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A feedback attribution analysis is conducted for the ENSO‐related atmospheric and surface temperature anomalies in boreal winter. Local temperature anomalies are decomposed into partial temperature changes due to changes in oceanic dynamics/heat storage, water vapor, clouds, atmospheric dynamics, ozone, and surface albedo. It is shown that atmospheric dynamics plays distinctly different roles in establishing the tropical and extratropical temperature response to El Niño. The atmospheric dynamics serves as a primary negative feedback to the tropical (tropospheric) warming by transporting out of the tropics excessive energy production associated with oceanic dynamical forcing. In the northern extratropics, it is the main forcing of atmospheric temperature changes and also modulates surface temperatures via longwave radiative heating and cooling. This provides an alternative view of the “atmospheric bridge” mechanism from the perspective of local energetics and temperature feedback attribution. Substantial tropospheric cooling over the eastern North Pacific is found to be collectively contributed by water vapor, cloud, and atmospheric dynamical feedbacks, driven at least partly by the equatorward shift of the Pacific storm track during El Niño. Polar stratospheric warming (cooling), largely due to atmospheric dynamics, is seen over the Eurasian‐Pacific (Atlantic) sector, with ozone feedback contributing significantly to the midstratospheric cooling over the Atlantic sector. Water vapor (atmospheric dynamical) feedback has an overall warming (cooling) effect throughout the tropical troposphere, and cloud feedback cools (warms) the tropical lower to middle (upper) troposphere. Atmospheric dynamics induces stratospheric warming over the entire northern extratropics and drives over northern midlatitudes (high latitudes) a tropospheric cooling (warming) that generally intensifies with altitude. Key Points Decomposition of global temperature responses to ENSO Contribution of partial temperature to ENSO‐related temperature anomalies Application of the feedback response analysis to temperature decomposition

Based upon the climate feedback-responses analysis method, a quantitative attribution analysis is conducted for the annual-mean surface temperature biases in the Community Earth System Model version 1 (CESM1). Surface temperature biases are decomposed into partial temperature biases associated with model biases in albedo, water vapor, cloud, sensible/latent heat flux, surface dynamics, and atmospheric dynamics. A globally-averaged cold bias of −1.22 K in CESM1 is largely attributable to albedo bias that accounts for approximately −0.80 K. Over land, albedo bias contributes −1.20 K to the averaged cold bias of −1.45 K. The cold bias over ocean, on the other hand, results from multiple factors including albedo, cloud, oceanic dynamics, and atmospheric dynamics. Bias in the model representation of oceanic dynamics is the primary cause of cold (warm) biases in the Northern (Southern) Hemisphere oceans while surface latent heat flux over oceans always acts to compensate for the overall temperature biases. Albedo bias resulted from the model’s simulation of snow cover and sea ice is the main contributor to temperature biases over high-latitude lands and the Arctic and Antarctic region. Longwave effect of water vapor is responsible for an overall warm (cold) bias in the subtropics (tropics) due to an overestimate (underestimate) of specific humidity in the region. Cloud forcing of temperature biases exhibits large regional variations and the model bias in the simulated ocean mixed layer depth is a key contributor to the partial sea surface temperature biases associated with oceanic dynamics. On a global scale, biases in the model representation of radiative processes account more for surface temperature biases compared to non-radiative, dynamical processes.

Urban heat island (UHI) in conjunction with heatwave (HW) leads to exacerbation of thermal stress in urban areas. Prior research on UHI and HW has predominantly concentrated on examining the thermal conditions at the surface and near-surface, with few investigations extending to the radiative and dynamical interactions of UHI and HW, particularly with a focus on the inhomogeneities across local climate zones (LCZs). Here, we analyse the temperature disparity between HW and non-HW conditions across LCZs in the Sydney area by quantifying the contributions of individual radiative and dynamical processes using the coupled surface-atmosphere climate feedback-response analysis method (CFRAM). Three moist HW events in 2017, 2019, and 2020 are simulated using the Weather Research and Forecasting (WRF) model coupled with the single-layer urban canopy model (SLUCM). It is found that the maximum surface and 900 hPa temperature difference between HW and non-HW days may reach up to 10 K, with the increased net solar radiation during HWs being comparable to the typical level of anthropogenic heat flux in urban areas. It is also found that the reduction of clouds, the presence of vapour, and the increase of sensible heat contribute to the warming effect to various degrees, with the contribution of clouds being the most dominant. Conversely, the generation of dry convection and the increase of latent heat flux lead to cooling effects, with the latter being more dominant and capable of causing up to 10 K surface temperature difference between LCZ1 (compact high-rise) and LCZ9 (sparsely built). The differences in the contributions of climate feedback processes across different LCZs become more evident during more severe and humid HWs. These findings underscore the necessity of implementing LCZ-tailored heat mitigation strategies.

