Highest daily maximum temperature during the first two weeks of January. Australian Bureau of Meteorology
By Claire
In Australia at the moment, it’s summer, and it’s hot! And not just hot, record breaking hot.
So far this year, at least 52 weather stations have broken the record for the highest recorded maximum January temperature (32 of these are also the hottest day ever recorded for these stations) and around 20 stations have recorded their highest minimum January temperature on record.
But it’s not only individual stations that are breaking records – Australia as a whole is the hottest it has ever been.
“On Monday and Tuesday last week (January 7 and 8) the Australian-averaged maximum temperature rose to over 40°C. Monday’s temperature of 40.33°C set a new record, beating the previous highest Australian daily maximum of 40.17°C set in 1972. Tuesday’s temperature came in as the 3rd highest on record at 40.11°C,” writes a team of meteorologist from the Bureau of Meteorology for blog site, “The Conversation“.
“Perhaps more unusually, the Australian mean temperature (representing the average of the daytime maximum and night-time minimum) set record high values on both days at 32.22 (January 7) and 32.32°C (January 8), that were well above the previous high of 31.86°C, set in 1972.”
This naturally leads to the question, “Are these extreme temperatures due to climate change?”
Unfortunately, the answer to that question is yes.
So far, the Earth has warmed by an average of 1°C during the last 100 years. The weather we see from day to day is occurring on top of this 1°C warming. It’s for this reason that we’re seeing so many warm records broken.
What we’ve essentially done is “load the dice” due to the underlying warming that has already occurred. What this means is that because of climate change, it is more likely that we will see temperatures above average conditions.
Increase in the number of extremes due to climate change. From Coumou et al, 2013.
A new article just published in the journal “Climatic Change” concludes that “on average, there are now five times as many record-breaking hot months worldwide than could be expected without long-term global warming. In parts of Europe, Africa and southern Asia the number of monthly records had increased even by a factor of ten.”
The extreme weather that we have seen in Australia will not be called extreme in the future, it will become the norm.
Seeing as records have only been kept in Australia,continuously, for about 100 years, records are bound to be broken. The use of about 740 weather sites to provide a multi-point average for 10.6 million sq km of Australia, means 1 site would be responsible for 10,000 sq km ,if they were equidistant.
That’s true. The problem with keeping records of weather events in Australia is that Australia is huge, with big areas that are largely uninhabited.
Despite this, the records that we do have (and the way that these records have been calculated in the past) are still being broken.
I think that if we were basing our claim of climate change on Australian temperatures only, then there is a good argument that the records aren’t extensive enough for us to see any kind of “long-term” pattern.
But, there are temperature records all over the world that are being broken, suggesting that these extreme temperatures are not only happening in Australia and it’s on the basis of this that we can say that climate change is happening.
See this report on the USA’s record breaking heat as an example
http://www.nytimes.com/2013/01/09/science/earth/2012-was-hottest-year-ever-in-us.html?_r=1&
And yet worldwide. it is only in recent times, via satellite that we have had the option of recording a lot of this data. Even then, it is still to some extent limited. Adding data points may make the assessment more certain, but can you then rely on historical data in conjunction with these new sites, or should we just use these recent data points.
Where sites have changed, is the data comparable or are we introducing biases, one way or the other?
The world certainly is changing and has done over millenia. Is the problem too large a population, pollution of all types, data points not correctly factored into the models, or a combination.
‘Even Prof Jones admitted that he and his colleagues did not understand the impact of ‘natural variability’ – factors such as long-term ocean temperature cycles and changes in the output of the sun. However, he said he was still convinced that the current decade would end up significantly warmer than the previous two.’
Read more: http://www.dailymail.co.uk/sciencetech/article-2217286/Global-warming-stopped-16-years-ago-reveals-Met-Office-report-quietly-released–chart-prove-it.html#ixzz2IgH7hhnv
If you don’t know how the inputs interact, how can you possibly believe the models?
The website you link to in your last post has a few major issues that skews the data to make the point of the author.
Data is available prior to 1998 – I’m not sure why they choose to start there graph in this year. If you look at the long-term records of temperature, you’ll see that even the “plateau” that the authors speak about is occurring at temperatures well above the long-term mean.
http://data.giss.nasa.gov/gistemp/graphs_v3/
Secondly, there are no actual references to the original data that was supposed to have been “issued quietly on the internet”. From what I can see, there are no references to any of the claims the authors make – no links to interviews or webpages of the people they “quote” and no links to the data that they claim shows climate change has stopped.
When you go to a reputable data source – like NASA GISS linked above, or the Bureau of Meteorology (http://www.bom.gov.au/climate/change/), you’ll see that recent temperatures are the warmest of at least the last century. Palaeoclimate reconstructions extend instrumental records back in time, and suggest that recent temperature are the warmest they’ve been for at least 2000 years (http://www.ncdc.noaa.gov/paleo/globalwarming/paleolast.html)
But the piece I posted was about the MODELS.
