Global harmonization of climate & temperature data since 1850

A recent post, DCENT: Dynamically Consistent ENsemble of Temperature at the earth surface in Harvard Dataverse V1, is a complete dataset to accompany a paper in Nature. This came in perfectly, as a significant extension of the CLIWOC dataset originally posted 20 yrs. ago and reposted here just last week! Rationalizing disparate climate datasets spanning the last 1¾ c. make my efforts look puerile - indeed mine were before breakfast on the then-new internet per earlier blogpost - yet the message and the need are never greater than in the current Climate Emergency. Re: ongoing changes in climate modelling & temperature increases, my comment on an "Media Tell the Truth" WhatsApp group underscores the stark message:

Aaaand it appears someone rationalized ancient & modern climate data... only to find a lower baseline, ergo a higher rate of warming w.r.t. pre-industrial levels! Will it ever stop?

A first blog on CLIWOC reloaded  went from time-intervals to time-series on ship captains' logs, as early climate data collection pre-1880: roughly the start of systematic meteo data gathering.  Then time series graduated to time-enabled maps that compacted vast and complex datasets - ½ M points over 1½ c. with 20+ attributes each from ½ doz. sailing nations - into simple maps. Here is Generation 3 with multi-dimensional datasets in netCDF formats that combine time series into composite files.

Here are three videos at 1, 5 & 10 yr. intervals of the ensemble mean monthly resolution of land and sea surface temperatures in 5×5° squares worldwide since 1850.  

YouTube decadal above (1 min.), quinquennial (2 min.) and annual (8 min.)

The eagle-eyed will note early (19th c.) records are mainly maritime upscaling - 5×5° areas, compared to points in original video reposted below - of ships tracks, and that continental data take ascendance in later (20th. c.) records:

CLIWOC wind force direction (YouTube)

CLIWOC tall ships nationality (YouTube)

Without going into details, the Jupyter Notebook tools (advanced, not used here) display the raw global temperature trends seen across publications on climate warming:

Annual global mean difference w.r.t. 2088

It also shows a bell distribution of temperature data, remarkable considering the harmonization of a 200-member ensemble of climate data.

Another set on monthly climatological data details the above. It is meant to adjust and calibrate the temporal data shown above. Here are the monthly global stats for 1982-2014 stepping from months 1 thru 12. It shows what I call the annual breathing of the globe thru the seasons. 

Last but not least, polar effects are the most severe for climate change: that's where the most ice reside on the globe that are at risk of melt and sea level rise, and that's where the wind patterns converge toward and multiply climate change effects; so here are the Norh and South polar Arctic and Antarctic views of the same, projected onto previous maps, say, here... Don't the annual breathing of the poles thru the seasons show well?

North polar view of monthly climatological data (YouTube)

South polar view of monthly climatological data (YouTube)