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This dataset originates from a new CESM2 CAM6 perturbed parameter ensemble (PPE) designed to explore climate and hydroclimate dynamics under a wide range of sea surface temperature (SST) conditions. The SST varies from 4 degrees Celsius colder to 16 degrees Celsius warmer than preindustrial levels, encompassing a broad spectrum of mean temperatures spanning the past 65 million years. This dataset offers valuable insights into climate and hydroclimate responses, as well as weather and climate extremes under diverse conditions.The dataset includes results from nine PPE simulations with different SST scenarios: preindustrial (PREI), 4K cooler (M04K), and 4K, 8K, 12K, and 16K warmer (P04K to P16K). For SSTs exceeding 8K warming, sea ice was removed to improve numerical stability. Each PPE set consists of 250 ensemble members, with 45 parameters related to microphysics, convection, turbulence, and aerosols perturbed using Latin Hypercube Sampling. An additional simulation with default parameter settings brings the total to 251 simulations, each running for five years using CAM6.3 (https://github.com/ESCOMP/CAM/tree/cam6_3_026; with additional paleo modifications).Post-processing converted the data into compressed NetCDF-4 format. All 251 runs were concatenated using ncecat to minimize the number of files. For example, the following file contains monthly surface temperature data from the preindustrial PPE: f.c6.F1850.f19_f19.paleo_ppe.sst_prei.ens251/atm/proc/tseries/month_1/f.c6.F1850.f19_f19.paleo_ppe.sst_prei.ens251.cam.h0.TS.000101-000512.ncA detailed variable list [https://rda.ucar.edu/OS/web/datasets/d651038/docs/detailed_vars.txt] can be found in the Documentation Tab.Parameter values are provided in the PPE Parameter File. More details can be found in the paper: Zhu et al. (2025). Investigating the State Dependence of Cloud Feedback Using a Suite of Perturbed Parameter Ensembles, Journal of Climate. 

Abstract The state dependence of cloud feedback—its variation with the mean state climate—has been found in many paleoclimate and contemporary climate simulations. Previous results have shown inconsistencies in the sign, magnitude, and underlying mechanisms of state dependence. To address this, we utilize a perturbed parameter ensemble (PPE) approach with fixed sea surface temperature (SST) in the Community Atmosphere Model, version 6. Our suites of PPEs span a wide range of global mean surface temperatures (GMSTs), with spatially uniform SST perturbations of −4, 0, 4, 8, 12, and 16 K from the preindustrial. The results reveal a nonmonotonic variation with GMSTs: Cloud feedback increases under both cooler and warmer-than-preindustrial conditions, with a rise of ∼0.1 W m⁻²K⁻¹under a 4-K colder climate and ∼0.4 W m⁻²K⁻¹under a 12-K warmer climate. This complexity arises from differing cloud feedback responses in high and low latitudes. In high latitudes, cloud feedback consistently rises with warming, likely driven by a moist adiabatic mechanism that influences cloud liquid water. The low-latitude feedback increases under both cooler and warmer conditions, likely influenced by changes in the lower-tropospheric stability. This stability shift is tied to nonlinearity in thermodynamic responses, particularly in the tropical latent heating, alongside potential state-dependent changes in tropical circulations. Under warmer-than-preindustrial conditions, the increase in cloud feedback with warming is negatively correlated with its preindustrial value. Our PPE approach takes the model parameter uncertainty into account and emphasizes the critical role of state dependence in understanding past and predicting future climates. Significance StatementThis study focuses on how cloud feedback—one of the most uncertain aspects of climate change—varies as global temperatures rise. We found that the cloud feedback decreases at first with warming and then increases, showing significant variation. This complexity stems from nonlinear thermodynamics, such as the Clapeyron–Clausius relationship, which describes how temperature affects moisture in the atmosphere. Our results indicate that the cloud feedback depends on the level of global warming, which is a significant factor rooted in fundamental physics. Recognizing this dependence is important for studies that aim to interpret past climates and predict future climate changes. 

