1 Detecting Regional Deep Ocean Warming Below 2000M Based on Altimetry

1 1 Detecting regional deep ocean warming below 2000m based on altimetry, 2 GRACE, Argo, and CTD data 3 Yuanyuan YANG1,2, Min ZHONG1,2,3, Wei FENG*1,3 Dapeng MU4 4 1 State Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodesy and 5 Geophysics, Innovation Academy for Precision Measurement Science and 6 Technology, Chinese Academy of Sciences, Wuhan 430077, China. 7 2 University of the Chinese Academy of Sciences, Beijing 100049, China. 8 3 School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 9 519082, China. 10 4Institute of Space Sciences, Shandong University, Weihai 264209, China. 11 ABSTRACT 12 The deep ocean below 2000m is a large water body with the sparsest data coverage, 13 challenging the closure of sea level budget and estimate of the Earth’s energy 14 imbalance. Whether the deep ocean below 2000m is warming globally has been debated 15 in the recent decade. However, as the regional signals are generally larger than global 16 average, it is intriguingin to investigate press the regional temperature changes. Here we adopt 17 an indirect method that combines altimetry, GRACE, and Argo data to examine the 18 global and regional deep ocean temperature changes below 2000m. The consistency 19 between high quality conductivity-temperature-depth (CTD) data from repeated *Corresponding author: Wei FENG Email: [email protected]) 2 20 hydrographic sections and our results confirms the validity of the indirect method. We 21 find that the deep oceans are warming in the Middle East Indian Ocean, subtropical 22 North and Southwest Pacific, and Northeast Atlantic, but cooling in the Northwest 23 Atlantic and Southern oceans from 2005 to 2015. 24 Key words: Deep ocean warming, GRACE, Argo, GO-SHIP, Altimetry 25 http://doi.org/10.1007/s00376-021-1049-3 26 Article Highlights: 27 An indirect method is used to estimate global and regional deep ocean temperature 28 changes below 2000m 29 Repeated hydrographic sections confirm the effectiveness of the indirect method 30 to detect potential deep ocean changes. 31 The deep ocean changes are inhomogeneous and contain robust warming and 32 cooling signals at different locations. 33 in press 34 1. Introduction 35 Ocean absorbs more than 90% of heat excess accumulated in the climate system 36 caused by greenhouse gases since 1970s and contributes about 40% to global sea level 37 rise since 1990s (IPCC, 2013). Thus ocean heat content (OHC) change and steric sea 3 38 level change are vital climate metrics. Recent estimates of OHC and associated steric 39 sea level rise based on observational records and models indicate that ocean warming 40 is significant in the past half century and the warming is accelerating in recent decades 41 (Durack et al., 2018; Cheng et al., 2019; Cheng et al., 2020). However, these estimates 42 are limited to the upper 2000m of the ocean, mainly due to the lack of sufficient 43 temperature observations in the deep ocean below 2000m. Whether the deep ocean 44 below 2000m is warming has been a great controversy (Meyssignac et al., 2019). The 45 deep ocean mentioned in this study refers to the ocean below 2000m, exceeding the 46 current maximum sampling range of the Argo floats. 47 Both the international World Ocean Circulation Experiment (WOCE) 48 Hydrographic Programme and Global Ocean Ship-based Hydrographic Investigations 49 Program (GO-SHIP) have offered the direct and high-quality measurements (including 50 temperature, salinity etc.) to monitor the deep ocean changes based on repeated 51 hydrographic sections (Desbruyeres et al., 2016; Desbruyères et al., 2016). However, it 52 is still a challenge to give a time series for the deep ocean changes, instead, only a long- 53 term trend estimatein is provided press (e.g., Purkey and Johnson, 2010; Desbruyères et al., 54 2014; Talley et al., 2015). In addition to the in situ observations, there are many model 55 or reanalysis products available, but these data suffer from model bias or strength of 56 the observation constraint, therefore, how does the deep ocean change is still debatable 57 (Song and Colberg, 2011; Palmer et al., 2017; Garry et al., 2019). 4 58 At the beginning of this century, the emergence of Gravity Recovery and Climate 59 Experiment (GRACE) satellites, altimetry satellites, along with in situ Argo floats has 60 provided a completely new perspective to study the deep ocean warming (Purkey et al., 61 2014; Volkov et al., 2017). Theoretically, one can deduct the barystatic sea-level 62 change (GRACE-derived) and steric sea-level change of the upper 2000m depth (Argo- 63 derived) from the total sea-level change (altimeter-derived), and estimate the steric 64 contribution from the deep ocean, which is an affirmative indicator of ocean warming 65 (Llovel et al., 2014; Chen et al., 2018; Asbjørnsen et al., 2019; Chang et al., 2019; 66 Royston et al., 2020). Previous studies found that the global sea-level budget (SLB) 67 based on altimetry, GRACE, and Argo (0-2000m) appears to be closed within 68 uncertainty over various time scales, and the remaining part (the deep ocean under 2000 69 m) contributes almost zero with great uncertainty (Llovel et al., 2014; Dieng et al., 70 2015; Kleinherenbrink et al., 2016; Volkov et al., 2017; WCRP, 2018; Frederikse et al., 71 2020; Royston et al., 2020). This indicates that the deep ocean changes below 2000m 72 are currently still too small to be detectable from data uncertainty with current Earth 73 Observation System.in press 74 However, regional changes are always larger than global averages, where the 75 signals are cancelled out, it is desirable to estimate regional changes based on altimetry, 76 GRACE, and Argo data. In this study, we estimate the regional pattern of deep steric 77 sea-level (DSSL) change from 2005 to 2015 by using an indirect method with multiple 78 datasets of altimetry, GRACE, and Argo, i.e. “Alt.–Argo–GRACE”. And then we 5 79 assume that the DSSL changes are mainly due to temperature changes rather than 80 salinity. Consequently, the pattern of global temperature in the deep ocean can be 81 obtained. This pattern has great interest to the community as it highlights the “hot spots” 82 or “cold spots”, which are important to the deep ocean heat uptake. This regional pattern 83 could also guide the deployment of Deep-Argo floats. 84 As indicated before, our study relies on a basic assumption that the DSSL change 85 is dominated by the temperature change. This assumption is valid in most of the global 86 regions except for the birthplace of deep-water mass, such as the subpolar region of 87 North Atlantic and Southern Oceans (Stammer et al., 2013). Therefore, the location 88 where the DSSL changes drastically is probably a good indicator of the deep ocean 89 temperature changes. To test this assumption, we have compared our results with direct 90 hydrographical measurements from GO-SHIP and such a comparison supported this 91 assumption. 92 In addition, recent investigations found a significant signal of long-term 93 accelerated deep-ocean warming in the subtropical South Pacific Basin based on GO- 94 SHIP and pilot Deep-Argoin floats press from 2005 to 2018 (Johnson and Doney, 2006; Volkov 95 et al., 2017; Johnson et al., 2019; Purkey et al., 2019), which is also confirmed by our 96 indirect method. 97 The paper is structured as follows. Section 2 introduces the data and methodology 98 used in this study. Section 3 presents the spatial-temporal variability of the deep ocean 6 99 steric sea-level change and the results of verification. Section 4 shows the main 100 discussion and summary. 101 2.Data and Methodology 102 To derive the deep ocean steric sea level changes, estimate their uncertainty, and 103 verify the results, four groups of datasets are used: satellite altimetry, satellite gravity, 104 Argo, and GO-SHIP CTD data (the detailed information is provided in Table 1 in the 105 supporting information). 106 Three monthly sea surface height (SSH) grid products derived from a series of 107 satellite altimeter missions are released by the Archiving, Validation, and Interpretation 108 of Satellite Oceanographic(AVISO), the Copernicus Marine Environment Monitoring 109 Service (CMEMS) and the Commonwealth Scientific and Industrial Research 110 Organisation (CSIRO) covering the period from 1993 to present. We remove glacial 111 isostatic adjustment (GIA) effect correction on long-term ocean bottom deformation 112 (Roy and Peltier, 2015,2017) and elastic loading ocean bottom deformation (OBD) 113 correction using GRACE data (García-García et al., 2006; Vishwakarma et al., 2020) 114 before joint calculation.in press 115 Ten monthly temperature and salinity datasets used to calculate steric sea-level 116 change from the sea surface to a depth of 2000 m (SSL2000 ) (Jayne et al., 2003) are 117 retrieved from the following institutions: the Second Institute of Oceanography, 118 Ministry of Natural Resources, China (BOA), the Coriolis Ocean database for 119 ReAnalysis (CORA), France, the UK Met Office (EN4), the Institute of Atmospheric 7 120 Physics, Chinese Academy of Sciences, China (IAP), the International Pacific Research 121 Center (IPRC) at the University of Hawaii, USA, the Laboratory for Ocean Physics and 122 Satellite remote sensing (ISAS15), France, the Japan Agency for Marine-Earth Science 123 and Technology (JAMSTEC), the National Centers for Environmental Information 124 (NCEI), USA, and the Scripps Institution of Oceanography (SIO) at the University of 125 California San Diego, USA. These datasets are mainly derived from Argo floats that 126 have been deployed since 1999 and achieved near global coverage since 2005 (Jayne 127 et al., 2017). Some of these products also merge Argo data with other instrumental data 128 such as CTD, Mooring, XBTs, Glider etc., hereafter, referred to simply as Argo.

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