2.1 The influences of terrestrial fluxes and transport on interhemispheric CO₂ differences

The growth rate and concentration of atmospheric CO₂ depend on many mechanisms including fossil fuel emissions, surface fluxes, such as associated with the growth and decay of vegetation, and atmospheric mean and eddy transport. The CO₂ growth rate and IH gradients in CO₂ vary on daily, monthly, yearly, and multi-year timescales, where there is a quasi-periodic variability associated with the influence of ENSO (e.g. Thoning et al., 1989). This reflects the response of tropical vegetation to rainfall variations and both hemispheres are also affected through dynamical coupling.

A number of recent inversion studies have largely attributed growth anomalies in atmospheric CO₂ concentrations to anomalous responses of the terrestrial biosphere. However, the variability in the responses within dynamic global vegetation models (DGVMs) is significant. Le Quéré et al. (2018), for example, note that the “standard deviation of the annual CO₂ sink across the DGVMs averages to ±0.8 GtC year⁻¹ for the period 1959 to 2016”. This is significantly larger than the reported extratropical sink anomalies during, for example, the major 2009–2010 step in CO₂ concentrations (Poulter et al., 2014; Trudinger et al., 2016). Francey and Frederiksen (2015) presented reasons supporting a dynamical contribution to the cause of the 2009–2010 C_mlo−cgo step.

For the 2015–2016 period of particular relevance here there are two studies that stand out. Keenan et al. (2016) interpret slowing CO₂ growth in 2016 as strong uptake by Northern Hemisphere terrestrial forests. Yue et al. (2017) examine the reasons for the strong positive anomalies in atmospheric CO₂ growth rates during 2015. They present evidence of the Northern Hemisphere terrestrial response to El Niño events by way of satellite observations of vegetation greenness. To reconcile increased greenness with increased CO₂ growth, their inversion modelling requires the “largest ever observed” transition from sink to source in the tropical biosphere at the peak of the El Niño, “but the detailed mechanisms underlying such an extreme transition remain to be elucidated”.

In this study, we find that the 2015–2016 El Niño also corresponds to unprecedented anomalies in both mean and eddy IH CO₂ transport characterized by indices of these transfers that we introduce. As for the anomalies in CO₂ IH gradient during the 2009–2010 El Niño, studied in FF16, this again suggests a contributing role for anomalous IH transport during the 2015–2016 event. We examine this possibility in detail and study the relationships between the extremes in IH CO₂ differences and transport anomalies for 1992 to 2016 and associated correlations between C_mlo−cgo (and other trace gases) and dynamical indices of transport.

2.2 Dynamical influences on IH exchange

Figure 2b confirms that much of the year-to-year variability in C_mlo−cgo, particularly preceding 2010, occurs in the boreal winter–spring (December–May), when eddy transport is expected to make a more active contribution to IH exchange (FF16). The step jump in annual values between 2009 and 2010, which was the focus of FF16, is the most prominent feature. A similar relationship with u_duct is supported prior to 2010 as indicated here by vertical dashed grid lines aligned with the beginning of the calendar years when u_duct is unusually low (u_duct≤3 ms⁻¹). At these times u_duct generally corresponds to above-average C_mlo−cgo, consistent with an accumulation of CO₂ in the Northern Hemisphere at a time when the Pacific duct transfer is small.

A notable exception occurs in 2015–2016, and this is a particular focus of this study that we address in the context of the unusual C_mlo−cgo behaviour since 2010. For example, since 2010 the annual average C_mlo−cgo in Fig. 2a shows reduced scatter and a slight decrease at a time when fossil fuel emissions continue to grow. As shown in Fig. 2b, this C_mlo−cgo decrease is more marked in the boreal summer–autumn (June–November) than in boreal winter–spring (December–May) when it is relatively stable (and even recovers in the last 3 years).

Figure 3Hovmöller diagrams of 300 hPa zonal wind (ms⁻¹) averaged between 5^∘ S and 5^∘ N as a function of longitude and time for (a) 1 January 2008 to 31 December 2010, (b) 1 January 2011 to 31 December 2013, and (c) 1 January 2014 to 31 December 2016. Green through to red represent westerly winds (u winds, ms⁻¹) over the Western Hemisphere. White solid rounded rectangles denote the longitudinal extent of the Pacific duct, and the height denotes the February to April period.

