very cool Dan P. If I were you I would contact the folks at Google and get in on the earth engine beta.

I think back to few posts ago when folks discussed whether they "hoped" for a new record or not. While I expected 2013 to come close to matching 2012, I hoped for a recovery of sorts, or at least some sort of slow down. For two reasons: 1. Losing the ice early rather than later will not be good for us. 2. Having an oddball year that we cant make sense of immediately is a great scientific opportunity. back to lurking. this is a wonderful place to learn.

@ Timothy, sorry about the lack of linkage, in a rush. @ Wipneus, great work, looking fwd to more.. back to lurking at one of my favorite places.

Timothy "Climate models are based on physics, and while they can't include all of the different processes that are involved, the interactions or unlimited resolution. They can be expected to at least get at some of the core processes at sufficient resolution to see some of what is going on." One of the things that climate models do poorly is absolute temperature. I'm currently working with NARCCAP data and the problem of future heatwaves ( http://www.narccap.ucar.edu/) and one difficulty is that absolute values of things like temperature are off by physically significant amounts. For the arctic, I would suspect that the cold bias exhibited in many models may be a problem.. dunno. See below "We usually focus on temperature anomalies, rather than the absolute temperature that the models produce, and for many purposes this is sufficient. Figure 1 instead shows the absolute temperature evolution from 1850 till present in realizations of the coupled climate models obtained from the Coupled Model Intercomparison Project phase 3 (CMIP3) [Meehl et al., 2007] and phase 5 (CMIP5) [Taylor et al., 2012] multimodel datasets available to us at the time of writing, along with two temperature records reconstructed from observations [Brohan et al., 2006]. There is considerable coherence between the model realizations and the observations; models are generally able to reproduce the observed 20th century warming of about 0.7 K, and details such as the years of cooling following the volcanic eruptions, e.g., Krakatau (1883) and Pinatubo (1991), are found in both the observed record and most of the model realizations. [6] Yet, the span between the coldest and the warmest model is almost 3 K, distributed equally far above and below the best observational estimates, while the majority of models are cold-biased. Although the inter-model span is only one percent relative to absolute zero, that argument fails to be reassuring. Relative to the 20th century warming the span is a factor four larger, while it is about the same as our best estimate of the climate response to a doubling of CO2, and about half the difference between the last glacial maximum and present. To parameterized processes that are non-linearly dependent on the absolute temperature it is a prerequisite that they be exposed to realistic temperatures for them to act as intended. Prime examples are processes involving phase transitions of water: Evaporation and precipitation depend non-linearly on temperature through the Clausius-Clapeyron relation, while snow, sea-ice, tundra and glacier melt are critical to freezing temperatures in certain regions. The models in CMIP3 were frequently criticized for not being able to capture the timing of the observed rapid Arctic sea-ice decline [e.g., Stroeve et al., 2007]. While unlikely the only reason, provided that sea ice melt occurs at a specific absolute temperature, this model ensemble behavior seems not too surprising"

I'm gunna stick with my june numbers. 2.9 on area, 4.29 on extent.

+1. awesome

Nice work neven.

Neven, I'm aching to see some of your awesome animations..not to give you homework or anything. I'm still pretty confident about a new record this sept (4.29), but would not be shocked by

Ghoti. Environment canada has insolation data in it's Hourly format (hly11) but I believe you have to order the CDs. The online hourly, daily, and monthly products which you can download to csv dont have this variable. My current project ( CHCN) allows you to download all the monthly data-- using R, but I've been unable to locate any online insolation data from Envcanada. You can look on their web page under products and services.. then look at the technical documentation. There are, as others note, other sources for insolation data.

"othesis to which more sophisticated physical models might be compared. Can other models from October the year before outperform this approach? One might hope so, but it seems a good challenge. Continuing in this vein, the same approach yields a September 2012 mean NSIDC area prediction of 3 million km2, with confidence interval from 2.2 to 3.7 (Figure 2). 2012_Gompertz_area1b Figure 2 The PIOMAS volume prediction is 4 thousand km3, with confidence interval from 2 to 5.9 (Figure 3). 2012_Gompertz_volume1 Figure 3 But all these are monthly means. What about daily values? The dailies have higher entertainment value on this blog and elsewhere, although they are often disdained (by me among others) as being too random to reasonably predict. In a sporting mood I tried it out anyway, coming up with a minimum 1-day Cryosphere Today prediction of 2.7 million km2, with confidence interval from 2.1 to 3.3 (Figure 4). 2012_Gompertz_CTarea1 Figure 4 I was going to follow Figure 4 with a remark about daily values being harder to predict, but then hesitated and wondered, are they really? The surprising answer is no, at least with respect to Cryosphere Today area. The Gompertz model for minimum daily area graphed above leaves a residual standard deviation of just .29 million km2. A very similar model for CT September mean has a slightly higher residual sd (.34). That wasn't what I expected, so I'll explore the idea more systematically in the future. And for better or worse, I'll revisit these estimates in October and fill out a table: Sep 2012 Sep 2012 Predicted Observed NSIDC extent 4.3 ___ NSIDC area 3.0 ___ PIOMAS volume 4.0 ___ CT area 1-day 2.7 ___ Posted by L. Hamilton on June 07, 2012 at 06:03 | Permalink Comments DrTskoul For the quick understanding of trends, I have always liked mathematical formulas which possess the correct asymptotic limits (imposed by the underlying physical processes). ########### +1. Nice work L. hamilton.

ha crandles, i like to think great minds. Last year sure looked like a record breaker in the making and I considered it to be fluke that the record didnt fall. This year the ice looks worse, so I cant imagine fluking out two years in a row. With the "right" weather the record could be crushed. At some point I want to chat about your regression as I want to steal it for some unrelated work.. see you at the finish line

hehe. nice crandles. 4.29 That's the bet I have in over at Lucia's

2.9 4.29 records falling this year.

