Editing IPCC:AR6/SROCC/Chapter-4 (section)

==== 4.2.2.2 Contributions to Global Mean Sea Level Change During the Instrumental Period ====

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The different contributions to the GMSL rise are independently observed over various time scales. They are compared with simulated estimates from climate model experiments of CMIP5 (Taylor et al., 2012 <sup>[[#fn:r160|160]]</sup> ) when available (see Table 4.1). The observations are compared with experiments beginning in the mid-19th century, forced with past time-dependent anthropogenic changes in atmospheric composition, natural forcings due to volcanic aerosols and variations in solar irradiance (Taylor et al., 2012 <sup>[[#fn:r161|161]]</sup> ). The objective is first, to assess understanding of the causes of observed sea level changes and second, to evaluate the ability of coupled climate models to simulate these causes. It enables the evaluation of the confidence level there is in current coupled climate models that form the basis of future sea level projections.

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<span id="thermal-expansion-contribution"></span>
===== 4.2.2.2.1 Thermal expansion contribution =====

The ocean thermal expansion is caused by excess heat being absorbed by the ocean, as the climate warms. Thermal expansion is estimated from ''in situ'' ocean observations and ocean heat content reanalyses that rely on assimilation of data into numerical models (Storto et al., 2017 <sup>[[#fn:r162|162]]</sup> ; Sections 1.8.1.1 and 1.8.1.4; WCRP Global Sea Level Budget Group, 2018 <sup>[[#fn:r163|163]]</sup> ) . Full-depth, high-quality and unbiased ocean temperature profile data with adequate metadata and spatio-temporal coverage are required to estimate thermal expansion and to understand drivers of variability and long-term change (Pfeffer et al., 2018 <sup>[[#fn:r164|164]]</sup> ; Section 5.2.2.2.2) .

Historically, however, observational gaps exist and some ocean regions remain under-sampled to date (Sections 1.8.1.1 and 5.2.2.2.2; Figure 1.3; Appendix 1.A, Figure 1.1). Other factors also introduce uncertainty in estimates of thermal expansion like changes in instrumentation, systematic instrumental errors, changes in the quality control of the data and the mapping method used to produce regular grids (Section 5.2.2.2.2; Palmer et al., 2010 <sup>[[#fn:r165|165]]</sup> ) . In the upper 700 m, the largest sources of uncertainty for estimates of global mean thermal expansion from 1970 to 2004 are the choice of mapping methods (Boyer et al., 2016 <sup>[[#fn:r166|166]]</sup> ) , followed by the choice of bias correction for the bathythermographic observations (Cheng et al., 2016 <sup>[[#fn:r167|167]]</sup> ; Section 5.2.2.2.2). From 2006 onwards, the uncertainty is considerably reduced (Roemmich et al., 2015 <sup>[[#fn:r168|168]]</sup> ; von Schuckmann et al., 2016 <sup>[[#fn:r169|169]]</sup> ; Wijffels et al., 2016 <sup>[[#fn:r170|170]]</sup> ) , because the Argo array reached its targeted near-global ( up to ±60° latitude) coverage for the upper 2000 m in November 2007 (Riser et al., 2016 <sup>[[#fn:r171|171]]</sup> ; Section 5.2.2.2.2) .

Since AR5, in a community effort, the (WCRP Global Sea Level Budget Group, 2018 <sup>[[#fn:r172|172]]</sup> ) revisited the global mean thermal expansion estimates based on observations only. On the basis of a full-depth 13-member ensemble of global mean thermal expansion time series developed with the latest data and corrections available, they estimated that the global thermal expansion was 1.40 (1.08 – 1.72) mm yr <sup>–1</sup> for 2006–2015, 1.36 (0.96 – 1.76) mm yr <sup>–1</sup> for 1993–2015 (see Table 4.1). While the relative contribution of the upper 300 m did not change (~70%) between 2006–2015 and 1993–2015, the 700–2000 m contribution increased around 10% over the Argo decade (2006–2015), when observations for that depth interval soared ( Figure 1.3; Appendix 1.A, Figure 1.1) . This suggests that observed changes for 700–2000 m may have been underestimated for 1993 – 2005. Before 1993, estimates are based on a smaller ensemble of 4 datasets in which no thermal expansion is assumed below 2000 m because of lack of data (see Section 5.2.2.2.2 for more details). This ensemble shows a thermal expansion linear rate of 0.89 (0.84 – 0.94) mm yr <sup>–1</sup> for 1970–2015 (see Table 4.1).

