On depth and temperature biases in bathythermograph data: Development of a new correction scheme based on analysis of a global ocean database

Gouretski, Viktor; Reseghetti, Franco

doi:10.1016/j.dsr.2010.03.011

Cited by 335 publications

(382 citation statements)

References 35 publications

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“…There is no doubt that monthly global mean temperatures during the Argo era are accurate at all levels in the upper 2,000 m. We estimated uncertainties in the global mean Gouretski and Reseghetti (2010). These bias corrections were used in addition to the common bias correction (Hanawa et al 1995) which is not time-varying.…”

Section: Uncertainties In Global Mean Temperaturesmentioning

confidence: 99%

Accuracy of Global Upper Ocean Heat Content Estimation Expected from Present Observational Data Sets

Ishii¹,

Fukuda

Hirahara

et al. 2017

SOLA

149

153

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The simplest global mapping method and dense data coverage for the global oceans by the latest observation network ensure an estimate of global ocean heat content (OHC) within a satisfactory uncertainty for the last 60 years. The observational database conditionally presented a level high enough for practical use for the global OHC estimation when applying bias corrections of expendable bathythermograph, assuming that the other severe observational biases are not included in the database. Uncertainties in annual global mean temperatures averaged vertically from the surface to 1,500 m are within 0.01 K for the period from 1955 onward, when only sampling errors are taken into account. Those in annual mean global OHC of an improved objective analysis for 0−1,500 m depth is 16ZJ on average throughout the period. Compared to previous studies, the new objective analysis provides a higher estimation of the global 0−1,500 m OHC trend for a longer period from 1955 to 2015, which is an increase of 350 ± 57ZJ with a 95% confidence interval.(Citation: Ishii, M., Y. Fukuda, H. Hirahara, S. Yasui, T. Suzuki, and K. Sato, 2017: Accuracy of global upper ocean heat content estimation expected from present observational data sets.

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Section: Uncertainties In Global Mean Temperaturesmentioning

confidence: 99%

Accuracy of Global Upper Ocean Heat Content Estimation Expected from Present Observational Data Sets

Ishii¹,

Fukuda

Hirahara

et al. 2017

SOLA

149

153

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“…2.2.1.1, the calculation of κ considers the increase in temperature for depths below 700 m by scaling observations from the upper part of the ocean. Of the six OHC datasets, Ishii and Kimoto (2009) results in the smallest value for κ (0.43 W m −2 °C −1 ) and Gouretski and Reseghetti (2010) leads to the largest value (1.52 W m −2 °C −1 ). All of the values of κ found using various time series for OHC fall within the range of empirical estimates and coupled ocean-atmosphere model behavior that is shown in Fig.…”

Section: Model Outputsmentioning

confidence: 99%

“…These simulations reveal a second degeneracy of the climate record, which further impacts our ability to define ECS. If the export of heat from the atmosphere to the oceans is truly as large as suggested by the OHC record of Gouretski and Reseghetti (2010), then Earth's climate exhibits considerably larger sensitivity to the doubling of atmospheric CO 2 than if the OHC record of Ishii and Kimoto (2009) is correct.…”

Section: Equilibrium Climate Sensitivitymentioning

confidence: 99%

“…Gouretski and Reseghetti (2010) as well as the average of the data from these six studies. Ostensibly, all of the studies make use of similar (if not the same) measurements from expendable bathy-thermograph (XBT) devices and the more accurate conductivity temperature depth (CTD) probes.…”

Section: Model Inputsmentioning

confidence: 99%

“…2.8, the OHC record is also quite uncertain. Figure 2.10c makes use of the OHC record of Gouretski and Reseghetti (2010) that yields the largest value of κ, 1.52 W m −2 °C −1 . The OHC record of Levitus et al (2012), which lies closest to the average of the six OHC determinations (Fig.…”

Section: Equilibrium Climate Sensitivitymentioning

confidence: 99%

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Forecasting Global Warming

et al. 2017

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This chapter provides an overview of the factors that will govern the rise in global mean surface temperature (GMST) over the rest of this century. We evaluate GMST using two approaches: analysis of archived output from atmospheric, oceanic general circulation models (GCMs) and calculations conducted using a computational framework developed by our group, termed the Empirical Model of Global Climate (EM-GC). Comparison of the observed rise in GMST over the past 32 years with GCM output reveals these models tend to warm too quickly, on average by about a factor of two. Most GCMs likely represent climate feedback in a manner that amplifies the radiative forcing of climate due to greenhouse gases (GHGs) too strongly. The GCM-based forecast of GMST over the rest of the century predicts neither the target (1.5 °C) nor upper limit (2.0 °C warming) of the Paris Climate Agreement will be achieved if GHGs follow the trajectories of either the Representative Concentration Pathway (RCP) 4.5 or 8.5 scenarios. Conversely, forecasts of GMST conducted in the EM-GC framework indicate that if GHGs follow the RCP 4.5 trajectory, there is a reasonably good probability (~75 %) the Paris target of 1.5 °C warming will be achieved, and an excellent probability (>95 %) global warming will remain below 2.0 °C. Uncertainty in the EM-GC forecast of GMST is primarily caused by the ability to simulate past climate for various combinations of parameters that represent climate feedback and radiative forcing due to aerosols, which provide disparate projections of future warming.

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EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates

2013

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[1] We present version 4 of the Met Office Hadley Centre ''EN'' series of data sets of global quality controlled ocean temperature and salinity profiles and monthly objective analyses, which covers the period 1900 to present. We briefly describe the EN4 data sources, processing, quality control procedures, and the method of generating the analyses. In particular, we highlight improvements relative to previous versions, which include a new duplicate profile removal procedure and the inclusion of three new quality control checks. We discuss in detail a novel method for providing uncertainty estimates for the objective analyses and improving the background error variance estimates used by the analysis system. These were calculated using an iterative method that is relatively robust to initial misspecification of background error variances. We also show how the method can be used to identify issues with the analyses such as those caused by misspecification of error variances and demonstrate the impact of changes in the observing system on the uncertainty in the analyses.Citation: Good, S. A., M. J. Martin, and N. A. Rayner (2013), EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates,

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On depth and temperature biases in bathythermograph data: Development of a new correction scheme based on analysis of a global ocean database

Cited by 335 publications

References 35 publications

Accuracy of Global Upper Ocean Heat Content Estimation Expected from Present Observational Data Sets

Accuracy of Global Upper Ocean Heat Content Estimation Expected from Present Observational Data Sets

Forecasting Global Warming

EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates

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