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  1. Abstract  
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  2. 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.

     
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    Free, publicly-accessible full text available March 5, 2025
  3. Free, publicly-accessible full text available June 12, 2025
  4. Abstract

    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 the 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−2K−1) as compared to CAM6.0 (0.81 W m−2K−1) owing primarily to reductions in low clouds over the tropics and midlatitudes. The work highlights the large sensitivity of cloud feedbacks to both parameter values and structural details in CAM6.

     
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  5. 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. 
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    Free, publicly-accessible full text available March 22, 2025
  6. Abstract For the Community Atmosphere Model version 6 (CAM6), an adjustment is needed to conserve dry air mass. This adjustment exposes an inconsistency in how CAM6’s energy budget incorporates water—in CAM6 water in the vapor phase has energy, but condensed phases of water do not. When water vapor condenses, only its latent energy is retained in the model, while its remaining internal, potential, and kinetic energy are lost. A global fixer is used in the default CAM6 model to maintain global energy conservation, but locally the energy tendency associated with water changing phase violates the divergence theorem. This error in energy tendency is intrinsically tied to the water vapor tendency, and reaches its highest values in regions of heavy rainfall, where the error can be as high as 40 W m −2 annually averaged. Several possible changes are outlined within this manuscript that would allow CAM6 to satisfy the divergence theorem locally. These fall into one of two categories: 1) modifying the surface flux to balance the local atmospheric energy tendency and 2) modifying the local atmospheric tendency to balance the surface plus top-of-atmosphere energy fluxes. To gauge which aspects of the simulated climate are most sensitive to this error, the simplest possible change—where condensed water still does not carry energy and a local energy fixer is used in place of the global one—is implemented within CAM6. Comparing this experiment with the default configuration of CAM6 reveals precipitation, particularly its variability, to be highly sensitive to the energy budget formulation. Significance Statement This study examines and explains spurious regional sources and sinks of energy in a widely used climate model. These energy errors result from not tracking energy associated with water after it transitions from the vapor phase to either liquid or ice. Instead, the model used a global fixer to offset the energy tendency related to the energy sources and sinks associated with condensed water species. We replace this global fixer with a local one to examine the model sensitivity to the regional energy error and find a large sensitivity in the simulated hydrologic cycle. This work suggests that the underlying thermodynamic assumptions in the model should be revisited to build confidence in the model-simulated regional-scale water and energy cycles. 
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  7. Abstract. Global climate models (GCMs) have advanced in many ways ascomputing power has allowed more complexity and finer resolutions. As GCMsreach storm-resolving scales, they need to be able to produce realisticprecipitation intensity, duration, and frequency at fine scales withconsideration of scale-aware parameterization. This study uses astate-of-the-art storm-resolving GCM with a nonhydrostatic dynamical core – theModel for Prediction Across Scales (MPAS), incorporated in the atmosphericcomponent (Community Atmosphere Model, CAM) of the open-source CommunityEarth System Model (CESM), within the System for Integrated Modeling of theAtmosphere (SIMA) framework (referred to as SIMA-MPAS). At uniform coarse (here, at 120 km) gridresolution, the SIMA-MPAS configuration is comparable to the standardhydrostatic CESM (with a finite-volume (FV) dynamical core) with reasonableenergy and mass conservation on climatological timescales. With thecomparable energy and mass balance performance between CAM-FV (workhorse dynamical core) and SIMA-MPAS (newly developed dynamical core), it gives confidence inSIMA-MPAS's applications at a finer resolution. To evaluate this, we focuson how the SIMA-MPAS model performs when reaching a storm-resolving scale at3 km. To do this efficiently, we compose a case study using a SIMA-MPASvariable-resolution configuration with a refined mesh of 3 km covering thewestern USA and 60 km over the rest of the globe. We evaluated the modelperformance using satellite and station-based gridded observations withcomparison to a traditional regional climate model (WRF, the WeatherResearch and Forecasting model). Our results show realistic representationsof precipitation over the refined complex terrains temporally and spatially.Along with much improved near-surface temperature, realistic topography, andland–air interactions, we also demonstrate significantly enhanced snowpackdistributions. This work illustrates that the global SIMA-MPAS atstorm-resolving resolution can produce much more realistic regional climatevariability, fine-scale features, and extremes to advance both climate andweather studies. This next-generation storm-resolving model could ultimatelybridge large-scale forcing constraints and better inform climate impactsand weather predictions across scales. 
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  8. Abstract

    Cloud microphysics is one of the most time‐consuming components in a climate model. In this study, we port the cloud microphysics parameterization in the Community Atmosphere Model (CAM), known as Parameterization of Unified Microphysics Across Scales (PUMAS), from CPU to GPU to seek a computational speedup. The directive‐based methods (OpenACC and OpenMP target offload) are determined as the best fit specifically for our development practices, which enable a single version of source code to run either on the CPU or GPU, and yield a better portability and maintainability. Their performance is first examined in a PUMAS stand‐alone kernel and the directive‐based methods can outperform a CPU node as long as there is enough computational burden on the GPU. A consistent behavior is observed when we run PUMAS on the GPU in a practical CAM simulation. A 3.6× speedup of the PUMAS execution time, including data movement between CPU and GPU, is achieved at a coarse horizontal resolution (8 NVIDIA V100 GPUs against 36 Intel Skylake CPU cores). This speedup further increases up to 5.4× at a high resolution (24 NVIDIA V100 GPUs against 108 Intel Skylake CPU cores), which highlights the fact that GPU favors larger problem size. This study demonstrates that using GPU in a CAM simulation can save noticeable computational costs even with a small portion of code being GPU‐enabled. Therefore, we are encouraged to port more parameterizations to GPU to take advantage of its computational benefit.

     
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