The Ross-Amundsen sector is experiencing an accelerating warming trend and a more intensive advective influx of marine air streams. As a result, massive surface melting events of the ice shelf are occurring more frequently, which puts the West Antarctica Ice Sheet at greater risk of degradation. This study shows the connection between surface melting and the prominent intrusion of warm and humid air flows from lower latitudes. By applying the Climate Feedback-Response Analysis Method (CFRAM), the temporal surge of the downward longwave (LW) fluxes over the surface of the Ross Ice Shelf (RIS) and adjacent regions are identified for four historically massive RIS surface melting events. The melting events are decomposed to identify which physical mechanisms are the main contributors. We found that intrusions of warm and humid airflow from lower latitudes are conducive to warm air temperature and water vapor anomalies, as well as cloud development. These changes exert a combined impact on the abnormal enhancement of the downward LW surface radiative fluxes, significantly contributing to surface warming and the resultant massive melting of ice.

The seasonal phase-locking feature of the Indian Ocean Dipole (IOD) is well documented. However, the seasonality tendency of sea surface temperature anomalies (SSTAs) during the development of the IOD has not been widely investigated. The SSTA tendencies over the two centers of the IOD peak in September-October-November are of different monthly amplitudes. The SSTA tendency over the west pole is small before June–July–August but dramatically increases in July–August–September. Meanwhile, the SSTA tendency over the east pole gradually increases before June-July-August and decreases since then. The growth rate attribution of the SSTAs is achieved by examining the roles of radiative and non-radiative air-sea coupled thermodynamic processes through the climate feedback-response analysis method (CFRAM). The CFRAM results indicate that oceanic dynamic processes largely contribute to the total SSTA tendency for initiating and fueling the IOD SSTAs, similar to previous studies. However, these results cannot explain the monthly amplitudes of SSTA tendency. Four negative feedback processes (cloud radiative feedback, atmospheric dynamic processes, surface sensible, and latent heat flux) together play a damping role opposite to the SSTA tendency. Nevertheless, the sea surface temperature-water vapor feedback shows positive feedback. Specifically, variations in SSTAs can change water vapor concentrations through evaporation, resulting in anomalous longwave radiation that amplifies the initial SSTAs through positive feedback. The effect of water vapor feedback is well in-phase with the monthly amplitudes of SSTA tendency, suggesting that the water vapor feedback might modulate the seasonally dependent SSTA tendency during the development of the IOD.

Previous studies have found amplified warming over Europe-West Asia and Northeast Asia in summer since the mid-1990s relative to elsewhere on the Eurasian continent, but the cause of the amplification in these two regions remains unclear. In this study, we compared the individual contributions of influential factors for amplified warming over these two regions through a quantitative diagnostic analysis based on CFRAM (climate feedback-response analysis method). The changes in surface air temperature are decomposed into the partial changes due to radiative processes (including CO 2 concentration, incident solar radiation at the top of the atmosphere, surface albedo, water vapor content, ozone concentration, and clouds) and non-radiative processes (including surface sensible heat flux, surface latent heat flux, and dynamical processes). Our results suggest that the enhanced warming over these two regions is primarily attributable to changes in the radiative processes, which contributed 0.62 and 0.98 K to the region-averaged warming over Europe-West Asia (1.00 K) and Northeast Asia (1.02 K), respectively. Among the radiative processes, the main drivers were clouds, CO 2 concentration, and water vapor content. The cloud term alone contributed to the mean amplitude of warming by 0.40 and 0.85 K in Europe-West Asia and Northeast Asia, respectively. In comparison, the non-radiative processes made a much weaker contribution due to the combined impact of surface sensible heat flux, surface latent heat flux, and dynamical processes, accounting for only 0.38 K for the warming in Europe-West Asia and 0.05 K for the warming in Northeast Asia. The resemblance between the influential factors for the amplified warming in these two separate regions implies a common dynamical origin. Thus, this validates the possibility that they originate from the Silk Road pattern.

