It was cold in the northern prairies and N Canada, but surprisingly, warm on average in E USA. Cold in Europe, extending through Morocco to Senegal. As much noted, very warm in the Arctic, and warm in a band from E Mediterranean to N India. Mixed in Antarctica, probably on average cool. Interactive map here.
I expect that Arctic-sensitive indices like GISS, Cowtan and Way, and TempLS mesh will show more warming than the others. Here is a temperature map looking down on the N Pole
This post is part of a series that has now run for some years. The NCEP/NCAR integrated average is posted daily here, along with monthly averages, including current month, and graph. When the last day of the month has data (usually about the 3rd) I write this post.
The TempLS mesh data is reported here, and the recent history of monthly readings is here. Unadjusted GHCN is normally used, but if you click the TempLS button there, it will show data with adjusted, and also with different integration methods. There is an interactive graph using 1981-2010 base period here which you can use to show different periods, or compare with other indices. There is a general guide to TempLS here.
The reporting cycle starts with a report of the daily reanalysis index on about the 4th of the month. The next post is this, the TempLS report, usually about the 8th. Then when the GISS result comes out, usually about the 15th, I discuss it and compare with TempLS. The TempLS graph uses a spherical harmonics to the TempLS mesh residuals; the residuals are displayed more directly using a triangular grid in a better resolved WebGL plot here.
A list of earlier monthly reports of each series in date order is here:
The TempLS mesh data is reported here, and the recent history of monthly readings is here. Unadjusted GHCN is normally used, but if you click the TempLS button there, it will show data with adjusted, and also with different integration methods. There is an interactive graph using 1981-2010 base period here which you can use to show different periods, or compare with other indices. There is a general guide to TempLS here.
The reporting cycle starts with a report of the daily reanalysis index on about the 4th of the month. The next post is this, the TempLS report, usually about the 8th. Then when the GISS result comes out, usually about the 15th, I discuss it and compare with TempLS. The TempLS graph uses a spherical harmonics to the TempLS mesh residuals; the residuals are displayed more directly using a triangular grid in a better resolved WebGL plot here.
A list of earlier monthly reports of each series in date order is here:
Posts
The February estimate of global mean surface temperature from UM CCI based on preliminary GFS daily estimates was also up +0.058C and from WeatherBELL based on CFSV2 was up +0.075C from January. In contrast, the UAH TLT was down -0.06C, but it does not include the highest polar latitudes.
ReplyDeleteGISS should be high 80s. Maybe even 90? Regardless, sky high for a La Niña February.
ReplyDeleteA number of Pacific variability indicators now swinging strongly back upwards so looks like the January anomaly should be one of the lowest of the year in GISS. I would think March through May will be at a similar level of around 0.75-0.8ºC before heading upwards with an annual average similar to 2015 and 2017. Then about evens chance of a record in 2019 and a clear record in 2020, assuming no major eruption.
ReplyDeletePaulS - an average of .2 ℃ over the first 2 decades of the 21st century. Using a hoax theory, the IPCC pulls off a Robin Hood level bullseye! It would be hilarious. I would add to your caveat, the Divine Wind (M. England).
ReplyDeleteThe following is something I was thinking about wrt our reliance on blindfolded predictions as validators of theories and models.
ReplyDeletePredictions aren't necessary to validate conventional ocean tidal analysis models, as historical data has always been perfectly adequate to prove cause and effect of tidal forcing with regard to SLH data. The models work that well -- several days worth of measurements and one can make an accurate backcast estimate. I have found that the same holds true with tidally-forced ENSO models -- any randomly selected ~20 year interval is sufficient to reproduce the rest of the historical ENSO data set with uncanny accuracy. (not talking about GCMs here which are intentionally configured as stochastic AFAICT)
The point is that we do not have to wait for predictions to come true before we come to an understanding of the fundamental physical process involved. Contrary to popular belief, these are not the dark ages of scientific analysis, where the PTB requires us to make blindfolded predictions to prove their worth. Sometimes I think there is a misguided conventional wisdom indoctrination going on that demanding predictions is the knee-jerk response pushback to any proposed model. Especially when the behavior is deterministic and robust against perturbations (e.g. as with Marston's topological insulators), there should be no need for such a contrived requirement.
More importantly, why do we have to wait 20 years when the future is being decided now with respect to policy? That's what I don't get. The models for CO2-induced warming are already baked in with much less validation than a tidal analysis model. I have no problem with that, so what's up with all the pushback on the truly interesting stuff?