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Lijing Cheng, Jiang Zhu, Rebecca Cowley, Tim Boyer, and Susan Wijffels

Abstract

Systematic biases in historical expendable bathythermograph (XBT) data are examined using two datasets: 4151 XBT–CTD side-by-side pairs from 1967 to 2011 and 218 653 global-scale XBT–CTD pairs (within one month and 1°) extracted from the World Ocean Database 2009 (WOD09) from 1966 to 2010. Using the side-by-side dataset, it was found that both the pure thermal bias and the XBT fall rate (from which the depth of observation is calculated) increase with water temperature. Correlations between the terminal velocity A and deceleration B terms of the fall-rate equation (FRE) and between A and the offset from the surface terms are obtained, with A as the dominant term in XBT fall-rate behavior. To quantify the time variation of the XBT fall-rate and pure temperature biases, global-scale XBT–CTD pairs are used. Based on the results from the two datasets, a new correction scheme for historical XBT data is proposed for nine independent probe-type groups. The scheme includes corrections for both temperature and depth records, which are all variable with calendar year, water temperature, and probe type. The results confirm those found in previous studies: a slowing in fall rate during the 1970s and 2000s and the large pure thermal biases during 1970–85. The performance of nine different correction schemes is compared. After the proposed corrections are applied to the XBT data in the WOD09 dataset, global ocean heat content from 1967 to 2010 is reestimated.

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Rebecca Cowley, Susan Wijffels, Lijing Cheng, Tim Boyer, and Shoichi Kizu

Abstract

Because they make up 56% of ocean temperature profile data between 1967 and 2001, quantifying the biases in expendable bathythermograph (XBT) data is fundamental to understanding the evolution of the planetary energy and sea level budgets over recent decades. The nature and time history of these biases remain in dispute and dominate differences in analyses of the history of ocean warming. A database of over 4100 side-by-side deployments of XBTs and conductivity–temperature–depth (CTD) data has been assembled, and this unique resource is used to characterize and separate out the pure temperature bias from depth error in a way that was not previously possible. Two independent methods of bias extraction confirm that the results are robust to bias model and fitting method. It was found that there is a pure temperature bias in Sippican probes of ~0.05°C, independent of depth. The temperature bias has a time dependency, being larger (~0.1°C) in the earlier analog acquisition era and being likely due to changes in recorder type. Large depth errors are found in the 1970s–80s in shallower-measuring Sippican T4/T6 probe types, but the deeper-measuring Sippican T7/Deep Blue (DB) types have no error during this time. The Sippican T7/DB fall rate slows from ~1990 onward. It is found that year-to-year variations in fall rate have a bigger effect on corrections to the global XBT database than do any small effects of ocean temperature on fall rate. This study has large implications for the future development of better schemes to correct the global historical XBT archive.

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Benjamin S. Giese, Gennady A. Chepurin, James A. Carton, Tim P. Boyer, and Howard F. Seidel

Abstract

Historical bathythermograph datasets are known to be biased, and there have been several efforts to model this bias. Three different correction models of temperature bias in the historical bathythermograph dataset are compared here: the steady model of Hanawa et al. and the time-dependent models of Levitus et al. and Wijffels et al. The impact of these different models is examined in the context of global analysis experiments using the Simple Ocean Data Assimilation system. The results show that the two time-dependent bias models significantly reduce warm bias in global heat content, notably in the 10 years starting in the early 1970s and again in the early 1990s. Overall, the Levitus et al. model has its greatest impact near the surface and the Wijffels et al. model has its greatest impact at subtropical thermocline depths. Examination of the vertical structure of temperature error shows that at thermocline depths the Wijffels et al. model overcompensates, leading to a slight cool bias, while at shallow levels the same model causes a slight warm bias in the central and eastern subtropics and at thermocline depths on the equator in the Pacific Ocean as a result of reduced vertical entrainment. The results also show that the bias-correction models may alter the representation of interannual variability. During the 1997/98 El Niño and the subsequent La Niña the Levitus et al. model, which has its main impact at shallow depths, reduces the 50-m temperature anomalies in the eastern equatorial Pacific by 10%–20% and strengthens the zonal currents by up to 50%. The Wijffels et al. correction, which has its main impact at deeper levels, has much less effect on the oceanic expression of ENSO.

