On the “Cal-Mode” Correction to TOPEX Satellite Altimetry and Its Effect on the Global Mean Sea Level Time Series

Studies of the global sea-level budget (SLB) and ocean-mass budget (OMB) are essential to assess the reliability of our knowledge of sea-level change and its contributors. The SLB is considered closed if the observed sea-level change agrees with the sum of independently assessed steric and mass contributions. The OMB is considered closed if the observed ocean-mass change is compatible with the sum of assessed mass contributions.&#160;Here we present results from the Sea-Level Budget Closure (SLBC_cci) project conducted in the framework of ESA&#8217;s Climate Change Initiative (CCI). We used data products from CCI projects as well as newly-developed products based on CCI products and on additional data sources. Our focus on products developed in the same framework allowed us to exercise a consistent uncertainty characterisation and its propagation to the budget closure analyses, where the SLB and the OMB are assessed simultaneously.&#160;We present time series of global mean sea-level changes from satellite altimetry; new time series of the global mean steric component generated from Argo drifter data with incorporation of sea surface temperature data; time series of ocean-mass change derived from GRACE satellite gravimetry; time series of global glacier mass change from a global glacier model; time series of mass changes of the Greenland Ice Sheet and the Antarctic Ice Sheet both from satellite radar altimetry and from GRACE; as well as time series of land water storage change from the WaterGAP global hydrological model. Our budget analyses address the periods 1993&#8211;2016 (covered by the satellite altimetry records) and 2003&#8211;2016 (covered by GRACE and the Argo drifter system). In terms of the mean rates of change (linear trends), the SLB is closed within uncertainties for both periods, and the OMB, assessable for 2003&#8211;2016 only, is also closed within uncertainties. Uncertainties (1-sigma) arising from the combined uncertainties of the elements of the different budgets considered are between 0.26 mm/yr and 0.40 mm/yr, that is, on the order of 10% of the magnitude of global mean sea-level rise, which is 3.05 &#177; 0.24 mm/yr and 3.65 &#177; 0.26 mm/yr for 1993-2016 and 2003-2016, respectively. We also assessed the budgets on a monthly time series basis. The statistics of monthly misclosure agrees with the combined uncertainties of the budget elements, which amount to typically 2-3 mm for the 2003&#8211;2016 period. We discuss possible origins of the residual misclosure.

Download Full-text

Assessment of the Jason-2 Extension to the TOPEX/Poseidon, Jason-1 Sea-Surface Height Time Series for Global Mean Sea Level Monitoring

Marine Geodesy ◽

10.1080/01490419.2010.491029 ◽

2010 ◽

Vol 33 (sup1) ◽

pp. 447-471 ◽

Cited By ~ 53

Author(s):

B. D. Beckley ◽

N. P. Zelensky ◽

S. A. Holmes ◽

F. G. Lemoine ◽

R. D. Ray ◽

...

Keyword(s):

Time Series ◽

Sea Level ◽

Sea Surface Height ◽

Sea Surface ◽

Mean Sea Level ◽

Global Mean Sea Level ◽

Global Mean ◽

Level Monitoring

Download Full-text

Year by year closure adjustment of global mean sea level budget, inclusive of lumped snow, water vapor, and permafrost mass components

Journal of Geodetic Science ◽

10.1515/jogs-2020-0109 ◽

2020 ◽

Vol 10 (1) ◽

pp. 83-90

Author(s):

H. Bâki Iz ◽

C. K. Shum

Keyword(s):

Water Vapor ◽

Sea Level ◽

Satellite Altimetry ◽

Linear Trend ◽

The Other ◽

Mean Sea Level ◽

Lump Sum ◽

Snow Water ◽

Global Mean Sea Level ◽

Global Mean

AbstractGlobal mean sea level budget is rigorously adjusted during the period 2005–2015 with an emphasis on closing the budget on a year by year basis as opposed to using linear trends of global mean sea level components. The adjustment also accounts for the effect of snow, water vapor, and permafrost mass components as a lump sum. The approach provides better resolution for evaluating individual contribution of each budget component year by year in tandem with the other components. Year by year budget misclosures and the confidence intervals of the year by year adjusted budget components are suggestive of an increasing non-linearity in satellite altimetry derived global mean sea level measurements starting in 2012, which are not present in the other components. The solution also generates time series iteratively for the lumped snow, water vapor, and permafrost mass components as well as an estimate for its linear trend, 0.06±0.59 mm/yr. Nonetheless, its standard error is markedly large because of the un-modeled variability in satellite altimetry observed yearly averaged global mean sea level anomalies.

Download Full-text

The certitude of a global sea level acceleration during the satellite altimeter era

Journal of Geodetic Science ◽

10.1515/jogs-2020-0101 ◽

2020 ◽

Vol 10 (1) ◽

pp. 29-40

Author(s):

H. Bâki İz ◽

C.K. Shum

Keyword(s):

Sea Level ◽

Satellite Altimetry ◽

Mean Sea Level ◽

Sea Level Acceleration ◽

Kinematic Models ◽

Sea Level Variations ◽

Global Mean Sea Level ◽

Global Sea Level ◽

Omission Errors ◽

Global Mean

AbstractRecent studies reported a uniform global sea level acceleration during the satellite altimetry era (1993–2017) by analyzing globally averaged satellite altimetry measurements. Here, we discuss potential omission errors that were not thoroughly addressed in detecting and estimating the reported global sea level acceleration in these studies. Our analyses results demonstrate that the declared acceleration in recent studies can also be explained equally well by alternative kinematic models based on previously well-established multi-decadal global mean sea level variations of various origins, which suggests prudence before declaring the presence of an accelerating global mean sea level with confidence during the satellite altimetry era.