Coupled climate models simulate the historical thermal expansion (see Table 4.1). However, for models that omit the volcanic forcing in their control experiment, the imposition of the historical volcanic forcing during the 20th century results in a spurious time mean negative forcing and a spurious persistent ocean cooling related to the control climate (Gregory, 2010 <sup>[[#fn:r173|173]]</sup> ; Gregory et al., 2013 <sup>[[#fn:r174|174]]</sup> ) . Since AR5, the magnitude of this effect has been estimated from historical simulations forced by only natural radiative forcing. Then it has been used to correct the historical simulations forced with the full 20th century forcing (Slangen et al., 2016 <sup>[[#fn:r187|187]]</sup> ; Slangen et al., 2017b <sup>[[#fn:r188|188]]</sup> ) . The resulting ensemble mean of simulated thermal expansion provides a good fit to the observations within the uncertainty ranges of both models and observations (Slangen et al., 2017b <sup>[[#fn:r189|189]]</sup> ; Cheng et al., 2019 <sup>[[#fn:r190|190]]</sup> ; Table 4.1) . The spread, which is essentially due to uncertainty in radiative forcing and uncertainty in the modelled climate sensitivity and ocean heat uptake efficiency (Melet and Meyssignac, 2015 <sup>[[#fn:r191|191]]</sup> ) , is still larger than the observational uncertainties (Gleckler et al., 2016 <sup>[[#fn:r192|192]]</sup> ; Cheng et al., 2017 <sup>[[#fn:r193|193]]</sup> ; Table 4.1) . Compared to AR5, the availability of improved observed and modelled estimates of thermal expansion and the good agreement between both confirm the ''high confidence'' level in the simulated thermal expansion using climate models and the ''high confidence'' level in their ability to project future thermal expansion.

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<span id="table-4.1"></span>
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'''Table 4.1'''
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Global mean sea level (GMSL) budget over different periods from observations and from climate model base contributions. All values are in mm yr–1. Values in brackets in 4.2 are uncertainties ranging from 5–95%. The climate model historical simulations end in 2005; projections for Representative Concentration Pathway (RCP)8.5 are used for 2006–2015. The modelled thermal expansion, glacier and ice sheet surface mass balance (SMB) contributions are computed from the Coupled Model Intercomparison Project Phase 5 (CMIP5) models as in Slangen et al. (2017b). For the model contributions, uncertainties are estimated from the spread of the ensemble of model simulations following Slangen et al. (2017b), see the footnotes for the details on the uncertainty propagation. GIS is Greenland Ice Sheet.
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[[File:bede59e395134bf8a28829a10c2702b3 table4.1.png]]

Notes:

# (a)  The number is built from WCRP Global Sea Level Budget Group (2018) estimate of the 0–700 m depth thermal expansion, assuming no trend below 2000 m depth before 1992 and the mean value from Purkey and Johnson (2010), and Desbruyères et al. (2017) afterwards.
# (b)  The number is calculated as the mean between the estimate from a reconstruction of glacier mass balance based on glacier length (update of Leclercq et al. (2011)) and the estimate from a mass balance model forced with atmospheric observations (Marzeion et al., 2015). The uncertainty is assumed to be a gaussian with a standard deviation of half the difference between the two estimates.
# (c)  The number is calculated as the sum of the Greenland Ice Sheet (GIS) contribution from Kjeldsen et al. (2015) and the peripheral glaciers’ contribution. The peripheral glaciers’ contribution and the associated uncertainty are computed from a mass balance model forced with atmospheric observations (Marzeion et al., 2015). The total uncertainty is computed assuming that both uncertainties from the GIS contribution and from the peripheral glaciers’ contribution are independent.
# (d)  Numbers from Bamber et al. (2018). See Section 3.3.1 for more details.
# (e)  These numbers are the weighted average of the numbers from Bamber et al. (2018) and from The Imbie team (2018). The weights in the average are based on the uncertainty associated to each estimate. See Section 3.3.1 for more details.
# (f)  Only direct anthropogenic contribution, from Wada et al. (2016).
# (g)  Land water storage estimated from Gravity Recovery and Climate Experiment (GRACE) excluding glaciers, from WCRP Global Sea Level Budget Group (2018).
# (h)  Direct estimate of ocean mass from GRACE from WCRP Global Sea Level Budget Group (2018).
# (i)  Sum of the thermal expansion and the contributions from glaciers, GIS, Antarctica Ice Sheet (AIS) and land water storage. Uncertainties in the different contributions are assumed as independent.
# (j)  Sea level reconstructions that end before 2015 have been extended to 2015 with the satellite altimetry record from Legeais et al. (2018). The uncertainty is derived from the uncertainty of individual sea level reconstructions over the longest period available that start in 1970. The uncertainty from different sea level reconstructions are assumed as independent.
# (k)  The mean estimate is from the satellite altimetry estimate in WCRP Global Sea Level Budget Group (2018) corrected for GIA and for the elastic response of the ocean crust to present day mass redistribution (Frederikse et al., 2017; Lickley et al., 2018). The uncertainty is computed using the updated error budget of Ablain et al. (2015).
# (l)  Land water storage is estimated from Wada et al. (2016) and ice discharge is deduced from Shepherd et al. (2012). The ice discharge contribution is assumed to be zero before 1992. The uncertainties in the different contributions from coupled climate models are assumed independent.
# (m)  The uncertainties in the observed GMSL and the coupled climate models’ estimate of GMSL are assumed independent for the computation of the uncertainties in the residuals.
# (n)  Numbers taken from Appendix 2.A.
# (o)  Numbers taken from Zemp et al. (2019), see Sections 2.2.3 and 3.3.2 for more details.
# (p)  The Number is calculated as the mean of the estimates of Zemp et al. (2019) and Bamber et al. (2018). The uncertainties of the two estimates are assumed to be independent of each other to obtain the uncertainty estimate of the mean.

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<span id="ocean-mass-observations-from-grace-and-grace-follow-on"></span>
===== 4.2.2.2.2 Ocean mass observations from GRACE and GRACE Follow-On =====

The ocean mass changes correspond to the sum of land ice and terrestrial water storage changes. Since 2002, the GRACE and GRACE follow-on missions provide direct estimates of the ocean mass changes and thus they provide an independent estimate of the sum of land ice and terrestrial water storage contributions to sea level. Since AR5, GRACE-based estimates of the ocean mass rates are increasingly consistent (WCRP Global Sea Level Budget Group, 2018 <sup>[[#fn:r194|194]]</sup> ) because of the extended length of GRACE missions’ observations (over 15 years), the improved understanding of data and methods for addressing GRACE limitations (e.g., noise filtering, leakage correction and low-degree spherical harmonics estimates), and the improved knowledge of geophysical corrections applied to GRACE data (e.g., GIA). The most recent estimates (Dieng et al., 2015b <sup>[[#fn:r195|195]]</sup> ; Reager et al., 2016 <sup>[[#fn:r196|196]]</sup> ; Rietbroek et al., 2016 <sup>[[#fn:r197|197]]</sup> ; Chambers et al., 2017 <sup>[[#fn:r198|198]]</sup> ; Blazquez et al., 2018 <sup>[[#fn:r199|199]]</sup> ; Uebbing et al., 2019 <sup>[[#fn:r200|200]]</sup> ) report a global ocean mass increase of 1.7 (1.4 – 2.0) mm yr <sup>–1</sup> over 2003–2015 (see also Table 4.1). The uncertainty arises essentially from differences in the inversion method to compute the ocean mass (Chen et al., 2013 <sup>[[#fn:r201|201]]</sup> ; Jensen et al., 2013 <sup>[[#fn:r202|202]]</sup> ; Johnson and Chambers, 2013 <sup>[[#fn:r204|204]]</sup> ; Rietbroek et al., 2016 <sup>[[#fn:r205|205]]</sup> ) , uncertainties in the geocentre motion and uncertainty in the GIA correction (Blazquez et al., 2018 <sup>[[#fn:r205|205]]</sup> ; Uebbing et al., 2019 <sup>[[#fn:r206|206]]</sup> ) . The consistency between estimates of the global mean ocean mass on a monthly time scale has also increased since AR5.