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Matthew D. Palmer, Tim Boyer, Rebecca Cowley, Shoichi Kizu, Franco Reseghetti, Toru Suzuki, and Ann Thresher

Abstract

Time-varying biases in expendable bathythermograph (XBT) instruments have emerged as a key uncertainty in estimates of historical ocean heat content variability and change. One of the challenges in the development of XBT bias corrections is the lack of metadata in ocean profile databases. Approximately 50% of XBT profiles in the World Ocean database (WOD) have no information about manufacturer or probe type. Building on previous research efforts, this paper presents a deterministic algorithm for assigning missing XBT manufacturer and probe type for individual temperature profiles based on 1) the reporting country, 2) the maximum reported depth, and 3) the record date. The criteria used are based on bulk analysis of known XBT profiles in the WOD for the period 1966–2015. A basic skill assessment demonstrates a 77% success rate at correctly assigning manufacturer and probe type for profiles where this information is available. The skill rate is lowest during the early 1990s, which is also a period when metadata information is particularly poor. The results suggest that substantive improvements could be made through further data analysis and that future algorithms may benefit from including a larger number of predictor variables.

Open access
Boyin Huang, William Angel, Tim Boyer, Lijing Cheng, Gennady Chepurin, Eric Freeman, Chunying Liu, and Huai-Min Zhang

Abstract

The difficulty in effectively evaluating sea surface temperature (SST) analyses is finding independent observations, since most available observations have been used in the SST analyses. In this study, the ocean profile measurements [from reverse thermometer, CTD, mechanical bathythermograph (MBT), and XBT] above 5-m depth over 1950–2016 from the World Ocean Database (WOD) are used (data labeled pSSTW). The biases of MBT and XBT are corrected based on currently available algorithms. The bias-corrected pSSTW over 1950–2016 and satellite-based SST from the European Space Agency (ESA) Climate Change Initiative (CCI) over 1992–2010 are used to evaluate commonly available SST analyses. These SST analyses are the Extended Reconstructed SST (ERSST), versions 5, 4, and 3b, the Met Office Hadley Centre Sea Ice and SST dataset (HadISST), and the Japan Meteorological Administration (JMA) Centennial In Situ Observation-Based Estimates of SST version 2.9.2 (COBE-SST2). Our comparisons show that the SST from COBE-SST2 is the closest to pSSTW and CCI in most of the Pacific, Atlantic, and Southern Oceans, which may result from its unique bias correction to ship observations. The SST from ERSST version 5 is more consistent with pSSTW than its previous versions over 1950–2016, and is more consistent with CCI than its previous versions over 1992–2010. The better performance of ERSST version 5 over its previous versions is attributed to its improved bias correction applied to ship observations with a baseline of buoy observations, and is seen in most of the Pacific and Atlantic.

Open access
Boyin Huang, Peter W. Thorne, Viva F. Banzon, Tim Boyer, Gennady Chepurin, Jay H. Lawrimore, Matthew J. Menne, Thomas M. Smith, Russell S. Vose, and Huai-Min Zhang

