Within the Copernicus Climate Change Service (C3S), ECMWF is producing the ERA5 reanalysis which, once completed, will embody a detailed record of the global atmosphere, land surface and ocean waves from 1950 onwards. This new reanalysis replaces the ERA-Interim reanalysis (spanning 1979 onwards) which was started in 2006. ERA5 is based on the Integrated Forecasting System (IFS) Cy41r2 which was operational in 2016. ERA5 thus benefits from a decade of developments in model physics, core dynamics and data assimilation. In addition to a significantly enhanced horizontal resolution of 31 km, compared to 80 km for ERA-Interim, ERA5 has hourly output throughout, and an uncertainty estimate from an ensemble (3-hourly at half the horizontal resolution). This paper describes the general setup of ERA5, as well as a basic evaluation of characteristics and performance, with a focus on the dataset from 1979 onwards which is currently publicly available. Re-forecasts from ERA5 analyses show a gain of up to one day in skill with respect to ERA-Interim. Comparison with radiosonde and PILOT data prior to assimilation shows an improved fit for temperature, wind and humidity in the troposphere, but not the stratosphere. A comparison with independent buoy data shows a much improved fit for ocean wave height. The uncertainty estimate reflects the evolution of the observing systems used in ERA5. The enhanced temporal and spatial resolution allows for a detailed evolution of weather systems. For precipitation, global-mean correlation with monthly-mean GPCP data is increased from 67% This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

A new sea surface temperature (SST) analysis on a centennial time scale is presented. In this analysis, a daily SST field is constructed as a sum of a trend, interannual variations, and daily changes, using in situ SST and sea ice concentration observations. All SST values are accompanied with theory-based analysis errors as a measure of reliability. An improved equation is introduced to represent the ice–SST relationship, which is used to produce SST data from observed sea ice concentrations. Prior to the analysis, biases of individual SST measurement types are estimated for a homogenized long-term time series of global mean SST. Because metadata necessary for the bias correction are unavailable for many historical observational reports, the biases are determined so as to ensure consistency among existing SST and nighttime air temperature observations. The global mean SSTs with bias-corrected observations are in agreement with those of a previously published study, which adopted a different approach. Satellite observations are newly introduced for the purpose of reconstruction of SST variability over data-sparse regions. Moreover, uncertainty in areal means of the present and previous SST analyses is investigated using the theoretical analysis errors and estimated sampling errors. The result confirms the advantages of the present analysis, and it is helpful in understanding the reliability of SST for a specific area and time period.

The simplest global mapping method and dense data coverage for the global oceans by the latest observation network ensure an estimate of global ocean heat content (OHC) within a satisfactory uncertainty for the last 60 years. The observational database conditionally presented a level high enough for practical use for the global OHC estimation when applying bias corrections of expendable bathythermograph, assuming that the other severe observational biases are not included in the database. Uncertainties in annual global mean temperatures averaged vertically from the surface to 1,500 m are within 0.01 K for the period from 1955 onward, when only sampling errors are taken into account. Those in annual mean global OHC of an improved objective analysis for 0−1,500 m depth is 16ZJ on average throughout the period. Compared to previous studies, the new objective analysis provides a higher estimation of the global 0−1,500 m OHC trend for a longer period from 1955 to 2015, which is an increase of 350 ± 57ZJ with a 95% confidence interval.(Citation: Ishii, M., Y. Fukuda, H. Hirahara, S. Yasui, T. Suzuki, and K. Sato, 2017: Accuracy of global upper ocean heat content estimation expected from present observational data sets.

The ERA-Interim and JRA-55 reanalyses of synoptic data and several conventional analyses of monthly climatological data provide similar estimates of global-mean surface warming since 1979. They broadly agree on the character of interannual variability and the extremity of the 2015/2016 warm spell to which a strong El Niño and low Arctic sea-ice cover contribute. Nevertheless global and regional averages differ on various time-scales due to differences in data coverage and sea-surface temperature analyses; averages from those conventional datasets that infill where they lack direct observations agree better with the averages from the reanalyses. The latest warm event is less extreme when viewed in terms of atmospheric energy, which gives more weight to variability in the Tropics, where the thermal signal has greater vertical penetration and latent energy is a larger factor.Surface Centennial trends from the conventional datasets, HadCRUT4 on the one hand and GISTEMP and NOAAGlobalTemp on the other, differ mainly because sea-surface temperatures differ. Infilling of values where direct observations are lacking is more questionable for the data-sparse earlier decades. Change since the eighteenth century is inevitably more uncertain than change over and after a modern baseline period. The latter is arguably best estimated separately for taking stock of actions to limit climate change, exploiting reanalyses and using satellite data to refine the conventional approach. Nevertheless, early in 2016 the global temperature appears to have first touched or briefly breached a level 1.5• C above that early in the Industrial Revolution, having touched the 1.0 • C level in 1998 during a previous El Niño.

Bias estimation for sea surface temperature is discussed and recommendations for improving data, observational metadata, and uncertainty modeling are given. T he global surface temperature record is constructed by blending sea surface temperature (SST) with air temperature over land and ice (see also section S1 of the supplement , which is available online at http://dx.doi.org/10.1175/BAMS-D-15-00251.2). Both SST and land air temperature require adjustments to account for changes in, for example, depth or height of measurement, instrumentation, and siting. Improvement of estimated biases in historical measurements of SST will have a major effect on estimates of global surface temperature change and their uncertainty (Jones 2016).