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1. Improvements in Wintertime Surface Temperature Variability in the Community Earth System Model Version 2 (CESM2) Related to the Representation of Snow Density

   https://doi.org/10.1029/2021MS002880  

   Simpson, Isla R.; Lawrence, David M.; Swenson, Sean C.; Hannay, Cecile; McKinnon, Karen A.; Truesdale, John E. (April 2022, Journal of Advances in Modeling Earth Systems)

   Abstract The Community Earth System Model (CESM) is widely used for the prediction and understanding of climate variability and change. Accurate simulation of the behavior of near surface air temperature (T_2m) is critical in such a model for addressing societally relevant problems. However, previous versions of CESM suffered from an overestimation of wintertimeT_2mvariability in Northern Hemisphere (NH) land regions. Here, it is shown that the latest version of CESM (CESM2) exhibits a much improved representation of wintertimeT_2mvariability compared to its predecessor and it now compares well with observations. A series of targeted experiments reveal that an important contributor to this improvement is the local effects of changes to the representation of snow density within the land surface component. Increased snow densities in CESM2 lead to enhanced conductance of the snow layer. As a result, larger heat fluxes across the snow layer are induced in the presence ofT_2manomalies, leading to a greater dampening of surface and near surface atmospheric temperature anomalies. The implications for future projections with CESM2 are also considered through comparison of the CESM1 and CESM2 large ensembles. Aligned with the reduction in surface temperature variability, compared to CESM1, CESM2 exhibits reduced ensemble spread in future projections of NH winter mean temperature and a smaller decline in daily wintertimeT_2mvariability under climate change. Overall, this improvement has increased the accuracy of CESM2 as a tool for the study of wintertimeT_2mvariability and change. 

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2. Skillful Multi‐Month Predictions of Ecosystem Stressors in the Surface and Subsurface Ocean

   https://doi.org/10.1029/2023EF003605  

   Mogen, Samuel_C; Lovenduski, Nicole_S; Yeager, Stephen; Keppler, Lydia; Sharp, Jonathan; Bograd, Steven_J; Quiros, Nathali_Cordero; Di_Lorenzo, Emanuele; Hazen, Elliott_L; Jacox, Michael_G; et al (November 2023, Earth's Future)

   Abstract Anthropogenic carbon emissions and associated climate change are driving rapid warming, acidification, and deoxygenation in the ocean, which increasingly stress marine ecosystems. On top of long‐term trends, short term variability of marine stressors can have major implications for marine ecosystems and their management. As such, there is a growing need for predictions of marine ecosystem stressors on monthly, seasonal, and multi‐month timescales. Previous studies have demonstrated the ability to make reliable predictions of the surface ocean physical and biogeochemical state months to years in advance, but few studies have investigated forecast skill of multiple stressors simultaneously or assessed the forecast skill below the surface. Here, we use the Community Earth System Model (CESM) Seasonal to Multiyear Large Ensemble (SMYLE) along with novel observation‐based biogeochemical and physical products to quantify the predictive skill of dissolved inorganic carbon (DIC), dissolved oxygen, and temperature in the surface and subsurface ocean. CESM SMYLE demonstrates high physical and biogeochemical predictive skill multiple months in advance in key oceanic regions and frequently outperforms persistence forecasts. We find up to 10 months of skillful forecasts, with particularly high skill in the Northeast Pacific (Gulf of Alaska and California Current Large Marine Ecosystems) for temperature, surface DIC, and subsurface oxygen. Our findings suggest that dynamical marine ecosystem prediction could support actionable advice for decision making. 

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3. Arctic sea ice seasonal transition metrics from coupled climate model simulations, 1979-2013

   https://doi.org/10.18739/A2C53F235  

   Smith, Abigail; Jahn, Alexandra (January 2020, NSF Arctic Data Center)

