Organizing the Fragments:
Organizing the Fragments: This blog returns to the series (linked below) where I have tried to give some insight into the issues of the management and politics within the climate community. I hope that this series ultimately provides transparency to the readers, and perhaps contributes to improving the use and acquisition of resources. In the previous blog in this series I wrote about the community being fragmented for both good and bad reasons. In this blog I will talk about one of the prominent community activities to organize these fragments. But, first, it is worth considering the federal budget for climate science.
Here is a budget chart from the U.S. Global Change Research Program website. (I put the legend at the bottom of the page.) This is the budget in 2005 dollars from Fiscal Year 1989 through 2008. (Here is direct link to budget information.)
Figure 1: Federal budget for climate science sorted by agencies from Fiscal Year 1989-2008. Legend is at bottom of page.
There are several things that often surprise people, both scientists and non-scientists. First is the size of NASA’s budget; it has been and is larger than all of the other agencies combined. This is due to the fact that NASA builds the instruments and satellites and launches the rockets for much of the observing system. There are four agencies which have, historically, been known as the “big four”: NASA, NOAA, NSF and DOE. In recent years the Department of Transportation and Health and Human Services have seen an increasing budget for climate science. (Remember this blog on Gulf Roads.)
There are, of course, a number of ways to calculate how much is “spent on climate.” (I pose, for example, what is the relation between weather and climate, especially with regard to the observing network?) Let’s assume that there is a consistent and rational approach represented in this figure. Note that there was a peak in 1995. Much of this peak was due to the Earth Observing System program at NASA, which was a program advocated for and funded by George H. W. Bush. During the Clinton-Gore years, this program was downsized tremendously – by numbers far larger than indicated in the graph. It is surprising to many to see the budget decrease steadily during the second Clinton administration. (Note, I am not analyzing the reasons for this decrease.) The total budget per year from about 1994 to 2005 fluctuated between, about, 1.7 and 2.0 billion dollars per year. (Is this the right amount, too much, too little?)
Since 2004 there has been a steady drop in the budget for studying climate change. This has been most present in the NASA budget. However, throughout the federal climate community, there has been a decrease in the budget, especially when considering the cost of inflation. There have been many scientists leaving the federal laboratories, and often leaving the field. (See this article in Science.) (Yes, there are unemployed and laid off scientists. This stands in stark contrast to some of the rhetoric in the blogosphere.)
It is the decrease in the NASA budget that I want to focus on. Throughout the long effective decline in the NASA budget, that I would argue started in about 1994, the problem of fragmentation has amplified the impact of the budget pressure. The scientists that represent the atmospheric, oceanic, land surface, chemistry, geological, etc. disciplines have each had and advocated their priorities. There were reviews and de-scoping of the observing program. We now stand at a time when Earth science observations are in significant decline, and the projection for the next few years is for more declines.
One response of the community was to initiate a Decadal Survey, which was run by the National Academy of Science, National Research Council. (The National Academy of Sciences in NOT a governmental organization. It is often asked by the government to review important questions. About the National Academy) The first Decadal Survey was published in 2007. The request to the National Academy was to
1.Review the status of the field to assess recent progress in resolving major scientific questions outlined in relevant prior NRC, NASA, and other relevant studies and in realizing desired predictive and applications capabilities via space-based Earth observations;
2.Develop a consensus of the top-level scientific questions that should provide the focus for Earth and environmental observations in the period 2005–2015;
3.Take into account the principal federal- and state-level users of these observations and identify opportunities for and challenges to the exploitation of the data generated by Earth observations from space;
4.Recommend a prioritized list of measurements, and identify potential new space-based capabilities and supporting activities within NASA ESE [Earth Science Enterprise] and NOAA NESDIS to support national needs for research and monitoring of the dynamic Earth system during the decade 2005–2015; and
5.Identify important directions that should influence planning for the decade beyond 2015.
This was an effort to address the fragmentation that permeates the field, to form a consensus of the priority measurements that must be made if we are to characterize the climate and how it is changing.
From the executive summary of the report:
“The committee found that fundamental improvements are needed in existing observation and information systems because they only loosely connect three key elements: (1) the raw observations that produce information; (2) the analyses, forecasts, and models that provide timely and coherent syntheses of otherwise disparate information; and (3) the decision processes that use those analyses and forecasts to produce actions with direct societal benefits.
Taking responsibility for developing and connecting these three elements in support of society’s needs represents a new social contract for the scientific community. The scientific community must focus on meeting the demands of society explicitly, in addition to satisfying its curiosity about how the Earth system works.”
