Yi Qin

Recent research has revealed a correlation within the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations: models exhibiting more pronounced warming due to positive cloud feedback simultaneously show enhanced cooling from aerosol‐cloud interactions (ACI). However, the interplay between ACI and cloud feedback is not well understood in these models. Our study addresses this gap by modifying the autoconversion rate in two Earth system models (ESMs), elucidating how ACI could connect with cloud feedback through the warm rain process. We find that higher autoconversion rates, which are associated with stronger ACI, suppress the increase in cloud liquid water path (LWP) and cloud optical depth with warming by enhancing the precipitation efficiency over the extratropical regions, leading to a larger positive cloud feedback. This study offers new insights into the compensatory mechanism between ACI and cloud feedback through the warm rain process and highlights the importance of constraining the autoconversion parameterization in models.

This paper introduces an Earth system modeling testbed for predicting aerosols and aerosol‐cloud interactions (ACIs) at convection‐permitting scales. Using the Energy Exascale Earth System Model (E3SM) version 2 with a four‐mode Modal Aerosol Module, we conduct simulations at 3.25 km resolution on a regionally refined mesh (RRM) across four regions with distinct aerosol and cloud regimes. Results are compared with the standard 100 km E3SM configuration and evaluated against satellite, aircraft, and ground‐based observations. We find that increasing model resolution improves heavy precipitation simulation but amplifies positive bias in light drizzle at coarse resolution. These resolution‐induced changes affect cloud and aerosol properties to varying degrees across regions. Generally, cloud cover and liquid water path (LWP) show better agreement with satellite retrievals at 3.25 km, though surface‐based comparisons suggest otherwise. Aerosol composition remains poorly represented at both resolutions. The RRM increases Aitken mode aerosol number concentrations via enhanced new particle formation. However, accumulation mode aerosols are decreased at higher resolution as aerosol removals become more efficient. This partially contributes to fewer cloud condensation nuclei (CCN) and lower cloud droplet number concentrations (), which produces larger model biases in some scenarios. These findings suggest that solely increasing horizontal resolution to kilometer scales is insufficient to broadly improve aerosol and cloud predictions without concurrent advancements in physical and chemical process representations. Nonetheless, the RRM moderately improves key ACI relationships such as CCN‐ correlation, reflecting enhanced aerosol activation representation. The LWP‐ relationship is also better captured by RRM, suggesting a better characterization of LWP adjustment.

Using the global 3.25‐km Simple Cloud Resolving E3SM Atmosphere Model (SCREAM 3 km), a pair of 13‐month Cess‐Potter simulations are performed to quantify the radiative feedbacks and the hydrologic and circulation responses to warming. Large‐scale aspects of SCREAM 3 km's top‐of‐atmosphere radiative fluxes, precipitation rates, and circulations are in good agreement with observations and reanalysis, with notable differences, including a drier lower free‐troposphere in the Tropics, reduced precipitation and humidity over the Tropical West Pacific, and poleward shifted Southern Hemisphere midlatitude jet. In response to warming, SCREAM 3 km predicts a total radiative feedback within the top 15% of the CMIP5 and CMIP6 models, which puts it substantially higher than the feedback reported by other kilometer‐scale models. SCREAM 3 km's high radiative feedback stems from a strongly positive shortwave cloud feedback, most prominent over the mid‐ and high‐latitudes. SCREAM 3 km's high precipitation response also puts it among the highest of CMIP models, whereas its circulation response is within the spread of CMIP models. An ensemble of five perturbed initial condition Cess‐Potter simulations with a 12 km version of SCREAM (SCREAM 12 km) is performed to characterize uncertainty and resolution sensitivity. It suggests that the uncertainty from analyzing a pair of 1‐year simulations is small compared to the inter‐model spread in feedbacks and precipitation response. SCREAM 12 km also produces a strong precipitation response to warming but a much lower cloud feedback and total radiative feedback. The results from these experiments suggest that the spread in climate feedbacks will likely persist in the next generation of kilometer‐scale models.

