Global mean thermosteric sea level (GThSL) and global ocean heat content (GOHC) timeseries for the upper 700m


Plot of OHC and ThSL time series


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Version history: Global mean thermosteric sea level (GThSL) and global ocean heat content (GOHC) timeseries for the upper 700 m
(updates from Domingues et al., 2008)

 

Recons.
version

Period/Area
available

Historical data

Argo
data

XBT corrections

Climatology

Sat. altimeter
EOF period/number

Used in refs.

 

3.1

1960-2012

65°N-65°S

EN3v1b

1960-2004

BO,CTD,XBT

CSIRO
2000-2012

(J. Dunn)

Wijffels et al. [2008], Table 1

Wijffels et al. [2008]
BO,CTD, Argo-Dunn

(monthly mean)

CSIRO

1993-2011

30 EOFs

 

Click to download:

Near-global yearly estimates smoothed by a 3-year running mean:

GThSL_recons_version3.1_1950_2012_CLIM_sbca12tmosme_OBS_bcax_0700m.dat

Read_me_GThSL_recons_version3.1_1950_2012_CLIM_sbca12tmosme_OBS_bcax_0700m.txt

GOHC_recons_version3.1_1950_2012_CLIM_sbca12tmosme_OBS_bcax_0700m.dat

Read_me_GOHC_recons_version3.1_1950_2012_CLIM_sbca12tmosme_OBS_bcax_0700m.txt

Note:

As version 3.0 but with timeseries extending to 2012.

Argo data downloaded on January 2013.

 

3.0

1960-2011

65°N-65°S

EN3v1b

1950-2004

BO,CTD,XBT

CSIRO
2000-2011

(J. Dunn)

Wijffels et al. [2008], Table 1

Wijffels et al. [2008]
BO,CTD, Argo-Dunn
(monthly mean)

CSIRO

1993-2011

30 EOFs

(4), (5), (6), (7)

Click to download:

Near-global yearly estimates smoothed by a 3-year running mean:

GThSL_recons_version3.0_1950_2011_CLIM_sbca12tmosme_OBS_bcax_0700m.dat

Read_me_GThSL_recons_version3.0_1950_2011_CLIM_sbca12tmosme_OBS_bcax_0700m.txt

GOHC_recons_version3.0_1950_2011_CLIM_sbca12tmosme_OBS_bcax_0700m.dat

Read_me_GOHC_recons_version3.0_1950_2011_CLIM_sbca12tmosme_OBS_bcax_0700m.txt

Note:

Bug fixed: missing square of one of the errors related to number of observations when combined in quadrature. Thanks to Didier Monselesan.

Argo data downloaded on February 2012.

 

2.0

1960-2009

65°N-65°S

EN3v1b

1950-2004

BO,CTD,XBT

CSIRO
2000-2009

(P. Barker)

Wijffels et al. [2008], Table 1

Wijffels et al. [2008]
BO,CTD, Argo-Barker
(monthly mean)

CSIRO

1993-2009

30 EOFs

(2), (3), (8)

Click to download:

Near-global yearly estimates smoothed by a 3-year running mean:

GThSL_recons_version2.0_1950_2009_CLIM_sbca08tmosme_OBS_bcax_0700m.dat

Read_me_GThSL_recons_version2.0_1950_2009_CLIM_sbca08tmosme_OBS_bcax_0700m.txt

GOHC_recons_version2.0_1950_2009_CLIM_sbca08tmosme_OBS_bcax_0700m.dat

Read_me_GOHC_recons_version2.0_1950_2009_CLIM_sbca08tmosme_OBS_bcax_0700m.txt

Note:

An incorrect version of the above estimates was used in Ref. (2) and which accidentally omitted the contribution of the South Indian Ocean. A corrigendum will be available soon.

 

 

1.0

1950-2003

65°N-65°S

EN3v1b

1950-2004

BO,CTD,XBT

CSIRO
2000-2003

(P. Barker)

Wijffels et al. [2008], Table 1

Wijffels et al. [2008]
BO,CTD,XBT, Argo-Barker
(monthly mean)

CSIRO

1993-2006

30 EOFs

(1)

Click to download:

Near-global yearly estimates smoothed by a 3-year running mean:

GThSL_recons_version1.0_1950_2003_CLIM_sbcax08tmosme_OBS_bcax_0100m_0300m_0700m.dat

Read_me_GThSL_recons_version1.0_1950_2003_CLIM_sbcax08tmosme_OBS_bcax_0100m_0300m_0700m.txt

GOHC_recons_version1.0_1950_2003_CLIM_sbcax08tmosme_OBS_bcax_0100m_0300m_0700m.dat

Read_me_GOHC_recons_version1.0_1950_2003_CLIM_sbcax08tmosme_OBS_bcax_0100m_0300m_0700m.txt

 

 