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<span id="glaciers-1"></span>
===== 4.2.2.2.3 Glaciers =====

To assess the mass contribution of glaciers to sea level change, global estimates are required. Recent updates and temporal extensions of estimates obtained by different methods continue to provide ''very high confidence'' in continuing glacier mass loss on the global scale during the past decade (Bamber et al., 2018 <sup>[[#fn:r207|207]]</sup> ; Wouters et al., 2019; Zemp et al., 2019 <sup>[[#fn:r208|208]]</sup> ), see Section 2.2.3 and Appendix 2.A for a detailed discussion also on regional scales). Updates of the reconstructions of Cogley (2009) <sup>[[#fn:r210|210]]</sup> , Leclercq et al. (2011) <sup>[[#fn:r211|211]]</sup> and Marzeion et al. (2012) <sup>[[#fn:r212|212]]</sup> , presented and compared in Marzeion et al. (2015) <sup>[[#fn:r212|212]]</sup> , show increased agreement on rates of mass loss during the entire 20th century (Marzeion et al., 2015 <sup>[[#fn:r213|213]]</sup> ), compared to earlier estimates reported by AR5. The contribution of glaciers that may be missing in inventories or have already melted during the 20th century is hard to constrain (Parkes and Marzeion, 2018 <sup>[[#fn:r215|215]]</sup> ), and there is ''low confidence'' in their estimated contribution. These glaciers are thus neglected in the assessment of the sea level budget (Table 4.1).

While the agreement between the observational estimates of glacier mass changes and the modelled estimates from glacier models forced with climate model simulations has increased since AR5 (Slangen et al., 2017b <sup>[[#fn:r216|216]]</sup> ), there is only ''medium'' ''confidence'' in the use of glacier models to reconstruct sea level change because of the limited number of well-observed glaciers available to evaluate models on long time scales, and because of the small number of model-based global glacier reconstructions.

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<span id="greenland-and-antarctic-ice-sheets"></span>
===== 4.2.2.2.4 Greenland and Antarctic ice sheets =====

Frequent observations of ice sheet mass changes have only been available since the advent of space observations (see Section 3.3.1). In the pre-satellite era, mass balance was geodetically reconstructed only for the GIS (Kjeldsen et al., 2015 <sup>[[#fn:r217|217]]</sup> ) . These geodetic reconstructions empirically constrain the contribution of the GIS to SLR between 1900 and 1983 to 17.2 (10.7 – 23.2; Kjeldsen et al., 2015 <sup>[[#fn:r218|218]]</sup> ) . During the satellite era, three approaches have been developed to estimate ice sheet mass balance: 1) Mass loss is estimated by direct measurements of ice sheet height changes with satellite laser or radar altimetry in combination with climatological/glaciological models for firn density and compaction, 2) the input–output method combines measurements of ice flow velocities estimated from satellite (synthetic aperture radar or optical imagery) across key outlets with estimates of net surface balance derived from ice thickness data, 3) space gravimetry data yields direct estimate of the mass changes by inversion of the anomalies in the gravity field (see Section 3.3.1 for more details). AR5 concluded that the three space-based methods give consistent results. They agree in showing that the rate of SLR due to the GIS and AIS’ contributions has increased since the early 1990s. Since AR5, up-to-date observations confirm this statement with increased confidence for both ice sheets (Rignot et al., 2019 <sup>[[#fn:r219|219]]</sup> ; see Section 3.3.1) . The assessment of the literature since AR5 made in Section 3.3.1 shows that the contribution from Greenland to SLR over 2012–2016 (0.68 (0.64 – 0.72) mm yr <sup>–1</sup> ) was similar to the contribution over 2002–2011 (0.73 (0.67 – 0.79) mm yr <sup>–1</sup> ) and ''extremely likely'' greater than over 1992–2001 (0.02 (0.21 – 0.25) mm yr <sup>–1</sup> ). The contribution from Antarctica over 2012–2016 (0.55 (0.48 – 0.62) mm yr <sup>–1</sup> ) was ''extremely likely'' greater than over the 2002–2011 period (0.23 (0.16 – 0.30) mm yr <sup>–1</sup> ) and ''likely'' greater than over the period 1992–2001 (0.14 (0.12 – 0.16); see Section 3.3.1 for more details).

