Showing posts with label extreme weather. Show all posts
Showing posts with label extreme weather. Show all posts
Friday, 5 February 2016
Malcolm Turnbull, how should Australia adapt to climate change without science?
Adapting to climate change needs information on local changes in the mean, weather variability and extremes. Observed changes in the means are not enough.
The "conservative" government of Australia plans to gut its climate research and kill the groups doing climate research at Australia's main research institute, CSIRO. Australia's opposition leader rightly said the Prime Minister Malcolm Turnbull should "hang his head in shame".
The destruction is not for lack of quality of the research. CSIRO's new chief Larry Marshall send an email to its employees stating:
Just because we know climate change is real, does not mean that we understand everything. Projecting increases in the global mean temperature is easy. Saying something about the changes in the hydrological cycle is much harder. We know how much the global mean precipitation will increase because we can estimate the additional evaporation and what goes up must go down, but say where and how it goes down is hard. These assessments naturally have their uncertainties and it certainly pays to reduce them to make better political decisions.
Much more important than the uncertainties in the changes in the global means, for "solutions for the climate we will be living with" (adapting to climate change) we will need local predictions. That is a lot harder and very uncertain. Locally the changes can be very different from the global change. As Roger Pielke Jr. writes about storms on the US East Coast: "So those who argue for a simple relationship between increasing water content of the atmosphere and storm strength, data do not support such a claim over this multi-decadal period, in this region." (my emphasis)
open flames and smoke in a rural Texas landscape
Much more important than the uncertainties in the changes in the global means, for adaptation we need information on changes in weather variability and extremes. Especially for a country like Australia that knows very large variations due, for example, to El Nino.
One of the strategies of the mitigation skeptics is to pretend that adaptation is straightforward and cheap. When the sea level goes up 1 mm, just make the dikes 1 mm higher. However, the sea dikes will break during spring tide and a strong storm. Thus we also need to understand the storms to know how much stronger the dikes need to be. They will break during a once in a century storm. Or at least during what used to be a once in a century storm. Try to estimate from observations during a changing climate whether the 100-year storms are getting worse.
Was the flooding of New Orleans due to [[Hurricane Katrina]] a unique event or the "new normal"? During the flooding last year in South Carolina in some locations the rain amounted to a 1,000-year event (in a given year there is a 1 in 1,000 chance of observing rainfall of that magnitude of more). Does South Carolina have to adapt to this because this will happen more often or will this remain an outlier? Parts of the United Kingdom were hit three times by 100 year rain events the last few years. How often will they have to suffer this before we know from waiting and seeing that the weather has changed, people will have to move and the infrastructure needs to be more more robust?
The same goes less visibly for droughts. When your farm takes a hit due to a drought, do you build it up again when the rain comes back or is your land no longer profitable. Do you want to do this blindly? Or do you prefer some scientific guidance? For planning crops and managing reservoirs during droughts, seasonal and decadal climate predictions reduce costs and hardship. For planning new reservoirs and desalination plants long-term climate projects give guidance.
Meteorologists and climatologists are building seamless prediction systems. Going from short term weather predictions and nowcasting using observations during severe weather, to long-term weather predictions to prepare for bad weather, to seasonal and decadal predictions for planning and climate projections for adaptation. In many wealthy countries governments are setting up national climate service centers to help their societies adapt. The World Meteorological Organization is building a Global Framework for Climate Services (GFCS) to coordinate such efforts and help poorer countries understand the changes their region will see. While Australia sticks its head in the sand.
We will need very good science, a very good understanding of the coming climatic changes to adapt. The Australian government destroys climatology at a moment people, communities and companies need it most to adapt to the climatic changes that we have set in motion. This is about as stupid as the US states where the civil servants are no longer allowed to talk about climate change, which will mean that these communities will suffer the consequences without being prepared for the changes.