The simulation of ice sheet-climate interaction such as surface massbalance fluxes are sensitive to model grid resolution. Here we simulatethe multicentury evolution of the Greenland Ice Sheet (GrIS) and itsinteraction with the climate using the Community Earth System Modelversion 2.2 (CESM2.2) including an interactive GrIS component (theCommunity Ice Sheet Model v2.1 [CISM2.1]) under an idealized warmingscenario (atmospheric CO2 increases by 1% yr−1 until quadrupling thepre-industrial level and then is held fixed). A variable-resolution (VR)grid with 1/4◦ regional refinement over broader Arctic and 1◦ resolutionelsewhere is applied to the atmosphere and land components, and theresults are compared to conventional 1◦ lat-lon grid simulations toinvestigate the impact of grid refinement. An acceleration of GrIS massloss is found at around year 110, caused by rapidly increasing surfacemelt as the ablation area expands with associated albedo feedback andincreased turbulent fluxes. Compared to the 1◦ runs, the VR run featuresslower melt increase, especially over Western and Northern Greenland,which slope gently towards the peripheries. This difference patternoriginates primarily from the weaker albedo feedback in the VR run,complemented by its smaller cloud longwave radiation. The steeper VRGreenland surface topography favors slower ablation zone expansion, thusleading to its weaker albedo feedback. The sea level rise contributionfrom the GrIS in the VR run is 53 mm by year 150 and 831 mm by year 350,approximately 40% and 20% smaller than the 1◦ runs, respectively. 

Abstract Global Storm Resolving Models (GSRMs) provide a way to understand weather and climate events across scales for better‐informed climate impacts. In this work, we apply the recently developed and validated CAM (Community Atmosphere Model)—MPAS (Model for Prediction Across Scales) modeling framework, based on the open‐source Community Earth System Model (CESM2), to examine the tropical convection features at the storm resolving scale over the Maritime Continent region at 3 km horizontal spacing. We target two global numerical experiments during the winter season of 2018 for comparison with observation in the region. We focus on the investigation of the representations of the convective systems, precipitation statistics, and tropical cyclone behaviors. We found that regional‐refined experiments show more accurate precipitation distributions, diurnal cycles, and better agreement with observations for tropical cyclone features in terms of intensity and strength statistics. We expect the exploration of this work will further advance the development and use of the storm‐resolving model in precipitation predictions across scales. 

Abstract The simulation of ice sheet‐climate interactions, such as surface mass balance fluxes, is sensitive to model grid resolution. Here we simulate the multi‐century evolution of the Greenland Ice Sheet (GrIS) and its interaction with the climate using the Community Earth System Model version 2.2 (CESM2.2) including an interactive GrIS component (the Community Ice Sheet Model v2.1 [CISM2.1]) under an idealized warming scenario (atmospheric increases by 1% until quadrupling the pre‐industrial level and then is held fixed). A variable‐resolution (VR) grid with 1/ regional refinement over the broader Arctic and resolution elsewhere is applied to the atmosphere and land components, and the results are compared with conventional lat‐lon grid simulations to investigate the impact of grid refinement. Compared with the runs, the VR run features a slower rate of surface melt, especially over the western and northern GrIS, where the ice surface slopes gently toward the periphery. This difference pattern originates primarily from higher snow albedo and, thus, weaker albedo feedback in the VR run. The VR grid better captures the CISM ice sheet topography by reducing elevation discrepancies between CAM and CISM and is, therefore, less reliant on the downscaling algorithm, which is known to underestimate albedo gradients. The sea level rise contribution from the GrIS in the VR run is 53 mm by year 150 and 831 mm by year 350, approximately 40% and 20% less than that of the runs, respectively. 

Marine cloud brightening (MCB) is the deliberate injection of aerosol particles into shallow marine clouds to increase their reflection of solar radiation and reduce the amount of energy absorbed by the climate system. From the physical science perspective, the consensus of a broad international group of scientists is that the viability of MCB will ultimately depend on whether observations and models can robustly assess the scale-up of local-to-global brightening in today’s climate and identify strategies that will ensure an equitable geographical distribution of the benefits and risks associated with projected regional changes in temperature and precipitation. To address the physical science knowledge gaps required to assess the societal implications of MCB, we propose a substantial and targeted program of research—field and laboratory experiments, monitoring, and numerical modeling across a range of scales.