Pythons, pandas and ferrets: The zoo of tools for atmospheric scientists

Sitting in a postgraduate group meeting a few weeks ago, the modelling students were discussing the various tools available used. One social scientist commented that to an outsider the conversation sounded highly amusing, and to those not familiar with the tools, I suppose discussing ferrets, pythons, Fortran, C and R sounds like a cross between a zoo and alphabet soup.

Why all these different tools? Legacy....

Fortran is the backbone of all climate models, used mainly for its numerical core that some argue is still faster then C etc. (they just don't want to port the system to a newer language). One programmer described reading Fortran is like scratching your own eyes out...pretty graphic!

Then comes C, the quintessential language of the engineer.

Ferret is a scripting language for environmental applications.

Python is an interpreted language that is has evolved over time, it used to be incredibly slow to manipulate data, but the modifications have sped up calculations and new modules have made python a welcome addition to the scientific zoo. SciPy, NumPy, pandas make it possible to do R comparable (actually fixed issues in R) calculations. Matrix manipulations have never been better.

Climate Change Negotiation Simulation

Today IIASA staged a Climate Change Negotiation Simulation as part of our YSSP. YSSP participants we assigned a country to represent one of: Brazil, Canada, China, Germany, Grenada, India, Japan, Russian Federation, Sweden, Sudan, UK, USA, Australia, Poland, Hungary etc etc. Basically a mixture og G8, G20 and G77 countries.

My initial reason for taking part was to experience a negotiation situation as I am always frustrated by the reports in the newspapers of failure following these negotiations. I can now understand why. We were each assigned a country, or given NGO status to observe the proceedings. I was assigned Australia, an environmental activist of sorts that was campaigning for tough emissions targets and to introduce the concept of "climate change refugees". My simulation tip notes featured some countries I should negotiate with and some that needed more persuasion. I was told to criticise African countries (which goes against my personal feelings) and push for developing countries to accept that they are not exempt from the guilt just because they are still growing economically.

M&M Negotiations

Before the official negotiations began we played a simple negotiaton game where each person is given five pieces of paper in different colours, you have to negotiate to get 5 pieces of the same colour. It was hilarious as some people walked around talking your way into a colour while others just sat and waited for people to come to them. Quite interesting to try.

Negotiation Simulation

Throughout the negotiation process we discovered other's country mandates, game instructions and a few hidden agendas. Ariel as game master observed the proceedings and pushed forward negotiations with ad-hoc instructions for countries to carry out. Examples of these were criticising the chair for bias, not following due process, changing stance mid negotiation, stage a walk out, start forming a coalition. It made the process quite baffling at times.

There are few keys thing I have learnt from the experience. When negotiating, there must be a clear language for all parties, such as what emissions means vs emissions intensities, what does respected mean etc. I also learnt that one needs negotiators with technical know-how to negotiate, not political figures that do not understand the implications of the various proposals tabled for discussion.

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.

Wicked Problem

Today's presentation on the Science on Global Problem Area: Energy & Climate Change by IIASA highlighted their second strategic research plan stream. Climate change was described as a "wicked problem": messy problem with different definitions, no single solution. Officially a "wicked problem" is a phrase used in social planning to describe a problem that is difficult or impossible to solve because of incomplete, contradictory, and changing requirements that are often difficult to recognize. Moreover, because of complex interdependencies, the effort to solve one aspect of a wicked problem may reveal or create other problems. I think its appropriate.

ENE (Energy Program) presented the current energy challenges. There is a growing demand for energy services (globally) that are reliable and has a secure supply. There are currently 2 billion people with access to energy sources. IIASA is looking at energy policies, savings and options available. There is a Global Energy Assessment will be released next year of which IIASA is a coordinator and author. Should be an interesting coffee table book :) The slide comes from the presentation given and highlights just how many factors one needs to consider and all the complexity of integrated assessments.

