I am grateful to D Kelly O'Day, who created these files to illustrate the use of R to plot current climate data.
More examples of his work can be found at his Climate Charts & Graphs website.
Here's a model of the Keeling data (xlispstat)
Since writing that, HadCRUT4 is out (Fri Aug 14 14:11:38 EDT 2020). So that data has been subbed in. One can compare results against his analyses above, circa 2011.
Gavin uses the same data (Variance adjusted version of combined land [CRUTEM3] and marine [sea surface temperature (SST) anomalies from HadSST2, see Rayner et al., 2006] temperature anomalies on a 5by 5grid-box basis) to conclude that
Here's the general temperature download location for Hadley data.
O'Day: "The 5 global land-ocean temperature anomaly (LOTA) series use different baseline periods, making direct comparisons between the series more difficult than it would be if each series had the same baseline period.
This post shows how to convert the 5 major LOTA series to a common baseline. Links to on-line source data file and RClimate script are provided. Here is long term LOTA trends using a 133 month moving average and 1979-2008 baseline."
O'Day: "I show how to map NASA GISS's 2x2 degree temperature anomaly data using R mapping tools. Rather than rely on a single value to reflect monthly global temperature anomaly, this map shows the anomalies in each of the 16,200 cells in a 2 degree lon/lat grid. This lets us see the details that make up the global mean, we can see which areas are warmer and which are cooler. I provide a link to my RClimate script and data file so that interested R users can make their own maps."
Here is a current data source for L-OTI
yutakashino had the original, from July, 2010:
This 5 panel chart and table shows the year-to-date anomaly trends for the 5 major global temperature anomaly series and a table that shows how the current year ranks over the entire record for each series."
Decadal means shown in blue steps and last monthly value highlighted in red. Decadal means show steep rise since mid 1970s."
Annual means are shown in blue steps and last monthly value highlighted in red."
Here's a regression line for the same RSS data:
The seasonal signal has been removed and an inverted barometer has been applied. No glacial isostatic adjustment (GIA) has been made. The overall rate of increase is 2.9 mm/yr. GIA estimates are in the 0.2 - 0.5 mm/yr range."Wed Apr 11 07:19:48 EDT 2018: ael: I've added in Jason-3 data.
Arctic sea ice extent includes areas with at least 15% sea ice concentrations.
Since sea ice extent varies seasonally, the chart shows the trend for the current month for each year from 2002 to the present. The latest daily reading is shown in red for the current year and previous years."
Note: this file uses a file that must be recreated using my JAXA comparison file below: file Andy_JAXA.R
I used NOAA's more complete and recent data.
At this point in time (11/18/2016) something crazy is going on in the Arctic -- ice is supposed to be growing, but it's declining.... to new record levels. Scary data in the Arctic, and Antarctic:
Antarctic Sea Ice Extent Trend by Month, Based on NSIDC Monthly Data: 1/1979 - 2016. I went after this graphic upon seeing this striking image of total sea ice.
Now I'm playing with ice thickness:
Periods with negative anomalies (La Nina like conditions) are shown in blue and periods with positive anomalies (El Nino like conditions) are shown in red.
Most recent reading is highlighted in black. Here's where I got the data.
(Consol_GISS_SATO_Nino34A_RSS.csv obtained from this archive -- not the best source, perhaps.)
from their paper Global Temperature in 2014 and 2015:
I obtained some of the data I needed from this site.
Here's an updated version, with the data through August, 2016:
I used NOAA's more complete and recent data.
R script includes data retrieval, conversion of wide format table to long format, calculation of monthly averages and production of 2 figures in one chart."
Andy: "My problem is that the data file no longer contains the long-term averages, so I have to base averages off the current period." Actually it appears that data collection failed in March, 2013 (3/28, and a few days prior to that).
I think that this is the data, but since the channel died in 2013, there's been no change since then. Don't know if they're hoping to resurrect it or what. (Sun Nov 20 17:08:00 EST 2016)
Ironically, this file was created to make a Faithful Reproduction of one of Kelly's graphs:
Data from The Post's Pulitzer-winning series is available on GitHub (including R Scripts)