"For instance, why go with 72N" It's not that hard to understand. The idea was this. Every year ice below 72N melts out. So, if you want to look for the effect that added warming has on ice loss, you look at ice above 72N. Although I dont necessarily agree with the approach, I didn't think it was crazy. plus, the data and code was open, so its not like people would go blind by looking at data in a different way. Its not like puppies would die if somebody merely asked the question "whats the realtionship between warming and ice loss above 72N?" turns out you find a stronger relationship by looking at it that way. interesting. But in general I didnt stop believing that doubling C02 is a bad idea merely by looking at somebody trying something different. I didnt suddenly unknow that GHGs cause warming because somebody looked at data differently. No science got undone by the mere calculation of a number. Since it was well described and documented I could actually see what was done and didnt have to guess. Go figure. curiousity might kill cats, but I didnt figure it would frighten grown men

Who needs balance? I come here for three reasons. 1. it's the only place that discusses the weather and its role. 2. the players have a long history of looking at this data. 3. they have a perspective that they are entitled to have and defend. perspectives are good. The balancing happens in my head. err hopefully.

Chris, As I noted there is nothing wrong with being a bit aggressive on your forecasts. And there is nothing wrong with folks noting it. When my skeptical friends ( yes I do consort with disbelievers ) predict really high numbers for extent, I handicap them as well. My daily forecast would consist of averaging all other forecasters and after applying a correction factor for their past performance. Think of it as ensemble averaging.. collectively we are smarter than the individual. At this stage with the state of the ice ( uncharted terriority in many ways) you have to have a lot of balls to put a number down. So, kudos. I learn a lot by reading your take on things. I like crandles most of all cause he speaks my language. Personally, I think its going to be really close with 2007, but the blowout potential is still there. Either way, I get to go beat up some friends who were predicting 5M+.

Bob, yes. My multiplier is about the same for Chris, just checking back up thread. Nothing wrong with being a bit aggressive as Chris is wont to do, but I find that if I adjust his forecasts consistently downward, I'm rarely disappointed

beware of readings from microwave in bad weather. On the UB site No warranty, expressed or implied, is made regarding the accuracy or utility of the data! False ice concentrations can occur due to bad weather systems. prolly wise to wait. also concretration at the edge is much more prone to error.

Paul It be they continue QA the datasets for a long period. Im new to these datsets, but ive seen that before. neat mystery

Paul "Look, its really clear that IJIS-JAXA is not using simple two-day averaging. " The part you are not getting is that the data that is being averaged changes. You get a daily update. Let say today was 43. Well tommorrow you get another another update that says: Today is 44, AND oh by the way, yesterday was actually 45.

Mosher-- I for one would need to see multiple years worth of MASIE vs. IJIS data before I would consider Paul K's theory of the lag to have been tested. I'm not sure how many years-- but likely 5" ## ha Lucia, you know my standard. I wanna see the code. There were a bunch of times I thought I had the CRU algorithms nailed through reverse engineering.. except for tiny tiny little bits in the early century that were off. looked like a rounding error. That was my theory. Till i wrote them and they explained the very subtle difference between my reverse engineering and their actual algorithm..( they dont actually publish this step to my knowledge ). Some of the folks who work on this stuff are 'R' buddies. Just a matter of time, maybe by AGU, there is going to be a special session on R. I'm waiting on one guy to come back from the arctic and will see if I can interest him in the project. I need a bigger machine. Satillite raw data is a PITA.

Paul. You and I have a different standard of proof and different objectives. Personally, Im not satisfied if the regional maps fit like a glove. I am satisfied when I can re construct the data from SOURCE. That is, take the source data, apply the source algorithms and confirm why it fits like a glove. Assume it does fit like a glove. Assume that as a given. you have a hypothesis that this is related to delays. good hypothesis. probably correct. How do I rule out other explanations? It basically comes down to a reverse engineering perspective which is fine and dandy and a first principles re built from scratch approach. I prefer the latter because I like doing it. So even if the delay hypothesis satisfies you, I'm looking to do different things. namely, build tools that allow people to re build things from source. two entirely different problems. So, go in peace. No argument here just a different problem and different approach.

Thanks Alan, i'll add that to my reading list. There are two ways to look at data. look at the results and try to intuit or reverse engineer the process from that, or start with the source data. Personally, i'm a source data kinda guy. The other thing that was bothering me was the method of determining ice from brightness temperature. That relies on being able to determine a brightness temp for the ocean. Fine and dandy if there isnt a bunch of little chucks of ice.. a different animal if there is. Plus the susceptibility to storms that microwave suffers from.

Paul look back on this thread 6 days ago or check my posting and you can see the places where I talk about MASIE being an entirely different system. Not a time shifted shifted version of some other measure, not 10 days late or 6 days late, but entirely different. with a human square in the middle of the loop. You seem to think that MASIE must be time delayed. The documentation, if you read further, explains that it is a daily product. The difference in reporting would be to be an accuracy issue for the other services and not a time delay for MASIE Also, MASIE includes more than satillite data it includes ship observations and other real time input. In terms of resolution, and diversity of sources it would appear to be the best. You know that micro wave underestimates concentration and isnt especially accurate at the margin of the ice since the algorithms depend upon the difference in brightness temp between open ocean and ice. That underestimation can be corrected with visible band. If A micro wave product ( IJIS) shows more loss that a visisible + microwave product ( MASIE) the likely explanation is the microwave product is under estimating the concentration ( especially at the edge) and the product that has a micro wave + visible is doing a better job of estimating concentration at the edge.

Thanks Ned, That was most helpful. When I finish the R package I'm working on i'll try to do one for Ice data, i'll let you guys know when its done.