The same is true for (nearly?) every impact of climate change. In the past we could use long-term observations to determine what kind of extremes we could expect. Now, after all the delays to solve the problem, humanity is becoming more and more dependent on climate science and climate models, the models the mitigation skeptics who campaign for more global warming claim not to trust.
If you do not know which climatic changes you need to adapt to, you need to adapt to everything. Preparing for the worst case scenario in every direction is very expensive.
When Australia notices what a blunder they are making it will easily take over a decade until Australia's climate research is again where it started. It takes years until you understand a climate model or a data set well and start to be productive. Science is a social profession and once you are proficient you can start building your network. Then you notice the kinds of expertise still missing in your freshly build up institute. Unfortunately, like trust losing scientific expertise goes much faster than building it up.
Tweet
The CSIRO and farming in a changing climate
'Misleading, inaccurate and in breach of Paris': CSIRO scientist criticises cuts. Stefan Rahmstorf: "Closing down climate research capacity at a time of rapid global warming is not just short-sighted, it borders on the insane."
The Sydney Morning Herald: Climate science to be gutted as CSIRO swings jobs axe
Australia's CSIRO dims the lights on climate and environment
Thomas Peterson chair of WMO Commission on Climate explains the need for climate research by example how to deal with a drought.
Top photo. Severe suburban flooding in New Orleans, USA. Aftermath of Hurricane Katrina. Photo by ark Moran, NOAA Corps, NMAO/AOC (CC BY 2.0)
Second photo. Flames burn out of control at Possum Kingdom Lake near Pickwick, TX, on April 15, 2011. Photo by Texas Military Forces, available through a CC license.
Last photo. Flash flooding stalls traffic on I-45 in Houston on May 26, 2015. Photo by Bill Shirley, available through a CC license.
If you don't like what #climate science is telling you, just fire all the climate scientists
Miles Grant
The "conservative" government of Australia plans to gut its climate research and kill the groups doing climate research at Australia's main research institute, CSIRO. Australia's opposition leader rightly said the Prime Minister Malcolm Turnbull should "hang his head in shame".
The destruction is not for lack of quality of the research. CSIRO's new chief Larry Marshall send an email to its employees stating:
"CSIRO pioneered climate research ... Our climate models are among the best in the world and our measurements honed those models to prove global climate change.From this the strange conclusion is drawn:
That question has been answered, and the new question is what do we do about it, and how can we find solutions for the climate we will be living with?"[UPDATE. Judith Curry agrees with this strange sentiment: "Now that the UN’s community of nations has accepted consensus climate science to drive international energy and carbon policy, what is the point of heavy government funding of climate research, particularly global climate modelling?"]
Just because we know climate change is real, does not mean that we understand everything. Projecting increases in the global mean temperature is easy. Saying something about the changes in the hydrological cycle is much harder. We know how much the global mean precipitation will increase because we can estimate the additional evaporation and what goes up must go down, but say where and how it goes down is hard. These assessments naturally have their uncertainties and it certainly pays to reduce them to make better political decisions.
Much more important than the uncertainties in the changes in the global means, for "solutions for the climate we will be living with" (adapting to climate change) we will need local predictions. That is a lot harder and very uncertain. Locally the changes can be very different from the global change. As Roger Pielke Jr. writes about storms on the US East Coast: "So those who argue for a simple relationship between increasing water content of the atmosphere and storm strength, data do not support such a claim over this multi-decadal period, in this region." (my emphasis)
open flames and smoke in a rural Texas landscape
Much more important than the uncertainties in the changes in the global means, for adaptation we need information on changes in weather variability and extremes. Especially for a country like Australia that knows very large variations due, for example, to El Nino.
One of the strategies of the mitigation skeptics is to pretend that adaptation is straightforward and cheap. When the sea level goes up 1 mm, just make the dikes 1 mm higher. However, the sea dikes will break during spring tide and a strong storm. Thus we also need to understand the storms to know how much stronger the dikes need to be. They will break during a once in a century storm. Or at least during what used to be a once in a century storm. Try to estimate from observations during a changing climate whether the 100-year storms are getting worse.
