Global Navigation Satellite System-Reflectometry (GNSS-R) measurements have demonstrated ability in high precision altimetry applications due to their potential for centimeter-level ranging precision, small spatial footprint sizes, and frequent revisit times. However, centimeter-level ranging precision is only achievable when the reflected signal can be coherently tracked, and coherent reflections more commonly occur when the incident ray is at a low elevation angle. A challenging factor is the tropospheric delay, which is significantly larger at low elevation angles, and becomes a dominant source of error in GNSS-R carrier-phase based altimetry. In this paper, we present a tropospheric error budget at low grazing angles specifically for GNSS-R applications using the Global Pressure and Temperature- 3 model. The error budget is created by comparing GPT3 to a truth reference over 328 days in 2019 at specific elevation angles of 45, 30, 20, 15, 10, 7, and 5 degrees. The tropospheric error budget was generated at the KUUJ station in Hudson Bay and the CPNM station in the Java Sea to account for performance differences between dry and humid climates, respectively. Overall, we find clear regional, seasonal, and elevation angle dependencies in the tropospheric error budget. At 45 degrees elevation, the monthly mean tropospheric error ranges from -6.8 cm to 5.4 cm at KUUJ, and -18.4 cm to 20 cm at CPNM. When the elevation decreases to 5 degrees, the tropospheric error range significantly increases to a range of -46.7 cm to 38.3 cm at KUUJ, and -143.3 cm to 146.3 cm at CPNM.

Present-day global mean sea level rise is caused by ocean thermal expansion, ice mass loss from glaciers and ice sheets, as well as changes in terrestrial water storage. For that reason, sea level is one of the best indicators of climate change as it integrates the response of several components of the climate system to internal and external forcing factors. Monitoring the global mean sea level allows detecting changes (e.g., in trend or acceleration) in one or more components. Besides, assessing closure of the sea level budget allows us to check whether observed sea level change is indeed explained by the sum of changes affecting each component. If not, this would reflect errors in some of the components or missing contributions not accounted for in the budget. Since the launch of TOPEX/Poseidon in 1992, a precise 27-year continuous record of sea level change is available. It has allowed major advances in our understanding of how the Earth is responding to climate change. The last two decades are also marked by the launch of the GRACE satellite gravity mission and the development of the Argo network of profiling floats. GRACE space gravimetry allows the monitoring of mass redistributions inside the Earth system, in particular land ice mass variations as well as changes in terrestrial water storage and in ocean mass, while Argo floats allow monitoring sea water thermal expansion due to the warming of the oceans. Together, satellite altimetry, space gravity, and Argo measurements provide unprecedented insight into the magnitude, spatial variability, and causes of present-day sea level change. With this observational network, we are now in a position to address many outstanding questions that are important to planning for future sea level rise. Here, we detail the network for observing sea level and its components, underscore the importance of these observations, and emphasize the need to maintain current systems, improve their sensors, and supplement the observational network where gaps in our knowledge remain.

Global mean sea level is an integral of changes occurring in the climate system in response to unforced climate variability as well as natural and anthropogenic forcing factors. Its temporal evolution allows changes (e.g., acceleration) to be detected in one or more components. Study of the sea-level budget provides constraints on missing or poorly known contributions, such as the unsurveyed deep ocean or the still uncertain land water component. In the context of the World Climate Research Programme Grand Challenge entitled Regional Sea Level and Coastal Impacts, an international effort involving the sea-level community worldwide has been recently initiated with the objective of assessing the various datasets used to estimate components of the sea-level budget during the altimetry era (1993 to present). These datasets are based on the combination of a broad range of space-based and in situ observations, model estimates, and algorithms. Evaluating their quality, quantifying uncertainties and identifying sources of discrepancies between component estimates is extremely useful for various applications in climate research. This effort involves several tens of scientists from about 50 research teams/institutions worldwide (www.wcrp-climate.org/grand-challenges/gc-sea-level, last access: 22 August 2018). The results presented in this paper are a synthesis of the first assessment performed during 2017–2018. We present estimates of the altimetry-based global mean sea level (average rate of 3.1 ± 0.3 mm yr−1 and acceleration of 0.1 mm yr−2 over 1993–present), as well as of the different components of the sea-level budget (http://doi.org/10.17882/54854, last access: 22 August 2018). We further examine closure of the sea-level budget, comparing the observed global mean sea level with the sum of components. Ocean thermal expansion, glaciers, Greenland and Antarctica contribute 42 %, 21 %, 15 % and 8 % to the global mean sea level over the 1993–present period. We also study the sea-level budget over 2005–present, using GRACE-based ocean mass estimates instead of the sum of individual mass components. Our results demonstrate that the global mean sea level can be closed to within 0.3 mm yr−1 (1σ). Substantial uncertainty remains for the land water storage component, as shown when examining individual mass contributions to sea level.

