About this page
When discussing climate change, there is much misinformation about the science as well as the observational data. This page is about the latter as at January 1st, 2014 with updates from time to time. There is no discussion about the implications of these diagrams. Captions to diagrams shown in italics are copied from the source material. Sources are quoted.
These are of fundamental importance if global climate change is a result of using fossil fuels.
Monthly mean atmospheric carbon dioxide at Mauna Loa Observatory, Hawaii
The annual mean carbon dioxide growth rates for Mauna Loa. In the graph, also decadal averages of the growth rate are plotted, as horizontal lines for 1960 through 1969, 1970 through 1979, and so on.
(Source - http://www.esrl.noaa.gov/gmd/ccgg/trends/ )
Global annual average near-surface temperature anomalies from HadCRUT18.104.22.168 (Black line and grey area indicating the 95% uncertainty range), GISTEMP (blue) and NOAAGlobalTemp (orange). The average for 2015 is a provisional figure based on the months January to October 2015.
(Source: Met Office Hadley Centre.
From https://www.wmo.int/media/content/wmo-2015-likely-be-warmest-record-2011-2015-warmest-five-year-period )
This next version is from the Japanese Meteorological Agency and is the provisional assessment early January, 2016.
Historical weather data are difficult to handle for many reasons such as changes of site, instrumentation, land use etc. The following was produced by Berkley, USA and is known as the BEST data set.
Unadjusted data results are shown in the blue curve. The green curve shows the results if only metadata breakpoints are considered. The red curve depicts all adjustments.
For the period since 1980 the BEST data have been compared with the other data analyses used in IPCC/WMO reports.
Series Comparison. Anomalies of the global temperature index of provided by several groups is depicted for the 1980 to present time period. The base period is 1951-1980.
(Source - http://berkeleyearth.org/land-and-ocean-data/ )
A global view of the warming that has occurred in recent years is shown here.
Average temperature anomalies for January to October 2015 from the HadCRUT.22.214.171.124 data set. Crosses (+) indicate temperatures that exceed the 90th percentile, signifying unusual warmth, and dashes (-) indicate temperatures below the 10thpercentile, indicating unusually cold conditions. Large crosses and large dashes indicate temperatures outside the range of the 2nd to 98th percentiles.
(Source - https://www.wmo.int/media/content/wmo-2015-likely-be-warmest-record-2011-2015-warmest-five-year-period -
Source: Met Office Hadley Centre)
Alternative ways of measuring global change in temperatures
The next diagram is another way of obtaining sea level temperature data. The Europe Centre for Medium range weather Forecasting produces data analyses to start every forecast. These analyses include all data from the surface of the earth right up into the stratosphere. All elements are included in a 4-D analysis scheme. Values of surface temperature are extracted with the results shown here.
The narrower, darker bars denote complete global averages, while the lighter, broader bars denote averages taken only over grid boxes which exclude most of the Arctic and Antarctic. Evidently the ranking of average temperatures depends on the data coverage, although the differences are within the bounds of uncertainty associated with the dataset.
For the purpose of this illustration we used HadCRUT4 geographical coverage for each month to sample ERA-Interim estimates. The November 2014 HadCRUT4 coverage was used for December 2014 as HadCRUT4 data were not yet available for the latest month.
El Nino/La Nina effects
There has been much talk about the effects of El Nino and La Nina years. This is a diagram on that aspect of the data.
Global annual average temperatures anomalies (relative to 1961-1990) based on an average of three global temperature data sets (HadCRUT.126.96.36.199, GISTEMP and NOAAGlobalTemp) from 1950 to 2014. The 2015 average is based on data from January to October. Bars are coloured according to whether the year was classified as an El Niño year (red), a La Niña year (blue) or an ENSO-neutral year (grey).Note uncertainty ranges are not shown, but are around 0.1°C.
A data set that does not use and in situ data at all has been derived from satellite data. Micro-wave sensors can be used to get a measure of the temperature in a considerable depth of the lower troposphere. This is shown here.
Latest Global Average Tropospheric Temperatures.
Another version of the same information is
Global (80S to 80N) Mean TLT (lower troposphere temperatures) Anomaly plotted as a function of time. The thick black line is the observed time series from RSS V3.3 MSU/AMSU Temperatures. The yellow band is the 5% to 95% range of output from CMIP-5 climate simulations. The mean value of each time series average from 1979-1984 is set to zero so the changes over time can be more easily seen. Note that after 1998, the observations are likely to be below the simulated values, indicating that the simulation as a whole are predicting too much warming (of the air temperatures..)
Another way of looking at global temperatures from satellite is the total water vapour content. The warmer the air, the more water it can hold.
Time series of total column vapor anomaly, averaged over the world's oceans, from 60S to 60N.
Probably the most consistent and reliable data on sea level are from satellite altimetry although other measures can be found in IPCC reports. The latest is from IPCC, AR5, https://www.ipcc.ch/pdf/assessment-report/ar5/wg1/WG1AR5_Chapter13_FINAL.pdf, Satellite data used in that report are shown here.
Altimetry data sets from five groups (University of Colorado (CU), National Oceanic and Atmospheric Administration (NOAA), Goddard Space Flight Centre (GSFC), Archiving, Validation and Interpretation of Satellite Oceanographic (AVISO), Commonwealth Scientific and Industrial Research Organisation (CSIRO)) with mean of the five shown as bright blue line.
(Source - http://sealevel.colorado.edu/)
There are tidal gauge data dating back to the late 19th century.
Global average sea level from 1860 to 2009 as estimated from the coastal and island sea-level data (blue). The one standard deviation uncertainty estimates plotted about the low passed sea level are indicated by the shading. The Church and White (2006) estimates for 1870–2001 are shown by the red solid line and dashed magenta lines for the 1 standard deviation errors. The series are set to have the same average value over 1960–1990 and the new reconstruction is set to zero in 1990. The satellite altimeter data since 1993 is also shown in black.
A long data set is available using sea surface temperatures measured from ships on passage. Until the 1940s, there was considerable scatter in the data. Since then observing procedures on ships have become more standardised and since the 1960s satellite sensing has produced even more consistent data.
This graph shows how the average surface temperature of the world’s oceans has changed since 1880. This graph uses the 1971 to 2000 average as a baseline for depicting change. Choosing a different baseline period would not change the shape of the data over time. The shaded band shows the range of uncertainty in the data, based on the number of measurements collected and the precision of the methods used'
Ocean heat content down to a depth of 700m (left) and 2000m (right). Three-month (red), annual (black) and 5-year (blue) averages are shown.
Antarctic land ice amount
The melting of the land ice over the Antarctic continent is a measure of warming of that area. Paradoxically, the melting of the land ice has led to an increase in the area of sea ice. However, the volume of sea ice gain is substantially less than the loss of land ice.
The continent of Antarctica has been losing more than 100 cubic kilometers (24 cubic miles) of ice per year since 2002.