"With climate change, we can’t drive by looking in the rear view mirror. We’re in a new normal."
Climate scientists Berrien Moore and Katharine Hayhoe
Climate scientists Berrien Moore and Katharine Hayhoe
Was the flooding of New Orleans due to [[Hurricane Katrina]] a unique event or the "new normal"? During the flooding last year in South Carolina in some locations the rain amounted to a 1,000-year event (in a given year there is a 1 in 1,000 chance of observing rainfall of that magnitude of more). Does South Carolina have to adapt to this because this will happen more often or will this remain an outlier? Parts of the United Kingdom were hit three times by 100 year rain events the last few years. How often will they have to suffer this before we know from waiting and seeing that the weather has changed, people will have to move and the infrastructure needs to be more more robust?
"There's no point putting in flood defenses that respond to mean climate change if you haven't thought of what a one-in-a-hundred-year event will look like in a warmer world... They don't want to know what the climate will be like, they want to know what the weather will be like in 20, 30, 50 years time."
The same goes less visibly for droughts. When your farm takes a hit due to a drought, do you build it up again when the rain comes back or is your land no longer profitable. Do you want to do this blindly? Or do you prefer some scientific guidance? For planning crops and managing reservoirs during droughts, seasonal and decadal climate predictions reduce costs and hardship. For planning new reservoirs and desalination plants long-term climate projects give guidance.
Meteorologists and climatologists are building seamless prediction systems. Going from short term weather predictions and nowcasting using observations during severe weather, to long-term weather predictions to prepare for bad weather, to seasonal and decadal predictions for planning and climate projections for adaptation. In many wealthy countries governments are setting up national climate service centers to help their societies adapt. The World Meteorological Organization is building a Global Framework for Climate Services (GFCS) to coordinate such efforts and help poorer countries understand the changes their region will see. While Australia sticks its head in the sand.
We will need very good science, a very good understanding of the coming climatic changes to adapt. The Australian government destroys climatology at a moment people, communities and companies need it most to adapt to the climatic changes that we have set in motion. This is about as stupid as the US states where the civil servants are no longer allowed to talk about climate change, which will mean that these communities will suffer the consequences without being prepared for the changes.
The same is true for (nearly?) every impact of climate change. In the past we could use long-term observations to determine what kind of extremes we could expect. Now, after all the delays to solve the problem, humanity is becoming more and more dependent on climate science and climate models, the models the mitigation skeptics who campaign for more global warming claim not to trust.
If you do not know which climatic changes you need to adapt to, you need to adapt to everything. Preparing for the worst case scenario in every direction is very expensive.
Never attribute to maladaptation that which can be adequately explained by stupidity.
When Australia notices what a blunder they are making it will easily take over a decade until Australia's climate research is again where it started. It takes years until you understand a climate model or a data set well and start to be productive. Science is a social profession and once you are proficient you can start building your network. Then you notice the kinds of expertise still missing in your freshly build up institute. Unfortunately, like trust losing scientific expertise goes much faster than building it up.
Tweet
Related reading
CSIRO boss’s failed logic over climate science could waste billions in taxes by Andy Pitman, Director of the Centre for Climate System Science.The CSIRO and farming in a changing climate
'Misleading, inaccurate and in breach of Paris': CSIRO scientist criticises cuts. Stefan Rahmstorf: "Closing down climate research capacity at a time of rapid global warming is not just short-sighted, it borders on the insane."
The Sydney Morning Herald: Climate science to be gutted as CSIRO swings jobs axe
Australia's CSIRO dims the lights on climate and environment
Thomas Peterson chair of WMO Commission on Climate explains the need for climate research by example how to deal with a drought.
Top photo. Severe suburban flooding in New Orleans, USA. Aftermath of Hurricane Katrina. Photo by ark Moran, NOAA Corps, NMAO/AOC (CC BY 2.0)
Second photo. Flames burn out of control at Possum Kingdom Lake near Pickwick, TX, on April 15, 2011. Photo by Texas Military Forces, available through a CC license.
