Heat Waves (4) A Climate Case Study:
Heat Waves (4) A Climate Case Study:
In the last article I wrote that the extreme events of 2011 were providing us with the opportunity to think about climate and how to cope with a warming world. The U.S. is experiencing an extreme heat event this week (Masters @ WU). This heat wave is the consequence of a strong, stationary high pressure system over the central U.S., and it will move to the east over the next few days. Back on July 14th The Capital Weather Gang did a nice write up on the forecast of the heat wave. At the end of this blog are links to my previous blogs on heat waves and human health.
When thinking about weather, climate, and extreme events an important idea is “persistence.” For example, a heat wave occurs when there are persistent high temperatures. Persistent weather patterns occur when high and low pressure systems get large and stuck; that is, they don’t move. In the Figure below, you need to imagine North America and the United States. There is a high pressure center over the proverbial Heartland. With blue arrows I have drawn the flow of air around the high pressure system, and in this case moist air. There is moisture coming from the Gulf of Mexico and, in fact on the date when this was drawn, from the Pacific. This is common in the summer to see both the Gulf of Mexico and Pacific as sources of continental moisture.
Figure 1: Schematic of a high pressure system over the central United States in July. While generic, this is drawn to represent some of the specifics of 2011. The green-shaded area is where there have been floods in 2011. The brown-shaded area represents sustained drought in the southern part of the nation.
At the center of this high pressure system there is a suppression of rain, because the air is moving downward. This sets up a situation where the surface heats from the Sun’s energy. There is not much mixing and cooling, because of the suppression of the upward motion that produces rain. Hence, if this high pressure system gets stuck, then there is persistent heat. This is a classic summer heat wave.
Let’s think about it some more. There is lot of moisture being drawn around the edge of the high pressure system, and this moisture contributes to the discomfort of people. People – just a short aside about people: if we think about heat and health, then we are concerned about people’s ability to cool themselves. It is more difficult to cool people when it is humid because sweat does not evaporate. Suppose that in addition to this moisture, there is a region where the ground is soaked with water from flooding. Then on top of already moist air coming from the Gulf, there is local evaporation into the air being warmed by the Sun. If on the interior of the high, where the rain is suppressed, there is hot, wet air, then it becomes dangerous heat.
It’s not easy to derive a number that describes dangerous heat. But in much of the eastern U.S. a number that somehow combines temperature and humidity is useful. Meteorologists often use the heat index. It’s the summer time version of “it’s 98 degrees, but it feels like 105.” For moist climates, the heat index is one version of the “it feels like” temperature. Jeff Masters tells me that in Newton, Iowa yesterday, July 17, 2011, the heat index was 126 degrees F. (see here, and 131 F in Knoxville, Iowa on July 18)
Another measure of heat and humidity is the dew point; that is, the temperature at which dew forms, and effectively limits the nighttime low. The dew points in Iowa, South Dakota, Minnesota, and Wisconsin are currently very high and setting records. Here is a map of dew point for July 19, 2011.
Figure 2: Exceptionally high dew points centered on Iowa.
Now if I was a public health official, and I was trying to understand how a warming planet might impact my life, then here is how I would think about it. First, the Gulf of Mexico and the Pacific are going to be warmer, and hence, there will be more humid air. This will mean, with regard to human health for the central U.S., heat waves will become more dangerous, without necessarily becoming hotter. It is also reasonable to expect heat waves will become more frequent and last longer, because those persistent, stuck high pressure systems are, in part, forced by the higher sea surface temperatures. If I am a public health official here is my algorithm – heat waves are already important to my life, and they are likely to get more dangerous, more frequent, and of longer duration. But by how much? Do I need to know by how much before I decide on a plan for action?
If I think about the air being more humid, then I might expect to see trends in the heat index. I might expect to see trends in dew points, and trends in the nighttime minimum temperatures getting higher. (That’s where a greenhouse effect really matters.) I worry about persistent heat, warm nights, and the inability of people and buildings to cool themselves. I worry about their being dangerous heat in places where people and emergency rooms are not used to dangerous heat – not acclimated to heat – not looking for heat-related illness.
Let’s go back to the figure. Rain is suppressed in the middle of the high pressure system, but around the edge of the high pressure system it will rain; there will be storms. (see Figure 3 at the end) The air around the edge of high is warm and very wet. Wet air is energetic air, and it is reasonable to expect local severe storms. (See Severe Storm on Lake Michigan) And if the high pressure is persistent, stuck, then days of extreme weather are possible. If this pattern sets up, then there is increased likelihood of flooding. If I am that public health official, then I am alerted to the possibility of more extreme weather and the dangers thereof. But, again, can the increase of extreme weather be quantified? Do I need to quantify it before I decide on a plan of action?
