Phil D. Jones

Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set

[1]xa0Recent developments in observational near-surface air temperature and sea-surface temperature analyses are combined to produce HadCRUT4, a new data set of global and regional temperature evolution from 1850 to the present. This includes the addition of newly digitized measurement data, both over land and sea, new sea-surface temperature bias adjustments and a more comprehensive error model for describing uncertainties in sea-surface temperature measurements. An ensemble approach has been adopted to better describe complex temporal and spatial interdependencies of measurement and bias uncertainties and to allow these correlated uncertainties to be taken into account in studies that are based upon HadCRUT4. Climate diagnostics computed from the gridded data set broadly agree with those of other global near-surface temperature analyses. Fitted linear trends in temperature anomalies are approximately 0.07°C/decade from 1901 to 2010 and 0.17°C/decade from 1979 to 2010 globally. Northern/southern hemispheric trends are 0.08/0.07°C/decade over 1901 to 2010 and 0.24/0.10°C/decade over 1979 to 2010. Linear trends in other prominent near-surface temperature analyses agree well with the range of trends computed from the HadCRUT4 ensemble members.

A large discontinuity in the mid-twentieth century in observed global-mean surface temperature

Data sets used to monitor the Earth’s climate indicate that the surface of the Earth warmed from ∼1910 to 1940, cooled slightly from ∼1940 to 1970, and then warmed markedly from ∼1970 onward. The weak cooling apparent in the middle part of the century has been interpreted in the context of a variety of physical factors, such as atmosphere–ocean interactions and anthropogenic emissions of sulphate aerosols. Here we call attention to a previously overlooked discontinuity in the record at 1945, which is a prominent feature of the cooling trend in the mid-twentieth century. The discontinuity is evident in published versions of the global-mean temperature time series, but stands out more clearly after the data are filtered for the effects of internal climate variability. We argue that the abrupt temperature drop of ∼0.3u2009°C in 1945 is the apparent result of uncorrected instrumental biases in the sea surface temperature record. Corrections for the discontinuity are expected to alter the character of mid-twentieth century temperature variability but not estimates of the century-long trend in global-mean temperatures.

Revisiting radiosonde upper air temperatures from 1958 to 2002

HadAT is a new analysis of the global upper air temperature record from 1958 to 2002 based upon radiosonde data alone. This analysis makes use of a greater number of stations than previous radiosonde analyses, combining a number of digital data sources. Neighbor buddy checks are applied to ensure that both spatial and temporal consistency are maintained. A framework of previously quality controlled stations is used to define the initial station network to minimize the effects of any pervasive biases in the raw data upon the adjustments. The analysis is subsequently expanded to consider all remaining available long-term records. The final data set consists of 676 radiosonde stations, with a bias toward continental Northern Hemisphere midlatitudes. Temperature anomaly time series are provided on 9 mandatory reporting pressure levels from 850 to 30 hPa. The effects of sampling and adjustment uncertainty are calculated at all scales from the station series to the global mean and from seasonal to multidecadal. These estimates are solely parametric uncertainty, given our methodological choices, and not structural uncertainty which relates to sensitivity to choice of approach. An initial analysis of HadAT does not fundamentally alter our understanding of long-term changes in upper air temperature changes.

Testing E-OBS European high-resolution gridded data set of daily precipitation and surface temperature

Gridded data sets derived through interpolation of station data have a number of potential inaccuracies and errors. These errors can be introduced either by the propagation of errors in the station data into derived gridded data or by limitations in the ability of the interpolation method to estimate grid values from the underlying station network. Recently, Haylock et al. (2008) reported on the development of a new high-resolution gridded data set of daily climate over Europe (termed E-OBS). E-OBS is based on the largest available pan-European data set, and the interpolation methods used were chosen after careful evaluation of a number of alternatives, yet the data set will inevitably have errors and uncertainties. In this paper we assess the E-OBS data set with respect to: (1) homogeneity of the gridded data; (2) evaluation of inaccuracies arising from available network density, through comparison with existing data sets that have been developed with much denser station networks; and (3) the accuracy of the estimates of interpolation uncertainty that are provided as part of E-OBS. We find many inhomogeneities in the gridded data that are primarily caused by inhomogeneities in the underlying station data. In the comparison of existing data with E-OBS, we find that while correlations overall are high, relative differences in precipitation are large, and usually biased toward lower values in E-OBS. From the analysis of the interpolation uncertainties provided as part of E-OBS, we conclude that the interpolation standard deviation provided with the data significantly underestimates the true interpolation error when cross validated using station data, and therefore will similarly underestimate the interpolation error in the gridded E-OBS data. While E-OBS represents a valuable new resource for climate research in Europe, users of the data need to be aware of the limitations in the data set and use the data appropriately.

A Further Extension of the Tahiti-Darwin SOI, Early ENSO Events and Darwin Pressure

Abstract An extension of the Tahiti minus Darwin Southern Oscillation Index (SOI) from 1882 back to 1876 is reported following the recovery of early Darwin mean sea-level pressure data spanning the period 1865–81. As a result, we are able to compare, for the first time, the major 1877–78 and 1982–83 ENSO events on the basis of this commonly used index. Early Darwin and Jakarta data are also examined in terms of a measure of the Australian response to documented El Nino and/or ENSO events in 1866, 1868, 1871, 1873, 1874 and 1875. The SOI during the 1877–78 ENSO event has a similar temporal response to that in 1982–83, but the index is slightly weaker than in the recent event. Examination of documentary evidence confirms the severity of the drought conditions that affected the Australian continent during the 1877–78 ENSO, and shows that this response is in line with the wider Indo-Pacific impacts reported in the literature. Earlier El Nino phases in 1868 and 1873 are not resolved distinctly in either the Da...

