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The variability of the summer North Pacific Subtropical High (NPSH) has substantial socioeconomic impacts. However, state‐of‐the‐art climate models significantly disagree on the response of the NPSH to anthropogenic warming. Inter‐model spread in NPSH projections originates from models' inconsistency in simulating tropical precipitation changes. This inconsistency in precipitation changes is partly due to inter‐model spread in tropical sea surface temperature (SST) changes, but it can also occur independently of uncertainty in SST changes. Here, we show that both types of precipitation uncertainty influence the NPSH via the Matsuno‐Gill wave response, but their relative impact varies by region. Through the modulation of low cloud fraction, inter‐model spread of the NPSH can have a further impact on extra‐tropical land surface temperature. The teleconnection between tropical precipitation and the NPSH is examined through a series of numerical experiments. Plain Language Summary The North Pacific Subtropical High (NPSH) is a semi‐permanent high‐pressure system located in the subtropical North Pacific. The variability in the summer NPSH has a significant impact on the monsoon and typhoons over East Asia and the hydroclimate of California. However, future projections of the NPSH using state‐of‐the‐art climate models remain highly uncertain. By evaluating how much individual models deviate from the multi‐model mean at different locations, we find four hot spots of high uncertainty in NPSH projections. Our analysis further reveals that the primary source of model variance in changes in the NPSH is tropical precipitation, which can be attributed to both inter‐model SST‐driven and non‐inter‐model SST‐driven factors. Through numerical experiments, we demonstrate that the teleconnection between tropical precipitation and the NPSH is achieved through wave propagation. Key Points Model spread in the response of the summer North Pacific Subtropical High (NPSH) to CO2 stems from model spread in simulating tropical processes Model spread in tropical sea surface temperature (SST) changes modulates the NPSH by influencing tropical precipitation Model spread in tropical precipitation changes independent of model spread in SST changes also adds to the uncertainty of the NPSH response

Dynamic sea‐level change (ΔDSL)$({\\Delta }\\mathrm{D}\\mathrm{S}\\mathrm{L})$is a key process in shaping the pattern of future sea‐level rise. CMIP6 models predict a range of ΔDSL${\\Delta }\\mathrm{D}\\mathrm{S}\\mathrm{L}$under 1% increase of CO2${\\text{CO}}_{2}$per year. We analyze this CMIP6 spread into contributions from: (a) surface flux change (dF)$(\\mathrm{d}\\mathrm{F})$and (b) model sensitivity to it (Φ)$({\\Phi })$ . Specifically, we force the pre‐industrial simulation of an ocean model with space‐ and time‐varying dF$\\mathrm{d}\\mathrm{F}$diagnosed from different CMIP6 models (one at a time). The CMIP6 spread is thus decomposed into a flux‐driven spread and a residual; the latter is linked to model spread of Φ${\\Phi }$ . We improve upon previous studies by: (a) deriving the perturbed forcing ensemble using an ocean‐only setup and (b) comparing it with the CMIP6 ensemble for both variance and correlation. This reveals distinct roles of surface forcing in driving the CMIP6 spread in different regions. In the North Pacific, differences in windstress forcing primarily explain the CMIP6 spread, while in the North Atlantic, differences in model sensitivity are more important. For the latter region, although buoyancy forcing drives a ΔDSL${\\Delta }\\mathrm{D}\\mathrm{S}\\mathrm{L}$spread there, it correlates poorly with the CMIP6 spread. In the Southern Ocean, differences in both surface forcing and model sensitivity are important for explaining the CMIP6 spread. The surface forcing affects the spread along 40°S via windstress and the spread around the Antarctic via buoyancy flux. Plain Language Summary Climate model simulations provide important information to support planning for future sea‐level rise. Contemporary climate models exhibit large differences in simulated regional sea‐level change under strong CO2${\\text{CO}}_{2}$emission. These model differences can be analyzed in terms of: (a) model differences in simulated surface flux changes (heat, freshwater and wind) and (b) model differences in simulated ocean response to a given surface flux change. We find that model differences in surface flux changes explain most of model diversity in sea‐level change in the North Pacific and part of that in the Southern Ocean, but little of that in the North Atlantic. These results pave the way for reducing sea‐level projection uncertainties in future research. Key Points CMIP6 spread of dynamic sea‐level projections results from both surface forcing and model sensitivity to it Windstress forcing explains the CMIP6 spread in the North Pacific, while model sensitivity is more important in the North Atlantic In the Southern Ocean, both surface forcing and model sensitivity are important for explaining the CMIP6 spread

Energy‐balance models (EBM) constitute a useful framework for summarizing the first‐order physical properties driving the magnitude of the global mean surface air temperature response to an externally imposed radiative perturbation. Here the contributions of these properties to the spread of the temperature responses of an ensemble of coupled Atmosphere‐ocean General Circulation Models (AOGCM) of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are evaluated within the framework of a state‐of‐the‐art EBM. These partial contributions are quantified (in equilibrium and transient conditions) using the analysis of variance method. The radiative properties, particularly the strength of the radiative feedback to the global equilibrium surface warming, appear to constitute the most primary source of the spread. Moreover, the adjusted radiative forcing is found to play an important role in the spread of the transient response. Key Points The total radiative feedback is the main contributor to the inter‐model spread The adjusted radiative forcing is also an important source of spread of the TCR The role of the efficacy of deep‐ocean heat uptake is found to be not negligible