   This dataset includes annual, gridded Arctic sea ice seasonal transition metrics (dates and periods) for fifteen Coupled Model Intercomparison Project version 6 (CMIP6) models and the Community Earth System Model version 1.1 (CESM1.1) Large Ensemble (CESM LE) (Kay, et al., 2015). Seasonal transition dates include melt onset, opening, break-up, freeze onset, freeze-up and closing. Seasonal transition periods include the melt period, the seasonal loss-of-ice period, the freeze period, the seasonal gain-of-ice period, the melt season, the open water period and the outer ice-free period. Data are provided for one ensemble member of the following models: Australian Community Climate and Earth System Simulator CM2 (ACCESS-CM2), Beijing Climate Center Climate System Model 2 MR (BCC-CSM2-MR), Beijing Climate Center Earth System Model 1 (BCC-ESM1), Community Earth System Model 2 (CESM2), Community Earth System Model 2 FV2 (CESM2-FV2), Community Earth System Model 2 Whole Atmosphere Community Climate Model (CESM2-WACCM), Community Earth System Model 2 Whole Atmosphere Community Climate Model FV2 (CESM2-WACCM-FV2), Centre National de Recherches Météorologiques ESM 2-1 (CNRM-ESM2-1), Centre National de Recherches Météorologiques CM 6-1 (CNRM-CM6-1), EC-Earth3, Meteorological Research Institute Earth System Model 2-0 (MRI-ESM2-0), Norwegian Earth System Model 2 LM (NorESM2-LM) and Norwegian Earth System Model 2 MM (NorESM2-MM). Data are provided for 40 members of the Community Earth System Model Large Ensemble (CESM LE), 35 members of Canadian Earth System Model 5 (CanESM5) and 30 members of Institut Pierre Simon Laplace CM6A LR (IPSL-CM6A-LR). The data is stored in netcdf format, and includes metadata in the netcdf files. The raw CMIP6 and CESM LE model output that these transition metrics are calculated from are publicly available at https://esgf-node.llnl.gov/projects/cmip6/ and https://www.earthsystemgrid.org/ respectively. This dataset was created to evaluate climate model projections of Arctic sea ice using seasonal transition metrics in the context of both observations and internal variability. It is used in the article Smith, Jahn, Wang (2020), Seasonal transition dates can reveal biases in Arctic sea ice simulations, The Cryosphere, in press. The discussion paper with a link to the final paper can be found at https://doi.org/10.5194/tc-2020-81. This work was conducted at the University of Colorado Boulder from 2019-2020. 

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4. The Idealized Aquaplanet Maritime Continent Barrier Effect on the MJO Predictability

   https://doi.org/10.1175/JCLI-D-22-0155.1  

   Kim, Hyemi; Benedict, James J. (September 2023, Journal of Climate)

   Abstract Studies have indicated exaggerated Maritime Continent (MC) barrier effect in simulations of the Madden–Julian oscillation (MJO), a dominant source of subseasonal predictability in the tropics. This issue has plagued the modeling and operational forecasting communities for decades, while the sensitivity of MC barrier on MJO predictability has not been addressed quantitatively. In this study, perfect-model ensemble forecasts are conducted with an aquaplanet configuration of the Community Earth System Model version 2 (CESM2) in which both basic state and tropical modes of variability are reasonably simulated with a warm pool–like SST distribution. When water-covered terrain mimicking MC landmasses is added to the warm pool–like SST framework, the eastward propagation of the MJO is disturbed by the prescribed MC aqua-mountain. The MJO predictability estimate with the perfect-model experiment is about 6 weeks but reduces to about 4 weeks when the MJO is impeded by the MC aqua-mountain. Given that the recent operational forecasts show an average of 3–4 weeks of MJO prediction skill, we can conclude that improving the MJO propagation crossing the MC could improve the MJO skill to 5–6 weeks, close to the potential predictability found in this study (6 weeks). Therefore, more effort toward understanding and improving the MJO propagation is needed to enhance the MJO and MJO-related forecasts to improve the subseasonal-to-seasonal prediction. 

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5. Seasonality of Pacific Decadal Oscillation Prediction Skill

   https://doi.org/10.1029/2025GL116122  

   Meeker, E D; Maroon, E A; Deppenmeier, A L; Thompson, L A; Vimont, D J; Yeager, S G (July 2025, Geophysical Research Letters)

   Abstract We investigate coupled climate model initialized predictions of the Pacific Decadal Oscillation (PDO) prediction skill in the Community Earth System Model (CESM) Seasonal to Multi Year Large Ensemble (SMYLE). The PDO is predictable up to a year in advance in SMYLE; however, the predictability depends on verification month, with skill degrading most rapidly in boreal spring for all initializations. To examine the role of teleconnections from El Niño–Southern Oscillation (ENSO) in the prediction skill of the PDO, we use a multi‐linear regression model. The linear model shows that initial value persistence explains most of the PDO prediction skill in SMYLE. In addition, the PDO prediction skill's seasonal dependence is fully reproduced only when ENSO is included as a predictor. These results suggest that ENSO has a strong influence on the seasonality of PDO predictions. 

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