These are important words and conclusions, but they are also words that have been stated in different ways for as long as I have been in the field. This version of the words is new in the sense that they mention “a social contract,” and they carry the weight of a National Academy review.
However, this report, like many earlier reports, falls into the fragmented environment of the agencies. The agencies see opportunity and they see threat. They cherry pick the report to stand as advocacy for the programs and projects that they would like to carry forward. This idea of how to connect those elements that are loosely connected, that need to be more tightly connected, defies implementation, if not comprehension. Efforts to integrate activities are added on with little recognition that the power structure is aligned with the existing fragments and that the incentive structure does not reward integration. Despite the hard work, the good intentions, and the rational conclusions of reports like the Decadal Survey, we are left with a set of fundamental human-systems that must be addressed. These reports are, perhaps, necessary, but clearly not sufficient to address the strategic management, organization, and optimized effectiveness of the community.
r
Legend for Figure
USDA: U.S. Department of Agriculture
DOC / NOAA: Department of Commerce / National Oceanic and Atmospheric Administration
DOE: Department of Energy
HHS: Department of Health and Human Services
DOI / USGS: Department of Interior / U.S. Geological Survey
DOT: Department of Transportation
USAID: U.S. Agency for International Development
EPA: Environmental Protection Agency
NASA: National Aeronautics and Space Administration
NSF: National Science Foundation
SI: Smithsonian Institution
Links to relevant blogs.
Importance of Justification
Buying Big Computers
Fragmented Climate
This series of blogs collected.
This is a report that I was lead author on in the year 2000. Many of the conclusions of this report still hold today.
High End Climate Science: Development of Modeling and Related Computing Capabilities
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Updated: 5:19 PM GMT on October 05, 2010
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How Do We Know?
How Do We Know?: In general I have tried to steer clear of the arguments about the reality of climate change that pervade, mostly now, the web. But I got a note this week from a reader that had as one part of the note “How do we know?” This question coupled with a number of events got me thinking. How do we know?
When I was at NASA I was involved in the assessment of the environmental impact of airplanes. Often we were investigating new types of airplanes, like commercial supersonic jets. These would fly higher than the current fleet, in the stratosphere, and their pollutants would stay in the atmosphere a long time. These studies were, of course, based on atmospheric transport and chemistry models. Over the years there were many numerical experiments and measurement campaigns. We developed a collection of knowledge, which included a catalog of what the models did well and what the models did poorly. With all of this knowledge we could state with some certainty what different fleets might or might not do to, say, the ozone layer. Occasionally we would get a request for what a VERY small fleet, say less than five, of very high flying airplanes might do. Based on our experience with both observations and models we could state, without further experiments, that this very small fleet would not (or would) have significant impact. Still though, the sponsor of the research would want us to do a numerical experiment to “prove it.” Without the experiment – how would we know?
Of course some would argue that a numerical experiment does not give us a definitive yes or no answer. In fact, there are those in the world who maintain that we never know until something happens, and we can experience it or measure it. There are those in the world who believe that the process of past experience, measurements, theory (remember theory is derived from testable hypotheses), and prediction tell us with high probability what will and will not happen. That knowledge is actionable.
This train of thought reminded me of the spring of 2005 when I was finishing up a sabbatical at the Lawrence Livermore National Laboratory. I was in the barbershop on Main Street getting my last Livermore haircut, and there was a property appraiser in the chair next to me. The property appraiser was planning to quit his job because he had just been asked to reappraise a house to add $50,000 dollars to the price. He had gone out and said that a 100 square feet of tile in the entrance, which he had “overlooked,” was worth $50,000 dollars. He felt dishonest, used, and liable for lying. We talked about the way the housing market was working, and how the run up in prices did not sit on a foundation that even rose to the level of “house of cards.” It was a situation that was unsustainable and stunningly stupid. There was little we could do outside of “not participate.” (I never imagined, however, that this stunning stupidity could reach so far and so deep.)
How did we, this house appraiser and I, know about the housing market? Experience is one way. Some simple math is another way – the robustness of the housing market required exponential growth of wealth and that just doesn’t work for very long. (And exponential growth of waste in the environment will also not work for very long.) But in the end, we did not really know -- did we?
How do we know? Sometimes this question can be quite informed. How did Columbus and Magellan know that they would not fall off the edge of the world? (I limit the question to Columbus and Magellan and don’t include the Vikings and the Chinese and the Polynesians, because I know the European world had been indoctrinated that the world was flat. They drew maps.) There were writings and measurements that supported the idea that the Earth was round. There were, perhaps, even reports from other cultures of experiences in lands across the water. So the answer is that Columbus and Magellan did not know. They set out to find out. They set out, I assert, with some information.