Last photo. Flash flooding stalls traffic on I-45 in Houston on May 26, 2015. Photo by Bill Shirley, available through a CC license.
Wednesday, 5 March 2014
Be careful with the new daily temperature dataset from Berkeley
The Berkeley Earth Surface Temperature project now also provides daily temperature data. On the one hand this is an important improvement, that we now have a global dataset with homogenized daily data. On the other hand, there was a reason that climatologists did not publish a global daily dataset yet. Homogenization of daily data is difficult and the data provided by Berkeley is likely better than analyzing raw data, but still insufficient for robust conclusions about changes in extreme weather and weather variability.
The new dataset is introuduced by Zeke Hausfather and Robert Rohde on Real Climate:
The post explains in more detail how the BEST daily method works and presents some beautiful visualizations and videos of the data. Worth reading in detail.
The example of a move from black roof in a city to an airport is also a good example that not only the mean can change. The black roof will show more variability because on hot sunny days the warm bias is larger than on windy cloudy days. Thus part of this variability is variability in solar insolation and wind.
Also the urban heat island could be a source of variability, the UHI is strongest on wind and cloud free days. Thus part of the variability in observed temperature will be due to variability in wind and clouds.
A nice illustration of the problem can be found in a recent article by Blair Trewin. He compares the distribution of two stations, one in a city near the coast and one at an airport more inland. In the past the station was in the city, nowadays it is at the airport. The modern measurements in the city that are shown below have been made to study the influence of this change.
For this plot he computed the 0th to the 100th percentile. The 50th percentile is the median, 50% of the data has a lower value. The 10th percentile is the value where 10% of the data is smaller, and so on. The 0th and 100th percentile in this plot are the minimum and maximum. What is displayed is the temperature difference between these percentiles. On average the difference is about 2°C, the airport is warmer. However, for the higher percentiles (95th) the difference is much larger. Trewin explains this by cooling of the city station by a land-sea circulation (sea breeze) often seen on hot summer days. For the highest percentiles (99th), the difference becomes smaller again because offshore wind override the sea breeze.
Clearly if you would homogenize this time series for the transition from the coast to the inland by only correcting the mean, you would still have a large inhomogeneity in the higher percentiles, which would still lead to non-climatic spurious trends in hot weather.
Thus we would need a bias correction of the complete probability distribution and not just its mean.
Or we should homogenize the indices we are interested in, for example percentiles or the number of days above 40°C. etc. The BEST algorithm being fully automatic could be well suited for such an approach.
The new dataset is introuduced by Zeke Hausfather and Robert Rohde on Real Climate:
Daily temperature data is an important tool to help measure changes in extremes like heat waves and cold spells. To date, only raw quality controlled (but not homogenized) daily temperature data has been available through GHCN-Daily and similar sources. Using this data is problematic when looking at long-term trends, as localized biases like station moves, time of observation changes, and instrument changes can introduce significant biases.
For example, if you were studying the history of extreme heat in Chicago, you would find a slew of days in the late 1930s and early 1940s where the station currently at the Chicago O’Hare airport reported daily max temperatures above 45 degrees C (113 F). It turns out that, prior to the airport’s construction, the station now associated with the airport was on the top of a black roofed building closer to the city. This is a common occurrence for stations in the U.S., where many stations were moved from city cores to newly constructed airports or wastewater treatment plants in the 1940s. Using the raw data without correcting for these sorts of bias would not be particularly helpful in understanding changes in extremes.
The post explains in more detail how the BEST daily method works and presents some beautiful visualizations and videos of the data. Worth reading in detail.
Daily homogenization
When I understand the homogenization procedure of BEST right, it is based on their methods for the monthly mean temperature and this only accounts for non-climatic changes (inhomogeneities) in the mean temperature.The example of a move from black roof in a city to an airport is also a good example that not only the mean can change. The black roof will show more variability because on hot sunny days the warm bias is larger than on windy cloudy days. Thus part of this variability is variability in solar insolation and wind.
