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Lijing Cheng
and
Jiang Zhu

Abstract

A complete map of the ocean subsurface temperature is essential for monitoring aspects of climate change such as the ocean heat content (OHC) and sea level changes and for understanding the dynamics of the ocean/climate variation. However, global observations have not been available in the past, so a mapping strategy is required to fill the data gaps. In this study, an advanced mapping method is proposed to reconstruct the historical ocean subsurface (0–700 m) temperature field from 1940 to 2014 by using ensemble optimal interpolation with a dynamic ensemble (EnOI-DE) approach and a multimodel ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5) historical and representative concentration pathway 4.5 simulations. The reconstructed field is a combination of two parts: a first guess provided by the ensemble mean of CMIP5 models and an adjustment by minimizing the analysis error with the assistance of error covariance determined by the CMIP5 models. The uncertainty of the field can also be assessed. This new approach was evaluated using a series of tests, including subsample tests by using data from the Argo period, idealized tests by specifying a truth field from the models, and withdrawn-data tests by removing 20% of the observations for validation. In addition, the authors showed that the ocean mean state, long-term trends, and interannual and decadal variability are all well represented. Furthermore, the most significant benefit of this method is to provide an improved estimate of the long-term historical OHC changes since 1940, which have important implications for Earth’s energy budget.

Full access
Andrea Storto
,
Lijing Cheng
, and
Chunxue Yang

Abstract

Recent estimates of the global warming rates suggest that approximately 9% of Earth’s excess heat has been cumulated in the deep and abyssal oceans (below 2000-m depth) during the last two decades. Such estimates assume stationary trends deducted as long-term rates. To reassess the deep ocean warming and potentially shed light on its interannual variability, we formulate the balance between Earth’s energy imbalance (EEI), the steric sea level, and the ocean heat content (OHC), at yearly time scales during the 2003–18 period, as a variational problem. The solution is achieved through variational minimization, merging observational data from top-of-atmosphere EEI, inferred from Clouds and the Earth’s Radiant Energy System (CERES), steric sea level estimates from altimetry minus gravimetry, and upper-ocean heat content estimates from in situ platforms (mostly Argo floats). Global ocean reanalyses provide background-error covariances for the OHC analysis. The analysis indicates a 2000-m–bottom warming of 0.08 ± 0.04 W m−2 for the period 2003–18, equal to 13% of the total ocean warming (0.62 ± 0.08 W m−2), slightly larger than previous estimates but consistent within the error bars. The analysis provides a fully consistent optimized solution also for the steric sea level and EEI. Moreover, the simultaneous use of the different heat budget observing networks is able to decrease the analysis uncertainty with respect to the observational one, for all observation types and especially for the 0–700-m OHC and steric sea level (more than 12% reduction). The sensitivity of the analysis to the choice of the background time series proved insignificant.

Significance Statement

Several observing networks provide complementary information about the temporal evolution of the global energy budget. Here, satellite observations of Earth’s energy imbalance (EEI) and steric sea level and in situ–derived estimates of ocean heat content anomalies are combined in a variational analysis framework, with the goal of assessing the deep ocean warming. The optimized solution accounts for the uncertainty of the different observing networks. Furthermore, it provides fully consistent analyses of global ocean heat content, steric sea level, and EEI, which show smaller uncertainty than the original observed time series. The deep ocean (below 2000-m depth) exhibits a significant warming of 0.08 ± 0.04 W m−2 for the period 2003–18, equal to the 13% of the total ocean warming.

