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?

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 MTG5: Urban Parameterization for CCSM Part II

The paper this week is "An Urban Parameterization for the Global Climate Model. Part II: Sensitivity to Input Parameters and the Simulated Urban Heat Island in Offline Simulations" by Oleson et al. The paper builds on the previous paper and looks at the sensitivity to changing parameters within the model. The split the parameter types in 3 categories, morphological, radiance and thermal. Each category was combined using every possible combination within its own category. The results of this highlight that previous surfaces, or the lack thereof, greatly influence the latent heat available and the sensible heat is very sensitive to both the morphological and radiance parameters. The paper moves on to simulating heat islands and compared this is Oke(1981) who derived a logarithmic equation based on measurements of heat islands in US and European cities. The model simulates the increased diurnal cycle and temperature difference between urban and rural environments. The sensitivity studies also real that parameter uncertainty mainly effects the amplitude of the diurnal cycle and not the phase. Overall the paper has a few confusing parts and many graphs that could be simplified or removed, but I felt this study did a much better job than some sensitivity studies of other areas in the past.

Journal Club MTG2: Urban Parameterization for CCSM

As part of the journal meetings we are reviewing "An Urban Parameterization for a Global Climate Model. Part 1: Formulation and Evaluation for Two Cities" by KW Oleson et al. The paper reviews efforts to capture land use change in urban areas and the effects this has on the atmosphere. The global models currently divides the world into a grid and each grid cell is further divided into various plant function types and wetland, urban etc. Each is dealt with separately however there is no scheme for urban areas. This paper presents an "urban canyon" solution that supposedly captures the urban climatology. There are separate energy balances and surface temperatures for each canyon facet. I really appreciate the added heat sources from waste (air conditioners) and traffic. A 1D heat conduction is solved numerically for fluxes in and out of surfaces. The paper then describes an experiment looking at two different cities areas, Mexico City and Vancouver. The results suggest an increased understanding of fluxes but says that the timing is off in the model, but note that no one has perfected the urban diurnal cycle. I want to note that the observations are rather sparse though. The paper also states that implementing this system globally might be problematic as no urban map yet exists categorising urban types. Something to look forward to in the future. Lastly a relevant question is, at what resolution does this actually matter? Perhaps a global view is not the answer but maybe regional is.

Journal Club MTG1: Atmospheric Chemistry

I attended my first ever Journal Club today with my supervisor here and fellow officemates. The topic: Atmospheric oxidation capacity sustained by a tropical forest by Lelieveld et al. Interesting paper from Nature that explores a theory which opposes known science in atmospheric chemistry. The idea is that OH is recycled through some mechanism in a low NOx environment like a rainforest. The paper is not great, requires a lot of supplementary material but it has jumpstarted my organic chemistry understanding, last attempted in high school. It also started to fill in the gaps of knowledge I have about various plugins in global climate models. However unless you are an organic chemist or have one handy (my officemate Ahmed very kindly spent an hour patiently explaining things) don't bother.