This study investigates in-cloud ground icing over Fagernesfjellet, a complex terrain site in northern Norway, using new kilometer-scale simulations from the Weather Research and Forecasting model (WRF) during the year 2022-2023. The WRF-derived icing results and meteorological parameters relevant to in-cloud icing are validated using measurement data (MEAS), with focus on model resolution and terrain height. Our findings indicate that WRF effectively represented the temporal evolution of icing events, with higher altitudes indicating more severe icing and an increased number of annual icing hours. However, in the current model setup, WRF underestimates the magnitude and variability of icing loads; an improvement in icing load amount is found when accurate terrain height point is used. The highest spatial resolution improved the simulation of key meteorological parameters, such as temperature and wind speed, but struggled with relative humidity and wind direction. Our study shows that high-resolution simulation and accurate terrain height are essential for improving atmospheric in-cloud ground icing simulations over complex terrain.

Abstract. Earth system models (ESMs) provide essential insight into large-scale climate variability and change but often lack the spatial resolution required to represent fine-scale processes critical for regional impacts and adaptation planning. To help address this gap, we present an updated high-resolution regional climate simulation for North America (NA) as part of the Coordinated Regional Downscaling Experiment (CORDEX). We evaluate a new reanalysis forced NA-CORDEX simulation at 12 km resolution against observational datasets, an earlier NA-CORDEX CMIP5 simulation (25 km), and the convection-permitting CONUS-404 simulation (4 km). Through these comparisons, we assess how horizontal resolution and regional model configuration influence historical biases and extremes, with a particular focus on precipitation processes given that convection is parameterized at 12 km. Relative to previous NA-CORDEX-CMIP5 simulations, the new CMIP6-based evaluation run reduces mean biases in temperature and precipitation, improves the magnitude and timing of the diurnal precipitation cycle across North America, and substantially improves the representation of tropical cyclone structure and intensity. Notably, extreme precipitation rates are well captured at 12 km when compared to the convection-permitting simulations. While long-term convection-permitting climate simulations remain a key objective for regional modeling, the current generation of CORDEX simulations provides a practical balance between computational efficiency and physical realism for continental-scale climate assessment.

Downscaled precipitation projections were created using the Intermediate Complexity Atmospheric Research (ICAR) model over the western United States to increase the physical realism in orographic precipitation changes. End-of-century simulations from eight models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) were down-scaled with ICAR and compared to the widely utilized statistically downscaled dataset, localized constructed analogs (LOCAs), to understand where and why projections of cool-season (September-May) precipitation differed. ICAR and LOCA precipitation projections were similar, but their sign differed in hydrologically relevant regions likely due to ICAR's simulation of microphysics and mesoscale dynamics with high-resolution topography (6 km). In the Pacific Northwest, cool-season precipitation projections from ICAR showed an increase on the windward side of the Cascades and no significant change within the lee. This difference between the windward and leeward side was attributed to reduced zonal wind speeds, allowing more time for microphysical processes within ICAR. This contrast is enhanced by rain's faster fall speed compared to snow, limiting transport into the lee. Meanwhile, LOCA projected an increase in precipitation across the Cascades. In the Upper Colorado River basin, LOCA projected an increase in precipitation in high elevation regions (>3000 m), but ICAR projected no significant change or a decrease in precipitation. High elevation differences were most evident in the spring and fall and were also attributed to a snow-to-rain transition and dynamical processes that impacted orographic enhancement within ICAR. Idealized, controlled studies are needed to better isolate individual processes, but these results underscore the importance of including microphysics and mesoscale dynamics within regional-scale precipitation projections. SIGNIFICANCE STATEMENT: A set of global climate model simulations was downscaled using an atmosphericmodel that contains key physical equations, referred to as Intermediate Complexity Atmospheric Research (ICAR).ICAR was used to examine projected changes in end-of-century cool-season precipitation over mountains in the west-ern United States. Precipitation projections from ICAR were similar to projections that used statistical relationships todownscale climate projections. However, projections differed between ICAR and statistically downscaled datasets inwhether they increased, decreased, or stayed the same in specific, hydrologically relevant regions such as the easternCascades and high elevation areas of the Upper Colorado River basin. These differences were attributed to the simula-tion of physical processes in ICAR. The results highlight the importance of kilometer-scale atmospheric processes inregional climate projections

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-2 K-1 under a 4-K colder climate and-0.4 W m-2 K-1 under 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.