Open access
Kevin E. Trenberth
,
John T. Fasullo
,
Karina von Schuckmann
, and
Lijing Cheng

Abstract

The current Earth’s energy imbalance (EEI) can best be estimated from changes in ocean heat content (OHC), complemented by top-of-atmosphere (TOA) radiation measurements and an assessment of the small non-ocean components. Sustained observations from the Argo array of autonomous profiling floats enable near-global estimates of OHC since 2005, which reveal considerable cancellation of variations in the upper 300 m. An analysis of the monthly contributions to EEI from non-ocean components (land and ice) using the Community Earth System Model (CESM) Large Ensemble reveals standard deviations of 0.3–0.4 W m−2 (global); largest values occur in August, but values are below 0.75 W m−2 greater than 95% of the time. Global standard deviations of EEI of 0.64 W m−2 based on top-of-atmosphere observations therefore substantially constrain ocean contributions, given by the tendencies of OHC. Instead, monthly standard deviations of many Argo-based OHC tendencies are 6–13 W m−2, and nonphysical fluctuations are clearly evident. It is shown that an ocean reanalysis with multivariate dynamical data assimilation features much better agreement with TOA radiation, and 44% of the vertically integrated short-term OHC trend for 2005–14 of 0.8 ± 0.2 W m−2 (globally) occurs below 700-m depth. Largest warming occurs from 20° to 50°S, especially over the southern oceans, and near 40°N in all ocean analyses. The EEI is estimated to be 0.9 ± 0.3 W m−2 for 2005–14.

Full access
Jing Duan
,
Yuanlong Li
,
Lijing Cheng
,
Pengfei Lin
, and
Fan Wang

Abstract

The heat content in the Indian Ocean has been increasing owing to anthropogenic greenhouse warming. Yet, where and how the anthropogenic heat is stored in the Indian Ocean have not been comprehended. Analysis of various observational and model-based datasets since the 1950s reveals a robust spatial pattern of the 0–700 m ocean heat content trend (ΔOHC), with enhanced warming in the subtropical southern Indian Ocean (SIO) but weak to minimal warming in the tropical Indian Ocean (TIO). The meridional temperature gradient between the TIO and SIO declined by 16.4% ± 7.5% during 1958–2014. The heat redistribution driven by time-varying surface winds plays a crucial role in shaping this ΔOHC pattern. Sensitivity experiments using a simplified ocean dynamical model suggest that changes in surface winds over the Indian Ocean, particularly those of the SIO, caused a convergence trend in the upper SIO and a divergence trend in the upper TIO. These wind changes primarily include the enhancements of westerlies in the Southern Ocean and the subtropical anticyclone in the SIO. Albeit with systematic biases, the ΔOHC pattern and surface wind changes simulated by phase 6 of the Coupled Model Intercomparison Project (CMIP6) models broadly resemble the observation and highlight the essence of external forcing in causing these changes. This heat storage pattern is projected to persist in the model-projected future, potentially impacting future climate.

Restricted access
Lijing Cheng
,
Grant Foster
,
Zeke Hausfather
,
Kevin E. Trenberth
, and
John Abraham

Abstract

The increased concentrations of greenhouse gases in the atmosphere create an increase in Earth’s thermal energy, which is mainly stored in the ocean. Quantification of the rate of increase in ocean heat content (OHC) is vital for understanding the current and future climate of Earth. Linear trend lines have been frequently used to quantify long-term rates of change, but are inappropriate because they cannot capture nonlinearity in trends, have large start- and end-point sensitivity, and the assumption of linearity is nonphysical. Here observed and model-based linear regressions with higher-order polynomial (quadratic), piecewise linear, and locally weighted scatterplot smoothing (LOWESS) are compared. Piecewise linear and LOWESS perform best in depicting multidecadal trends. It is shown that linear rates are valid for up to about 15-yr segments (i.e., it is valid to compute linear rates within a 15-yr time window). Using the recommended methods, ocean warming for the upper 2000 m increases from about 0 to 0.06 ± 0.08 W m−2 for 1958–73 to 0.58 ± 0.08 W m−2 for 2003–18, indicating an acceleration of ocean warming that happens in all four ocean basins and from near the sea surface to 2000 m. There is consistency between multimodel-mean historically forced climate models and observations, which implies that the contribution of internal variability is small for global 0–2000 m OHC. Notable increases of OHC in the upper ocean (i.e., 0–300 m) after about 1980 and the deeper ocean (300–2000 m) after the late 1980s are also evident. This study suggests alternative methods to those currently used to estimate ocean warming rates to provide a more accurate quantification of long-term Earth’s energy changes.

