• Extreme precipitation patterns across different regions of WNP are evaluated. • Two concentrated precipitation belts are observed throughout the four seasons. • Mid-21st century projections are derived from HighResMIP ensemble means. • Future precipitation tends to get wetter in high intensity region, and vice versa. Extreme precipitation in the western North Pacific (WNP) is jointly controlled by complex circulation systems, which can be better captured by enhancing model resolution. Despite progress in evaluating precipitation in the Coupled Model Intercomparison Project Phase 6 (CMIP6), most assessments have focused on land monsoon regions or individual river basins, leaving the land-ocean transitional WNP insufficiently investigated. This study, leveraging the climate model outputs from the High-Resolution Model Intercomparison Project (HighResMIP) of the CMIP6, examines the spatiotemporal characteristics of precipitation in the WNP and evaluates the simulation biases across different models. The results indicate that most HighResMIP models are capable of simulating spatial patterns of double‑banded precipitation zones that are consistent with observations, with higher skill scores compared to their lower‑resolution counterparts. Among these models, ECMWF-IFS-HR and EC-Earth3P-HR exhibit superior performance in capturing both annual mean precipitation and extreme precipitation characteristics. The multi-model ensemble means from optimal models reduce uncertainty significantly by much narrowed inter-model spread. Precipitation projections in the mid-21st century (2031–2050) exhibit a "wet-get-wetter, dry-get-drier" divergent development characteristic, indicating an eastward expansion and an increased influence of extreme precipitation toward lower- and higher-latitude regions under future warming.

Patient-tailored brachytherapy applicator development in compliance with the EU medical device regulation.

Abstract Interactions between the tropical Pacific and the tropical Atlantic (TP–TA) play a crucial role in tropical Pacific climate variability and predictability on a broad range of time scales. However, the ability of state-of-the-art climate models to simulate these cross-basin interactions remains uncertain. This study systematically evaluates the representation of TP–TA interactions in 34 climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6). While models generally reproduce the spatial patterns of key tropical climate modes, significant biases are found in their amplitudes, seasonality (particularly for the equatorial Atlantic mode), and spectral characteristics. Notably, most models substantially underestimate Atlantic impacts on spatiotemporal aspects of El Niño–Southern Oscillation (ENSO). To disentangle the bidirectional coupling mechanisms, we employ a linear inverse model (LIM) that allows selective isolation of Atlantic-to-Pacific versus Pacific-to-Atlantic coupling. Our analysis reveals two key aspects of TP–TA interactions: 1) internal Atlantic variability enhances Pacific climate variance across interannual and decadal time scales and 2) Pacific-driven Atlantic variability reduces tropical Pacific low-frequency variability. Although these influences qualitatively agree with observations, their simulated intensities are markedly weaker. Furthermore, we identify considerable intermodel spread in representing TA impacts on TP variability, highlighting persistent challenges in achieving robust model consensus. Our findings underscore the need to improve the representation of TP–TA interactions in climate models, particularly through more realistic simulations of tropical Atlantic dynamics and their seasonal evolution, to make progress in seasonal-to-decadal climate predictions.

