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 (
- NSF-PAR ID:
- 10417787
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- Geoscientific Model Development
- Volume:
- 15
- Issue:
- 16
- ISSN:
- 1991-9603
- Page Range / eLocation ID:
- 6451 to 6493
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract T 2m ) is critical in such a model for addressing societally relevant problems. However, previous versions of CESM suffered from an overestimation of wintertimeT 2m variability in Northern Hemisphere (NH) land regions. Here, it is shown that the latest version of CESM (CESM2) exhibits a much improved representation of wintertimeT 2m variability 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 2m anomalies, 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 2m variability under climate change. Overall, this improvement has increased the accuracy of CESM2 as a tool for the study of wintertimeT 2m variability and change. -
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Abstract 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.
Significance Statement 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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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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Abstract 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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Abstract Subseasonal tropical cyclone (TC) reforecasts from the Community Earth System Model version 2 (CAM6) subseasonal prediction system are examined in this study. We evaluate the modeled TC climatology and the probabilistic forecast skill of basin‐wide TC genesis at weekly temporal resolution. Prediction skill is calculated using the Brier skill score relative to a constant annual mean climatology and to a monthly varying seasonal climatology during TC season. The model captures the observed basin‐wide climatological TC seasonality and spatial distributions at weeks 1–6, but TC genesis is largely underestimated from Week 2 onward. For some basins and lead times, the predicted TC genesis is primarily controlled by the number of TC “seeds” and the mean‐state climate condition. The model has good prediction skill relative to the constant climatology across all the basins and lead times, but is only skillful in the eastern Pacific, North Indian Ocean, and Southern Hemisphere at Week 1 when compared to the seasonal climatology, indicating limited skill in predicting deviations from the seasonal cycle. We find strong modulations of the predicted TC genesis at up to 3 weeks of forecast lead time by the Madden‐Julian Oscillation. The interannual variability of predicted TC genesis and accumulated cyclone energy are skillfully predicted in the North Atlantic and the Northwestern Pacific, with a strong modulation by the El Nino‐Southern Oscillation.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/gmd-15-6451-2022
