Editing IPCC:AR6/SROCC/Chapter-5 (section)

==== 5.2.2.2 Changing Temperature, Salinity, Circulation ====

<div id="section-5-2-2-2changing-temperature-salinity-circulation-block-1"></div>

Historically, scientific research expeditions starting in the 19th century have provided occasional sections measuring deep ocean properties (Roemmich et al., 2012 <sup>[[#fn:r11|11]]</sup> ). Greater spatial and temporal coverage of temperatures down to about 700 m was obtained using expendable bathythermographs along commercial shipping tracks starting in the 1970s (Abraham et al., 2013 <sup>[[#fn:r12|12]]</sup> ). Since the early 2000s, thousands of autonomous profiling floats (Argo floats) have provided high-quality temperature and salinity profiles of the upper 2000 m in ice-free regions of the ocean (Abraham et al., 2013 <sup>[[#fn:r13|13]]</sup> ; Riser et al., 2016 <sup>[[#fn:r14|14]]</sup> ). Further advances in autonomous floats have been developed that now allow these floats to operate in seasonally ice covered oceans (Wong and Riser, 2011 <sup>[[#fn:r15|15]]</sup> ; Wong and Riser, 2013 <sup>[[#fn:r15|15]]</sup> ), and more recently to profile the entire depth of the water column down to 4000 or 6000 m (Johnson et al., 2015 <sup>[[#fn:r17|17]]</sup> ; Zilberman, 2017 <sup>[[#fn:r18|18]]</sup> ) and to include biogeochemical properties (Johnson et al., 2017 <sup>[[#fn:r19|19]]</sup> ). Autonomous floats have revolutionised our sampling and accuracy of the global ocean temperature and salinity records and increased certainty and confidence in global estimates of the earth heat (temperature) budget, particularly since 2004 (Von Schuckmann et al., 2014; Roemmich et al., 2015 <sup>[[#fn:r20|20]]</sup> ; Riser et al., 2016 <sup>[[#fn:r21|21]]</sup> ), as demonstrated by the convergence of observational estimates of the changes in the heat budget of the upper 2000 m (Figure 5.1). New findings using data collected from such observing platforms mark significant progress since AR5.

To understand the recent and future climate, we use ensembles of coupled ocean-atmosphere-cryosphere-ecosystem models (ESMs) with the full-time history of atmospheric forcing (greenhouse gases, aerosols, solar radiation and volcanic eruptions) for the historical period and projections of the concentrations or emissions of these forcings to 2100. For these projections the RCPs of atmospheric emissions scenarios are used as specified by the Coupled Model Intercomparison Project, Phase 5 (CMIP5) (see Section 1.8.2.3, Cross-Chapter Box 1, and also IPCC AR5) <sup>[[#fn:3|3]]</sup> . This chapter focuses on the low and high emissions scenarios RCP2.6 and RCP8.5, respectively. When these scenarios are used to drive ESMs, it is possible to simulate the recent and future patterns of changes in the ocean temperature, salinity and circulation (and other oceanic properties such as ocean oxygen concentration and acidification, Section 5.2.2.3 and 5.2.2.4). Finally, the projections of ocean changes also informs the detection, attribution and projection of risk and impacts on ecosystems (Sections 5.2.3, 5.2.4 and 5.3), ecosystem services (Section 5.4.1) and human well-being (Section 5.4.2) under climate change.

<div id="section-5-2-2-2changing-temperature-salinity-circulation-block-2"></div>

<span id="observed-and-projected-global-ocean-heat-uptake"></span>
===== 5.2.2.2.1 Observed and projected global ocean heat uptake =====

As AR5 concluded, the ocean is warming as a direct result of anthropogenic changes to the radiative properties of the atmosphere and the heat budget of the Earth ( ''very likely'' ) (Bindoff et al., 2013 <sup>[[#fn:r22|22]]</sup> ). Over the past few decades our ocean observing system has measured an increase in ocean temperature (Figure 5.1). This temperature increase corresponds to an uptake of over 90% of the excess heat accumulated in the Earth system over this period (Bindoff et al., 2013 <sup>[[#fn:r23|23]]</sup> ; Rhein et al., 2013 <sup>[[#fn:r24|24]]</sup> ). This heat in the ocean also causes it to expand and has contributed about 43% of the observed global mean SLR from 1970–2015 (Section 4.2.2.3.6).

Since AR5, there have been further improvements in our ability to understand and correct instrumental errors and new estimates also attempt to minimise biases in estimating temperature changes arising from traditional data-void filling strategies (Abraham et al., 2013 <sup>[[#fn:r25|25]]</sup> ; Durack, 2015 <sup>[[#fn:r26|26]]</sup> ; Cheng and Chen, 2017 <sup>[[#fn:r27|27]]</sup> ; Cheng et al., 2017 <sup>[[#fn:r28|28]]</sup> ). New estimates from ocean observations of ocean heat uptake in the top 2000 m between 1993 and 2017 ''very likely'' range from 9.2 ± 2.3 ZJ yr -1 to 12.1 ± 3.1 ZJ yr -1 (Johnson et al., 2018 <sup>[[#fn:r29|29]]</sup> ) <sup>[[#fn:4|4]]</sup> . Three recent independent estimates do a better job of accounting for instrumental biases and the sparseness of historical ocean temperature measurements than the older studies assessed in AR5, and provide larger and more consistent estimates of heat uptake rates for the 0-2000 m layer of 5.8 ± 1.0 ZJ yr -1 (Cheng and Chen, 2017 <sup>[[#fn:r30|30]]</sup> ; Cheng et al., 2017 <sup>[[#fn:r31|31]]</sup> ; Ishii et al., 2017 <sup>[[#fn:r32|32]]</sup> ), 6.0 ± 0.8 ZJ yr -1 (updated from Domingues et al. (2008)) and 6.3 ± 1.8 ZJ yr -1 (Cheng and Chen, 2017 <sup>[[#fn:r33|33]]</sup> ; Cheng et al., 2017 <sup>[[#fn:r34|34]]</sup> ; Ishii et al., 2017 <sup>[[#fn:r35|35]]</sup> ) for the 1971–2010 period assessed by AR5.

Based on these new published methods and revised atlases we update the estimates for ocean heat uptake (Table 5.1, and SM5.1). For all of the periods assessed in Table 5.1, it is ''virtually certain'' that the upper ocean (0–700 m) has warmed. These results are consistent with earlier research into the duration of record needed to detect a significant signal in global ocean heat content (Gleckler et al., 2012 <sup>[[#fn:r36|36]]</sup> ). Critically, the ''high confidence'' and ''high'' ''agreement'' in the ocean temperature data means we can detect discernable rates of increase in ocean heat uptake (Gleckler et al., 2012 <sup>[[#fn:r37|37]]</sup> ; Cheng et al., 2019 <sup>[[#fn:r38|38]]</sup> ). The rate of heat uptake in the upper ocean (0–700 m) is ''very likely'' higher in the 1993–2017 (or 2005–2017) period compared with the 1969–1993 period (see Table 5.1). The deeper layer (700–2000 m) heat uptake rate is ''likely'' to be higher in the 1993–2017 period compared with the 1969–1993 period.

<span id="table-5.1"></span>
<!-- START TABLE -->
'''Table 5.1'''

The assessed rate of increase in ocean heat content in the two depth layers 0–700 m and 700–2000 m and their ''very likely'' ranges. Fluxes in Wm -2 are averaged over the Earth’s entire surface area. The four periods cover earlier and more recent trends; the 2005–2017 period has the most complete interior ocean data coverage and the greatest consistency between estimates, while longer trends are better for distinguishing between forced changes and internal variability. These observationally-estimated rates come from an assessment of the recent research (see SM5.1), while the Coupled Model Intercomparison Project Phase 5 (CMIP5) Earth System Models (ESM) estimates are based on a combined 28-member ensemble of historical, Representative Concentration Pathway (RCP)2.6 and RCP8.5 simulations.