Abstract This study examines future changes in precipitation and temperature across Myanmar using bias-corrected multimodel ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6) archive under shared socioeconomic pathway 2-4.5 (SSP2-4.5) and SSP5-8.5 scenarios. Thirteen general circulation models (GCMs) were evaluated against observed data from 38 meteorological stations (1985–2014), and seven models were selected based on performance metrics. Bias correction was applied using the power transformation method for precipitation and variance scaling for temperature, and its performance was assessed for reproducing both baseline climate and extreme indices. Decomposition of uncertainty sources for the baseline period revealed that model and temporal variability are the dominant contributors to projection uncertainty. Future projections were analyzed for near-future (2021–50), mid-future (2051–80), and far-future (2081–2100) periods. Results indicate increasing precipitation across most regions, particularly during the rainy season, with the central dry zone and coastal areas showing the most notable changes. Minimum temperatures show a consistent warming trend across all scenarios and regions. In contrast, maximum temperatures exhibit mixed trends under SSP2-4.5 but a pronounced increase under SSP5-8.5, with the northern hilly region projected to warm by up to 4.9°C by the end of the century. Extreme indices also show a clear intensification of extremes toward the end of the century, especially under SSP5-8.5. These findings offer essential insights into Myanmar’s future climate risks and provide a scientific foundation for developing region-specific climate adaptation and resilience strategies. Significance Statement This study applies CMIP6 multimodel ensembles to project long-term precipitation and temperature changes across Myanmar under shared socioeconomic pathway 2-4.5 (SSP2-4.5) and SSP5-8.5. By bias-correcting model outputs and selecting high-performing general circulation models (GCMs) and analyzing both mean climate and extreme indices, we provide robust regional and seasonal climate projections through 2100. The decomposition of uncertainty sources for the base period and projection period shows how model and temporal variability affect the projections. The results reveal significant warming and shifting precipitation patterns, particularly affecting the dry and hilly zones. These changes have critical implications for agriculture, water resources, and climate risk management. Our work addresses a regional knowledge gap in climate projection literature and offers actionable information for adaptation planning in one of Southeast Asia’s most climate-vulnerable countries.

Optimal large-scale logistics project portfolio selection considering cascading failure among projects: A project termination strategy

Research methodology This case study is developed using secondary data from publicly available sources, including news articles, reports and government publications about the cancellation of the New Mexico City International Airport (NAIM). This approach is useful to understand individual, group, organisational, social, political and related phenomena involving a contemporary set of events to explore the complexity and richness of the phenomenon of study, namely, the termination of infrastructure megaprojects. While the details about Rodrigo Montes de la Vega and his role in Grupo Aeroportuario de la Ciudad de México are fictional, the scenario is inspired by real-world challenges and decision-making processes faced by leaders during the termination of large-scale infrastructure projects. By combining factual context with a fictional character, the case provides a realistic but imaginative framework for analysing leadership dilemmas, organisational resistance to change and the broader implications of political decisions on public projects. Case overview/synopsis The cancellation of the NAIM by President Andrés Manuel López Obrador represents one of the most controversial decisions in recent Mexican history. Initially conceived as a state-of-the-art infrastructure project to alleviate congestion at the existing airport, the NAIM was well underway, with billions of pesos already invested. However, upon taking office, Lopez Obrador cancelled the project, citing financial concerns, environmental issues and a public consultation that favoured an alternative plan: converting the Santa Lucía military base into a commercial airport. Rodrigo Montes de la Vega, a young and ambitious Chair of the Board of Grupo Aeroportuario de la Ciudad de México (GACM), found himself at the centre of this controversy. As a key figure responsible for NAIM’s development, Rodrigo faced a profound dilemma. Should he support the government’s decision, preserving his career prospects but compromising his professional ethics and vision for the project? Or should he resist publicly, defending the NAIM’s merits but risking professional isolation and losing his ability to influence future decisions? This case examines Rodrigo’s tension-filled decision-making process, offering students an opportunity to explore how leaders navigate high-stakes choices involving conflicting interests, uncertain outcomes and organisational resistance to change. Complexity academic level This case is designed for students and professionals in engineering, business management and public policy who are interested in project management, organisational change and leadership decision-making. The case is particularly suitable for courses focused on project governance, decision-making under uncertainty and managing stakeholder resistance. It is versatile enough to be used at various academic and professional levels, including: The case encourages multidisciplinary thinking and fosters engagement among students from diverse academic backgrounds, making it a valuable teaching tool for institutions worldwide.

