Editing IPCC:AR6/SROCC/Chapter-1 (section)

=== 1.8.1 Scientific Knowledge ===

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==== 1.8.1.1 Ocean and Cryosphere Observations  ====

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Long-term sustained observations are critical for detecting and understanding the processes of ocean and cryosphere change (Rhein et al., 2013 <sup>[[#fn:r392|392]]</sup> ; Vaughan et al., 2013 <sup>[[#fn:r393|393]]</sup> ). Scientific knowledge of the ocean and cryosphere has increased through time and geographical space (Figure 1.3). ''In situ'' ocean subsurface temperature and salinity observations have increased in spatial and temporal coverage since the middle of the 19th century (Abraham et al., 2013 <sup>[[#fn:r394|394]]</sup> ), and near global coverage (60°S–60°N) of the upper 2,000 m has been achieved since 2007 due to the international Argo network (Riser et al., 2016 <sup>[[#fn:r395|395]]</sup> ; Figure 1.3). Improved data quality and data analysis techniques have reduced uncertainties in global ocean heat uptake estimates (Sections 1.4.1, 5.2.2). In addition to providing deep ocean measurements, repeated hydrographic physical and biogeochemical observations since AR5 have led to improved estimates of ocean carbon uptake and ocean deoxygenation (Sections 1.4.1, 5.2.2.3, 5.2.2.4). Targeted observational programmes have improved scientific knowledge for specific regions and physical processes of particular concern in a warming climate, including the Greenland and West Antarctic ice sheets (Section 3.3), and the AMOC (Section 6.7). Ocean and cryosphere mass changes and sea level studies have benefited from sustained or newly implemented satellite-based remote sensing technologies, complemented by ''in situ'' data such as tide gauges measurements (Sections 3.3, 4.2; Dowell et al., 2013 <sup>[[#fn:r396|396]]</sup> ; Raup et al., 2015 <sup>[[#fn:r397|397]]</sup> ; PSMSL, 2016 <sup>[[#fn:r398|398]]</sup> ). Glacier length measurements in some locations go back many centuries (Figure 1.3), but it is the systematic high resolution satellite monitoring of a large number of the world’s glaciers since the late 1970s that has improved global assessments of glacier mass loss (Sections 2.2.3, 3.3.2).

L imitations in knowledge of ocean and cryosphere change remain, creating knowledge gaps for the SROCC assessment. Ocean and cryosphere datasets are frequently short, and do not always span the key IPCC assessment time intervals (Cross-Chapter Box 1 in Chapter 1), so for many parameters the full magnitude of changes since the pre-industrial period is not observed (Figure 1.3). The brevity of ocean and cryosphere measurements also means that some expected changes cannot yet be detected with confidence in direct observations (e.g., Antarctic sea ice loss in Section 3.2.1, AMOC weakening in Section 6.7.1), or other observed changes cannot yet be robustly attributed to anthropogenic factors (e.g., ice sheet mass loss in Section 3.3.1). Observations for many key ocean variables (Bojinski et al., 2014 <sup>[[#fn:r399|399]]</sup> ), such as ocean currents, surface heat fluxes, oxygen, inorganic carbon, subsurface salinity, phytoplankton biomass and diversity, etc., do not yet have global coverage or have not reached the required density or accuracy for detection of change. Some ocean and cryosphere areas remain difficult to observe systematically, for example, the ocean under sea ice, subsurface permafrost, high mountain areas, marginal seas, coastal areas (Section 4.2.2.3) and ocean boundary currents (Hu and Sprintall, 2016 <sup>[[#fn:r400|400]]</sup> ), basin interconnections (Section 6.6) and the Southern Ocean (Sections 3.2, 5.2.2). Measurements that reflect ecosystem change are often location or species specific, and assessments of long-term ocean ecosystem changes are currently only feasible for a limited subset of variables, for example coral reef health (e.g., coral reef health) (Section 5.3; Miloslavich et al., 2018 <sup>[[#fn:r401|401]]</sup> ). The deep ocean below 2,000 m is still rarely observed (Talley et al., 2016 <sup>[[#fn:r402|402]]</sup> ), limiting (for example) the accurate estimate of deep ocean heat uptake and, consequently the full magnitude of Earth’s energy imbalance (e.g., von Schuckmann et al., 2016 <sup>[[#fn:r403|403]]</sup> ; Johnson et al., 2018 <sup>[[#fn:r404|404]]</sup> ; Sections 1.2, 1.4, 5.2.2).

