Li-Wei Chao

Cloud feedback remains the main source of uncertainty in climate sensitivity estimated by global climate models (GCMs), largely because subgrid cloud responses are parameterized in GCMs due to their coarse resolution. This study examines cloud feedback in the global 3.25-km Simple Cloud-Resolving Energy Exascale Earth System Model (E3SM) Atmosphere Model (SCREAM 3 km) through a pair of 1-yr atmosphere-only simulations with control and +4-K sea surface temperature perturbations. SCREAM 3 km produces a positive cloud feedback that falls within but at the upper end of the range of Coupled Model Intercomparison Project phase 5 (CMIP5) and CMIP phase 6 (CMIP6) models and expert judgment. The positive cloud feedback arises from positive contributions from both high- and low-level clouds, with increases in high-cloud altitude and decreases in low-cloud amount and optical depth playing key roles. The stronger-than-CMIP-average feedback is mainly attributable to the high-cloud altitude feedback, owing to cloud tops rising nearly isothermally in SCREAM 3 km. The positive low-cloud amount feedback is weaker in SCREAM than in GCMs because estimated inversion strength (EIS) increases more dramatically with warming. A coarser 12-km resolution version of SCREAM exhibits a weaker positive cloud feedback than SCREAM 3 km, mainly because its low-cloud-radiative flux is more sensitive to EIS, leading to a stronger negative low-cloud amount feedback. With this process-level assessment of cloud feedback, this study reveals where SCREAM aligns with and diverges from conventional GCMs and expert assessment, providing insights to inform further model improvement and future expert assessment.

This study systematically investigates biases in cloud radiative effects (CREs) within the recently released Energy Exascale Earth System Model version 3 (E3SMv3). Compared to its previous version (E3SMv2), E3SMv3 shows excessively strong shortwave CRE over tropical and subtropical oceans, the Southern Ocean, and the Northern Hemisphere storm tracks, which is primarily caused by an overestimation of optically intermediate low clouds. The model also displays excessive longwave CRE over the Maritime Continent and other tropical deep convection regions, resulting from an overestimation of optically thick high clouds. Implementing the Predicted Particle Properties (P3) scheme for stratiform clouds played the most significant role in these cloud changes. In addition, using a double‐moment scheme for convective clouds contributed to the increase in intermediate low clouds and optically thick clouds in tropical deep convection regions. Error metrics for total cloud amount (E _TCA ), cloud properties (E _ctp‐τ ), and cloud properties weighted by their SW and LW radiative impacts (E _SW and E _LW ) indicate that E3SMv3's ability to reproduce observed cloud radiative effect of low, middle, and high clouds has not been improved compared to E3SMv2. Nevertheless, the performance of E3SMv3 remains well within the spread of CMIP6 models, with E _SW and E _LW values smaller than those in most CMIP6 models. This study underscores the importance of integrating diverse satellite observations for robust cloud evaluation and using cloud‐radiation relationships as consistency check for model errors. It suggests that further model developments focus on improving cloud microphysics and their interactions with radiation.

Accurately simulating clouds remains a key challenge in global climate models, primarily because cloud formation involves sub‐grid processes that are parameterized and crudely represented in models. This study examines the performance of DOE's Simple Cloud‐Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) in simulating cloud properties and their spatio‐temporal distribution by comparing against satellite observations. Two horizontal resolutions of SCREAM (3 and 12 km) are examined, and both depict a realistic spatial structure of mean‐state cloud cover but underestimate its global mean magnitude. SCREAM 3 km reasonably reproduces the distribution of mean‐state cloud properties across various cloud optical thickness and cloud‐top pressure regimes, with performance comparable to CMIP5 and CMIP6 ensemble and marginally outperforming SCREAM 12 km. Still, SCREAM 3 km tends to underpredict low clouds and optically thin clouds, highlighting the need for continued improvement in representing unresolved processes. This study provides a basis for confidence in the representation of clouds in SCREAM, as simulating mean‐state clouds is a necessary prerequisite for trusting its cloud responses to changes in aerosols and greenhouse gases.

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.

This study systematically investigates biases in cloud radiative effects (CREs) within the recently released Energy Exascale Earth System Model version 3 (E3SMv3). Results show that the excessively strong shortwave CRE over tropical and subtropical oceans, the Southern Ocean, and the Northern Hemisphere storm tracks is primarily caused by an overestimation of optically intermediate low clouds. The excessive longwave CRE over the Maritime Continent and other tropical deep convection regions results from an overestimation of optically thick high clouds. Implementing the Predicted Particle Properties (P3) scheme for stratiform clouds played the most significant role in these cloud changes. In addition, using a double-moment scheme for convective clouds contributed to the increase in intermediate low clouds and optically thick clouds in tropical deep convection regions. Error metrics for total cloud amount (ETCA), cloud properties, and cloud properties weighted by their SW and LW radiative impacts (ESW and ELW) indicate that E3SMv3’s ability to reproduce observed cloud radiative effect of low, middle, and high clouds has not been improved compared to E3SMv2, as biases in ESW and ELW are slightly larger although minor improvements are seen in ETCA. Nevertheless, the performance of E3SMv3 remains well within the spread of CMIP6 models, with ESW and ELW values smaller than those in most CMIP6 models. This study underscores the importance of integrating diverse satellite observations for robust cloud evaluation and using cloud-radiation relationships as consistency check for model errors. It suggests that further model developments focus on improving cloud microphysics and their interactions with radiation.

Accurately simulating clouds remains a key challenge in global climate models, primarily because cloud formation involves sub-grid processes that are parameterized and crudely represented in models. This study examines the performance of DOE’s Simple Cloud-Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) in simulating cloud properties and their spatio-temporal distribution by comparing against satellite observations. Two horizontal resolutions of SCREAM (3 and 12km) are examined, and both depict a realistic spatial structure of mean-state cloud cover but underestimate its global mean magnitude. SCREAM 3km faithfully reproduces the distribution of mean-state cloud properties across various cloud optical thickness and cloud-top pressure regimes, with an error that is smaller than that of the 12km and most CMIP5 and CMIP6 models. Still, SCREAM tends to underpredict low clouds and optically thin clouds, highlighting the need for continued improvement in representing unresolved processes. This study provides a basis for confidence in the representation of clouds in SCREAM, as simulating mean-state clouds is a necessary prerequisite for trusting its cloud responses to changes in aerosols and greenhouse gases.

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.

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 are unremarkable compared to 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 one-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.

Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction.