the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Extreme blocking ridges are associated with large wildfires in England
Abstract. Persistent positive anomalies in 500 hPa geopotential heights (PPAs) are an event-based paradigm for tracking specific large scale atmospheric patterns that often correspond to blocking events. PPAs are associated with hot, dry surface weather conditions that promote fuel aridity and wildfire activity. We examine the importance of PPA events for surface fire weather across the UK and wildfires in England, a temperate, emerging fire prone region. Surface fire weather is more extreme under PPAs, characterised by reduced precipitation and anomalously high temperatures. Overall, 34 % of England’s burned area and 16 % of all wildfire events occur during or up to five days following the presence of a PPA event. PPAs are generally more strongly associated with wildfire burned area than ignition frequency. The percentage of PPAs associated with wildfire events increases with increasing fire size, with PPAs being associated with half of wildfire events > 500 ha. PPAs are most important for heathland/moorland (40 % burned area) followed by grassland (30 % burned area) wildfires and are more important during the summer wildfire season. Synoptic-scale indicators of wildfire activity like PPAs may improve longer-term fire weather forecasts beyond surface fire weather indices alone, aiding wildfire preparedness and management decision-making. This is particularly important in emerging fire prone regions where wildfire risk is increasing but established tools for assessing fire danger may not yet exist.
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Status: open (until 13 Dec 2024)
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RC1: 'Comment on nhess-2024-161', Anonymous Referee #1, 03 Dec 2024
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Little et al. explore the relationship between persistent high-pressure systems, surface weather conditions, fire indicators and actual wildfires using a dataset which ranges from March – October 2001-2021 (UK) and 2010-2020 (England), respectively. They find significant relationships between high pressure systems and local weather conditions and wildfire characteristics across seasons. This is an interesting assessment, which could be further improved by accounting for following comments:
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Minor:
1.     Fig. 1 this is a very informative figure. It could be improved even further, by adding information on months and seasons by e.g. dashed vertical lines or shaded background.
2.     L. 144 is this the best way to implement this? Wouldn’t daily max. Temperatures be more informative? Or is this essentially the same value for most of the days.
3.     L. 245 do these numbers refer to each grid-cell individually or are events defined based on some sort of spatial integration (all anomalous neighboring grid-cells are counted as one event)?
4.     This does not refer to the content, but rather to the way the paper is structured. Data and Methods sections seem to include some results already, which is why the results section is fairly short (mostly the figure, the figure caption and 1-2 sentences of description). The manuscript would be easier to follow if methods and related results were merged.Â
5.     Figure 3. It would be helpful (also for the description of the results in the paragraph before and for the caption) to have the subplot labeled with e.g. letters a-i. Abbreviations should be written out fully in the caption.Â
6.     Figure 4. The subplot labeling and the figure caption could be made more intuitive by labeling each plot separately. Abbreviated variables should be written out. A clear description of what the boxplot boundaries, whiskers and dots refer to is missing.Â
7.     I would suggest to merge the conclusions into the discussion section and possibly add a bit more context.Â
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Major:Â
1.     How are PPA events being associated with surface anomalies? Are relationships being quantified for events that are detected over the same grid-point, during the same time period only? How are the lead lag relationships accounted for and how are vertical shears in PPAs and surface response integrated in the regression shown in Fig. 4?
2.     The Dataset is fairly short for a trend analysis, but it would be very insightful to acknowledge and assess changes in wildfire characteristics over the past decade(s). Have increased temperatures, precipitation or landcover changes led to significant increases (or decreases in regional wildfires?). An increased threat from Wildfires to the UK is mentioned in l.363 but is not backed up with any quantitative results.
3.     Various fire weather indicators were calculated in this study. Which indicator is best suited to predict actual fires in the UK?
Citation: https://doi.org/10.5194/nhess-2024-161-RC1
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