Still with the figure - what about that region of extended drought and the heat from the high pressure system? Dehydration becomes a more important issue. As a public health official, I start to see the relation of the heat event to other aspects of the weather, the climate. I see the relation to drought. I see the flood, and it’s relation to the winter snow pack and spring rains.
So what I have presented here is to look at the local mechanisms of the weather – what are the basic underlying physics responsible for hot and cold, wet and dry – for moist air? If I stick to these basic physics, and let the climate model frame the more complex regional and global picture, what can I say about the future? Do I have to have a formal prediction to take action? Here in 2011, I see drought and flood and hot weather and warm oceans that interact together to make a period of sustained, dangerous heat. It does not have to “set a record” to convey the reality of the warming earth. It tells me the type of event that is likely to come more often, of longer duration, and of, perhaps, of greater intensity. If I am a public health planner, then I can know this with some certainty. The question becomes, how do I use that information in my planning?
r
Figure 3: Radar loop showing precipitation around the edge of the large high pressure system in the middle of the continent. July 19, 2011.
Previous Blogs on Heat Waves
Hot in Denver: Heat Waves (1)
Heat Waves (2): Heat and Humans
Heat Waves (3): Role of Global Warming
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Updated: Saat: 04:25 AM GMT Tarih: 20 Temmuz 2011
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Drought, Fire, Flood: In the News
Drought, Fire, Flood: In the News
I have been writing about a variety of issues that I know are of interest to only a small number of people – U.S. science organizations, climate model software, and validation of climate models. I am going to move away from that arcane set of subjects for a while and spend a little more time in the climate mainstream. In this entry I want to touch on several subjects – starting with my garden.
My garden is in the flat land that is the western edge of the Great Plains, just east of Boulder, Colorado. Weather wise, it is a complex and difficult environment: more than 5000 feet above sea level, reliant upon water from the winter snow pack in the mountains, huge swings of hot and cold. In terms of climate types, I have seen region defined as both arid and semiarid. In the last week, we have had three or more inches of rain – hard driving rain with much lightning. There is water standing between the rows in the garden. The week of July 4 it was so dry there was a fire ban, and many firework fires.
Last summer in Boulder we had the Fourmile Fire, which burned thousands of acres and dozens of houses. With this rain, we have mudslides, rock slides and flash floods (Longmont Times Call). It all makes you appreciate the importance of the weather and the climate. Wet and dry. Hot and cold. ( 485 Billion Dollar Impact of Weather)
Boulder is a microcosm of what is going on in the U.S. There have been overwhelming fires in Arizona, New Mexico, and Texas. (Texas Fires). Dangerous drought and heat is spreading all across the southern half of the U.S. The dust storm last week in Arizona was reminiscent of pictures of the Dust Bowl. (more here). We were overwhelmed not long ago by the Mississippi River flooding. I have almost forgotten about the Missouri River flooding.
Figure 1: From KFAB Omaha News Radio. Photo Credit AP: Missouri River flood of Calhoun Nuclear Power Plant.
We see here the persistence of weather, climate, snow cover, drought, floods - one extreme after another. Jeff Master’s wrote an excellent summary of 2010-2011 as being a year of the most extreme events since 1816 – the year of Mount Tambora, a definitive and understood climate anomaly. Jeff writes that June 2011 continues the run. July 2011 is looking strong. It has been more than 300 months since there was a “below average” mean temperature. That’s a little compelling.
We are being handed one case study after another, where we see the impact that weather and climate have on us. And what is that impact? We see vulnerable people losing their homes, their crops. But where is the real threat? What does it mean that 213 counties in Texas are primary disaster areas?
Energy, economy, population – markets. We all know that the weather affects our economy. We rely on a stable climate. We see here and now an interconnected world, where extreme heat kills thousands and destroys crops and send food prices soaring. We see multiple billion dollar liens placed on our economy by floods, droughts, and tornadoes. These costs come at a time when economies all around the world are weak. There is a debt crisis, and the weather is demanding more loans. Right here and now the world is providing one climate disaster after another. The weather and climate are showing the need for more planning, for building resilience and recovery strategies. The weather and climate are revealing our vulnerabilities. While there is the obvious, the family fleeing the flood, the destroyed Joplin, Missouri hospital, there is also the accumulated impact felt through markets, higher food prices, emergency relief, things that will not be fixed, people relocating.
We are being offered lessons. I have written this far and not strung together the words “climate change” or mentioned “global warming.” This is the weather in our warming climate. The take away message from climate models, Be Prepared.
r
Rood on To the Point
Open Climate Modeling:
Greening of the Desert
Stickiness and Climate Models
Open Source Communities, What are the Problems?
A Culture of Checking
Organizing U.S. Climate Modeling:
Something New in the Past Decade?