This research explores how Building Information Modeling (BIM), green building design, and post-pandemic resilience contribute to sustainable, healthy buildings. Although studies have explored BIM’s impact on green buildings, its role after COVID-19 remains largely unexplored. This study fills this gap through a “Green BIM post-pandemic square” taxonomy, integrating findings from a 1999–2024 literature review and BIM software evaluation. The taxonomy delves into project phases, green attributes, BIM features, and post-pandemic considerations to uncover links between BIM, green buildings, and post-pandemic cycles. It reveals how BIM enhances green design, construction, operation, and retrofitting. Additionally, BIM scrutinizes green building parameters such as energy efficiency and supply chain management. The study underscores BIM’s potential in sustainability, occupant health, and supply chain resilience through Green Building Evaluations (GBE). Ultimately, it synthesizes data to guide building researchers and practitioners, identifying gaps and suggesting future initiatives. Relevant journals and software are incorporated to provide comprehensive insights into the subject.

This article presents the development of an e-learning teaching project focused on Ancient Egyptian language, initially launched as an academic experiment among Portuguese and Brazilian universities. Since 2020, the initiative has expanded into a robust network, culminating in the creation of a digital, open-access anthology of sources. The rise of Digital Humanities has introduced new research possibilities by offering specialized digital tools and methodologies that integrate into Humanities studies. This became particularly crucial during the COVID-19 pandemic of 2020, when universities were compelled to shift classes and meetings to online platforms due to physical restrictions. This transition fostered greater international collaboration and networking. The project key achievement was the production of recorded lectures on Middle Egyptian, delivered in Portuguese and made freely accessible across various platforms. It represents a groundbreaking effort in the digitization of Egyptology resources for Portuguese-speaking students, significantly improving access to academic materials in the field.

Abstract. Studies of the Late Pliocene have frequently been used as a means to improve our understanding of the climate system in a warmer state. Large scale features of Late Pliocene climate, such as Arctic Amplification, will impact global circulation including the jet stream. To date, the majority of Late Pliocene studies have focused on long term mean climate. However, considering interannual variability is important to fully understand the response of the climate system to different forcings. Using data from the Pliocene Model Intercomparison Project Phase 2, we find a more poleward, yet weaker jet stream in the North Pacific during winter months, and increased interannual jet stream variability in the Late Pliocene compared to the pre-industrial control. This result is consistent across the majority of models, although there is variation in the magnitude of change across the ensemble. Using new simulations from the Hadley Centre Climate Model Version 3 (HadCM3), we find that changes in jet stream variability are due to orographic boundary conditions and are correlated with sea ice feedbacks. Carbon dioxide has little impact on the interannual variability in HadCM3 suggesting that the Late Pliocene is not an analogue for future jet stream variability. This change in jet stream variability in the Late Pliocene could lead to a change in the distribution of temperature and precipitation which could have implications for how proxy data and model simulations are compared.

Abstract. The Land and Land Ice Theme in the Coupled Model Intercomparison Project Phase 7 (CMIP7) represents the current understanding of physical processes in land surface ecosystems, hydrology, cryosphere, and their physical interactions with other Earth system components. Simulations from Earth system models (ESMs) could provide crucial information for assessing planetary safety, such as critical tipping elements, and be used to inform climate risks for improving climate impact assessments and policy decisions. This paper presents a collaborative effort to identify scientific opportunities in the Land and Land Ice Theme of the CMIP7 Data Request. The proposed opportunities build upon advances in ESMs, including new freshwater system and land ice processes being included in CMIP7, as well as the scientific community's demand for high-frequency and sub-grid-scale land surface outputs. In total, 25 variable groups that contain 716 variables have been identified to be potentially available to the broad scientific audience for performing analysis in land–atmosphere coupling, hydrological processes and freshwater systems, glacier and ice sheet mass balance and their influence on the sea levels, land use, and plant phenology. Key reflections from this data request effort include advocacy for closer engagement between the user community and modeling groups, reduction in the technical barriers to tracking existing parameters and defining new variables, and more streamlined variable management. These will be essential to enhance the usability and reliability of CMIP7 outputs for climate and Earth system research and applications to a broad audience that relies on the CMIP7 endeavor.

