The Ocean's Role in Climate

All people on Earth depend directly or indirectly on the ocean. The global ocean covers 71% of the Earth surface and contains about 97% of the Earth’s water. The ocean supports unique habitats, and are interconnected with other components of the climate system through global exchange of water, energy and carbon. The projected responses of the ocean to past and current human-induced greenhouse gas emissions and ongoing global warming include climate feedbacks, changes over decades to millennia that cannot be avoided, thresholds of abrupt change, and irreversibility. Therefore, monitoring ocean's changes is a critical step for combating climate change.

--Temperature and ocean heat content: owing to its large heat capacity, more than 90% of Earth’s residual heat related to global warming is absorbed by the ocean. As such, the global ocean heat content record robustly represents the signature of global warming and is impacted less by weather-related noise and climate variability such as El Niño and La Niña events. Ocean thermal expansion due to ocean temperature change contributes substantially (30%~50%) to the sea level change, which can considerably influence human populations in coastal and island regions and marine ecosystems. Therefore, monitoring the OHC changes and understanding its variation are crucial for climate change.

-- Salinity: the global hydrological cycle is comprised of the movement of water through the ocean, atmosphere, cryosphere, and land systems. It is a central element of Earth’s climate system, yet it is also one of the most poorly observed and modeled aspects of Earth’s climate system. The ocean accounts for ~80% of the global surface freshwater flux. In fact, given that salinity integrates the highly variable E and P fields in space and time, ocean salinity is among the best recorders of long-term changes in E-P.

-- Stratification: Sea water generally forms stratified layers with lighter waters near the surface and denser waters at greater depth. This stable configuration acts as a barrier to water mixing that impacts the efficiency of vertical exchanges of heat, carbon, oxygen and other constituents.

IAP group provides observational reconstructions to ocean temperature, salinity, density, stratification state, and aims to provide the most state-of-the-art observational data products to support a wide range of scientific researches and services.

This webpage contains the following products and some techniques:

--Temperature, ocean heat content, steric sea level gridded product and time series

--Salinity gridded product and time series

--Stratification gridded product and time series

-- In situ ocean temperature profile observations after quality-control and bias-correction

Climate Data Guide (UCAR) has a webpage hosting IAP gridded temperature data, OCEAN TEMPERATURE ANALYSIS AND HEAT CONTENT ESTIMATE FROM INSTITUTE OF ATMOSPHERIC PHYSICS

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IAP Ocean Salinity gridded product is now available (version 1.0)!!!

Ocean salinity gridded product: 1 by 1 degree, 0-2000m, 1940-present, Monthly. The data descreption and evaluation paper has been submitted to J.Climate (see reference below).

DATA: Gridded ocean 0-2000m salinity data (ftp) [Link]

A backup data link: Gridded ocean 0-2000m salinity data, time series and figures (cloud disk)  [Link]

DATA: Salinity-Contrast time series (SSS, 0-100m, 0-300m, 0-700m, 0-2000m) [Link]

Remarks at 24-May-2020: previous runs of 2018/2019 salinity data are found to have some errors, the past two years data are re-generated and updated at 24-May-2020

Fig. (upper)  Salinity-contrast time series from 1960 to 2017 at upper 2000m. Salinity-contrast is defined as the difference between the salinity in regions of high and low salinity averaged over the top 2000 m (SC2000). Monthly anomaly time series and its 2σ error bars relative to a 2008-2017 baseline. (bottom) Ocean salinity trends within 1960 to 2017 from sea surface to 2000m as the zonal mean sections in each ocean basin organized around the Southern Ocean in the center. Grey contours show the climatological mean salinity, with intervals of 0.2 g kg^-1.

Reference: Cheng L., K. E. Trenberth, N. Gruber, J. P. Abraham, J. Fasullo, G. Li, M. E. Mann, X. Zhao, Jiang Zhu, 2020: Improved estimates of changes in upper ocean salinity and the hydrological cycle. Journal of Climate. In press, doi: https://doi.org/10.1175/JCLI-D-20-0366.1.

Source plots (.eps, .ai) and more [Link]

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IAP Ocean Stratification gridded product is now available (version 1.0)!!!

Ocean stratification (Brunt–Väisälä frequency: N^2) and potential density gridded product:1 by 1 degree, 0-2000m, 1960-present, Monthly. The data descreption and evaluation see our paper in Nature Climate Change (see reference below).

