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The cloud feedback may result in amplification or damping of Arctic warming. Two common techniques used to diagnose the top‐of‐the‐atmosphere cloud feedback are the Adjusted Cloud Radiative Effect (AdjCRE) method and the Cloud Radiative Kernel (CRK) method. We apply both to CMIP5 and CMIP6 model data, finding that the AdjCRE calculated Arctic shortwave cloud feedback is twice as correlated with sea ice loss in CMIP5, and four times in CMIP6, as the CRK method. We find that the CRK method produces Arctic all‐sky residual percentages exceeding 20% in 15 of 18 models. We use the CRK method to decompose the feedback in CMIP5 and CMIP6 finding that its median value changed from negative to positive driven by a less‐negative cloud optical depth feedback. Despite its lack of closure, we conclude that the CRK method is better suited for Arctic SW feedbacks as it is less impacted by surface albedo changes. Plain Language Summary: The cloud feedback is the process by which cloud property changes in a warming climate can either further enhance warming or damp it. The Arctic is warming faster than the rest of the globe, and one of the largest sources of uncertainty in its climate projections is the cloud feedback. There are two popular methods to calculate the cloud feedback: the Adjusted Cloud Radiative Effect technique, and the Cloud Radiative Kernel technique. In this paper we compare the two methods in a suite of climate models by considering the extent to which changes in Arctic sea ice impact the cloud feedbacks. From this analysis we conclude that the Cloud Radiative Kernel method is less affected by sea ice loss. We then apply the Cloud Radiative Kernel technique to data from the two most recent generations of global climate models to investigate how polar day Arctic cloud feedbacks have changed between these generations. We find that the median value of these Arctic feedbacks is slightly positive in the newest generation of models, a change from slightly negative in the previous generation that is largely fueled by a weakening of the feedback associated with changes in cloud optical depth. Key Points: The Cloud Radiative Kernel method is less sensitive to surface albedo changes than the Adjusted Cloud Radiative Effect techniqueThe Cloud Radiative Kernel method provides poor radiative closure in a suite of global climate modelsThe median shortwave Arctic cloud feedback in recent climate models is slightly positive due to a weakened cloud optical depth feedback [ABSTRACT FROM AUTHOR]

Apparent hydrological sensitivity (ηa), the change in the global mean precipitation per degree K of global surface warming, is a key aspect of the climate system's response to increasing CO2 forcing. To determine whether ηa depends on the forcing amplitude we analyze idealized experiments over a broad range of abrupt CO2 forcing, from 2× to 8× preindustrial values, with two distinct climate models. We find little change in ηa between 2× and 4×CO2, and almost no change beyond 5×CO2. We validate this finding under transient CO2 forcing at 1%‐per‐year, up to 8×CO2. We further corroborate this result by analyzing the 1%‐per‐year output of more than 15 CMIP5/6 models. Lastly, we examine the 1,000‐year long LongrunMIP model output, and again find little change in ηa. This wealth of results demonstrates that ηa is a very weak function of CO2 forcing. Plain Language Summary: Hydrological sensitivity (HS) is defined as the change in globally‐averaged precipitation per degree K of surface temperature increase caused by increasing concentrations of greenhouse gasses, such as CO2. It is important to understand how HS changes with different levels of CO2 in the atmosphere. To do this we analyzed model experiments with varying increases of CO2. We find little change in HS between 2× to 4× the pre‐industrial levels of CO2, and almost no change beyond 5×CO2. Additionally, we analyze model experiments where CO2 concentrations increase by 1% per year and see similar results. Finally, we validate this finding with models with significantly longer run times. We thus conclude that HS is independent of the level of CO2 in the atmosphere. Key Points: We examine the dependence of apparent hydrological sensitivity (ηa, defined as ΔP/ΔT) on the magnitude of CO2 forcingWe find little change in ηa in abrupt 2× to 8×CO2 experiments and a transient 1%/year experiment up to 8×CO2Little change in ηa, notably at large CO2, is also found in most CMIP5, CMIP6, and Longrun‐MIP models [ABSTRACT FROM AUTHOR]

Water vapor in the Arctic can be attributed to local evaporation and transport from mid‐latitudes. Here we investigate the response of Arctic water vapor to declining sea ice in a climate model, using an idealized warming scenario in which the Arctic becomes ice free year‐round. We find distinct monthly patterns in water vapor change with warming, with a strong influence of sea ice loss on changes to evaporation, except in summer when transports dominate. Before the Arctic becomes ice‐free, total column water increases at a rate slower than that indicated by the Clausius‐Clapeyron relationship; once ice‐free, total column water increases at a rate equal to Clausius‐Clapeyron. The exception is summer when the increase is initially faster then becomes slower when ice‐free. The boundary layer structure changes, under global warming, from stratified to well mixed in all seasons except summer, which remains largely unchanged throughout the ice‐free transition. Plain Language Summary: Surface evaporation from the Arctic Ocean is limited by the presence of sea ice, which creates a natural barrier between the ocean and atmosphere above. We show the loss of Arctic sea ice, in a climate model affects atmospheric water vapor and find that this varies throughout the year. The greatest increase in atmospheric water vapor is in winter, during the Arctic night, whilst there is very little change in summer. There are two reasons why sea ice loss does not impact water vapor in the summer: first, the summer air temperature barely changes between ice‐covered and ice‐free, whereas in winter the loss of ice cover can increase air temperatures by at least 20°C; second the summer Arctic water budget is dominated by transport of water vapor from midlatitudes rather than local evaporation. We also show changes in the vertical structure of the near‐surface atmosphere and cloud types, with a transition from stable to turbulent regimes in autumn and winter. Key Points: As Arctic sea ice declines with increased warming, Arctic evaporation and water vapor increase most in autumn and winterArctic sea ice limits water vapor availability, but once the Arctic is ice‐free, it increases with the Clausius‐Clapeyron relationshipArctic boundary layer changes from stably stratified to well mixed, during winter and autumn, but less change in spring and summer [ABSTRACT FROM AUTHOR]