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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. 

Abstract. The McMurdo Dry Valleys (MDV) are home to a unique microbial ecosystem that is dependent on the availability of freshwater. This is a polar desert and freshwater originates almost entirely from surface and near-surface melt of the cold-based glaciers. Understanding the future evolution of these environments requires the simulation of the full chain of physical processes from net radiative forcing, surface energy balance, melt, runoff and transport of meltwater in stream channels from the glaciers to the terminal lakes where the microbial community resides. To establish a new framework to do this, we present the first application of WRF-Hydro/Glacier in the MDV, which as a fully distributed hydrological model has the capability to resolve the streams from the glaciers to the bare land that surround them. Given that meltwater generation in the MDV is almost entirely dependent on small changes in the energy balance of the glaciers, the aim of this study is to optimize the multi-layer snowpack scheme that is embedded in WRF-Hydro/Glacier to ensure that the feedbacks between albedo, snowfall and melt are fully resolved. To achieve this, WRF-Hydro/Glacier is implemented at a point scale using automatic weather station data on Commonwealth Glacier to physically model the onset, duration and end of melt over a 7-month period (1 August 2021 to 28 February 2022). To resolve the limited energetics controlling melt, it was necessary to (1) limit the percolation of meltwater through the ice layers in the multi-layer snowpack scheme and (2) optimize the parameters controlling the albedo of both snow and ice over the melt season based on observed spectral signatures of albedo. These modifications enabled the variability of broadband albedo over the melt season to be accurately simulated and ensured that modelled surface and near-surface temperatures, surface height change and runoff were fully resolved. By establishing a new framework that couples a detailed snowpack model to a fully distributed hydrological model, this work provides a stepping stone to model the spatial and temporal variability of melt and streamflow in the future, which will enable some of the unknown questions about the hydrological connectivity of the MDV to be answered. 

Abstract Aerosol‐cloud interactions (ACI) in warm clouds are the primary source of uncertainty in effective radiative forcing (ERF) during the historical period and, by extension, inferred climate sensitivity. The ERF due to ACI (ERFaci) is composed of the radiative forcing due to changes in cloud microphysics and cloud adjustments to microphysics. Here, we examine the processes that drive ERFaci using a perturbed parameter ensemble (PPE) hosted in CAM6. Observational constraints on the PPE result in substantial constraints in the response of cloud microphysics and macrophysics to anthropogenic aerosol, but only minimal constraint on ERFaci. Examination of cloud and radiation processes in the PPE reveal buffering of ERFaci by the interaction of precipitation efficiency and radiative susceptibility.