Here, the approach of Section 3.3.1 is followed, using the two multi-method assessments from Bamber et al. (2018) <sup>[[#fn:r220|220]]</sup> and the IMBIE team (2018) to evaluate the contribution of ice sheet mass loss to SLR over 1993–2015 and 2006–2015 (see Table 4.1). These two studies agree with results from the WCRP Global Sea Level Budget Group (2018) . For the estimation of the AIS contribution, Bamber et al. (2018) <sup>[[#fn:r221|221]]</sup> and the The IMBIE team (2018) use similar but not identical data sources and processing. Both studies find consistent results within uncertainties over both periods. In Table 4.1, the results of these two studies were averaged, and weighted the average on the basis of their uncertainties, because there is no apparent reason to discount either study. For the estimation of the GIS contribution only the Bamber et al. (2018) <sup>[[#fn:r222|222]]</sup> estimate is used, as there is no other multi-method assessment available.

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<span id="contributions-from-water-storage-on-land"></span>
===== 4.2.2.2.5 Contributions from water storage on land =====

Water is stored on land not only in the form of ice but snow, surface water, soil moisture and groundwater. Temporal changes in land water storage, defined as all forms of water stored on land excluding land ice, contribute to observed changes in ocean mass and thus sea level on annual to centennial time scales (Döll et al., 2016 <sup>[[#fn:r225|225]]</sup> ; Reager et al., 2016 <sup>[[#fn:r226|226]]</sup> ; Hamlington et al., 2017 <sup>[[#fn:r227|227]]</sup> ; Wada et al., 2017 <sup>[[#fn:r228|228]]</sup> ) . They are caused by both climate variability and direct human interventions, at the multi-decadal to centennial time scales. Over the past century, the main cause for land water storage changes are the groundwater depletion and impoundment of water behind dams in reservoirs (Döll et al., 2016 <sup>[[#fn:r229|229]]</sup> ; Wada et al., 2016 <sup>[[#fn:r230|230]]</sup> ) . While the rate of groundwater depletion and thus its contribution to SLR increased during the 20th century and up to today (Wada et al., 2016 <sup>[[#fn:r231|231]]</sup> ) , its effect on sea level was more than balanced by the increase in land water storage due to dam construction between 1950 and 2000 (Wada et al., 2016 <sup>[[#fn:r232|232]]</sup> ) . Since about 2000, based on hydrological models, the combined effect of both processes is a positive contribution to SLR (Wada et al., 2016 <sup>[[#fn:r233|233]]</sup> ) . Decreased water storage in lakes, wetlands and soils due to human activities are less important for ocean mass changes (Wada et al., 2016 <sup>[[#fn:r234|234]]</sup> ) . Overall, the integrated effects of the direct human intervention on land hydrology have reduced land water storage during the last decade, increasing the rate of SLR by 0.15–0.24 mm yr <sup>–</sup> <sup>1</sup> (Wada et al., 2016 <sup>[[#fn:r235|235]]</sup> ; Wada et al., 2017 <sup>[[#fn:r236|236]]</sup> ; Scanlon et al., 2018 <sup>[[#fn:r237|237]]</sup> ; WCRP Global Sea Level Budget Group, 2018 <sup>[[#fn:r238|238]]</sup> ) . Over periods of a few decades, land water storage was affected significantly by climate variability (Dieng et al., 2015a <sup>[[#fn:r239|239]]</sup> ; Reager et al., 2016 <sup>[[#fn:r240|240]]</sup> ; Dieng et al., 2017 <sup>[[#fn:r241|241]]</sup> ) . Net land water storage change driven by both climate and direct human interventions can be determined based on GRACE observations and global hydrological modelling. They indicate different estimates of the rate of SLR. Over the period 2002–2014 GRACE-based estimates of the net land water storage (i.e., not including glaciers) show a negative contribution to sea level (e.g., Scanlon et al., 2018) resulting in the negative value after 2006 in Table 1while hydrological models determined a slightly positive one. The reasons for this difference between estimates are not elucidated. There is scientific consensus that uncertainties of both net land water storage contribution to sea level and its individual contributions remain high (WCRP Global Sea Level Budget Group, 2018 <sup>[[#fn:r242|242]]</sup> ) . The differences in estimates and the lack of multiple consistent studies give ''low confidence'' in the net land water storage contribution to current SLR.