In any given situation if we make assumptions, we are likely to introduce biases. The only way not to introduce such a bias is if all our assumptions are correct.
Take a simple example- in the equation a=100/x- if we assume x=3 or 5 when in fact it is 4; then we have introduced a bias. Either a positive or negative bias.
We know intuitively in a multiplication or adding scenario,if we use a larger number, it will be a positive bias. Similarly if we use a smaller number in dividing or subtracting. Also vice versa.
With all the different models available, they all seem to vary. Even though the underlying data sets are the same. It is only the differing assumptions then that make that variability.
A maximum of only one model can be correct, and a minimum of zero.
By the way don’t you think Palaeoclimate reconstructions make assumptions?
What you are saying about models is true, they are models and are therefore simply the best representation of the real world. No single model can be heralded as having all of the answers, which is why there are so many climate models being used for climate change projections. When you take the average of all of the model outputs, you have the best guess at an answer.
The real way to test these model outputs is to compare the model forecasts with observed data. This is done regularly in publications, by bloggers and by the IPCC in their assessment reports.
(http://www.ipcc.ch/publications_and_data/ar4/wg1/en/faq-8-1.html)
Of course there are discrepancies, but generally, the models seem to be doing a good job.
The question I’d like to ask you then is what do you suggest as an alternative to models? In the absence of a better way, isn’t it still valid to use models (with their inherent errors and assumptions) to make predictions for future climate change – given that models are rigorously tested and compared and improved as knowledge develops? The alternative, it would seem, is to stick to observations and see trends as they emerge from the data. The problem with this is that we see things when it’s too late to do anything about it.
But the IPCC tops and tails the models to provide a midrange. Do you consider that a scientific method of selection? At the moment the models are the science.
I don’t have answers. But to use a system with acknowledged flaws as some type of predictive service seems illogical.
I notice Prof. Jones is quoted as saying 15-16 years is too short a period from which to draw conclusions. If this is so then it is too early to use the models as a predictive service.
The models are tested using “hindcasting” where they attempt to recreate the conditions from the past. The idea is that if they are successful at replicating the conditions from the past (which we know from observations and proxy records) then they can be considered more robust in predicting conditions in the future.
So while the models have only been projecting climate change for a few decades, they have been equilibrated using data from the past, which increases their effective model time out to hundreds of years (thousands if you include palaeoclimate models – but these aren’t usually used for climate change projections).
I think the bottom line is that models are flawed and no one will disagree on that point, which is why there are so many uncertainty and confidence bands shown when you look at model output. But models have proven themselves to be realistic in their short term forecasts, which we can test by comparing predictions with observations.
The uncertainty increases the further you go into the future, but the general trend is going in the right direction.
I’d be very hesitant to throw away models altogether just because there are uncertainties surrounding the result. They are a good indicator of future trends and the models all suggest that the world will warm (the exact amount varies between models, but that’s not a deal breaker in my view).
We know the world is going to warm because we know how much CO2 has been released and although we may not know the specifics of how much warming, feedbacks etc, we know that on long time scales, CO2 and temperature change together. It’s a huge risk to take to continue to release CO2 and not do anything about it because the predictions are uncertain.
I haven’t said the world wasn’t warming. I am arguing the models and causes.
Some interesting extracts from
‘Spatial Analysis to Quantify Numerical Model Bias and
Dependence: How Many Climate Models Are There?’
http://www.image.ucar.edu/~nychka/manuscripts/A06624R1.pdf
’4.1 ..‘Although model 10 has exceptionally large DJF and JJA rms means, it does not have the largest trend rms. The results to some degree question the common assumption that a model, which does well in simulating the climate mean state, can be trusted to project changes into the future (the latter being temperature increase over time, i.e. a trend). This assumption is either explicit (e.g. Tebaldi et al. (2005)) or implicit (e.g. IPCC (2001)) in many studies.’
4.2 ..’ While the noise contribution is small in a 30 year climatological mean, linear trends at a single grid point can be influenced substantially by noise, i.e. the internal unforced variability in the climate system
5. ..Conclusions
’ We presented the results of quantification of AOGCM model biases and their dependence across different models. Based on our analysis, many AOGCM models, especially those developed by the same parent organizations, have highly correlated biases and thus the effective number of “independent” AOGCM models are much less than the actual number of models. This lets us form subgroups of models that share “common” features and to find better strategy in combining the informations from different model outputs rather than taking a naive average. We also demonstrated that the performance of AOGCM models on the mean temperature state has little relationship with its performance in reproducing the observed spatial temperature trend. This conflicts with a standard assumption used to interpret different AOGCM projections of future climate. Our results suggest the need for better model validation procedures that are multivariate.’
And
‘Our results show that the statistical approaches in Tebaldi et al. (2005), Furrer et al. (2007) and Smith et al. (2006) may need to be extended due to the biasedness of the climate models and more importantly the dependence among biases from different AOGCMs.’
So why do some proponents insist on 100 year scenarios?