We evaluate cloud simulations using satellite simulators against multiple observational datasets. These simulators have been run within the Geophysical Fluid Dynamics Laboratory’s Atmosphere Model version 4.0 (AM4.0), as well as an alternative configuration where a two-moment Morrison-Gettelman bulk cloud microphysics with prognostic precipitation (MG2) is applied, denoted as AM4-MG2. The modelled cloud spatial distributions, vertical profiles, phase partitioning, cloud-to-precipitation transitions, and radiative effects from AM4.0 and AM4-MG2 compare reasonably well with satellite observations. Model biases include the underestimate of total and low-level clouds, especially optically thin/intermediate clouds, but the overestimate of optically thick clouds, indicating “too few, too bright’ biases. These biases compensate each other and result in reasonable estimates of cloud radiative effects. The underestimate of low-level clouds is associated with too frequent and too early drizzle/precipitation formation. The precipitation bias is improved in AM4-MG2, where the autoconversion scheme initiates the precipitation more realistically.

Earth’s climate sensitivity is greatly affected by the compensation between temperature feedback and water vapor feedback. Using abrupt 4xCO2 experiments, we show that the global-mean water vapor feedback is nearly a linear function of the temperature feedback, the slope of which is explained by the longwave radiative efficiency of water vapor (ε). Although ε remains constant across models in the global mean, it exhibits substantial spatial variations and is particularly weak in Antarctica, where near-surface inversions decouple the surface from the free troposphere. We introduce a surface-free troposphere temperature difference (SFTD) metric, showing that positive SFTD (e.g., high lifting condensation level) amplifies ε, while negative SFTD (e.g., strong inversion) suppresses it. These findings provide a clear explanation of how local climate conditions modulate the radiative compensation between temperature and water vapor feedbacks.

We examine tropical rainfall from Geophysical Fluid Dynamics Laboratory’s Atmosphere Model version 4 (GFDL AM4) at three horizontal resolutions of 100 km, 50 km, and 25 km. The model produces more intense rainfall at finer resolutions, but a large discrepancy still exists between the simulated and the observed frequency distribution. We use a theoretical precipitation scaling diagnostic to examine the frequency distribution of the simulated rainfall. The scaling accurately produces the frequency distribution at moderate-to-high intensity (≥10 mm day-1). Intense tropical rainfall at finer resolutions is produced primarily from the increased contribution of resolved precipitation and enhanced updrafts. The model becomes more sensitive to the grid-scale updrafts than local thermodynamics at high rain rates as the contribution from the resolved precipitation increases. On the contrary, the observed tropical precipitation extremes do not show a strong sensitivity to the grid-scale updrafts.

In this study, a new open‐source package for cloud and precipitation modeling is introduced. Based on Mie theory and existing ice crystal datasets, the scheme generates optical properties for user‐defined gas bands, particle size distributions, and crystal habits, ensuring continuity across wide spectral bands and from small particles (clouds) to large particles (precipitation). Compared with existing schemes in GFDL’s AM4‐MG2, it reduces shortwave reflection of liquid clouds at the top of the atmosphere (TOA) by 1.50 Wm-2 and increases that of ice clouds by 1.62 Wm-2, based on offline radiative calculations.Using the new scheme, we find that cloud radiative effects are sensitive to microphysics variables such as particle size and habit, which affect the effective radius. Systematic flux biases may arise if the effective radius is not fully predicted in microphysics due to predefined size and habit distributions. We show that assuming spherical ice crystals underestimates ice‐cloud radiative effects by 3.20 Wm-2 in the longwave TOA and 2.76 Wm-2 in the shortwave TOA. These biases can be addressed by improving the effective radius approximation with a volume‐to‐radius ratio derived from in‐situ measurements.Combining these findings, we propose that climate models use a set of optics parameterizations for each hydrometeor type while adequently accounting for radiation effects caused by size and habit distributions. Uncertainties due to this simplification are evaluated. This study offers a consistent and physically based representation of radiative processes of clouds and precipitation in weather and climate simulations.

