Data is currently at
https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.csv
or
https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.txt
(or such updated location for this Gistemp v4 LOTI data)
January 2024 might show as 124 in hundredths of a degree C, this is +1.24C above the 1951-1980 base period. If it shows as 1.22 then it is in degrees i.e. 1.22C. Same logic/interpretation as this will be applied.
If the version or base period changes then I will consult with traders over what is best way for any such change to have least effect on betting positions or consider N/A if it is unclear what the sensible least effect resolution should be.
Numbers expected to be displayed to hundredth of a degree. The extra digit used here is to ensure understanding that +1.20C resolves to an exceed 1.195C option.
Resolves per first update seen by me or posted as long, as there is no reason to think data shown is significantly in error. If there is reason to think there may be an error then resolution will be delayed at least 24 hours. Minor later update should not cause a need to re-resolve.
1,000@zenarxy April anomaly has been cooler than March anomaly in last couple decades but this month looks like a repeat of last month at least spatially ... last 5 days missing so it will be warmer than shown, but the spatially warm/cold anomalies look somewhat similar.
The medium range ensembles generally following the very basic climatological rise you'd get from a statistical model...
I've been on the cool side repeatedly for last several months, so I've updated my error adjustment likewise to include all my own past forecast errors now, rather than excluding a couple tails (this raises the adjustment to +0.02 whatever the adjusted superensemble gives rather than a further +0.01)...
I don't think I will make any improvements in the ERA5->GISTEMP model as it stands for a long while -- until I can do a MCS type simulation essentially over station availability to calculate subbox/box biases/variances -- this means getting the weights for each station for each subbox & box for each year and then figuring out how to map ERA5/ECM onto those weights, in order to produce some spatial model that outputs bias, variances for the gridded data and incorporating that into my already complicated super ensemble pipeline sounds like a nightmare).
edit: Compared to forecast 4 days ago, notably more modest warm anomaly across central/eastern US and also generally elsewhere in the western hemisphere, but more anomalously warm poles (Greenland and Antarctica); However, looks like generally, northern Africa and Asia has warmed up a bit.