Abstract

The monthly global 2° × 2° Extended Reconstructed Sea Surface Temperature (ERSST) has been revised and updated from version 4 to version 5. This update incorporates a new release of ICOADS release 3.0 (R3.0), a decade of near-surface data from Argo floats, and a new estimate of centennial sea ice from HadISST2. A number of choices in aspects of quality control, bias adjustment, and interpolation have been substantively revised. The resulting ERSST estimates have more realistic spatiotemporal variations, better representation of high-latitude SSTs, and ship SST biases are now calculated relative to more accurate buoy measurements, while the global long-term trend remains about the same. Progressive experiments have been undertaken to highlight the effects of each change in data source and analysis technique upon the final product. The reconstructed SST is systematically decreased by 0.077°C, as the reference data source is switched from ship SST in ERSSTv4 to modern buoy SST in ERSSTv5. Furthermore, high-latitude SSTs are decreased by 0.1°–0.2°C by using sea ice concentration from HadISST2 over HadISST1. Changes arising from remaining innovations are mostly important at small space and time scales, primarily having an impact where and when input observations are sparse. Cross validations and verifications with independent modern observations show that the updates incorporated in ERSSTv5 have improved the representation of spatial variability over the global oceans, the magnitude of El Niño and La Niña events, and the decadal nature of SST changes over 1930s–40s when observation instruments changed rapidly. Both long- (1900–2015) and short-term (2000–15) SST trends in ERSSTv5 remain significant as in ERSSTv4.

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Tim Boyer, V. V. Gopalakrishna, Franco Reseghetti, Amit Naik, V. Suneel, M. Ravichandran, N. P. Mohammed Ali, M. M. Mohammed Rafeeq, and R. Anthony Chico

Abstract

Long time series of XBT data in the Bay of Bengal and the Arabian Sea are valuable datasets for exploring and understanding climate variability. However, such studies of interannual and longer-scale variability of temperature require an understanding, and, if possible, a correction of errors introduced by biases in the XBT and expendable conductivity–temperature–depth (XCTD) data. Two cruises in each basin were undertaken in 2008/09 on which series of tests of XBTs and XCTDs dropped simultaneously with CTD casts were performed. The XBT and XCTD depths were corrected by comparison with CTD data using a modification of an existing algorithm. Significant probe-to-probe fall-rate equation (FRE) velocity and deceleration coefficient variability was found within a cruise, as well as cruise-to-cruise variability. A small (∼0.01°C) temperature bias was also identified for XBTs on each cruise. No new FRE can be proposed for either the Bay of Bengal or the Arabian Sea for XBTs. For the more consistent XCTD, basin-specific FREs are possible for the Bay of Bengal, but not for the Arabian Sea. The XCTD FRE velocity coefficients are significantly higher than the XCTD manufacturers’ FRE coefficient or those from previous tests, possibly resulting from the influence of temperature on XCTD FRE. Mean temperature anomalies versus a long-term mean climatology for XBT data using the present default FRE have a bias (which is positive for three cruises and negative for one cruise) compared to the mean temperature anomalies for CTD data. Some improvement is found when applying newly calculated cruise-specific FREs. This temperature error must be accounted for in any study of temperature change in the regions.

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Yan Xue, Magdalena A. Balmaseda, Tim Boyer, Nicolas Ferry, Simon Good, Ichiro Ishikawa, Arun Kumar, Michele Rienecker, Anthony J. Rosati, and Yonghong Yin

Abstract

Ocean heat content (HC) is one of the key indicators of climate variability and also provides ocean memory critical for seasonal and decadal predictions. The availability of multiple operational ocean analyses (ORAs) now routinely produced around the world is an opportunity for estimation of uncertainties in HC analysis and development of ensemble-based operational HC climate indices. In this context, the spread across the ORAs is used to quantify uncertainties in HC analysis and the ensemble mean of ORAs to identify, and to monitor, climate signals. Toward this goal, this study analyzed 10 ORAs, two objective analyses based on in situ data only, and eight model analyses based on ocean data assimilation systems. The mean, annual cycle, interannual variability, and long-term trend of HC in the upper 300 m (HC300) from 1980 to 2009 are compared.

The spread across HC300 analyses generally decreased with time and reached a minimum in the early 2000s when the Argo data became available. There was a good correspondence between the increase of data counts and reduction of the spread. The agreement of HC300 anomalies among different ORAs, measured by the signal-to-noise ratio (S/N), is generally high in the tropical Pacific, tropical Indian Ocean, North Pacific, and North Atlantic but low in the tropical Atlantic and extratropical southern oceans where observations are very sparse. A set of climate indices was derived as HC300 anomalies averaged over the areas where the covariability between SST and HC300 represents the major climate modes such as ENSO, Indian Ocean dipole, Atlantic Niño, Pacific decadal oscillation, and Atlantic multidecadal oscillation.

