PhD: Chapter 6, 7 & Conclusion

Chapter 6: Decision Frameworks for Added Information Use

Chapter 7 & Conclusion: Seasonal Forecasting Decision Frameworks

PhD: Chapter 4 & 5

Chapter 4: Constraints for Seasonal Forecast Use

Chapter 5: Mobile Dissemination of Climate Information (mClimate)

I've been playing around with the direction of the text, the font, colours and excluding various words. It seems that the program cannot distinguish between common nouns or phrases such as et al.

PhD: Chapter 2 & 3

Chapter 2: Seasonal Forecasting techniques and uncertainty

Chapter 3: Current producers and dissemination of information

PhD: Chapter 1

I've been slowly feeding my thesis chapters in wordle.net. It certainly makes it look more impressive in key word form

PhD: Abstract

What my abstract looks like according to wordle:

Is the MEAN meaningless?

After a rather heated debate last night about climate models, betting on the model runs and trying to analyse the data from these massive collections of model runs (ensembles) I have a few thought provoking questions for people:

Is the mean meaningless or useful?
What would you do if the mean is consistently wrong?
Would you bet on the mean of an ensemble?
How do you cater for the variability shown by the forecasts, especially if they encompass extreme that can feasibly happen?
Do you feel its responsible to give a policy-maker or decision-maker the mean without a proper feeling for the envelope of possibilities? Could policy-makers design policies that used that variability? Would these be more robust than the current solutions?

The Seeds of Time

"If you can look into the seeds of time, and say which grain will grow and which will not, speak then unto me." - William Shakespeare, Macbeth Act 1 Scene 3

What farmers want and what forecasters provide are two vastly different sets of information. I'm becoming increasing more frustrated with researchers who say 'but its not possible'. Well then why did you give the farmer the chance to ask for it in the first place, you're creating false expectations of the capabilities of science.

With this in mind, what can we the forecasters offer? Tailored forecasts for one. Industry specific? Individual time frames? Spatial resolution? These products seem to be lacking in South Africa and to some respect the rest of Africa.

Pop quiz: Would you trust the mean of the data below to tell what is going to happen in the next 3 months?

The trouble with (weather) forecasting is that it's right too often for us to ignore it and wrong too often for us to rely on it. - Patrick Young

We as a scientific community are improving our climate models to accurately reflect the climate conditions but they are not so accurate that we can rely on them completely. Would you place a bet based on your forecast?

There must be a way to create a robust solution that can cater for the inconsistencies in model skill. What is it? I'm not sure, that is part of the reason I came to IIASA, to discover, to explore. Its turning into a long path to an even more distant discovery but when have I ever chosen the easy path in the past.

Applying Climate Information in Africa

I'm currently making my way through what seems to be a literature bible for applications of climate information in various African countries across sectors and scales: "Applying Climate Information in Africa, An assessment of current knowledge". The report was written by Anthony Patt of IIASA for NOAA. I would highly recommend it for anyone doing climate work in South Africa.

Visiting SAWS

Simon Mason from IRI is visiting South Africa for a project with the South Africa Weather Bureau (SAWS). I had the opportunity to go to Pretoria to ask a few questions to the expert himself. Below I have listed a few things I learnt on my trip:

There are 3 attributes that one must consider in verification of forecasts:
• Resolution: Conditioning on the forecast. If you forecast something, does it happen? A forecast is useful if you have some resolution. Difficult to measure (need lots of data). Good for regions not grid boxes.
• Discrimination: Conditioning on outcomes. If it rains, did we forecast it. Needs less data. Can get map
• Reliability: Measures confidence and bias. Not useful on its own. Need to use (Resolution ∪ Discrimination) ∩ Reliability

Defining a region of study:
It is worth starting at 20 degrees latitude and longitude on each side of area interested and then narrow down by looking at diagnostic maps from GCM to downscaled. Easy to do with CCA. If looking at geo-potential height as an indicator, then the region may need to be bigger to include larger scale synoptic conditions.

Some extra literature for my spare time:
[1] R. Hagedorn and L. A. Smith. Communicating the value of probabilistic forecasts with weather roulette. Meteorological Applications, 2008.
[2] S. J. Mason. Recommended procedures for the verification of operational seasonal climate forecasts. 2010.
[3] A. H. Murphy and D. S. Wilks. A case study of the use of statistical models in forecast veri- fication: Precipitation probability forecasts. Weather and Forecasting, 13:795–810, September 1998.
[4] M. S. Roulston and L. A. Smith. Evaluating probabilistic forecasts using information theory. Monthly Weather Review, 130:1653–1660, June 2002.
[5] M. K. Tippett and A. G. Barnston. Skill of multimodel enso probability forecasts. Monthly Weather Review, 136:3933–3946, 2008.

SASAS Conference

South African Society for Atmospheric Scientists (SASAS) host an annual conference to bring together the Atmospheric Scientists and interested parties (agriculture, atmospheric chemists, hydrologists, oceanographers) a chance to meet and present their current research.

This year Climate Systems Analysis Group (CSAG) were tasked with running the conference. SASAS was held at Waterval in Tulbagh, a team building and conference venue just outside the picturesque wine producing town. There was snow on the mountains and fog in the mornings..nice and chilly.

The theme this year was BEYOND THE BOX. Trying to slowly shuffle atmospheric scientists from the traditional methods to the innovative. The conference structure changed to include 2 new sections; Grill the Climatologist and Beyond the Box.

Grill the Climatologist consisted of thought provoking questions put before a panel of three scientists. The questions were anonymously submitted to the chairman of the session. They included geoengineering, why we bother running more simulations when we can't cope with what we have already and carbon trading.

Beyond the Box was split into 2 sections, a short introduction by four scientists on what they think is the future of atmospheric science. These presentations highlighted the current failures and their visions of hope. The second section expanded on these ideas and included a few extra presentations on innovative approaches to climate science.

A few take home messages from this conference are that carbon trading is a disastrous idea, geoengineering is not a solution and that we need to stop looking to new and better models and tell a story with the data we already have.