It would seem to me by far the most important thing would be to find an advisor who was a top researcher in the field (which a new graduate can probably figure out from google scholar), and who is tolerably likable and has a track record of graduating PhDs who go on to be successful researchers in their own right. So pick a school/major that makes several such folks potentially available.
It seems to me (a climate scientist) that if someone has “a deep-seated desire to help the planet remain as habitable as possible” they are probably better off studying engineering than climate science. Studying the planet is something altogether different from “helping” it.
Excellent advice!! I gave my new graduate students a starter problem, then told them to define 3-5 more and different problems, assemble what it took to solve the problems, solve the problems, and publish the solutions. When they could tell me they could take care of themselves professionally, they were ready to graduate. The objective was to learn how to learn, and to create self confidence by repeated success. Diversity is critical. The things that will allow you to be productive, competitive, and satisfied 10-15 years from now have yet to be invented!! You are living in interesting times.
Newly graduated math major wrote: “I am someone with a deep-seated desire to help the planet remain as habitable as possible in the face of the trials humanity is putting it through. I’d like to devote my career to this cause …”
With all due respect to climate scientists, I would suggest that a career in climate science is not the best way to “devote your career” to “helping the planet remain as habitable as possible”.
Climate science has, of course, elucidated the problem of anthropogenic global warming, and in doing so it is fair to say that climate science MAY have indeed “saved the world” — but ONLY if we, as individuals and as a global society, ACT on the knowledge that climate science has given us.
And that is where I would suggest you direct your efforts: not to further STUDY of the problem, but towards ACTION to SOLVE the problem, by developing and deploying the technologies, policies and practices needed to eliminate anthropogenic greenhouse gas emissions and draw down the already dangerous anthropogenic excess of GHGs.
My response would be similar, but for a specific reason. Find an area that you are passionate about, whether its a specific branch of mathematics or an application of math, or even a broad area. A PhD is a hugely committing process, with many ups and downs along the way. It takes more than just a logical rationale to provide motivation through the process – a healthy mixture of logic and emotional attraction will have a much higher likelihood of success.
I think you showed real wisdom in not trying dictate your own ideals and goals to the student but instead encouraging them to find their own path, plus offering some specific, practical suggestions for a maths student.
I teach environmental law, including climate law, to environmental management, science, planning and engineering students in Australia and I am often asked about study and career options.
Three things that I commonly do when asked about this are:
1. Find out what interests the student and encourage them to pursue their own path using the skills and experiences that are unique to them.
2. Suggest that they think of their careers as a marathon, not a sprint, so they can view their initial jobs and study as stepping stones in which they aquire knowledge and skills to do higher roles later in their careers.
3. Remind them that they need to balance their careers with family, friends and fun so that they don’t burn-out and fall off their chosen path. Burn-out is a real problem for people working on climate change because the problems often seem over-whelming. I tell them that I often go bushwalking to reconnect with nature and to recharge.
For students who are struggling for answers and need a wider framework within which to anchor themselves, one book that I often recommend is the late Stephen Covey’s book, “The Seven Habits of Highly Effective People”, particularly the chapter on personal leadership.
I’ll share my own perspective from a graduate student:
My Bachelor’s degree and current graduate track is all in “pure” Atmospheric Science. I chose to supplement that with some additional physics in undergrad, and many of my peers have made similar choices with mathematics or computer science “minors” or “double majors.”
Depending on the school you go to, if you choose to go this route, you’ll almost certainly be required to take courses in atmospheric thermodynamics, a couple fluid dynamics courses, and possibly radiation. These are the “bare bones” courses that atmospheric science students take regardless of whether they branch off into climate, synoptic meteorology, tropical hurricane dynamics, numerical weather prediction, etc, etc. After these essential courses are taken, the student will typically take elective courses that are suited for their own research interests.
As soon as you hit the research realm, you’ll need statistics and programming background, as Gavin mentioned. I was hit with this realization as a student not too long ago, and the unfortunate fact is that many atmospheric science programs do not offer adequate “training” in those subjects. So, you need to take electives and learn it on your own. I’m still in the process of trying to use “R” as a toolkit for getting my work done, and many others around me use MATLAB. Fortran is popular in this discipline though I think is becoming an ancient language at University (I’ve had computer science roommates at Columbia who looked at me funny when I mentioned fortran).
I’ve found through atmospheric science courses and even a bit of astrophysics, that these courses are to a physics background what physics is to mathematics (this has been especially true in dynamics and synoptic meteorology). What I mean by this, is that you will gain an immense appreciation for being able to look at a complex equation and be astonished at how much you can apply it to the real world. It’s fascinating to be able to look at a weather map, and then from an equation you just spent an hour deriving, tell yourself where the clouds are going to form. With all due to respect to pure physicists, I also think it is also much more interesting than studying the behavior of a proton in a box. It’s stuff that I can also explain to friends, whereas with other theoretical ideas in physics or math, it became difficult for me to explain to others (or even myself) why they were interesting or important.
Just keep in mind that the field is becoming hyperspecialized (for better or worse). People are going that way in response to the pressures of the current academic/research/funding environment.
Why not use your math talents or other forms of genius to create ‘The Global 50/50 Lottery’? That is, what if we could exploit the infinite power of human greed to fight global warming? The USA and UN could use half the winnings from a global lottery to pay for the purchase and installation of vast arrays of clean electricity generating systems like windmill farms, solar panels, ocean and water powermakers, to make electricity to replace the juice from coal burning electric power plants, which are emitting the carbon dioxide that is causing global warming.