Greenhouse gases (GHGs) are gases that absorb and emit thermal energy. In a warming climate, GHGs modulate the thermal cooling to space from the surface and atmosphere, which is a fundamental feedback process that affects climate sensitivity. Previous studies have stated that the thermal cooling to space with global warming is primarily emitted from the surface, rather than the atmosphere. Using a millennium-length coupled general circulation model (Geophysical Fluid Dynamics Laboratory’s CM3) and accurate line-by-line radiative transfer calculations, here we show that the atmospheric cooling to space accounts for 12 % to 50 % of Earth’s clear-sky longwave feedback parameter from the poles to the tropics. The atmospheric cooling to space is an efficient stabilizing feedback process because water vapor and non-condensable GHGs tend to emit at higher temperatures with surface warming as the thermodynamic structure of the atmosphere evolves. A simple yet comprehensive model is proposed in this study for predicting the clear-sky longwave feedback over a wide range of surface temperatures. It achieves good spectral agreement when compared to line-by-line calculations. Our study provides a theoretical way for assessing Earth’s climate sensitivity, with important implications for Earth-like planets.

Changes in the concentration of greenhouse gases within the atmosphere lead to changes in radiative fluxes throughout the atmosphere. The value of this change, called the instantaneous radiative forcing, varies across climate models, due partly to differences in the distribution of clouds, humidity, and temperature across models, and partly due to errors introduced by approximate treatments of radiative transfer. This paper describes an experiment within the Radiative Forcing Model Intercomparision Project that uses benchmark calculations made with line-by-line models to identify parameterization error in the representation of absorption and emission by greenhouse gases. The clear-sky instantaneous forcing by greenhouse gases to which the world has been subject is computed using a set of 100 profiles, selected from a re-analysis of present-day conditions, that represent the global annual mean forcing with sampling errors of less than 0.01 \si{\watt\per\square\meter}. Six contributing line-by-line models agree in their estimate of this forcing to within 0.025 \si{\watt\per\square\meter} while even recently-developed parameterizations have typical errors four or more times larger, suggesting both that the samples reveal true differences among line-by-line models and that parameterization error will be readily resolved. Agreement among line-by-line models is better in the longwave than in the shortwave where differing treatments of the water vapor vapor continuum affect estimates of forcing by carbon dioxide and methane. The impacts of clouds on instantaneous radiative forcing are roughly estimated, as are adjustments due to stratospheric temperature change. Adjustments are large only for ozone and for carbon dioxide, for which stratospheric cooling introduces modest non-linearity.

We investigate the dependence of radiative feedback on the pattern of sea-surface temperature (SST) change in fourteen Atmospheric General Circulation Models (AGCMs) forced with observed variations in SST and sea-ice over the historical record from 1871 to near-present. We find that over 1871-1980, the Earth warmed with feedbacks largely consistent and strongly correlated with long-term climate sensitivity feedbacks (diagnosed from corresponding atmosphere-ocean GCM abrupt-4xCO2 simulations). Post 1980 however, the Earth warmed with unusual trends in tropical Pacific SSTs (enhanced warming in the west, cooling in the east) that drove climate feedback to be uncorrelated with – and indicating much lower climate sensitivity than – that expected for long-term CO2 increase. We show that these conclusions are not strongly dependent on the AMIP II SST dataset used to force the AGCMs, though the magnitude of feedback post 1980 is generally smaller in eight AGCMs forced with alternative HadISST1 SST boundary conditions. We quantify a ‘pattern effect’ (defined as the difference between historical and long-term CO2 feedback) equal to 0.44 ± 0.47 [5-95%] W m-2 K-1 for the time-period 1871-2010, which increases by 0.05 ± 0.04 W m-2 K-1 if calculated over 1871-2014. Assessed changes in the Earth’s historical energy budget are in agreement with the AGCM feedback estimates. Furthermore satellite observations of changes in top-of-atmosphere radiative fluxes since 1985 suggest that the pattern effect was particularly strong over recent decades, though this may be waning post 2014 due to a warming of the eastern Pacific.

