Emulating Mean Patterns and Variability of Temperature across and within Scenarios in Anthropogenic Climate Change Experiments
The pattern scaling methodology, which allows to emulate climate model output for scenarios that have not been run by coupled climate models, is extended to add onto the mean patterns of temperature the simulation of internal variability, using the large and medium ensemble to develop such approach for the emulation of RCP4.5 on the basis of RCP85, and validate the results. Specifically, we emulate both the internal variability affecting the long-term trends across initial condition ensemble members, and the variability superimposed on the long-term trend within individual ensemble members. We view this approach as a step forward in providing relevant climate information for avoided impacts studies, and more broadly for impact models, since we allow both forced changes and internal variability to play a role in determining future impact risks.
The pattern scaling methodology, which allows to emulate climate model output for scenarios that have not been run by coupled climate models, is extended to add onto the mean patterns of temperature the simulation of internal variability, using the large and medium ensemble to develop such approach for the emulation of RCP4.5 on the basis of RCP85, and validate the results. Specifically, we emulate both the internal variability affecting the long-term trends across initial condition ensemble members, and the variability superimposed on the long-term trend within individual ensemble members. We view this approach as a step forward in providing relevant climate information for avoided impacts studies, and more broadly for impact models, since we allow both forced changes and internal variability to play a role in determining future impact risks.
The pattern scaling methodology, which allows to emulate climate model output for scenarios that have not been run by coupled climate models, is extended to add onto the mean patterns of temperature the simulation of internal variability, using the large and medium ensemble to develop such approach for the emulation of RCP4.5 on the basis of RCP85, and validate the results. Specifically, we emulate both the internal variability affecting the long-term trends across initial condition ensemble members, and the variability superimposed on the long-term trend within individual ensemble members. We view this approach as a step forward in providing relevant climate information for avoided impacts studies, and more broadly for impact models, since we allow both forced changes and internal variability to play a role in determining future impact risks.