Global temperature projections from a statistical energy balance model using multiple sources of historical data

TY - JOUR

T1 - Global temperature projections from a statistical energy balance model using multiple sources of historical data

AU - Bennedsen, Mikkel

AU - Hillebrand, Eric

AU - Lykke, Jingying Zhou

PY - 2023/10

Y1 - 2023/10

N2 - This paper estimates a two-component energy balance model as a linear state-space system (EBM-SS model) using historical data. It is a joint model for the temperature in the mixed layer, the temperature in the deep ocean layer, and radiative forcing. The EBM-SS model allows for the modeling of nonstationarity in forcing and the incorporation of multiple data sources for the unobserved processes.We estimate the EBM-SS model using historical datasets at the global level for the period 1955-2020 by maximum likelihood. We show in the empirical estimation and in simulations that using multiple data sources for the unobserved processes reduces parameter estimation uncertainty. When fitting the EBM-SS model to six observational global mean surface temperature (GMST) anomaly series, the GMST projections under representative concentration pathway scenarios are comparable to those from CoupledModel Intercomparison Project models. The results show that a simple statistical climate model estimated on the historical period can produce GMST projections compatible with output from large-scale Earth system models.

AB - This paper estimates a two-component energy balance model as a linear state-space system (EBM-SS model) using historical data. It is a joint model for the temperature in the mixed layer, the temperature in the deep ocean layer, and radiative forcing. The EBM-SS model allows for the modeling of nonstationarity in forcing and the incorporation of multiple data sources for the unobserved processes.We estimate the EBM-SS model using historical datasets at the global level for the period 1955-2020 by maximum likelihood. We show in the empirical estimation and in simulations that using multiple data sources for the unobserved processes reduces parameter estimation uncertainty. When fitting the EBM-SS model to six observational global mean surface temperature (GMST) anomaly series, the GMST projections under representative concentration pathway scenarios are comparable to those from CoupledModel Intercomparison Project models. The results show that a simple statistical climate model estimated on the historical period can produce GMST projections compatible with output from large-scale Earth system models.

KW - Anthropogenic effects/forcing

KW - Energy budget/balance

KW - Kalman filters

KW - Statistics

KW - Stochastic models

KW - Temperature

UR - http://www.scopus.com/inward/record.url?scp=85173579091&partnerID=8YFLogxK

U2 - 10.1175/JCLI-D-22-0460.1

DO - 10.1175/JCLI-D-22-0460.1

M3 - Journal article

SN - 0894-8755

VL - 36

SP - 6817

EP - 6838

JO - Journal of Climate

JF - Journal of Climate

IS - 19

ER -