Aerosol-cloud interactions (ACI) in warm clouds alter reflected shortwave radiation by influencing cloud microphysical and macrophysical properties. The variable of state controlling ACI is the cloud droplet number concentration (Nd). Here, we examine the perturbations in Nd due to anthropogenic aerosols (ΔNd, PD-PI) using a perturbed parameter ensemble (PPE) hosted in the sixth Community Atmosphere Model (CAM6). Surrogate models are created for the CAM6 PPE outputs and are used to generate 1 million model variants of CAM6 by sampling 45 sources of parameter uncertainty. The range of uncertain physical parameters related to ACI are constrained with observations of aerosol and cloud properties from SOCRATES. The likely range of uncertain parameters and the associated range of ΔNd, PD-PI are more strongly constrained with observations of Nd relative to observations of cloud condensation nuclei. We conduct sensitivity tests of how constraints on ΔNd, PD-PI are affected by systematic uncertainties in observations and our limitations in our surrogate models created for CAM6 PPE outputs. Based on this, we provide guidance on the impact of reducing systematic uncertainty in airborne microphysical observations and in surrogate models.

Constraining cloud feedback in global climate models (GCMs) using observations is important for establishing accurate predictions of future climate. Uncertainty in shortwave cloud feedback (SW FB ) dominates uncertainty in total cloud feedback. Recent studies show a shift toward more positive extratropical SW FB in the latest generations of GCMs leading to the emergence of very high equilibrium climate sensitivity (ECS). In this study, we use precipitation efficiency and albedo susceptibility to constrain liquid water path (LWP) response to warming and SW FB in the Southern Ocean (SO; 50°–80°S). We analyze precipitation in extratropical cyclones (ECs) to learn about extratropical condensed water sink processes, combined with observations of clouds and moisture convergence, and use the analysis to better understand and constrain SW FB . We utilize a perturbed parameter ensemble (PPE) hosted in the Community Atmosphere Model, version 6 (CAM6), to provide a constraint on SW FB based on observations from Clouds and the Earth’s Radiant Energy System (CERES) and Multisensor Advanced Climatology of LWP (MAC-LWP). We apply Gaussian process regression to emulate the model response to all parameters perturbed in the PPE. Confronting the emulator output with observations provides a new estimated response of Earth to global warming. Our new estimates of SO LWP reduce the PPE range by 66%–72%, which results in a shortwave cloud radiative effect estimated range that is 27%–34% less than the PPE range. Observations suggest a more positive SO SW FB than the Community Earth System Model, version 2 (CESM2), and consequently do not reject the high climate sensitivity GCMs emerging from the Coupled Model Intercomparison Project phase 6 (CMIP6).

Earth's albedo is symmetric between the northern and southern hemispheres (NH and SH, respectively) because SH clouds compensate for higher NH clear‐sky albedo, a feature that climate models have difficulty capturing. We assess how parameterized processes affect a model's cloud albedo and albedo symmetry using a perturbed parameter ensemble (PPE) of atmospheric simulations. Parameters most significant to simulated albedo symmetry impact precipitation, turbulent dissipation, and sea salt aerosol emissions. Constraining the PPE's shortwave cloud feedbacks using the observed albedo symmetry yields a range of +0.61 0.24 W m −2 K −1 (66% confidence), which is stronger than that of the model's control simulation due to parameter settings that lead to greater loss of subtropical low clouds and weaker negative cloud phase feedback. Although these settings would reduce cloud albedo bias compared to the control simulation, we find that albedo symmetry has limited potential as a constraint for cloud feedbacks on its own.

By examining the historical temperature record during the industrial era, we can infer the climate's sensitivity to radiative perturbations, given knowledge of historical forcings. Energy conservation enforces a negative correlation between the climate feedback and historical forcing for a given change in global‐mean temperature. Here, we examine the negative correlation between the radiative forcing due to aerosol‐cloud interactions and the shortwave cloud feedback to warming that appears in a perturbed parameter ensemble (PPE). The PPE is not tuned to match the historical record, yet a negative correlation emerges over the extratropics due to the combined effects of liquid cloud precipitation efficiency and radiative saturation in the shortwave. Using an energy balance model, we argue that these processes combine to push Earth System Models to yield a temperature record in keeping with observations, but also limit our ability to constrain future warming posterior with the temperature record.