When we set out for the Moon, did we really know that it was not the staring eye of monster?
How do we know? There are people who will never accept the prediction of global warming. They will not accept the evidence from the experiential base, nor will they accept the utility of models. They will not accept the process of evaluation of models, which documents both what we know well and what we know poorly. As long as there is any discrepancy in the knowledge base they will find a foundation on which to question and dismiss the conclusion that: carbon dioxide released by the industry of humans into the atmosphere will warm the Earth, cause sea level to rise, and the weather to change. How do we know? How do they know otherwise? Why do people believe what they believe?
I sit and write this (tardy) blog on an airplane. This is a mass of metal held up above the ground by air that I cannot see. This particular mass of metal was largely designed by model simulations based on observations and theory (remember theory is derived from testable hypotheses). (And based on this model-assisted design, some pilot sat in this mass of metal the first time it was built and accelerated to some speed that, in a crash, would crush her or his aorta. They sat in that new airplane with the reasonable expectation that they would have dinner when they got home in the evening.) This metal shell moves forward at more than 500 miles per hour towards Denver, with the Earth turning below it, and with some knowledge that what lies ahead is more invisible air that can hold the plane up. We fly with presumed knowledge of the density of the air, the temperature, the wind speed and that the air is not full of hard ice. Likely, this plane will land no more than a few inches from where it was planned to land when it started down the runway in Romulus. How do I know I will get there? I don’t. But I have reason to expect that I will. (And if you read this, I did.)
We are placing massive amounts of carbon dioxide, waste, into the atmosphere. We know that carbon dioxide in an atmosphere irradiated by the Sun heats up the atmosphere. The amount of carbon dioxide we emit is increasing exponentially. How do I know? How do we know?
Does smoking lead to lung cancer? Does two pounds of barbeque a day lead to obesity? Does obesity lead to heart disease? How do we know? (And yes, I can know that something is potentially bad for me, and I will still do it.)
It is true that some people get tired of hearing about global warming. At this point, I am tired of hearing about the collapsing economy. I am angry that because the stunning stupidity of wanting people has threatened my savings, my retirement, my future. I am upset because I am placed in a position where my experiential base does not tell me what to do, or how to figure out what to do … if there is anything that I can do. I am hopeful that the policies developed during the 1930s and 70 years of generation of economic knowledge offers strategies to keep us from another great depression. I am wary that economic theory and economic models are based on quantification of markets and the behavior of people. Yes, I am tired of hearing of the reality of the economic instability, but that makes it no less real. I can commiserate with those who are tired of hearing of global warming. Global warming – an important reality that is based in the principles of predictable classical physics and billions of observations. Global warming – an important reality that we can do something about.
We have knowledge, and the ability to make informed decisions about what will happen. We might not always “know” to the satisfaction of all people. We can view this knowledge as a blessing or a curse. I view the knowledge as a blessing, an opportunity, something to be thankful for.
r
NEWS: The Sun Shows Signs of Life
Figure 1: New-cycle sunspot group 1007 emerges on Halloween and marches across the face of the sun over a four-day period in early November 2008. Credit: Solar and Heliospheric ObservatoryThe Solar and Heliospheric Observatory (SOHO). From Science at NASA. (Thanks again to Judith Lean.)
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Updated: 2:05 PM GMT on November 23, 2008
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Fragmented Climate
Fragmented Climate: In the previous two blogs (linked below) I have tried to give some insight into some of the issues that climate scientists are thinking about as we move into the future. I started from the discussions initiated at the World Modeling Summit for Climate Prediction at the European Center for Medium-range Weather Forecasts. (See also article in Nature). I provided some analysis with the discussion focused on computational resources. In that analysis I tried to show some of the tensions within the climate community and how the community operates, good or bad, efficiently or inefficiently.
Science communities tend to be fragmented for many reasons. Some argue that this arises because of the very nature of scientific discovery and the personalities of scientists. Scientists are trained to question; the very process of science requires challenging results; discovery is unpredictable and today’s discovery will be altered, perhaps rejected, by new observations and new studies. In addition to this cultural predisposition towards fragmentation, the way science is funded in the U.S. supports, if not encourages, fragmentation. There are multiple agencies that “own” the problem, or different aspects of the “problem.” Program managers within the agencies act with various degrees of autonomy. Virtually all decisions are, ultimately, based on some flavor of competition. When this is combined with the predisposition of the culture of science to reduce, to make smaller, to fragment rather than to consolidate; if there is to be a balanced program it arises out of a messy, sometimes contentious, time-consuming process.
