Scenario set-up and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Model Intercomparison Project (ISIMIP3a)

Abstract

Abstract. This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, www.isimip.org) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs)

“…However, such approaches, often based on the idea of pattern scaling of global-mean temperature, can only be used for emulation of ESM outputs, and without adequate ESM training pathways will not be able to capture key features of peak-and-decline or stabilisation pathways. Furthermore, none of those emulators is yet able to provide the full richness of variables, spatial and temporal and cross-variable correlations that ESMs are able to provide -and which are key for a range of specific impact models like those linked together in the ISIMIP project (Frieler et al, 2023;Warszawski et al, 2014). Thus, while emulators will be able to play an increasingly important role, they do not provide sufficient capabilities to address the science and policy questions identified above.…”

“…Such are hereafter referred to as framing pathways since they frame how ESMs can be run with a consistent set of drivers (emissions, concentrations, land surface states, solar activity etc.) to build a range of climate futures which in turn provide a common framing input to impact and vulnerability studies can be conducted (Frieler et al, 2023;Warszawski et al, 2014). The framing pathways thereby can provide a backbone of integration across the IPCC physical science (Working Group I) and impact (WG II) communities 1 and also to link with socio-economic and mitigation information (WG III) (Figure 1).…”

Global burned area increasingly explained by climate change

“…individually, within this IPCC framework 29,32 . Two transient historical simulations were performed with seven dynamic re-vegetation models: one experiment using reanalysis climate and historical atmospheric CO 2 forcing ("Factual"), and another employing de-trended historical climate and atmospheric CO 2 xed at 1901 levels ("Counterfactual").…”

“…In this study, we attribute historical changes in burned area to changes in observed climate (meteorological conditions and CO 2 ) and direct human forcings (ignitions and land-use change) individually, within this IPCC framework 29,32 . Two transient historical simulations were performed with seven dynamic re-vegetation models: one experiment using reanalysis climate and historical atmospheric CO 2 forcing ("Factual"), and another employing de-trended historical climate and atmospheric CO 2 xed at 1901 levels ("Counterfactual").…”

“…We then calculate the Probability Ratio (PR) [32][33][34][35] of higher than average burned area, where the ratio is between the probability of being above the counterfactual mean (area above the blue vertical line, Fig. 1) under (i) the factual forcing simulation (orange shading), and (ii) the counterfactual simulation (blue shading; see 'Relative Anomaly and Probability Ratio' in Methods).…”