Also the urban heat island could be a source of variability, the UHI is strongest on wind and cloud free days. Thus part of the variability in observed temperature will be due to variability in wind and clouds.
A nice illustration of the problem can be found in a recent article by Blair Trewin. He compares the distribution of two stations, one in a city near the coast and one at an airport more inland. In the past the station was in the city, nowadays it is at the airport. The modern measurements in the city that are shown below have been made to study the influence of this change.
For this plot he computed the 0th to the 100th percentile. The 50th percentile is the median, 50% of the data has a lower value. The 10th percentile is the value where 10% of the data is smaller, and so on. The 0th and 100th percentile in this plot are the minimum and maximum. What is displayed is the temperature difference between these percentiles. On average the difference is about 2°C, the airport is warmer. However, for the higher percentiles (95th) the difference is much larger. Trewin explains this by cooling of the city station by a land-sea circulation (sea breeze) often seen on hot summer days. For the highest percentiles (99th), the difference becomes smaller again because offshore wind override the sea breeze.
Clearly if you would homogenize this time series for the transition from the coast to the inland by only correcting the mean, you would still have a large inhomogeneity in the higher percentiles, which would still lead to non-climatic spurious trends in hot weather.
Thus we would need a bias correction of the complete probability distribution and not just its mean.
Or we should homogenize the indices we are interested in, for example percentiles or the number of days above 40°C. etc. The BEST algorithm being fully automatic could be well suited for such an approach.
Monday, 15 July 2013
WUWT not interested in my slanted opinion
Today Watts Up With That has a guest post by Dr. Matt Ridley. In this post he seems to refer to a story that was debunked more than a year ago:
Naturally, I did not formulate it that way, but in a perfectly neutral way suggested that people could find more information about the above quote as my blog. I see no way my comment could have gone against the WUWT commenting policy. Still the response was:
[sorry, but we aren't interested in your slanted opinion - mod]
Strange, people calling themselves skeptics that are not interested in hearing all sides. I see that some people from WUWT still find their way here to see what the moderator does not allow. Here it is:
Investigation of methods for hydroclimatic data homogenization
(削除) (I may remove this redirect in some days, as this post does not really provide any new information.)
(削除ここまで)
UPDATE: Sou at Hotwhopper wrote a post, WUWT comes right out and says "We Aren't Interested" in facts , about his post. Thank you, Sou. So I guess I will have to keep this post up. And that also makes it worthwhile to add another gem to be found in the WUWT guest post of Dr. Matt Ridley.
And this is even before you take into account the exaggeration that seemed to contaminate the surface temperature records in the latter part of the 20th century – because of urbanisation, selective closure of weather stations and unexplained “adjustments”. Two Greek scientists recently calculated that for 67 per cent of 181 globally distributed weather stations they examined, adjustments had raised the temperature trend, so they almost halved their estimate of the actual warming that happened in the later 20th century.I tried to direct those WUWT readers that are interested in both sides of the conversation to an old post of mine about why these Greek scientist were wrong and mainly how their study was abused and exaggerated by WUWT.
Naturally, I did not formulate it that way, but in a perfectly neutral way suggested that people could find more information about the above quote as my blog. I see no way my comment could have gone against the WUWT commenting policy. Still the response was:
[sorry, but we aren't interested in your slanted opinion - mod]
Strange, people calling themselves skeptics that are not interested in hearing all sides. I see that some people from WUWT still find their way here to see what the moderator does not allow. Here it is:
Investigation of methods for hydroclimatic data homogenization
(削除ここまで)
UPDATE: Sou at Hotwhopper wrote a post, WUWT comes right out and says "We Aren't Interested" in facts , about his post. Thank you, Sou. So I guess I will have to keep this post up. And that also makes it worthwhile to add another gem to be found in the WUWT guest post of Dr. Matt Ridley.
