the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluation of CMIP6 model performances in simulating fire weather spatiotemporal variability on global and regional scales
- 1Centre for Agroecology, Water and Resilience, Coventry University, Coventry, CV8 3LG, UK
- 2Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Alnarp, 230 53, Sweden
- 3Institut de recherche sur les forêts, Université du Québec en Abitibi-Témiscamingue (UQAT), QC J9X 5E4, Canada
- 4Forest Research Institute of the Karelian Research Centre of the Russian Academy of Sciences, Petrozavodsk, 185910, Russia
- 5School of Forestry, Northern Arizona University, Flagstaff, Arizona, 86011, USA
- 6Disaster Risk Management Unit, Directorate for Space, Security and Mitigation, Joint Research Centre (JRC), European Commission, Ispra, 21027, Italy
- 7School of Energy, Construction and Environment, Coventry University, Coventry, CV1 5FB, UK
- 1Centre for Agroecology, Water and Resilience, Coventry University, Coventry, CV8 3LG, UK
- 2Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Alnarp, 230 53, Sweden
- 3Institut de recherche sur les forêts, Université du Québec en Abitibi-Témiscamingue (UQAT), QC J9X 5E4, Canada
- 4Forest Research Institute of the Karelian Research Centre of the Russian Academy of Sciences, Petrozavodsk, 185910, Russia
- 5School of Forestry, Northern Arizona University, Flagstaff, Arizona, 86011, USA
- 6Disaster Risk Management Unit, Directorate for Space, Security and Mitigation, Joint Research Centre (JRC), European Commission, Ispra, 21027, Italy
- 7School of Energy, Construction and Environment, Coventry University, Coventry, CV1 5FB, UK
Abstract. Weather and climate play an important role in shaping global wildfire regimes and geographical distributions of burnable area. As projected by the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6), in the near future, fire danger is likely to increase in many regions due to warmer temperatures and drier conditions. General Circulation Models (GCMs) are an important resource in understanding how fire danger will evolve in a changing climate but, to date, the development of fire risk scenarios has not fully accounted for systematic GCM errors and biases. This study presents a comprehensive global evaluation of the spatiotemporal representation of fire weather indicators from the Canadian Forest Fire Weather Index System simulated by 16 GCMs from the 6th Coupled Model Intercomparison Project (CMIP6). While at the global scale, the ensemble mean is able to represent variability, magnitude and spatial extent of different fire weather indicators reasonably well when compared to the latest global fire reanalysis, there is considerable regional and seasonal dependence in the performance of each GCM. To support the GCM selection and application for impact studies, the evaluation results are combined to generate global and regional rankings of individual GCM performance. The findings highlight the value of GCM evaluation and selection in developing more reliable projections of future climate-driven fire danger, thereby enabling decision makers and forest managers to take targeted action and respond to future fire events.
Carolina Gallo et al.
Status: open (until 02 Jan 2023)
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RC1: 'Comment on gmd-2022-223', Anonymous Referee #1, 22 Nov 2022
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Synopsis
This paper evaluates the skill of 16 CMIP6 models in reproducing historical observed fire weather conditions as viewed through the CFDRS. The paper is well-written, well-structured and the methodology is robust. I think the paper has the potential to be published but I have a few comments below that need to be addressed before publication.
Major comments
Section 2.3 – Are authors aware of the hot model problem in CMIP6 ? See the paper below. This should be discussed and acknowledged.
https://www.nature.com/articles/d41586-022-01192-2
Line 166-170 – Defining a unique fire season for each GFED region is questionable given their spatial extent. The timing of the fire season has already been reported in a number of previous studies and has been shown to be much variable in space. The present coarse-scale analysis is thus likely to mix a number of different seasonalities within each GFED region. I would suggest to define a fire season locally (e.g. at the pixel scale) as done in most previous studies. Moreover, the model ranking (currently based on GFED region) would be much more relevant if authors would consider the spatial extent (number of pixels) where a model falls within a specific tercile. The current ranking is dependent on the size of each GFED region (a small region contributes as much as a large one).
Minor comments
Line 65-68 - GCMs have also been used in attribution studies of FWI to quantify the current risk
Barbero R, Abatzoglou J T, Pimont F, Ruffault J and Curt T. 2020 Attributing increases in fire weather to anthropogenic climate change over France. Frontiers in Earth Science
https://doi.org/10.3389/feart.2020.00104
Line 130-131 – I am not sure to understand how links between fire events and fire weather trends relate to the performance of ERA-5 in reproducing observed fire weather conditions Two independent variables can follow similar trends !
Line 150-155 – As stated by authors, the record length of GFED is a bit short to draw such conclusions. What about using a land cover map to define what is burnable or not?
Line 157 – why this specific time period? This needs clarification
Line 158 – Are you referring to the 90th percentile of daily values?
Figure 2 – the title indicates (mon_mean) while the caption reads annual mean. Please use the same terminology for consistency.
Figure 2 and 3 – I wonder if the (minor) differences between average and extreme statistics need to be reported. Please consider moving Figure 3 to the supplementary information and reduce the text accordingly.
Line 197-198 – Given the (rather expected) similarities in the results, I suggest moving DSR results to the supplementary information.
Line 217-219 – this belongs to the methods
Figure 4 – please increase label size (x and y labels) as well as the colorbar. They are very hard to read.
Figures 5-6 – Would that make sense to add the MMM for comparison with each individual GCM?
Figure 5-6 – Please indicate the RMSE error on the figure for clarity
Figure 6 – please consider moving this figure to SI given the similarity with figure 5
Line 349-350 – this is a very interesting result
Line 355-358 – Does it mean that a model performing well in reproducing historical variability is more credible in simulating future changes to fire weather conditions? I am just asking.
Line 402-405 – Yes, other regionalization such as the pyroregions presented in Galizia et al. (2021) over Europe would be more relevant in terms of fire activity and management.
Galizia, L. F. C., Curt, T., Barbero, R., Rodrigues, M. (2021). Understanding fire regimes in Europe, International Journal of Wildland Fire, 31, 56-66. https://doi.org/10.1071/WF21081
A future interesting research question would be to examine what meteorological variables (temp, prcp, RH and wind) are responsible for the difference between observed and simulated CFDRS components. This could be discussed somewhere in the paper.
Carolina Gallo et al.
Carolina Gallo et al.
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