WMO confirms 2017 among the three warmest years on record

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January 2018 (WMO) – In a clear sign of continuing long-term climate change caused by increasing atmospheric concentrations of greenhouse gases, 2015, 2016 and 2017 have been confirmed as the three warmest years on record. 2016 still holds the global record, whilst 2017 was the warmest year without an El Niño, which can boost global annual temperatures.

A consolidated analysis by the World Meteorological Organization of five leading international datasets showed that the global average surface temperature in 2017 was approximately 1.1° Celsius above the pre-industrial era.

The year 2016 remains the warmest year on record (1.2°C above preindustrial era). Global average temperatures in 2017 and 2015 were both 1.1°C above pre-industrial levels. The two years are virtually indistinguishable because the difference is less than one hundredth of a degree, which is less than the statistical margin of error.

“The long-term temperature trend is far more important than the ranking of individual years, and that trend is an upward one, “ said WMO Secretary-General Petteri Taalas. “Seventeen of the 18 warmest years on record have all been during this century, and the degree of warming during the past three years has been exceptional. Arctic warmth has been especially pronounced and this will have profound and long-lasting repercussions on sea levels, and on weather patterns in other parts of the world.”

The globally averaged temperature in 2017 was about 0.46°C above the 1981-2010 long-term average (14.3°C). This 30-year baseline is used by national meteorological and hydrological services to assess the averages and variability of key climate parameters, such as temperature, precipitation and wind, which are important for climate sensitive sectors such as water management, energy, agriculture and health.

In addition to the global warming due to rising greenhouse gas levels in the atmosphere, the climate also has a naturally occurring variability due to phenomena such as El Niño, which has a warming influence, and La Niña, which has a cooling influence. The strong 2015/2016 El Niño contributed to the record temperature in 2016. By contrast, 2017 started with a very weak La Niña and also finished with a weak La Niña.

For more information:  https://public.wmo.int/en/media/press-release/wmo-confirms-2017-among-three-warmest-years-record

2017-temperature-infographic

Notes for Editors:

The three conventional surface temperature data sets are NOAA’s NOAAGlobalTemp data set, Met Office Hadley Centre and Climatic Research Unit HadCRUT.4.6.0.0 data set and NASA GISS’s GISTEMP data set. They use measurements of air temperature over land and sea-water temperature measurements over oceans to estimate temperature anomalies around the globe. The reanalyses are the ERA-Interim of the European Centre for Medium Weather Forecasts and the JRA-55 of the Japan Meteorological Agency. Despite the very different approach, the estimates of global average temperatures produced by these reanalyses are in good agreement with the conventional surface temperature datasets

WMO now uses 1981-2010 as a baseline for computing Temperature variations at monthly, seasonal and annual time-scales. This replaces the 1961-1990 baseline used previously. The 1981-2010 period is also recommended by WMO to compute the climatological standard normal for operational climate monitoring as it is more representative of current climatic conditions. It allows a consistent reporting of information from satellite and reanalysis systems, some of which do not extend back to 1960, alongside with traditional data sets based on surface-based-observations managed by the National Meteorological and Hydrological Services of the 191 WMO Member States and territories. For global average temperatures, the 1981-2010 period is approximately 0.31±0.02°C warmer than that of 1961-1990. The change in the baselines has no influence on trend analysis.

WMO uses the period 1880-1900 as a reference period for pre-industrial conditions allowing early instrumental observations to be used for estimating pre-industrial temperature conditions.

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