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The steadily decreasing u_duct since 2012 occurs all year round in Fig. 2c. Similar decreases occur in indices ω_H and v_H in Fig. 2d, which measure the strength and location of the Hadley circulation. Here, ω_H is the 300 hPa vertical velocity in pressure coordinates ($\mathit{\omega }=\mathrm{d}p/\mathrm{d}t$ where p is pressure) averaged zonally (0–360^∘) and between 10 and 15^∘ N (Table 1). Also, v_H is the 200 hPa south–north meridional wind averaged zonally and between 5 and 10^∘ N (Table 1). Both ω_H and v_H become more negative and the mean transport from the Northern to Southern Hemisphere increases with a strengthening of the Hadley circulation. As noted by Freitas et al. (2017; and references therein), the Hadley circulation strengthens during El Niños, and particularly for strong events such as during 2015–2016 (L'Heureux et al., 2017). There are subtle relationships between the latitudinal width of the equatorial heating during El Niño and global warming (Freitas et al., 2017) and the Hadley circulation. However, it is expected that there will be an increasing frequency of extreme El Niño events with increasing global warming (Cai et al., 2014; Yeh et al., 2018).

Before examining the Hadley component further, we clarify factors associated with the eddy transfer through the Pacific duct.

3.1 Eddy generation in the equatorial zone

The u_duct zonal wind based on the peak climatological correlation with SOI (140–170^∘ W) is also largely representative of near-equatorial (5^∘ N to 5^∘ S) zonal winds and their variability in the larger region between 90^∘ W and 180^∘. Pattern correlations between the two vary from r∼0.9 for November to April to r∼0.7 for May to October.

In Fig. 3, Hovmöller diagrams for the Western Hemisphere (180 to 0^∘ W) between 2008 and 2016 show the time–longitude of daily 300 hPa zonal winds between 5^∘ N and 5^∘ S. The cool blue background represents easterly winds (negative u), while warm colours, green to yellow through to red, depict westerlies (positive u). Frederiksen and Webster (1988) found that near-equatorial upper tropospheric transient kinetic energy generation is approximately linearly related to zonal wind strength (their Fig. 6) and is strongest for westerlies when the winds oppose the earth's rotation. The longitudinal limits of u_duct determined from the SOI correlation are enclosed by solid white rounded rectangles in Fig. 3, while the time period (rectangle height) represents those months when C_mlo−cgo is at a seasonal maximum and winds in the Pacific duct are normally westerly (February–April).

In most years, u_duct is positive in boreal winter–spring and Rossby waves generated by, for example, the Himalayas (height of 8.8 km) can propagate through the downstream Pacific duct region (140–170^∘ W, 5^∘ N–5^∘ S), producing and transporting turbulent kinetic energy southwards. In some years, such as the boreal winter–spring of 2009–2010 and 2015–2016, u_duct is anomalously weak and the peak equatorial 300 hPa zonal winds are over the Atlantic Ocean, particularly in the Atlantic duct region defined as 10–40^∘ W, 5^∘ N–5^∘ S. The Atlantic duct region, most conspicuously, is downstream of the Rockies (height of 4.4 km). Our Fig. S1 and Fig. 4a of FF16 show that the SOI and Atlantic duct zonal winds are strongly anti-correlated in contrast to the strong correlation with u_duct; in the Eastern Hemisphere the correlation between the SOI and equatorial zonal winds is quite weak as well. We note that u_duct and the Atlantic duct winds are anti-correlated with $r=-\mathrm{0.66}$. Further, while u_duct is anti-correlated with C_mlo−cgo, the Atlantic duct winds are correlated with a similar magnitude. This indicates that changes in u_duct are the primary determinant of interhemispheric CO₂ duct transfer via eddy processes and C_mlo−cgo and the opening of the Atlantic duct is mainly important through the associated closing of the Pacific duct. This is consistent with the idea that Rossby wave dispersion from the smaller topographic features of the Rockies is less important than from the comparatively massive Himalayas, as further discussed in the Supplement.

Figure 4Correlation of vertical velocity ω (Pas⁻¹) at 300 hPa with SOI for February–April and 1948–2016.