– many of the blogs in the sidebar, for thoughts
– The Azimuth Project which isn’t in the sidebar — but might warrant being added
“The Azimuth Project is an international collaboration to create a focal point for scientists and engineers interested in saving the planet. Our goal is to make clearly presented, accurate information on the relevant issues easy to find, and to help people work together on our common problems.”
No connection to me. Good and getting better for ideas, broadly.
While there is no way to guaranty the giving of specific advice about what to study for an individual there are some ideas that an individual can use to discover direction. The end result is a better informed ability to give oneself the best advice possible. But all in a path to discovery.
Gavin’s answer provides wonderful clues about how as one wanders through the interests at large in order that one might find where one resonates better, or possibly even best.
I am reminded of something Jonas Salk once told me. He said “Life is a mosaic and each of us are colored tiles. The trick is to get our (as individuals) colored tiles in the right place so that the we benefit the bigger picture”.
Following one strengths and interests along with ability, opportunity, and potential, including creating opportunity proactively in line with these considerations, is likely a good key to understanding just what color is our tile, and where it fits in the big picture.
I am just wrapping up my dissertation. In recent years I have become convinced that the spatial and temporal scope of the best science in my field (ecology) does not line up spatial and temporal scope of the social actions it needs to inform. If more smart people dedicated themselves to interfacing the ivory tower with communities and non-scientific institutions that act or form policy, and did not view such as a demotion, they could have as much or more impact than leading scientists. Remember that citations don’t equal real world impact, but the only the chance of impact…when science affects policy or is implemented on the ground, it isn’t cited in a journal. I hold pure science as sacred, but the community of scientists needs to look at talent and decide whether or not to cloister it all away as young post-docs writing scripts or communicate that there is plenty of room for talent to move out into the world. There is a separateion of science from society that will ruin us, if we don’t invest in the bridges. It was suggested that this young graduate learn some programming languages. That’s a good suggestion. A smart person can really learn those fairly quickly. Really, I would say to ignore laundry lists. Instead identify the task and learn the skills on the fly. If you care about it, it won’t be that much trouble. A better suggestion is to seek out opportunities that allow one to teach and/or extend beyond the research community. What those experiences have to teach cannot be learned in a class or at a computer, but the best scientists and the best activists both have those skills. As far as careers go, look at some of the opportunities available with the government or NGOs that might not involve modeling climate, but modeling or testing responses to ranges of climate scenarios.
I once got a doctorate in physics. This is my personal opinion for what it might be worth. For me there was no choice, as Rilke said, a writer writes because he must, for me, I had to do physics.
Do what you love. Else you will regret it for the rest of your life.
Grad school in hard science is like putting your head in a vice and tightening it until your head explodes or you graduate. You will be ruthlessly exploited, you will work insane hours, you will be paid a pittance, you will be screamed at, sneered at, dressed down by dyspeptic professors, your theories will be derided, your data will be dismissed, and job prospects in academia are below dismal. That’s how science works today. All these things will happen, that’s why you better love what you are doing. The most promising student in my class in grad school quit because he decided he didn’t really love physics.
Remember you can quit grad school anytime, and get paid better anywhere for the hours you put in. That’s your ultimate and only weapon when your advisor/superior tells you to do something particularly disgusting.
Don’t do this to save the world, do it because you love it. Don’t worry about saving the world. The world will save itself, as long as each of us saves just a little of whatever we can.
Deciding what to study based on today’s problems is tenuous at best. By the time a newly-entering student graduates and advances enough to “make a difference”… well, if the problems the newly minted PHD is poised to solve have already been solved…..
Yeah, like others have said, we ALREADY KNOW most of what we need to know scientifically about climate. The rest is important but inevitably solved. There’s probably very little a new student can do to “change the world” via pure climate science. Ya need some engineering to turn the concepts into reality.
Of course, 99.9% of what we need CAN’T be taught in school. Note how most of the most successful folks dropped out of college and did things their way. (And, of course, ever so many who tried that path ended up asking, “Do you want fries with that?”)
As a university academic for over 30 years involved in both teaching and research, my advice , for what it’s worth, is get involved in something you like/are interested in. An earlier poster (David Douglas) has said get involved in a project you are passionate about. While I don’t take any issue at all with that advice, finding out what you are passionate about may only happen once you’ve been immersed for some time in a particular area. If you’ve already selected the area or areas that you are interested in/like, it is usually fairly straightforward to select a particular area of study after you’ve spent time looking at various aspects of the area. Whatever career you eventually decide to pursue my very best wishes for your future.
I graduated from mathematics (with focus on “pure” mathematics) before completing PhD thesis in atmospheric sciences. Based on my experience, the following things are useful:
- Programming skills: after you more or less master basic concepts and one non-trivial programming language, the rest becomes easier to learn. Personally, I have used Fortran, C++, IDL, and Matlab.
- Basic physics and chemistry along with some courses in statistics.
- Overview of the physics of the atmosphere of the Earth.
In my personal and limited opinion, I’d choose applied statistics over applied mathematics if you want to pursue career in atmospheric sciences. However, I do believe that PhD in atmospheric sciences is best way to go as you can probably learn (at least) some of the skills I mentioned above along the way. This is not granted with mathematics and statistics as they are somewhat limited in scope compared to atmospheric sciences which draws from several disciplines (mathematics, chemistry, computing science etc.)
I have to say that in order to help humanity climate science is not the way to go. The broad strokes have been developed for a long time now and the major problems are not with the science.
