Observational uncertainty is the primary source of climate uncertainty in impacts projections
Abstract
Projections of future climate damages underpin environmental policy and risk management but depend on inherently uncertain climate data. Uncertainty in historical climate observations, used to train impact models and calibrate climate projections to the observed record, is rarely considered. We show this “observational uncertainty” contributes more than the spread across or within global climate models to projections of mortality, GDP per capita growth, and payroll. Most of this uncertainty stems from training impact models; even small differences in historic weather estimates can cause large differences in projected damages. Incorporating this uncertainty increases the spread in climate uncertainty by up to 300%, highlighting the importance of impact model design and the need for better constraints on observations, and implying a higher Social Cost of Carbon.
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BibTeX
@misc{schwarzwald2026observational,
title = {{Observational uncertainty is the primary source of climate uncertainty in impacts projections}},
author = {Kevin Schwarzwald and Nathan Lenssen and Tom Bearpark and Alia Bonanno and Radley Horton and Gernot Wagner},
howpublished = {EarthArXiv},
year = {2026},
month = oct,
doi = {10.31223/x5bz39},
url = {https://eartharxiv.org/repository/view/15692/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Observational uncertainty is the primary source of climate uncertainty in impacts projections
AU - Kevin Schwarzwald
AU - Nathan Lenssen
AU - Tom Bearpark
AU - Alia Bonanno
AU - Radley Horton
AU - Gernot Wagner
PY - 2026
DA - 2026/10/22
PB - EarthArXiv
DO - 10.31223/x5bz39
UR - https://eartharxiv.org/repository/view/15692/
AB - Projections of future climate damages underpin environmental policy and risk management but depend on inherently uncertain climate data. Uncertainty in historical climate observations, used to train impact models and calibrate climate projections to the observed record, is rarely considered. We show this “observational uncertainty” contributes more than the spread across or within global climate models to projections of mortality, GDP per capita growth, and payroll. Most of this uncertainty stems from training impact models; even small differences in historic weather estimates can cause large differences in projected damages. Incorporating this uncertainty increases the spread in climate uncertainty by up to 300%, highlighting the importance of impact model design and the need for better constraints on observations, and implying a higher Social Cost of Carbon.
ER -