Changes in cloud scattering properties and emissivity that arise from atmospheric warming cause substantial radiative feedbacks in model projections of anthropogenic climate change, and the relative importance of the underlying mechanisms is poorly understood. One leading hypothesis is that ice-to-liquid conversions cause clouds to optically thicken, producing a major negative feedback. We test this hypothesis by developing a method to decompose cloud radiative feedbacks by cloud-top phase. The method is applied to an ensemble of six state-of-the-art global climate models run with prescribed sea surface temperature. In these simulations, the global mean of the net cloud scattering and emissivity feedback from cloud-phase conversions ranges from −0.17 to −0.01 W m⁻² K⁻¹, while the overall net cloud feedback ranges from 0.02 to 0.91 W m⁻² K⁻¹. The multimodel mean of the cloud scattering and emissivity feedback from cloud-phase conversions is approximately 19% of the magnitude of the multimodel mean of the overall cloud feedback (−0.10 vs 0.52 W m⁻² K⁻¹). These results indicate that cloud-phase conversions cause a robust negative feedback by changing cloud scattering and emissivity, but this mechanism makes a modest contribution to the overall cloud feedback at the global scale. Significance Statement: Climate warming changes Earth’s cloud properties, which then change the temperature further. One cloud-climate feedback mechanism involves the conversion of cloud ice particles to liquid droplets with warming. This makes clouds more opaque, causing them to reflect more solar radiation back to space. It is widely accepted that this mechanism dampens climate warming, but its importance relative to other feedback mechanisms has been unclear. This study develops a method to estimate the cloud opacity feedback from ice-to-liquid conversions in climate-model simulations. On average, cloud-opacity changes from phase conversions explain approximately 19% of the overall cloud–climate feedback in an ensemble of six climate models. This finding clarifies the importance of cloud-phase conversions in projections of anthropogenic climate change.

Recent studies reveal an anti‐correlation between global cloud feedback (CF) and effective radiative forcing due to aerosol‐cloud interaction (ERFaci) in Earth system models, but the physical mechanisms underlying it remain uncertain. Here we investigate how different turbulence representations contribute to this relationship over the global ocean using an ensemble of Energy Exascale Earth System Model version 2 simulations with perturbed turbulence parameters. The anti‐correlation appears only in the tropical ascent regime. In the Northern Hemisphere midlatitude and high latitude regimes, there is no significant correlation, and in the tropical marine low cloud and Southern Ocean regimes, the correlation is positive. These opposite correlations are primarily driven by opposing CF responses to perturbed parameters. We find that the mean‐state turbulent mixing strength affects both CF and ERFaci, enabling strong correlations in certain regimes. This study highlights the complex linkages between CF and ERFaci through turbulent processes across diverse cloud regimes.

The representations of aerosol and aerosol-cloud interactions (ACI) in conventional Earth system models are overly simplified due to computational constraints. These simple process representations limit the models’ predictive power as they contribute to significant errors in various parts of the simulated climate system. To address this challenge, we developed neural networks to replace aerosol and ACI processes (optics, activation, and precipitation) in the U.S. Department of Energy’s Energy Exascale Earth System Model (E3SM). These neural networks are trained on high-fidelity-high-resolution data and achieve remarkably high accuracy in offline tests. When implemented in E3SM, robust tests and guardrails are needed to ensure that the model produces correct and stable simulations and that their computational cost is low enough so that multi-year global simulations are possible. The hybrid E3SM produces a much more accurate characterization of aerosol and ACI, which leads to a very different climate simulation. To evaluate E3SM, an observationally based emulator has also been developed for understanding aerosol’s role in modulating various atmospheric features across scales in the real world. We highlight that the new hybrid approach, combining physics and artificial intelligence, provides ample opportunities for advancing understanding and predictability of the role of aerosols in the Earth system.

Earth system models struggle to accurately simulate aerosol’s interactions with weather and climate. This is largely attributed to structural uncertainty including insufficient process representation and model resolution due to limited computer power, and model tuning has become a low-cost remedy for improving performance. With unprecedented computational capability, improved understanding, modern software, and novel machine learning algorithms, high-resolution Earth system modeling with accurate and yet expensive process representations has become possible. In this study, we quantify the impacts of longstanding structural uncertainty on aerosol effective radiative forcing (ERF) by incorporating much more sophisticated process representations in U.S. Department of Energy’s Energy Exascale Earth System Model (E3SM). The ERF associated with aerosol-cloud interactions is further decomposed into the Twomey effect, liquid water path (LWP) adjustment, and cloud fraction adjustment using a satellite-based radiative kernel so that the impacts of each new process representation on aerosol ERF can be evaluated against observations. We find that while increasing model resolution to kilometer scale changes aerosol ERF by 30%, model physics representations (aerosol mixing assumption, condensational growth, secondary organic and sulfate aerosol formation, aerosol optics, aerosol activation, emission, giant aerosol, chemistry, warm rain process, and aerosol-turbulence coupling) contributes to a factor-of-two variation in aerosol ERF. As opposed to model tuning, this approach improves understanding and increases confidence in simulations as they are traceable to physics. Furthermore, even though the model’s total aerosol ERF or the Twomey effect alone can be brought to agree well with satellite estimate, significant biases in LWP and cloud fraction adjustments remain, highlighting the importance of improving aerosol interactions with cloud macrophysics in the model.