For me the issues remaining on this are:
1, Policy and political frameworks
2, Financial and economic methods of making the change to a sustainable economy
3, Engineering and developing solutions to the many problems that are the result of all the changes.
Plenty of scope for a maths graduate in all three areas.
My advice would be to really strive to do a PhD in a group that is active in the field you want to be in. While working in a related field may get you the skills and the qualifications, it won’t get you the contacts or the recognition within the community that you get from actively doing the research. But more fundamentally, I always say that the two most important things to get right when embarking on a science PhD are that you enjoy what you’re researching and that you get on with your supervisor/advisor. Neither of these stem from approaching it with a career ‘means to an end’ attitude.
Whereas climate science is hugely important in understand what is going on – and I and so many others benefit hugely from the posts on Real Climate – surely the question of a way to make the planet habitable is a political/ social question, and not science alone. Prof Gavin’s response is appropriate for his immediate needs perhaps, but in addition to following that advice, it would be important for that student to try to understand and intervene in the larger social context in which he is located. There has been so little progress re global warming, not because the science has not been understood or communicated, but because there is not adequate political will and popular pressure from below on governments in countries that are largest emitters of CO2 – China, the US and India (where I am located). More fundamentally, global warming and most of the other planetary ecological crises are linked to the logic of industrial capitalism, and this student needs to understand that systemic logic. That includes the drive to profit, to use the cheapest sources of inputs (energy included) and labour, and to externalize waste. It makes resolving global warming that much more difficult, and is why climate negotiations have gone nowhere in 20 years. For this student, a wider social engagement along with the technical specifics you suggest would help.
I´d go with Eric and Secular Animist on this one.
Sure, there are things worse for “the planet” (I prefer to call it “the people living on the planet”) than being a climate scientiest, but I can imagine a lot of more helpful things, too. Engineering in renewable energies, for instance, or politics/activism. Unless flying around the globe a lot to a bunch of conferences each year somehow gives him better conscience about helping make the planet stay more habitable).
Listerers listen. Leaders lead. Ask someone what to do, and you will get a list of what they wish they had done. Just do something. Change your mind often. Your niche will appear. In all training programs you are being taught how to work for someone, think for someone. Think for yourself. Stop looking for a job, stop looking for others to help. Just do something.
Most of the responses have fallen toward the high end of this spectrum….
On the ideal end……. we all want a career that is so rewarding we would still work for free after winning a record breaking Powerball lottery.
On the other end…. do something that brings a paycheck without killing your heart’s ability to give your free time to your passions (I’m fond of this retired prof’s theme http://www.youtube.com/watch?v=x5OYmRyfXBY)
I agree with others who say the bottleneck is our inaction, not the lack of climate-science research. Also the geo- socio- and publicworks engineering type careers, as well as international conflict resolution will all be exponentially increasing
After studying physics, chemistry and logic. Read as much many books and research papers as possible from peer-reviewed scientists. Then perhaps you could follow the advise of Numbers 11. and 14. They have the insight into what is now needed to get something done — the public and politicians have to be motivated and most of them have no science background in the field of climate science. I suggest a prime time television series that explains the science to the layman and the urgency for addressing climate change with sustainable living and new laws, such as a carbon tax, etc. When you finally see something being addressed politically through your help with this urgent problem you will surely feel a great satisfaction that something is finally being done to save our planet. I wish you all the luck in the world that you find a passion for communication in this direction — as it is what is missing to motivate change!
Comment by Sharon Hawkins-Fauster — 16 Jan 2013 @ 10:50 AM
Learn at least one programming language no matter what you end up doing. Modelers running computationally expensive simulations often learn older, low-level languages like FORTRAN or C/C++. High-level languages like Matlab or Python are most useful for the rest of us. The trade-off is between computational speed (low-level languages run much faster) vs. coding speed (high-level languages are easier to write), so pick one appropriate to your problems of interest. A good compromise is to go with Python – it’s quick to pick up, open-source (read:free), marketable, and has an elegant coding style, but the Cython extension lets you add in pieces of C code for bits that need to run faster.
As far as acute needs go, I wanted to highlight my field, which doesn’t get talked about much in the climate community. As a recent climate PhD grad, I found one area with a lot of job opportunities was in the insurance/reinsurance business. This part of the private sector is on the front lines of adaptation, and is actively hiring climate scientists to improve decision-making. Not only do I think that getting the business community on board can improve our progress towards actually dealing with climate change, it’s an area that currently has fairly limited access to climate expertise, so could really see a lot of benefit.
A statistical background with some spatial data analysis skills would be most useful for this field – we work on a lot of problems like how sea level rise will change the probability of hurricane storm surge flooding, how frequencies of extreme events might be expected to change in the future, and how sea surface temperatures relate to precipitation patterns – so, heavy on stats.
I’m about to submit my PhD thesis in Computational Neurosicence. I went into the PhD programme because I found the topic really exciting and interesting – and I still do! But I think a problem that afflicts a lot of people is being interested in more than one thing. I recently went to Malawi to visit my partner who was studying the use of Moringa as a nutritional supplement in a feeding programme (random controlled trial). There were lots of people talking about its de-worming powers, but there’s no evidence (I can find) in the literature. So I got permission to take stool samples and get them tested for worms…
Anyway, the results of the mini-study aren’t what I’m talking about. I found this mini-study was as exciting/interesting as my PhD back home, but even better: The results would have immediate implications, and I felt like it was a much more direct and important potential discovery (this might be because it’s a very under-researched field! so easy to find big interesting problems to solve???).
