National average climate information from ClimGen

Climate projections & observations

Data sources: climate observations & projections

How to cite: Osborn et al. (2016)

To access national average climate information, click the Locations link, select a region and then a country. The country name will appear in the menu at the left-hand side, select either the Observations or Projections link listed under the country name.

Climate observations - overview

The climate observations used in ClimGen, and for the national average data provided here, are from the CRU TS dataset. The latest version was published by Harris et al. (2014), though the average climate for 1961-1990 is unchanged from the climatology published by (New et al., 1999; called CRU CL 1.0).

The average 1961-1990 normals (CRU CL 1.0) were estimated by New et al. (1999) from a dense network of weather station observations, interpolated to a regular grid taking into account the elevation of the stations and the mean elevation of each grid cell. Thus, the average climate is adjusted to represent climate at the mean elevation of each grid cell. The accuracy of the interpolations was assessed using cross-validation and by comparison with other climatologies.

The gridded CRU TS data, including timeseries of monthly values for each 0.5° grid cell, and the underlying station data, are also available in scientific formats from here including via a convenient Google Earth interface.

Climate projections - overview

The climate projections generated by ClimGen, and used to produce the national average data provided here, are based upon simulations from General Circulation Model (GCM) based climate models. However, the GCM-based projections are not used directly but are instead approximated using the "pattern-scaling" technique (Santer et al., 1990; see Tebaldi and Arblaster, 2014, for a recent review).

The pattern-scaling technique (as implemented in ClimGen - Osborn et al., 2016) diagnoses patterns of climate changes from GCM simulations and scales them to represent the climate change that we might expect that GCM to predict for different amounts of global warming. Therefore the climate projections presented on this website are based on GCM simulations but they are not the same as the direct GCM output. See Tebaldi and Arblaster (2014) and Osborn et al. (2018) for two recent evaluations of how well pattern-scaling is able to emulate the GCM projections: the errors are generally small compared with the spread between GCMs. This is sufficient accuracy for the present purpose, since the aim of this climate information website is to facilitate exploration of the spread in results between different GCMs.

Patterns diagnosed from three archives of GCM simulations have been used here:

  • 21 GCM patterns from CMIP5 (Taylor et al., 2012): a multi-model ensemble of different GCMs (2010-2014 vintage).
  • 21 GCM patterns from CMIP3 (Meehl et al., 2007): a multi-model ensemble of different GCMs (2003-2007 vintage).
  • 17 GCM patterns from QUMP (Murphy et al., 2004): a perturbed-physics ensemble of a single GCM (HadCM3).
Lists of these GCMs are given in Osborn et al. (2016) and in the GCM symbol key.

These patterns of climate change are scaled to make projections for a range of global temperature increases, either specific levels of warming or under a standard scenario.

  • Specific levels of global-mean warming for CMIP5, CMIP3 and QUMP ensembles:
    • 0.6, 1.0, 1.6, 2.0, 2.6, 3.0, 3.6, 4.0, 4.6, 5.0 and 5.6 °C above the 1961-1990 baseline
    • These are approximately equivalent to 1.0, 1.4, 2.0, 2.4, 3.0, 3.4, 4.0, 4.4, 5.0, 5.4 and 6.0 °C above the pre-industrial (Hawkins et al., 2017)
  • Representative Concentration Pathway (RCP) for CMIP5 and CMIP3 ensembles:
    • RCP2.6 (sometimes called RCP3PD), RCP4.5, RCP6 and RCP8.5
  • Special Report on Emissions Scenarios (SRES) for CMIP3 ensemble:
    • SRES B1, B2, A1T, A1B, A2, A1FI

Not all GCMs have simulated climate changes under all of these scenarios and specific warming levels. Indeed, this is the motivation for using pattern scaling rather direct GCM simulations: pattern scaling can emulate the GCM results even for cases that the GCM has not simulated. All that is needed is that GCM's pattern of change and the global-mean temperature change.

For the scenarios (RCP and SRES) the global-mean warming for each GCM is obtained by:

  • CMIP5 GCMs: global-mean temperature change simulated directly by the GCM for the RCP scenarios that had been simulated by that GCM. For any RCP scenario that had not been run by a particular GCM, the global-mean temperature change for that GCM-RCP combination was emulated by scaling its simulation of the RCP4.5 scenario by the multi-model mean ratio between the scenarios.
  • CMIP3 GCMs: global-mean temperature change simulated by MAGICC5.2 (as used by IPCC AR4), having been tuned to emulate each individual CMIP3 GCM (Meinshausen et al., 2011).

In addition to the climate change projections, an indication of natural internal climate varibility is also given for each national average. This is based on the variability of 30-year averages of temperature and precipitation. Since our observational records are not long enough to estimate the variability of 30-year averages, the estimates included here are taken from a 1000-year control run of the CMIP3/CMIP5 HadCM3 climate model.


  • Harris I, Jones PD, Osborn TJ and Lister DH (2014) Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset. International Journal of Climatology 34, 623-642 (doi: 10.1002/joc.3711).
  • Hawkins E, Ortega P, Suckling E, Schurer A, Hegerl G, Jones P, Joshi M, Osborn TJ, Masson-Delmotte V, Mignot J, Thorne P and van Oldenburgh GJ (2017) Estimating changes in global temperature since the pre-industrial period. Bulletin of the American Meteorological Society 98, 1841-1856 (doi: 10.1175/BAMS-D-16-0007.1).
  • Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchel JFB, Stouffer RJ and Taylor KE (2007) The WCRP CMIP3 multimodel dataset: a new era in climate change research. Bull. Amer. Meteor. Soc. 88, 1383-1394 (doi:10.1175/BAMS-88-9-1383).
  • Meinshausen M, Raper SCB and Wigley TML (2011) Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 - Part 1: Model description and calibration. Atmos. Chem. Phys. 11, 1417-1456 (doi: 10.5194/acp-11-1417-2011).
  • Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ, Collins M and Stainforth DA (2004) Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430, 768-772 (doi:10.1038/nature02771).
  • New M, Hulme M and Jones PD (1999) Representing twentieth century space-time climate variability. Part 1: development of a 1961-90 mean monthly terrestrial climatology. Journal of Climate 12, 829-856 (doi: 10.1175/1520-0442(1999)012<0829:RTCSTC>2.0.CO;2).
  • Osborn TJ, Wallace CJ, Harris IC and Melvin TM (2016) Pattern scaling using ClimGen: monthly-resolution future climate scenarios including changes in the variability of precipitation. Climatic Change 134, 353-369 10.1007/s10584-015-1509-9).
  • Osborn TJ, Wallace CJ, Lowe JA and Bernie D (2018) Performance of pattern-scaled climate projections under high-end warming, part I: surface air temperature over land. Journal of Climate 31, 5667-5680 (doi:10.1175/JCLI-D-17-0780.1).
  • Santer BD, Wigley TML, Schlesinger ME and Mitchell JFB (1990) Developing climate scenarios from equilibrium GCM results. MPI report, 47, Max Planck Institute for Meteorology, Hamburg, Germany.
  • Taylor KE, Stouffer RJ and Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc. 93, 485-498 (doi:10.1175/BAMS-D-11-00094.1).
  • Tebaldi C and Arblaster JM (2014) Pattern scaling: its strengths and limitations, and an update on the the latest model simulations. Climatic Change 122, 459-471 (doi:10.1007/s10584-013-1032-9).