-Pg C loss). The models indicate that substantial net losses of ecosystem carbon would not occur until after 2100. This assessment suggests that effective mitigation efforts during the remainder of this century could attenuate the negative consequences of the permafrost carbon-climate feedback.

The enhanced vegetation growth by climate warming plays a pivotal role in amplifying the seasonal cycle of atmospheric CO 2 at northern lands (>50° N) since 1960s. However, the correlation between vegetation growth, temperature and seasonal amplitude of atmospheric CO 2 concentration have become elusive with the slowed increasing trend of vegetation growth and weakened temperature control on CO 2 uptake since late 1990s. Here, based on in situ atmospheric CO 2 concentration records from the Barrow observatory site, we found a slowdown in the increasing trend of the atmospheric CO 2 amplitude from 1990s to mid-2000s. This phenomenon was associated with the paused decrease in the minimum CO 2 concentration ([CO 2 ] min ), which was significantly correlated with the slowdown of vegetation greening and growing-season length extension. We then showed that both the vegetation greenness and growing-season length were positively correlated with spring but not autumn temperature over the northern lands. Furthermore, such asymmetric dependences of vegetation growth upon spring and autumn temperature cannot be captured by the state-of-art terrestrial biosphere models. These findings indicate that the responses of vegetation growth to spring and autumn warming are asymmetric, and highlight the need of improving autumn phenology in the models for predicting seasonal cycle of atmospheric CO 2 concentration.

Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m−2 yr−1), most models produced higher NPP (309 ± 12 g C m−2 yr−1) over the permafrost region during 2000–2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982–2009, there was a twofold discrepancy among models (380 to 800 g C m−2 yr−1), which mainly resulted from differences in simulated maximum monthly GPP (GPPmax). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vcmax_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO2 concentration. These results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPPmax as well as their sensitivity to climate change.

This article provides a validation framework for research on the development and use of science Learning Progressions (LPs). The framework describes how evidence from various sources can be used to establish an interpretive argument and a validity argument at five stages of LP research--development, scoring, generalisation, extrapolation, and use. The interpretation argument contains the interpretation (i.e. the LP and conclusions about students' proficiency generated based on the LP) and the use of the LP. The validity argument specifies how the evidence from various sources supports the interpretation and the use of the LP. Examples from our prior and current research are used to illustrate the validation activities and analyses that can be conducted at each of the five stages. When conducting an LP study, researchers may use one or more validation activities or analyses that are theoretically necessary and practically applicable in their specific research contexts. [For the grantee submission, see ED595026.]

A significant portion of the large amount of carbon (C) currently stored in soils of the permafrost region in the Northern Hemisphere has the potential to be emitted as the greenhouse gases CO2 and CH4 under a warmer climate. In this study we evaluated the variability in the sensitivity of permafrost and C in recent decades among land surface model simulations over the permafrost region between 1960 and 2009. The 15 model simulations all predict a loss of near-surface permafrost (within 3 m) area over the region, but there are large differences in the magnitude of the simulated rates of loss among the models (0.2 to 58.8 × 103 km2 yr−1). Sensitivity simulations indicated that changes in air temperature largely explained changes in permafrost area, although interactions among changes in other environmental variables also played a role. All of the models indicate that both vegetation and soil C storage together have increased by 156 to 954 Tg C yr−1 between 1960 and 2009 over the permafrost region even though model analyses indicate that warming alone would decrease soil C storage. Increases in gross primary production (GPP) largely explain the simulated increases in vegetation and soil C. The sensitivity of GPP to increases in atmospheric CO2 was the dominant cause of increases in GPP across the models, but comparison of simulated GPP trends across the 1982–2009 period with that of a global GPP data set indicates that all of the models overestimate the trend in GPP. Disturbance also appears to be an important factor affecting C storage, as models that consider disturbance had lower increases in C storage than models that did not consider disturbance. To improve the modeling of C in the permafrost region, there is the need for the modeling community to standardize structural representation of permafrost and carbon dynamics among models that are used to evaluate the permafrost C feedback and for the modeling and observational communities to jointly develop data sets and methodologies to more effectively benchmark models.

A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyse simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models, and compare them with observations from 268 Russian stations. There are large cross-model differences in the simulated differences between near-surface soil and air temperatures (ΔT; 3 to 14 °C), in the sensitivity of soil-to-air temperature (0.13 to 0.96 °C °C-1), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, hence guide improvements to the model's conceptual structure and process parameterisations. Models with better performance apply multilayer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (13.19 to 15.77 million km2). However, there is not a simple relationship between the sophistication of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, because several other factors, such as soil depth used in the models, the treatment of soil organic matter content, hydrology and vegetation cover, also affect the simulated permafrost distribution.