Changes in cloud scattering properties and emissivity that arise from atmospheric warming cause substantial radiative feedbacks in model projections of anthropogenic climate change, and the relative importance of the underlying mechanisms is poorly understood. One leading hypothesis is that ice-to-liquid conversions cause clouds to optically thicken, producing a major negative feedback. We test this hypothesis by developing a method to decompose cloud radiative feedbacks by cloud-top phase. The method is applied to an ensemble of six state-of-the-art global climate models run with prescribed sea-surface temperature. In these simulations, the global mean of the net cloud scattering and emissivity feedback from cloud-phase conversions ranges from -0.17 to -0.01 W m-2 K-1, while the overall net cloud feedback ranges from 0.02 to 0.91 W m-2 K-1. The multi-model mean of the cloud scattering and emissivity feedback from cloud-phase conversions is approximately 18% of the magnitude of the multi-model mean of the overall cloud feedback (-0.10 W m-2 K-1 vs. 0.52 W m-2 K-1). These results indicate that cloud-phase conversions cause a robust negative feedback by changing cloud scattering and emissivity, but this mechanism makes a modest contribution to the overall cloud feedback at the global scale.

Aerosol–cloud interactions (ACIs) are a leading source of uncertainty in estimates of the historical effective radiative forcing (ERF). One reason for this uncertainty is the difficulty in estimating the ERF from aerosol–cloud interactions (ERFaci) in climate models, which typically requires multiple calls to the radiation code. Most commonly used methods also cannot disentangle the contributions from different processes to ERFaci. Here, we develop a new, computationally efficient method for estimating the shortwave (SW) ERFaci from liquid clouds using histograms of monthly averaged cloud fraction partitioned by cloud droplet effective radius (r_e) and liquid water path (LWP). Multiplying the histograms with SW cloud radiative kernels gives the total SW ERFaci from liquid clouds, which can be decomposed into contributions from the Twomey effect, LWP adjustments, and cloud fraction (CF) adjustments. We test the method with data from five CMIP6-era models, using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument simulator to generate the histograms. Our method gives similar total SW ERFaci estimates to other established methods in regions of prevalent liquid cloud and indicates that the Twomey effect, LWP adjustments, and CF adjustments have contributed −0.34 ± 0.23, −0.22 ± 0.13, and −0.09 ± 0.11 W m⁻², respectively, to the effective radiative forcing of the climate since 1850 in the ensemble mean (95 % confidence). These results demonstrate that widespread adoption of a MODIS r_e–LWP joint histogram diagnostic would allow the SW ERFaci and its components to be quickly and accurately diagnosed from climate model outputs, a crucial step for reducing uncertainty in the historical ERF.

The cloud radiative kernel method is a popular approach to quantify cloud feedbacks and rapid cloud adjustments to increased CO₂ concentrations and to partition contributions from changes in cloud amount, altitude, and optical depth. However, because this method relies on cloud property histograms derived from passive satellite sensors or produced by passive satellite simulators in models, changes in obscuration of lower-level clouds by upper-level clouds can cause apparent low-cloud feedbacks and adjustments, even in the absence of changes in lower-level cloud properties. Here, we provide a methodology for properly diagnosing the impact of changing obscuration on cloud feedbacks and adjustments and quantify these effects across climate models. Averaged globally and across global climate models, properly accounting for obscuration leads to weaker positive feedbacks from lower-level clouds and stronger positive feedbacks from upper-level clouds while simultaneously removing a mostly artificial anti-correlation between them. Given that the methodology for diagnosing cloud feedbacks and adjustments using cloud radiative kernels has evolved over several papers, and obscuration effects have only occasionally been considered in recent papers, this paper serves to establish recommended best practices and to provide a corresponding code base for community use.