RealClimate logo


AGU Chapman Conference on Climate Science Communication

Filed under: — gavin @ 8 July 2013

A couple of weeks ago, there was a small conference on Climate Science communication run by the AGU. Both Mike and I attended, but it was very notable that it wasn’t just scientists attending – there were also entertainers, psychologists, film-makers and historians. There were a lot of quite diverse perspectives and many discussions about the what’s, why’s and how’s of climate science communication.

There were a couple of notable features: the conference had a lively twitter hashtag (#climatechapman), and almost the entire proceedings were webcast live (schedule). The video from this has now been posted on YouTube in more bite-sized chunks.

While our own presentations (Mike here and Gavin here) are available, it is worth watching the presentations from people you might not have heard of, as well as a few from more established people. We’ll embed a few here, but please point out some of the other ones of interest in the comments.
More »

2012 Updates to model-observation comparisons

Time for the 2012 updates!

As has become a habit (2009, 2010, 2011), here is a brief overview and update of some of the most discussed model/observation comparisons, updated to include 2012. I include comparisons of surface temperatures, sea ice and ocean heat content to the CMIP3 and Hansen et al (1988) simulations.
More »

What to study?

Filed under: — gavin @ 15 January 2013

I recently got an email from newly graduated Math(s) major (mildly edited):

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, but am young and haven’t chosen a definitive career path yet. My bachelors is in pure math and I am considering graduate study in either applied math or statistics. I’m curious what you would recommend to someone in my position. Between getting, say, a PhD in statistics vs. one in applied math, what positions me best for a career in the climate science community? What are its acute needs, where are the job opportunities, and how competitive is it?

My response was as follows (also slightly edited):

As you may know I too started out as a mathematician, and then moved to more climate related applications only in my post-doc(s).

I can’t possibly give you ‘the’ answer to your question – but I do suggest working from the top down. What do you see specifically as something where someone like you could have maximum impact? Then acquire the skills needed to make that happen. If that seems too hard to do now, spend time on the developing your basic toolkits – Bayesian approaches to statistics, forward modeling, some high level coding languages (R, python, matlab etc.), while reading widely about applications.

One of the things I appreciated most in finding my niche was being exposed to a very large number of topics – which while bewildering at the start, in the end allowed me to see the gaps where I could be most useful. At all times though, I pursued approaches and topics that were somewhat aesthetically pleasing to me, which is to say, I didn’t just take up problems just for the sake of it.

I’ve found that I get more satisifaction from focusing on making some progress related to big problems, rather than finding complete solutions to minor issues, but this probably differs from person to person.

But what do other people think? How should people prepare to work on important problems? Are there any general rules? What advice did people give you when you were starting out? Was it useful, or not? Any advice – from existing researchers, graduate students or interested public – will be welcome.

On sensitivity: Part I

Filed under: — gavin @ 3 January 2013

Climate sensitivity is a perennial topic here, so the multiple new papers and discussions around the issue, each with different perspectives, are worth discussing. Since this can be a complicated topic, I’ll focus in this post on the credible work being published. There’ll be a second part from Karen Shell, and in a follow-on post I’ll comment on some of the recent games being played in and around the Wall Street Journal op-ed pages.

More »

Improving the Tropical Cyclone Climate Record

Filed under: — gavin @ 16 December 2012

Guest Commentary by Christopher Hennon (UNC Asheville)

Get involved in a new citizen science project at CycloneCenter.org.

The poor quality of the tropical cyclone (TC) data record provides severe constraints on the ability of climate scientists to: a) determine to what degree TCs have responded to shifts in climate, b) evaluate theories on how TCs will respond to climate change in the future. The root cause for the poor data is the severity of the TC conditions (e.g. high wind, rough seas) and the remoteness of these storms – the vast majority of which form and remain well away from most observing networks. Thus, most TCs are not observed directly and those that are (with buoys, aircraft reconnaissance, ships) are often not sampled sufficiently (see the IBTrACS, (Knapp et al., 2010)).

This leaves tropical cyclone forecasters, who are ultimately responsible for recording TC tracks and intensities (i.e. maximum wind speeds), with a challenging problem. Fortunately, there is a tool called the Dvorak Technique which allows forecasters to make a reasonable determination of the TC intensity by simply analyzing a single infrared or visible satellite image, which is almost always available Velden et al., 2006). The technique calls for the analyst to determine the center location of the system, the cloud pattern type, the degree of organization of the pattern, and the intensity trend. A maximum surface wind speed is determined after the application of a number of rules and constraints.

