Philip Jones

Bio: Philip Jones is an academic researcher from University of East Anglia. The author has contributed to research in topics: Climate change & Climate model. The author has an hindex of 135, co-authored 644 publications receiving 90838 citations. Previous affiliations of Philip Jones include St. Vincent's Health System & University of New South Wales.

Updated high‐resolution grids of monthly climatic observations – the CRU TS3.10 Dataset

Abstract: This paper describes the construction of an updated gridded climate dataset (referred to as CRU TS3.10) from monthly observations at meteorological stations across the world's land areas. Station anomalies (from 1961 to 1990 means) were interpolated into 0.5° latitude/longitude grid cells covering the global land surface (excluding Antarctica), and combined with an existing climatology to obtain absolute monthly values. The dataset includes six mostly independent climate variables (mean temperature, diurnal temperature range, precipitation, wet-day frequency, vapour pressure and cloud cover). Maximum and minimum temperatures have been arithmetically derived from these. Secondary variables (frost day frequency and potential evapotranspiration) have been estimated from the six primary variables using well-known formulae. Time series for hemispheric averages and 20 large sub-continental scale regions were calculated (for mean, maximum and minimum temperature and precipitation totals) and compared to a number of similar gridded products. The new dataset compares very favourably, with the major deviations mostly in regions and/or time periods with sparser observational data. CRU TS3.10 includes diagnostics associated with each interpolated value that indicates the number of stations used in the interpolation, allowing determination of the reliability of values in an objective way. This gridded product will be publicly available, including the input station series (http://www.cru.uea.ac.uk/ and http://badc.nerc.ac.uk/data/cru/). © 2013 Royal Meteorological Society

An improved method of constructing a database of monthly climate observations and associated high-resolution grids

Abstract: A database of monthly climate observations from meteorological stations is constructed. The database includes six climate elements and extends over the global land surface. The database is checked for inhomogeneities in the station records using an automated method that refines previous methods by using incomplete and partially overlapping records and by detecting inhomogeneities with opposite signs in different seasons. The method includes the development of reference series using neighbouring stations. Information from different sources about a single station may be combined, even without an overlapping period, using a reference series. Thus, a longer station record may be obtained and fragmentation of records reduced. The reference series also enables 1961–90 normals to be calculated for a larger proportion of stations. The station anomalies are interpolated onto a 0.5° grid covering the global land surface (excluding Antarctica) and combined with a published normal from 1961–90. Thus, climate grids are constructed for nine climate variables (temperature, diurnal temperature range, daily minimum and maximum temperatures, precipitation, wet-day frequency, frost-day frequency, vapour pressure, and cloud cover) for the period 1901–2002. This dataset is known as CRU TS 2.1 and is publicly available (http://www.cru.uea.ac.uk/). Copyright  2005 Royal Meteorological Society.

The Twentieth Century Reanalysis Project

Abstract: The Twentieth Century Reanalysis (20CR) project is an international effort to produce a comprehensive global atmospheric circulation dataset spanning the twentieth century, assimilating only surface pressure reports and using observed monthly sea-surface temperature and sea-ice distributions as boundary conditions. It is chiefly motivated by a need to provide an observational dataset with quantified uncertainties for validations of climate model simulations of the twentieth century on all time-scales, with emphasis on the statistics of daily weather. It uses an Ensemble Kalman Filter data assimilation method with background ‘first guess’ fields supplied by an ensemble of forecasts from a global numerical weather prediction model. This directly yields a global analysis every 6 hours as the most likely state of the atmosphere, and also an uncertainty estimate of that analysis. The 20CR dataset provides the first estimates of global tropospheric variability, and of the dataset's time-varying quality, from 1871 to the present at 6-hourly temporal and 2° spatial resolutions. Intercomparisons with independent radiosonde data indicate that the reanalyses are generally of high quality. The quality in the extratropical Northern Hemisphere throughout the century is similar to that of current three-day operational NWP forecasts. Intercomparisons over the second half-century of these surface-based reanalyses with other reanalyses that also make use of upper-air and satellite data are equally encouraging. It is anticipated that the 20CR dataset will be a valuable resource to the climate research community for both model validations and diagnostic studies. Some surprising results are already evident. For instance, the long-term trends of indices representing the North Atlantic Oscillation, the tropical Pacific Walker Circulation, and the Pacific–North American pattern are weak or non-existent over the full period of record. The long-term trends of zonally averaged precipitation minus evaporation also differ in character from those in climate model simulations of the twentieth century. Copyright © 2011 Royal Meteorological Society and Crown Copyright.