Download Full-text

An approach to study on time series components and by using them to enumerate the height of sea level alteration for both Global Mean Sea Level(GMSL) and Bay of Bengal(BOB)

2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) ◽

10.1109/icecct.2019.8869397 ◽

2019 ◽

Author(s):

Anika Tabassum ◽

Masud Rabbani ◽

Saad Bin Omar

Keyword(s):

Time Series ◽

Sea Level ◽

Bay Of Bengal ◽

Mean Sea Level ◽

Global Mean Sea Level ◽

Global Mean

Download Full-text

Kinematics of global mean thermosteric sea level during 1993–2019

Journal of Geodetic Science ◽

10.1515/jogs-2020-0121 ◽

2021 ◽

Vol 11 (1) ◽

pp. 75-82

Author(s):

H. Bâki İz

Keyword(s):

Time Series ◽

Sea Level ◽

Temperature Gradients ◽

Sea Surface ◽

Sea Level Changes ◽

Mean Sea Level ◽

Mean Sea Surface ◽

Global Mean Sea Level ◽

Global Sea Level ◽

Global Mean

Abstract Because oceans cover 71% of Earth’s surface, ocean warming, consequential for thermal expansion of sea water, has been the largest contributor to the global mean sea level rise averaged over the 20 th and the early 21 st century. This study first generates quasi-observed monthly globally averaged thermosteric sea level time series by removing the contributions of global mean sea level budget components, namely, Glaciers, Greenland, Antarctica, and Terrestrial Water Storage from satellite altimetry measured global sea level changes during 1993–2019. A baseline kinematic model with global mean thermosteric sea level trend and a uniform acceleration is solved to evaluate the performance of a rigorous mixed kinematic model. The model also includes coefficients of monthly lagged 60 yearlong cumulative global mean sea surface temperature gradients and control variables of lunisolar origins and representations for first order autoregressive disturbances. The mixed kinematic model explains 94% (Adjusted R 2)1 of the total variability in quasi-observed monthly and globally averaged thermosteric time series compared to the 46% of the baseline kinematic model’s Adjusted R 2. The estimated trend, 1.19±0.03 mm/yr., is attributed to the long-term ocean warming. Whereas eleven statistically significant (α = 0.05) monthly lagged cumulative global mean sea surface temperature gradients each having a memory of 60 years explain the remainder transient global mean thermosteric sea level changes due to the episodic ocean surface warming and cooling during this period. The series also exhibit signatures of a statistically significant contingent uniform global sea level acceleration and periodic lunisolar forcings.

Download Full-text

Comparison of Global Mean Sea Level Time Series from TOPEX/Poseidon, Jason-1, and Jason-2

Marine Geodesy ◽

10.1080/01490419.2012.717862 ◽

2012 ◽

Vol 35 (sup1) ◽

pp. 20-41 ◽

Cited By ~ 50

Author(s):

D. Masters ◽

R. S. Nerem ◽

C. Choe ◽

E. Leuliette ◽

B. Beckley ◽

...

Keyword(s):

Time Series ◽

Sea Level ◽

Mean Sea Level ◽

Global Mean Sea Level ◽

Global Mean

Download Full-text

Uncertainty in Satellite estimate of Global Mean Sea Level changes, trend and acceleration

10.5194/essd-2019-10 ◽

2019 ◽

Cited By ~ 5

Author(s):

Michaël Ablain ◽

Benoit Meyssignac ◽

Lionel Zawadzki ◽

Rémi Jugier ◽

Aurélien Ribes ◽

...

Keyword(s):

Sea Level ◽

Covariance Matrix ◽

Confidence Level ◽

Satellite Altimetry ◽

Error Variance ◽

Anthropogenic Activity ◽

Mean Sea Level ◽

Global Mean Sea Level ◽

Global Mean ◽

Variance Covariance Matrix

Abstract. Satellite altimetry missions now provide more than 25 years of accurate, continuous and quasi-global measurements of sea level along the reference ground track of TOPEX/Poseidon. These measurements are used by different groups to build the Global Mean Sea Level (GMSL) record, an essential climate change indicator. Estimating a realistic uncertainty of the GMSL record is of crucial importance for climate studies such as estimating precisely the current rate and acceleration of sea level, analyzing the closure of the sea level budget, understanding the causes of sea level rise, detecting and attributing the response of sea level to anthropogenic activity, or estimating the Earth energy imbalance. (Ablain et al., 2015) estimated the uncertainty of the GMSL trend over the period 1993–2014 by thoroughly analyzing the error budget of the satellite altimeters and showed that it amounts to ±0.5 mm/yr (90 % confidence level). In this study, we extend (Ablain et al., 2015) analysis by providing a comprehensive description of the uncertainties in the satellite GMSL record. We analyse 25 years of satellite altimetry data and estimate for the first time the error variance-covariance matrix for the GMSL record with a time resolution of 10 days. Three types of errors are modelled (drifts, biases, noise) and combined together to derive a realistic estimate of the GMSL error variance-covariance matrix. From the error variance-covariance matrix we derive a 90 % confidence envelop of the GMSL record on a 10-day basis. Then we use a least square approach and the error variance-covariance matrix to estimate the GMSL trend and acceleration uncertainties over any time periods of 5 years and longer in between October 1992 and December 2017. Over 1993–2017 we find a GMSL trend of 3.35 ± 0.4 mm/yr within a 90 % Confidence Level (CL) and a GMSL acceleration of 0.12 ± 0.07 mm/yr2 (90 % CL). This is in agreement (within error bars) with previous studies. The full GMSL error variance-covariance matrix is freely available online: https://doi.org/10.17882/58344 (Ablain et al., 2018).

Download Full-text