Ocean temperature data

To estimate historical thermosteric sea level and ocean heat content (or potential temperature) for the upper 700 m, we use ocean temperature profiles available in the ENACT/ENSEMBLES version 3 (hereafter EN3; http://www.metoffice.gov.uk/hadobs/en3/) data set [Ingleby and Huddleston, 2007]. We discard profiles that have bad quality flags, have coarse vertical resolution, are shallower than the depth integration level or are from higher latitudes than 65°N and 65°S. We select only the temperature data that are clearly identified and measured by bottles (BO), conductivity-temperature-depth (CTD) profilers, and expendable bathythermographs (XBT) for which we can apply a correction for systematic fall-rate errors [Wijffels et al., 2008; Table 1]. We do not include temperature profiles from the remaining instrument types, of which a large fraction is from mechanical bathythermographs (MBT), because of their shallow depths and also lack of understanding of their systematic biases [Wijffels et al., 2008]. To complement the BOs, CTDs, and XBTs from the EN3 data set, we use the most recent version of our own quality-controlled Argo profiling floats (provided by P. Barker or J. Dunn), including data from floats corrected for pressure-sensor drifts [e.g., Barker et al., 2011; http://www.argo.ucsd.edu/Argo_data_and.html ]. Given the increased availability of high-quality delayed-mode Argo data with time, we always update the entire Argo data set (from 2000 onwards) for our estimates.

 

Temperature climatology

We use a temperature climatology (provided by S. Wijffels) which mapping technique includes spatially-dependent terms and annual, semi-annual and linear trend terms at each grid point [Wijffels et al., 2008]. We believe this is superior to most other available climatologies in which all years are simply averaged together, yielding young median observation dates in the Southern Hemisphere and old median dates in the data-rich areas of the Northern Hemisphere. Attempts to resolve more than a linear trend in time were also considered, but estimates were poorly constrained by the data.

 

Thermosteric sea level and ocean heat content

We convert temperature profiles into thermosteric sea level and ocean heat content relative to a number of fixed-depth reference levels, assuming climatological salinities from the World Ocean Atlas [Conkright et al., 2002]. We calculate anomalies relative to their monthly mean fields and map them into 1° latitude x 1° longitude grids for the ice-free ocean equatorward of 65°N and 65°S. Our deepest calculation is performed with respect to 700 m, because many XBTs measure to this depth [Wijffels et al., 2008]. To take advantage of the greater number of in situ observations in the upper ocean, the 0-700 m estimates are a sum of two depth integrations, 0-300 m and 300-700 m. Near-global timeseries are computed with equal-area weighting. Because of the sparse spatial in situ ocean coverage, particularly towards the earlier years of the record, the monthly reconstructed fields contain substantial noise which is then reduced by forming yearly averages smoothed by a three-year running mean filter.

 

Reconstruction details

In our reconstruction we use the sparse but relatively long record of thermosteric sea level anomalies to determine monthly amplitudes of leading empirical orthogonal functions (EOFs). The EOFs are used to model variability of the time-varying sea level and are calculated from satellite altimeter data (provided by CSIRO, http://www.cmar.csiro.au/sealevel/sl_data_cmar.html). An additional constant (essentially a spatially uniform field) is included in the reconstruction to represent changes in the global mean [e.g., Church et al., 2004; Church and White, 2006]. Before computing the EOFs, we apply an inverted barometer correction and remove annual and semi-annual signals as well as a global mean sea level trend from the altimeter data.

 

Error estimates

The reduced-space optimal interpolation formalism [Kaplan et al., 2000], designed to recover the large-scale robust patterns that can be derived from sparse data, provides estimates of errors on the basis of the data distribution and uncertainties in the in situ ocean observations (instrumental and geophysical errors) as well as ocean eddy variability determined from satellite altimeter data (provided by CSIRO, http://www.cmar.csiro.au/sealevel/sl_data_cmar.html). The latter two are combined in quadrature. The formal error estimates included in the downloadable files are for one standard deviation.

 

XBT bias corrections

We have significantly reduced systematic XBT fall-rate errors by applying the corrections proposed by Wijffels et al. [2008], listed in their Table 1. Further refinements in identifying and correcting XBT errors may be possible in the future. XBT bias corrections are a complex issue which is currently being addressed by an international working group (http://www.nodc.noaa.gov/OC5/XBT_BIAS/xbt_bias.html).

 

Ocean heat content regression

We convert the reconstructed near-global monthly gridded maps of thermosteric sea level into ocean heat content maps by using coefficients obtained from a spatially variable linear regression between observations of ocean heat content and thermosteric sea level. The regressions are calculated from the temperature profiles in 10° latitude x 10° longitude grid boxes (following the World Meteorological Organization squares). The resultant correlation coefficients are at least 0.99.

 

 

References

(7)

Abraham, J.P., Baringer, M., Bindoff, N.L., Boyer, T., Cheng, L.J., Church, J.A., Conroy, J.L., Domingues, C.M., Fasullo, J.T., Gilson, J., Goni, G., Good, S.A., Gorman, J. M., Gouretski, V., Ishii, M., Johnson, G.C., Kizu, S., Lyman, J.M., Macdonald, A. M., Minkowycz, W.J., Moffitt, S.E., Palmer, M.D., Piola, A., Reseghetti, F., Trenberth, K.E., Velicogna, I., von Schuckmann, K., and Willis, J.K. Monitoring systems of global ocean heat content and the implications for climate change, a review, commissioned for Rev. of Geophysics (submitted 24/04/2013).

