DOI: 10.17603/ds2-0rdq-9531
title: SSM projection results
title: in From Future Learning to Current Action: Long-Term Sequential Infrastructure Planning under Uncertainty

description: 1. The State Space Model (SSM) is used as a simplified surrogate model to assess future climate change uncertainty using several key physical parameters. The SSM parametric form is based on common two-layer Earth energy balance equations, with the modeling of surface air and deep ocean temperature. The SSM includes a state transition function which models the latent variables (in this case latent surface temperature and deep ocean temperature); and a measurement function which models the measurements of surface temperature and deep ocean temperature. The SSM parametric form integrates six different physical parameters, i.e., ECS, C1, C2, Beta, Gamma1, Gamma2; and as well as four parameters for the noise terms of latent and measurement variables of surface and deep ocean temperature. 

2. The methodology follows a common Bayesian inference approach. The GCM simulations of global average temperature are used as the input of the SSM (along with provided SSP forcing time series) to obtain parameter posteriors unique to each GCM. These parameter posteriors represent the information obtained from the GCMs. Subsequently, these parameter posteriors were processed and combined as GCM-informed priors to further process observations. 
 
3. Both pseudo observations and historical observations are used to obtain parameter posteriors and to obtain final future projections of global average temperature. Different amounts of observations are assumed and used, leading to analysis results on ECS and TCR posterior distributions based on different amounts of historical observations and as well as projection results for the pseudo observation series when different amounts of such observations are used. Projections from using historical observations up to 2022 are also provided.
description: This is the data repository for the "Sequential Learning of Climate Change via a Physical-parameter-based State-space Model and Bayesian Inference". Specifically, the datasets provided include the input files, simulations, and analysis results from the developed state space model (SSM). Additional details can be found at: Lai, Y. and M. Pozzi, (2023). Under Review. Sequential Learning of Climate Change via a Physical-parameter-based State-space Model and Bayesian Inference. 
 
Engineering decision-making for climate adaptation is subject to challenges related to future climate change uncertainty. An integrated, physical-parameter-based State-space model (SSM) is developed as a surrogate model to assess future climate change uncertainty to facilitate engineering flexible and sequential decision-making schemes. This SSM aims to provide updated and improved future projections of climate change based on additional observations that will become available in the future, therefore reducing future uncertainty and facilitating engineering applications. 
 
The SSM is a time series, statistical model that is implemented with physical parameters to describe climate change such as increase of global average temperature.  The parametric form of the SSM is based on the Earth energy balance and derived from two-layer energy balance equations. The SSM is used by applying the developed SSM parametric form to process global climate model (GCM) simulations to obtain climate-model-informed priors, which are subsequently used to process historical observations using the same SSM parametric form to obtain parameter posteriors conditional on observation data. 
 
The current files include 1) the input files of original GCM simulations and forcing time series under different Shared Socioeconomic Pathways (SSP) scenarios; 2) the parameter posteriors (means and confidence intervals of physical parameters) from the processing of individual GCM simulations; 3) the posteriors (means and confidence intervals) of ECS and TCR from the processing of historical observations; and 4) SSM simulated/projected global average temperature under different SSP scenarios using both pseudo observations and historical observations. Additional results for regional SSM projections will be provided once completed.  
 
This research was supported by the National Science Foundation (NSF project CMMI \#1663479, titled “From Future Learning to Current Action: Long-Term Sequential Infrastructure Planning under Uncertainty”).