Anthropogenic climate forcing is altering ocean circulation and water mass distribution across the Southern Ocean, reshaping the habitat of circumpolar marine predators such as threatened crested ( Eudyptes ) penguins. Understanding species vulnerability remains challenging due to substantial uncertainties in climate projections. Here, we integrate two state-of-the-art climate assessment tools—storylines and time of emergence—to evaluate the vulnerability of crested penguins to ocean warming while explicitly addressing projection uncertainties. Using this framework, we select a discrete set of projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) that capture qualitatively different global circulation responses and climate sensitivity. Uncertainty in global atmospheric circulation responses, particularly the degree of intensification of the Westerlies, strongly influences both the magnitude and spatial pattern of projected sea surface temperature (SST) warming within penguin foraging habitats. Storylines and climate sensitivity explain a greater proportion of overall projection uncertainty compared to conventional CMIP6 scenario ensembles. We identify two groups of SST sensitivity among crested penguins: (1) highly sensitive species, including Northern Rockhoppers ( E. moseleyi ) and Aotearoa/New Zealand endemic species, and (2) species with broader distributions, such as Southern and Eastern Rockhoppers ( E. chrysocome ) and Macaroni/Royal penguins ( E. chrysolophus/E. schlegeli ), which exhibit spatially heterogeneous exposure and sensitivity. Spatial variability in exposure among widely distributed species highlights opportunities for targeted monitoring to detect early climate change impacts. However, limited data on population dynamics, gene flow, and foraging ecology constrain vulnerability assessments, emphasizing the need for expanded ecological and tracking studies coupled with environmental monitoring. We advocate for interdisciplinary, uncertainty-aware approaches and transparent workflows, including open data and code sharing, to strengthen future climate vulnerability assessments for threatened species.

Abstract This paper presents the initial insights derived from the Digital Youth Formation Initiative (DYFI) research project, which accompanies Catholic initiatives across Europe and situates their experiences within a broader scholarly discourse. Supported by Porticus, DYFI projects experiment with digital content, hybrid gatherings, and new forms of youth-oriented faith communication. The meta-research maps the learning processes of project leaders and youth ministers with the aim of bringing them into dialogue and debating issues pertaining to the postdigital church. It further draws on collaborations with the International Association of the Study of Yoth Ministry (IASYM) research group on digital youth formation and on material from international encounters, such as the Sarajevo consultation Swipe//Post//Like and the Catholic Synod’s Study Group on The Mission in the Digital Environment . Through iterative qualitative analysis, six initial clusters were refined into four overarching themes: the ambivalent effects of the digital world, the role of theology in digital contexts, the needs and voices of young people, and contested forms of religious authority. These findings illuminate the central tensions and opportunities in digital youth faith formation and underscore their relevance pertaining to practical theology in a postdigital age.

Abstract. The terrestrial biosphere absorbs about one third of anthropogenic CO2 emissions, thereby significantly slowing human-induced climate change. Its capacity to act as a carbon sink strongly depends on climate conditions, particularly soil moisture (SM), which can constrain plant growth and amplify land–atmosphere feedbacks. Therefore, accurately capturing these effects in Earth System Models (ESMs) is critical. Using dedicated experiments of the Land Feedback Intercomparison Project (LFMIP, an experiment within the Land Surface, Snow, and Soil Moisture Model Intercomparison Project, LS3MIP) from the latest generation of ESMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we show that projected SM changes substantially reduce the land carbon sink by the end of the century (2070–2099). This reduction is mainly driven by SM variability, highlighting the importance of SM extremes, which are projected to become more frequent and intense under climate change. Our results confirm those of the previous model generation based on the Global Land–Atmosphere Climate Experiment-Coupled Model Intercomparison Project phase 5 (GLACE-CMIP5). The results show that the strong negative impact of SM changes on the land carbon sink shown for GLACE-CMIP5 is less severe in LFMIP. A more in-depth analysis reveals that this is due at least in part to the specific ESM sampling of the respective experiments, with participating ESMs from CMIP5 generally showing a stronger drying trend. Despite agreement on the negative impact of SM on the land carbon sink in most tropical and mid-latitude ecosystems in both sets of multi-model experiments, there are large intermodel differences in the projected magnitudes. As SM can influence land carbon uptake both directly and indirectly via land–atmosphere coupling, we conduct a contribution analysis on the impact of direct and indirect SM effects on carbon uptake, which reveals that SM–atmosphere interaction dominate SM-induced changes globally. However, models show disagreement on the magnitude of these effects. Intermodel differences arise mainly from varying sensitivities of GPP to SM-related direct and indirect effects, suggesting that differences likely stem from varying representations of water-stress related processes across ESMs. Our findings highlight SM–atmosphere coupling as a critical factor for future land carbon uptake. Improving the representation of water stress processes, plant hydraulics, and vegetation characteristics in ESMs is essential for reducing uncertainty in projections. Maintaining and possibly extending the experimental setup to a larger set of models in future CMIP generations will be key to advancing our understanding of SM-carbon interactions and consequently of the evolution of the land carbon sink under human-induced climate change.

