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1. Subseasonal Earth System Prediction with CESM2

   https://doi.org/10.1175/WAF-D-21-0163.1  

   Richter, Jadwiga H. ; Glanville, Anne A. ; Edwards, James ; Kauffman, Brian ; Davis, Nicholas A. ; Jaye, Abigail ; Kim, Hyemi ; Pedatella, Nicholas M. ; Sun, Lantao ; Berner, Judith ; et al ( June 2022 , Weather and Forecasting)

   Prediction systems to enable Earth system predictability research on the subseasonal time scale have been developed with the Community Earth System Model, version 2 (CESM2) using two configurations that differ in their atmospheric components. One system uses the Community Atmosphere Model, version 6 (CAM6) with its top near 40 km, referred to as CESM2(CAM6). The other employs the Whole Atmosphere Community Climate Model, version 6 (WACCM6) whose top extends to ∼140 km, and it includes fully interactive tropospheric and stratospheric chemistry [CESM2(WACCM6)]. Both systems are utilized to carry out subseasonal reforecasts for the 1999–2020 period following the Subseasonal Experiment’s (SubX) protocol. Subseasonal prediction skill from both systems is compared to those of the National Oceanic and Atmospheric Administration CFSv2 and European Centre for Medium-Range Weather Forecasts (ECMWF) operational models. CESM2(CAM6) and CESM2(WACCM6) show very similar subseasonal prediction skill of 2-m temperature, precipitation, the Madden–Julian oscillation, and North Atlantic Oscillation to its previous version and to the NOAA CFSv2 model. Overall, skill of CESM2(CAM6) and CESM2(WACCM6) is a little lower than that of the ECMWF system. In addition to typical output provided by subseasonal prediction systems, CESM2 reforecasts provide comprehensive datasets for predictability research of multiple Earth system components, including three-dimensional output for many variables, and output specific to the mesosphere and lower-thermosphere (MLT) region from CESM2(WACCM6). It is shown that sudden stratosphere warming events, and the associated variability in the MLT, can be predicted ∼10 days in advance. Weekly real-time forecasts and reforecasts with CESM2(CAM6) and CESM2(WACCM6) are freely available.

   We describe here the design and prediction skill of two subseasonal prediction systems based on two configurations of the Community Earth System Model, version 2 (CESM2): CESM2 with the Community Atmosphere Model, version 6 [CESM2(CAM6)] and CESM 2 with Whole Atmosphere Community Climate Model, version 6 [CESM2(WACCM6)] as its atmospheric component. These two systems provide a foundation for community-model based subseasonal prediction research. The CESM2(WACCM6) system provides a novel capability to explore the predictability of the stratosphere, mesosphere, and lower thermosphere. Both CESM2(CAM6) and CESM2(WACCM6) demonstrate subseasonal surface prediction skill comparable to that of the NOAA CFSv2 model, and a little lower than that of the ECMWF forecasting system. CESM2 reforecasts provide a comprehensive dataset for predictability research of multiple aspects of the Earth system, including the whole atmosphere up to 140 km, land, and sea ice. Weekly real-time forecasts, reforecasts, and models are publicly available.

    

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2. Quantifying the Effect of Climate Change on Midlatitude Subseasonal Prediction Skill Provided by the Tropics

   https://doi.org/10.1029/2022GL098663  

   Mayer, Kirsten J. ; Barnes, Elizabeth A. ( July 2022 , Geophysical Research Letters)

   Subseasonal timescales (∼2 weeks–2 months) are known for their lack of predictability, however, specific Earth system states known to have a strong influence on these timescales can be harnessed to improve prediction skill (known as “forecasts of opportunity”). As the climate continues warming, it is hypothesized these states may change and consequently, their importance for subseasonal prediction may also be impacted. Here, we examine changes to midlatitude subseasonal prediction skill provided by the tropics under anthropogenic warming using artificial neural networks to quantify skill. The network is tasked to predict the sign of the 500 hPa geopotential height for historical and future time periods in the Community Earth System Model Version 2 ‐ Large Ensemble across the Northern Hemisphere at a 3 week lead using tropical precipitation. We show prediction skill changes substantially in key midlatitude regions and these changes appear linked to changes in seasonal variability with the largest differences in accuracy occurring during forecasts of opportunity.

    

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3. 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)

   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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4. 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)

   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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5. Quantifying sources of subseasonal prediction skill in CESM2

   https://doi.org/10.1038/s41612-024-00595-4  

   Richter, Jadwiga H. ; Glanville, Anne A. ; King, Teagan ; Kumar, Sanjiv ; Yeager, Stephen G. ; Davis, Nicholas A. ; Duan, Yanan ; Fowler, Megan D. ; Jaye, Abby ; Edwards, Jim ; et al ( March 2024 , npj Climate and Atmospheric Science)

   Subseasonal prediction fills the gap between weather forecasts and seasonal outlooks. There is evidence that predictability on subseasonal timescales comes from a combination of atmosphere, land, and ocean initial conditions. Predictability from the land is often attributed to slowly varying changes in soil moisture and snowpack, while predictability from the ocean is attributed to sources such as the El Niño Southern Oscillation. Here we use a set of subseasonal reforecast experiments with CESM2 to quantify the respective roles of atmosphere, land, and ocean initial conditions on subseasonal prediction skill over land. These reveal that the majority of prediction skill for global surface temperature in weeks 3–4 comes from the atmosphere, while ocean initial conditions become important after week 4, especially in the Tropics. In the CESM2 subseasonal prediction system, the land initial state does not contribute to surface temperature prediction skill in weeks 3–6 and climatological land conditions lead to higher skill, disagreeing with our current understanding. However, land-atmosphere coupling is important in week 1. Subseasonal precipitation prediction skill also comes primarily from the atmospheric initial condition, except for the Tropics, where after week 4 the ocean state is more important.

    

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