DATA: Gridded ocean 0-2000m stratification data (ftp) [Link]

DATA: Gridded ocean 0-2000m potential density data (ftp) [Link]

A backup data link: Gridded ocean 0-2000m Stratification/Potential Density data (cloud disk)  [Link]

DATA: Global and basin-averaged N^2 time series, spatial trend data and other data associated with Li et al. 2020 NCC [Link]

Fig. (upper) Time evolution of the global  0-2000m ocean stratification changes. The solid lines are after application of a LOWESS (Locally Weighted Scatterplot Smoothing) smoother (span width of 20 years) to depict the interdecadal and longer-term changes. The 90% confidence interval for IAP estimate (shadings) accounts for both spatial sampling of historical observations and instrumental errors. (bottom) zonal and vertical sections of the 0-2000m N2 trends during 1960-2018 in the three main basins (Pacific, Atlantic, Indian) surrounding the meridional mean section of the Southern Ocean (30°S south). Black contour lines show the climatology of potential density with 0.5 kg m^-3 intervals.


Reference: Li, Guancheng, L. Cheng*, J. Zhu*, K. E. Trenberth, M. E. Mann, J. P. Abraham, 2020: Increasing ocean stratification over the past half century. Nature Climate Change. https://doi.org/10.1038/s41558-020-00918-2

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News

《Science》Perspective: Cheng, L., J. Abraham, Z. Hausfather, K. E. Trenberth, 2019: How fast are the oceans warming? Science, 363, 128-129. doi: 10.1126/science.aav7619.

DATA: Here is the data presented in Cheng et al. 2019 Science: Global OHC0-2000m changes (Unit: Zeta Joules; Resolution: annual mean) from 1955 to 2100 from CMIP5 models and four latest observational datasets:

CMIP5 models:

(Historical Simulations 1955-2005 + RCP2.6 projections 2006-2100)

(Historical Simulations 1955-2005 + RCP4.5 projections 2006-2100)

(Historical Simulations 1955-2005 + RCP8.5 projections 2006-2100)

Observations:

Four observational time series 1955-2017 (IAP0-2000m, Domingues0-700m+Levitus700-2000m, Ishii0-2000m, Resplandy)

Significant Warming Has Already Been Observed in The Ocean

Fig. Mean ocean warming rate (0-2000m, linear trend) from 1960 ro 2019 based on IAP ocean analyses. Figure shows vertical section of the ocean temperature trends within 1960 to 2019 from sea surface to 2000 m (60-year Ordinary Least Squares linear trend). Shown are the zonal mean sections in each ocean basin organized around the Southern Ocean (south of 60°S) in the center. Black contours show the associated climatological mean temperature with intervals of 2°C (In the Southern Ocean, 1°C intervals are provided in dashed contours). IAP gridded data are used.

2019 was the warmest year on record for the global ocean

Fig. (upper) Change in global upper-level (0–2000 m) ocean heat content since 1958. Each bar shows the annual mean relative to a 1981–2010 baseline. (lower) Annual mean ocean heat content anomaly in 2019 relative to a 1981–2010 baseline.

DATA in (a):OHC0-2000m annual time series (baseline 1981-2010, Unit: Zetta Joules)

OHC0-2000m monthly time series (baseline 1981-2010, Unit: Zetta Joules)

DATA in (b):Spatial map of OHC0-2000m anomaly relative to 1981-2010 baseline (Unit: 10^9 Joules per square meter) (This data organized as [latitude, longitude, OHC value])

All figures (.png, .eps) in Cheng et al. 2020 AAS, and an updated animation of ocean warming from 1940 to 2019 can be found in [Link], free to download and use.

Note: Other monthly 0-700/700-2000m time series and gridded product are available below (IAP data) .

Reference 1 (2020): Cheng L. et al. 2020: Record-Setting Ocean Warmth Continued in 2019, Advances in Atmospheric Science, 37(2), 137, Doi: 10.1007/s00376-020-9283-7.