<!-- TABLE -->
{| class="wikitable"
|-
|
| colspan="4"| '''Ocean Heat Uptake Rate, ZJ yr''' '''-1'''
| colspan="4"| '''Ocean Heat Uptake as Average Fluxes, W m''' '''-2'''
|-
| '''Period'''
| '''1969–1993'''
| '''1993–2017'''
| '''1970–2017'''
| '''2005–2017'''
| '''1969–1993'''
| '''1993–2017'''
| '''1970–2017'''
| '''2005–2017'''
|-
| colspan="9"| '''Observationally Based Ocean Heat Uptake Estimates:'''
|-
| '''0–700 m'''
| 3.22 ± 1.61
| 6.28 ± 0.48
| 4.35 ± 0.80
| 5.31 ± 0.48
| 0.20 ± 0.1
| 0.39 ± 0.03
| 0.27 ± 0.05
| 0.33 ± 0.03
|-
| '''700–2000 m '''
| 0.97 ± 0.64
| 3.86 ± 2.09
| 2.25 ± 0.64
| 4.02 ± 0.97
| 0.06 ± 0.04
| 0.24 ± 0.13
| 0.14 ± 0.04
| 0.25 ± 0.06
|-
| colspan="9"| '''CMIP5 ESM Ensemble-mean Ocean Heat Uptake with 90% Certainty Range from Ensemble Spread:'''
|-
| '''0–700 m'''
| 3.60 ± 1.92
| 7.37 ± 2.09
| 5.64 ± 1.90
| 7.85 ± 2.71
| 0.22 ± 0.12
| 0.46 ± 0.13
| 0.35 ± 0.12
| 0.49 ± 0.17
|-
| '''700–2000 m '''
| 1.32 ± 1.49
| 2.72 ± 1.41
| 1.99 ± 1.51
| 3.33 ± 1.75
| 0.08 ± 0.09
| 0.17 ± 0.09
| 0.12 ± 0.09
| 0.21 ± 0.11
|}
<!-- END TABLE -->

The direct comparison of the observed changes in ocean heat content and the simulated historical changes is undertaken to detect climate change, to attribute the causes of climate change to the forcings in the system, and to evaluate the performance of ESMs. Attribution studies also reject competing hypotheses to explain the global ocean changes such as natural forcing from solar variability or volcanic eruptions (see Section 1.3) (Bindoff et al., 2013 <sup>[[#fn:r39|39]]</sup> ). Detection and attribution studies have since been used to detect changes in the rate of ocean heat uptake and to attribute these changes to human activity (Gleckler et al., 2016 <sup>[[#fn:r40|40]]</sup> ).

Updated observationally-based estimates of ocean heat uptake are consistent with simulations of equivalent time-periods from an ensemble of CMIP5 ESMs (Table 5.1 and the inset panel in Figure 5.1) ( ''high confidence'' ), once the limitations of the historical ocean observing network and the internally generated variability with a single realisation of the real world are taken into account (see Section 5.2.2.2). Following the CMIP5 protocol, the ESMs are radiatively forced with observationally derived estimates of greenhouse gas concentrations and aerosols, including natural forcing variations from volcanic eruptions and solar forcing, through 2005; after 2006 each of the ESMs uses either the RCP2.6 or RCP8.5 emissions scenarios. The ''very likely'' ranges of the observed trends of heat uptake for the four periods and two layers all fall within the ''very likely'' range of simulated heat uptake from the ESM ensemble (Table 5.1). The difference between observations and average of the simulations in the upper ocean is an overestimate of heat uptake by about 20% and for the deeper layer there an underestimate by a similar amount, but this difference is still well within the ''very likely'' range from the ensemble of simulations. The overall consistency between observationally-based estimates and ESM simulations of the historical period gives greater confidence in the projections; it is ''very likely'' that historical simulations agree with observations of the global ocean heat uptake (Table 5.1).

While the collection of the worlds’ ESMs have been criticised for having an ensemble mean that does not exhibit the observed ‘hiatus’ or ‘slowdown’ of global mean surface temperature increase in the early 21st century (Meehl et al., 2011 <sup>[[#fn:r57|57]]</sup> ; Trenberth et al., 2016 <sup>[[#fn:r58|58]]</sup> ) , it is increasingly clear that this is at least in part due to the redistribution of heat within the climate system from the surface into the interior ocean and between ocean basins. Individual realisations of ESMs do show decades with slow increases in mean surface temperature change comparable to what was observed, even though these cases exhibit continued interior ocean heat uptake, and every ensemble member exhibits surface warming closer to the ensemble-mean over multi-decadal timescales (Meehl et al., 2011 <sup>[[#fn:r59|59]]</sup> ; England et al., 2015 <sup>[[#fn:r60|60]]</sup> ; Knutson et al., 2016 <sup>[[#fn:r61|61]]</sup> )

<span id="figure-5.1"></span>
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'''Figure 5.1'''

<span id="figure-5.1-time-series-of-globally-integrated-upper-2000-m-ocean-heat-content-changes-in-zj-relative-to-the-20002010-period-average-as-inferred-from-observations-magenta-and-as-simulated-for-historical-tan-representative-concentration-pathway-rcp2.6-blue-and-rcp8.5-red-forcing-by-a-25-member-ensemble-of-coupled-model-intercomparison-project-phase-5-cmip5"></span>
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'''Figure 5.1 | Time series of globally integrated upper 2000 m ocean heat content changes in ZJ, relative to the 2000–2010 period average, as inferred from observations (magenta) and as simulated for historical (tan), Representative Concentration Pathway (RCP)2.6 (blue) and RCP8.5 (red) forcing by a 25-member ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5) […]'''
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[[File:e2518a5a96f60c4991d9797d8d3fea43 IPCC-SROCC-CH_5_1.jpg]]

Figure 5.1 | Time series of globally integrated upper 2000 m ocean heat content changes in ZJ, relative to the 2000–2010 period average, as inferred from observations (magenta) and as simulated for historical (tan), Representative Concentration Pathway (RCP)2.6 (blue) and RCP8.5 (red) forcing by a 25-member ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5) Earth System Models (ESMs) (Cheng et al. 2019 <sup>[[#fn:r41|41]]</sup> ). The shaded magenta in the outer panel is the very likely range determined by combining data from 4 long-term estimates (Palmer et al. 2007 <sup>[[#fn:r42|42]]</sup> ; Levitus et al. 2012 <sup>[[#fn:r43|43]]</sup> ; Lyman and Johnson, 2014 <sup>[[#fn:r44|44]]</sup> ; Cheng and Chen, 2017 <sup>[[#fn:r45|45]]</sup> ; Cheng et al. 2017 <sup>[[#fn:r46|46]]</sup> ; Ishii et al. 2017 <sup>[[#fn:r47|47]]</sup> ) processed as in Johnson et al. (2018) <sup>[[#fn:r48|48]]</sup> . The tan, blue and red lines are the ESM ensemble means, while shading shows each ensemble’s 5th to 95th percentile range. In the inset subpanel, the four different shaded magenta areas are the reported very likely range of heat content changes as inferred from observations by four independent groups (Magenta shading; Palmer et al. 2007 <sup>[[#fn:r49|49]]</sup> ; Lyman and Johnson, 2014 <sup>[[#fn:r50|50]]</sup> ; Cheng and Chen, 2017 <sup>[[#fn:r51|51]]</sup> ; Cheng et al. 2017 <sup>[[#fn:r52|52]]</sup> ; Ishii et al. 2017) <sup>[[#fn:r53|53]]</sup> processed as in Johnson et al. (2018) <sup>[[#fn:r54|54]]</sup> . In the inset subpanel the RCP2.6 and RCP8.5 projections after 2005 are combined into a single ensemble with the historical simulations.