The integration of digital tools and environmental design methods is widely recognised as essential for sustainable architectural practice. However, their influence on early design decisions and lifecycle continuity remains limited. This study introduces the concept of integration readiness and operationalises it through the Integrated Environmental Design Framework (ILPP+), which links environmental methods to project phases, decision leverage, and organisational conditions. An exploratory survey of 37 architectural design offices in the Lower Silesian region of Poland was conducted to examine how BIM, life cycle assessment (LCA), passive strategies, performance-based analysis, and monitoring practices are embedded in design workflows. The analysis combines descriptive statistics with exploratory correlation analysis to identify relationships between selected integration dimensions. The results indicate uneven patterns of integration. Passive strategies and simulations show moderate coupling (ρ = 0.60), while weaker relationships between simulations and structured decision processes (ρ = 0.40) suggest that analytical tools are not consistently used as decision-support mechanisms. Similarly, BIM shows only partial integration with LCA (ρ = 0.41) and post-occupancy evaluation (ρ = 0.46), indicating limited lifecycle continuity within the analysed sample. These findings suggest that environmental integration may be constrained not by the availability of tools but by their positioning within decision processes and across project phases. The study highlights the importance of aligning analytical methods with high-leverage design stages and strengthening feedback loops between design and operation.

Physicians routinely document specifics of patient encounters in clinic visit notes, a critical but potentially time-consuming task. Ambient listening artificial intelligence (AI) technology is being integrated into clinical workflows to reduce documentation burden by creating draft visit notes. While this technology is promising, it is not perfect, and the potential for patient harm needs to be understood and mitigated. We developed and piloted an efficient, standardized approach to evaluating AI-generated notes for safety concerns in ambulatory care visits. The objective of this quality improvement project was to develop and pilot an efficient, standardized, and scalable approach to evaluating AI-generated notes for safety concerns in ambulatory care visits. During a 2-month pilot (July to August 2024), 31 physicians across multiple specialties used an ambient listening AI scribe to assist with the creation of 7545 clinic notes. A novel survey instrument was developed to assess note quality, focusing on 4 error types: accidental inclusions, accidental omissions, hallucinations, and bias. Physicians evaluated 356 (4.7%) AI-generated notes. Where an error was present, physicians rated its severity based on its potential to cause patient harm if it was not corrected, on a 0 to 5 scale. Additionally, a vendor-reported metric on the percentage of note content edited by physicians was analyzed. Of the 356 evaluated notes, accidental omissions were the most frequent error (n=64, 18%), followed by hallucinations (n=41, 11.5%), and accidental inclusions (n=33, 9.3%). Bias was rare (n=4, 1.1%). Most (119/142, 83.8%) errors were rated as mild to moderate (severity 1-3), with only 19 (5.3%) notes containing errors rated as posing serious or imminent risk (severity 4-5). Editing metrics across all AI-created notes showed a median of 9.0% (IQR 2.5%-21.9%) of AI-generated words were changed, with 14.9% (143/960) of notes left entirely unedited. Physician editing practices varied widely, with average percentages of AI-generated words changed ranging from 1.9% to 69.3% (median 9.0%, IQR 2.5%-21.9%). AI-generated clinical notes were generally of high quality, with 94.7% (337/356) free from significant errors. However, because a small number contained errors that carried the risk of serious harm if not corrected, careful clinician review of notes remains imperative. Prior to deploying an AI scribe, organizations should pilot the technology and include an efficient review process to understand the nature and type of errors common at their organization. This pilot provides a scalable model for other health systems seeking to implement AI scribe technology responsibly.

