Reliable and accurate temperature data acquisition is not only important for hydroclimate research but also crucial for the management of water resources and agriculture. Gridded data products (GDPs) offer an opportunity to estimate and monitor temperature indices at a range of spatiotemporal resolutions; however, their reliability must be quantified by spatiotemporal comparison against in situ records. Here, we present spatial and temporal assessments of temperature indices (
Future estimates of global climate patterns are directly concomitant with climate variations at regional scale [
In recent decades, the development of temperature gridded data products (hereafter GDPs) has been proven to be reliable and cost-effective for retrieving gridded data at various scales across the globe [
The evaluation of climatic GDPs has been proven to be useful for quantifying trends, variability, and various other hydroclimate applications for different regions across the globe [
In recent years, few studies have reported the spatiotemporal variation in climate with limited literature focused on evaluation and assessment of global climate products with in situ records over the different regions of the country. Reference [
In this study, we aim to evaluate the performance of the widely used GDP temperature indices (
Pakistan is located in SW Asia. The country has an area of 8 × 106 km2, including diverse landscapes ranging from the Karakoram and Himalayan mountains in the north and northwest to the agricultural plains of the Indus River basin in the center and the Arabian Sea along the southern coast [
Location of the study area and its climatic stations.
Historical variations in annual air temperature indices (
Numerous methods have been used to detect the outliers and inhomogeneities in the gauge records [
The degree or significance of autocorrelation was checked in the observed data series of air temperature indices by using time series autocorrelation technique prior to detecting the trends significance, trend magnitude, and abrupt transition over time by using Mann–Kendall (MK), Sen’s slope, and Sequential Mann–Kendall (SQMK) methods [
In this study, six temperature index data products (GHCN, UDEL, APHRODITE, CPC, CRU, and PGF) were evaluated against the reference data (Table
Information on global gridded products used in the study.
Datasets | Variable | Resolution | Frequency | Study temporal coverage | Source | Reference |
---|---|---|---|---|---|---|
APHRODITE | 0.5° × 0.5° | Monthly | 1979–2015 | High Asian Product, Japan | [ | |
CRU | 0.5° × 0.5° | Monthly | 1979–2015 | University of East Anglia | [ | |
CPC | 0.5° × 0.5° | Monthly | 1979–2015 | Climate Prediction Center | [ | |
UDEL | 0.5° × 0.5° | Monthly | 1979–2015 | University of Delaware | [ | |
GHCN | 0.5° × 0.5° | Monthly | 1979–2015 | National Climatic Data Center | [ | |
PGF | 0.5° × 0.5° | Monthly | 1979–2015 | Princeton University Global Meteorological Forcing (NCEP-ncar) Reanalysis Dataset | [ |
The Climate Research Unit (CRU) TS V4 product was developed by the University of East Anglia, UK, and is continuously updated with the support from National Centre for Atmospheric Science (NCAS) and Natural Environment Research Council (NERC), UK. The product comprises several climate variables including temperature indices (
The UDEL V5.01 product was developed by the University of Delaware, USA. The primary data of air surface temperature and precipitation were acquired from various sources including Global Historical Climatology Network (GHCN2, daily GHCN) at the National Centers for Environmental Information, Atmospheric Environment Services Canada, Institute of Hydrometeorology in St. Petersburg, daily records from Greenland Climate Network data, daily records from the Global Surface Summary of the Day, the National Center for Atmospheric Research (NCAR) India, and Nicholson’s precipitation data archive of Africa, station records from South America, and monthly records from the Automatic Weather Station Project Greenland [
The CPC gauge-based product is the first product covering both land and ocean data from the CPC Unified Precipitation Project at the National Oceanic and Atmospheric Administration (NOAA). The CPC acquired data and constructed various climate parameters on daily and monthly scales; these include precipitation, temperature, snow cover, and degree-days. The product collected data reports from 30,000 stations, including reports from the Global System of Telecommunication (GTS) data, Cooperative Observer Network (COOP), and other national meteorological agencies (NMAs) [
