An assessment of oceanic variability in the NCEP climate forecast system reanalysis

Xue, Yan; Huang, Boyin; Hu, Zeng‐Zhen; Kumar, Arun; Wen, Caihong; Behringer, David; Nadiga, Sudhir

doi:10.1007/s00382-010-0954-4

Cited by 150 publications

(111 citation statements)

References 68 publications

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“…CFSR (Fig. 9a) shows very some large, almost oscillatory, changes in OHC below 700 m that may be linked to previously documented discontinuities in deep ocean temperature and salinity (Xue et al 2011). For the MOVE products (Fig.…”

Section: Time Series Of Ohc Changesupporting

confidence: 55%

Ocean heat content variability and change in an ensemble of ocean reanalyses

et al. 2015

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regions of the Pacific and Indian Oceans where the interannual variability of the ensemble mean exceeds ensemble spread, indicating that OHC variations are well-constrained by the available observations over the period [1993][1994][1995][1996][1997][1998][1999][2000][2001][2002][2003][2004][2005][2006][2007][2008][2009]. At deeper levels, the ORAs are less well-constrained by observations with the largest differences across the ensemble mostly associated with areas of high eddy kinetic energy, such as the Southern Ocean and boundary current regions. Spatial patterns of OHC change for the period 1997-2009 show good agreement in the upper 300 m and are characterized by a strong dipole pattern in the Pacific Ocean. There is less agreement in the patterns of change at deeper levels, potentially linked to differences in the representation of ocean dynamics, such as water mass formation processes. However, the Atlantic and Southern Oceans Abstract Accurate knowledge of the location and magnitude of ocean heat content (OHC) variability and change is essential for understanding the processes that govern decadal variations in surface temperature, quantifying changes in the planetary energy budget, and developing constraints on the transient climate response to external forcings. We present an overview of the temporal and spatial characteristics of OHC variability and change as represented by an ensemble of dynamical and statistical ocean reanalyses (ORAs). Spatial maps of the 0-300 m layer show large This paper is a contribution to the special issue on Ocean estimation from an ensemble of global ocean reanalyses, consisting of papers from the Ocean Reanalyses Intercomparsion Project (ORAIP), coordinated by CLIVAR-GSOP and GODAE OceanView. The special issue also contains specific studies using single reanalysis systems. The current work emphasizes the need to better observe the deep ocean, both for providing observational constraints for future ocean state estimation efforts and also to develop improved models and data assimilation methods.

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Section: Time Series Of Ohc Changesupporting

confidence: 55%

Ocean heat content variability and change in an ensemble of ocean reanalyses

et al. 2015

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“…Figure 1 indicates that the RMSE of the CFSv2 forecasts is about twice that of most other models, which have RMSE less than 0.25 °C. Pertinent to this high RMSE is the fact that several studies have noted a discontinuity in the forecast bias of the CFSv2 SST hindcasts for the central and eastern tropical Pacific occurring around 1999, which has been related to a discontinuity in the data assimilation and initialization procedure (Xue et al 2011;Kumar et al 2012;Barnston and Tippett 2013;Tippett et al 2017). The time series of the difference between the ensemble mean prediction and the observed anomalies (not shown) indicates that CFSv2 first-lead forecasts tend to be too cool prior to 1999 and too warm after 1999.…”

Section: Methodsmentioning

confidence: 99%

Deterministic skill of ENSO predictions from the North American Multimodel Ensemble

et al. 2017

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the presence of calibration errors in some of the models. In particular, the amplitudes of some model predictions are too high when predictability is limited by the northern spring ENSO predictability barrier and/or when the interannual variability of the SST is near its seasonal minimum. The skill of the NMME system is compared to that of the MME from the IRI/CPC ENSO prediction plume, both for a comparable hindcast period and also for a set of real-time predictions spanning 2002-2011. Comparisons are made both between the MME predictions of each model group, and between the average of the skills of the respective individual models in each group. Acknowledging a hindcast versus real-time inconcsistency in the 2002-2012 skill comparison, the skill of the NMME is slightly higher than that of the prediction plume models in all cases. This result reflects well on the NMME system, with its large total ensemble size and opportunity for possible complementary contributions to skill.Abstract Hindcasts and real-time predictions of the eastcentral tropical Pacific sea surface temperature (SST) from the North American Multimodel Ensemble (NMME) system are verified for 1982-2015. Skill is examined using two deterministic verification measures: mean squared error skill score (MSESS) and anomaly correlation. Verification of eight individual models shows somewhat differing skills among them, with some models consistently producing more successful predictions than others. The skill levels of MME predictions are approximately the same as the two best performing individual models, and sometimes exceed both of them. A decomposition of the MSESS indicates This paper is a contribution to the special collection on the North American Multi-Model Ensemble (NMME) seasonal prediction experiment. The special collection focuses on documenting the use of the NMME system database for research ranging from predictability studies, to multi-model prediction evaluation and diagnostics, to emerging applications of climate predictability for subseasonal to seasonal predictions

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“…CFSR uses 3D-Var as the data assimilation method (Saha et al, 2010). The reanalysis system assimilates temperature profiles from XBT (expendable bathythermograph), moorings, Argo floats and SST only in the top 750 m (Xue et al, 2011). CFSR also assimilates synthetic salinity profiles (Xue et al, 2011) and SIC (Saha et al, 2010).…”

Section: -2010mentioning

confidence: 99%

“…The reanalysis system assimilates temperature profiles from XBT (expendable bathythermograph), moorings, Argo floats and SST only in the top 750 m (Xue et al, 2011). CFSR also assimilates synthetic salinity profiles (Xue et al, 2011) and SIC (Saha et al, 2010). The atmospheric model is based on the previous National Center for Environmental Prediction (NCEP) operational global forecast system (Saha et al, 2010).…”

Section: -2010mentioning

confidence: 99%