Significance Statement

Quantifying long-term rates of change is needed to understand the time evolution of ocean warming and to assess the changing ocean and Earth’s energy budgets. Linear trend lines have been frequently used but cannot capture nonlinearity in trends, and have large start- and end-point sensitivity. Based on an analysis of the statistical features of ocean heat content time series, this study proposes two alternative methods to quantify the rates of change, including piecewise linear fit and LOWESS. Robust increases in warming for the upper 2000 m detected through observational records and climate models from 1958 to 2020, indicate a robust acceleration of ocean warming. Slow penetration of heat from the upper ocean into the deeper ocean is also evident.

Open access
Kevin E. Trenberth
,
Yongxin Zhang
,
John T. Fasullo
, and
Lijing Cheng

Abstract

Ocean meridional heat transports (MHTs) are deduced as a residual using energy budgets to produce latitude versus time series for the globe, Indo-Pacific, and Atlantic. The top-of-atmosphere (TOA) radiation is combined with the vertically integrated atmospheric energy divergence from atmospheric reanalyses to produce the net surface energy fluxes everywhere. The latter is then combined with estimates of the vertically integrated ocean heat content (OHC) tendency to produce estimates of the ocean heat divergence. Because seasonal sea ice and land runoff effects are not fully considered, the mean annual cycle is incomplete, but those effects are small for interannual variability. However, there is a mismatch between 12-month inferred surface flux and the corresponding OHC changes globally, requiring adjustments to account for the Earth’s global energy imbalance. Estimates are greatly improved by building in the constraint that MHT must go to zero at the northern and southern extents of the ocean basin at all times, enabling biases between the TOA and OHC data to be reconciled. Zonal mean global, Indo-Pacific, and Atlantic basin ocean MHTs are computed and presented as 12-month running means and for the mean annual cycle for 2000–16. For the Indo-Pacific, the tropical and subtropical MHTs feature a strong relationship with El Niño–Southern Oscillation (ENSO), and in the Atlantic, MHT interannual variability is significantly affected by and likely influences the North Atlantic Oscillation (NAO). However, Atlantic and Pacific changes are linked, suggesting that the northern annular mode (as opposed to NAO) is predominant. There is also evidence of decadal variability or trends.

Open access
Lijing Cheng
,
Kevin E. Trenberth
,
John T. Fasullo
,
Michael Mayer
,
Magdalena Balmaseda
, and
Jiang Zhu

Abstract

As the strongest interannual perturbation to the climate system, El Niño–Southern Oscillation (ENSO) dominates the year-to-year variability of the ocean energy budget. Here we combine ocean observations, reanalyses, and surface flux data with Earth system model simulations to obtain estimates of the different terms affecting the redistribution of energy in the Earth system during ENSO events, including exchanges between ocean and atmosphere and among different ocean basins, and lateral and vertical rearrangements. This comprehensive inventory allows better understanding of the regional and global evolution of ocean heat related to ENSO and provides observational metrics to benchmark performance of climate models. Results confirm that there is a strong negative ocean heat content tendency (OHCT) in the tropical Pacific Ocean during El Niño, mainly through enhanced air–sea heat fluxes Q into the atmosphere driven by high sea surface temperatures. In addition to this diabatic component, there is an adiabatic redistribution of heat both laterally and vertically (0–100 and 100–300 m) in the tropical Pacific and Indian oceans that dominates the local OHCT. Heat is also transported and discharged from 20°S–5°N into off-equatorial regions within 5°–20°N during and after El Niño. OHCT and Q changes outside the tropical Pacific Ocean indicate the ENSO-driven atmospheric teleconnections and changes of ocean heat transport (i.e., Indonesian Throughflow). The tropical Atlantic and Indian Oceans warm during El Niño, partly offsetting the tropical Pacific cooling for the tropical oceans as a whole. While there are distinct regional OHCT changes, many compensate each other, resulting in a weak but robust net global ocean cooling during and after El Niño.