Aerosol–cloud interactions (ACIs) are the largest source of uncertainty in inferring the magnitude of future warming consistent with the observational record. The effective radiative forcing due to ACI (ERFaci) is dominated by liquid clouds and is composed of two terms: the change in cloud albedo due to redistributing liquid over a larger number of cloud droplets (Nd) and the change in cloud macrophysical properties due to changes in cloud microphysics. These terms are, respectively, referred to as the radiative forcing due to ACI (RFaci) and aerosol–cloud adjustments. While the magnitude of RFaci is uncertain, its sign is confidently negative and results in a cooling in the historical record. In contrast, the adjustment of cloud liquid water path (LWP) to enhanced Nd and associated radiative forcing is uncertain in sign. Increased LWP in response to increased Nd is consistent with precipitation suppression, while decreased LWP in response to increased Nd is consistent with enhanced evaporation from cloud top. Observational constraints of these processes are poor in part because of causal ambiguity in the relationship between Nd and LWP. To better understand this relationship, precipitation (P), Nd, and LWP surface observations from the Eastern North Atlantic (ENA) atmospheric observatory are combined with the output from a perturbed parameter ensemble (PPE) hosted in the Community Atmosphere Model version 6 (CAM6). This allows for causal interpretation of observed covariability. Observations of precipitation and cloud from ENA constrain the range of possible LWP aerosol–cloud adjustments relative to the prior from the PPE by 15 %, resulting in a global value that is confidently positive (a historical cooling) ranging from 2.1 to 6.9 g m−2. It is found that observed covariability between Nd and LWP is dominated by coalescence scavenging and that this observed covariability is not strongly related to aerosol–cloud adjustments.

We present a new additive method, referred to as sage for Simplified Additive Gaussian processes Emulator, for emulating climate model Perturbed Parameter Ensembles (PPEs). sage estimates the value of a climate model output as the sum of additive terms. Each additive term is the mean of a Gaussian Process, and corresponds to the impact of a parameter or parameter group on the variable of interest. This design caters to the sparsity of PPEs, which are characterized by limited ensemble members and high dimensionality of the parameter space and raise the issue of parameter sensitivity in the emulator design. sage quantifies the variability explained by different parameters and parameter groups, providing additional insights on the parameter-climate model output relationship. We apply sage to two climate model PPEs and compare it to a fully connected Neural Network. The two methods have comparable performance with both PPEs, but sage provides insights on parameter and parameter group importance as well as diagnostics useful for optimizing PPE design. Insights gained from applying the method and comparing its performance with Neural Network are pointed out which have not been previously addressed. Our work highlights that analyzing the PPE used to train an emulator is different from analyzing data generated from an emulator trained on the PPE, as the former provides more insights on the data structure in the PPE which could help inform the emulator design. Our work also proposes new questions on the optimal way of working with climate model PPEs.

A Perturbed Parameter Ensemble (PPE) with the Community Atmosphere Model version 6 (CAM6) is used to better understand the sensitivity of simulated clouds to both aerosol forcing and cloud feedbacks and the interactions between them. Aerosol forcing through aerosol-cloud interactions is mostly negative (a cooling) due to shortwave radiation, while feedbacks are positive or negative in different regions due to contrasting longwave and shortwave effects. Both forcing and feedbacks are related to the mean climate state. Higher magnitude cloud radiative effects generally mean larger net forcing and larger net feedback.Aerosol forcing is broadly related to the susceptibility of clouds to drop number. Feedbacks are less related to susceptibility, and in different regions. Aerosol forcing and cloud feedbacks are anti-correlated in the CAM6 PPE such that stronger negative forcing is associated with stronger positive feedbacks. Even the processes governing forcing and feedback sensitivity in the PPE are similar. These include the warm rain formation process, ice loss processes and deep convective intensity.

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.

This paper documents the methodology and preliminary results from a perturbed parameter ensemble (PPE) technique, where multiple parameters are varied simultaneously and the parameter values are determined with Latin hypercube sampling. This is done with the Community Atmosphere Model version 6 (CAM6), the atmospheric component of the Community Earth System Model version 2 (CESM2). We apply the PPE method to CESM2-CAM6 to understand climate sensitivity to atmospheric physics parameters. The initial simulations vary 45 parameters in the microphysics, convection, turbulence and aerosol schemes with 263 ensemble members. These atmospheric parameters are typically the most uncertain in many climate models. Control simulations and targeted simulations to understand climate forcing due to aerosols and fast climate feedbacks are analyzed. The use of various emulators is explored in the multi-dimensional space mapping input parameters to output metrics. Parameter impacts on various model outputs, such as radiation, cloud and aerosol properties, are evaluated. Machine learning is also used to probe optimal parameter values against observations. Our findings show that PPE is a valuable tool for climate uncertainty analysis. Furthermore, by varying many parameters simultaneously, we find that many different combinations of parameter values can produce results consistent with observations, and thus careful analysis of tuning is important. The CESM2-CAM6 PPE is publicly available and extensible to other configurations to address questions of other model processes in the atmosphere and other model components (e.g., coupling to the land surface).