Anyway, what’s my advice? I’m actually reading these comments looking for advice: I’m trying to jump sideways, hopefully taking my Informatics and Science background with me – to find useful things to do in more of a “development environment” (is that a real expression?). But the topic is diverse, with considerable silos of domain-specific knowledge. At first I thought to look to development studies/Africa studies etc, but these are in the humanities. While other topics are scattered throughout Nutrition, Geography, Public Health, Education,…etc.
Plan B – is to head off, with some contacts and organisations and universities ahead, and physically visit them – am hoping interesting things appear as I go :) [worse case is my travels turn into a year-long-holiday!]
Anyway, not sure that ramble was useful, but I feel in a fairly similar situation, but slightly later in my career :)
Chris Colose said: “I’ve had computer science roommates at Columbia who looked at me funny when I mentioned fortran”. I think people who use Matlab should be looked-at funny too because it is locked behind a paywall making it inaccessible to people like me who work independently and work at unfunded projects in voluntary service of their communities. Using Matlab adds an unnecessary workload to our part of collaboration with the rest of the scientific research community. Python can do everything Matlab can and more, and using it will serve all of us and the research community better.
The answers have been mostly pretty good. I’ll just add something entertaining (hopefully) which I once read, but sorry I can’t remember the source. So reconstructed out of memory:
The physics major asks: “How does it work?”
The engineering major asks: “How do we build it?”
The accounting major asks: “How much will it cost?”
The liberal arts major asks: “Do you want fries with that?”
I have to say I think Mike Smith @29 will end up doing just fine. Anyone who can get excited at the chance to collect stool samples will probably find fulfillment with little difficulty. My advice is to have an attitude like that.
Everyone is going to hate this. IEHO the physical basis(WGI) is pretty well understood. Regional climate modeling is crowded. The real action is going to be in the biological interface (WGII), which needs good statistics and modeling.
As a side, a good set of gardening skills is always a good thing to have. Maybe combine it with the math and start to formalize sustainable agriculture practises, optimization of yield per water/fertiliser unit consumed is one way to go. In theory this might fall into discrete maths, since the available land areas are projected rather to decrease than to increase. And anyway discrete maths is what imho should be used in closed systems like earth (well that’s not entirely correct.) But I guess this is too simplistic for an academic career. Organic gardening is also pretty labor intensive, so some physical education would be good. (Stopping before going hyperbole and Hunger Games)
Excellent math skills can take you in many different directions, including being the basis for physics and engineering. Know what you love and what you are good at, and take a few courses in several different areas. Knowledge is not wasted.
I would also say that we seem to know enough in a general sense about how our world is reacting to increasing concentrations of greenhouse gases, so we really need to know how to run our civilization without burning fossil fuels. Research in chemistry, physics, or biology might find some basic information that would be useful in 30 to 50 years, but engineering using what is known now is probably what will best enable us to reduce emissions in the next ten or twenty years.
Disagreeing to some extent with Candide #34, sociology, applied psychology, and political science will all be important in making the changes needed to kick our fossil fuel addiction.
I agree with Secular Animist (post 5) – At this stage what is needed most to improve the world is political skills, in addition to scientific skills. Uncovering the science helps the world little if policy makers do not act on it. Your student might look to Frank Rowland as a model.
I am slightly disturbed by many of the posts advising that “helping the planet remain as habitable” would be outside a climate science career, and that the individual would be better in activism or engineering. As a climate activist (for a depressingly long time), I can say that activism on climate is dependent on the science. The better the science on climate, the better the quality of activism can be. Activists across the world need climate scientists to continue working on climate, getting better predictions on sea level rises (for example) and shifting weather patterns, and then communicating that to people outside of science (hence the value of this site). Even a casual reading of Real Climate suggests that there are many gaps in our understanding of climate and related, there is more to be discovered, and if someone has an interest in dedicating his/her life to figuring these unknowns out, fantastic. Activists benefit, engineers benefit, and, hopefully, the planet too.
I agree with others that if you want to change the world you should really take up engineering or social sciences but this graduate is into maths so that is what he should exploit.
The biggest threat to humanity is abrupt climate change. To understand that, i.e. Tipping Points, you need a grasp of Chaos and Catastrophe Theory. Chaos Theory arose out of meteorology and computing, so those skills would also be useful for a prospective climate scientist.
But going back to my first point, it is important to understand that mathematics is just theory, and that it should be used as a tool not as a paradigm when addressing real world problems.
Comment by Alastair McDonald — 17 Jan 2013 @ 8:13 AM
“Bayesian approaches to statistics”
is there one or a couple of books you would recommend?
(a book, please, not a website)
[Response: Jayne's book is a classic, but I have been looking for something more up-to-date and accessible, but have not yet found one. - gavin]
Yes, there are many routes into Earth Science and Climate Change studies. But it seems to me that if you KNOW you want to study these fields as a senior in college, then the best course of action would be a graduate program in Earth Science. I don’t think that either math or statistics would be the best PhD track here, especially if someone had little or no exposure to Earth Science as an undergraduate.
As Earth Science becomes more complex and specialized, I think it will be come increasingly difficult for someone with ABSOLUTELY NO background in the field to just jump in as a post doc. No amount of linear algebra or Bayesian statistics can substitute for actual understanding of our planet.