The Scientific Organization
A Science-Organized Community
Validation and the Scientific Organization
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Updated: Saat: 04:05 PM GMT Tarih: 12 Temmuz 2011
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Validation and the Scientific Organization: Organizing U.S. Climate Modeling (4)
Validation and the Scientific Organization: Organizing U.S. Climate Modeling (4)
This entry is the last I will be writing about organizing U.S. climate modeling, software, and open source communities – for a while. At the end of this entry are links to the blogs/articles in a couple of series. I am going to start by quoting a comment from atmoaggie on the previous entry.
“The difference between all of those (rbr: types of models in a previous comment) and climate models is the ability to study their validity.
I would like to see a climate modeling 10 year forecast of some parameters, such as, maybe, average SST for the month of June 2021. Too specific? How about average global SST for JJA (summer) 2021. Still too specific? Maybe the average global SST for the next 10 years.
I, too, work in modeling. In storm surge modeling, one can very easily tune a model to better match the results for one storm (by adjusting air-sea drag, e.g.) only to find that the model is not useful for forecasting as another parameter or physical calculation is incorrect (the sea floor friction formulation, e.g.)
I bring this up to illustrate what can go wrong when modeling a hindcast, tuning to match observations, and applying that model to forecasts. And climate is far more complex, I think, than tides and TC wind and pressure-forced storm surges.”
I want to bring together two streams of thought that I have pursued over the past few months – validation and the scientific organization. First, I will discuss whether or not climate models can be validated and then argue that the development of a validation plan is at the center of developing a scientific organization.
Validation: As suggested in some of my earlier entries the question about whether or not climate models can be validated is a controversial issue. The controversy lies, first, in philosophy. The formal discussion of whether or not climate models can or cannot be validated often starts with a greatly cited paper by Naomi Oreskes et al. entitled Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences. In fact quoting the first two sentences in the abstract:
“Verification and validation of numerical models of natural systems is impossible. This is because natural systems are never closed and because model results are always nonunique.”
Oreskes et al. argues that the performance of the models can be “confirmed” by comparison with observations. However, if the metric of “validation” is a measure of absolute truth, then such absolute validation is not possible. By such a definition little of the science of complex systems, which would include most biological science, medical science, and nuclear weapons management, can stand up to formal validation.
I will return to the stream I started with the quote from atmoaggie, which makes reference to storm surge model (see here for excellent discussion of storm surges Resio and Westerink). The point of the comment is that the storm surge model can be tuned and thereby calibrated based on observations of past storm surges and theory, but the model may still fail in future predictions of storm surges. This points out a weakness in the development of models of natural systems, that the adjustments of the models to represent a historical situation does not assure that model correctly represents the physics of cause and effect. In fact, this is a general problem with modeling of complex natural systems, if you get the answer “right,” then that does not mean you get it right for the right reason. Hence, in the spirit of Oreskes et al. validation is not possible – there is no absolute to be had.
Yet, aren’t storm surge models useful and usable? The same situation is true for weather models and river forecast models, their correctness cannot be assured in any absolute sense, but aren’t they useful and usable? Atmoaggie poses a set of predictions, all of which are reasonable propositions, that may or may not be convincing to him or her. These do not represent a complete set of metrics to evaluate models, and the success or failure of these predictions does not state in any absolute sense whether or not the models have usable information. There are many more elements of model evaluation that determine our level of confidence in the use of models.
It is easy, therefore, to establish that models that cannot be formally validated can be both useful and usable. The results of these models might not be certain, but the degree of confidence that can be attributed to their calculations is very high. This confidence is, in general, established by many forms of model evaluation and additional sources of relevant information, most importantly, observations and basic physical principles.
Validation, verification, evaluation, certification, confirmation, calibration: All of the words in this list have been used in discussions of how to assess the quality of models. For some, there are nuanced differences between the words, but in the general discussion they are all likely to take on the same meaning – some quantitative measure of model quality. The word “validation;” however, is special. Within political or philosophical arguments, the statement “models cannot be validated,” carries a powerful message, especially if one establishes as a principle that the elimination or the reduction of uncertainty is required prior to taking action (see Shearer and Rood). Many scientists take on the mantra that climate models cannot be validated. When I worked at NASA, the culture was that measurements of temperature (for example) could be validated, but that models could not. But if one is talking about temperatures from satellites over a deep layer of the atmosphere, in the spirit of Oreskes et al., can satellite temperature measurements be validated? We can state with stunning confidence that the satellite temperatures are within a certain closeness of a more intuitive or accepted measure of temperature – like a thermometer on a balloon. This is, to me, more calibration than validation, but in my world at NASA, calibration was done in a lab with standards (and that is why we have NIST). At NASA we talked about models being “evaluated.”