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3.2 Transport of surface CO₂ emissions to the upper troposphere

Transport of CO₂ emissions from the surface to the upper troposphere is explored next. We find that when the Pacific duct is open there is also large-scale uplift slightly downstream of Asia so that in a given winter–spring season the substantial regional emissions are effectively transported directly through the duct via Rossby wave dispersion, including by the Himalayan wave train. Figure 4 shows the February–April correlation between the 500 hPa ω (the vertical wind in pressure coordinates with negative values corresponding to uplift in height coordinates) and the SOI from 1948 to 2016. The most prominent correlations occur within ±30^∘ of the Equator at longitudes 120^∘ E to 170^∘ W, upstream, and at the longitudes of the Pacific duct, and this is in fact the case at all levels between the surface and 100 hPa (not shown). Broadly similar correlations are obtained between the 500 hPa ω and u_duct for February–April (and for 500 hPa ω and SOI for January–December). At other times, for example in 2010 and 2015–2016, when there have been persisting easterlies in the Pacific duct region, there has been descent slightly downstream of the Asian region. Thus the recent record Asian emissions may play a significant role in direct episodic IH CO₂ transfer through the Pacific duct. To the extent that Asian emissions might be preferentially represented in direct IH CO₂ transfer, it is relevant that uncertainty and possibly variability in Asian emissions are greater than the reported uncertainty and variability in the global totals (Andres et al., 2014).

Figure 5Latitude–height cross section of June to August 120–240^∘ E average (a) ω (Pas⁻¹) – vertical velocity in pressure coordinates – for 1979–2016, (b) ω difference of 2016 minus 1979–2016, and(c) ω difference of 2016 minus 2010.

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6.1 Pacific westerly duct and eddy IH transport of CSIRO-monitored trace gases

The IH exchange of the trace gas species, CH₄, CO, and H₂, in addition to CO₂, and the role of the Pacific westerly wind duct were also considered in FF16. In particular, the covariance, of the mlo–cgo difference in these routinely monitored CSIRO species with u_duct, is shown in Fig. 5 of FF16. We recall that the u_duct index is the average zonal wind in the region 5^∘ N to 5^∘ S, 140 to 170^∘ W at 300 hPa. As noted in FF16, the extreme cases of Pacific westerly duct closure in 1997–1998 and 2009–2010 show up in the reduction of seasonal IH exchange for CH₄ and CO as well as CO₂. The similar behaviour of detrended anomalies of mlo–cgo difference in CH₄, CO, and CO₂ and their correlations with u_duct is shown in Table 3 for February–April. We note the quite high correlations of CH₄ and CO with CO₂ (r=0.697 and r=0.645 respectively) and the significant anti-correlations of all these three species with $u_{\mathrm{duct}}(r=-\mathrm{0.448}$, $r=-\mathrm{0.605}$ and $r=-\mathrm{0.500}$ respectively). In fact, for March–May the correlation between CH₄ and CO₂ is even larger at r=0.728 (and with u_duct it is $r=-\mathrm{0.474}$), while between CO and CO₂ it is r=0.611 (and with u_duct it is $r=-\mathrm{0.507}$). These results are of course consistent with Fig. 5 of FF16 and are further evidence of similarities of IH transient eddy transport of these three gases. Table 3 also shows that the February–April correlation of H₂ with CO₂ and anti-correlation with u_duct have smaller magnitudes (r=0.296 and $r=-\mathrm{0.218}$, respectively). These results for anomalies are probably related to corresponding similarities and differences in the seasonal mean values (not shown) of these gases in February–April, as discussed below.

Table 3Correlations (r) between the detrended mlo–cgo gas anomalies for CO₂, CH₄, CO, and H₂ with CO₂ and u_duct index of transient transport averaged between February and April for 1992–2016. Also shown are corresponding correlations for N₂O and 1993–2016 and for SF₆ and 1998–2012.

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Anomalies in mlo–cgo differences in CSIRO-monitored N₂O are generally poorly correlated with those in CO₂ as shown for February–April and June–August in Tables 3 and 4 respectively (the maximum 3-month average correlation is r=0.274 for March–May), and this is mirrored in generally poor correlation with the dynamical indices shown in Tables 3 and 4. This reflects the fact that natural exchanges with equatorial agriculture and oceans are the main sources (Ishijima et al., 2009), and the seasonal range in mlo–cgo difference is only around 0.2 % of the mean N₂O level, more than 10 times less than is the case for the other species.

6.2 Hadley circulation and mean IH transport of CSIRO-monitored trace gases

We examine the role of the Hadley circulation in the mean transport of trace gases focusing on the boreal summer period of June–August. Table 4 shows the correlations between the detrended anomalies of mlo–cgo difference in CH₄, CO, and H₂ with CO₂ and with the dynamical indices ω_P and v_P (Table 1). We note that the largest June–August correlation is between H₂ and CO₂ (r=0.680) and the correlations between CH₄ and CO with CO₂ are considerably smaller (r=0.246 and r=0.108, respectively), while for April–June the latter correlations are more comparable at r=0.583 and r=0.496 respectively.