<!-- END IMG -->
<span id="figure-5.2"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 5.2'''

<span id="figure-5.2-heat-uptake-by-the-top-700-m-of-the-ocean-as-determined-by-differences-between-the-averages-over-two-5--or-20-year-intervals-converted-to-a-heat-flux-into-the-ocean-w-m2-either-from-observationally-based-analyses-or-a-38-member-ensemble-of-coupled-model-intercomparison-project-phase-5-cmip5-earth-system-models"></span>
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'''Figure 5.2 | Heat uptake by the top 700 m of the ocean, as determined by differences between the averages over two 5- or 20-year intervals converted to a heat flux into the ocean (W m–2), either from observationally-based analyses or a 38-member ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5) Earth System Models […]'''
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[[File:b7d41eb2445844a63fceb78ad98cdcb4 IPCC-SROCC-CH_5_2-1.jpg]]

Figure 5.2 | Heat uptake by the top 700 m of the ocean, as determined by differences between the averages over two 5- or 20-year intervals converted to a heat flux into the ocean (W m–2), either from observationally-based analyses or a 38-member ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5) Earth System Models (ESMs). (a) Change between (1971–1990) and (1998–2017) as inferred from observations (Good et al. 2013 <sup>[[#fn:r55|55]]</sup> ); (b) The ensemble mean change in CMIP5 ESMs for the same time periods as in (a); (c) Projected ensemble mean change in CMIP5 ESMs between (1986–2005) and (2081–2100) for the RCP8.5 forcing scenario. In panels (b) and (c), stippling indicates regions where the ensemble mean change is not significantly different from 0 at the 95% confidence level based on the models’ temporal variability. (d) Change between (2004–2008) and (2013–2017) as inferred from observations by the SODA 3.4.2 reanalysis product (Carton et al. 2018 <sup>[[#fn:r56|56]]</sup> ); (e) and (f) Estimates of change in heat uptake as in (d) but from two individual realisations of the CCSM ESM (Table SM5.2). These two realisations are identical apart from their initial conditions, which leads to different timing in their internal modes of variability; they were selected from the full CMIP5 ensemble as examples where one is reminiscent of the recent observed changes while the other has regional changes that have dissimilar timing.

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<div id="section-5-2-2-2changing-temperature-salinity-circulation-block-3"></div>

The ocean will continue to take up heat in the coming decades for all plausible scenarios. As depicted in Figure 5.1, the ensemble of CMIP5 ESMs used by Cheng et al. (2019) project that under RCP2.6, the top 2000 m of the ocean will take up 935 ZJ of heat between 2015 and 2100 (with a ''very likely'' range of 650–1340 ZJ based on the 5th and 95th percentiles of the 25 ESMs used here that have available data from the historical, scenario and control runs for RCP2.6). Under RCP8.5 this ensemble projects heat uptake of 2180 ZJ (with a ''very likely'' range of 1710–2790 ZJ, based on 35 ESMs) between 2015 and 2100. By 2100 the ocean is ''very likely'' to warm by 2 to 4 times as much for low emissions (RCP2.6) and 5 to 7 times as much for the high emissions scenario (RCP8.5) compared with the observed changes since 1970. With the RCP8.5 scenario, the ocean is ''very likely'' to take up about twice as much heat as RCP2.6 (Figure. 5.1). Even under RCP2.6 the ocean will continue to warm for several centuries to come (Collins et al., 2013 <sup>[[#fn:r52|52]]</sup> ). It is ''virtually certain'' that the ocean will continue to take up heat throughout the 21st century, and the rate of uptake will depend upon on the emissions scenario we collectively choose to follow.

<div id="section-5-2-2-2changing-temperature-salinity-circulation-block-4"></div>

<span id="figure-5.3"></span>
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<!-- IMG TITLE -->
'''Figure 5.3'''

<span id="figure-5.3-side-view-basin-averaged-zonal-mean-trends-change-per-century-in-water-mass-properties-in-the-top-2000-m-by-basin-a-as-inferred-from-observations-average-of-20132017-minus-average-of-20052009-and-b-coupled-model-intercomparison-project-phase-5-cmip5-model-projections-with-representative-concentration-pathway-rcp8.5-forcing-average-of-20812100-minus-average-of"></span>
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'''Figure 5.3 | Side-view basin-averaged zonal-mean trends (change per century) in water-mass properties in the top 2000 m by basin (a) as inferred from observations (average of 2013–2017 minus average of 2005–2009) and (b) Coupled Model Intercomparison Project Phase 5 (CMIP5) model projections with Representative Concentration Pathway (RCP)8.5 forcing (average of 2081–2100 minus average of […]'''
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[[File:e6f8060870293eb7dd25c79ffa06efb5 IPCC-SROCC-CH_5_3.jpg]]

Figure 5.3 | Side-view basin-averaged zonal-mean trends (change per century) in water-mass properties in the top 2000 m by basin (a) as inferred from observations (average of 2013–2017 minus average of 2005–2009) and (b) Coupled Model Intercomparison Project Phase 5 (CMIP5) model projections with Representative Concentration Pathway (RCP)8.5 forcing (average of 2081–2100 minus average of 1981–2000) trends in water-mass changes forcing. Subpanels within each group: top-to-bottom (Atlantic, combined Pacific and Indian, Global); left-to-right (Temperature, in situ Density, Salinity). Shaded areas show where the projected changes are not statistically significant at the 95% level. This figure uses the same observationally-derived reanalysis datasets and ensemble of Earth System Models (ESMs) as in Figure 5.2c and 5.2d. Solid lines show present contours of these fields; the notable structure in the northern hemisphere of the global-zonal mean contours of density and salinity are due to the relatively salty Mediterranean and fresh Black seas.

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<div id="section-5-2-2-2changing-temperature-salinity-circulation-block-5"></div>

<span id="structure-of-anthropogenic-climate-changes-in-the-ocean"></span>
===== 5.2.2.2.2 Structure of anthropogenic climate changes in the ocean =====

The ensemble average of the CMIP5 ESMs projects widespread ocean warming over the coming century, concentrated in the upper ocean (Figures 5.2c and 5.3) (Kuhlbrodt and Gregory, 2012). The anthropogenic heat will penetrate into the ocean following well-established circulation pathways (Jones et al., 2016a <sup>[[#fn:r63|63]]</sup> ). The greatest vertically integrated heat uptake occurs where there is already the formation of interior waters, such as Antarctic Intermediate Water along the Antarctic Circumpolar Current (Frölicher et al., 2015 <sup>[[#fn:r64|64]]</sup> ) or NADW precursors in the Nordic Seas (Figure 5.2c), but all water-masses <sup>[[#fn:5|5]]</sup> that are subducted over decades are

expected to experience significant warming (see Figure 5.3). The warming in the subtropical gyres penetrates deeper into the ocean than other gyres (roughly 15°N–45°N and 15°S–45°S in Figure 5.3), following the wind-driven bowing down of the density surfaces (the solid lines in Figure 5.3) in these gyres (Terada and Minobe, 2018 <sup>[[#fn:r65|65]]</sup> ). The greater warming at 700-2000 m in the Atlantic than the Pacific or Indian Oceans (Figure 5.3) reflects the strong southward transport of recently formed NADW at these depths by the AMOC. Two areas that commonly exhibit substantially reduced near-surface warming over the course of the 21st century are the northern north Atlantic, where a slowing AMOC (see Section 6.7.1.1) reduces the northward heat transport and brings the surface temperatures closer to what is found in other ocean basins at these latitudes (Collins et al., 2013 <sup>[[#fn:r66|66]]</sup> ), and the southern side of the Southern Ocean, where water upwells that has been submerged for so long that it has not yet experienced significant anthropogenic climate change (Armour et al., 2016 <sup>[[#fn:r67|67]]</sup> ). Most of these projected warming patterns are broadly consistent across the current and previous generations of climate models (Mitchell et al., 1995 <sup>[[#fn:r68|68]]</sup> ; Collins et al., 2014 <sup>[[#fn:r69|69]]</sup> ) as well as observations and theoretical understanding. These multiple lines of evidence give ''high confidence'' that the projections describe the changes in the real world ( ''high agreement, robust evidence'' ).