Balance: One of the points of previous blogs was that there is a need for a balanced approach to the expenditures on climate research. This balance considers, for instance, a small number of big, unreliable computers versus a larger number of more reliable, medium size computers versus data systems versus sustained observations versus new observations versus applied research versus basic research versus education versus all of the other elements that are part of the undertaking of science.
Over the past two decades climate science became “big science.” This big science followed from increasing societal concern about the global environment. The big science was and is most defined by the cost to take observations; there is a concentration of money in space observations to support climate research, weather research and forecasting, and more general research of the Earth system. The big science is also defined by the need for big computers, consistent with the computational resources needed for several fields of research – either applied or basic research. (The biggest of all computer problems has traditionally been Stockpile Stewardship, and computational requirements for climate science are of a similar scale.) There are, consistent, with these resources a hoard of scientists and technicians and software specialists. Climate research is big science, and compared with many worthy research efforts, it is well funded (More than 15,000 scientists come to the December American Geophysical Union meeting, only one of many relevant meetings.). While well funded, there is also a mountain of work that needs to be done, that can be done, and for which the funds are not adequate. It is also true that if the funds were there the human and technical resources do not pre-exist; that is, there is not a large store of people and machines and observations on the margin that could immediately exploit additional funds.
This suggests that for climate science to move to a place to best serve society it would benefit from more formalized management, with a plan for balanced, integrated expenditures. This would suggest the need to manage the current resources more effectively. There is a need to distinguish the mission that supports the essential observations and modeling that might stand as a basis for climate services in contrast to those equally essential fundamental research topics to answer the questions that arise from the thousands of studies and papers that are published every year. There is a need for workforce development.
This need for more formalized, integrative management stands in contrast to the fragmented state of field – fragmentation that is both natural and perhaps in the best interest of basic research. The question arises whether or not the fragmentation is greater than optimal, and if so, are there ways to effectively optimize the expenditures by the reduction of fragmentation. I pose that the answer is yes, and I also know that 20 years of trying to move towards this optimization is characterized by slow, measurable, difficult progress. I know that most efforts to “bureaucratically centralize,” to reorganize, are not obviously successful; I assert that they are more negative than positive.
Hence we are faced with the need for organization in a fragmented environment that is fragmented for both good and bad reasons. The challenge is how do we sustain the healthy fragmentation, that is the diversity that supports innovation, creativity, and the scientific process, and how do we reduce the fragmentation that exists, for instance, because of bureaucratic ownership and competition for limited resources? What are the forms of management that exist between bureaucratic centralization and anarchy? The next blog in this series will explore some of the self organization efforts of the field.
r
Importance of Justification
Buying Big Computers
High End Climate Science: Development of Modeling and Related Computing Capabilities
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Updated: 3:53 PM GMT on November 29, 2008
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Buying Big Computers
Climate Supercomputing: In the previous blog I started an analysis of some of the arguments that are made by scientists in looking for resources, that is, money to support climate science. I took as a starting point the World Modeling Summit for Climate Prediction at the European Center for Medium-range Weather Forecasts. (See also article in Nature).
I argued that the potential impact on society that follows from climate change research is one of the reasons that climate change research motivates both broad and deep argument about funding climate change research. This distinguishes climate science from, for example, astronomy and high-energy physics.
Focusing specifically on the modeling aspects of climate science, expenditures on high-performance computers come to the forefront in the discussion. In fact, in the Statement from the Modeling Summit it was stated that in a facility to accelerate climate prediction, “The central component of this world facility will be one or more dedicated highend computing facilities …” There is no doubt that high-performance computers are essential to climate science and that climate modeling is an application that drives the requirements for high-performance computing. To some, expenditures on high-performance computers are among the highest priorities of the field. To others this is not so obvious.
More generally, expenditures in the United States on all aspects of high-performance computing are controversial. Like funding for climate change research, expenditures by the U.S. government on high performance computing has been examined and justified by one document after another. A recent document published is one by the National Academies Press, The Potential Impact of High-End Capability Computing on Four Illustrative Fields of Science and Engineering. At the core of this study was an examination of whether or not there were investigations that would benefit in the here and now from more high-performance computers, or would it be better to defer expenditures.