Reference 2 (2019): Cheng L. et al. 2019: 2018 Continues Record Global Ocean Warming, Advances in Atmospheric Science, 36(3), 249-252, Doi: 10.1007/s00376-019-8276-x

Reference 3 (2018):Cheng L. and J. Zhu, 2018: 2017 is the Warmest Year on Record for the Global Ocean. Advances in Atmospheric Science, 35(3), 261-263, Doi:10.1007/s00376-018-8011-z

IAP Observational OHCs VS. Radiative imbalance at TOA (Cheng et al. 2017 Science Advances)

Fig. Estimate of the ocean energy budget in Cheng et al. (2017). The three major volcano eruptions are marked. The energy budgets are relative to 1958-1962 base period. The integrated net radiative imbalance from Allan et al. 2014 estimated from the top of atmosphere (TOA) is included in yellow multiplied by 0.93 to be comparable with the ocean energy budget. The TOA radiation is adjusted to the value of OHC within 2013-2014. The dashed grey lines encompass the 95% confidence interval. [Download high-resolution figure]

DATA:Ocean energy budget (based on OHC) [data shown in Cheng et al. (2017) ]

Reference: Cheng L. *, K. Trenberth, J. Fasullo, T. Boyer, J. Abraham, J. Zhu, 2017: Improved estimates of ocean heat content from 1960 to 2015, Science Advances. 3,e1601545c.

Observational OHCs VS. CMIP5 model simulations (Cheng et al. 2016 Ocean Science)

Fig. OHC trends during the 1970–2005 period in observations and CMIP5 models. (a) 0–700 m. (b) Full depth. For models, the histograms are the distribution of CMIP5 results, and the median of the CMIP5 multimodel results is shown in solid line, with the 5–95% confidence interval in dashed lines. For observations, we present the linear trends by different studies: this study (both CZ14 and the flexiblegrid method), five estimates in Durack et al. (2014) after adjustment, and five ensembles of ORAS4 reanalysis. The 5–95% confidence intervals for observations are shaded in light green.

DATA: OHC in 26 CMIP5 models (quadratic or linear climate-drfit correction)

DATA: OHC time series by CZ14 gaps-filling (This is NOT our recommended method, our official OHC time series see below)

Reference: Cheng L. *, K. Trenberth, M. D. Palmer, J. Zhu, J. Abraham, 2016: Observed and simulated full-depth ocean heat content changes for 1970-2005, Ocean Science. doi:10.5194/os-2016-16.

                IAP Ocean temperature gridded product

              (1 by 1 degree, from 1m to 2000m, 1940-present monthly)

AND   IAP OHC, Steric Sea Level estimate (1940-present)

Based on Cheng&Zhu 2016 Journal of Climate and Cheng et al. 2017 Science Advances

 

Fig. IAP estimate of Global 0-2000m averaged temperature change since 1940, with 95% Confidence Interval shown in red shading  [Download high-resolution figure] DATA in this plot: Ocean 0-2000m averaged temperature anomaly (1940-present, 1981-2010 baseline, Unit: Degree Celsius) (monthly mean and 95% error bar), (annual mean,and 95% error bar)

DATA: Gridded temperature dataset (version-3, 1940-present, 1 by 1 degree, monthly, 0-2000m), based on EnOI-DE gaps-filling method

DATA: Monthly/Global mean OHC time series of 0-700m/700-2000m

DATA: Monthly/Basin mean OHC time series of 0-700m/700-2000m (Pacific, Atlantic, Indian, Southern oceans)

DATA:Gridded OHC 0-100m/300m/700m/1500m/2000m (2-D)[Link]

DATA: Gridded OHC anomaly relative to 1981-2010 climatology after removing seasonal cycle, 0-100m/300m/700m/1500m/2000m (2-D)[Link]

DATA: Gridded Steric Sea Level (SSL) Anomaly data based on IAP temperature/salinity data (2-D, SSL0-100m/300m/700m/1500m/2000m)[Link]

 

A back-up for IAP data in Cloud Disk. IAP temperature gridded data and time series [Link]; IAP OHC/OHCa gridded data [Link]; IAP Steric sea level gridded data [Link]

 

An animation: An animation of historical (1940-2017) 0-2000m averaged temperature change based on IAP_Gridded dataset [DOWNLOAD]

Reference 1: Cheng L. and J. Zhu*, 2016, Benefits of CMIP5 multimodel ensemble in reconstructing historical ocean subsurface temperature variation, Journal of Climate, 29(15), 5393–5416, doi: 10.1175/JCLI-D-15-0730.1. [OA]

Reference 2: Cheng L. *, K. Trenberth, J. Fasullo, T. Boyer, J. Abraham, J. Zhu, 2017: Improved estimates of ocean heat content from 1960 to 2015, Science Advances. 3, e1601545.

