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  1. Abstract

    Atmospheric rivers (ARs) manifest as transient filaments of intense water vapor transport that contribute to synoptic‐scale extremes and interannual variability of precipitation. Despite these influences, the synoptic‐ to planetary‐scale processes that lead to ARs remain inadequately understood. In this study, North Pacific ARs within the November–April season are objectively identified in both reanalysis data and the Community Earth System Model Version 2, and atmospheric patterns preceding AR landfalls beyond 1 week in advance are examined. Latitudinal dependence of the AR processes is investigated by sampling events near the Oregon (45°N, 230°E) and southern California (35°N, 230°E) coasts. Oregon ARs exhibit a pronounced anticyclone emerging over Alaska 1–2 weeks before AR landfall that migrates westward into Siberia, dual midlatitude cyclones developing over southeast coastal Asia and the northeast Pacific, and a zonally elongated band of enhanced water vapor transport spanning the entire North Pacific basin that guides anomalous moisture toward the North American west coast. The precursor high‐latitude anticyclone corresponds to a significant increase in atmospheric blocking probability, suppressed synoptic eddy activity, and an equatorward‐shifted storm track. Southern California ARs also exhibit high‐latitude blocking but have an earlier‐developing and more intense northeast Pacific cyclone. Compared to reanalysis, Community Earth System Model Version 2 underestimates Northeast Pacific AR frequencies by 5–20% but generally captures AR precursor patterns well, particularly for Oregon ARs. Collectively, these results indicate that the identified precursor patterns represent physical processes that are central to ARs and are not simply an artifact of statistical analysis.

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  2. Abstract

    The Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative‐convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique among intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud‐resolving models (CRMs), large eddy simulations (LES), and global cloud‐resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self‐aggregation in large domains and agree that self‐aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self‐aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than unaggregated simulations.

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    Abstract The emergence of a spatial pattern in the externally forced response (FR) of dynamic sea level (DSL) during the altimeter era has recently been demonstrated using climate models but our understanding of its initial emergence, drivers, and implications for the future is poor. Here the anthropogenic forcings of the DSL pattern are explored using the Community Earth System Model Large Ensemble (CESM-LE) and Single-Forcing Large Ensemble, a newly available set of simulations where values of individual forcing agents remain fixed at 1920 levels, allowing for an estimation of their effects. Statistically significant contributions to the DSL FR are identified for greenhouse gases (GHGs) and industrial aerosols (AERs), with particularly strong contributions resulting from AERs in the mid-twentieth century and GHGs in the late twentieth and twenty-first century. Secondary, but important, contributions are identified for biomass burning aerosols in the equatorial Atlantic Ocean in the mid-twentieth century, and for stratospheric ozone in the Southern Ocean during the late twentieth century. Key to understanding regional DSL patterns are ocean heat content and salinity anomalies, which are driven by surface heat and freshwater fluxes, ocean dynamics, and the spatial structure of seawater thermal expansivity. Potential implications for the interpretation of DSL during the satellite era and the longer records from tide gauges are suggested as a topic for future research. 
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  5. null (Ed.)
    Abstract As the greenhouse gas concentrations increase, a warmer climate is expected. However, numerous internal climate processes can modulate the primary radiative warming response of the climate system to rising greenhouse gas forcing. Here the particular internal climate process that we focus on is the Atlantic meridional overturning circulation (AMOC), an important global-scale feature of ocean circulation that serves to transport heat and other scalars, and we address the question of how the mean strength of AMOC can modulate the transient climate response. While the Community Earth System Model version 2 (CESM2) and the Energy Exascale Earth System Model version 1 (E3SM1) have very similar equilibrium/effective climate sensitivity, our analysis suggests that a weaker AMOC contributes in part to the higher transient climate response to a rising greenhouse gas forcing seen in E3SM1 by permitting a faster warming of the upper ocean and a concomitant slower warming of the subsurface ocean. Likewise the stronger AMOC in CESM2 by permitting a slower warming of the upper ocean leads in part to a smaller transient climate response. Thus, while the mean strength of AMOC does not affect the equilibrium/effective climate sensitivity, it is likely to play an important role in determining the transient climate response on the centennial time scale. 
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  6. null (Ed.)
    Abstract The Southern Hemisphere summertime eddy-driven jet and storm tracks have shifted poleward over the recent few decades. In previous studies, explanations have mainly stressed the influence of external forcing in driving this trend. Here we examine the role of internal tropical SST variability in controlling the austral summer jet’s poleward migration, with a focus on interdecadal time scales. The role of external forcing and internal variability are isolated by using a hierarchy of Community Earth System Model version 1 (CESM1) simulations, including the pre-industrial control, large ensemble, and pacemaker runs. Model simulations suggest that in the early twenty-first century, both external forcing and internal tropical Pacific SST variability are important in driving a positive southern annular mode (SAM) phase and a poleward migration of the eddy-driven jet. Tropical Pacific SST variability, associated with the negative phase of the interdecadal Pacific oscillation (IPO), acts to shift the jet poleward over the southern Indian and southwestern Pacific Oceans and intensify the jet in the southeastern Pacific basin, while external forcing drives a significant poleward jet shift in the South Atlantic basin. In response to both external forcing and decadal Pacific SST variability, the transient eddy momentum flux convergence belt in the middle latitudes experiences a poleward migration due to the enhanced meridional temperature gradient, leading to a zonally symmetric southward migration of the eddy-driven jet. This mechanism distinguishes the influence of the IPO on the midlatitude circulation from the dynamical impact of ENSO, with the latter mainly promoting the subtropical wave-breaking critical latitude poleward and pushing the midlatitude jet to higher latitudes. 
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  7. null (Ed.)
    Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections. 
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