Guest Post by Willis Eschenbach
Inspired by the work done by Robert Rohde attempting to link May to October temperatures and rainfall to fires, I thought I’d take a look at the acres burned over the years. Rohde compared the rainfall and temperature records and highlighted the largest fires. However, this gives only a few data points. I wanted a larger view of the situation.
So instead of major fires, I looked at the areas burned every year, which are available here. There is complete data from 1959 to 2016, and the last two years are available here and here.
The first thing I did was run a multiple regression on the data, using both May to October temperature and May to October rainfall to see how well they would predict the area burned. To my great surprise, I found out that rainfall is not significantly correlated with the area burned. Here is that result:
Estimate Std. Error t value Pr(>|t|) (Intercept) -13393785 2502402 -5.352 1.61e-06 Temperature 203834 35791 5.695 4.52e-07 Rainfall -46812 35591 -1.315 0.194
Temperature is significant (right-hand column, p-value 4.52e-7), but rainfall is far from significant (p-value = .19). So I ignored rainfall for the rest of the analysis.
Next, I graphed the acres burned, and ran a linear regression on the data. Figure 1 shows that result:
Figure 1. Total areas burned by year, 1959-2018 (red line) and linear least squares trend line (blue line).
Note that the p-value of the line is quite good (right column, p-value = .00000004). The R^2 value (bottom line) shows that the straight line explains 41% of the variance in the acres burned.
Then I looked at the connection between temperature and acres burned. Figure 2 shows that result:
Figure 2. Total areas burned by year, 1959-2018 (red line) and acreage estimated from the variation in May – Oct temperatures (blue line).
Curiously, that looks a lot better than the straight line … but note that there is only a slight increase in the amount of variance explained (44% variance explained by temperature versus 41% for the straight line). This proves once again that our eyes are tuned to see patterns even when none are there … consider the constellations of the night sky as a prime example.
Finally, I looked at the errors in the temperature based estimate of the acres burned. Figure 3 shows the difference between the temperature-based estimate of the area burned and the actual acreage burned.
Figure 3. Errors of the estimate.Total areas burned by year, 1959-2018, minus the acreage estimated from the variation in May-Oct temperatures. The red line is a seven years Full-Width Half Maximum (FWHM) Gaussian average of the data. The vertical dotted blue line shows that in 1994, logging was stopped in much of the state in a vain attempt to protect the Spotted Owl.
Here is the oddity of the Figure 3 graph. In the first part of the record, up to the early part of the 21st Century, the temperature generally overestimates the acres burned.
But since then, the temperature has greatly underestimated the number of acres burned.
This is clear evidence that the recent large wildfires are not due to the variations in temperature as is widely claimed.
Conclusions
• Using variations in May to October rainfall do not improve the estimate of the acreage burned. In other words, May to October rainfall doesn’t add anything to an estimate done using May to October temperature alone.
• The variations in May to October temperature are only slightly better than a straight line in estimating the variations in area burned.
• The recent very large areas burned are not the result of variations in May to October temperature. As I pointed out in my last post, the decade over decade changes in temperature are nowhere near large enough to explain the recent increase in the area burned. We must look elsewhere for the causes of these large fires.
h/t to Steven Mosher for pointing out the Rohde analysis.
[UPDATE] Some folks didn’t like the data I used, so here’s some more:
I’m home now, and the smoke is not too bad. Not as bad as it was in the Central Valley or in San Francisco on the way back here. We’re in the yellow area on the California coast north of San Francisco. Smoke map available here, click on “Vertically Integrated Smoke” or “Near Surface Smoke”.
Figure 4. Smoke map. Red is the thickest smoke. The Camp Fire is burning north-east of Sacramento.
My best wishes and hopes for the future for all of those affected by the fires, and my condolences for those who have lost friends, family, or homes.
Regards,
w.
PS—My usual request. When you comment, please quote the exact words you are referring to, so we can all be clear on what you are discussing.