High resolution figures in Cheng et al. 2017, Sci. Adv. can be downloaded here.

 

Ocean subsurface in-situ temperature profile dataset (IGOT)

DATA: IGOT (IAP Global Ocean Temperature) dataset (1940-to-2017 in situ ocean subsurface temperature profiles dataset, QC-ed and Bias-corrected. All instruments are included in this dataset: XBT, CTD, Bottle, Argo, MBT, OSD, Mooring, Glider etc. ) [Latest update: Jan. 2018]

An alternative link, for IGOT profile dataset, check for the real-time update[Latest version]

DATA: Annual Mean time series of 0-700m OHC from 1965 to 2015 [Latest update: July.2016] (Note: this is not our recommended OHC estimate, IAP official OHC time series see above)

Reference: Cheng L. *, J. Zhu, and J. Abraham, 2015: Global upper ocean heat content estimation: recent progress and the remaining challenges. Atmospheric and Oceanic Science Letters, 8, 6, 333-338. DOI:10.3878/AOSL20150031.[OA]

                                    How OHC is calculated?

Fig. Schematic illustration of how annual mean ocean heat content is calculated from the ocean temperature profile observations (raw data).

C1: IAP-OHC uses WOD2013 QC flags (more guide for QC can be found in IQuOD project)

C2/C3:see below.

C4: 1° grid

C5: Mapping method. IAP provides two options: (1). in Cheng and Zhu, 2014 (simple adjustment); (2). in Cheng and Zhu, 2016 (objective analysis, strongly recommended by the authors!!).

Reference: Cheng L. *, J. Zhu, and J. Abraham, 2015: Global upper ocean heat content estimation: recent progress and the remaining challenges. Atmospheric and Oceanic Science Letters, 8, 6, 333-338. DOI:10.3878/AOSL20150031.[OA]


                              XBT Data and XBT error

Expendable bathythermograph (XBT) data were the major component of the ocean temperatureprofile observations from the late 1960s through the early 2000s, and XBTs still continue to providecritical data to monitor surface and subsurface currents, meridional heat transport, and ocean heat content. Systematic errors have been identified in the XBT data, some of which originate from computingthe depth in the profile using a theoretically and experimentally derived fall-rate equation(FRE). After in-depth studies of these biases and discussions held in several workshops dedicated todiscussing XBT biases, the XBT science community met at the Fourth XBT Science Workshop andconcluded that XBT biases consist of 1) errors in depth values due to the inadequacy of the probemotion description done by standard FRE and 2) independent pure temperature biases. The deptherror and temperature bias are temperature dependent and may depend on the data acquisition andrecording system. In addition, the depth bias also includes an offset term. Some biases affecting theXBT-derived temperature profiles vary with manufacturer/probe type and have been shown to be time dependent. (quote in Cheng et al. 2016 [BAMS]):

Now there is a community-recommended XBT correction scheme (Cheng et al. 2014 [JAOT], CH14 method), which takes accounts of all of the influencing factors. And this method is carefully evaluated and shown to be able to significantly reduce the XBT bias [Figure]. We applied this  method to historical XBT data and then used this data to calculate IAP OHC change.

Fig. Temperature difference between XBT and CTD/OSD/PFL profiles before (left side) and after (right side) bias corrections. The data are from WOD2013 dataset(NOAA/NODC)

Here we provide our bias-corrected XBT dataset, CH14 correction scheme and a Matlab code showing how CH14 is implemented.

CH14 correction has been recommended by XBT community as the BEST correction scheme to XBT data (Cheng et al. 2016, BAMS)

DATA: 1966-2017 XBT profiles dataset (QC-ed and Bias-corrected) [Latest update: Jan. 2018]

An alternative link, for XBT dataset after CH14 correction, check for the real-time update [Latest version]

DATA: CH14 correction scheme for XBT data, Table-1, Table-2 [Latest update: Oct. 2016, an introduction on the update]

CODE: CH14 correction (Matlab codes), [Latest update: Oct. 2016] 

Link: NOAA/NCEI (U.S.) provides CH14-corrected WOD data (World Ocean Database)

Link: Met Office (U.K.) will provide CH14-corrected EN data

Reference: Cheng L., J. Zhu*, R. Cowley, T. P. Boyer and S. Wijffels, 2014: Time, probe type and temperature variable bias corrections on historical expendable bathythermograph observations. Journal of Atmospheric and Oceanic Technology, 31(8), 1793-1825.