The near surface salinity of the ocean is both observed and projected to evolve in ways that reflect the increased intensity of the Earth’s hydrologic cycle (Durack, 2015 <sup>[[#fn:r70|70]]</sup> ) and the increasing near-surface ocean stratification (Zika et al., 2018 <sup>[[#fn:r71|71]]</sup> ). As described in WGI AR5, the ocean surface in areas that currently have net evaporation are expected to become saltier, while areas with net precipitation are expected to get fresher (Rhein et al., 2013 <sup>[[#fn:r72|72]]</sup> ), as the patterns of precipitation and evaporation are generally expected to be amplified (Held and Soden, 2006 <sup>[[#fn:r73|73]]</sup> ). At longer time-scales of decades, the larger scale changes in the ocean circulation and basin integrated freshwater imbalances emerge in the near-surface salinity changes, as shown in Figure 5.3b, with an increasingly salty tropical and subtropical Atlantic and Mediterranean contrasting with a freshening Pacific and polar Arctic emerging as robust signals across the suite of ESMs (Collins et al., 2013 <sup>[[#fn:r74|74]]</sup> ). The freshening of the high latitudes in the north Atlantic and Arctic basin is consistent with the widely expected weakening of the AMOC (also discussed in Section 6.7), hydrological cycle changes and a decline in the volume of sea ice (discussed in Section 3.2.2).

Projected salinity changes in the subsurface ocean reflect changes in the rates of formation of water masses or their newly formed properties (Purich et al., 2018 <sup>[[#fn:r75|75]]</sup> ). Thus, projected freshening of the Southern Ocean surface leads to a freshening of the Antarctic Intermediate Water that is subducted there, flowing northward from the Southern Ocean as a relatively fresh water-mass at depths of 500–1500 m (Figure 5.3b). Increased surface salinity in the Atlantic subtropical gyres are pumped into the interior by the winds, leading to an increased salinity of the interior subtropical gyres, along with contributions from increasingly salty Mediterranean water (Jordà et al., 2017 <sup>[[#fn:r76|76]]</sup> ). Conversely, freshwater capping of the northwestern north Atlantic is projected to inhibit deep convection in the Labrador Sea and the consequent production of Labrador Sea Water in some models (Collins et al., 2013 <sup>[[#fn:r77|77]]</sup> ), and contributes to the increased salinity of the north Atlantic between 1000–2000 m depths (Figure 5.3b).

Identifying the specific patterns of anthropogenic climate changes in oceanic observations is complicated by the presence of basin-scale natural variability with timescales ranging from tidal to multi-decadal, and due to the difficulties associated with maintaining high-precision observing systems spanning the ocean basins and limited observational coverage of the extratropical Southern Hemisphere before 2006 (Rhein et al., 2013 <sup>[[#fn:r78|78]]</sup> ). Inferences based on oceanographic observations from the 1970s onward show wide-spread warming of the upper 700 m (Figure 5.2a), in broad agreement with the ensemble of historical CMIP5 ESM simulations (Figure 5.2b). These ESMs indicate that anthropogenic regional warming over the past half-century should be discernable at the 95% confidence level in much of the upper oceans (un-stippled areas in Figure 5.2b). Most of the areas where observational analyses (Figure 5.2a) exhibit long-term cooling are either regions where the internally generated variability is large enough to mask the trends (e.g., the Eastern Tropical Pacific, Northwest Atlantic, and Kurushio extension east of Japan, which are stippled in Figure 5.2b), or where the observational coverage early in the record is limited and different analyses can disagree about trends (e.g., the Southern Ocean and extratropical South Pacific). When internal variability is taken into account, the broad consistency in the magnitude and regional distribution of observed and simulated 50-year trends gives confidence to the ESM projections of longer-term oceanic changes described previously.

Detailed regional patterns of trends in temperature and heat content at depths of 0–2000 m during the early 21st century are consistent in various analysis, owing to the improved observing network (Roemmich et al., 2015 <sup>[[#fn:r81|81]]</sup> ; Desbruyères et al., 2016a <sup>[[#fn:r82|82]]</sup> ) (Figure 5.2d). At depths of 700–2000 m, observations in all of the ocean basins show broadly warming trends in the well-observed Argo era (2006 to present), with particularly significant warming patterns in the Southern Hemisphere extratropics around 40 o S and the subpolar north Atlantic (Figure 5.3a). These observed changes support the notion that deep ocean heat content has been continuously increasing. As a result, regional climate change signatures emerge from confounding natural variability sooner in the 700–2000 m depth range than in upper 700 m of the ocean, where interannual modes of variability have a larger influence on the circulation (for a more complete discussion see Johnson et al. (2018). Despite regional patches of cooling water in the upper 700 m (Figure 5.2d), every one of the world’s ocean basins volume averaged over depths of 0–2000 m has experienced significant warming over the last decade (Figure 5.3, and also Desbruyères et al. (2016a) <sup>[[#fn:r82|82]]</sup> ). The greatest warming of the top 2000 m has been in the Southern Ocean (Roemmich et al., 2015 <sup>[[#fn:r83|83]]</sup> ; Trenberth et al., 2016 <sup>[[#fn:r84|84]]</sup> ), the tropical and subtropical Pacific Ocean (Roemmich et al., 2015 <sup>[[#fn:r85|85]]</sup> ), and the tropical and subtropical Atlantic Ocean (Cheng and Chen, 2017 <sup>[[#fn:r86|86]]</sup> ). The Southern Hemisphere extratropical oceans accounted for 67–98% of the total ocean heat increase in the uppermost 2000 m for the period of 2006–2013 (Roemmich et al., 2015 <sup>[[#fn:r87|87]]</sup> ). Shi et al. (2018) <sup>[[#fn:r88|88]]</sup> suggest that the dominant ocean heat uptake by the Southern Hemisphere in the early 21st century is expected to become more balanced between the hemispheres as the asymmetric cooling by aerosols decreases.

Large-scale patterns of natural variability at interannual to decadal time scales can mask the long-term warming trend in the upper 700 m, particularly in the tropical Pacific and Indian Oceans (England et al., 2014 <sup>[[#fn:r89|89]]</sup> ; Liu et al., 2016 <sup>[[#fn:r90|90]]</sup> ) and in the north Atlantic (Buckley and Marshall, 2015 <sup>[[#fn:r91|91]]</sup> ). The most significant upper 700 m warming between five-year averages centered on 2007–2015 occurred in a large extratropical band of the Southern Hemisphere between 30ºS–60ºS, and in the tropical Indian Ocean, the eastern North Pacific and western subtropical north Atlantic (Figure 5.2d). Warming of the southern hemisphere subtropical gyres is driven, in part, by an intensification of Southern Ocean winds in recent decades, facilitating the penetration of heat to deeper depths (Gao et al., 2018 <sup>[[#fn:r92|92]]</sup> ). Marginal seas, such as the Mediterranean and Red seas have also exhibited notable warming. Conversely, over this timeframe there were also regions of cooling in the upper 700 m, notably in the north Atlantic around 40 o N–60 o N and in the western tropical Pacific (Figure 5.2d). Recent relatively cold and fresh surface and subsurface conditions in the north Atlantic have been attributed to anomalous atmospheric forcing (Josey et al., 2018 <sup>[[#fn:r93|93]]</sup> ) or weakened transport by the north Atlantic Current and AMOC (Smeed et al., 2018 <sup>[[#fn:r94|94]]</sup> ), and in turn may have contributed to an intensification of deep convection in the Labrador Sea since 2012 (Yashayaev and Loder, 2017 <sup>[[#fn:r95|95]]</sup> ). All these observed decadal changes can be related to internal decadal variability (Robson et al., 2014 <sup>[[#fn:r96|96]]</sup> ; Yeager et al., 2015 <sup>[[#fn:r97|97]]</sup> ) even though they resemble expected longer-term anthropogenically forced trends. Substantial decadal-scale warming and cooling trends in the tropical Pacific and Indian oceans can arise from natural El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole variability (Han et al., 2014 <sup>[[#fn:r98|98]]</sup> ). Large ensembles of freely running CMIP5 ESM simulations also show that internal variability can dominate the regional manifestation of the anthropogenic climate signal on decadal timescales (Kay et al., 2014 <sup>[[#fn:r99|99]]</sup> ). This is illustrated by the differing warming trends in Figure 5.2e and 5.2f from two identical ESMs that differ only in the weather in their 1850 initial conditions, averaged over the whole 21st century, by contrast, the ensemble of CMIP5 models project statistically significant anthropogenic regional upper 700 m heat content trends almost everywhere (Figure 5.2c).