These correlations with CO₂ are also reflected in the respective correlations of the other trace gases with ω_P and v_P. We note from Table 4 that the June–August correlations of H₂ with ω_P and v_P are r=0.427 and r=0.442 respectively, which is slightly less than the corresponding correlations between CO₂ and the dynamical indices (r=0.522 and r=0.539, respectively), but considerably larger than for CH₄ and CO. For May–July the correlation of H₂ with ω_P is slightly larger, with r=0.526.

Again, the different behaviour of the trace gas anomalies may be related to their different seasonal mean values; the seasonal mean IH difference for H₂ peaks in boreal summer, while for CH₄ and CO, it is relatively low with a minimum in August. The distribution and variability of surface exchange is different for each of the trace gases and there is potential for this to interact with the restricted extent and seasonal meandering of the regions of uplift to influence IH exchange of a species. For example, 70 % of the global total CH₄ emissions are from mainly equatorial biogenic sources that include wetlands, rice agriculture, livestock, landfills, forests, oceans, and termites (Denman et al., 2007), and CO emissions receive a significant contribution from CH₄ oxidation and from tropical biomass burning.

Table 4Correlations (r) between the detrended mlo–cgo gas anomalies for CO₂, CH₄, CO, and H₂ with CO₂ and indices of mean transport, ω_P, and v_P averaged between June–August for 1992–2016. Also shown are corresponding correlations for N₂O and 1993–2016 and for SF₆ and 1998–2012.

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A more detailed examination of the inter-annual variation of the mlo–cgo difference in H₂ during boreal summer is presented in Fig. 8. It shows the detrended H₂ data in comparison with the corresponding CO₂ data and with the ω_P and v_P indices.

First we note that the detrended CO₂ data in the top panel have very similar inter-annual variation to the FF-adjusted $C_{\mathrm{mlo}-\mathrm{cgo}}^{\ast }$ in Fig. 7b. We also see that the qualitative behaviour of H₂ mirrors many aspects of CO₂, as expected from the correlations in Table 4. In particular, the increase in the IH difference of H₂ in 2010 is even more pronounced than for CO₂. For CO₂ and for H₂ there is a steady reduction in the IH difference from around 2013, leading to a local minimum in 2016. In both of these respects these gases broadly follow the changes in the Hadley circulation, including the strengthening during 2015–2016. Vertical lines in Fig. 8 indicate other times between 1992 and 2016 when transitions occur in both these trace gases and in the Hadley circulation characterized by ω_P and v_P.

Surface exchanges of H₂ have similarities to those of CO₂ in that they occur mostly at mid–northern latitudes and are mainly due to emissions from fossil fuel combustion. However H₂ also has mid–northern-latitude photochemical sources peaking in August (Price et al., 2007). These boreal summer sources are almost offset by a combined soil and hydroxyl sink, but the overall interhemispheric partial pressure difference is boosted by a significant reduction in the Southern Hemisphere photochemical source at that time. For both species, the most northern excursions of the intertropical convergence zone that occurs at Pacific latitudes encounter increasing concentrations of both gases.

As noted above, anomalies in mlo–cgo differences in N₂O are poorly correlated with those in CO₂ and in dynamical indices (Tables 3 and 4). Indeed the 3-month average anti-correlation with u_duct that has the largest magnitude is $r=-\mathrm{0.133}$ for March–May and the largest correlation with ω_P is r=0.359 for April–June and with v_P is r=0.350 for May–July.

6.3 Interhemispheric exchange of SF₆

In the case of SF6 we have analysed the mlo–cgo difference in available NOAA HATS data from 1998 to 2012 when cgo HATS measurements ceased. Correlations (Tables 3 and 4) of detrended anomalies in IH differences in SF₆ with those in CO₂ are as follows: the February–April correlation is r=0.619, the March–May correlation is r=0.722, the April–June correlation is r=0.595, the May–July correlation is r=0.303, and the June–August correlation is r=0.223. The corresponding correlations with dynamical indices are as follows: for February–April the correlation with u_duct is $r=-\mathrm{0.617}$, the May–July correlations with ω_P is r=0.465, the June–August correlation with ω_P is r=0.433, the May–July correlation with v_P is r=0.517, and the June–August correlation with v_P is r=0.385. We note that SF₆ has an anti-correlation with u_duct for February–April that has a larger magnitude than for CO₂ and even CO. Thus, again there is a significant influence of the Pacific westerly duct, in late boreal winter and spring, and of the Hadley circulation, in boreal summer and late spring, as measured by these indices, on the mlo–cgo differences of SF₆; these SF₆ differences exhibit a similar step change in 2009–2010 as shown for CO₂ in Figs. 2 and 7.