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==== 1.8.1.2 Reanalysis Products ====

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Advances have been made over the past decade in developing more reliable and more highly resolved ocean and atmosphere reanalysis products. Reanalysis products combine observational data with numerical models through data assimilation to produce physically consistent, and spatially complete ocean and climate products (Balmaseda et al., 2015 <sup>[[#fn:r404|404]]</sup> ; Lellouche et al., 2018 <sup>[[#fn:r405|405]]</sup> ; Storto et al., 2018 <sup>[[#fn:r406|406]]</sup> ; Zuo et al., 2018 <sup>[[#fn:r407|407]]</sup> ). Ocean reanalyses are widely used to understand changes in physical properties (Section 3.2.1, 5.2), extremes (Sections 6.3 to 6.6), circulation (Section 6.6, 6.7) and to provide climate diagnostics (Wunsch et al., 2009 <sup>[[#fn:r408|408]]</sup> ; Balmaseda et al., 2013 <sup>[[#fn:r409|409]]</sup> ; Hu and Sprintall, 2016 <sup>[[#fn:r410|410]]</sup> ; Carton et al., 2018 <sup>[[#fn:r411|411]]</sup> ). Reanalysis products are used in SROCC for assessing climate change process that cause changes in the ocean and cryosphere (e.g., Sections 2.2.1, 3.2.1, 3.3.1, 3.4.1, 5.2.2, 6.3.1, 6.6.1, 6.7.1). Improvements in reanalysis products provide more realistic forcing for regional models, which are used for assessing regional ocean and cryosphere changes that cannot be resolved in global-scale models (e.g., Section 2.2.1; Mazloff et al., 2010 <sup>[[#fn:r412|412]]</sup> ; Fenty et al., 2017 <sup>[[#fn:r413|413]]</sup> ). The weather forecasts, and seasonal to decadal predictions building on reanalysis products have important applications in the early warning systems that reduce risk and aid human adaptation to extreme events (Sections 6.3.4, 6.4.3, 6.5.3, 6.7.3, 6.8.5).

<div id="section-1-8-1-3model-simulation-data"></div>

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==== 1.8.1.3 Model Simulation Data ====

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Models are numerical approximations of the Earth system that allow hypotheses about the mechanisms of ocean and cryosphere change to be tested, support attribution of observed changes to specific forcings (Section 1.3), and are the best available information for assessing future change (Figure 1.3). General Circulation Models (GCMs) typically simulate the atmosphere, ocean, sea ice, and land surface, and sometimes also incorporate terrestrial and marine ecosystems. Earth System Models (ESM) are climate models that explicitly include the carbon cycle and may include additional components (e.g., atmospheric chemistry, ice sheets, dynamic vegetation, nitrogen cycle, but also urban or crop models). The systematic set of global-scale model experiments (Taylor et al., 2012 <sup>[[#fn:r414|414]]</sup> ) used in SROCC were produced by CMIP5 (Cross-Chapter Box 1 in Chapter 1), including both GCMs and ESMs.

Models may differ in their spatial resolution, and in the extent to which processes are explicitly represented or approximated (parameterised). Model output can be biased due to uncertainties in their physical equations or parameterisations, specification of initial conditions, knowledge of external forcing factors, and unaccounted processes and feedbacks (Hawkins and Sutton, 2009 <sup>[[#fn:r415|415]]</sup> ; Deser et al., 2012 <sup>[[#fn:r416|416]]</sup> ; Gupta et al., 2013 <sup>[[#fn:r417|417]]</sup> ; Lin et al., 2016 <sup>[[#fn:r418|418]]</sup> ). Since AR5 there have been advances in modelling the dynamical processes of the Greenland and Antarctica ice sheets, leading to better representation of the range of potential future sea level rise scenarios (Sections 4.2.3). Downscaling, including the use of regional models, makes it possible to improve the spatial resolution of model output in order to better resolve past and future climate change in specific areas, such as high mountains and coastal seas (e.g., Sections 2.2.2, 3.2.3, 3.5.4, 4.2.2, 6.3.1). For biological processes, such as nutrient levels and organic matter production, model uncertainty at regional scales is the main issue limiting confidence in future projections (Sections 5.3, 5.7). While model projections of range shifts for fishes agree with theory and observations, at a regional scale there are known deficiencies in the ways models represent the impacts of ocean variables such as temperature and productivity (Sections 5.2.3, 5.7).