Climate models show considerable discrepancies in their future projections around the Atlantic, mainly due to uncertainties in the fate of the Atlantic Meridional Overturning Circulation (AMOC). Climate models suggest a reduction in AMOC strength of 32 ± 37% by 2100 (90% probability, Shared Socioeconomic Pathways 2-4.5 scenario, Coupled Model Intercomparison Project Phase 6). To refine this estimate and reduce its uncertainty, we use four different observational constraint methods. The best one, which provides the lowest leave-one-out error, integrates a large set of observable variables using ridge-regularized linear regression-a method unusual in climate science. It gives an estimate of the AMOC slowdown of 51 ± 8% (90% probability), i.e., a weakening ∼ 60% stronger than suggested by the multimodel mean. This refinement mainly results from correcting a bias in South Atlantic surface salinity, consistent with recent studies emphasizing its role in the proximity to an AMOC tipping point. This more substantial AMOC weakening has key implications for future adaptation strategies.

Purpose This study aims to investigate the evolution of research, development and innovation (R&D&I) projects addressing public health crises, analyzing the institutional and technological dynamics that shape project continuity, coordination and governance. Design/methodology/approach The study adopts a mixed-methods approach, combining a literature review, documentary analysis and relational network analysis to identify patterns of interaction among actors, technologies and thematic areas across different stages of R&D&I projects. Findings The findings indicate that public health crises function less as disruptive events and more as vectors of institutional consolidation. Technologies, particularly artificial intelligence, exhibit a continuous presence throughout project phases. Project governance becomes centralized around a limited number of actors and themes, constraining cognitive diversity and knowledge recombination. In addition, the study identifies a reconfiguration of the state’s role, with universities and hybrid organizations assuming greater operational responsibility. Originality/value This study offers an innovative perspective by reframing public health crises as mechanisms of institutional strengthening, integrating network analysis, R&D&I governance and mission-oriented innovation policies, with particular attention to the challenges and specificities of the Global South.

Abstract Tropical South American summer precipitation is primarily controlled by the intensity and position of the South American monsoon and the intertropical convergence zone, both of which respond to sea surface temperature anomalies over the surrounding tropical oceans. Our analysis examines how well contemporary, high-complexity Earth system models from the Coupled Model Intercomparison Project phase 6 (CMIP6) simulate the summer precipitation distribution and its interannual variability under preindustrial climate conditions. Specifically, we investigate how El Niño–Southern Oscillation (ENSO) and Atlantic Niño—two major zonal modes of variability in the tropical ocean—shape tropical South American precipitation through remote atmospheric teleconnections. The quality of the simulated climatological mean state and interannual variability across models is primarily evaluated using pattern correlations with the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) product. The three models with the highest and the three models with the lowest field correlation with the ERA5 reference are selected for a more detailed study of their representation of major modes of variability and associated teleconnection patterns. We show that models with large discrepancies in the location and abundance of core monsoon precipitation also typically fail to accurately represent atmospheric deep convection and teleconnections associated with the zonal modes. Differences in the ability to simulate South American summer precipitation, even under preindustrial forcings, emphasize the importance of selecting an appropriate model for studying the regional hydroclimate. Our study further calls for future research using high-resolution models that explicitly resolve deep convection to realistically capture South American monsoon rainfall. Significance Statement Tropical South America receives abundant rainfall from December through February, which is dominated by the South American monsoon system and the deep convection in the intertropical convergence zone. Naturally occurring climate modes in the surrounding tropical oceans, such as El Niño–Southern Oscillation and Atlantic Niño, also drive large variability in summer rainfall. Accurately representing how rainfall over tropical South America responds to modes of climate variability in preindustrial simulations is essential for evaluating the robustness and realism of climate models. Our study shows that models with more realistic atmospheric convection (upward vertical motion in the lower to midtroposphere) better depict the observed rainfall patterns and year-to-year changes over tropical South America, including the rainfall patterns shaped by the modes of variability. Models that better replicate these influences can be instrumental not only in understanding the future of South American rainfall but also in attribution studies of extreme events like droughts and floods.