Open access
Yuanyuan Song
,
Yuanlong Li
,
Aixue Hu
,
Lijing Cheng
,
Gaël Forget
,
Xiaodan Chen
,
Jing Duan
, and
Fan Wang

Abstract

As the major sink of anthropogenic heat, the Southern Ocean has shown quasi-symmetric, deep-reaching warming since the mid-20th century. In comparison, the shorter-term heat storage pattern of the Southern Ocean is more complex and has notable impacts on regional climate and marine ecosystems. By analyzing observational datasets and climate model simulations, this study reveals that the Southern Ocean exhibits prominent decadal (> 8 years) variability extending to ~700 m depth and is characterized by out-of-phase changes in the Pacific and Atlantic-Indian Ocean sectors. Changes in the Pacific sector are larger in magnitude than those in the Atlantic-Indian Ocean sectors and dominate the total heat storage of the Southern Ocean on decadal timescales. Instead of heat uptake through surface heat fluxes, these asymmetric variations arise primarily from wind-driven heat redistribution. Pacemaker and pre-industrial simulations of the Community Earth System Model version-1 (CESM1) suggest that these variations in Southern Ocean winds arise primarily from natural variability of the tropical Pacific, as represented by the Interdecadal Pacific Oscillation (IPO). Through atmospheric teleconnection, the positive phase of the IPO gives rise to higher-than-normal sea-level pressure and anti-cyclonic wind anomalies in the 50°–70°S band of the Pacific sector. These winds lead to warming of 0–700 m by driving the convergence of warm water. The opposite processes, involving cyclonic winds and upper-layer divergence, occur in the Atlantic-Indian Ocean sector. These findings aid our understanding of the time-varying heat storage of the Southern Ocean and provide useful implications on initialized decadal climate prediction.

Restricted 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
Lijing Cheng
,
Kevin E. Trenberth
,
Nicolas Gruber
,
John P. Abraham
,
John T. Fasullo
,
Guancheng Li
,
Michael E. Mann
,
Xuanming Zhao
, and
Jiang Zhu

Abstract

Ocean salinity records the hydrological cycle and its changes, but data scarcity and the large changes in sampling make the reconstructions of long-term salinity changes challenging. Here, we present a new observational estimate of changes in ocean salinity since 1960 from the surface to 2000 m. We overcome some of the inconsistencies present in existing salinity reconstructions by using an interpolation technique that uses information on the spatiotemporal covariability of salinity taken from model simulations. The interpolation technique is comprehensively evaluated using recent Argo-dominated observations through subsample tests. The new product strengthens previous findings that ocean surface and subsurface salinity contrasts have increased (i.e., the existing salinity pattern has amplified). We quantify this contrast by assessing the difference between the salinity in regions of high and low salinity averaged over the top 2000 m, a metric we refer to as SC2000. The increase in SC2000 is highly distinguishable from the sampling error and less affected by interannual variability and sampling error than if this metric was computed just for the surface. SC2000 increased by 1.9% ± 0.6% from 1960 to 1990 and by 3.3% ± 0.4% from 1991 to 2017 (5.2% ± 0.4% for 1960–2017), indicating an acceleration of the pattern amplification in recent decades. Combining this estimate with model simulations, we show that the change in SC2000 since 1960 emerges clearly as an anthropogenic signal from the natural variability. Based on the salinity-contrast metrics and model simulations, we find a water cycle amplification of 2.6% ± 4.4% K−1 since 1960, with the larger error than salinity metric mainly being due to model uncertainty.

Open access