Reference: Cheng L.*, John Abraham, Gustavo Goni, Timothy Boyer, Susan Wijffels, Rebecca Cowley, Viktor Gouretski, Franco Reseghetti, Shoichi Kizu, Shenfu Dong, Francis Bringas, Marlos Goes, Loïc Houpert, Janet Sprintall, Jiang Zhu, 2016: XBT Science: assessment of instrumental biases and errors, Bulletin of the American Meteorological Society, 97, 924-933. doi: http://dx.doi.org/10.1175/BAMS-D-15-00031.

 OHC time series applying different XBT correction schemes (Cheng et al. 2016, BAMS)

XBT bias is one of the major source of error in OHC estimate, Cheng et al. 2016, BAMS provided an illustration for the impact of XBT bias on OHC estimate. In the following figure, we applied ten of the existing XBT correction schemes to calculate OHC. The mapping method is the simple-gridded average (potentially assuming that the OHC in the data-gaps is filled by the mean of OHCs in the sampled regions). By comparison, the dashed curve is without applying XBT correction.

Fig. Upper (0–700 m) OHC calculated using corrected XBT data and the uncorrected XBT data (Uncor; black curve). The XBT data are corrected using 10 of the schemes, including Cheng et al. (2014; CH14), Wijffels et al. (2008; W08), Ishii and Kimoto (2009; IK09), Good (2011; GD11), Hamon et al. (2012; H12), Cheng et al. (2011; CH) method in Cowley et al. (2013; CWCH), Cowley et al. (2013; CW13), Gouretski and Reseghetti (2010; GR10), Levitus et al. (2009; L09), and Gouretski (2012; G12). The annual mean of global OHC anomaly (OHCA) is calculated by simply averaging the 1° × 1° grid means of OHCA over the global ocean. [Update of Fig.3 in Cheng et al. BAMS paper]

DATA: OHC time series applying different XBT correction schemes (Mapping: simple gridded average) [Update of Fig. 3 in Cheng et al. BAMS paper] [Latest update: Oct. 2016]

Reference: Cheng L.*, John Abraham, Gustavo Goni, Timothy Boyer, Susan Wijffels, Rebecca Cowley, Viktor Gouretski, Franco Reseghetti, Shoichi Kizu, Shenfu Dong, Francis Bringas, Marlos Goes, Loïc Houpert, Janet Sprintall, Jiang Zhu, 2016: XBT Science: assessment of instrumental biases and errors, Bulletin of the American Meteorological Society, 97, 924-933. doi: http://dx.doi.org/10.1175/BAMS-D-15-00031.

                                                    Climatology

One need to remove a monthly climatology from each temperature profile, which results in temperature anomaly profile. There are several different temperature climatologies constructed by different data centers, such as the World Ocean Atlas (WOA) (Locarnini et al., 2010) and Argo climatology (Roemmich and Gilson, 2009). The irregular data coverage in historical period could lead to bias in OHC calculation tied to climatology. Cheng and Zhu (2015) proposed one of the solutions, to use a climatology constructed by data with a period with consistent global coverage (i.e. Argo era).

In our analysis, 12 monthly climatologies are constructed by using all of the observations from 2008 to 2012. All of the temperature profiles in each month within 2008–2012 are interpolated to standard vertical levels and then averaged to 1° × 1° grids. The 5° × 5° median filter is then applied to the obtained field to smooth the climatologies.

Fig. August of 2008-2012 Climatology, in values of 0-700m averaged temperature.

