Observation-Based Estimates of Global and Basin Ocean Meridional Heat Transport Time Series
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.
For many years, the observed average oceanic meridional heat transport (MHT) has been best estimated as a residual of the top-of-atmosphere (TOA) radiation and the atmospheric energy budget (Trenberth 1991, 1997; Trenberth and Caron 2001; Trenberth and Fasullo 2008; Mayer and Haimberger 2012). Carissimo et al. (1985) made an early attempt that subsequently has been substantially refined. In Trenberth and Caron (2001), the estimates were based upon the Earth Radiation Budget TOA measurements of the mid-1980s, which have been supplanted by higher-quality Clouds and the Earth’s Radiant Energy System (CERES) observations since March of 2000 (Loeb et al. 2009). At that time, atmospheric energy transports and their divergence were estimated from two different atmospheric reanalyses that have now been much improved with later generations; in particular, the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) has proven to be the most stable and best quality overall, at least until now (2018) (Trenberth et al. 2011; Trenberth and Fasullo 2013, 2017). Further improvements in the methodology, in particular adjustments for the inevitable spurious mass imbalance and allowance for the enthalpy associated with precipitation (Trenberth and Fasullo 2018), have changed the results in small ways. As well as global estimates, the net surface flux of energy can be estimated locally and combined with estimates of changes in ocean heat content (OHC) to allow for an estimate of the movement of ocean heat around.
The challenge is to produce these energy flows annually and even monthly in order to fully understand and appreciate the interannual and longer-term variability and trends of energy on Earth. How the atmospheric and oceanic energy disposition and transports vary and change is an important question because it influences where the surplus heat resides and potentially influences the subsequent atmospheric state and climate (Roberts et al. 2017).
Tremendous progress has been achieved with the global ocean observing system (Abraham et al. 2013). Argo is a system of autonomous profiling floats that drift with the currents at 1-km depth and provide profiles of temperature and salinity every 10 days or so for the top 2 km (Riser et al. 2016). Argo became global after about 2005 with over 3500 routinely deployed. Accordingly, the knowledge of OHC and its changes in space and time has increased enormously and enabled better reconstructions of the past (Cheng et al. 2017). Nevertheless, observations of currents and heat transports remain spartan, although a major advance occurred in April 2004 with the establishment of RAPID-MOCHA (Rapid Climate Change–Meridional Overturning Circulation and Heatflux Array) across about 26.5°N in the Atlantic (henceforth simply the RAPID array) (Baringer and Larsen 2001; Cunningham et al. 2007; Kanzow et al. 2007; Johns et al. 2011; McCarthy et al. 2012, 2015; Srokosz and Bryden 2015). The variability in the Atlantic MHT arises mainly from changes in the AMOC volume transports (Johns et al. 2011) and is surprisingly large (Srokosz and Bryden 2015). Several other smaller arrays have also been established (e.g., Mignac et al. 2018), but the expense and difficult logistics limit the capabilities for direct ocean observations.
Accordingly, complementary approaches that enable global coverage and extensions of the records back in time can help build understanding of variability and trends. Indeed, the zonal mean oceanic MHT can be readily computed from the TOA budget plus vertically integrated atmospheric energy transports, although some adjustments are necessary to deal with Earth’s energy imbalance (EEI) and the changes in storage of OHC (Trenberth and Fasullo 2017).
Trenberth et al. (2016) examined six OHC analyses plus one dynamic ocean reanalysis and found that the implied change from month to month was not physically possible—energy was not conserved—as it was far too large compared with the changes inferred from CERES. The CERES 12-month running mean EEI variations had a standard deviation of 0.4 W m−2 (global), but all OHC analyses had values over 3.6 W m−2 with the sole exception being ORAP5 (Ocean Re-Analysis Pilot v5) from ECMWF (Zuo et al. 2017), which was 1.4 W m−2 (Trenberth et al. 2016). In several products, the monthly anomaly standard deviation was analyzed to be >10 W m−2 versus the CERES estimate of 0.64 W m−2. Hence OHC analyses remain a substantial source of uncertainty. Trenberth and Fasullo (2017) combined estimated surface fluxes with OHC changes to deduce time series of vertically integrated ocean MHT throughout the Atlantic that could be verified by direct ocean observations at 26.5°N from RAPID. Roberts et al. (2017) used gridded analyses of OHC along with surface flux estimates to explore the main drivers of OHC variability, and also compared with the RAPID results.
As well as short-term weather-related variations, interannual variations in EEI associated with El Niño–Southern Oscillation (ENSO) are substantial, and typically are on the order of ±0.5 W m−2. They are associated with fluctuations in global mean surface temperature (GMST) (Trenberth et al. 2002, 2014; Mayer et al. 2014) as heat is stored in the oceans before being redistributed and some is released back into the atmosphere with an El Niño event (Cheng et al. 2019).
