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Abstract Overly smooth topography in general circulation models (GCMs) underestimates the blocking effect of the steep mountain ranges flanking the eastern Pacific. We explore the impact of this bias on common biases in Pacific climate simulation [i.e., the unrealistic cross-equatorial symmetry of near-surface winds, sea surface temperatures (SSTs), and precipitation] through sensitivity experiments with modified Central and/or South American topography in an atmosphere–ocean coupled GCM. Quantifying orographic blocking potential via the Froude number, we determine that an envelope topographic interpolation scheme best captures observed blocking patterns. Implementing envelope topography only in Central America reduced model biases as greater blocking of the trade winds warmed SST and enhanced convergence in the northeastern Pacific. Doing so additionally over the Andes improved the simulation of South Pacific circulation and the South Pacific convergence zone as stronger deflection of the westerlies intensified the South Pacific anticyclone. This mitigated convection biases in the southeast Pacific by increasing subsidence and cooling SST. However, remote impacts of the Andes exacerbated the dry bias in the northeast tropical Pacific, resulting in negligible improvement in the East Pacific double-ITCZ. We find that, due to the significant role of large-scale convergence in driving precipitation patterns, other model biases, such as cloud-radiative biases, may modulate the impact of altering topography. Our results highlight the importance of considering alternate methods for calculating model topographic boundary conditions, though the optimal interpolation scheme will vary with model resolution and the impact of topography on GCM biases can be sensitive to choices made in formulating parameterizations. Significance StatementIn this study, we explore how the mountain ranges spanning Central and South America shape the climate of the Pacific by blocking large-scale midlatitude and tropical winds. We show that the height of these mountains is typically too low in climate models and that elevating them can improve patterns of rainfall, surface ocean temperatures, and near-surface winds in the Pacific. This is important because model biases in the Pacific climate limit their utility for understanding current and future climate variability. Improving the representation of blocking by mountains can thus be a simple method for reducing uncertainties in future climate projections. 

Abstract A neural network (NN) surrogate of the NASA GISS ModelE atmosphere (version E3) is trained on a perturbed parameter ensemble (PPE) spanning 45 physics parameters and 36 outputs. The NN is leveraged in a Markov Chain Monte Carlo (MCMC) Bayesian parameter inference framework to generate a secondposteriorconstrained ensemble coined a “calibrated physics ensemble,” or CPE. The CPE members are characterized by diverse parameter combinations and are, by definition, close to top‐of‐atmosphere radiative balance, and must broadly agree with numerous hydrologic, energy cycle and radiative forcing metrics simultaneously. Global observations of numerous cloud, environment, and radiation properties (provided by global satellite products) are crucial for CPE generation. The inference framework explicitly accounts for discrepancies (or biases) in satellite products during CPE generation. We demonstrate that product discrepancies strongly impact calibration of important model parameter settings (e.g., convective plume entrainment rates; fall speed for cloud ice). Structural improvements new to E3 are retained across CPE members (e.g., stratocumulus simulation). Notably, the framework improved the simulation of shallow cumulus and Amazon rainfall while not degrading radiation fields, an upgrade that neither default parameters nor Latin Hypercube parameter searching achieved. Analyses of the initial PPE suggested several parameters were unimportant for output variation. However, many “unimportant” parameters were needed for CPE generation, a result that brings to the forefront how parameter importance should be determined in PPEs. From the CPE, two diverse 45‐dimensional parameter configurations are retained to generate radiatively‐balanced, auto‐tuned atmospheres that were used in two E3 submissions to CMIP6. 

Abstract Aerosol‐cloud interactions (ACI) in warm clouds are the primary source of uncertainty in effective radiative forcing (ERF) during the historical period and, by extension, inferred climate sensitivity. The ERF due to ACI (ERFaci) is composed of the radiative forcing due to changes in cloud microphysics and cloud adjustments to microphysics. Here, we examine the processes that drive ERFaci using a perturbed parameter ensemble (PPE) hosted in CAM6. Observational constraints on the PPE result in substantial constraints in the response of cloud microphysics and macrophysics to anthropogenic aerosol, but only minimal constraint on ERFaci. Examination of cloud and radiation processes in the PPE reveal buffering of ERFaci by the interaction of precipitation efficiency and radiative susceptibility. 

Abstract Using multiple independent satellite and reanalysis datasets, we compare relationships between mesoscale convective system (MCS) precipitation intensity P max , environmental moisture, large-scale vertical velocity, and system radius among tropical continental and oceanic regions. A sharp, nonlinear relationship between column water vapor and P max emerges, consistent with nonlinear increases in estimated plume buoyancy. MCS P max increases sharply with increasing boundary layer and lower free tropospheric (LFT) moisture, with the highest P max values originating from MCSs in environments exhibiting a peak in LFT moisture near 750 hPa. MCS P max exhibits strikingly similar behavior as a function of water vapor among tropical land and ocean regions. Yet, while the moisture– P max relationship depends strongly on mean tropospheric temperature, it does not depend on sea surface temperature over ocean or surface air temperature over land. Other P max -dependent factors include system radius, the number of convective cores, and the large-scale vertical velocity. Larger systems typically contain wider convective cores and higher P max , consistent with increased protection from dilution due to dry air entrainment and reduced reevaporation of precipitation. In addition, stronger large-scale ascent generally supports greater precipitation production. Last, temporal lead–lag analysis suggests that anomalous moisture in the lower–middle troposphere favors convective organization over most regions. Overall, these statistics provide a physical basis for understanding environmental factors controlling heavy precipitation events in the tropics, providing metrics for model diagnosis and guiding physical intuition regarding expected changes to precipitation extremes with anthropogenic warming.