Here we provide our 2008-2012 climatology (in MATLAB ".mat" format)

DATA: 2008-2012 Climatology [Latest update: Oct. 2016]

Reference: Cheng L. and J. Zhu*, 2015: Influences of the choice of climatology on ocean heat content estimation, Journal of Atmospheric and Oceanic Technology. 32(2), 388-394, doi: http://dx.doi.org/10.1175/JTECH-D-14-00169.1


                                           Vertical resolution

It has been shown that the typical vertical resolution of historical temperature profiles is insufficient (Cheng and Zhu, 2014a), with a global mean of 10–20 m for the upper 100 m and 50–100 m for 300–700 m prior to 2000. The insufficiency of the vertical resolution of temperature data leads to a systematic bias in OHC calculation. This error is calculated by sampling a high-vertical-resolution climatological ocean according to the depth intervals of in-situ subsurface temperature observations, and then the difference between the OHC calculated by subsampled profiles and the OHC of the climatological ocean is defined as the resolution-induced error.

Fig. Annual mean vertical resolution of historical subsurface temperature profiles. a). All observations. b) 7 main instruments: left side is the data amount, right side is the annual mean resolution.

Reference: Cheng L., and J. Zhu*, 2014: Uncertainties of the ocean heat content estimation induced by insufficient vertical resolution of historical ocean subsurface observations, Journal of Atmospheric and Oceanic Technology, 31(6), 1383-1396.

2001-2003 OHC shift: an artifact of the observation system evolution

There is an OHC shift during 2001-2003 in most of the OHC estimates, which is due to the observation system change from Ship-based system (Figure b) pre-2001 to Argo-based system (Figure c).

The key messege is: to be careful in using OHC values within 2000-2004 period, considering the observation system change.

Figure. a). OHC shift during 2001-2003 as identified in NODC-OHC estimate. b) Ship-based observation system. c) Argo system. d) Argo-Ship Area.

Reference: Cheng L., and J. Zhu*, 2014: Artifacts in variations of ocean heat content induced by the observation system changes, Geophysical Research Letters, 41(20), 7276-7283, DOI: 10.1002/2014GL061881. [OA]

                                        Gaps-filling (Mapping)

There are regions of ocean grids without any data. Therefore, it is impossible to calculate the global integration, which requires full data coverage. To overcome this data paucity, “mapping” strategies have been proposed to fill those data gaps.

Our group proposed two methods: (1) in Cheng and Zhu, 2014, which is a simple/transparent method. (2) in Cheng and Zhu, 2016, which is Ensemble Optimum Interpolation method with Dynamic Ensemble of CMIP5 simulations (EnOI-DE/CMIP5) method. This method is then updated in Cheng et al, 2016, Science Advances. Method (2) is recommended method! 


                                                    Useful Links

NOAA's offical estimate on historical OHC changes, Steric Sea Levl change etc.

World Ocean Database

XBT Science Team website

International Quality-Controlled Ocean Database (IQuoD) website

Guide for XBT corrections in World Ocean Dataset 2013 (WOD13)

Ongoing projects by IAP ocean data group:

1. Using the Synthetic data to understand the existing mapping methods

Mapping methods have been shown to be one of the major error sources in OHC calculation. Understanding the behavior of recent existing mapping strategies is the first step to solve this problem. Synthetic data for OHC will be used to gain knowledge about the existing mapping methods.

2. Ocean heat content

Improving ocean heat content estimate, investigating the global and regional ocean heat uptake.

3. MBT bias correction

Developing a new correction scheme for historical MBT data.

Published:  Gouretski, V. * and L. Cheng*, 2020: Correction for systematic errors in the global data set of temperature profiles from mechanical bathythermographs. J. Atmos. Oceanic Technol. 37 (5): 841–855. https://doi.org/10.1175/JTECH-D-19-0205.1

4. Ocean gridded data at 0.5 degree spatial resolution

Developing gridded temperature and salinity data at 0.5 by 0.5 degree spatial resolution.

                                   Media coverage




                                                        Publications
  • Cheng L., J. Abraham, Z. Hausfather, K. E. Trenberth, 2019: How fast are the oceans warming? Science, 363, 128-129. https://doi.org/10.1126/science.aav7619. [Altmetric score: 2780. Top 300 of >12,000,000 papers]

    Cheng L., K. E. Trenberth, J. T. Fasullo, M. Mayer, M. Balmaseda, J. Zhu, 2019: Evolution of ocean heat content related to ENSO. Journal of Climate, 32, 3529–3556, https://doi.org/10.1175/JCLI-D-18-0607.1.