Asian Precipitation-Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) products V1808 and V1101 of daily surface air temperature and precipitation were developed by the Meteorological Research Institute of the Meteorological Agency and Japan Institute for Humanity and Nature. The gauge dataset is first interpolated at 0.05-degree resolution. Further datasets are generated after regridding to 0.25- and 0.5-degree resolution by considering the local attributes, with the aid of improved algorithms for the weighting function [
The Global Historical Climatological Network-monthly (GHCN) data product V3 was developed by the National Climatic Data Center (NCDC) and National Centers for Environmental Information. It provides the temperature monthly mean data for 7280 stations in 226 countries [
The product was developed by assimilating the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis datasets with several global observed databases [
In the present study, the spatiotemporal performance of each GDP was assessed against reference or gauge data by using significant statistical indicators. Several quantitative evaluation metrics, including the Pearson correlation coefficient (CC), root mean square error (RMSE), and standard deviation, were applied using a Taylor diagram, which is an accurate method of quantifying the degree of agreement between GDPs and reference data [
where
The nonparametric MK trend analysis was used to evaluate the GDPs against reference data series. The MK test is robust against missing values and outliers [
In these equations,
The nonparametric TSS technique is used to quantify the magnitude of slope in linear trends [
The SQMK trend test is used to detect significant positive or negative turning point in a time series data [
Subsequently, the test statistics, PS and RS, are calculated by the following equation:
Similarly,
The annual averages and temporal variability of temperature indices (
Average annual (a)
Temporal comparison of observation data and GDPs for (a)
Spatial distributions of average annual temperature indices over Punjab. (a)
The performances of GDPs relative to the reference data were further evaluated based on statistical metrics as presented in Table
Statistical metrics for the evaluation of temperature indices GDPs.
Indices | RMSE | CC | rBias (%) | |
---|---|---|---|---|
( | ||||
CRU | 0.18 | 0.93 | 0.80 | −0.49 |
CPC | 0.44 | 0.80 | 0.65 | −7.2 |
PGF | 0.20 | 0.90 | 0.71 | −5.1 |
( | ||||
CRU | 0.28 | 0.90 | 0.79 | −2.3 |
CPC | 0.39 | 0.83 | 0.69 | −6.8 |
PGF | 0.32 | 0.85 | 0.72 | −3.3 |
(DTR) | ||||
CRU | 0.44 | 0.63 | 0.45 | −0.6 |
( | ||||
APHRODITE | 0.22 | 0.90 | 0.87 | −3.1 |
CRU | 0.13 | 0.97 | 0.93 | −1.5 |
GHCNM | 0.19 | 0.93 | 0.90 | −2.7 |
UDEL | 0.15 | 0.95 | 0.91 | −1.84 |
CPC | 0.20 | 0.94 | 0.88 | −3.4 |
Statistical comparison of observation data and GDPs for (a)
The spatial distributions of Bias, RMSE, and CC used to evaluate
Spatial distribution of Bias, RMSE, and CC of temperature indices when comparing GDPs against observations for (a)
Meanwhile, the CPC temperature indices were the least accurate among all the products when capturing the spatial distribution pattern. Most products showed similar patterns, with positive Bias in the northern part of the study region, indicating that temperatures were underestimated in the high altitude of northern Punjab. The temperature index products were less able to capture the spatial distribution pattern, particularly in northern Punjab. This could be attributed to topographic effects and the relatively coarse spatial resolution of GDPs when attempting to capture variability in high altitude areas. Overall, the range of statistical parameters in these areas demonstrates the importance of the bias correction of GDPs before their use in climate studies [
The annual trends of GDPs and reference data series acquired by the MK and TSS approaches at 95% confidence interval (CI) during the study period of 1979–2015 are presented in Figures
Comparison of MK trend slopes (
Furthermore, the APHRODITE, CRU, GHCNM, UDEL, and CPC
Abrupt changes in climate data series reveal the transition from one climate state to another, due to some external factors, at a rate determined by the climate system [
Comparison of abrupt transitions in observation data and GDPs for (a)
The results of abrupt transition in the
The detection of rapid change points and comparison of
Accurate and reliable spatiotemporal temperature data acquisition is not only important for studies of climate variations but also crucial for the water resources and agriculture management [