There are well documented changes in observed ocean temperatures and salinities (Abraham et al., 2013 <sup>[[#fn:r100|100]]</sup> ; Ishii et al., 2017 <sup>[[#fn:r101|101]]</sup> ). However, attributing these changes in the state of the ocean to anthropogenic causes can be challenging due to the presence of internally generated variability, which can swamp the underlying climate change signal in short records and on regional scales. As can be seen in Figure 5.2, the observed long-term trends (Figure 5.2a) exhibit a striking similarity to the CMIP5 ensemble mean in areas where the models suggest that anthropogenic changes should be statistically significant (Figure 5.2b). However, the trends in the shorter well-observed period covering 2005–2017 (Figure 5.2d) exhibits strong trends from internal variability, as illustrated by the differences of two ensemble members of the same ESM with the same forcing but initialised with different weather (Figure 5.2e and 5.2f). Detection and Attribution studies take the internal variability into account and separate the underlying climate signals with the same spatio-temporal sampling as the observations, and apply a range of statistical tests to determine the coherence of the observations with the co-sampled observations (Bindoff et al., 2013 <sup>[[#fn:r102|102]]</sup> ; AR5 WG1 Box 10.1).

Since AR5, the use of different and updated oceanographic data sets and increase in the number of ensembles of the CMIP5 simulations (Kay et al., 2014 <sup>[[#fn:r103|103]]</sup> ) has improved the overall detection and attribution of human influence. Together these measures increase the coherence of the simulations and reduce noise. For example, an isotherm approach used to reduce the noise from the displacement of isotherms in the upper water column allowing detection in each of the mid-latitude ocean basins was achieved on 60-year time series (Weller et al., 2016 <sup>[[#fn:r104|104]]</sup> ). Using all the available ocean temperature and salinity profiles from the Southern Ocean, Swart et al. (2018) show that the warming and freshening patterns were consistent primarily with increased human induced greenhouse gases and secondarily from ozone depletion in the stratosphere, but inconsistent with internal variability. Together the evidence from the AR5, and the discussion above with the new evidence on regional scales across the global oceans, we conclude that the observed long-term upper ocean temperature changes are ''very likely'' to have a substantial contribution from anthropogenic forcing.

The wind-driven ocean circulation at the end of the 21st century is expected to be qualitatively similar to that in the present day, even as important buoyancy-loss driven overturning circulations are expected to weaken. ESM projections suggest that some major ocean current transports will exhibit a modest increase (such as the Kuroshio Extension (Terada and Minobe, 2018) or a small decrease such as for the Indonesian Throughflow (Sen Gupta et al., 2016); many predominantly wind-driven current-system transports are expected to exhibit smaller than 20% changes by 2100 with RCP8.5 forcing. Climate-change induced changes of the circulation in other mid-latitude basins may be difficult to detect or reliably project because of significant natural variability at inter-annual (e.g., El Niño) to decadal (e.g., the Pacific Decadal Oscillation) timescales. The Antarctic Circumpolar Current is projected to be subject to strengthening westerly winds and substantially reduced rates of Antarctic Bottom Water (AABW) formation, as assessed in the Cross-Chapter Box 7 in Chapter 3. The heat transported by the buoyancy-loss driven AMOC, in particular, contributes to the relatively clement climate of northern Europe and the north Atlantic Basin as a whole, although the wind-driven ocean gyres also contribute to the meridional ocean heat transport (see the review by Buckley and Marshall (2015). As a result, there is a concern that significant changes in ocean circulation could lead to localised climate changes that are much larger than the global mean. Projected and observed changes in the AMOC and the rates of formation of deep water-masses in the north Atlantic are discussed in Chapter 6.7.1, along with the possibility of abrupt or enduring changes resulting from forcing by Greenlandic meltwater. A significant reduction in AMOC would, in turn, modestly weaken the Gulf Stream transport, which also has a substantial wind driven component (Frajka-Williams et al., 2016 <sup>[[#fn:r105|105]]</sup> ). Most aspects of the large-scale wind-driven ocean circulation are ''very likely'' to be qualitatively similar to the circulation in the present day, with only modest changes in transports and current location.

The global ocean below 2000 m has warmed significantly between the 1980s and 2010s (Figure 5.4), contributing to ocean heat uptake and through thermal expansion to SLR (Purkey and Johnson, 2010 <sup>[[#fn:r106|106]]</sup> ; Desbruyères et al., 2016b <sup>[[#fn:r107|107]]</sup> ). The observed deep warming rate varies regionally and by depth reflecting differences in the waters influencing particular regions. The deep and abyssal north Atlantic, fed by North Atlantic Deep Water (NADW), has reversed from warming to cooling over the past decade, possibly associated with the North Atlantic Oscillation (NAO) (e.g., Yashayaev, 2007; Desbruyères et al., 201 <sup>[[#fn:r108|108]]</sup> 4) or longer-term weakening in north Atlantic overturning circulation (Caesar et al., 2018 <sup>[[#fn:r109|109]]</sup> ; Thornalley et al., 2018 <sup>[[#fn:r110|110]]</sup> ). The strongest warming is observed in regions of the deep ocean AABW (Purkey et al., 2014 <sup>[[#fn:r111|111]]</sup> ). Regions of the ocean fed by AABW from the Weddell Sea have exhibited a possible slowdown in local AABW warming rates (Lyman and Johnson, 2014 <sup>[[#fn:r112|112]]</sup> ), while the Pacific, fed by AABW from the shelves along the Ross and Adelie Coast, has continued to warm at an accelerating rate between 1990 and 2018 (Desbruyères et al., 2016b <sup>[[#fn:r113|113]]</sup> ).

To date, assessment of deep ocean (below 2000 m) heat content has mostly been from ship-based data collected along decadal repeats of oceanographic transects (Figure 5.4b) (Talley et al., 2016 <sup>[[#fn:r114|114]]</sup> ). While relatively sparse in space and time compared to the upper ocean, these transects were positioned to optimise sampling of most deep ocean basins and provide the highest quality of salinity, temperature and pressure data. Argo floats capable of sampling to 6000 m have just started to populate select deep ocean basins; this Deep Argo data has just started providing regional deep ocean warming estimates (Johnson et al., 2019 <sup>[[#fn:r115|115]]</sup> ). Decadal monitoring by the full global Deep Argo array (Johnson et al., 2015 <sup>[[#fn:r116|116]]</sup> ), complemented by indirect estimates from space (Llovel et al., 2014 <sup>[[#fn:r117|117]]</sup> ; Von Schuckmann et al., 2014), will strongly reduce the currently large uncertainties of deep ocean heat content change estimates in the future.