    Cheng, L., J, Zhu., 2018: 2017 was the warmest year on record for the global ocean. Adv. Atmos. Sci., 35, 261-263, doi: 10.1007/s00376-018-8011-z. [LINK]

    Cheng L., K. E. Trenberth, J. Fasullo, J. Abraham, T. P. Boyer, K. von Schuckmann, and J. Zhu 2018: Taking the pulse of the planet, Earth and Space Science News, Eos, 99, 14-16. doi: 10.1029/2017EO081839. [LINK]

    Wang G., L. Cheng*, J. Abraham, C. Li, 2017: Consensuses and discrepancies ofbasin-scale ocean heat content changes in different ocean analyses. ClimateDynamic, doi: 10.1007/s00382-017-3751-5. [LINK]

    Wang, G., L. Cheng*, T. Boyer, C. Li, 2017: Halosteric Sea Level Changes during theArgo Era. Water, 9, 484. [LINK]

    Cheng L., K. Trenberth, J. Fasullo, T. Boyer, J. Abraham, J. Zhu, 2017: Improved estimates of ocean heat content from 1960 to 2015, Science Advances, 3, e1601545. [LINK]

    Cheng L., J. Abraham, G. Goni, T. Boyer, S. Wijffels, R. Cowley, V. Gouretski, F. Reseghetti, S. Kizu, S. Dong, F. Bringas, M. Goes, L. Houpert, J. Sprintall, and J. Zhu, 2016: XBT Science: assessment of instrumental biases and errors, Bulletin of the American Meteorological Society, 97, 924-933. [LINK]

    Cheng L., K. Trenberth, M. D. Palmer, J. Zhu, J. Abraham, 2016: Observed and simulated full-depth ocean heat content changes for 1970-2005, Ocean Science. doi:10.5194/os-2016-16. [LINK]

    Cheng L. and J. Zhu, 2016, Benefits of CMIP5 multimodel ensemble in reconstructing historical ocean subsurface temperature variation, Journal of Climate, 29(15), 5393–5416. doi: 10.1175/JCLI-D-15-0730.1. [LINK]

    Cheng L., J. Zhu, and J. Abraham, 2015: Global upper ocean heat content estimation: recent progress and the remaining challenges. Atmospheric and Oceanic Science Letters, 8. DOI:10.3878/AOSL20150031. (LINK)(LINK)

    Cheng, L., and J. Zhu, 2015: Influences of the choice of climatology on ocean heat content estimation, J. Atmos. Ocean Tech., 32, 388–394. (LINK)(LINK)

    Cheng, L., and J. Zhu, 2014a: Uncertainties of the ocean heat content estimation induced by insufficient vertical resolution of historical ocean subsurface observations, J. Atmos. Ocean Tech., 31, 1383–1396. (LINK)(LINK)

    Cheng, L., and J. Zhu, 2014b: Artifacts in variations of ocean heat content induced by the observation system changes, Geophys. Res. Lett., 20, 7276–7283. (LINK)

    Cheng, L., Zhu, R. Cowley, T. P. Boyer, S. Wijffels, 2014: Time, probe type and temperature variable bias corrections to historical expendable bathythermograph observations, J. Atmos. Ocean Tech., 31, 1793–1825. (LINK)(LINK)

  • Abraham, J. P., F. Reseghetti, M. Baringer, T. Boyer, L. Cheng, J. Church, C. Domingues, J. T. Fasullo, J. Gilson, G. Goni, S. Good, J. M. Gorman, V. Gouretski, M. Ishii, G. C. Johnson, S. Kizu, J. Lyman, A. MacDonald, W. J. Minkowycz, S. E. Moffitt, M. Palmer, A. Piola, K. E. Trenberth, I. Velicogna, S. Wijffels and J. Willis, 2013: A review of global ocean temperature observations: Implications for ocean heat content estimates and climate change, Reviews of Geophysics, 51, 450-483.(LINK)
  • Cowley, R., S. Wijffels, L. Cheng, T. Boyer, S. Kizu, 2013: Biases in Expendable Bathythermograph Data: A New View Based on Historical Side-by-Side Comparisons, Journal of Atmospheric and Oceanic Technology, 30(6), 1195-1225. (LINK)

    L. Cheng's publications can be downloaded here [link]

Contact
Lijing Cheng
(ResearchID)
(GoogleScholar)
Jiang Zhu 

    from IAP/CAS


Web page provided by IAP, L.C.