Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset

Harris, Ian; Osborn, Timothy J.; Jones, Phil; Lister, David

doi:10.1038/s41597-020-0453-3

Cited by 2,983 publications

(1,888 citation statements)

References 38 publications

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“…For near-surface temperature observations we used the high-resolution data Climatic Research Unit (CRU) Time-Series (TS) version 4.02 (CRU TS4.02) [12]. This station-based gridded dataset is produced by angular-distance weighting interpolation of station observations onto a 0.5 • grid [7].…”

Section: Observational Data For Evaluationmentioning

confidence: 99%

“…By contrast, temperature datasets have been less in the spotlight, despite the fact that temperature plays a key role in modulating surface hydrology and the severity of droughts (see, e.g., SPEI-the Standardized Precipitation-Evapotranspiration Index). Furthermore, even recent temperature products like the Climatic Research Unit (CRU) Time-Series (TS) Version 4 (CRU TS4.02)) [12], Climatic Research Unit TEMperature, version 4 (CRUTEM4) [13], the NASA GISS Surface Temperature Analysis (GISTEMP) [14], NOAA MLOST [15] or UDEL [16] are spatially and temporally coarser than the precipitation counter parts.…”

Section: Introductionmentioning

confidence: 99%

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Did ERA5 Improve Temperature and Precipitation Reanalysis over East Africa?

2020

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Reanalysis products are often taken as an alternative solution to observational weather and climate data due to availability and accessibility problems, particularly in data-sparse regions such as Africa. Proper evaluation of their strengths and weaknesses, however, should not be overlooked. The aim of this study was to evaluate the performance of ERA5 reanalysis and to document the progress made compared to ERA-interim for the fields of near-surface temperature and precipitation over Africa. Results show that in ERA5 the climatological biases in temperature and precipitation are clearly reduced and the representation of inter-annual variability is improved over most of Africa. However, both reanalysis products performed less well in terms of capturing the observed long-term trends, despite a slightly better performance of ERA5 over ERA-interim. Further regional analysis over East Africa shows that the representation of the annual cycle of precipitation is substantially improved in ERA5 by reducing the wet bias during the rainy season. The spatial distribution of precipitation during extreme years is also better represented in ERA5. While ERA5 has improved much in comparison to its predecessor, there is still demand for improved products with even higher resolution and accuracy to satisfy impact-based studies, such as in agriculture and water resources.

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Section: Observational Data For Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Did ERA5 Improve Temperature and Precipitation Reanalysis over East Africa?

2020

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“…We perform ADW to the selected group of stations used for the creation of the CRU data set to explore whether a station network used for gridding may explain differences between station and the nearest gridpoint. The stations contributing to the calculation of a given gridpoint in the CRU data set are found out using a freely available utility in Google Earth (which is maintained by the team around CRU; Harris et al, 2020). Then, temperature anomalies at eight or less selected stations (this condition is also used in the CRU data set) are interpolated in each time step.…”

Section: /2020jd033254mentioning

confidence: 99%

“…However, the sensitivity of relationships with atmospheric circulation to the type of surface climate data has not been studied yet. Here we compare the temporal evolution of relationships of circulation modes detected in ERA-40 reanalysis and temperature between stations and their nearest gridpoints in the CRU data set (Harris et al, 2020). A particular attention is paid to the pairs of the station and gridpoint data, for which the time series exhibit substantial discrepancies.…”

Section: Introductionmentioning

confidence: 99%

Gridded Versus Station Temperatures: Time Evolution of Relationships With Atmospheric Circulation

Hynčica

Huth

2020

JGR Atmospheres

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Interpolated data sets are often considered to be a reliable source of information on a variety of meteorological variables, such as temperature and precipitation. Users expect the interpolated data to be rather similar to those directly observed at stations, which is not always true: well documented is the influence of interpolation on, e.g., extremes. Here another kind of discrepancy between gridpoints and station observations is presented: the time evolution of relationships between temperature and atmospheric circulation. One of the most widely utilized gridded temperature data sets, CRU TS (Climatic Research Unit gridded Time Series), is compared with 634 station time series from GHCN (Global Historical Climatology Network) in the Northern Extratropics. We analyze running correlations (calculated for 15-year windows) of monthly values between modes of atmospheric circulation variability (identified in the ERA-40 reanalysis) and temperature anomalies in winter from 1957 to 2002. The smallest differences in the running correlations are found in Europe and North America due to a dense station network. On the other hand, the sites with considerable differences are located mainly in mountainous regions or in isolated locations. In order to uncover causes of these differences, we analyze two sites in more detail. Mike (the North Sea) is an isolated site where the gridpoint temperature is affected by rather distant Scandinavian stations. At Songpan (central China; 2,852 m a.s.l), the terrain configuration in mountainous region influences the gridpoint value, in which the effect of stations with much lower altitude and different climate conditions is dominant.

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“…Some historically significant climate mutations and their environmental responses cannot be recorded by modern instruments, making it difficult to grasp the current state of environmental changes and to predict future trends [20,21]. On the one hand, the climate data has a relatively short time scale, e.g., climatic research unit (CRU) data began in 1901 [22]. On the other hand, since most of the weather stations in arid regions of China began to observe in the 1950s, the applicability of the CRU data covering Chia is also worthy of exploring.…”

Section: Introductionmentioning

confidence: 99%

Variations in Sediment Grain Size from a Lake in the Tianshan Mountain of Central Asia: Implications for Paleoprecipitation Reconstruction

Abuduwaili

Liu

et al. 2020

Applied Sciences

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The Tianshan Mountain is the largest mountain range in Central Asia, and the source area of many river systems. Changes in precipitation result in significant alterations to regional hydrological processes. Lake sediment from the Tian Shan representative of the last 90 years was chosen as the object of this research study. The grain-size data were used in conjunction with instrumental data to provide a method for determining changes in paleoprecipitation. The results showed the three-point moving average curve of the silty fraction content with a size of 16 to 32 μm to be significantly consistent with the curve of total precipitation from April to September since 1950. The total content of clay and fine-silty fraction (0–16 μm) was clearly consistent with the monthly precipitation in July. The total precipitation from April to September showed a significant downward trend from 1930 to 1975, and then an overall increasing trend beginning in 1975, which may have been influenced by the North Atlantic Oscillation. The change in precipitation reconstructed by the grain size of lake sediments was significantly different from the high-resolution gridded datasets (Climatic Research Unit Time-Series version 4.04) because of the lack of data from meteorological stations in China before 1950. The conclusions of this study are significant for evaluating the validity of climatic research unit (CRU) data in arid areas of Western China. In addition, the results of this study serve as a bridge between modern instrumental records and long time-scale paleoclimate research and provide important reference values for future reconstructions of long time-scale paleoclimate.

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Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset

Cited by 2,983 publications

References 38 publications

Did ERA5 Improve Temperature and Precipitation Reanalysis over East Africa?

Did ERA5 Improve Temperature and Precipitation Reanalysis over East Africa?

Gridded Versus Station Temperatures: Time Evolution of Relationships With Atmospheric Circulation

Variations in Sediment Grain Size from a Lake in the Tianshan Mountain of Central Asia: Implications for Paleoprecipitation Reconstruction

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