Results showed that the spatial and temporal performances of the CRU temperature indices outperformed the other GDPs as indicated by their high values of CC and
Furthermore, the current results indicated significant warming trends during the period of 1979–2015 over the study region, which could be attributed to rapid urbanization, deforestation, or population growth across the study region [
Overall, we found considerable spread in the magnitude and temporal variability among the different GDPs over the study region, with differences reaching 1-2°C between different products within the same category as well as between GDPs and the reference data. The range of uncertainties was more notable in the extreme temperature (
The spatial and temporal performances of the datasets depend on a number of factors related to the processing of the GDPs, for example, data sources, interpolation techniques, temporal domain, missing data, topography, and spatial resolution [
Our study summarizes and compares some potential GDPs for temperature indices over Punjab Province. The evaluation results can improve our understanding of the use of GDPs in arid and semiarid regions like Punjab Province. Meanwhile, spatial and temporal discrepancies were also identified, which will be useful in the further application of these GDPs in hydrometeorological applications. The reference data were used as a standard for the assessment of different global products. However, different systematic errors related to the reference data compromise the quality of evaluation process. Thus, the regions with less number of meteorological stations available with sufficient geographic distribution and the use of global products could be proved satisfactory [
Through comparison with reference data, this study highlights the spatial and temporal strengths and weaknesses of different temperature GDPs; this will help in the selection of potential GDPs for Punjab Province, Pakistan. Notable differences and similarities in the bias, trends, and abrupt transition in temperature were identified for these different GDPs over the target region. The core findings of the study are listed below.
The spatial and temporal performances of the CRU product were better than those of the other GDPs in terms of the higher values of CC and
In conclusion, this research provides an inclusive comparison of the widely used temperature indices GDPs and enumerates the spatial and temporal inconsistencies in selected GDPs. The results and associated procedures are useful for assessing potential GDPs and for improving our understanding of their application over the study region. Despite finding that the CRU product performed better than other GDPs, uncertainties remain when applying GDPs over semiarid and arid regions like Punjab, as demonstrated by the ranges of RMSE and Bias. The spatial and temporal comparisons of GDPs with reference data showed large discrepancies, with temperature differences of up to 1-2°C between different products within the same category. These results highlight the need for a further improvement in GDPs and for better accuracy over the arid and semiarid regions. It will also be important to note the number of stations used at each grid scale by GDPs for different regions across the globe. The magnitude of Bias for the GDPs, particularly in northern Punjab, demonstrates the importance of bias corrections before using GDPs in hydroclimate studies. This evaluation of GDPs in the study area was limited to annual time scales; future studies should focus on higher temporal and spatial resolutions. Our evaluation of the different GDPs for temperature indices will be useful when assessing potential products and their weaknesses before their reliable utilization in hydrological and meteorological applications.
Data used in this study are available from the corresponding author upon request.
The authors declare no conflicts of interest.
Xin Li and Yingying Chen supervised and designed this study. Zain Nawaz conducted the research and wrote the manuscript. Yanlong Guo and Xufeng Wang assisted in formatting and data analysis. Kun Zhang, Naima Nawaz, and Akynbekkyzy Meerzhan helped in editing the manuscript.
The authors acknowledge the financial support of the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant no. XDA20100104) and the National Natural Science Foundation of China (Grant no. 41630856) for this study. The authors used in situ data from the Pakistan Meteorological Department (PMD). The authors cordially appreciate the PMD, whose efforts made it possible for them to access the data. The authors also acknowledge the support of the CAS-TWAS president fellowship program for PhD.