A method is proposed for combining information from several emergent constraints into a probabilistic estimate for a climate sensitivity proxy $Y$ such as equilibrium climate sensitivity (ECS) or the climate feedback parameter $\lambda$. The method is based on fitting a multivariate Gaussian PDF for $Y$ and the emergent constraints using an ensemble of global climate models (GCMs). For a single perfectly-observed constraint $X$, it reduces to a linear regression-based estimate of $Y$. The method accounts for uncertainties in sampling this multidimensional PDF with a small number of models, for observational uncertainties in the constraints, and for overconfidence about the correlation of the constraints with the climate sensitivity. Two methods are presented. Method C accounts for correlations between emergent constraints but can fail if some constraints are too strongly related. Method U assumes constraints are uncorrelated except through their mutual relationship to the climate proxy; it is robust to small GCM sample size and is appealingly interpretable. These methods are applied to ECS and $\lambda$ using a previously-published set of 11 possible emergent constraints derived from climate models in the Coupled Model Intercomparison Project (CMIP). This study corroborates and quantifies past findings that most constraints predict higher climate sensitivity than the CMIP mean. The $\pm2\sigma$ posterior range of ECS for Method C with no overconfidence adjustment is $4.1 \pm 0.8$ K. For Method U with a large overconfidence adjustment, it is $4.0 \pm 1.3$ K.

Marine boundary layer clouds tend to organize into closed or open mesoscale cellular convection (MCC). Here, two-dimensional wavelet analysis is applied for the first time to passive microwave retrievals of cloud water path (CWP), water vapor path (WVP), and rain rate from AMSR-E in 2008 over the Northeast and Southeast Pacific, and the Southeast Atlantic subtropical stratocumulus to cumulus transition regions. The (co-)variability between CWP, WVP, and rain rate in 160x160 km2 analysis boxes is partitioned between four mesoscale wavelength octaves (20, 40, 80, and 160 km). The cell scale is identified as the wavelength of the peak CWP variance. Together with a machine-learning classification of cell type, this allows the statistical characteristics of open and closed MCC of various scales, and its relation to WVP, rain rate and potential environmental controlling factors to be analyzed across a very large set of cases. The results show that the cell wavelength is most commonly 40-80 km. Cell-scale CWP perturbations are good predictors of the WVP and rain rate perturbations. A universal cubic dependence of rain rate on CWP is found in closed and open cells of all scales. This suggests that aerosol control on precipitation susceptibility is not as important for open cell formation as are processes that cause increases in cloud water. For cells larger than 20 km, there is no obvious dependence of cell scale on the environmental controlling factors tested, suggesting that the cell scale may depend more on its historical evolution than the current environmental conditions.

Global atmospheric ‘storm-resolving’ models with horizontal grid spacing of less than 5~km resolve deep cumulus convection and flow in complex terrain. They promise to be reference models that could be used to improve computationally affordable coarse-grid global climate models across a range of climates, reducing uncertainties in regional precipitation and temperature trends. Here, machine learning of nudging tendencies as functions of column state is used to correct the physical parameterization tendencies of temperature, humidity, and optionally winds, in a real-geography coarse-grid model (FV3GFS with a 200~km grid) to be closer to those of a 40-day reference simulation using X-SHiELD, a modified version of FV3GFS with a 3~km grid. Both simulations specify the same historical sea-surface temperature fields. This methodology builds on a prior study using a global observational analysis as the reference. The coarse-grid model without machine learning corrections has too little cloud, causing too much daytime heating of land surfaces that creates excessive surface latent heat flux and rainfall. This bias is avoided by learning downwelling radiative flux from the fine-grid model. The best configuration uses learned nudging tendencies for temperature and humidity but not winds. Neural nets slightly outperform random forests. Forecasts of 850 hPa temperature gain 18 hours of skill at 3–7 day leads and time-mean precipitation patterns are improved 30\% by applying the ML correction. Adding machine-learned wind tendencies improves 500 hPa height skill for the first five days of forecasts but degrades time-mean upper tropospheric temperature and zonal wind patterns thereafter.

This study uses cloud and radiative properties collected from in-situ and remote sensing instruments during two coordinated campaigns over the Southern Ocean between Tasmania and Antarctica in January-February 2018 to evaluate the simulations of clouds and precipitation in nudged-meteorology simulations with the CAM6 and AM4 global climate models sampled at the times and locations of the observations. Fifteen SOCRATES research flights sampled cloud water content, cloud droplet number concentration, and particle size distributions in mixed-phase boundary-layer clouds at temperatures down to -25 C. The six-week CAPRICORN2 research cruise encountered all cloud regimes across the region. Data from vertically-pointing 94 GHz radars deployed was compared with radar-simulator output from both models. Satellite data was compared with simulated top-of-atmosphere (TOA) radiative fluxes. Both models simulate observed cloud properties fairly well within the variability of observations. Cloud base and top in both models are generally biased low. CAM6 overestimates cloud occurrence and optical thickness while cloud droplet number concentrations are biased low, leading to excessive TOA reflected shortwave radiation. In general, low clouds in CAM6 precipitate at the same frequency but are more homogeneous compared to observations. Deep clouds are better simulated but produce snow too frequently. AM4 underestimates cloud occurrence but overestimates cloud optical thickness even more than CAM6, causing excessive outgoing longwave radiation fluxes but comparable reflected shortwave radiation. AM4 cloud droplet number concentrations match observations better than CAM6. Precipitating low and deep clouds in AM4 have too little snow. Further investigation of these microphysical biases is needed for both models.