StISSM (Stochastic Ice Sheet and Sea Level System Model) is a stochastic implementation of ISSM that integrates uncertainty quantification methods into ice sheet and sea level simulations. Historical background : StISSM was developed by the StISSM team to provide uncertainty quantification capabilities for ice sheet and sea level predictions. The development of this model responds to the growing concern about the uncertainty of ice sheet predictions, particularly in assessing the impacts of climate change. Technical features : • Integrate uncertainty quantification framework to provide probability prediction • Implement Monte Carlo sampling and ensemble prediction techniques • Provide probability distribution and confidence interval for ice sheet prediction • Multi scenario probability prediction for sea level rise • Analyze the uncertainty propagation of climate forcing • Seamless integration with ISSM, sharing core functions Core functions : • Quantification of uncertainty in ice sheet prediction, including uncertainty in parameters and initial conditions • Multiple scenario probability prediction for sea level rise • Analysis of climate forcing uncertainty propagation • Sensitivity Study of Ice Sheet Model Parameters • Identify key sources of uncertainty • Evaluate the probability distribution of ice sheet response under different climate scenarios Application case : • Uncertainty assessment of Greenland ice sheet mass balance prediction • Probability analysis of Antarctic ice sheet instability • Multiple scenario probability prediction for sea level rise • Sensitivity analysis of ice sheet model parameters • Assessment of the impact of climate forcing uncertainty on ice sheet predictions • Risk analysis of ice sheet sea level system Limitations : • High computational cost, requiring a large amount of Monte Carlo simulation • High demand for computing resources and high-performance computing facilities • The quantification of uncertainty results depends on the choice of input probability distribution • Coupling with certain climate models requires additional development • The learning curve is steep and requires familiarity with probability methods and ISSM Input parameters : • Geometric and physical parameters of ice sheet (with uncertainty distribution) • Climate forcing data (with uncertainty range) • Monte Carlo sampling parameters (sample size, sampling method, etc.) • Prior distribution of parameters for ice sheet models • Uncertainty range of boundary conditions and initial conditions Output result : • Probability distribution and confidence interval of ice sheet prediction • Multi scenario probability prediction of sea level rise • Sensitivity analysis results of model parameters • Identification of key sources of uncertainty • Probability response under different climate scenarios