Sunday, 5 May 2013
The age of Climategate is almost over
It seems as if the age of Climategate is over (soon). Below you can see the number of Alexa (social bookmarking) users that visited What Up With That? At the end of 2009 you see a jump upwards. That is where Anthony Watts made his claim to fame by violating the privacy of climate scientist Phil Jones of the Climate Research Unit (CRU) and some of his colleagues.
Criminals broke into the CRU backup servers and stole and published their email correspondence. What was Phil Jones' crime? The reason why manners and constitutional rights are not important? The reason to damage his professional network? He is a climate scientist!
According to Watts and co the emails showed deliberate deception. However, there have been several investigations into Climategate, none of which found evidence of fraud or scientific misconduct. It would thus be appropriate to rename the Climategate to Scepticgate. And it is a good sign that this post-normal age is (almost) over and the number of visitors to WUWT is going back to the level before Climategate.
Since the beginning of 2012, the number of readers of WUWT is in a steady decline. It is interesting coincidence that I started commenting once in a while since February 2012. Unfortunately for the narcissistic part of my personality: correlation is not causation.
The peak in mid 2012 is Anthony Watts first failed attempt in writing a scientific study.
According to WUWT Year in review (Wordpress statistics), WUWT was viewed about 31,000,000 times in 2011 and 36,000,000 times in 2012. However, a large part of the visitors of my blog are robots and that problem is worse here as for my little read German language blog. Alexa more likely only counts real visitors.
Criminals broke into the CRU backup servers and stole and published their email correspondence. What was Phil Jones' crime? The reason why manners and constitutional rights are not important? The reason to damage his professional network? He is a climate scientist!
According to Watts and co the emails showed deliberate deception. However, there have been several investigations into Climategate, none of which found evidence of fraud or scientific misconduct. It would thus be appropriate to rename the Climategate to Scepticgate. And it is a good sign that this post-normal age is (almost) over and the number of visitors to WUWT is going back to the level before Climategate.
Since the beginning of 2012, the number of readers of WUWT is in a steady decline. It is interesting coincidence that I started commenting once in a while since February 2012. Unfortunately for the narcissistic part of my personality: correlation is not causation.
The peak in mid 2012 is Anthony Watts first failed attempt in writing a scientific study.
According to WUWT Year in review (Wordpress statistics), WUWT was viewed about 31,000,000 times in 2011 and 36,000,000 times in 2012. However, a large part of the visitors of my blog are robots and that problem is worse here as for my little read German language blog. Alexa more likely only counts real visitors.
Tuesday, 18 September 2012
Future research in homogenisation of climate data – EMS2012 in Poland
By Enric Aguilar and Victor Venema
The future of research and training in homogenisation of climate data was discussed at the European Meteorological Society in Lodz by 21 experts. Homogenisation of monthly temperature data has improved much in the last years, as seen in the results of the COST-HOME project. On the other hand the homogenization of daily and subdaily data is still in its infancy and this data is used frequently to analyse changes in extreme weather. It is expected that inhomogeneities in the tails of the distribution are stronger than in the means. To make such analyses on extremes more reliable, more work on daily homogenisation is urgently needed. This does not mean than homogenisation at the monthly scale is already optimal, much can still be improved.
When gathering parallel measurements the metadata is very important. INSPIRE (an EU Directive) has a standard format for metadata, which could be used.
It may be difficult to produce an open database with parallel measurements as European national meteorological and hydrological services are often forced to sell their data for profit.(Ironically, in the Land the Free (markets), climate data is available freely, the public already paid for it with their tax money after all.) Political pressure to free climate data is needed. Finland is setting a good example and will free its data in 2013.
The future of research and training in homogenisation of climate data was discussed at the European Meteorological Society in Lodz by 21 experts. Homogenisation of monthly temperature data has improved much in the last years, as seen in the results of the COST-HOME project. On the other hand the homogenization of daily and subdaily data is still in its infancy and this data is used frequently to analyse changes in extreme weather. It is expected that inhomogeneities in the tails of the distribution are stronger than in the means. To make such analyses on extremes more reliable, more work on daily homogenisation is urgently needed. This does not mean than homogenisation at the monthly scale is already optimal, much can still be improved.