The energy imbalance clearly varies over time (Trenberth et al. 2014, 2016). The only way to determine the EEI is to perform a detailed inventory of the changes in energy in various forms in the climate system, the dominant component of which is changes in OHC (von Schuckmann et al. 2016). Because of changes in atmospheric temperatures, sea and land ice, land temperatures, and snow and water on land, there is no requirement for a match between TOA radiation and OHC tendencies on a monthly basis, but globally the mismatch is strongly constrained to be less than about 0.2 W m−2 [see Trenberth et al. (2014) for an estimate both from observations and a climate model]. But the TOA radiation once calibrated plus the atmospheric reanalyses and OHC then provide the best information on the variability of the EEI and its regional manifestations. As shown here, it is necessary to adjust the OHC tendencies to match the EEI values, and by doing so, the results are “cleaned up” enormously.
In this paper we compute monthly MHT estimates, first globally, and then for two ocean basins. The first is the joint Indo-Pacific region, which is combined through the Indonesian Throughflow (ITF). Independent estimates of ITF would allow the results to be further broken out into the contributions from the Indian and Pacific Oceans, but this will be done elsewhere. The second is the combined Arctic and Atlantic Oceans. In both cases, as discussed below, the flow through the Bering Strait is sufficiently small that we can ignore it. We briefly consider the Indo-Pacific results in the context of ENSO, and we update the Atlantic results and compare with MHT from the RAPID array at 26°N and further consider the influence of the North Atlantic Oscillation (NAO).
We use monthly TOA CERES Energy Balanced and Filled (EBAF) Ed. 4.0 radiation on 1° × 1° grids (Loeb et al. 2009, 2012), from Langley Atmospheric Science Data Center (http://ceres.larc.nasa.gov/order_data.php). Observations from CERES begin in March 2000, and have been extended back in time (Allan et al. 2014) using model results and other constraints. Nevertheless, as questions remain about the earlier reconstruction, those values are used here only for January–February 2000. The global mean net TOA radiation RT is too small to measure directly from satellite, and raw CERES analyzed values of global EEI of 6.5 W m−2 were much larger than the estimated 0.85 W m−2 owing to what are thought to be primarily systematic errors that were adjusted in a somewhat ad hoc manner based on overall OHC changes (Loeb et al. 2009). However, instruments are far more stable than they are absolutely accurate, with calibration stability −2 per decade (95% confidence) (Loeb et al. 2009), and hence there is considerable confidence in the changes from year to year. Therefore, the long-term average absolute value of global mean RT is established from an inventory of the energy and, in particular, estimates of mean ocean heat uptake (Loeb et al. 2012; Trenberth 2009; Trenberth et al. 2016).
The atmospheric computations here all utilize only the ERA-Interim (ERA-I) (Dee et al. 2011) (http://data-portal.ecmwf.int/data/d/interim_daily/), as they are superior in several assessments and much improved over earlier reanalyses (e.g., Trenberth et al. 2011; Trenberth and Fasullo 2013). They have been comprehensively evaluated for conservation properties by Berrisford et al. (2011) and for air temperatures and humidity (Simmons et al. 2010, 2014) and the water and energy cycles (Trenberth et al. 2011; Trenberth and Fasullo 2013). ERA-I did not include comprehensive TOA forcings and volcanic aerosols, such as those from the eruption of Mount Pinatubo in 1991, and the TOA radiation is biased (Trenberth and Fasullo 2013). Accordingly, we have here confined diagnostics to after 2000. The budget computations for the atmosphere are performed on 60 model levels every 6 h at T255 (about 79 km) resolution, and results are mass corrected to ensure that the atmospheric mass is conserved (see Trenberth and Stepaniak 2003a,b; Trenberth and Fasullo 2018). All datasets were averaged or interpolated to a 1° resolution for the computations presented.
Here we mainly make use of ocean reanalyses from ECMWF, using ORAS5 (Zuo et al. 2018), which was developed from ORAP5 (Trenberth and Fasullo 2017), which was the best ocean reanalysis in the Arctic (Uotila et al. 2019). ORAS5 uses the same ocean and sea ice model as for ORAP5 and was produced using the V3.4.1 of the NEMO ocean model at a resolution of 0.25° in the horizontal and 75 nonuniformly spaced levels in the vertical. ORAS5 uses four-dimensional data assimilation of multivariate fields, including sea surface height from altimetry, with a full global ocean circulation model used to carry information from past observations forward in time (Balmaseda et al. 2013a,b; Zuo et al. 2018; Mayer et al. 2018). ORAS5 is an eddy-permitting ocean reanalysis with a prognostic thermodynamic–dynamic sea ice model with assimilation of sea ice concentration data and surface forcing from ERA-I. Zuo et al. (2018) note advances and improvements in a number of areas for ORAS5 versus ORAP5. It has been run in reanalysis mode through 2014 with consistent data streams, quality control, and forcing fluxes and has been extended into an operational setting in January 2015. For ORAS5, five ensemble members are generated by perturbing both observations and forcing fields, to reflect the main uncertainties in both, and we have analyzed results using all five, as well as the ensemble mean.