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'''Figure 1.3 | Illustrative examples of the availability of ocean and cryosphere data relative to the major time periods assessed in the Special Report on the Oceans and Cryosphere in a Changing Climate (SROCC). Upper panel; observed (Keeling et al., 1976) and reconstructed (Bereiter et al., 2015) atmospheric CO2 concentrations, as well as the Representative […]'''
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Figure 1.3 | Illustrative examples of the availability of ocean and cryosphere data relative to the major time periods assessed in the Special Report on the Oceans and Cryosphere in a Changing Climate (SROCC). Upper panel; observed (Keeling et al., 1976 <sup>[[#fn:r425|425]]</sup> ) and reconstructed (Bereiter et al., 2015 <sup>[[#fn:r426|426]]</sup> ) atmospheric CO2 concentrations, as well as the Representative Concentration Pathways (RCPs) of CO2 for low (RCP2.6) and high (RCP8.5) future emission scenarios (van Vuuren et al., 2011a <sup>[[#fn:r427|427]]</sup> ; Cross-Chapter Box 1 in Chapter 1). Lower panel; illustrative examples of data availability for the ocean and cryosphere (Section 1.8.1; Taylor et al., 2012 <sup>[[#fn:r428|428]]</sup> ; Boyer et al., 2013 <sup>[[#fn:r429|429]]</sup> ; Dowell et al., 2013 <sup>[[#fn:r430|430]]</sup> ; McQuatters-Gollop et al., 2015 <sup>[[#fn:r431|431]]</sup> ; Raup et al., 2015 <sup>[[#fn:r432|432]]</sup> ; Olsen et al., 2016 <sup>[[#fn:r433|433]]</sup> ; PSMSL, 2016; PAGES2K Consortium, 2017 <sup>[[#fn:r434|434]]</sup> ; WGMS, 2017 <sup>[[#fn:r435|435]]</sup> ). The amount of data available through time is shown by the heights of the time series for observational data, palaeoclimate data and model simulations, expressed relative to the maximum annual data availability (maximum values given on plot; M = million, k = thousand). Spatial coverage of data across the globe or the relevant domain is shown by colour scale. See SM1.4 for further details.

| Illustrative examples of the availability of ocean and cryosphere data relative to the major time periods assessed in the Special Report on the Oceans and Cryosphere in a Changing Climate (SROCC). Upper panel; observed (Keeling et al., 1976) and reconstructed (Bereiter et al., 2015) atmospheric CO2 concentrations, as well as the Representative Concentration Pathways (RCPs) of CO2 for low (RCP2.6) and high (RCP8.5) future emission scenarios (van Vuuren et al., 2011a; Cross-Chapter Box 1 in Chapter 1). Lower panel; illustrative examples of data availability for the ocean and cryosphere (Section 1.8.1; Taylor et al., 2012; Boyer et al., 2013; Dowell et al., 2013; McQuatters-Gollop et al., 2015; Raup et al., 2015; Olsen et al., 2016; PSMSL, 2016; PAGES2K Consortium, 2017; WGMS, 2017). The amount of data available through time is shown by the heights of the time series for observational data, palaeoclimate data and model simulations, expressed relative to the maximum annual data availability (maximum values given on plot; M = million, k = thousand). Spatial coverage of data across the globe or the relevant domain is shown by colour scale. See SM1.4 for further details.

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==== 1.8.1.4 Palaeoclimate Data ====

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Palaeoclimate data provide a way to establish the nature of ocean and cryosphere changes prior to direct measurements (Figure 1.3), including natural variability and early anthropogenic climate change (Masson-Delmotte et al., 2013 <sup>[[#fn:r419|419]]</sup> ; Abram et al., 2016 <sup>[[#fn:r420|420]]</sup> ). Palaeoclimate records utilise the accumulation of physical, chemical or biological properties within natural archives that are related to climate at the time the archive formed. Commonly used palaeoclimate evidence for ocean and cryosphere change comes from marine and lake sediments, ice layers and bubbles, tree growth rings, past shorelines and shallow reef deposits. In many mountain areas, centuries to millennia of palaeoclimate information is now being lost through widespread melting of glacier ice (Cross-Chapter Box 6 in Chapter 2). Palaeoclimate data are spatially limited (Figure 1.3), but often represent regional to global-scale climate patterns, either individually or as syntheses of networks of data (PAGES2K Consortium, 2017 <sup>[[#fn:r421|421]]</sup> ) .

Palaeoclimate data provide evidence for multi-metre global sea level rises and shifts in climate zones and ocean ecosystems during past warm climate states where temperatures were similar to those expected later this century (Hansen et al., 2016 <sup>[[#fn:r422|422]]</sup> ; Fischer et al., 2018 <sup>[[#fn:r423|423]]</sup> ; Section 4.2.2). Palaeoclimate reconstructions give context to recent ocean and cryosphere changes that are unusual in the context of variability over past centuries to millennia, including acceleration in Greenland and Antarctic Peninsula ice-melt (Section 3.3.1), declining Arctic sea ice (Section 3.2.1), and emerging evidence for a slowdown of AMOC (Section 6.7.1). Assessments of climate model performance across a wider-range of climate states than is possible using direct observations alone also draws on palaeoclimate data (Flato et al., 2013 <sup>[[#fn:r424|424]]</sup> ), and since AR5 important progress has been made to calibrate modelled ice sheet processes and future sea level rise based on palaeoclimate evidence (Cross-Chapter Box 8 in Chapter 3).

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