After spending 32 years as medical school faculty, I followed some ideas into biotech where business considerations dominate. The greatest challenge was to be creative, not on my ideas, but someone else’s!! It can be done, and is just as rewarding.norvebci subjected
A math background certainly opens up a lot of possibilities; might as well lay them all out and see where personal interests intersect with best citizenship. Might also include geography (physical and cultural), systems and industrial engineering, urban planning, policy and administrative studies, landscape architecture, risk management, etc. etc. Also could extend into dealing with the potential aftermath of inaction; disease control, hydrology and water policy, disaster relief, environmental clean up…
I am living, walking proof that one can do everything bassackwards, step way, way off the career track and still wind up having a career by accident. My recommendation is to look at what you do well and whether those skills are common. For instance, most people do not think statistically. If stats comes naturally to you, exploit that. I wound up being a stats autodidact, mainly because so few people understood it, and it was a skill that was needed…desperately.
Get the best background in basic science and math you can and try to use all of it. I’ve even used Stirling’s approximation to the factorial a couple of times when looking at reliability of large systems.
Marcus @43 — E.T. Jaynes, “Probability Theory: The Logic of Science” remains the best introduction to Bayesian statistics for those with a decent math & physics background. I am currently reading Christian Robert’s “The Bayesian Choice” which is a more difficult text concentrating on decision theory; quite good but not the place to start.
Comment by David B. Benson — 17 Jan 2013 @ 5:42 PM
Outside research, there’s plenty of consultancies around looking for technical people who can help them design more energy-efficient buildings and business processes, for example. Bigger companies might do it in-house also.
We don’t need more scientists to tell us what what they have already told us. No career choice anyone can make individually is going to help the planet’s survivability. The only collective political action (mass movements) that put pressure on the government and corporations to legislate radical changes has any hope of succeeding.
It’s fine to give someone career advice, but as peak oil facilitates more economic collapse, fewer and fewer people will have careers. This reality is already here, as colleges and universities cut enrollment. It behooves us to remember that it was only starting in the early 1960s that a university education became a widespread, middle-class phenomenon. It is as short-lived as the oil age.
My PhD is in the biomedical sciences and so I can’t offer specific advice to a maths major. But I think simply asking a leader in the field for advice is a very good first start. If the planet and specifically climate science is your interest then identifying the movers and shakers and seeking their input is valuable. But remember that a PhD can be more of a learning experience than a stepping stone to a defined career path. More than likely during your PhD and/or postdoc you will find your niche. What really matters is finding mentors to guide you. Don’t settle for second best, chase the best candidates until they accept you into their sphere.
Comment by David B. Benson — 17 Jan 2013 @ 10:29 PM
OK, my third comment, I hope it’s not too much.
The more I think about it, the more I feel this question may be ill-posed.
I believe that climate change will be one of the defining issues of our times. It will affect every human life on this planet. BILLIONS of people will be involved in action, one way or another, to try to keep our planet habitable (or not).
As others have noted, the world will need professionals of all stripes devoting their careers to keeping this planet habitable: lawyers, politicians, engineers, community organizers, and yes, scientists, to name a few. It would be hubris to assume that we, as scientists, are somehow the most important people in that effort.
Scientists do play role in this narrative, one I am proud to be a part of. Earth Science in general is multidisciplinary, climate change even more so. It sucks in technical people from all sorts of backgrounds with all sorts of skills. Whenever you decide you want to participate in Earth Science, THAT is the time to come over and start immersing yourself in the field. There is FAR more opportunity here than in, say, particle physics or pure math or applied math or statistics.
For anyone starting out: a good career choice requires an intersection between the kinds of things you want to achieve (in this case, saving our planet) and your skills and aptitudes. I have only cursory knowledge of both these issues in your case, and there are likely no easy answers — it took me over 15 years to find that intersection! But maybe you can do it faster, if you work hard at knowing yourself, and you’re willing to try different things, and you’re mature.
I think people who use Matlab should be looked-at funny too because it is locked behind a paywall making it inaccessible to people like me who work independently and work at unfunded projects in voluntary service of their communities. Using Matlab adds an unnecessary workload to our part of collaboration with the rest of the scientific research community. Python can do everything Matlab can and more, and using it will serve all of us and the research community better.
I agree with Bert. My division is spending considerable effort to replace existing Matlab and IDL code with Python, largely due to the prohibitive cost of licensing for both commercial languages. Take the trouble to learn Python, and avoid getting hooked on proprietary languages. It’ll make you more employable if nothing else.
I’d go with David Benson on this. Jaynes’ book is full of the sorts of insights that come only from someone who has been thinking about a subject for over 50 years. It is interesting that Gavin refers to it as a “classic”, as its publication date was only in the past 10 years after Jaynes’ death. That was probably the only way it would ever be published, given that Jaynes was never satisfied with it and kept tweaking it right up to his death.
My suggestion is to study what you find most interesting while ensuring that you progress your mathematical/coding/physical science skills. I wouldn’t be too specific beyond that and certainly not tie yourself to a must having computer language. You never know where your career may go in the future. For example, my PhD was on Schumann resonances which is primarily an electromagnetic phenomenon in the Earth-ionosphere cavity due to lightning. A little later did I think about the relationship between global lightning activity and climate (though the relationship is not straightforward). Much later did I realise the potential connection between the work I was doing on stochastic trends and autoregressive process and climate statistics. With the work being done in the 80’s and early 90’s in coded in FORTRAN. The move to C++ and R later was straightforward. So I would say, don’t tie yourself down too early – study what interests you.
A junior member of my department keeps trying to tell me computer science needs no mathematical background. I can only suppose he’s never had to solve an interesting problem. I would advise your student not to shy away from the hard stuff especially as applied to real-world problems. If you have a strong base and good ability to formalize you can switch fields and handle the complexity of multidisciplinary research much better than if you go for a narrow purely theoretical specialization and follow the line of least resistance. Aside from working climate scientists, John von Neumann is a great role model.
