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Abstract. The Arctic poses many challenges for Earth system and snow physics models, which are commonly unable to simulate crucial Arctic snowpack processes,such as vapour gradients and rain-on-snow-induced ice layers. These limitations raise concerns about the current understanding of Arctic warming and its impact on biodiversity, livelihoods, permafrost, and the global carbon budget. Recognizing that models are shaped by human choices, 18 Arctic researchers were interviewed to delve into the decision-making process behind model construction. Although data availability, issues of scale, internal model consistency, and historical and numerical model legacies were cited as obstacles to developing an Arctic snowpack model, no opinion was unanimous. Divergences were not merely scientific disagreements about the Arctic snowpack but reflected the broader research context. Inadequate and insufficient resources, partly driven by short-term priorities dominating research landscapes, impeded progress. Nevertheless, modellers were found to be both adaptable to shifting strategic research priorities – an adaptability demonstrated by the fact that interdisciplinary collaborations were the key motivation for model development – and anchored in the past. This anchoring and non-epistemic values led to diverging opinions about whether existing models were “good enough” and whether investing time and effort to build a new model was a useful strategy when addressing pressing research challenges. Moving forward, we recommend that both stakeholders and modellers be involved in future snow model intercomparison projects in order to drive developments that address snow model limitations currently impeding progress in various disciplines. We also argue for more transparency about the contextual factors that shape research decisions. Otherwise, the reality of our scientific process will remain hidden, limiting the changes necessary to our research practice. 

The framework of Representative Key Risks (RKRs) has been adopted by the Intergovernmental Panel on Climate Change Working Group II (WGII) to categorize, assess and communicate a wide range of regional and sectoral key risks from climate change. These are risks expected to become severe due to the potentially detrimental convergence of changing climate conditions with the exposure and vulnerability of human and natural systems. Other papers in this special issue treat each of eight RKRs holistically by assessing their current status and future evolution as a result of this convergence. However, in these papers, such assessment cannot always be organized according to a systematic gradation of climatic changes. Often the big-picture evolution of risk has to be extrapolated from either qualitative effects of “low”, “medium” and “high” warming, or limited/focused analysis of the consequences of particular mitigation choices (e.g., benefits of limiting warming to 1.5 or 2C), together with consideration of the socio-economic context and possible adaptation choices. In this study we offer a representation – as systematic as possible given current literature and assessments – of the future evolution of the hazard components of RKRs. We identify the relevant hazards for each RKR, based upon the WGII authors’ assessment, and we report on their current state and expected future changes in magnitude, intensity and/or frequency, linking these changes to Global Warming Levels (GWLs) to the extent possible. We draw on the assessment of changes in climatic impact-drivers relevant to RKRs described in the 6th Assessment Report by Working Group I supplemented when needed by more recent literature. For some of these quantities - like regional trends in oceanic and atmospheric temperature and precipitation, some heat and precipitation extremes, permafrost thaw and Northern Hemisphere snow cover - a strong and quantitative relationship with increasing GWLs has been identified. For others - like frequency and intensity of tropical cyclones and extra-tropical storms, and fire weather - that link can only be described qualitatively. For some processes - like the behavior of ice sheets, or changes in circulation dynamics - large uncertainties about the effects of different GWLs remain, and for a few others - like ocean pH and air pollution - the composition of the scenario of anthropogenic emissions is most relevant, rather than the warming reached. In almost all cases, however, the basic message remains that every small increment in CO2 concentration in the atmosphere and associated warming will bring changes in climate phenomena that will contribute to increasing risk of impacts on human and natural systems, in the absence of compensating changes in these systems’ exposure and vulnerability, and in the absence of effective adaptation. Our picture of the evolution of RKR-relevant climatic impact-drivers complements and enriches the treatment of RKRs in the other papers in at least two ways: by filling in their often only cursory or limited representation of the physical climate aspects driving impacts, and by providing a fuller representation of their future potential evolution, an important component – if never the only one – of the future evolution of risk severity. 