The sensitivity of cloud feedbacks to atmospheric model parameters is evaluated using a CAM6 perturbed parameter ensemble (PPE). The CAM6 PPE perturbs 45 parameters across 262 simulations, 206 of which are used here. The spread in total cloud feedback and its six components across the CAM6 PPE are comparable to the spread across the CMIP6 and AMIP ensembles, indicating that parametric uncertainty mirrors structural uncertainty. However, the high-cloud altitude feedback is generally larger in the CAM6 PPE than WCRP assessment, CMIP6, and AMIP values. We evaluate the influence of each of the 45 parameters on the total cloud feedback and each of the six cloud feedback components. We also explore whether the CAM6 PPE can be used to constrain the total cloud feedback, with inconclusive results. Further, we find that despite the large parametric sensitivity of cloud feedbacks in CAM6, a substantial increase in cloud feedbacks from CAM5 to CAM6 is not a result of changes in parameter values. Notably, the CAM6 PPE is run with a more recent version of CAM6 (CAM6.3) than was used for AMIP (CAM6.0), and has a smaller total cloud feedback (0.56 W m$^{-2}$ K$^{-1}$) as compared to CAM6.0 (0.81 W m$^{-2}$ K$^{-1}$) owing primarily to reductions in low clouds over the tropics and middle latitudes. The work highlights the large sensitivity of cloud feedbacks to both parameter values and structural details in CAM6.

Glaciers, in general, are sensitive to changes in the climate but Himalayan glaciers, in particular, are highly affected by climate change. Mass balance (MB) of glaciers is one of the important parameters to examine the response of glaciers to climate variability and change. The study of mass balance sensitivity (MBS) due to climate perturbations for glaciers is also important to understand future behavior of the glaciers. For Chhota Shigri Glacier, research on the estimation of long-term annual and seasonal MB and MBS as well as equilibrium-line altitude (ELA) and accumulation area ration (AAR) sensitivity analysis is not reported in detail. Accordingly, the present study carries out a detailed analysis of annual and seasonal MBS from 1953 to 2014 using annual and monthly climate perturbations as well as ELA and AAR sensitivities for the Chhota Shigri Glacier. The long-term annual and seasonal MB of Chhota Shigri Glacier from 1953 to 2014 is reconstructed using distributed temperature-index model by simulating minimal model parameters, namely melt factor, snow, and ice radiations using Monte-Carlo simulation. The mean annual MB of Chhota Shigri was -0.28 ± 0.41 m w.e./year during 1953–2014. The annual MB decreased from − 0.09 ± 0.41 m w.e./year (1953–1968) to − 0.57 ± 0.41 m w.e./year (2000–2014). The estimated MBS of Chhota Shigri Glacier is 0.57 m w.e./°C due to temperature change which is high and can be attributed to the presence of significantly less debris-covered ice in Chhota Shigri Glacier. It is analyzed that ELA and AAR of Chhota Shigri Glacier will change to + 107.7 m a.s.l. and − 15.03

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.

The South America Affinity Group (SAAG) was established in early 2019 by the National Center for Atmospheric Research (NCAR) Water Systems Program as a community effort focused on improving hydroclimate science over South America. SAAG supports large research efforts such as the ANDEX Regional Hydroclimate Program (Espinoza et al. 2020) as well as individual research groups. The group started with a dozen members and quickly grew to over 100 participants from more than 10 countries. For the past four years, the SAAG has been meeting online every two weeks and has organized sessions at international conferences such as the American Geophysical Union Fall Meeting and the Convection-Permitting Climate Workshop (Prein et al. 2022). At the core of the SAAG effort are two multidecadal convection-permitting (CP) model simulations with 4-km grid spacing for historical and future climates over the South American continent. Additionally, a major observational data collection effort has been undertaken, including in situ station data from South American meteorological and water services, gridded products, satellite-based observations, and field campaign data (NCAR 2023a). This article discusses the research needs and scientific goals that drive this community of scientists with diverse backgrounds and interests.

Abstract. Cloud microphysics is critical for weather and climate prediction. In this work, we document updates and corrections to the cloud microphysical scheme used in the Community Earth System Model (CESM) and other models. These updates include a new nomenclature for the scheme, now called Parameterization of Unified Microphysics Across Scales (PUMAS), and the ability to run the scheme on graphics processing units (GPUs). The main science changes include refactoring an ice number limiter and associated changes to ice nucleation, adding vapor deposition onto snow, and introducing an implicit numerical treatment for sedimentation. We also detail the improvements in computational performance that can be achieved with GPU acceleration. We then show the impact of these scheme changes on the (a) mean state climate, (b) cloud feedback response to warming, and (c) aerosol forcing. We find that corrections are needed to the immersion freezing parameterization and that ice nucleation has important impacts on climate. We also find that the revised scheme produces less cloud liquid and ice but that this can be adjusted by changing the loss process for cloud liquid (autoconversion). Furthermore, there are few discernible effects of the PUMAS changes on cloud feedbacks but some reductions in the magnitude of aerosol–cloud interactions (ACIs). Small cloud feedback changes appear to be related to the implicit sedimentation scheme, with a number of factors affecting ACIs.