Steric sea-level change estimates from 2005-2015 and 1993-2017

We collected and analyzed 15 publicly available gridded datasets of monthly ocean temperature and salinity published by different research groups worldwide (article, Table 1). The datasets were divided according to their data type: Argo – for datasets that have only data from Argo floats; Multiple in-situ (MiS) – for products that combine several sources of in-situ observations, in addition to Argo data; ocean reanalysis (REA). From the T,S datasets, we computed steric sea-level anomalies (SLA) using the TE0-10 as the equation of state. First, the steric SLA was computed in the native resolution of each dataset. Afterwards, we standardized the varying resolution by remapping all datasets to a 1˚ by 1˚ grid. Next, we selected the data within 66˚N to 66˚S of latitude, and applied a land mask based on ETOPO1 (Amante and Eakins, 2009). Next, we computed a mean dataset for each of the three categories (Argo, MiS, REA) and a total ensemble mean, creating four new steric SLA datasets. Using an area-weighted mean, we computed a global mean steric SLA for each dataset. The trends and respective uncertainties were estimated using the Hector software (Bos et al., 2013). We used 8 different noise-models to obtain the trends: WN, PL, PLWN, GGM, AR(1), AR(5), AR(9), ARFIMA.<br>v3: Added new file with the preferred trend and uncertainty (output of analysis shown in Figure 7 and Figure 8 of paper)

本研究收集并分析了全球不同研究团队公开发布的15套网格化逐月海洋温度与盐度数据集（详见论文表1）。本研究按数据类型将这些数据集划分为三类：仅包含Argo浮标（Argo floats）观测数据的Argo类数据集；除Argo数据外还融合了多类原位观测（in-situ observations）资料的多源原位（Multiple in-situ, MiS）数据集；以及海洋再分析（ocean reanalysis, REA）数据集。基于上述温度-盐度（T,S）数据集，本研究以TE0-10作为状态方程，计算得到比容海平面异常（steric sea-level anomalies, SLA）。首先，我们在各数据集的原始分辨率下计算比容海平面异常；随后，为统一各数据集的差异化分辨率，将所有数据重映射至1°×1°的网格。接下来，我们筛选出纬度范围介于66°N至66°S之间的数据，并基于ETOPO1地形数据（Amante与Eakins, 2009）应用陆地掩膜。随后，我们分别对Argo、MiS、REA三类数据集计算均值，并计算整体集合均值，最终生成4套新的比容海平面异常数据集。通过面积加权平均法，我们为每套数据集计算得到全球平均比容海平面异常。利用Hector软件（Bos等人, 2013），我们估算了各数据集的趋势及其对应不确定性。本研究采用8种不同的噪声模型进行趋势估算，分别为：WN、PL、PLWN、GGM、AR(1)、AR(5)、AR(9)及ARFIMA。v3版本：新增了包含最优趋势与不确定性分析结果的文件（分析结果详见论文图7与图8）。