Other arguments I have heard about climate modes defying validation are based on to what do we chose to validate against – what is our standard? Suppose that you are interested in how well the model represents the Pacific Ocean, and I am interested in how well it represents the Arctic Ocean. And the scientist down the hall wants to know how well it represents the ice-age cycles, and another wants to know how well it represents the 20th century temperature variability. There is no absolute way to make these choices. More fundamentally, if it is a climate model then how do we measure “climate?”
The list goes on – I have frequently heard arguments of one community making critical remarks about the “science” of other communities. The weather forecast community relies strongly on forecast skill scores, but these measures are by no means unique and for a variety of reasons often only indirectly relevant to the quality of climate models. There is no fundamental reason that an excellent climate model would automatically be an excellent weather forecasting model. The opposite is true as well. Over the years of my career there have been criticisms of climate science by other fields of physics. The gist of their arguments is that they don’t validate models the same way we do, and since we do a good job, they don’t. These arguments make great fuel for political argument and the maintenance of doubt. (Here is an interesting article by Oreskes and Renouf.)
Validation is, therefore, both controversial and important. I pose that validation is at the center of the development of the scientific organization.
Validation and the Scientific Organization: The definition I have posed for the scientific organization is an organization that as a whole functions according to the scientific method. Therefore, if it is a climate modeling organization the model development path, the modeling problems that are being addressed, are determined in a unified way. In that determination, it is required that ways to measure success be identified. This leads to a strategy of evaluation that is determined prior to the development and implementation of model software. With the existence of an evaluation strategy, a group of scientists who are independent of the developers can be formed to serve as the evaluation team.
The development of an evaluation plan requires that a fundamental question be asked? What is the purpose of the model development? What is the application? If the model is being developed to do “science,” then there is no real constraint that balances the interests of one scientific problem versus another. There is little or no way to set up a ladder of priorities.
Again, I will emphasize that to achieve this, and it can be achieved, is a matter of governance and management. It is a process of developing organizational rather than individual goals. It is a myth to imagine that if a group of individuals are each making the “best” scientific decisions, the accumulation of their activities will be the best integrated science. Science and scientists are not immune to the The Tragedy of the Commons. If one wants to achieve scientifically robust results from a unified body of knowledge, then one needs to manage the components of that body of knowledge so that as a whole the scientific method is honored. Enough on that pulpit.
Back to evaluation and validation – Minimally, the arguments about the nuanced meaning of validation and evaluation are a subject about which the climate modeling community needs to develop a standard. By my interpretation, the evaluation of climate models can be structured and quantified as “validation.”
When I was at NASA I had a programmatic requirement to develop a validation plan. And, yes, my friends and colleagues would tell me that that validation was “impossible.” But I am stubborn, and not so smart, so I persisted and still persist with the notion. That old plan can still be found here in Algorithm Theoretical Basis Document for Goddard Earth Observing System Data Assimilation System (GEOS DAS) with a Focus on Version 2.
The software we produced was an amalgam of weather forecasting and climate modeling. For the validation plan the strategy was taken to define a quantitative baseline of model performance for a set of geophysical phenomena. These phenomena were broadly studied and simulated well enough that they described a credibility threshold for system performance. They were chosen to represent the climate system. Important aspects of this validation approach were that it is defined by a specific suite of phenomena, formally separated validation from development, and relied on both quantitative and qualitative analysis.
The validation plan separated "scientific" validation from "systems" validation. It included steps of routine point-by-point monitoring of simulation and observations, formal measures of quality assessment by measure of fit of simulations and observations, and calculation of skill scores to a set of "established forecasts." There was a melding of methodologies of practices of the study of weather and the study of climate. We distinguished the attributes of the scientific validation from the systems validation. The systems validation, focused on the credibility threshold described above, used simulations that were of longer time scales than the established forecasts and brought attention to a wider range of variables important to climate. The scientific validation was a more open-ended process, often requiring novel scientific investigation of new problems. The modeling software system was released for scientific validation and use after a successful systems validation.
The end result of this process was the quantitative description of the modeling system against a standard set of measures over the course of one modeling release to the next. Did it meet the criterion of the absolute validation? No. Did it provide a defensible quantitative foundation for scientific software and its application? Yes.
All told, it does little to base a body of scientific knowledge on the premise that validation is “impossible.” Rather than following such a premise, which immediately devalues the knowledge base, it is more useful to develop a systematic approach to robust, appropriate validation. This stands to represent the complexity of the Earth’s climate and its investigation that serves not only the scientific method, but the communication of that science to other scientists, and to those with a stake in those scientific results. It sets a standard.
r
Open Climate Modeling:
Greening of the Desert
Stickiness and Climate Models
Open Source Communities, What are the Problems?
A Culture of Checking
Organizing U.S. Climate Modeling:
Something New in the Past Decade?
The Scientific Organization
A Science-Organized Community
Validation and the Scientific Organization
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Updated: Saat: 06:19 PM GMT Tarih: 04 Temmuz 2011
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