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<span id="budget-of-global-mean-sea-level-change"></span>
===== 4.2.2.2.6 Budget of global mean sea level change =====

Drawing on previous sections, the budget of GMSL rise (Table 4.1, Figure 4.5) is assessed with observations over 4 periods: 1901–1990 (which corresponds to the period in the 20th century that is prior to the increase in ice sheet contributions to GMSL rise), 1970–2015 (when ocean observations are sufficiently accurate to estimate the global ocean thermal expansion and when glacier mass balance reconstructions start), 1993–2015 (when precise satellite altimetry is available) and 2006–2015 (when GRACE data is available in addition to satellite altimetry and when the Argo network reaches a near-global coverage). The budget of GMSL rise is also assessed with sea level contributions simulated by climate models over the same periods (Table 4.1, Figure 4.5). The periods 1993–2015 and 2006–2015 are only 23 and 10 years long respectively, short enough so that they can be affected by internal climate variability. Therefore, it is not expected that observations over these periods will be precisely reproduced by climate model historical experiments. For the contribution from land water storage, the estimated effect of direct human intervention was used, neglecting climate-related variations until 2002 (Ngo ‐ Duc et al., 2005) . From 2002 to 2015, total land water storage estimated with GRACE was used. In general, historical simulations of climate models end in 2005. Historical simulations were extended here to 2015 using the RCP8.5 scenario. This choice of RCP scenario is not critical for the simulated sea level, as the different scenarios only start to diverge significantly after the year 2030 (Church et al., 2013 <sup>[[#fn:r243|243]]</sup> ) .

For 1993–2015 and 2006–2015, the observed GMSL rise is consistent within uncertainties with the sum of the estimated observed contributions (Table 4.1). Over the period 1993–2015 the two largest terms are the ocean thermal expansion (accounting for 43% of the observed GMSL rise) and the glacier mass loss (accounting for a further 20%). Compared to AR5, the extended observations corrected for the TOPEX-A drift (see Section 4.2.2.1.2) allow us now to identify an acceleration in the observed SLR over 1993–2015 and to attribute this acceleration mainly to Greenland ice loss along with an acceleration in Antarctic ice loss (Velicogna et al., 2014 <sup>[[#fn:r244|244]]</sup> ; Harig and Simons, 2015 <sup>[[#fn:r245|245]]</sup> ; Chen et al., 2017 <sup>[[#fn:r246|246]]</sup> ; Dieng et al., 2017 <sup>[[#fn:r247|247]]</sup> ; Yi et al., 2017 <sup>[[#fn:r248|248]]</sup> ; see also Sections 4.2.2.2.2, 4.2.2.3.4, 3.3.1) . Since 2006, land ice, collectively from glaciers and the ice sheets has become the most important contributor to GMSL rise over the thermal expansion with mountain glaciers contributing 20% and ice sheets 33% (see Table 4.1) . Over the periods 1993–2015, the sum of the observed sea level contributions is consistent with the total observed sea level within uncertainties at monthly-scales (not shown, e.g., Dieng et al., 2017) . This is also true for the period 2006–2015, when uncertainties are significantly smaller. This agreement at monthly time scales represents a significant advance since the AR5 in physical understanding of the causes of past GMSL change. It provides an improved basis for the evaluation of models. Given these elements there is ''high confidence'' that the current observing system is capable of resolving decadal to multidecadal changes in GMSL and its components (with an uncertainty of &lt;0.7 mm yr <sup>–1</sup> at decadal and longer time scales, see Table 4.1 and for example, WCRP Global Sea Level Budget Group, 2018) . However, despite this advance since AR5 there are still no comprehensive observations of ocean thermal expansion below 2000 m, in regions covered by sea ice and in marginal seas. The understanding of glacier mass loss can be improved at regional scale and the understanding of the land water storage contribution is still limited. Thus, for smaller changes in sea level of the order of a few tenths of a mm yr <sup>–1</sup> at decadal time scales and shorter time scales there is ''medium confidence'' in the capability of the current observing system to resolve them (e.g., WCRP Global Sea Level Budget Group, 2018) .