 

 

Barker, P.M., J.R. Dunn, C.M. Domingues and S.E. Wijffels (2011), Pressure Sensor Drifts in Argo and Their Impacts. Journal of Atmospheric and Oceaninc Technology, 28, 1036-1049, doi:10.1175/2011JTECHO831.1

 

 

Church, J. A., White, N. J., Coleman, R., Lambeck, K. & Mitrovica, J. X. Estimates of the regional distribution of sea-level rise over the 1950 to 2000 period. J. Clim. 17, 2609–2625 (2004)

 

 

Church, J. A. & White, N. J. A 20th century acceleration in global sea-level rise. Geophys. Res. Lett. 33 L01602 doi: 10.1029/2005GL024826 (2006)

 

(2)

Church, J.A., N.J. White, L.F. Konikow, C.M. Domingues, J.G. Cogley, E. Rignot, J.M. Gregory, M.R. van den Broeke, A.J. Monaghan, and I. Velicogna (2011), Revisiting the Earth's sea level and energy budgets from 1961 to 2008, Geophysical Research Letters, 38, L18601, doi:10.1029/ 2011GL048794.

 

(4)  

 

Church, J. A., D. Monselesan, J. M. Gregory, and B. Marzeion (2013), Evaluating the ability of process based models to project sea-level change, Environmental Research Letters, 8(1).

 

 

Conkright, M. E. et al. World Ocean Atlas 2001: Objective Analyses, Data Statistics, and Figures, CD-ROM Documentation (National Oceanographic Data Center, Silver Spring, MD, 2002)

 

(1)  

 

Domingues, C.M. , J.A. Church, N.J. White, P.J. Gleckler, S.E. Wijffels, P.M. Barker and J.R. Dunn (2008), Improved estimates of upper-ocean warming and multi-decadal sea-level rise. Nature, 453, 1090-1094, doi:10.1038/nature07080.

 

(3)  

Gleckler, P. J., Santer, B.D., Domingues, C. M., Pierce, D.W., Barnett, T.P., Church, J.A., Taylor, K.E., AchutaRao, K.M., Boyer, T.P., Ishii, M., and Caldwell, P.M. (2012). Human-induced global ocean warming on multidecadal timescales. Nature Climate Change, 2, 524-529, doi:10.1038/nclimate1553.

 

(8)  

 

Griffies, S.M., J. Yin, S.C. Bates, E. Behrens, M. Bentsen, D. Bi, A. Biastoch, C. Boening, A. Bozec, C. Cassou, E. Chassignet, G. Danabasoglu, S. Danilov, C.M. Domingues, H. Drange, P.J. Durack, R. Farneti, P. Goddard, R. J. Greatbatch, J. Lug, E. Maisonnave, S.J. Marsland, K. Lorbacher, A. J. George Nurser, D.S. Melian, J. B. Paltero, B. L. Samuels, M. Scheinert, D. Sidorenko, L. Terray, A.M. Treguier, Y.H. Tseng, H. Tsujino, P. Uotila, S. Valcke, A. Voldoire, Q. Wang, S. Yeager, Xuebin Zhang. Global and regional sea level in a suite of interannual CORE-II hindcast simulations. To be submitted to Ocean Modelling.

 

(6)  

Hanna, E, Navarro, F., Pattyn, F., Domingues, C.M., Fettweis, X., Ivins, E.R., Nicholls, R.J., Ritz, C., Smith, B., Tulaczyk, S., Whitehouse, P.L., Zwally. H.J. Ice sheet mass balance and climate change: a state of the science review. Nature, 498, Pages: 51–59 DOI: doi:10.1038/nature12238.

 

Ingleby, B., and M. Huddleston, 2007: Quality control of ocean temperature and salinity profiles—Historical and real-time data. J. Mar. Syst., 65, 158–175, doi:10.1016/j.jmarsys.2005.11.019.

 

 

Kaplan, A., Kushnir, Y. & Cane, M. A. Reduced space optimal interpolation of historical marine sea level pressure. J. Clim. 13, 2987–3002 (2000)

 

(5)  

Otto, A., F. E. L. Otto, O. Boucher, J. Church, G. Hegerl, P. M. Forster, N. P. Gillett, J. Gregory, G. C. Johnson, R. Knutti, N. Lewis, U. Lohmann, J. Marotzke, G. Myhre, D. Shindell, B. Stevens, and M. R. Allen. 2013. Energy budget constraints on climate response. Nature Geoscience, 6, 415-416, doi:10.1038/ngeo1836.

 

 

Wijffels, S.E., J. Willis, C.M. Domingues, P. Barker, N.J. White, A. Gronell, K. Ridgway, and J.A. Church (2008), Changing Expendable Bathythermograph Fall Rates and Their Impact on Estimates of Thermosteric Sea Level Rise. Journal of Climate, 21, 5657-5672, doi:10.1175/2008JCLI2290.1.