I’ve added the following plot to the head post:
Not seeing climate change in that … who knows.
w.
Willis,
I hope you will read this. I don’t expect to engage you in conversation.
Climate changes regionally. The prediction is that in general, dry areas will become drier and wet areas will become wetter. This is somewhat borne out by the evidence of U.S. patterns.
https://www.epa.gov/climate-indicators/climate-change-indicators-us-and-global-precipitation – Figure 3
If incidence of wildfire is related to precipitation, you would not necessarily expect a national increasing trend in acreage burned.
Quite apart from that is the fact that there are so many complicating factors, such as vegetation history and management, development patterns, presence of ignition sources such as electrical faults, etc. This is why I find your analysis so striking – there does seem to me to be a relationship between temperature and acreage burned. I don’t know why you find it offensive that I point this out.
While I understand Mosh’s point about thresholds, I’m not sure that’s the best way to look at this. For one thing, different vegetation (and forest) types are likely to have different thresholds. Another is that the precipitation over the course of years could have a large impact on tree health. If a forest has gone though 5 years of drought then has one rainy spring, that may not be enough to make up for the long-term stress, which can make trees more susceptible to disease and insect damage (in some areas – Yosemite, for instance – insects are responsible for a lot of dead and dying trees). Extreme precipitation events, the kind that quickly fill reservoirs, may not help that much, especially on hillsides where much of the rain flows above ground. A cold early winter with delayed snow, followed by an early spring melt could cause much of the water to run off over frozen ground. Assessing effects of precipitation on vegetation drought status is not at all straightforward…but temperature is definitely a significant factor.
We all have different views, Willis. I support the idea of AGW and believe it will come at a net cost, though it’s uncertain just how high it will be. There is much we don’t know about how the natural world will respond. This isn’t an environmentalist perspective, it’s a recognition of the fact that human welfare is dependent on the state of the natural world – the CA fires are a good example.
I’m not a hard-liner about policy. I recognize that there are economic and social factors that have to be taken into account. I think it’s more important to slow the rate of change than to limit it, allowing people and the biota time to adapt. This is why I believe doing something is better than doing nothing.
In CA, the most important thing to do will be to develop a plan in order to prioritize use of limited resources and engage the public. Wise development and home construction are essential (using fiber cement siding rather than wood, for example). Whether or not climate change is contributing to increasingly damaging fires, people have to adapt. Forest management is only one part of the picture.
I make mistakes, but I’m not stupid. I don’t whine.
Kristi, sorry. Reading without discussing is no fun, and discussing anything with you is even less fun.
Pass …
w.
Yeah, I realized I’d wasted my time as soon as I’d posted. I should know better.
“Is the California Drought a symptom of long term climate change?
The current drought is not part of a long-term change in California precipitation, which exhibits no appreciable trend since 1895.”
https://cpo.noaa.gov/Portals/0/Docs/MAPP/TaskForces/DTF/california_drought_report.pdf
peterhodges,
“The current drought, though extreme, is not outside the range of California hydro-climate variability and similar events have occurred before. Although there has been a drying trend in California since the late 1970s, when considering the full observational record since 1895, there is no appreciable trend to either wetter or drier California winters.”
This is a general pattern. Most of the climate change attributable to CO2 has appeared since the late 1970s. This is because before then other factors have played more of a role, particularly solar irradiance and aerosols.