I second (third?) comments on Matlab. Aside from python, where there’s work going on to replace Matlab functionality, R is a great tool. And there’s no harm in learning a range of programming langages for when you just can’t make the thing you’re trying to smack home look like a nail.
Maybe it is just youthfull exuberance, but after four or more years of university education and you express a deep-seated desire to help the planet remain as habitable as possible indicates a career in something other than science would be advisable. Deep-seated desires to help suggests you might be more suited to social programs than hard science. In all seriousness, you to biased out of the gate to be in the hard sciences.
First I have to say that what sidd says about the life of a graduate student in the hard sciences is outdated (at least in the U.S.) I have been at this for a long time (in Math/CS/Engineering depts), and from my point of view, the life of a grad student has improved significantly in the last 20 years. It is still intense, but not so exploitative as before.
The main point I want to make is that if you want to study climate change, I think you have to study numerical methods for partial differential equations. While this may sound like an esoteric, math-geek, topic, it is actually at the heart of all long term climate prediction.
In my (admittedly somewhat limited) opinion, the state of the art in multi-scale numerical modeling of the climate is currently lagging behind what scientists in fields like combustion or fusion are regularly adopting. So I will predict an increase interest in high-order,multi-physics, multi-scale numerical methods applied to climate in the next 10 years.
A book: “The Signal And The Noise,” by Nate Silver. I found about this site by reading it.
…is a relatively new book that is, essentially, *all* about Bayesian Statistics applied to a wide variety of fields, from baseball, cards, and the economy, to weather and climate science. Well, well worth the read, as it’s constantly pointing out the strengths and weaknesses of Bayesian logic (spolier alert: weaknesses are few, compared to other models). Nate Silver has also, recently, yet again proved himself a serious authority on Bayesian statistics by predicting (among other things) the last U.S. Presidential Election and related races with fairly stunning precision; or, as he would say – accuracy. His blog, for a quick primer, recently published an AMA transcript where he answers a lot of questions about his process http://fivethirtyeight.blogs.nytimes.com
[Response: Unfortunately, there are some things that Nate gets seriously wrong about climate change/climate modeling, etc. See my piece at Huffington Post on this. --mike]
I recommend you read Mike’s blog article (in response to post #68). If nothing else, this makes clear the difference between being smart and KNOWING a field.
Many responses here have mentioned tools: C++/Python/MATLAB/IDL/NCL/Fortran, Bayesian Statistics, PDE Solvers, etc. These are (some of) the technical tools that underlie all hard sciences these days (and many “soft sciences” as well). Sure, anyone wanting to be a scientist should have these skills. But more important than any of them is the CONTENT of the field. We are studying the Earth, not just solving PDEs or grinding data through statistical models.
First and foremost, a curiosity about the EARTH is required. When I do something, I ask myself “what does this tell us about the Earth?” If I’ve built a cool piece of software that uses sophisticated statistics but in the end it tells us nothing, then I’m wasting my time as a scientist.
As for Statistics… this tool shines through when you want to measure small things accurately — for example, the likely winner of a presidential election that’s close to 50-50 split. But climate change is different, less subtle. You don’t need fancy statistics to see that the glaciers and ice sheets are melting, or that CO2 is going up, or that measured temperatures are increasing. Statistics is useful, don’t get me wrong. But progress is made by scientists who USE statistics, not by statisticians who wonder whether they might apply their tools to climate change. That is true, even when the scientists use outdated methods of computer science, numerical analysis, statistics, etc. Better statistics will lead to better papers. But in the absence of better statistics, scientists will make do with inferior tools.
I disagree with Comment #65. There are certainly scientists who are motivate purely by curiosity. But there are plenty who want their curiosity to fit a larger social or human goal. Nothing wrong with that. Scientists are people, not robots, and it is only natural that we care about the subject of our study. That does not, in and of itself, introduce a “bias” into one’s work.
Anyway, you need to find a career path that fits your interests, skills and aptitudes. And from the sound of it, your skills and aptitudes seem to make you at least as well suited to be a scientist as any of the other careers mentioned.
[Response: Well put Bob. Thanks for stopping by :-) --mike]
I agree very much with with Bob F but would just like to return to..
‘Best’ book on Bayes.
Some others here, know much more about this topic than me , but in general for other subjects , I would usually recommend reading more than one book at once, especially if there is a chance to download one free. In the case of the book I suggested, you could do the opposite of what its author suggests. David MacKay suggests that Chapters 3 and 37 are not essential and could be skipped. I think that you could perhaps skip most of the rest of the book and browse or read just those two chapters and parts of chapter 2. They are not written as if they are restricted to information theorists. You don’t have to follow every line.
There is also a tiny “Further Reading” list at the end of chapter 37.
Regarding statistics and climate, I recommend some of the talks from a workshop at NCAR, October 2007, see Strange scholarship …, pp.67-70. Among the good talks (and one very bad one), was Statistical Issues Involving (Climate) Computer Models by Jim Bergman, including p.17 “How can statisticians help?” but especially, p.19
‘How can statisticians become involved?
The Key: Becoming involved in a ‗team environment‘ with scientists.
• NCAR, where teams operate
• SAMSI (and NPCDS), where teams can be formed
• National labs (both LANL and LLNL have climate/stat teams)
• Large interdisciplinary grants available today
• Statistics cannot generally fund involvement of statisticians in other
disciplines which, in turn, rarely have much money for statistics.
• Shortage of statisticians
• The time needed for a statistician to get deeply involved with
another science and to also learn the statistics needed for it.