Abstract This study evaluates the ability of the Coupled Model Intercomparison Project phase 6 (CMIP6) climate models to simulate the observed effects of tropical Pacific and Indian Ocean sea surface temperature anomalies (SSTAs) on Indian summer monsoon rainfall (ISMR) variability. Using observational data and the large ensemble historical simulations of seven CMIP6 models from 1950 to 2014, we applied a cyclostationary linear inverse model (CS-LIM) to isolate the impacts of tropical Pacific SSTAs, Indian Ocean SSTAs, and their interaction on the interannual variability of ISMR. Overall, these CMIP6 models well reproduced the observed enhanced (reduced) ISMR variability from Pacific SSTAs (Indian Ocean SSTAs and the Indo-Pacific interaction), though with varying spatial patterns and magnitudes. Among them, CESM2 and Energy Exascale Earth System Model version 2.0 (E3SM-2-0) showed the best agreement with observations for the effects of Pacific SSTAs and the Indo-Pacific interaction, respectively. Composite analysis of ISMR anomalies during the developing phases of pure and co-occurring El Niño–Southern Oscillation (ENSO) and Indian Ocean dipole (IOD) events revealed that the impacts from Pacific SSTAs were captured reasonably well by E3SM-2-0, CESM2, MIROC6, and MPI-ESM1-2-LR, while E3SM-2-0 also showed the best agreement with observations for the effects from the Indo-Pacific interaction. However, all seven models exhibited substantial biases in simulating the Indian Ocean SSTA impacts on ISMR, particularly during pure El Niño events. Overall, this study provides new insights into how individual CMIP6 models simulate the isolated impacts from the tropical Pacific and Indian Oceans, which have important applications for improving ISMR predictions and interpreting ISMR future projections.

Abstract. This paper presents a comprehensive overview of the Coupled Model Intercomparison Project Phase 7 (CMIP7) request for data pertaining to Earth systems science, and provides justification for the resources needed to produce this data. Topics within the CMIP7 Earth System (CMIP7-ES) theme centre around tracking of flows of energy, carbon, water and other fluxes across domains, and constraining feedbacks between these cycles and the climate system. These topics are summarized in this paper as scientific “opportunities” describing specific model intercomparison experiments and use cases for next-generation Earth System Model (ESM) output. These opportunities were submitted by modelling groups and scientific consortia following an extended public consultation process. Contained within each opportunity are requests for groups of Climate & Forecasting (CF) variables, which are bundled into variable groups representing all data required to address the opportunities' needs. Novel opportunities in CMIP7 compared with previous phases will include running `emissions-driven' simulations that integrate carbon emissions and removal scenarios with updated representations of the global carbon cycle, expanded variable groups needed to model marine trophic interactions and biogeochemistry, and data needed to understand the risk of global tipping points, among others. The production of these variables will close key gaps and uncertainties identified during previous rounds of CMIP, and support the 7th Intergovernmental Panel on Climate Change Assessment Report (AR7). We argue that CMIP7-ES data will be broadly used by scientific, policy, governmental, industry, and other communities that rely on climate model projections for research and decision making. As an author group we also reflect on the evolution of the CMIP7-ES data request as a part of a deliberative process in support of the global CMIP program.