Before 1992, observations are not sufficient to confidently estimate the ice sheet mass balance and before 1970, the space and time sampling of ocean observations are not sufficient to estimate the global ocean thermal expansion. For these reasons, it is difficult to assess the closure of the GMSL rise budget over 1901–1990 and 1970–2015 (Church et al., 2013 <sup>[[#fn:r249|249]]</sup> ; Gregory et al., 2013 <sup>[[#fn:r250|250]]</sup> ; Jevrejeva et al., 2017 <sup>[[#fn:r251|251]]</sup> ; Meyssignac et al., 2017c <sup>[[#fn:r252|252]]</sup> ; Slangen et al., 2017b <sup>[[#fn:r253|253]]</sup> ; Parkes and Marzeion, 2018 <sup>[[#fn:r254|254]]</sup> ) . For the period 1970–2015, the thermal expansion of the ocean represents 43% of the observed GMSL rise while the glaciers’ contribution represents 22% (see Table 4.1). This result indicates a slightly smaller contribution from glaciers than reported by AR5. If the GIS contribution and the Antarctic SMB is added, then the sum of the contributors to sea level is in agreement with the low end observed SLR estimates over 1970–2015 (Frederikse et al., 2018 <sup>[[#fn:r255|255]]</sup> ) . This result suggests that the contribution of Antarctica ice sheet dynamics to SLR has been small, if any, before the 1990s.

Since AR5, extended simulations along with recent findings in observations and improved model estimates allow for a new more robust, consistent and comprehensive comparison between sea level estimates based on observations and climate model simulations (e.g., Meyssignac et al., 2017c; Slangen et al., 2017b <sup>[[#fn:r256|256]]</sup> ; Parkes and Marzeion, 2018 <sup>[[#fn:r257|257]]</sup> ) . Compared to AR5, the simulated thermal expansion from climate models has improved with a new correction for the volcanic activity (see Section 4.2.2.2.1). The glacier contribution from glacier models forced with inputs from climate models is updated with a new glacier inventory and improvements to the glacier mass balance model (Marzeion et al., 2015 <sup>[[#fn:r258|258]]</sup> ) . The simulated Greenland SMB is estimated with a new regional SMB-component downscaling technique, which accounts for the regional variations in components of the Greenland SMB (Noël et al., 2015 <sup>[[#fn:r259|259]]</sup> ; Meyssignac et al., 2017a) <sup>[[#fn:r260|260]]</sup> . In addition, an updated groundwater extraction contribution from Döll et al. (2014) <sup>[[#fn:r261|261]]</sup> is now used for the land water storage contribution.

For the periods 1970–2015, 1993–2015 and 2006–2015 the simulated contributions from thermal expansion, glaciers mass loss and Greenland SMB explain respectively 84%, 81% and 77% of the observed GMSL (see Table 4.1). For all these periods the residual is consistent within uncertainty with the sum of the contribution from land water storage and ice discharge from Greenland and Antarctica. For each period the consistency is improved compared to AR5 (see Table 4.1) although the uncertainty on the residual is slightly larger because of a larger uncertainty in simulated Glaciers and Greenland SMB contributions.

