Tropical climate variability in the Community Earth System Model: Data Assimilation Research Testbed

Eliashiv, Jonathan; Subramanian, Aneesh; Miller, Arthur J.

doi:10.1007/s00382-019-05030-6

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…In a related study using CESM-DART (Eliashiv et al, 2018), the data assimilation in CESM significantly improved the performance of MJO events, which suggested that the flux coupling at the ocean-atmosphere interface may be important in organizing the convective activity inherent in MJO. Focusing on MJO events, which have well-known mechanisms involved, may give us added insight into the processes leading to the error growth in CESM-DART.…”

Section: Intraseasonal Explorationmentioning

confidence: 96%

“…For instance, since the study was executed in observations space, there was no assessment of the reliability of the surface fluxes because they were not included in the data assimilation procedure (cf. Eliashiv et al, 2018 who found enhanced MJO activity may be due to the surface flux coupling in CESM-DART). Additionally, evaluations using ensemble-based reanalysis products with more properly specified observational uncertainty and the high-density observations that are available in more recent decades may also provide clearer perspectives on how to pinpoint the processes in the model that need the most improvement in their parameterization.…”

Section: 1029/2019ms001678mentioning

confidence: 98%

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A Reliability Budget Analysis of CESM‐DART

Eliashiv

Subramanian

Miller

2020

J Adv Model Earth Syst

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A reliability budget is used to diagnose potential sources of error (departure from observations) in a new prototype coupled ocean‐atmosphere ensemble Kalman filter reanalysis product, the Community Earth System Model using the Data Assimilation Research Testbed (CESM‐DART). In areas with sufficient observations, the mean bias in zonal wind was generally very low compared to the spread due to ensemble variance, which did not exhibit patterns associated with Northern Hemisphere jet streams but did have regional enhancement over the Maritime Continent. However, the residual term was often the largest contributor to the budget, which is problematic, suggesting improper observational error statistics and inadequately represented ensemble variance statistics. The departure and residual exhibit significant seasonal variability, with a strong peak in boreal winter months, indicating the model's deficiencies during the energetic Northern Hemisphere winter. Ocean temperature contained large error in areas with eddy production indicating inadequate ensemble variance due to poor model resolution. Periods when the Madden‐Julian Oscillation (MJO) was active exhibited lower error, especially in the western equatorial Pacific during MJO phases with reduced convection. In contrast, during MJO phases with enhanced convection in that region, the ensemble variance is increased yet the error is comparable to non‐MJO conditions, suggesting a controlling effect of the precipitation parameterization. Further studies evaluating the impact of the coupled assimilation procedure on the reliability budget will be illuminating.

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Section: Intraseasonal Explorationmentioning

confidence: 96%

Section: 1029/2019ms001678mentioning

confidence: 98%

A Reliability Budget Analysis of CESM‐DART

Eliashiv

Subramanian

Miller

2020

J Adv Model Earth Syst

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Seasonal prediction skills in the CAMS-CSM climate forecast system

Liu

et al. 2021

Clim Dyn

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The seasonal predictability in the CAMS-CSM climate forecast system is evaluated with a set of retrospective forecast experiments during the period of 1981-2019. The CAMS-CSM, which has been registered for the sixth phase of the coupled model intercomparison project (CMIP6), is an atmosphereocean-land-sea ice fully coupled general model. The assimilation scheme used in the forecast system is the 3-dimentional nudging, including both the atmospheric and oceanic components.The analyses mainly focus on the seasonal predictable skill of sea surface temperature, 2-m air temperature, and precipitation anomalies. The analyses revealed that the model shows a good prediction skill for the SST anomalies, especially in the tropical Paci c, such as El Niño-Southern Oscillation (ENSO) events. The anomaly correlation coe cient (ACC) score for ENSO can reach 0.75 at 6-month lead time. Furthermore, the extreme warm/cold Indian Ocean dipole (IOD) events are successfully predicted at 3and even 6-month lead times. The whole ACC of IOD events between the observation and the prediction can reach 0.51 at 2-month lead time. There are reliable seasonal prediction skills for 2-m air temperature anomalies over most of the Northern Hemisphere, where the correlation is mainly above 0.4 at 2-month lead time, especially over the East Asia, North America and South America. However, the seasonal prediction for precipitation still faces a big challenge. The source of precipitation predictability over the East Asia can be partly related to strong ENSO events. Additionally, the anomalous anticyclone over the western North Paci c (WPAC) which connects the ENSO events and the East Asian summer monsoon (EASM) can be well predicted at 6-month lead time.

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