In this study, a coupled atmosphere-surface “climate feedback-response analysis method” (CFRAM) was applied to the slab ocean model version of the NCAR CCSM3.0 to understand the tropospheric warming due to a doubling of CO 2 concentration through quantifying the contributions of each climate feedback process. It is shown that the tropospheric warming displays distinct meridional and vertical patterns that are in a good agreement with the multi-model mean projection from the IPCC AR4. In the tropics, the warming in the upper troposphere is stronger than in the lower troposphere, leading to a decrease in temperature lapse rate, whereas in high latitudes the opposite it true. In terms of meridional contrast, the lower tropospheric warming in the tropics is weaker than that in high latitudes, resulting in a weakened meridional temperature gradient. In the upper troposphere the meridional temperature gradient is enhanced due to much stronger warming in the tropics than in high latitudes. Using the CFRAM method, we analyzed both radiative feedbacks, which have been emphasized in previous climate feedback analysis, and non-radiative feedbacks. It is shown that non-radiative (radiative) feedbacks are the major contributors to the temperature lapse rate decrease (increase) in the tropical (polar) region. Atmospheric convection is the leading contributor to temperature lapse rate decrease in the tropics. The cloud feedback also has non-negligible contributions. In the polar region, water vapor feedback is the main contributor to the temperature lapse rate increase, followed by albedo feedback and CO 2 forcing. The decrease of meridional temperature gradient in the lower troposphere is mainly due to strong cooling from convection and cloud feedback in the tropics and the strong warming from albedo feedback in the polar region. The strengthening of meridional temperature gradient in the upper troposphere can be attributed to the warming associated with convection and cloud feedback in the tropics. Since convection is the leading contributor to the warming differences between tropical lower and upper troposphere, and between the tropical and polar regions, this study indicates that tropical convection plays a critical role in determining the climate sensitivity. In addition, the CFRAM analysis shows that convective process and water vapor feedback are the two major contributors to the tropical upper troposphere temperature change, indicating that the excessive upper tropospheric warming in the IPCC AR4 models may be due to overestimated warming from convective process or underestimated cooling due to water vapor feedback.

[LANGUAGE=”English”] BackgroundThe university stage of education is a crucial period of stable and mature physical and mental growth that influences students’ career development. During this stage, students gain autonomy with respect to personal emotion management, living conditions, education, learning, and career planning. In university, students start to think independently, take responsibility for their actions, maintain and expand interpersonal relationships, and participate in public affairs, and therefore, this is a crucial phase of development in terms of cultivating independence and responsibility. For university students to develop self-control, decision-making skills, and responsibility for their learning, they must be provided with an experiential learning environment in which teachers can focus on the learner and empower them to engage in inquiry and reflection. Outdoor adventure education (OAE) is a form of experiential learning designed to promote positive individual and team development by enh

Peer assessment has been the subject of considerable research interest over the last three decades, with numerous educational researchers advocating for the integration of peer assessment into schools and instructional practice. Research synthesis in this area has, however, largely relied on narrative reviews to evaluate the efficacy of peer assessment. Here, we present a meta-analysis (54 studies, k = 141) of experimental and quasi-experimental studies that evaluated the effect of peer assessment on academic performance in primary, secondary, or tertiary students across subjects and domains. An overall small to medium effect of peer assessment on academic performance was found ( g = 0.31, p < .001). The results suggest that peer assessment improves academic performance compared with no assessment ( g = 0.31, p = .004) and teacher assessment ( g = 0.28, p = .007), but was not significantly different in its effect from self-assessment ( g = 0.23, p = .209). Additionally, meta-regressions examined the moderating effects of several feedback and educational characteristics (e.g., online vs offline, frequency, education level). Results suggested that the effectiveness of peer assessment was remarkably robust across a wide range of contexts. These findings provide support for peer assessment as a formative practice and suggest several implications for the implementation of peer assessment into the classroom.

An artificial intelligence-based chatbot, ChatGPT, was launched in November 2022 and is capable of generating cohesive and informative human-like responses to user input. This rapid review of the literature aims to enrich our understanding of ChatGPT’s capabilities across subject domains, how it can be used in education, and potential issues raised by researchers during the first three months of its release (i.e., December 2022 to February 2023). A search of the relevant databases and Google Scholar yielded 50 articles for content analysis (i.e., open coding, axial coding, and selective coding). The findings of this review suggest that ChatGPT’s performance varied across subject domains, ranging from outstanding (e.g., economics) and satisfactory (e.g., programming) to unsatisfactory (e.g., mathematics). Although ChatGPT has the potential to serve as an assistant for instructors (e.g., to generate course materials and provide suggestions) and a virtual tutor for students (e.g., to answer questions and facilitate collaboration), there were challenges associated with its use (e.g., generating incorrect or fake information and bypassing plagiarism detectors). Immediate action should be taken to update the assessment methods and institutional policies in schools and universities. Instructor training and student education are also essential to respond to the impact of ChatGPT on the educational environment.