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Tim Boyer, Catia M. Domingues, Simon A. Good, Gregory C. Johnson, John M. Lyman, Masayoshi Ishii, Viktor Gouretski, Josh K. Willis, John Antonov, Susan Wijffels, John A. Church, Rebecca Cowley, and Nathaniel L. Bindoff

Abstract

Ocean warming accounts for the majority of the earth’s recent energy imbalance. Historic ocean heat content (OHC) changes are important for understanding changing climate. Calculations of OHC anomalies (OHCA) from in situ measurements provide estimates of these changes. Uncertainties in OHCA estimates arise from calculating global fields from temporally and spatially irregular data (mapping method), instrument bias corrections, and the definitions of a baseline climatology from which anomalies are calculated. To investigate sensitivity of OHCA estimates for the upper 700 m to these different factors, the same quality-controlled dataset is used by seven groups and comparisons are made. Two time periods (1970–2008 and 1993–2008) are examined. Uncertainty due to the mapping method is 16.5 ZJ for 1970–2008 and 17.1 ZJ for 1993–2008 (1 ZJ = 1 × 1021 J). Uncertainty due to instrument bias correction varied from 8.0 to 17.9 ZJ for 1970–2008 and from 10.9 to 22.4 ZJ for 1993–2008, depending on mapping method. Uncertainty due to baseline mean varied from 3.5 to 14.5 ZJ for 1970–2008 and from 2.7 to 9.8 ZJ for 1993–2008, depending on mapping method and offsets. On average mapping method is the largest source of uncertainty. The linear trend varied from 1.3 to 5.0 ZJ yr−1 (0.08–0.31 W m−2) for 1970–2008 and from 1.5 to 9.4 ZJ yr−1 (0.09–0.58 W m−2) for 1993–2008, depending on method, instrument bias correction, and baseline mean. Despite these complications, a statistically robust upper-ocean warming was found in all cases for the full time period.

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Boyin Huang, Matthew J. Menne, Tim Boyer, Eric Freeman, Byron E. Gleason, Jay H. Lawrimore, Chunying Liu, J. Jared Rennie, Carl J. Schreck III, Fengying Sun, Russell Vose, Claude N. Williams, Xungang Yin, and Huai-Min Zhang

Abstract

This analysis estimates uncertainty in the NOAA global surface temperature (GST) version 5 (NOAAGlobalTemp v5) product, which consists of sea surface temperature (SST) from the Extended Reconstructed SST version 5 (ERSSTv5) and land surface air temperature (LSAT) from the Global Historical Climatology Network monthly version 4 (GHCNm v4). Total uncertainty in SST and LSAT consists of parametric and reconstruction uncertainties. The parametric uncertainty represents the dependence of SST/LSAT reconstructions on selecting 28 (6) internal parameters of SST (LSAT), and is estimated by a 1000-member ensemble from 1854 to 2016. The reconstruction uncertainty represents the residual error of using a limited number of 140 (65) modes for SST (LSAT). Uncertainty is quantified at the global scale as well as the local grid scale. Uncertainties in SST and LSAT at the local grid scale are larger in the earlier period (1880s–1910s) and during the two world wars due to sparse observations, then decrease in the modern period (1950s–2010s) due to increased data coverage. Uncertainties in SST and LSAT at the global scale are much smaller than those at the local grid scale due to error cancellations by averaging. Uncertainties are smaller in SST than in LSAT due to smaller SST variabilities. Comparisons show that GST and its uncertainty in NOAAGlobalTemp v5 are comparable to those in other internationally recognized GST products. The differences between NOAAGlobalTemp v5 and other GST products are within their uncertainties at the 95% confidence level.

Open access