• Scientists often have a hard time judging what they can do
themselves and when they should seek statistical help.’
In particular, while math/statistics skills are always important, it is another expression of the idea that one has to know something about the science as well. It is all too easy to generate statistics and nice graphs … that don’t actually contribute any insight into anything real.
On computing: as I’ve often said for 35 years, as in this:
‘USE EXISTING TOOLS
Work at the highest level possible’
Anything one can do to *find* code and *use* it rather than write it is good … because then, if you write it, you may actually solving something useful. [UNIX shell programming was created, in part, because I kept finding people struggling to write C code ... that mostly duplicated the effects of some combination of existing UNIX commands.] If I were still teaching CMPSC, I’d probably have a few programming assignments where the task was to be solved without writing much if any code, but by studying the various repositories for what already existed and using that, maybe with a little glue.
Of course, some people must write in FORTRAN or C++ or C, either for structural reasons or performance, but most people should be able to avoid needing to do that.
As a layperson, I’ve been holding back here. But it is very important that you write clearly and communicate by any means possible. Right now, the science is getting clearer by the day, and the obfuscation more forceful. We live in a marketing-based society and our model will not create the community of all we need to solve the many problems we face.
The letter seemed clear and to the point, which is part of what is needed. Being able to tell a story that grabs people’s attention would be a great additional asset.
I agree that a person with aptitude in math should consider pursuing engineering to “help the planet.” Many years ago one of my physics professors said I should go into photovoltaics if I wanted to stop global warming. (I should have listened to him.) Two other things that come to mind are writing software used to design energy efficient buildings, including cost analysis (which could have a very large immediate impact), or working on computational chemistry or computational engineering for clean technology applications.
My view is that we could use more and better statisticians in the field of climate change. There are many examples of the wrong statistical methods being used to interpret climate data normally by climate scientists whose primary knowledge is in physics / climatology
On statisticians in climate science or any other science for that matter:
The thing is that most scientists have little formal training in statistics. In my case, while doing particle physics research for my PhD, it was simply assumed that you would pick up what you needed. As a result, a lot of us go only as far as necessity and inclination take us. The downside of this is that often our statistical analysis would be cringeworthy to a professional statistician, even if we get it more or less correct. The upside is that when we do learn a technique, we tend to see how it applies, since we have an application readily at hand.
The obverse side of this coin is that some statisticians may apply an analysis that is patently unphysical–e.g. approximating global temperatures as a random walk.
Snow’s Two Cultures have continued to bifurcate multiple times since the 50s. What is needed is researchers sufficiently broad to bridge the gaps between them.
I did a BA in Mathematics, went on to do an MSc in Environmental Science and eventually after years working went back to college to do a PhD in Atmospheric Physics. The most useful degree was Environmental Science. I work now in the area of EnviroInformatics and build environmental management software (mainly geospatial). There is a demand of people with IT and mathematical skills who understand environmental problems (from scientific perspective).
Excellent,my first time reading this blog. I have no advanced education or degrees. I am Joe average.
Knowing all the math and physics you could possibly ever want is not going to do anything if you dont figure out how to change Americans mindsets on how they currently live.
99% of the people just want to gas up the suv and go to the mall to buy more electronics gadgets to distract themselves from there problems.
Climate change isnt on there radar at all!!!
Comment by chris in chicago — 26 Jan 2013 @ 1:10 PM
Excellent, first time reading this blog. I am Joe average. I have no advanced degrees.
Knowing all the math and physics in the world isnt going to make a bit of differance until someone figures out how to change the current mindset of Americans
99% of people just want to gas up there suv and go to the mall to buy more electronic gadgets and crap.
Climate change isnt even close to being on there radar!!
Comment by chris in chicago — 26 Jan 2013 @ 1:15 PM
This is a great thread and has helped me think about what I want to do. I’m a “retired” applied statistician and software developer. I have an MS plus 30 hours and am considering going back to school to get a PhD that would prepare me to support some kind of climate change effort – a purposely vague goal. My thoughts are to get a PhD in statistics with additional courses in “big data” techniques and also some numerical analysis. I spent 35 years working in industry and (as has been mentioned above) realize that many scientists and engineers do not get enough training in statistics to be able to apply it efficiently to their problems. I think I could help in that area. I would feel that going back to grad school and updating my skills would have been worth it if I could help some solar panel manufacturer increase yield by 10% or help develop an algorithm that would optimize a power grid or help a battery company properly design a set of experiments to evaluate a new battery design.
Go for it! So far In this thread the best advice regarding training in climate science has been provided by the professionals and students in this area, so I recommend that you ask some statisticians who are doing what you think you would like to train for and ask them how best to get there.
I am not a climate scientist: I work as an epidemiologist on the health effects of climate (and other environmental issues too). But my backgroung is in fluid mechanics, the proof that your entire professional life is not defined by your diplomas :-)
From my daily work, I understood that studying the climate is important, but if we want to change things we also need to study the impacts, and to understand the adaptation needs and possibilities for many different topics.
To do so, we need more people able to work on an integrated fashion, willing to share and develop new methods on a multidisciplinary / interdisciplinary basis. So if you already master enough maths and stats, you may want to broaden your view and study environmental or social sciences…
The new graduate posed the issue as follows: “I am someone with a deep-seated desire to help the planet remain as habitable as possible in the face of the trials humanity is putting it through.” This problem has many dimensions (scientific/technical, economic, sociopolitical, as starters), and any credible solution has to address these many dimensions in parallel. This calls for a systems engineering/systems optimization/operations research type of approach, and would be well suited for a person with good mathematical and logical skills. Having good climate science results is a necessary, but not sufficient, condition for addressing the overall problem. The climate science is central to setting the requirements for any technical, economic, and sociopolitical solutions. But, most importantly, all the components of the overall system must mesh.