This study explores the role of the stratiform cloud scheme in the inter‐model spread of cloud feedback. Six diagnostic cloud schemes used in various CMIP (Coupled Model Intercomparison Experiment] climate models are implemented (at low and midlevels) into two testbed climate models, and the impacts on cloud feedback are investigated. Results suggest that the choice of stratiform cloud scheme may contribute up to roughly half of the intermodel spread of cloud radiative responses in stratocumulus (Sc) regions, and may determine or favor a given sign of the feedback there. Cloud schemes assuming a probability density function for total water content consistently predict a positive feedback in Sc regions in our experiments. A large negative feedback in Sc regions is obtained only with schemes that consider variables other than relative humidity (e.g., stability). The stratiform cloud scheme also significantly affects cloud feedback at the scale of the tropics and at global scale. Results are slightly less consistent for tropical means, likely indicating coupling with other boundary layer processes such as convective mixing. Key Points The stratiform cloud scheme plays an important role in the spread of cloud feedback, both in Sc regions and globally Subgrid PDF schemes tend to impose a positive low cloud feedback and stability dependence a negative feedback Stability dependence does not fully determine the sign of the feedback, including in marine stratus regions

This study diagnoses the climate sensitivity, radiative forcing and climate feedback estimates from eleven general circulation models participating in the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5), and analyzes inter-model differences. This is done by taking into account the fact that the climate response to increased carbon dioxide (CO 2 ) is not necessarily only mediated by surface temperature changes, but can also result from fast land warming and tropospheric adjustments to the CO 2 radiative forcing. By considering tropospheric adjustments to CO 2 as part of the forcing rather than as feedbacks, and by using the radiative kernels approach, we decompose climate sensitivity estimates in terms of feedbacks and adjustments associated with water vapor, temperature lapse rate, surface albedo and clouds. Cloud adjustment to CO 2 is, with one exception, generally positive, and is associated with a reduced strength of the cloud feedback; the multi-model mean cloud feedback is about 33 % weaker. Non-cloud adjustments associated with temperature, water vapor and albedo seem, however, to be better understood as responses to land surface warming. Separating out the tropospheric adjustments does not significantly affect the spread in climate sensitivity estimates, which primarily results from differing climate feedbacks. About 70 % of the spread stems from the cloud feedback, which remains the major source of inter-model spread in climate sensitivity, with a large contribution from the tropics. Differences in tropical cloud feedbacks between low-sensitivity and high-sensitivity models occur over a large range of dynamical regimes, but primarily arise from the regimes associated with a predominance of shallow cumulus and stratocumulus clouds. The combined water vapor plus lapse rate feedback also contributes to the spread of climate sensitivity estimates, with inter-model differences arising primarily from the relative humidity responses throughout the troposphere. Finally, this study points to a substantial role of nonlinearities in the calculation of adjustments and feedbacks for the interpretation of inter-model spread in climate sensitivity estimates. We show that in climate model simulations with large forcing (e.g., 4 × CO 2 ), nonlinearities cannot be assumed minor nor neglected. Having said that, most results presented here are consistent with a number of previous feedback studies, despite the very different nature of the methodologies and all the uncertainties associated with them.

One of the main concerns in regional climate modeling is to which extent limited-area regional climate models (RCM) reproduce the large-scale atmospheric conditions of their driving general circulation model (GCM). In this work we investigate the ability of a multi-model ensemble of regional climate simulations to reproduce the large-scale weather regimes of the driving conditions. The ensemble consists of a set of 13 RCMs on a European domain, driven at their lateral boundaries by the ERA40 reanalysis for the time period 1961-2000. Two sets of experiments have been completed with horizontal resolutions of 50 and 25 km, respectively. The spectral nudging technique has been applied to one of the models within the ensemble. The RCMs reproduce the weather regimes behavior in terms of composite pattern, mean frequency of occurrence and persistence reasonably well. The models also simulate well the long-term trends and the inter-annual variability of the frequency of occurrence. However, there is a non-negligible spread among the models which is stronger in summer than in winter. This spread is due to two reasons: (1) we are dealing with different models and (2) each RCM produces an internal variability. As far as the day-to-day weather regime history is concerned, the ensemble shows large discrepancies. At daily time scale, the model spread has also a seasonal dependence, being stronger in summer than in winter. Results also show that the spectral nudging technique improves the model performance in reproducing the large-scale of the driving field. In addition, the impact of increasing the number of grid points has been addressed by comparing the 25 and 50 km experiments. We show that the horizontal resolution does not affect significantly the model performance for large-scale circulation.

Divergence in land carbon cycle simulation is persistent and widespread. Regardless of model intercomparison project, results from individual models diverge significantly from each other and, in consequence, from reference datasets. Here we link model spread to structure using a 15-member ensemble of land surface models from the Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as a test case. Our analysis uses functional benchmarks and model structure as predicted by model skill in a machine learning framework to isolate discrete aspects of model structure associated with divergence. We also quantify how initial conditions prejudice present-day model outcomes after centennial-scale transient simulations. Overall, the functional benchmark and machine learning exercises emphasize the importance of ecosystem structure in correctly simulating carbon and water cycling, highlight uncertainties in the structure of carbon pools, and advise against hard parametric limits on ecosystem function. We also find that initial conditions explain 90% of variation in global satellite-era values-initial conditions largely predetermine transient endpoints, historical environmental change notwithstanding. As MsTMIP prescribes forcing data and spin-up protocol, the range in initial conditions and high levels of predetermination are also structural. Our results suggest that methodological tools linking divergence to discrete aspects of model structure would complement current community best practices in model development.