As I mentioned in the previous blog, high-performance computers have a relatively short life span. After about five years the computers are obsolete, and there will be the request for a new computer. Expenditure in high performance computing is not likely to lead to the generation of a definitive answer; it will lead to more questions and more complex algorithms. A high-performance computing center is a multiple tens of millions dollars per year expenditure. These computers are essential to climate research and a sustained, stable funding stream is required. This requires a different strategy, a different business model, than comes from arguing for “a center,” or a sequence of computational centers. This sustained commitment does demand accountability and metrics to indicate progress and best use of resources.
High-performance computers are not simple to use. Therefore, the expenditure on high-performance computers demands related expenditures in software and personnel. In fact, the field struggles to keep computer models viable --- able to use the computational systems. While a high-performance computer might be advertised as able to deliver several trillions of calculations per second, climate models, and many other applications, are only able to realize a small percentage of this potential. Again, this leads to scrutiny about the efficacy of expenditures in high-performance computers. To me, this suggests that a balance of expenditures is needed, and that priority attention needs to be given to the software and the viability of the software. There needs to be an anticipation of new computational systems, placing the software and algorithms out in front. If hardware, big computer systems, big iron, big silicon are out in front, each new computer system leads to an exercise in urgency of molding the models to use the computer system. This leads to an apparent inefficient use of resources. From a funding point of view, it seems much easier to advocate for large computers than it is to advocate for focused expenditures in software and algorithms.
There is one more aspect of expenditures in computing that I will mention here. An effective computational system needs to have a comparable data system associated with it. That data system needs to have an interface with an exotic and quixotic computational platform. The data system must also support the analysis of the simulations generated by the computer; therefore, it must also serve an exotic and quixotic user community. So the expenditure in high-performance computing begets expenditure in high-performance data systems.
Where does this leave us?
High-performance computers, supercomputers, are only a piece of the system that is needed to support climate modeling. Climate modeling is only a piece of the activities associated with climate science. Balanced, reasoned expenditures across all of these elements of the climate research enterprise are needed.
There are many aspects to developing a strategic, sustained managed enterprise in climate research. Some of the issues raised above demonstrate the point of view that is taken by program managers, agency leads, and elected officials when there is argument by the science community for more funds for more resources. There are legitimate questions of how the resources are managed, how priorities are determined, and the payoff of that research. The argument that climate research is important does not stand as a convincing argument. There are many important areas of research, and virtually every field feels that their research is important --- and underfunded.
It was also stated in the initial blog of this series that the community of scientists never uniformly agree that expenditures on major facilities is the best way forward. This is especially true when the expenditures are focused towards high-performance computers. These are expensive, difficult to use systems. While it is often argued that high-performance computing systems serve the community, the vast majority of the community is not served, directly, by expenditures in high-performance computing. Funds are concentrated for the direct use by what is, in fact, a small sub-culture of the community. It is an important and essential part of the climate community, but the climate modeling activities should not be confused with the totality of the community, or even the majority of the community. It is natural therefore, for others in the community to advocate for their research, because they will argue that their research is important for the future of the field. They will argue that discovery comes more from observations than computational experimentation. They will argue that discovery follows from intellect and creativity, that more minds, not more computations are the best way forward. Their arguments are substantive.
The point of this series of blogs is to address some of the strategic challenges that the field of climate research faces going forward. There is no doubt that improved understanding and prediction of the climate is important to society. No doubt that the field needs computing facilities, data systems, and observing systems. No doubt that there is much to be done now. But perpetuation of the fragmented approach to developing the facilities and the infrastructure to support the field is inefficient and, in fact, damaging. The intellectual capacity that needs to be focused on climate change is immense, and there are communities of thinkers that are able to contribute to the climate change problem today. The engagement of this intellectual community, the extension and scaling across minds, is as or more important than the extension and scaling of models across silicon chips. The climate community must move away from the strategies to secure funding that worked when the research was held primarily in the community of scientists. It must move away from the strategies that worked when agency budgets were growing. Climate science needs to recognize that its importance stands in relation to other important fields of research, like energy security, whose urgency and impact will trump the needs of climate change if it is just a matter of competing importance.
Importance of Justification
Figure 1: Picture of the ENIAC computer, which was the first computer used in weather forecasting. This image is taken from the Computer Science Department at Virgina Tech and the article The ENIAC by Kevin W. Richey. An evolving history of general circulation models and the introduction of computers to forecasting can be found here American Institute of Physics Narrative History of General Circulation Modeling. Here is a link to the University of Pennsylvania ENIAC Museum.
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Updated: 7:10 PM GMT on November 11, 2008
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