Future sea-level change is characterized by both quantifiable and unquantifiable uncertainties. Effective communication of both types of uncertainty is a key challenge in translating sea-level science to inform long-term coastal planning. Scientific assessments play a key role in the translation process and have taken diverse approaches to communicating sea-level projection uncertainty. Here we review how past IPCC and regional assessments have presented sea-level projection uncertainty, how IPCC presentations have been interpreted by regional assessments and how regional assessments and policy guidance simplify projections for practical use. This information influenced the IPCC Sixth Assessment Report presentation of quantifiable and unquantifiable uncertainty, with the goal of preserving both elements as projections are adapted for regional application. 

Abstract. The Earth climate system is out of energy balance, and heat hasaccumulated continuously over the past decades, warming the ocean, the land,the cryosphere, and the atmosphere. According to the Sixth Assessment Reportby Working Group I of the Intergovernmental Panel on Climate Change,this planetary warming over multiple decades is human-driven and results inunprecedented and committed changes to the Earth system, with adverseimpacts for ecosystems and human systems. The Earth heat inventory providesa measure of the Earth energy imbalance (EEI) and allows for quantifyinghow much heat has accumulated in the Earth system, as well as where the heat isstored. Here we show that the Earth system has continued to accumulateheat, with 381±61 ZJ accumulated from 1971 to 2020. This is equivalent to aheating rate (i.e., the EEI) of 0.48±0.1 W m−2. The majority,about 89 %, of this heat is stored in the ocean, followed by about 6 %on land, 1 % in the atmosphere, and about 4 % available for meltingthe cryosphere. Over the most recent period (2006–2020), the EEI amounts to0.76±0.2 W m−2. The Earth energy imbalance is the mostfundamental global climate indicator that the scientific community and thepublic can use as the measure of how well the world is doing in the task ofbringing anthropogenic climate change under control. Moreover, thisindicator is highly complementary to other established ones like global meansurface temperature as it represents a robust measure of the rate of climatechange and its future commitment. We call for an implementation of theEarth energy imbalance into the Paris Agreement's Global Stocktake based onbest available science. The Earth heat inventory in this study, updated fromvon Schuckmann et al. (2020), is underpinned by worldwide multidisciplinarycollaboration and demonstrates the critical importance of concertedinternational efforts for climate change monitoring and community-basedrecommendations and we also call for urgently needed actions for enablingcontinuity, archiving, rescuing, and calibrating efforts to assure improvedand long-term monitoring capacity of the global climate observing system. The data for the Earth heat inventory are publicly available, and more details are provided in Table 4. 

Peatlands store substantial amounts of carbon and are vulnerable to climate change. We present a modified version of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model for simulating the hydrology, surface energy, and CO₂ fluxes of peatlands on daily to annual timescales. The model includes a separate soil tile in each 0.5° grid cell, defined from a global peatland map and identified with peat-specific soil hydraulic properties. Runoff from non-peat vegetation within a grid cell containing a fraction of peat is routed to this peat soil tile, which maintains shallow water tables. The water table position separates oxic from anoxic decomposition. The model was evaluated against eddy-covariance (EC) observations from 30 northern peatland sites, with the maximum rate of carboxylation (V_cmax) being optimized at each site. Regarding short-term day-to-day variations, the model performance was good for gross primary production (GPP) (r² =  0.76; Nash–Sutcliffe modeling efficiency, MEF  =  0.76) and ecosystem respiration (ER, r² =  0.78, MEF  =  0.75), with lesser accuracy for latent heat fluxes (LE, r² =  0.42, MEF  =  0.14) and and net ecosystem CO₂ exchange (NEE, r² =  0.38, MEF  =  0.26). Seasonal variations in GPP, ER, NEE, and energy fluxes on monthly scales showed moderate to high r² values (0.57–0.86). For spatial across-site gradients of annual mean GPP, ER, NEE, and LE, r² values of 0.93, 0.89, 0.27, and 0.71 were achieved, respectively. Water table (WT) variation was not well predicted (r² < 0.1), likely due to the uncertain water input to the peat from surrounding areas. However, the poor performance of WT simulation did not greatly affect predictions of ER and NEE. We found a significant relationship between optimized V_cmax and latitude (temperature), which better reflects the spatial gradients of annual NEE than using an average V_cmax value.