Abstract
Here we present an update to the FaIR model for use in probabilistic future climate and scenario exploration, integrated assessment, policy analysis, and education. In this update we have focussed on identifying a minimum level of structural complexity in the model. The result is a set of six equations, five of which correspond to the standard impulse response model used for greenhouse gas (GHG) metric calculations in the IPCC’s Fifth Assessment Report, plus one additional physically motivated equation to represent state-dependent feedbacks on the response timescales of each greenhouse gas cycle. This additional equation is necessary to reproduce non-linearities in the carbon cycle apparent in both Earth system models and observations. These six equations are transparent and sufficiently simple that the model is able to be ported into standard tabular data analysis packages, such as Excel, increasing the potential user base considerably. However, we demonstrate that the equations are flexible enough to be tuned to emulate the behaviour of several key processes within more complex models from CMIP6. The model is exceptionally quick to run, making it ideal for integrating large probabilistic ensembles. We apply a constraint based on the current estimates of the global warming trend to a million-member ensemble, using the constrained ensemble to make scenario-dependent projections and infer ranges for properties of the climate system. Through these analyses, we reaffirm that simple climate models (unlike more complex models) are not themselves intrinsically biased “hot” or “cold”: it is the choice of parameters and how those are selected that determines the model response, something that appears to have been misunderstood in the past. This updated FaIR model is able to reproduce the global climate system response to GHG and aerosol emissions with sufficient accuracy to be useful in a wide range of applications and therefore could be used as a lowest-common-denominator model to provide consistency in different contexts. The fact that FaIR can be written down in just six equations greatly aids transparency in such contexts.
Generated Summary
This research presents FaIRv2.0.0, an updated version of the FaIR (Finite amplitude Impulse Response) model designed for probabilistic climate and scenario exploration. The study focuses on simplifying the model’s structure while maintaining its ability to simulate the global climate system’s response to greenhouse gas (GHG) and aerosol emissions. The methodology involves developing a set of six equations, based on the standard impulse response model, and then tuning these equations to emulate the behavior of more complex Earth system models (ESMs). The researchers use the model to perform scenario-dependent projections and infer ranges for climate system properties, constrained by current estimates of global warming trends. The model’s performance is assessed by comparing its results to those of more complex models, and its potential for educational and policy applications is discussed.
Key Findings & Statistics
- The study finds that the Transient Climate Response (TCR) is constrained from 1.30 to 2.44 K.
- The Equilibrium Climate Sensitivity (ECS) is constrained from 1.94 to 6.59 K.
- The total aerosol forcing is constrained from -2.63 to -0.36 Wm-2.
- The anthropogenic forcing is constrained from 2.19 to 3.68 Wm-2.
- The best estimate for the TCRE (Transient Climate Response to Cumulative Emissions) is 1.53 with a 5%-95% range of 1.11–2.12 K TtC-1.
- The choice of observational dataset used in the Global Warming Index calculation can affect projections under an SSP2-45 pathway, with variations of over 0.2 K depending on the dataset.
- The full ensemble spans the range of carbon cycle behavior in 11 C4MIP models on decadal timescales.
Other Important Findings
- The updated FaIR model effectively reproduces the global climate system’s response to GHG and aerosol emissions.
- The model is transparent and simple, allowing for its easy integration into standard data analysis packages like Excel.
- The model is computationally efficient, enabling the generation of large probabilistic ensembles.
- The study reaffirms that simple climate models are not intrinsically biased towards being ‘hot’ or ‘cold’; the choice of parameters determines the model response.
- The model can be tuned to emulate key processes within more complex CMIP6 models.
- The FaIRv2.0.0 model includes state-dependent timescales to capture non-linearities in the carbon cycle.
Limitations Noted in the Document
- The study acknowledges that the highly idealized nature of the CMIP6 experiments used for tuning may limit the model’s ability to emulate complex model responses to more realistic scenarios.
- The choice of observational dataset used in the Global Warming Index calculation influences the results.
- The study focuses on the global mean surface temperature and the performance depends on the dataset definition (GMST vs. GSAT).
- The constraint methodology, although effective, may not be as sophisticated as other methods like Markov chain-Monte Carlo, potentially limiting the precision of the results.
Conclusion
The FaIRv2.0.0 model offers a simplified, transparent, and computationally efficient approach to climate modeling, suitable for a wide range of applications including education, policy analysis, and integrated assessment modeling. The study highlights the importance of parameter selection in determining model response and demonstrates the ability to reproduce the behavior of complex models while offering computational advantages. The results underscore the role of simple climate models in assessing climate sensitivities and projecting future climate scenarios, as well as the value of this approach in education and industry. The study’s findings suggest the potential of using FaIRv2.0.0 for emission and climate impact accounting in industry. Furthermore, the model’s ability to emulate complex models, run efficiently, and the simplified structure make it suitable for educational purposes and easily adaptable for those with limited programming expertise. The choice of observational dataset can influence results, particularly for projections, emphasizing the need for careful consideration of data sources in such assessments. The authors suggest further improvements via more advanced methods to enhance the precision of probabilistic projections, highlighting the model’s potential for widespread utility across various sectors and its ability to contribute to a deeper understanding of the climate system.