On the Average Value of Correlated Time Series, with Applications in Dendroclimatology and Hydrometeorology

Abstract: In a number of areas of applied climatology, time series are either averaged to enhance a common underlying signal or combined to produce area averages. How well, then, does the average of a finite number (N) of time series represent the population average, and how well will a subset of series represent the N-series average? We have answered these questions by deriving formulas for 1) the correlation coefficient between the average of N time series and the average of n such series (where n is an arbitrary subset of N) and 2) the correlation between the N-series average and the population. We refer to these mean correlations as the subsample signal strength (SSS) and the expressed population signal (EPS). They may be expressed in terms of the mean inter-series correlation coefficient r as SSS ≡ (Rn,N)2 ≈ n(1 + (N − 1)r)/ N(1 + (N − 1)r), EPS ≡ RN)2 ≈ Nr/1 + (N − 1)r.Similar formulas are given relating these mean correlations to the fractional common variance which arises as a parameter in a...

The ERA-Interim reanalysis: configuration and performance of the data assimilation system

Abstract: ERA-Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA-40, which will extend back to the early part of the twentieth century. This article describes the forecast model, data assimilation method, and input datasets used to produce ERA-Interim, and discusses the performance of the system. Special emphasis is placed on various difficulties encountered in the production of ERA-40, including the representation of the hydrological cycle, the quality of the stratospheric circulation, and the consistency in time of the reanalysed fields. We provide evidence for substantial improvements in each of these aspects. We also identify areas where further work is needed and describe opportunities and objectives for future reanalysis projects at ECMWF. Copyright © 2011 Royal Meteorological Society

Very high resolution interpolated climate surfaces for global land areas.

Abstract: We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered from a variety of sources and, where possible, were restricted to records from the 1950–2000 period. We used the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package for interpolation, using latitude, longitude, and elevation as independent variables. We quantified uncertainty arising from the input data and the interpolation by mapping weather station density, elevation bias in the weather stations, and elevation variation within grid cells and through data partitioning and cross validation. Elevation bias tended to be negative (stations lower than expected) at high latitudes but positive in the tropics. Uncertainty is highest in mountainous and in poorly sampled areas. Data partitioning showed high uncertainty of the surfaces on isolated islands, e.g. in the Pacific. Aggregating the elevation and climate data to 10 arc min resolution showed an enormous variation within grid cells, illustrating the value of high-resolution surfaces. A comparison with an existing data set at 10 arc min resolution showed overall agreement, but with significant variation in some regions. A comparison with two high-resolution data sets for the United States also identified areas with large local differences, particularly in mountainous areas. Compared to previous global climatologies, ours has the following advantages: the data are at a higher spatial resolution (400 times greater or more); more weather station records were used; improved elevation data were used; and more information about spatial patterns of uncertainty in the data is available. Owing to the overall low density of available climate stations, our surfaces do not capture of all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas. In future work, such variation might be captured through knowledgebased methods and inclusion of additional co-variates, particularly layers obtained through remote sensing. Copyright  2005 Royal Meteorological Society.

Updated world map of the Köppen-Geiger climate classification

Abstract: Although now over 100 years old, the classification of climate originally formulated by Wladimir Koppen and modified by his collaborators and successors, is still in widespread use. It is widely used in teaching school and undergraduate courses on climate. It is also still in regular use by researchers across a range of disciplines as a basis for climatic regionalisation of variables and for assessing the output of global climate models. Here we have produced a new global map of climate using the Koppen-Geiger system based on a large global data set of long-term monthly precipitation and temperature station time series. Climatic variables used in the Koppen-Geiger system were calculated at each station and interpolated between stations using a two-dimensional (latitude and longitude) thin-plate spline with tension onto a 0.1°×0.1° grid for each continent. We discuss some problems in dealing with sites that are not uniquely classified into one climate type by the Koppen-Geiger system and assess the outcomes on a continent by continent basis. Globally the most common climate type by land area is BWh (14.2%, Hot desert) followed by Aw (11.5%, Tropical savannah). The updated world Koppen-Geiger climate map is freely available electronically in the Supplementary Material Section.

Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century

Abstract: [1] We present the Met Office Hadley Centre's sea ice and sea surface temperature (SST) data set, HadISST1, and the nighttime marine air temperature (NMAT) data set, HadMAT1. HadISST1 replaces the global sea ice and sea surface temperature (GISST) data sets and is a unique combination of monthly globally complete fields of SST and sea ice concentration on a 1° latitude-longitude grid from 1871. The companion HadMAT1 runs monthly from 1856 on a 5° latitude-longitude grid and incorporates new corrections for the effect on NMAT of increasing deck (and hence measurement) heights. HadISST1 and HadMAT1 temperatures are reconstructed using a two-stage reduced-space optimal interpolation procedure, followed by superposition of quality-improved gridded observations onto the reconstructions to restore local detail. The sea ice fields are made more homogeneous by compensating satellite microwave-based sea ice concentrations for the impact of surface melt effects on retrievals in the Arctic and for algorithm deficiencies in the Antarctic and by making the historical in situ concentrations consistent with the satellite data. SSTs near sea ice are estimated using statistical relationships between SST and sea ice concentration. HadISST1 compares well with other published analyses, capturing trends in global, hemispheric, and regional SST well, containing SST fields with more uniform variance through time and better month-to-month persistence than those in GISST. HadMAT1 is more consistent with SST and with collocated land surface air temperatures than previous NMAT data sets.