Determining how the global mean sea level (GMSL) evolves with time is of primary importance to understand one of the main consequences of global warming and its potential impact on populations living near coasts or in low-lying islands. Five groups are routinely providing satellite altimetry-based estimates of the GMSL over the altimetry era (since late 1992). Because each group developed its own approach to compute the GMSL time series, this leads to some differences in the GMSL interannual variability and linear trend. While over the whole high-precision altimetry time span (1993–2012), good agreement is noticed for the computed GMSL linear trend (of \(3.1\pm 0.4\) mm/year), on shorter time spans (e.g., \({<}10~\hbox {years}\)), trend differences are significantly larger than the 0.4 mm/year uncertainty. Here we investigate the sources of the trend differences, focusing on the averaging methods used to generate the GMSL. For that purpose, we consider outputs from two different groups: the Colorado University (CU) and Archiving, Validation and Interpretation of Satellite Oceanographic Data (AVISO) because associated processing of each group is largely representative of all other groups. For this investigation, we use the high-resolution MERCATOR ocean circulation model with data assimilation (version Glorys2-v1) and compute synthetic sea surface height (SSH) data by interpolating the model grids at the time and location of “true” along-track satellite altimetry measurements, focusing on the Jason-1 operating period (i.e., 2002–2009). These synthetic SSH data are then treated as “real” altimetry measurements, allowing us to test the different averaging methods used by the two processing groups for computing the GMSL: (1) averaging along-track altimetry data (as done by CU) or (2) gridding the along-track data into \(2^{\circ }\times 2^{\circ }\) meshes and then geographical averaging of the gridded data (as done by AVISO). We also investigate the effect of considering or not SSH data at shallow depths \(({<}120~\hbox {m})\) as well as the editing procedure. We find that the main difference comes from the averaging method with significant differences depending on latitude. In the tropics, the \(2^{\circ }\times 2^{\circ }\) gridding method used by AVISO overestimates by 11 % the GMSL trend. At high latitudes (above \(60^{\circ }\hbox {N}/\hbox {S}\)), both methods underestimate the GMSL trend. Our calculation shows that the CU method (along-track averaging) and AVISO gridding process underestimate the trend in high latitudes of the northern hemisphere by 0.9 and 1.2 mm/year, respectively. While we were able to attribute the AVISO trend overestimation in the tropics to grid cells with too few data, the cause of underestimation at high latitudes remains unclear and needs further investigation.

RESUME Recently, multiple ensemble climate simulations have been produced for the forthcoming Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). Nearly two dozen coupled oceanatmosphere models have contributed output for a variety of climate scenarios. One scenario, the climate of the 20th century experiment (20C3M), produces model output that can be compared to the long record of sea level provided by altimetry. Generally, the output from the 20C3M runs is used to initialize simulations of future climate scenarios. Hence, validation of the 20C3M experiment results is crucial to the goals of the IPCC. We present comparisons of global mean sea level (GMSL), global mean steric sea level change, and regional patterns of sea level change from these models to results from altimetry, tide gauge measurements, and reconstructions.