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IntroductionA defining aspect of the Intergovernmental Panel on Climate Change (IPCC) assessment reports (AR) is a formal uncertainty language framework that emphasizes higher certainty issues across the reports, especially in the executive summaries and short summaries for policymakers. As a result, potentially significant risks involving understudied components of the climate system are shielded from view. MethodsHere we seek to address this in the latest, sixth assessment report (AR6) for one such component—the deep ocean—by summarizing major uncertainties (based on discussions of low confidence issues or gaps) regarding its role in our changing climate system. The goal is to identify key research priorities to improve IPCC confidence levels in deep ocean systems and facilitate the dissemination of IPCC results regarding potentially high impact deep ocean processes to decision-makers. This will accelerate improvement of global climate projections and aid in informing efforts to mitigate climate change impacts. An analysis of 3,000 pages across the six selected AR6 reports revealed 219 major science gaps related to the deep ocean. These were categorized by climate stressor and nature of impacts. ResultsHalf of these are biological science gaps, primarily surrounding our understanding of changes in ocean ecosystems, fisheries, and primary productivity. The remaining science gaps are related to uncertainties in the physical (32%) and biogeochemical (15%) ocean states and processes. Model deficiencies are the leading cited cause of low certainty in the physical ocean and ice states, whereas causes of biological uncertainties are most often attributed to limited studies and observations or conflicting results. DiscussionKey areas for coordinated effort within the deep ocean observing and modeling community have emerged, which will improve confidence in the deep ocean state and its ongoing changes for the next assessment report. This list of key “known unknowns” includes meridional overturning circulation, ocean deoxygenation and acidification, primary production, food supply and the ocean carbon cycle, climate change impacts on ocean ecosystems and fisheries, and ocean-based climate interventions. From these findings, we offer recommendations for AR7 to avoid omitting low confidence-high risk changes in the climate system. 

Abstract. We introduce a time-dependent, one-dimensional model ofearly diagenesis that we term RADI, an acronym accounting for the mainprocesses included in the model: chemical reactions, advection, molecularand bio-diffusion, and bio-irrigation. RADI is targeted for study ofdeep-sea sediments, in particular those containing calcium carbonates(CaCO3). RADI combines CaCO3 dissolution driven by organic matterdegradation with a diffusive boundary layer and integrates state-of-the-artparameterizations of CaCO3 dissolution kinetics in seawater, thusserving as a link between mechanistic surface reaction modeling andglobal-scale biogeochemical models. RADI also includes CaCO3precipitation, providing a continuum between CaCO3 dissolution andprecipitation. RADI integrates components rather than individual chemicalspecies for accessibility and is straightforward to compare againstmeasurements. RADI is the first diagenetic model implemented in Julia, ahigh-performance programming language that is free and open source, and itis also available in MATLAB/GNU Octave. Here, we first describe thescientific background behind RADI and its implementations. Following this, we evaluateits performance in three selected locations and explore other potentialapplications, such as the influence of tides and seasonality on earlydiagenesis in the deep ocean. RADI is a powerful tool to study thetime-transient and steady-state response of the sedimentary system toenvironmental perturbation, such as deep-sea mining, deoxygenation, oracidification events.