For the period 1901–1990 the simulated contributions from thermal expansion, glaciers mass loss and Greenland SMB explain only 60% of the observed GMSL and the residual is too large to be explained by the sum of the contribution from land water storage and ice discharge from Greenland and Antarctica. The gap can be explained by a bias in the simulated Greenland SMB and glacier ice loss around Greenland in the early 20th century (Slangen et al., 2017b <sup>[[#fn:r262|262]]</sup> ) . When the glacier model and the Greenland SMB downscaling technique are forced with observed climate from atmospheric reanalyses, rather than the simulated climate from coupled climate models, simulated SLR becomes consistent with the observed SLR (see the dashed blue line on Figure 4.5). This is because atmospheric reanalyses show an increase in air temperatures in and around Greenland over the period 1900–1940, which lead to increased melt in Greenland (Bjørk et al., 2012 <sup>[[#fn:r267|267]]</sup> ; Fettweis et al., 2017 <sup>[[#fn:r268|268]]</sup> ) and surrounding glaciers in the first half of the 20th century. This increase in air temperature over 1900–1940 is not reproduced by climate models (Slangen et al., 2017b <sup>[[#fn:r269|269]]</sup> ) . It may be because this increase in air temperature was due to internal climate variability on temporal and spatial scales that cannot be precisely reproduced by climate models. It may also be due to a bias in atmospheric circulation in climate models (Fettweis et al., 2017 <sup>[[#fn:r270|270]]</sup> ) , or an issue with the spatial pattern of the historical aerosol forcing.

In summary, the agreement between climate model simulations and observations of the global thermal expansion, glacier mass loss and Greenland SMB has improved compared to AR5 for periods starting after 1970. However, for periods prior to 1970, significant discrepancies between climate models and observations arise from the inability of climate models to reproduce some observed regional changes in glacier and GIS SMB around the southern tip of Greenland. It is not clear whether this bias in climate models is due to the internal variability of the climate system or deficiencies in climate models. For this reason, there is still ''medium confidence'' in the ability of climate models to simulate past and future changes in glaciers mass loss and Greenland SMB.

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'''Figure 4.5'''

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'''Figure 4.5 | Comparison of simulated (by coupled climate models as in Section 4.4.2.6) and observed global mean sea level change (GMSL) since 1901 (a) and since 1993 (b). The average estimate of 12 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model simulations is shown in blue with the 5–95% uncertainty range shaded in […]'''
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[[File:a361928b5f4ba7db98666b4e46217e56 IPCC-SROCC-CH_4_5-3000x2599.jpg]]

Figure 4.5 | Comparison of simulated (by coupled climate models as in Section 4.4.2.6) and observed global mean sea level change (GMSL) since 1901 (a) and since 1993 (b). The average estimate of 12 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model simulations is shown in blue with the 5–95% uncertainty range shaded in blue and calculated according to the procedures in Church et al. (2013) <sup>[[#fn:r263|263]]</sup> . The average of the 12 model estimates corrected for the bias in glaciers mass loss and Greenland surface mass balance (SMB) over 1900–1940 (see Section 4.2.2.2.6) is shown in dashed blue. The estimates from tide gauge reconstructions is shown in other colours in panel a), with the 5–95% uncertainty range shaded in grey. The satellite altimetry observations from Legeais et al. (2018) <sup>[[#fn:r264|264]]</sup> is shown in black in panel b). GMSL from altimetry corrected for the TOPEX-A drift (Watson et al., 2015 <sup>[[#fn:r265|265]]</sup> ) in orange as well as the tide gauge reconstruction. The 5–95% uncertainty range is shaded in orange (Ablain et al., 2015 <sup>[[#fn:r266|266]]</sup> ). All curves in (a) represent anomalies in sea level with respect to the period 1986–2005 (i.e., with zero time-mean over the period 1986–2005) in order to be consistent with sea level projections in Section 4.2.3. Vertical lines indicate the occurrence of major volcanic eruptions, which cause temporary drops in GMSL. Updated from Slangen et al. (2017b).

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