Parallel measurements
Parallel measurements with multiple measurement set-ups were seen as an important way to study the nature of inhomogeneities in daily and sub-daily data. It would be good to have a large international database with such measurements. The regional climate centres (RCC) could host such a dataset. Numerous groups are working on this topic, but more collaboration is needed. Also more experiments would be valuable.When gathering parallel measurements the metadata is very important. INSPIRE (an EU Directive) has a standard format for metadata, which could be used.
It may be difficult to produce an open database with parallel measurements as European national meteorological and hydrological services are often forced to sell their data for profit.(Ironically, in the Land the Free (markets), climate data is available freely, the public already paid for it with their tax money after all.) Political pressure to free climate data is needed. Finland is setting a good example and will free its data in 2013.
Labels:
climate data,
conference,
EMS,
extreme weather,
homogenization,
research ideas,
station data
Monday, 21 May 2012
What is a change in extreme weather?
What is a change in extreme weather?
The reason for changes in extremes can be divided up into two categories: changes in the mean (see panel a of the figure below) and other changes in the distribution (simplified as a change in the variance in panel b). Mixtures are of course also possible (panel c).
If you are interested in the impacts of climate change, you do not care why the the extremes are changing. If the dikes need to be made stronger or the sewage system needs larger sewers and larger reservoirs, all you need to know is how likely it is that a certain threshold is reached. Much research into changes in extreme weather is climate change impact research and thus does not care much about this distinction.
If you are interested in understanding the climate system, it does matter why the extremes are changing. Changes in the mean state of the climate are relatively well studied. Interesting questions are, for instance, whether a change in the mean changes the distribution via feedback processes or whether the reduced temperature contrasts between the poles and the equator or between day and night cause changes in the distribution.
If you are interested in understanding the climate system also the spatial and temporal averaging scales matter. If rain fronts move slower, they may locally produce more extreme daily precipitation sums, while on a global scale or instantaneously there is no change in the distribution of precipitation.
I hope scientists will distinguish between these two different ways in which extremes may change in future publications and, for example, not only compute the increase in the number of tropical days, but also how many of these days are due to the change in the mean and how many are due to changes in the distribution. I think this would contribute to a better understanding of the climate system.
Figure is taken from Real Climate, which took it from IPCC (2001).
The reason for changes in extremes can be divided up into two categories: changes in the mean (see panel a of the figure below) and other changes in the distribution (simplified as a change in the variance in panel b). Mixtures are of course also possible (panel c).
If you are interested in the impacts of climate change, you do not care why the the extremes are changing. If the dikes need to be made stronger or the sewage system needs larger sewers and larger reservoirs, all you need to know is how likely it is that a certain threshold is reached. Much research into changes in extreme weather is climate change impact research and thus does not care much about this distinction.
If you are interested in understanding the climate system, it does matter why the extremes are changing. Changes in the mean state of the climate are relatively well studied. Interesting questions are, for instance, whether a change in the mean changes the distribution via feedback processes or whether the reduced temperature contrasts between the poles and the equator or between day and night cause changes in the distribution.
If you are interested in understanding the climate system also the spatial and temporal averaging scales matter. If rain fronts move slower, they may locally produce more extreme daily precipitation sums, while on a global scale or instantaneously there is no change in the distribution of precipitation.
I hope scientists will distinguish between these two different ways in which extremes may change in future publications and, for example, not only compute the increase in the number of tropical days, but also how many of these days are due to the change in the mean and how many are due to changes in the distribution. I think this would contribute to a better understanding of the climate system.
Figure is taken from Real Climate, which took it from IPCC (2001).
Labels:
climate change,
climate variability,
extreme weather,
feedback
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