With the caveat that I have not been convinced that GW = AGW or that climate can be successfully modeled to make valid predictions, I do find myself largely in agreement with at least some of the ideas in most all of the above. I especially find #3, #5, #19, #22, #75, #80, #85, and #88 relevant. As many have suggested, I also would advise to consider a broad approach to hard science, engineering, and mathematics. This would enable you to adapt to whatever the future needs of society might entail as opposed to a narrow paradigm of the moment.
Concerning statistics: As noted by others, there are methodologies labeled “statistics” and then, with all due respect to Bayesians, there are other methodologies also so labeled. During my college years at an institution largely devoted to science and engineering, analogue computers were just beginning to be replaced with mainframe digital computers. During a summer internship, I also had the opportunity to work at a research facility that was engaged in silicon crystal growth. (The long — hopefully long — cylindrical silicon crystals would go elsewhere to be sliced and diced into chips.) My job entailed writing a Fortran program that used the output of the crystal growth furnace / kiln computer to do a statistical analysis of the data (temperatures, rate of rotation and vertical speed, etc.) that would then be plotted graphically and also used as re-inputs to the crystal growth kiln computer. The job entailed not only learning Fortran IV on the job but also learning statistics. My vocabulary thus increased with words such as “correlation,” “significant correlation,” “chi square”, etc. The advantage of the statistics we used was that the results of calculations could eventually be measured. That is, as a result of our work the crystal growth procedure either improved or else it didn’t. I can thus identify with the experience and goals of #85 Medina64. But I am skeptical about using statistical inference to predict future climates or even next week’s weather. Thus I would be leery of “big data techniques” if the goal is to infer a supposed truth certainty of the paradigm.
It has only been in recent years that I have stumbled across “Bayesian statistics” in my reading. As I understand matters, a great deal of skepticism is called for, at least in some uses. A problem stems from the introduction of subjective beliefs (including hypotheticals about hard data) as opposed to just hard empirical data and calculations that can be tested in the real world. This problem has been further compounded by the failure to always be transparent regards such subjectivity and underlying assumptions. Mario Bunge wrote an interesting article on this titled “In Praise of Intolerance to Charlatanism in Academia.” See Bunge’s article in THE FLIGHT FROM SCIENCE AND REASON edited by Gross, Levitt, and Lewis and published by the New York Academy of Sciences, 1996. Bunge wrote (p.103):
“When confronted with a random or seemingly random process, one attempts to build a probabilistic model that could be tested against empirical data; no randomness, no probability. Moreover, as Poincare pointed out long ago, talk of probability involves some knowledge; it is no substitute for ignorance. This is not how the Bayesians or personalists view the matter: when confronted with ignorance or uncertainty, they use probability — or rather their own version of it. This allows them to assign prior probabilities to facts and propositions in an arbitrary manner — which is a way of passing off mere intuition, hunch, or guess for scientific hypothesis. In other words, in the Bayesian perspective there is no question of objective randomness, randomization, random sample, statistical test, or even testability; it is all a game of belief rather than knowledge.”
Is my skepticism regards Bayesian inference (and the public’s skepticism regards AGW while accepting GW) misplaced? Also note, at Amazon, some of the seemingly quite expert reviews of Jaynes’ book. See also RealClimate’s informative FAQs regards modeling.
In any case, several of my more specific suggestions for any mathematician, scientist, or engineer who is just starting out, and whether or not you accept an extreme version of AGW, would be:
(1). a. Spend some time with GoogleMaps. Google the country as a whole, any region, or any locale. Then ask yourself this question: Does the country really need more “infrastructure”? It has often occurred to me that some disestablishmentarianism has long been called for.
b. Consider ‘the Traveling Salesman Problem’ especially ‘biological approaches to.’
(2). Consider the notion, in economics, of cost externalities, both positive and negative externalities. If our goal is to be as unbiased as possible, who ought to pay for or benefit from such currently unaccounted for externalities? Ask the same question regards what are termed incremental costs. Who ought to pay for school buses driving 5 miles out to the suburbs or for mail delivery 20 miles into the country? Etc.? Who ought to pay for the externalities of global trade? Etc.?
(3). Study some quantum physics and nanotechnology so that we might get on with the space elevator (and also origins of life issues). Contemplate the question, Would adding or deleting mass to the Moon-Earth system change the system’s orbit about the Sun or is the orbit totally dependent upon initial conditions?
(4). As scientists, engineers, and mathematicians, consider how many MBAs and Wall Street, real estate, and commodities “money changers” you all will be needing to manage you and what comparative values you would place on such managements.
All those of you who already have multimillion dollar or larger bank roles, come work with me ASAP.
Comment by Local Transportation Guy — 1 Feb 2013 @ 10:46 AM
Bradley Efron A 250-year argument: Belief, behavior and the bootstrap
Bull. Amer. Math. Soc. 50:1 January 2013
is an engaging, after dinner styled, 18 page paper which places Bayesian and frequentist notions of statistics in contrast, overlap and perspective. He ends with “[t]he two philosophies, Bayesian and frequentist, are more orthogonal and antithetical. And of course, practicing statisticians are free to sue whichever methods seem better for the problem at hand — which is just what I do.
“Meanwhile we can all get ready to wish Bayes rule a very happy 250th birthday [this] January.”