PREDICTION: Devastating fires will happen again and again in California and elsewhere. Wooden houses and their contents will be tragically lost, not to mention lives. The survivors will regroup and rebuild. Inexplicably, they will reproduce the same highly flamable structures as before using insurance money. Contractors are happy to build wood frame houses because it is cheap and they have it down to a science. Permitting authorities are happy because the codes and regulations allow it. Insurance companies are happy because when customers are living in fear of the next fire insurance makes a lot of sense. I don’t know if we can put an end to wild fires, but I know we can put an end to houses burning down. A wood frame house in a fire-prone area is little better than a bonfire waiting for a spark. Fire proof houses can and should be built in those areas. Do a search for “John Davis and Lorrie Brown” for one example from last year that withstood a major fire in California. Monolithic domes, concrete and steel, have survived fires as well. Normal looking houses can be built with concrete, walls and roof. Even so, very few will do anything like this. True, it may cost a bit more. What does it cost to rebuild your entire house? What price do you put on your belongings and memorabilia? What is the emotional price for the loss of your home? Evacuation may still be necessary, but you should have a place to go back to. Permitting authorities could require fireproof houses where needed. Insurance companies could decline to sell insurance for houses in danger from fire. The media could inform the public. I’m not optimistic that anything will be done. (rant over.)
Found another one: search for “Architect Eric Lloyd Wright” to see a video from 2007…concrete and steel is fireproof.
@ur momisugly Willis Eschenbach,
please read “Influence of wildfire induced land-cover changes on clouds and precipitation in Interior Alaska — A case study” (Atmospheric Research 84 (2007) 142–168) authored by Moelders and me.
Thanks, Gerhard. I went there expecting to see a study of the effect of wildfires on clouds and rain, which would have been quite interesting to me.
Unfortunately, when I got there I found out that the headline was totally misleading. You didn’t study wildfires, or clouds, or rain.
Instead, you studied a climate model called the “Mesoscale Model generation 5”, and you wrote about the results as if you were writing about the real world … a true bait-and-switch.
Here’s the noted genius Freeman Dyson on the question. You should consider his objections seriously.
My best to you, and I encourage you to give up studying models and start studying the real world …
w.
The acres burned has a lower bound near 0 while the temperature effectively does not have a lower bound (technically it does have a lower bound, but given how far you are above that bound you can treat the data as a random Gaussian distribution). I have found this messes up my analyses frequently. You might try taking the log of the acres burned.
In a post-hoc hand waving argument you could say something like: “The western United States is in a disequilibrium between low burn and high burn states. Each temperature rise will also raise the probability of continuing a burn linearly. However, as the probability increases the number of acres burned will not increase linearly. The number of fires started will increase linearly and the probability of sustaining a fire increase linearly, but more fires will cause more incidents that could be sustained. Blah blah blah and here is a monte carlo simulation…”
But in all reality, I think the log of the acres burned will give you a much better match. The problem is that the units are almost impossible to understand (log acres per degree C, seriously?).
@ur momisugly willis
I posted this a little bit ago, but it hasn’t shown up. I apologize if it posts twice.
I forget the name of the model, but it would be a checkerboard type model. Each square would have a probability of sustaining a wildfire, with the probability drawn randomly from a distribution model. As the temperature increases the mean of the probability distribution would increase. Then you seed fires and check the spread. The interesting thing about a model like this is that as you increase the mean the area affected would increase non-linearly.
I learned this model when studying the efficacy of radiation therapy on cancer treatment. I think it would have an application to this space. The model itself would be entirely linear (all that moves is the distribution center), but the effects are non-linear. The radiation model was a linear probability model but demonstrated why a ghastly amount of radiation was required to make it likely to kill that last cancer cell. The model was linear, but the effect was non-linear (I believe it was logarithmic).
What a year, Iceland low and Azores high.
Pollen dust and red Sahara sand.
From dormouse day to dormouse day.
Mark in calendars!
What a year 2018,
Iceland low and Azores high wandered.
Pollen dust and red Sahara sand.
Heavy rains and hailstorms in the EU.
The new normal.
From dormouse day to dormouse day.
Mark in calendars!
__________________________________________________
See ya 2019.
https://www.google.at/search?q=flood+damaged+cars+for+sale&client=ms-android-samsung&prmd=insv&source=lnms&tbm=isch&sa=X&ved=0ahUKEwiA3MiGpejeAhUCCCwKHSWcCJQQ_AUIEigB&biw=360&bih=560