The spatial and temporal sparseness of observations below 4000 m, along with significant differences between various ESMs, limits our understanding of the exact mechanisms driving the abyssal ocean variability. However, ESMs consistently predict an anthropogenic climate-change induced long-term abyssal warming trend originating in the Southern Ocean due to a reduction in the formation rates of cold AABW (Heuzé et al., 2015 <sup>[[#fn:r118|118]]</sup> ). Although the abyssal modes of natural variability are not as pronounced as closer to the surface, deep ocean heat content can vary on relatively short time scales through the communication of topographic and planetary waves driven by changes in the rate of deep water formation at high latitudes (Kawase, 1987 <sup>[[#fn:r119|119]]</sup> ; Masuda et al., 2010; Spence et al., 2017). AABW has shown variability in properties and production rates over the past half century (Purkey and Johnson, 2013 <sup>[[#fn:r121|121]]</sup> ; Menezes et al., 2017 <sup>[[#fn:r122|122]]</sup> ). A slowdown in AABW formation rates may arise from freshening of shelf waters, changes in local winds driving cross shelf mixing, or larger scale dynamics controlling the spin up or down of Southern Ocean gyres influencing the density of outflowing waters over deep sills. Large-scale circulation changes can also alter the properties of the ambient water that is entrained as dense water descends along the Antarctic continental slopes (Spence et al., 2017 <sup>[[#fn:r123|123]]</sup> ). Evolving AABW properties may also reflect changes in deep Southern Ocean convection. The Weddell Polynya is a large opening in the wintertime ice of the Weddell Sea that is kept ice-free despite intense heat loss to the atmosphere by convective mixing bringing up warm and salty water from the deep ocean. (See Box 3.2 for a more extensive discussion of polynyas and the Weddell Polynya in particular). The Weddell Polynya was present in three of the first years of infrared satellite observations of wintertime sea ice concentrations in the mid-1970s, but it has been closed since 1976, only to reopen in 2016 and 2017. The prominent Weddell Polynya in the mid-1970s greatly increased the volume of the coldest waters in the deep Weddell Sea. Weddell Polynyas are documented to drive abyssal cold and salty signals and can spread thermal signals as waves further and faster than could be explained by slow advective signals (Martin et al., 2015 <sup>[[#fn:r124|124]]</sup> ; Zanowski and Hallberg, 2017 <sup>[[#fn:r125|125]]</sup> ); these waves do not directly heat individual water parcels, but instead warm the ocean where they cause the coldest deep layers to spread laterally and thin. However, recovery from the large Weddell polynya of the early 1970s can only explain about 20% of the observed abyssal warming trend (Zanowski et al., 2015 <sup>[[#fn:r126|126]]</sup> ).

<span id="figure-5.4"></span>
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'''Figure 5.4'''

<span id="figure-5.4-observed-rates-of-warming-from-1981-to-2019-a-as-a-function-of-depth-globally-orange-and-south-of-the-sub-antarctic-front-the-purple-line-in-b-at-about-55s-purple-with-90-confidence-intervals-and-b-average-warming-rate-colours-in-the-abyss-below-4000-m-over-various-ocean-basins-whose"></span>
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'''Figure 5.4 | Observed rates of warming from 1981 to 2019 (a) as a function of depth globally (orange) and south of the Sub-Antarctic Front (the purple line in (b) at about 55°S) (purple) with 90% confidence intervals and (b) average warming rate (colours) in the abyss (below 4000 m) over various ocean basins (whose […]'''
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[[File:9f58ef729e7d153da43867c5b6e85cdf IPCC-SROCC-CH_5_4.jpg]]

Figure 5.4 | Observed rates of warming from 1981 to 2019 (a) as a function of depth globally (orange) and south of the Sub-Antarctic Front (the purple line in (b) at about 55°S) (purple) with 90% confidence intervals and (b) average warming rate (colours) in the abyss (below 4000 m) over various ocean basins (whose boundaries are shown in grey lines), with stippling indicating basins with no significant changes. The black lines show the repeat hydrographic sections used to make these estimates. These figures use updated GoShip data and the techniques of Purkey and Johnson (2010).

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<span id="figure-5.5"></span>
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'''Figure 5.5'''

<span id="figure-5.5-zonal-and-20-year-mean-stratification-averaged-over-the-top-200-m-of-the-ocean-for-the-coupled-model-intercomparison-project-phase-5-cmip5-ensemble-of-simulations-at-the-end-of-the-historical-runs-green-and-for-the-end-of-the-21st-century-for-representative-concentration-pathway-rcp2.6-blue-and-rcp8.5-red-scenarios."></span>
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'''Figure 5.5 | Zonal and 20-year mean stratification averaged over the top 200 m of the ocean for the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble of simulations at the end of the historical runs (green), and for the end of the 21st century for Representative Concentration Pathway (RCP)2.6 (blue) and RCP8.5 (red) scenarios. […]'''
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[[File:d071ea667782879532641679e563ef15 IPCC-SROCC-CH_5_5.jpg]]

Figure 5.5 | Zonal and 20-year mean stratification averaged over the top 200 m of the ocean for the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble of simulations at the end of the historical runs (green), and for the end of the 21st century for Representative Concentration Pathway (RCP)2.6 (blue) and RCP8.5 (red) scenarios. The values between the 5th and 95th percentiles of the ensembles are shaded, while the lines are the ensemble mean. These model results are not adjusted by the control-run, so the spread in the various estimates primarily reflect model formulation differences. The average squared buoyancy frequency shown here is nearly linearly proportional to the density difference between the surface and 200 m, and is a measure of the density stratification of the upper ocean.

The ocean’s properties are changing most rapidly in the near surface waters that are more immediately exposed to atmospheric forcing. As a result of the surface-intensified warming, the upper few hundred meters of the ocean are becoming more stably stratified (Helm et al., 2011 <sup>[[#fn:r126|126]]</sup> ; Talley et al., 2016 <sup>[[#fn:r128|128]]</sup> ). The combination of surface intensified warming and near-surface freshening at high latitudes leading to a projection of more intense near-surface stratification (the downward-increasing vertical gradient of density) across all ocean basins (Figures 5.3 and 5.5) is a robust result with a ''high'' ''agreement'' across successive generations of coupled climate models (Capotondi et al., 2012 <sup>[[#fn:r129|129]]</sup> ; Bopp et al., 2013 <sup>[[#fn:r130|130]]</sup> ). Based on the projected changes from individual models between 1986–2005 and 2081–2100, the mean stratification of the upper 200 m averaged between 60°S–60°N, normalised by the ensemble mean value from 1986–2005 will ''very likely'' increase by between 1.0–9.3% (with 95% confidence and a CMIP5 median change of 2.6%) for RCP2.6, and by between 12.2–30.0% (median value 21.2%) for RCP8.5. Inferences from oceanic observations (Good et al., 2013 <sup>[[#fn:r131|131]]</sup> ) suggest that the 20-year mean stratification averaged between 60°S–60°N and over the top 200 m ''very likely'' increased by between 2.18–2.42% from 1971–1990 to 1998–2017. By contrast, the bottom intensified warming in the abyss (see Figure 5.4) which is consistent with a slowing in the rate of AABW formation, is also associated with a reduction in the abyssal stratification of the ocean (Lyman and Johnson, 2014 <sup>[[#fn:r132|132]]</sup> ; Desbruyères et al., 2016b <sup>[[#fn:r133|133]]</sup> ). Both of these changes have consequences for the evolving turbulence and ocean water-mass structure. Based on observational evidence, theoretical understanding and robust ESM projections, it is ''very likely'' that stratification in the upper few hundred meters of the ocean below the mixed layer will increase significantly in the 21st century over most ocean basins as a result of climate change, and abyssal stratification will ''likely'' decrease.

Many dynamical consequences of increased stratification are understood with ''very high confidence'' (see, for instance, Gill (1982) and Vallis (2017)). For the same turbulent kinetic energy dissipation, locally increased stratification reduces the turbulent vertical diffusivity of heat, salinity, oxygen and nutrients (see Section 5.2.2.2.4). Increased stratification in the tropics and subtropical gyres will ''likely'' lead to a net reduction in the vertical diffusivities of nutrients and other gases within the main thermocline, reducing the flux of nutrients into the euphotic zone and increasing the gradient in oxygen concentrations between the near surface ocean and the interior. Increasing upper ocean stratification (Figure 5.5) acts to restrict the depth of the ocean’s surface mixed layer. Increasing stratification increases the buoyancy frequency and the lateral propagation speed of internal gravity waves and boundary waves by about half the percentage change of the stratification itself. Increasing stratification increases both the length of the internal deformation radius (a typical length scale in baroclinic eddy dynamics) and the horizontal scales of internal tides (see Section 5.2.2.2.3) proportionately with the changes in the internal gravity wave speeds. An increase in stratification will increase the lateral propagation of internal Rossby waves (which set up the basin-scale ocean density structure) proportionately. For the same forcing, increasing stratification reduces the geostrophically balanced slope of density surfaces, and hence the vertical extent of basin-scale wind-driven gyres or coastal upwelling circulations. The flattening of density surfaces by increased stratification inhibits advective exchange between the surface and interior ocean (Wang et al., 2015a <sup>[[#fn:r134|134]]</sup> ), with consequences for the uptake of anthropogenic carbon (Section 5.2.2.3), the evolving oxygen distribution (Section 5.2.2.4) and the supply of nutrients to support primary production (Section 5.2.2.5).

