An assessment of air–sea heat fluxes from ocean and coupled reanalyses

Valdivieso, Maria; Haines, Keith; Balmaseda, Magdalena; Chang, Yehui; Drévillon, Marie; Ferry, Nicolas; Fujii, Yosuke; Köhl, Armin; Storto, Andrea; Toyoda, Takahiro; Wang, Xiaochun; Waters, Jennifer; Xue, Yan; Yin, Yan; Barnier, Bernard; Hernández, Fabrice; Kumar, Arun; Lee, Tong; Masina, Simona; Peterson, K. Andrew

doi:10.1007/s00382-015-2843-3

Cited by 96 publications

(95 citation statements)

References 66 publications

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“…Differences in MDT and both hydrography and altimeter assimilation methods are particularly important in the Southern Ocean, and may explain the large spread and positive/negative dipoles exhibited in that region. Differences in the North Atlantic sector are likely associated with differing representation of the Atlantic meridional overturning circulation and the associated heat transport (Pohlmann et al 2013;Valdivieso et al 2015). While model resolution affects the simulation of the AMOC, studies have shown that the mean value can be reasonably well represented in ¼ degree models (Roberts et al 2013), although the associated heat transports are too weak (Haines et al 2013).…”

Section: Time-mean and Amplitude Of The Seasonal Cyclementioning

confidence: 99%

“…Disentangling the roles of differing ocean vertical mixing, (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) ocean circulation and surface forcings in determining these biases is a challenging problem and requires further and detailed analysis. Errors in both momentum and heat fluxes have a role here (Lee et al 2013;Valdivieso et al 2015) and consideration of mixed layer depths may also offer additional insights . The representation of vertical mixing in ocean models remains an area that requires particular attention due to the different mixing schemes, related parameters and the often poorly quantified effects of numerical mixing (e.g.…”

Section: Time-mean and Amplitude Of The Seasonal Cyclementioning

confidence: 99%

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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-mean and Amplitude Of The Seasonal Cyclementioning

confidence: 99%

Section: Time-mean and Amplitude Of The Seasonal Cyclementioning

confidence: 99%

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

et al. 2015

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“…Improvement in representing MLDs in this region is needed in the future. To do so, intercomparisons for sea ice (e.g., Smith et al 2014) and surface flux (e.g., Valdivieso et al 2014) might indicate important clues in association with deep convection following the sea ice formation by strong cooling. Further observations in the Southern Ocean, particularly for the sea ice region, are also important, indicated by large differences between MILA-GPV and EN3v2a/ ARMOR3D with their signs changing on small scales.…”

Section: Seasonal and Interannual Variations Of Mldsmentioning

confidence: 99%

Intercomparison and validation of the mixed layer depth fields of global ocean syntheses

et al. 2015

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5-7 (14-16) m with the vertical resolution of 25 (50) m when the criterion of potential density exceeding the 10-m value by 0.03 kg m −3 is used for the MLD estimation. Using the larger criterion (0.125 kg m −3 ) generally reduces the underestimations. In addition, positive biases greater than 100 m are found in wintertime subpolar regions when MLD criteria based on temperature are used. Biases of the reanalyses are due to both model errors and errors related to differences between the assimilation methods. The result shows that these errors are partially cancelled out through the ensemble averaging. Moreover, the bias in the ensemble mean field of the reanalyses is smaller than in the observation-only analyses. This is largely attributed to comparably higher resolutions of the reanalyses. The robust reproduction of both the seasonal cycle and interannual Abstract Intercomparison and evaluation of the global ocean surface mixed layer depth (MLD) fields estimated from a suite of major ocean syntheses are conducted. Compared with the reference MLDs calculated from individual profiles, MLDs calculated from monthly mean and gridded profiles show negative biases of 10-20 m in early spring related to the re-stratification process of relatively deep mixed layers. Vertical resolution of profiles also influences the MLD estimation. MLDs are underestimated by approximately 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.

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“…Valdivieso et al [2015] showed that most reanalysis products feature a bias during the period 1993-2009 in the global-mean net surface flux in the order of 1-2 W/m 2 which is larger than the energy gain derived from estimates of the top of atmosphere (TOA) imbalance of about 0.50 (±0.43) W/m 2 [Loeb et al, 2012]. For comparison, the current generation of climate models of the Coupled Model Intercomparison Project Phase 5 (CMIP5) exhibits an uncertainty in the net surface and TOA fluxes in the order of 10 W/m 2 and 5 W/m 2 , respectively [Wild et al, 2015].…”

Section: Introductionmentioning

confidence: 99%

Drivers of uncertainty in simulated ocean circulation and heat uptake

Huber

Zanna

2017

Geophysical Research Letters

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The impact of uncertainties in air‐sea fluxes and ocean model parameters on the ocean circulation and ocean heat uptake (OHU) is assessed in a novel modeling framework. We use an ocean‐only model forced with the simulated sea surface fields of the CMIP5 climate models. The simulations are performed using control and 1% CO2 warming scenarios. The ocean‐only ensemble adequately reproduces the mean Atlantic Meridional Overturning Circulation (AMOC) and the zonally integrated OHU. The ensemble spread in AMOC strength, its weakening, and Atlantic OHU due to different air‐sea fluxes is twice as large as the uncertainty range related to vertical and mesocale eddy diffusivities. The sensitivity of OHU to uncertainties in air‐sea fluxes and model parameters differs vastly across basins, with the Southern Ocean exhibiting strong sensitivity to air‐sea fluxes and model parameters. This study clearly demonstrates that model biases in air‐sea fluxes are one of the key sources of uncertainty in climate simulations.

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An assessment of air–sea heat fluxes from ocean and coupled reanalyses

Cited by 96 publications

References 66 publications

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

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

Intercomparison and validation of the mixed layer depth fields of global ocean syntheses

Drivers of uncertainty in simulated ocean circulation and heat uptake

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