A concern with ORAP5, noted by Trenberth and Fasullo (2017), was a problem area in the midlatitudes of the North Atlantic, evidently associated with Mediterranean exchanges, that developed after about 2007. The problem was only evident below 1000-m depths with a vertical dipole structure (hence cancelling in part in the OHC) and is still present in ORAS5 but has been reduced. ORAS5 experiments (Zuo et al. 2018) reveal maximum analyzed RMS temperature error at 1000 m associated with spurious convection between 1000 and 2000 m due to warm and salty Mediterranean outflow. A new “capping” procedure for salinity helps mitigate this effect. We computed all results using OHC down to both 1000 and 2000 m and, as the problem appears to have little effect on our results, we use the latter as it is more inclusive over the rest of the domain.
While other ocean analyses have been analyzed, we do not report the results here because the OHC is very noisy and the noise is spurious (Trenberth et al. 2016). Because the computation of OHC tendency inflates the noise (it is effectively a high-pass filter), we first take centered differences (thereby keeping the tendency centered on the time of interest), and then smooth the result with a 1/12 [1–3–4–3–1] filter that removes fluctuations shorter than 4 months.
For the global ocean, transports of energy (or any quantity) necessarily go to zero at the boundary. This applies universally at all times. Hence, an immediate check and constraint on the results of ocean heat transports is that they must go to zero at the North Pole and the edge of Antarctica. In general, we compute MHT results by integrating southward, so that values start at zero in the north, and the accumulated inferred value at Antarctica is the error. The errors primarily stem from the OHC mismatch with the CERES and Fs values, and we adjust the discrepancy on an annual basis to ensure that this constraint is satisfied. This is done uniformly over the ocean, so that a discrepancy at the Antarctic coast of 0.3 PW converts to an adjustment of about 0.8 W m−2 over the global ocean. This actually cleans up results from different OHC datasets by effectively bias correcting them. The documentation of this correction is given below (see Fig. 7). In reality, a small portion of the error may arise from CERES, and errors in energy divergence in the atmosphere (Trenberth and Fasullo 2017), and there may also be small contributions from changes in sea ice, land ice, and E − P over land, as documented for a model by Trenberth et al. (2016).
The details of the TOA radiation and atmospheric diabatic heating and energy transports are given in Trenberth and Fasullo (2017, 2018) and the latter presents results for 2000 to 2016. The net annual mean result deduced as a residual of the TOA net radiation and vertically integrated atmospheric energy divergence for the net surface flux of energy Fs (Fig. 2) reveals where there is a net flux of energy into the ocean (in blue areas) versus the losses of ocean heat (in red areas) so that in the absence of changes in storage of heat within the ocean, the ocean must transport heat via the ocean currents from the blue to the red areas. This is not easy to measure because here we are dealing with only the divergent transport, while in reality there is also a substantial rotational transport whereby the heat just moves around. This is especially the case for the Antarctic Circumpolar Current, for instance. The tropical Pacific is a large heat source region for the ocean while the largest sinks lie off the coasts of Asia in the Pacific and North America in the Atlantic in the vicinity of the Kuroshio and the Gulf Stream. These fluxes are largest in winter.
The fluxes of energy into the atmosphere over the Arctic and the Atlantic cannot be met by the flux of energy into the Atlantic tropical ocean and, as a result, there has to be a transport from the Pacific and Indian Oceans into the South Atlantic and a northward MHT throughout the Atlantic to balance the energy flows. In the Indian Ocean, there is a strong source region of heat in the equatorial zones that exceed transports out of the southern Indian Ocean and, combined with the net westward flow through the ITF, contributes to a net southward flow of heat across the southern boundary of the Indian Ocean.
The long-term 2000–16 annual average zonal mean MHTs (Fig. 3) show the overall energy transports that result from the integrated divergences. On the left-hand side, the CERES TOA radiation provides the total transport once the EEI is removed. The atmospheric reanalyses in turn provide the atmospheric transports [see Trenberth and Stepaniak (2003a,b) for details]. Because they involve the divergence, they automatically integrate to zero globally. The ocean MHTs are computed as a residual. Contributions from land are assumed to be small: the only meridional transports come from north–south flowing rivers, as diffusion within land is extremely small. The right-hand side (Fig. 3) then breaks the ocean down into the contributions from the basins, where use is now made of the OHC tendencies to match the TOA radiation and remove the energy imbalance or, equivalently, spurious OHC storage. The results are artificial for the Indian and Pacific between about 7° and 40°S because the ITF is ignored. The results are almost identical using all 5 ORAS5 ensemble members. Here the Arctic is combined with the Atlantic, and south of 35°S the ocean basin contributions become meaningless as the zonal transports become large (south of Africa), and indeed continuous around the globe near 60°S through the Drake Passage between South America and the Antarctic Peninsula.