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<div id="section-5-2-2-2changing-temperature-salinity-circulation-block-6"></div>

<span id="tides-and-coastal-physical-changes-in-a-changing-climate"></span>
===== 5.2.2.2.3 Tides and coastal physical changes in a changing climate =====

Coastal systems are subject to the same large-scale warming trends as the open ocean, but the local response may be dominated by a complex of localised changes in factors such as circulation, mixing, river plumes or the seasonal upwelling of cold water. Using ESMs to project how these factors will interact often requires much finer resolution than is currently affordable in global models, however regional high-resolution models can be effective, especially in marginal seas like the Mediterranean with restricted interactions with the open ocean and that respond primarily to local forcing (Adloff et al., 2015 <sup>[[#fn:r135|135]]</sup> ). High resolution regional models have also been used to project robust localised ocean climate changes in wide shelf seas with more extensive interactions with the open ocean, like those in northwestern Europe (Tinker et al., 2016 <sup>[[#fn:r136|136]]</sup> ). The technical difficulties of using nested regional models are much greater in coasts adjacent to energetic large-scale currents like the Gulf Stream, Kuroshio, and Agulhas, and projecting detailed coastal climate change such places may require the use of expensive high resolution global models (Saba et al., 2016 <sup>[[#fn:r137|137]]</sup> ). These physical coastal changes have consequences that cascade through ecosystems to people, as is illustrated in detail for eastern boundary upwelling systems in Box 5.2.

Both human structures and ecological systems in the coastal zone are directly impacted by tidal amplitudes, which contribute to high-water levels and the tidal flushing rates of estuaries, embayments, marshes and mangroves. The tides are the response of a forced-damped-resonance system (Arbic et al., 2009 <sup>[[#fn:r138|138]]</sup> ). The M 2 tide is the dominant tidal constituent in most places, with a period of half a lunar day, or 12 hours, 25 minutes; the M 2 tides are created by the differential motion of the solid Earth and oceans in response to the gravitational attraction of the moon (Newton, 1687 <sup>[[#fn:r139|139]]</sup> ; Laplace, 1799 <sup>[[#fn:r140|140]]</sup> ). The astronomical forcing evolves only slowly, however the tidal damping and basin resonance at tidal frequencies can change in response to changes in sea level, stratification and coastal conditions (Müller, 2012 <sup>[[#fn:r141|141]]</sup> ; Schindelegger et al., 2018 <sup>[[#fn:r142|142]]</sup> ). Several recent studies have analysed historical coastal tide gauge data and found amplitude trends of order 1 – 4% per century (Ray, 2009; Woodworth, 2010; Müller et al., 2011). In some locations, the changes in the tides have been of comparable importance to changes in mean sea level for explaining changes in high water levels (Jay, 2009). For many individual tide gauges, the trends in tidal amplitude are strongly positively or negatively correlated with local time-mean sea level trends (Devlin et al., 2017 <sup>[[#fn:r145|145]]</sup> ). Another source of secular tidal changes, changes in oceanic stratification, modifies the rate of energy conversion from the barotropic tides to the internal tides (Jayne and St. Laurent, 2001 <sup>[[#fn:r146|146]]</sup> ), the vertical profile of turbulent viscosity on shelves (Müller, 2012 <sup>[[#fn:r147|147]]</sup> ), and the propagation speed of the internal tides (Zhao, 2016 <sup>[[#fn:r148|148]]</sup> ). For example, Colosi and Munk (2006) found an increase in the amplitude of the principal lunar semidiurnal tide M 2 in Honolulu of about 1 cm over the past 100 years, which they attributed primarily to changes in oceanic stratification bringing about local changes in relative phases of the internal and external M 2 tides, increasing constructive interference. Both sea level and stratification are expected to exhibit robust secular positive trends in the coming century due to climate change, at rates that are significantly larger than historical trends, and people may choose to replace natural beaches and marshes with sea-walls in response to rising sea levels. As a result, it is ''very likely'' that the majority of coastal regions will experience statistically significant changes in tidal amplitudes over the course of the 21st century.

Because coastal tides are near resonance in many locations, small changes in sea level and bay shape can change the local tides significantly. For example, the insertion of tidal power plants can have a significant impact on the local tides (Ward et al., 2012 <sup>[[#fn:r149|149]]</sup> ). Various observational and modeling studies demonstrate that SLR has spatial heterogeneous impacts on the tides, with some locations experiencing decreased tidal amplitudes and others experiencing increased tidal amplitudes (Pickering et al., 2012 <sup>[[#fn:r150|150]]</sup> ; Devlin et al., 2017 <sup>[[#fn:r151|151]]</sup> ; Pickering et al., 2017 <sup>[[#fn:r152|152]]</sup> ). Projections of tidal changes indicate that the patterns and even the sign of changes in tidal amplitudes depend on whether the coastlines are allowed to recede with rising sea levels or are held in place (Pickering et al., 2017 <sup>[[#fn:r153|153]]</sup> ; Schindelegger et al., 2018 <sup>[[#fn:r154|154]]</sup> ) . Pelling et al. (2013) <sup>[[#fn:r155|155]]</sup> and Hwang et al. (2014) <sup>[[#fn:r156|156]]</sup> demonstrate that the rapid coastline changes in China’s Bohai Sea have already altered the tides in that region and throughout the Yellow Sea (Hwang et al., 2014 <sup>[[#fn:r157|157]]</sup> ). Pelling and Green (2014) examine the impact of flood defenses as well as SLR on tides on the European Shelf. Such tidal changes have implications for designing flood defenses, for tidal renewable energy, for tidal flushing timescales of estuaries and embayments, and for navigational dredging requirements (Pickering et al., 2012 <sup>[[#fn:r158|158]]</sup> ) (Section 5.4.2). The sign and amplitude of local changes to tides are ''very likely'' to be impacted by both human coastal adaptation measures and climate drivers (listed above).

<div id="section-5-2-2-2changing-temperature-salinity-circulation-block-7"></div>

<span id="systematic-sources-of-uncertainty-in-projections-of-ocean-physical-changes"></span>
===== 5.2.2.2.4 5.2.2.2.4 Systematic sources of uncertainty in projections of ocean physical changes =====

ESMs are able to capture the dynamics of the climate system, but all numerical models have approximations and biases. The most commonly used type of ocean component in ESMs is known to exhibit numerically induced vertical mixing that can be a significant fraction of the physical mixing (Ilıcak et al., 2012 <sup>[[#fn:r159|159]]</sup> ; Megann, 2018 <sup>[[#fn:r160|160]]</sup> ). Because so many ocean models exhibit the same sign of bias, there is a systematic warming of the lower-main thermocline that is not cancelled out when taking the average over the ensemble of all the models in CMIP5. These biases are widely known within the ocean modelling community, and various groups are working to reduce these biases in future ESMs with better ocean model numerics and parameterisations. To correct for model biases, ESM projections are always taken as the difference from a control run without the anomalous forcing. However, some aspects of the ocean response to climate change are nonlinear, and model biases can introduce uncertainties into climate projections. In the case of heat uptake, this is of the order of 10% uncertainty, while for the rate of steric SLR (which depends on the nonlinear equation of state of seawater) the uncertainty in CMIP5 models is of the order of 20% (Hallberg et al., 2012 <sup>[[#fn:r161|161]]</sup> ).

