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Amplification of sea surface temperature (SST) seasonality in response to global warming is a robust feature in climate model projections but season‐dependent regional disparities in this amplification and the associated mechanisms are not well addressed. Here, by analyzing large ensemble simulations using Community Earth System Model version 2, we investigate detailed spatiotemporal characteristics of the amplification of SST seasonality focusing on the North Pacific and North Atlantic, where robust changes are projected to emerge around 2050 under SSP3‐7.0 scenario. Our results indicate that atmosphere‐ocean coupled processes shape regional changes in SST seasonality differently between warm (MAMJJAS) and cold seasons (ONDJF). During the warm season, the projected warming tendency is mainly due to increased net surface heat flux and weakening of vertical mixing. On the other hand, in the cold season, the projected cooling tendency is driven by strengthened vertical mixing over the North Pacific associated with the northward shift of storm tracks but weakened horizontal advection and mixing due to changes in ocean currents over the North Atlantic. Plain Language Summary: In addition to the increase in annual mean sea surface temperature (SST), the amplitude of SST seasonal cycle is projected to intensify in response to greenhouse warming over many parts of the global ocean due to more warming during local summer than winter. By analyzing large ensemble simulations, we show how different atmospheric and ocean processes influence spatiotemporal differences in SST seasonality changes in the North Pacific and North Atlantic. Our findings indicate that during local summer, the projected warming tendency intensifies with the increased heat input into the ocean and weakening of ocean mixing. Meanwhile, during local winter, the projected cooling tendency intensifies with the strengthening of vertical mixing associated with the enhanced storm track activities in the North Pacific, but the weakening of ocean horizontal advection and mixing attributable to the weakened ocean circulation over the North Atlantic. They together contribute to the amplification of SST seasonality, which could have implications for marine ecosystems, including the plankton phenology. Key Points: Using CESM2 large ensemble simulations, this study explores the robustness of future sea surface temperature (SST) seasonality changes and the associated atmospheric and surface ocean processesIn the warm season, the future SST warming is mainly driven by the reduced upper ocean mixing and the increased net surface heat influx into the oceanIn the cold season, the future SST cooling is attributable to strengthened vertical mixing over the North Pacific but weakened horizontal advection and mixing in the North Atlantic [ABSTRACT FROM AUTHOR]

Climate change mitigation through negative CO2 emissions has been recognized as a crucial strategy to combat global warming. However, its potential effects on terrestrial productivity and agricultural activities remain uncertain. In this study, we utilized large ensemble simulations with an Earth system model of full complexity to investigate the response of Gross Primary Production (GPP) to CO2 forcings. Our findings reveal a significant asymmetry in the GPP response to CO2 ramp-up and symmetric ramp-down model experiments, especially in Europe, suggesting that GPP declines rapidly as CO2 levels decrease. Remarkably, during the CO2 removal period, the North Atlantic Sea surface temperature experienced cooling due to a delayed recovery of the Atlantic Meridional Overturning Circulation (AMOC). This cooling led to precipitation and soil moisture deficits, resulting in a rapid reduction in GPP. This asymmetry in GPP response holds consistent across multi-model simulations. These results underscore the potential implications of delayed recovery in ocean circulation, which could unexpectedly accelerate terrestrial GPP reduction. These insights are crucial for policymakers, aiding them in projecting agricultural activity and formulating targeted GPP control policies specific to the European region. [ABSTRACT FROM AUTHOR]

In every Intergovernmental Panel on Climate Change (IPCC) Assessment cycle, a multitude of scenarios are assessed, with different scope and emphasis throughout the various Working Group reports and special reports, as well as their respective chapters. Within the reports, the ambition is to integrate knowledge on possible climate futures across the Working Groups and scientific research domains based on a small set of "framing pathways" such as the so-called representative concentration pathways (RCPs) in the Fifth IPCC Assessment Report (AR5) and the shared socioeconomic pathway (SSP) scenarios in the Sixth Assessment Report (AR6). This perspective, initiated by discussions at the IPCC Bangkok workshop in April 2023 on the "Use of Scenarios in AR6 and Subsequent Assessments", is intended to serve as one of the community contributions to highlight the needs for the next generation of framing pathways that is being advanced under the Coupled Model Intercomparison Project (CMIP) umbrella, which will influence or even predicate the IPCC AR7 consideration of framing pathways. Here we suggest several policy research objectives that such a set of framing pathways should ideally fulfil, including mitigation needs for meeting the Paris Agreement objectives, the risks associated with carbon removal strategies, the consequences of delay in enacting that mitigation, guidance for adaptation needs, loss and damage, and for achieving mitigation in the wider context of societal development goals. Based on this context, we suggest that the next generation of climate scenarios for Earth system models should evolve towards representative emission pathways (REPs) and suggest key categories for such pathways. These framing pathways should address the most critical mitigation policy and adaptation plans that need to be implemented over the next 10 years. In our view, the most important categories are those relevant in the context of the Paris Agreement long-term goal, specifically an immediate action (low overshoot) 1.5 °C pathway and a delayed action (high overshoot) 1.5 °C pathway. Two other key categories are a pathway category approximately in line with current (as expressed by 2023) near- and long-term policy objectives, as well as a higher-emission category that is approximately in line with "current policies" (as expressed by 2023). We also argue for the scientific and policy relevance in exploring two "worlds that could have been". One of these categories has high-emission trajectories well above what is implied by current policies and the other has very-low-emission trajectories which assume that global mitigation action in line with limiting warming to 1.5 °C without overshoot had begun in 2015. Finally, we note that the timely provision of new scientific information on pathways is critical to inform the development and implementation of climate policy. Under the Paris Agreement, for the second global stocktake, which will occur in 2028, and to inform subsequent development of nationally determined contributions (NDCs) up to 2040, scientific inputs are required by 2027. These needs should be carefully considered in the development timeline of community modelling activities, including those under CMIP7. [ABSTRACT FROM AUTHOR]

This study describes a generalized computational mathematical framework, Scalable Feature Extraction and Tracking (SCAFET), that extracts and tracks features from large climate data sets. SCAFET utilizes novel shape-based metrics that can identify and compare features from different mean states, data sets, and between distinct regions. Features of interest such as atmospheric rivers, tropical and extratropical cyclones, and jet streams are extracted by segmenting the data based on a scale-independent bounded variable called the shape index (SI). The SI gives a quantitative measurement of the local geometric shape of the field with respect to its surroundings. Compared to other widely used frameworks in feature detection, SCAFET does not use a posteriori assumptions about the climate model or mean state to extract features of interest and levelize the comparison between different models and scenarios. To demonstrate the capabilities of the method, we illustrate the detection of atmospheric rivers, tropical and extratropical cyclones, sea surface temperature fronts, and jet streams. Cyclones and atmospheric rivers are extracted to show how the algorithm identifies and tracks both the nodes and areas from climate data sets. The extraction of sea surface temperature fronts exemplifies how SCAFET effectively handles curvilinear grids. Last, jet streams are extracted to demonstrate how the algorithm can also detect three-dimensional features. As a generalized framework, SCAFET can be implemented to extract and track many weather and climate features across scales, grids, and dimensions. [ABSTRACT FROM AUTHOR]