The SCAR READER Project: Toward a High-Quality Database of Mean Antarctic Meteorological Observations

A new dataset of monthly and annual mean near-surface climate data (temperature, surface and mean sea level pressure, and wind speed) for the Antarctic region has been created using historical observations [Scientific Committee on Antarctic Research (SCAR) Reference Antarctic Data for Environmental Research (READER)]. Where possible, 6-hourly surface synoptic and automatic weather station observations were used to compute the means. The ability to quality control the data at the level of individual observations has produced a more accurate series of monthly means than was available previously. At the time of writing, the mean data are available on the Internet (http://www.antarctica.ac.uk/met/programs-hosted.html). Data for 43 surface-staffed stations and 61 automatic weather stations are included in the database. Here, mean temperature, pressure, and wind speed data for 19 occupied stations with long records are provided.

Identifying Signatures of Natural Climate Variability in Time Series of Global-Mean Surface Temperature: Methodology and Insights

Abstract Global-mean surface temperature is affected by both natural variability and anthropogenic forcing. This study is concerned with identifying and removing from global-mean temperatures the signatures of natural climate variability over the period January 1900–March 2009. A series of simple, physically based methodologies are developed and applied to isolate the climate impacts of three known sources of natural variability: the El Nino–Southern Oscillation (ENSO), variations in the advection of marine air masses over the high-latitude continents during winter, and aerosols injected into the stratosphere by explosive volcanic eruptions. After the effects of ENSO and high-latitude temperature advection are removed from the global-mean temperature record, the signatures of volcanic eruptions and changes in instrumentation become more clearly apparent. After the volcanic eruptions are subsequently filtered from the record, the residual time series reveals a nearly monotonic global warming pattern since ...

An abrupt drop in Northern Hemisphere sea surface temperature around 1970.

The twentieth-century trend in global-mean surface temperature was not monotonic: temperatures rose from the start of the century to the 1940s, fell slightly during the middle part of the century, and rose rapidly from the mid-1970s onwards. The warming–cooling–warming pattern of twentieth-century temperatures is typically interpreted as the superposition of long-term warming due to increasing greenhouse gases and either cooling due to a mid-twentieth century increase of sulphate aerosols in the troposphere, or changes in the climate of the world’s oceans that evolve over decades (oscillatory multidecadal variability). Loadings of sulphate aerosol in the troposphere are thought to have had a particularly important role in the differences in temperature trends between the Northern and Southern hemispheres during the decades following the Second World War. Here we show that the hemispheric differences in temperature trends in the middle of the twentieth century stem largely from a rapid drop in Northern Hemisphere sea surface temperatures of about 0.3u2009°C between about 1968 and 1972. The timescale of the drop is shorter than that associated with either tropospheric aerosol loadings or previous characterizations of oscillatory multidecadal variability. The drop is evident in all available historical sea surface temperature data sets, is not traceable to changes in the attendant metadata, and is not linked to any known biases in surface temperature measurements. The drop is not concentrated in any discrete region of the Northern Hemisphere oceans, but its amplitude is largest over the northern North Atlantic.

Recent temperature trends in the Antarctic

It is important to understand how temperatures across the Antarctic have changed in recent decades because of the huge amount of fresh water locked into the ice sheet and the impact that temperature changes may have on the ice volume. Doran et al. claim that there has been a net cooling of the entire continent between 1966 and 2000, particularly during summer and autumn. We argue that this result has arisen because of an inappropriate extrapolation of station data across large, data-sparse areas of the Antarctic.

Historical SAM Variability. Part I: Century-Length Seasonal Reconstructions*

Seasonal reconstructions of the Southern Hemisphere annular mode (SAM) index are derived to extend the record before the reanalysis period, using station sea level pressure (SLP) data as predictors. Two reconstructions using different predictands are obtained: one [Jones and Widmann (JW)] based on the first principal component (PC) of extratropical SLP and the other (Fogt) on the index of Marshall. A regional-based SAM index (Visbeck) is also considered.These predictands agree well post-1979; correlations decline in all seasons except austral summer for the full series starting in 1958. Predictand agreement is strongest in spring and summer; hence agreement between the reconstructions is highest in these seasons. The less zonally symmetric SAM structure in winter and spring influences the strength of the SAM signal over land areas, hence the number of stations included in the reconstructions. Reconstructions from 1865 were, therefore, derived in summer and autumn and from 1905 in winter and spring. nThis paper examines the skill of each reconstruction by comparison with observations and reanalysis data. Some of the individual peaks in the reconstructions, such as the most recent in austral summer, represent a full hemispheric SAM pattern, while others are caused by regional SLP anomalies over the locations of the predictors. The JW and Fogt reconstructions are of similar quality in summer and autumn, while in winter and spring the Marshall index is better reconstructed by Fogt than the PC index is by JW. In spring and autumn the SAM shows considerable variability prior to recent decades.