Hemispheric and large‐scale land‐surface air temperature variations: An extensive revision and an update to 2010 (original) (raw)
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Updated high‐resolution grids of monthly climatic observations–the CRU TS3. 10 Dataset
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
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.
Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset
Scientific Data, 2020
CRU TS (Climatic Research Unit gridded Time Series) is a widely used climate dataset on a 0.5° latitude by 0.5° longitude grid over all land domains of the world except Antarctica. It is derived by the interpolation of monthly climate anomalies from extensive networks of weather station observations. Here we describe the construction of a major new version, CRU TS v4. It is updated to span 1901–2018 by the inclusion of additional station observations, and it will be updated annually. The interpolation process has been changed to use angular-distance weighting (ADW), and the production of secondary variables has been revised to better suit this approach. This implementation of ADW provides improved traceability between each gridded value and the input observations, and allows more informative diagnostics that dataset users can utilise to assess how dataset quality might vary geographically.
Frontiers in Environmental Science, 2020
The Land Surface Air Temperature (LSAT) climatology during the period of 1961-1990 and the anomalies (relative to the 1961-1990 climatology) have been developed over Pan-East Asian region at a (monthly) 0.5 • × 0.5 • resolution. The development of these LSAT data sets are based on the recently released C-LSAT station datasets and the high resolution Digital Elevation Model (DEM), and interpolated by the Thin Plate Spline (TPS) method (through ANUSPLIN software) and the Adjusted Inverse Distance Weighting (AIDW) method. Then they are combined into the high resolution gridded LSAT datasets (including the monthly mean, maximum, and minimum temperature). Considering the mean LSAT for example, the Cross Validation (CV) of the datasets indicates that the regional average of the Root Mean Square Error (RMSE) for the climatology is about 0.62 • C, and the average RMSE and Mean Absolute Error (MAE) for the anomalies are between 0.47-0.90 • C and 0.32-0.63 • C during the study period. The analysis also demonstrate that the gridded anomalies describe the spatial pattern fairly well for the coldest (1912, 1969) and the warmest (1948, 2007) years during the first and second half of the 20th century. Further analysis reveals that the high resolution dataset also performs well in the estimation of long-term LSAT change trend. Thus it can be concluded that this newly constructed datasets is a useful tool for regional climate monitoring, climate change research as well as climate model verification.
Gridded data as a source of missing data replacement in station records
Journal of Earth System Science
The quality of available station data over western Himalayan region (WHR) of India is poor due to missing values, and hence quantifying climate change information at the station level is more challenging. The present study investigated the extent to which the available two different resolutions gridded rainfall data from the India Meteorological Department (IMD) namely, IMD-0.25 • × 0.25 • (IMD.25) and IMD-1 • × 1 • (IMD1) and global observational gridded data from the Climate Research Unit (CRU-0.5 • × 0.5 •) of UK can be used as a substitute to replace the missing values in IMD station data. Long-term time series produced at each station location showed that IMD.25 data was much closer to observation both in magnitude and patterns compared to CRU and IMD1. The seasonal and annual scale performance of these gridded data has been evaluated through different agreement and error indices. The agreement indices showed higher values in case of IMD.25 data while IMD1 and CRU indicated poor results. Similarly, the estimated errors are minimum in IMD.25 and maximum in CRU data. Finally, an overall agreement index (OAI) was developed by the combined influence of all the agreement indices. The results of OAI for each station revealed that in more than 77% of cases, the performance of IMD.25 gridded data to reproduce station level rainfall is superior as compared to IMD1 (<63%) and CRU (<46%) data. Therefore, it is concluded that the IMD.25 gridded data may be reliably used as a substitute data source in place of missing rain-gauge data over the WHR.
A new global gridded radiosonde temperature data base and recent temperature trends
Geophysical Research Letters, 1997
We present a new analysis of global radiosonde temperature data. From 1979 onwards, the data from the Australasian region have been corrected for instrument-related discontinuities with the help of comparisons with collocated retrievals from satellite-based Microwave Sounding Units (MSU) and metadata: in future work, adjustments will be applied worldwide and extended to earlier years. The data are stored as monthly anomalies from a 1971-1990 reference period on a 5 ø latitude x 10 ø longitude grid at 8 levels from 50 hPa to 850 hPa. Seasonal and annual temperature anomalies have also been created on a 10 ø x 20 ø grid using an eigenvector reconstruction method to filter noise. Latitude-height profiles of zonal-mean temperature changes since the 1960s show significant cooling in the lower stratosphere, especially in middle and high latitudes of the Southern Hemisphere, but the cooling over Australasia is less than shown by unadjusted data. Warming dominates the troposphere but is not a maximum in the tropical upper troposphere. In the annual mean, tropospheric warming is greatest around 45øN and possibly in the data-sparse high latitudes of the Southern Hemisphere. global network of radiosondes (Angell, 1988) suggest a gradual relative cooling in the radiosonde data for the lower stratosphere of about 1 øC between 1979 and 1995 (IPCC, 1996). Our local comparisons imply that this gradual relative global cooling is likely to be the outcome of many asynchronous, instrument-related, sudden coolings at individual stations. So we have used, on a trial basis, comparisons with MSU data, and metadata, to specify the timing and magnitude of adjustments to a test set of radiosonde temperatures.
Carbon Dioxide Information Analysis Center (CDIAC) Datasets
Interest in global climate change has risen dramatically during the last several years. In a similar fashion, the number of data sets available to study global change has also increased. Unfortunately, these data sets have been compiled by mal_y different organizations/researchers, making it confusing and time consuming for individual researchers to acquire the "best" data. In response to this _apid growth in the number of global data sets, the Carbon Dioxide Information Analysis Center (CDIAC) and the National Climatic Data Center (NCDC) commenced the Global Historical Climatology Network (GHCN) project. The purpose of this project is to compile an improved global base-line data set of long-term monthly mean temperature, precipitation, sea level pressure, and station pressure for a dense network of worldwide meteorological stations. Specifically, the GHCN project seeks to consolidate the numerous preexisting national-, regional-, and global-scale data sets into a single global climate data base that can be updated, enhanced, mad distributed at regular intervals. The first version of the GHCN data base was completed during the summer of 1992. lt conr,tins 6039 temperature, 7533 precipitation, 1883 sea level pressure, and 1873 station pressure stations. Ali stations have at least 10 years of data, 40% have more than 50 years of data, and 10% have more than 100 years of data. "l_e majority of stations have fairly complete records (72% are missing less than 10% of their data). Furthermore, 80% of ali station records continue into the 1980s or 1990s. Spatial coverage is good over most of the globe, particularly for the United States and central Europe. In comparison to other major global data sets, dramatic improvements are evident over South America, Africa, and Asia. The GHCN data base has been subjected to a large amount of quality control. For example, ali impossibly extreme values have been set to missing. In addition, ali stations (some 17,000) were plotted and visually inspected for "gross" data processing errors and discontinuities, ali of which are documented in the data base itself. At this point, the GHCN data base is considered to be one of the best long-term climate data sets available for the study of global climate change. Furthermore, the data base will continue to evolve in the coming years. Planned improvements entail tile inclusion of additional data, the correction of erroneous data, the adjustment of data inhomogeneities, the addition of new variables, and the production of gridded data sets. The GHCN data base is available as a Ntuneric Data Package (NDP) from CDIAC. The NDP consists of this docmnent and two magnetic tapes that contain machine-readable data files :-and accompanying retrieval codes. This document describes, in detail, both the GHCN data base and the contents of the magnetic tapes.
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