This study assesses the effective climate sensitivity (EffCS) and transient climate response (TCR) derived from global energy budget constraints within historical simulations of 8 CMIP6 global climate models (GCMs). These calculations are enabled by use of the Radiative Forcing Model Intercomparison Project (RFMIP) simulations, which permit accurate quantification of the historical effective radiative forcing. We find that long-term historical energy budget constraints generally underestimate EffCS from CO2 quadrupling and TCR from CO2 ramping, both by 12%, owing to changes in radiative feedbacks and changes in ocean heat uptake efficiency. Atmospheric GCMs forced by observed warming patterns produce lower values of EffCS that are more in line with those inferred from observed historical energy budget constraints. Understanding the discrepancies between modeled and observed historical surface warming patterns remains critical for constraining EffCS and TCR from the historical record.

Mixed phase clouds, consisting of ice particles and super cooled liquid droplets, predominantly found in Arctic, are poorly represented in various climate models. A unique tethered balloon campaign carrying 4π radiometer, a cloud particle imager, and a meteorological package on board was conducted in Ny-Ålesund, Norway, located high in the Arctic at 78.91°N, 11.91°E during May-June 2008. The radiometric and cloud observations at 500nm and 800nm were collected during a month long experimental campaign. A state-of-the-art discrete ordinate radiative transfer model was used to estimate optimal cloud optical properties. These optical properties are contrasted with those of the cloud parameterization in the new NOAA-GFDL AM4 model that is presently being used in the Coupled Model Intercomparison Project 6 (CMIP6). The improved simulation of cloud optical properties will aid in understanding cloud radiation feedbacks and better representation of clouds in global climate model.

The newest atmospheric climate model at GFDL, AM4, succeeded at significantly reducing the toa radiative flux biases as compared to CERES observations. Despite a relatively low top-of-atmosphere sensitivity to uniform warming of SSTs (Cess warming experiments), the corresponding coupled climate model, CM4, has high transient and equilibrium climate sensitivities. We will present a systematic picture of the modeled clouds across a hierarchy of model configurations which utilize this atmospheric model. This hierarchy includes the CFMIP Aquaplanet and AMIP experiments, fully coupled model experiments (using GFDL’s CM4 model) as well as additional AMIP-like experiments with particular SST patterns. This demonstrates the large range of sensitivities that are possible from a single atmospheric climate model. Looking at the global mean radiative feedbacks across the different model configurations as well as in the context of CMIP5 and CMIP6 models will allow us to assess to what extent the cloud feedbacks in the idealized experiments relate to the fully coupled experiments and to observed clouds.

Meridional gradients in CO2 forcing are known to drive increases in poleward heat transport (Huang and Zhang 2014, Huang et al. 2017). However, the climate factors which control these meridional forcing gradients are not well understood. Building upon the work of Wilson (2012), we build a first-principles, analytical model for CO2 forcing which requires as input only the temperatures at the surface and roughly 30 hPa, as well as column relative humidity. This model quantitatively captures global variations in clear-sky CO2 forcing, and shows that the meridional forcing gradient is directly attributable to the meridional surface temperature gradient.

Clear-sky CO2 forcing is known to vary significantly over the globe, but the state dependence which controls this is not well understood. Here we construct a quantitatively accurate analytical model for spatially-varying CO2 forcing, which depends only on surface and stratospheric temperatures as well as column relative humidity. This model shows that CO2 forcing is primarily governed by surface-stratosphere temperature contrast, with the corollary that the meridional gradient in CO2 forcing is largely due to the meridional surface temperature gradient. The presence of H2O modulates this forcing gradient, however, by substantially reducing the forcing in the tropics, as well as introducing forcing variations due to spatially-varying column relative humidity.