Hurricane Gay (1992)The Dvorak Technique has been used for many years at all global tropical cyclone forecast centers and has been shown in many cases to yield a good estimate of maximum TC wind speed, when applied properly (Knaff et al., 2010). However, there is a level of analyst subjectivity inherent in the procedure; the cloud patterns are not always clear, it is sometimes difficult to accurately determine the storm center and the rules and constraints have been interpreted and applied differently across agencies. This introduces heterogeneity in the global TC record since the Dvorak Technique is usually the only available tool for assessing the maximum wind speed.

There has been recent work to eliminate the human element in the Dvorak Technique by automating the procedure. The Advanced Dvorak Technique (ADT) uses objective storm center and cloud pattern schemes to remove the subjectivity (Olander and Velden, 2007). All other classification rules and constraints are then applied and combined with additional statistical information to produce automated intensity estimates. Although the ADT skill is comparable to experienced human Dvorak analysts, large errors can occur if the scene type is not identified properly.

A new crowd sourcing project, called Cyclone Center, embraces the human element by enabling the public to perform a simplified version of the Dvorak Technique to analyze historical global tropical cyclone (TC) intensities (Hennon, 2012). Cyclone Center’s primary goal is to resolve discrepancies in the recent global TC record arising principally from inconsistent development of tropical cyclone intensity data. The Cyclone Center technique standardizes the classification procedure by condensing the Dvorak Technique to a few simple questions that can be answered by global, nonprofessional users.

One of the main advantages of this approach is the inclusion of thousands of users, instead of the 1-3 who would normally classify a TC image. This allows the computation of measures of uncertainty in addition to a mean intensity. Nearly 300,000 images, encompassing all global TCs that formed from 1978-2009, will be classified 30 times each – a feat that would take a dedicated team of twenty Dvorak-trained experts about 12 years to complete. Citizen scientists have already performed over 100,000 classifications since the project launch in September. Once the project is complete, a new dataset of global TC tracks and intensities will be made available to the community to contribute to our efforts to provide the best possible TC data record.

Interested readers are encouraged to learn more about and participate in the project at the cyclonecenter.org website (there are some FAQ on the project blog). The CycloneCenter project is a collaboration between the Citizen Science Alliance, NOAA National Climatic Data Center (NCDC), University of North Carolina at Asheville, and the Cooperative Institute for Climate and Satellites (CICS) – North Carolina.

References

  1. K.R. Knapp, M.C. Kruk, D.H. Levinson, H.J. Diamond, and C.J. Neumann, "The International Best Track Archive for Climate Stewardship (IBTrACS)", Bulletin of the American Meteorological Society, vol. 91, pp. 363-376, 2010. http://dx.doi.org/10.1175/2009BAMS2755.1
  2. C. Velden, B. Harper, F. Wells, J.L. Beven, R. Zehr, T. Olander, M. Mayfield, C. Guard, M. Lander, R. Edson, L. Avila, A. Burton, M. Turk, A. Kikuchi, A. Christian, P. Caroff, and P. McCrone, "The Dvorak Tropical Cyclone Intensity Estimation Technique: A Satellite-Based Method that Has Endured for over 30 Years", Bulletin of the American Meteorological Society, vol. 87, pp. 1195-1210, 2006. http://dx.doi.org/10.1175/BAMS-87-9-1195
  3. J.A. Knaff, D.P. Brown, J. Courtney, G.M. Gallina, and J.L. Beven, "An Evaluation of Dvorak Technique–Based Tropical Cyclone Intensity Estimates", Wea. Forecasting, vol. 25, pp. 1362-1379, 2010. http://dx.doi.org/10.1175/2010WAF2222375.1
  4. T.L. Olander, and C.S. Velden, "The Advanced Dvorak Technique: Continued Development of an Objective Scheme to Estimate Tropical Cyclone Intensity Using Geostationary Infrared Satellite Imagery", Wea. Forecasting, vol. 22, pp. 287-298, 2007. http://dx.doi.org/10.1175/WAF975.1
  5. C.C. Hennon, "Citizen scientists analyzing tropical cyclone intensities", Eos, Transactions American Geophysical Union, vol. 93, pp. 385, 2012. http://dx.doi.org/10.1029/2012EO400002

Switch to our mobile site