Historical reanalyses that span more than a century are needed for a wide range of studies, from understanding large‐scale climate trends to diagnosing the impacts of individual historical extreme weather events. The Twentieth Century Reanalysis (20CR) Project is an effort to fill this need. It is supported by the National Oceanic and Atmospheric Administration (NOAA), the Cooperative Institute for Research in Environmental Sciences (CIRES), and the U.S. Department of Energy (DOE), and is facilitated by collaboration with the international Atmospheric Circulation Reconstructions over the Earth initiative. 20CR is the first ensemble of sub‐daily global atmospheric conditions spanning over 100 years. This provides a best estimate of the weather at any given place and time as well as an estimate of its confidence and uncertainty. While extremely useful, version 2c of this dataset (20CRv2c) has several significant issues, including inaccurate estimates of confidence and a global sea level pressure bias in the mid‐19th century. These and other issues can reduce its effectiveness for studies at many spatial and temporal scales. Therefore, the 20CR system underwent a series of developments to generate a significant new version of the reanalysis. The version 3 system (NOAA‐CIRES‐DOE 20CRv3) uses upgraded data assimilation methods including an adaptive inflation algorithm; has a newer, higher‐resolution forecast model that specifies dry air mass; and assimilates a larger set of pressure observations. These changes have improved the ensemble‐based estimates of confidence, removed spin‐up effects in the precipitation fields, and diminished the sea‐level pressure bias. Other improvements include more accurate representations of storm intensity, smaller errors, and large‐scale reductions in model bias. The 20CRv3 system is comprehensively reviewed, focusing on the aspects that have ameliorated issues in 20CRv2c. Despite the many improvements, some challenges remain, including a systematic bias in tropical precipitation and time‐varying biases in southern high‐latitude pressure fields.

We present a new version of the sea ice concentration component of the Met Office HadleyCentre sea ice and sea surface temperature data set, HadISST.2.1.0.0. Passive microwave data are combined with historical sources, such as sea ice charts, to create global analyses on a 1°grid from 1850 to 2007. Climatology was used when no information about the sea ice was available. Our main aim was to create a homogenous data set by calculating and applying bias adjustments using periods of overlaps between the different data sources used. National Ice Center charts from 1995 to 2007 have been used as a reference to achieve this. In particular, large bias adjustments have been applied to the passive microwave data in both the Antarctic and Arctic summers. Overall, HadISST.2.1.0.0 contains more ice than HadISST1.1, with higher concentrations, shorter marginal ice zones, and larger extents and areas in some regions and periods. A new method for estimating the concentrations within the ice pack using the distance from the ice edge has been developed and evaluated. This was used when only the extents were known or the original concentration fields were heterogeneous. A number of discontinuities in the HadISST1.1 record are no longer found in HadISST.2.1.0.0.

Eight atmospheric general circulation models (AGCMs) are forced with observed historical (1871–2010) monthly sea surface temperature and sea ice variations using the Atmospheric Model Intercomparison Project II data set. The AGCMs therefore have a similar temperature pattern and trend to that of observed historical climate change. The AGCMs simulate a spread in climate feedback similar to that seen in coupled simulations of the response to CO2 quadrupling. However, the feedbacks are robustly more stabilizing and the effective climate sensitivity (EffCS) smaller. This is due to a pattern effect, whereby the pattern of observed historical sea surface temperature change gives rise to more negative cloud and longwave clear‐sky feedbacks. Assuming the patterns of long‐term temperature change simulated by models, and the radiative response to them, are credible; this implies that existing constraints on EffCS from historical energy budget variations give values that are too low and overly constrained, particularly at the upper end. For example, the pattern effect increases the long‐term Otto et al. (2013, https://doi.org/10.1038/ngeo1836) EffCS median and 5–95% confidence interval from 1.9 K (0.9–5.0 K) to 3.2 K (1.5–8.1 K).

Results are presented from a new homogenization of data since 1959 from 527 radiosonde stations. This effort differs from previous ones by employing an approach specifically designed to minimize systematic errors in adjustment, by including wind shear as well as temperature, by seasonally resolving adjustments, and by using neither satellite information nor station metadata. Relatively few artifacts were detected in wind shear, and associated adjustments were indistinguishable from random adjustments. Temperature artifacts were detected most often in the late 1980s-early 1990s. Uncertainty was characterized from variations within an ensemble of homogenizations and used to test goodness of fit with satellite data using reduced chi squared.The meridional variations of zonally aggregated temperature trend since 1979 moved significantly closer to those of the Microwave Sounding Unit (MSU) after data adjustment. Adjusted data from 5°S to 20°N continue to show relatively weak warming, but the error is quite large, and the trends are inconsistent with those at other latitudes. Overall, the adjusted trends are close to those of MSU for the temperature of the lower troposphere (TLT). For channel 2, they are consistent with two analyses (Remote Sensing Systems, p ϭ 0.54, and the University of Maryland, p ϭ 0.32) showing the strongest warming but not with the University of Alabama dataset ( p ϭ 0.0001). The troposphere warms at least as strongly as the surface, with local warming maxima at 300 hPa in the tropics and in the boundary layer of the extratropical Northern Hemisphere (ENH). Tropospheric warming since 1959 is almost hemispherically symmetric, but since 1979 it is significantly stronger in ENH and weaker in the extratropical Southern Hemisphere (ESH). ESH trends are relatively uncertain because of poor sampling. Stratospheric cooling also remains stronger than indicated by MSU and likely excessive.While this effort appears not to have detected all artifacts, trends appear to be systematically improved. Stronger warming is shown in the Northern Hemisphere where sampling is best. Several suggestions are made for future attempts. These results support the hypothesis that trends in wind data are relatively uncorrupted by artifacts compared to temperature, and should be exploited in future homogenization efforts.

Biases and uncertainties in large-scale radiosonde temperature trends in the troposphere are critically reassessed. Realistic validation experiments are performed on an automatic radiosonde homogenization system by applying it to climate model data with four distinct sets of simulated breakpoint profiles. Knowledge of the ''truth'' permits a critical assessment of the ability of the system to recover the large-scale trends and a reinterpretation of the results when applied to the real observations.The homogenization system consistently reduces the bias in the daytime tropical, global, and Northern Hemisphere (NH) extratropical trends but underestimates the full magnitude of the bias. Southern Hemisphere (SH) extratropical and all nighttime trends were less well adjusted owing to the sparsity of stations. The ability to recover the trends is dependent on the underlying error structure, and the true trend does not necessarily lie within the range of estimates. The implications are that tropical tropospheric trends in the unadjusted daytime radiosonde observations, and in many current upper-air datasets, are biased cold, but the degree of this bias cannot be robustly quantified. Therefore, remaining biases in the radiosonde temperature record may account for the apparent tropical lapse rate discrepancy between radiosonde data and climate models. Furthermore, the authors find that the unadjusted global and NH extratropical tropospheric trends are biased cold in the daytime radiosonde observations. Finally, observing system experiments show that, if the Global Climate Observing System (GCOS) Upper Air Network (GUAN) were to make climate quality observations adhering to the GCOS monitoring principles, then one would be able to constrain the uncertainties in trends at a more comprehensive set of stations. This reaffirms the importance of running GUAN under the GCOS monitoring principles.