Abstract
Abstract Global 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.
Highlights
IntroductionThe practice of evaluating GMSL budget is to quantify its geophysical contributors, i.e. budget components from various sources, and to calculate the misclosure of the budget, i.e. its deviation from zero during a period for which all time series data are available
Better understanding and improvement of current global sea level budget with a consistent set of estimates for its components is mandatory to accurately project/predict global sea level rise
Global 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
Summary
The practice of evaluating GMSL budget is to quantify its geophysical contributors, i.e. budget components from various sources, and to calculate the misclosure of the budget, i.e. its deviation from zero during a period for which all time series data are available. A recent study by Iz and Shum (2019) proposed and demonstrated adjusting the reported linear trends of the budget components and their uncertainties during the period of 2005 – 2015 under GMSL budget closure. This approach readjusts already estimated linear trends of budget components under GMSL budget closure constraint. The yearly averaged time series are ing the closure of the linear trends of the budget compo- expressed in mm of equivalent sea level during this period. The GMSL time series are from multi-mission satellite for GMSL budget closure as compared to the previous two altimetry; ensemble mean of 6 di erent sea level products practices. Pertinent references for the above time series can be found in WCRP, (2019)
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