Mesoscale eddies (geostrophic rotating vortices with spatial scales of 10–100 km that penetrate deeply into the water column, and are often described as the ocean’s weather) play an important role in regulating the changes to the larger scale ocean circulation, especially in the Antarctic Circumpolar current, as is discussed in Cross Chapter Box 7. In addition, sub-mesoscale eddies (rotationally influenced motions with smaller horizontal scales of hundreds of metres to about 10 km and intrinsic timescales of a few days that especially arise in association with fronts in the ocean’s surface properties) are known to be particularly important in the dynamics of the near-surface ocean boundary layer (see the review by Mahadevan (2016)). Sub-mesoscale instabilities are associated with re-stratifying overturning circulations that can limit the thickness of the well-mixed ocean surface boundary layer near fronts (Bachman et al., 2017 <sup>[[#fn:r162|162]]</sup> ). Moreover, sub-mesoscale motions generate strong vertical velocities that drive fluxes of nutrients from the interior ocean into the euphotic zone or create pockets of reduced mixing with increased phytoplankton residency time within the euphotic zone (Lévy et al., 2012 <sup>[[#fn:r163|163]]</sup> ). Intense mesoscale eddies are known to create favourable conditions for sub-mesoscale instabilities as shown in both observational (Bachman et al., 2017 <sup>[[#fn:r164|164]]</sup> ) and numerical studies (Brannigan et al., 2017 <sup>[[#fn:r165|165]]</sup> ). Intensifying Southern Ocean eddy fields will have a significant local impact on biological productivity, ecosystem structure, and carbon uptake, both directly and via sub-mesoscale processes. At typical CMIP5 ESM resolutions, it is only in the tropics that mesoscale eddies are adequately resolved to explicitly model their effects (Hallberg, 2013 <sup>[[#fn:r166|166]]</sup> ), while sub-mesoscale eddies are not resolved anywhere, so eddy effects need to be parameterised in ESMs. Despite great progress over the past 30 years in parameterising eddy effects, uncertainties in these parameterisations and how eddies will respond to novel conditions continue to contribute to uncertainties in projections of oceanic climate change ( ''medium confidence'' ).

Ocean turbulent mixing is a key process regulating the ocean circulation and climate. Turbulent mixing is important for the uptake and redistribution of heat, carbon, nutrients, oxygen and other tracers (properties that are carried along with the flow of water) in the ocean (Schmittner et al., 2009 <sup>[[#fn:r167|167]]</sup> ; MacKinnon et al., 2017 <sup>[[#fn:r168|168]]</sup> ). Both observations and theory indicate that turbulent mixing in the ocean is not constant in space or time. Global estimates of both the turbulent kinetic energy dissipation rate and the vertical diffusivity, two measures of ocean turbulence, vary over several orders of magnitude throughout the ocean (Figure 5.6) (Polzin et al., 1997 <sup>[[#fn:r170|170]]</sup> ; Waterman et al., 2012 <sup>[[#fn:r171|171]]</sup> ; Whalen et al., 2012 <sup>[[#fn:r172|172]]</sup> ; Alford et al., 2013 <sup>[[#fn:r173|173]]</sup> ; Hummels et al., 2013 <sup>[[#fn:r174|174]]</sup> ; Sheen et al., 2013 <sup>[[#fn:r175|175]]</sup> ; Waterhouse et al., 2014 <sup>[[#fn:r176|176]]</sup> ; Kunze, 2017 <sup>[[#fn:r177|177]]</sup> ). For a given energy dissipation rate, the turbulent diffusivities of heat, salinity, nutrients and other tracers tend to be smaller with stronger stratification. This dependency on stratification helps explain why the observationally inferred diffusivity in the heavily stratified main thermocline (250–1000 m depth) is of similar magnitude to those deeper in the water column, while the turbulent energy density and dissipation rate are much stronger at the shallower depths (Whalen et al., 2012 <sup>[[#fn:r178|178]]</sup> ). Oceanic turbulence also fluctuates in time, is modulated by tidal cycles (Klymak et al., 2008 <sup>[[#fn:r179|179]]</sup> ), the mesoscale eddy field and seasonal changes (Whalen et al., 2018 <sup>[[#fn:r180|180]]</sup> ). In the mixed layer and directly below, turbulence changes according to local conditions, such as the winds, heating rates and local stratification (Sloyan et al., 2010 <sup>[[#fn:r181|181]]</sup> ; Moum et al., 2013 <sup>[[#fn:r182|182]]</sup> ; D’Asaro, 2014 <sup>[[#fn:r183|183]]</sup> ; Tanaka et al., 2015 <sup>[[#fn:r184|184]]</sup> ) at diurnal to seasonal and longer timescales. These variations in near-surface turbulence need to be taken into account for ESMs to reproduce more accurately the observed seasonal cycle of surface properties and spatial structure of the depth of the thermally well-mixed near surface layer of the ocean. The spatial and temporal patterns of ocean turbulence help shape ocean tracer distributions (heat, dissolved greenhouse gases and nutrients) and how they will evolve in a changing climate ( ''high confidence'' ).

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'''Figure 5.6'''

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'''Figure 5.6 | Estimate of the average vertical turbulent diffusivity between 250–1000 m calculated by applying fine structure techniques to Argo float data from below the well-mixed near-surface boundary layer. Only bins with at least three estimates are plotted and regions with insufficient data are coloured grey. This figure was created using updated data through […]'''
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Figure 5.6 | Estimate of the average vertical turbulent diffusivity between 250–1000 m calculated by applying fine structure techniques to Argo float data from below the well-mixed near-surface boundary layer. Only bins with at least three estimates are plotted and regions with insufficient data are coloured grey. This figure was created using updated data through April, 2018 with the techniques from Whalen et al. (2012).

Ocean turbulent mixing requires energy sources, many of which are expected to change with a changing climate. Surface wind and buoyancy forcing, the mean and eddying larger-scale ocean circulation itself, and the barotropic tides are all thought to be significant sources of the energy that drives mixing (Wunsch and Ferrari, 2004 <sup>[[#fn:r185|185]]</sup> ). Often this energy first passes through the ocean’s pervasive field of internal gravity waves that propagate and refract through the varying ocean circulation, often breaking into turbulent mixing far from their sources (Eden and Olbers, 2014 <sup>[[#fn:r186|186]]</sup> ; Alford et al., 2016 <sup>[[#fn:r187|187]]</sup> ; Melet et al., 2016 <sup>[[#fn:r188|188]]</sup> ; Meyer et al., 2016 <sup>[[#fn:r189|189]]</sup> ; Zhao et al., 2016b <sup>[[#fn:r190|190]]</sup> ). The energy contributing to the internal waves from the winds and the subsequent turbulence will be altered by changes in tropical storm activity or sea ice coverage. For example, the increasing extent of ice-free Arctic Ocean has already been observed to lead to increased wind-driven internal waves (Dosser and Rainville, 2016 <sup>[[#fn:r191|191]]</sup> ). The Southern Annular Mode is expected to intensify as a result of climate change (Young et al., 2011 <sup>[[#fn:r192|192]]</sup> ; Jones et al., 2016b <sup>[[#fn:r193|193]]</sup> ), bringing with it stronger winds, and more wind-energy input over most of the Southern Ocean and a more intense mesoscale eddy field (Hogg et al., 2015 <sup>[[#fn:r194|194]]</sup> ). Changes in the near-bottom stratification will alter the rate that the barotropic tides generate internal waves, thereby altering the strength and distribution of the tidally generated mixing. Some of the parameterisations of interior ocean mixing used in CMIP5 ESMs take some changing turbulent energy sources into account (Jayne and St. Laurent, 2001 <sup>[[#fn:r195|195]]</sup> ) , and more comprehensive mixing treatments are being developed for use in future generations of ESMs (Eden and Olbers, 2014 <sup>[[#fn:r196|196]]</sup> ). However, not all of the physical processes leading to the rich structure of mixing shown in Figure 5.6 are well understood or included in ESMs; the prospect of significant changes in the patterns and intensity of ocean turbulent mixing is a potential source of uncertainty (probably at the 10% level) in projections of physical and ecological changes in the ocean, including heat uptake, stratification changes, steric SLR, deoxygenisation and nutrient fluxes ( ''medium confidence'' ).

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