APD (Atmospheric Pollution and Economic Development Program) reviewed how costly/ambitious the Annex I Mitigation Pledges are and are they comparable between Parties? The costs of these pledges range from -0.3% to 0.1% of the GDP of each country at 2020. It seems the Ukraine has the highest cost saving but New Zealand seem to be losing out.

RAV (Risk and Vulnerability)
Anthony Patt of the Decisions and Governance Group within RAV revealed the two paths within RAV, adaptation and mitigation. One study of interest to me was done on Mozambique and its disasters. Using two different techniques; linear extrapolation of current trends and coupled-model ensemble projections they looked at disaster frequency. Interestingly enough the ensemble model projections show that Mozambique isn't going to have an increase in disaster frequency but the statistical models do show an increase...what/who do you believe? The picture on the left is taken from the cover of Options Magazine, a presentation of IIASA and its research to a non-specialist audience. The last issue talks about looking beyond Copenhagen and the current issue explores Green growth.

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.

Journal Club MTG9: Land Use and Land Cover Change

My topic of research while in Michigan is on Land Use and Land Cover Change which made today's paper which I had to present especially interesting. The paper "Evaluating the effects of historical land cover change on summertime weather and climate in New Jersey: Land cover and surface energy budget changes" by Paul Wichansky et al (2008), investigates the sensitivity of a warm season climate of New Jersey to land cover change from 1880s versus 1990s. The areas of specific interest include the magnitude of land cover change and sensitivity on near surface air temperature, rainfall, dew point temperature, heat fluxes and climatological episodes.

Previous studies have shown that deforestation causes an increase in temperature and rainfall shifts, while reforestation causes cooling in the region. Urbanisation also causes an increase in temperatures and changes to the diurnal cycle. The interesting thing to note is that all three processes have occurred in the state of New Jersey within the last century. Reconstructing the land cover from historical topographical maps and land consensus, a full land cover map was available for direct comparison with NLCD data from 1992. Aggregated to 1km and combined into 8 vegetation classes, the researchers used RAMS and LEAF2 to compare atmospheric responses to land cover change.

They nested the grid in 3 levels (above-purple) and 3 ensemble members per land cover scenario, they used a 2 month period in June/July 1999. From NCEP data to force atmospheric boundary conditions, they used June as a spinup and July as a month for analysis. The soil moisture was described from observation.

The model comparisons showed an increase in near surface air temperature and dew point temperature in urban and deforested areas and cooler in reforested areas, just as previous research stated. The results overall come as no surprise but they way they use the ensemble without giving the spread or error is slightly weird.

Climate Saver Campaign by WWF

I attended a talk titled "The Business Response to Climate Change" with WWF's Matt Banks (Senior Program Officer of Business and Industry). I thought it would be about initiatives in industry within the USA but I learnt more about polar bears and how the Arctic ice sheet is disappearing. This I suppose was an emotional ploy to the audience but the speaker came across as bored. I lost interest within the first 10 minutes. I did learn though that HP, Johnson&Johnson and CocaCola have all signed on to this Climate Savers campaign.

Journal Club MTG4: Atmospheric Feedbacks

The article today was "Atmospheric / vegetation feedbacks: A mechanism for abrupt climate change over northern Africa" by CM Patricola and KH Cook. The paper discusses the sensitivity to the latitude of the desert border in the Sahara and the atmospheric feedbacks in the African easterly jet (AEJ). The idea is that if the desert border is located below 17.9N the climate becomes sensitive and the summer precipitation and a weakening in the AEJ. There are a few concerns with the paper as they use prescribed soil moisture and do not discuss the land cover model used.

Drowning in statistics

I drowned in statistics at a Royal Statistics Society day. The theme: Communicating uncertainty in Climate predictions. I hope to not see a summation sign for a few days. I'm going to have nightmares :(

In further news, I hate the tube during rush hour, the other people push and shove and I'm little and get squished. Its not a pleasant experience. I will avoid this if possible in future.