The need for carbon emissions-driven climate projections in CMIP7

Previous phases of the Coupled Model Intercomparison Project (CMIP) have primarily focused on simulations driven by atmospheric concentrations of greenhouse gases (GHGs), both for idealized model experiments, and for climate projections of different emissions scenarios. We argue that although this approach was pragmatic to allow parallel development of Earth System Model simulations and detailed socioeconomic futures, carbon cycle uncertainty as represented by diverse, process-resolving Earth System Models (ESMs) is not manifested in the scenario outcomes, thus omitting a dominant source of uncertainty in meeting the Paris Agreement. Mitigation policy is defined in terms of human activity (including emissions), with strategies varying in their timing of net-zero emissions, the balance of mitigation effort between short-lived and long-lived climate forcers, their reliance on land use strategy and the extent and timing of carbon removals. To explore the response to these drivers, ESMs need to explicitly represent complete cycles of major GHGs, including natural processes and anthropogenic influences. Carbon removal and sequestration strategies, which rely on proposed human management of natural systems, are currently represented upstream of ESMs in an idealized fashion during scenario development. However, proper accounting of the coupled system impacts of and feedback on such interventions requires explicit process representation in ESMs to build self-consistent physical representations of their potential effectiveness and risks under climate change. We propose that CMIP7 efforts prioritize simulations driven by CO2 emissions from fossil fuel use, projected deployment of carbon dioxide removal technologies, as well as land use and management, using the process resolution allowed by state-of-the-art ESMs to resolve carbon-climate feedbacks. Post-CMIP7 ambitions should aim to incorporate modeling of non-CO2 GHGs (in particular sources and sinks of methane) and process-based representation of carbon removal options. Such experiments would allow resources to be allocated to policy-relevant climate projections and better real-time information related to the detectability and verification of emissions reductions and their relationship to expected near-term climate impacts. Such efforts will provide information on the range of possible future climate states including Earth system processes and feedbacks which are increasingly well-represented in ESMs, thus forming a critical and complementary pillar underpinning proposed km-scale climate modeling activities and calls to better utilize novel machine learning approaches

A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs)

In every IPCC Assessment cycle, a multitude of scenarios are assessed, with different scope and emphasis throughout the various Working Group and Special Reports and 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 RCP pathways from the Fifth IPCC Assessment report (AR5) and the SSP-RCP 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 needs for the next generation of framing pathways that is being advanced under the CMIP umbrella for use in the IPCC AR7. Here we suggest a number of 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 needs over the next 5–10 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, and a higher emissions 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 that assume that global mitigation action in line with limiting warming to 1.5 °C without overshoot had begun in 2015. Finally, we note that timely provision of new scientific information on pathways is critical to inform the development and implementation of climate policy. For the second Global Stocktake under the Paris Agreement in 2028, and to inform subsequent development of Nationally Determined Contributions (NDCs) up to 2040, scientific inputs are required well before 2028. These needs should be carefully considered in the development timeline of community modelling activities including those under CMIP7

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease