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Abstract
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 (Tmax, Tmin, Tmean, and DTR) products against the reference data during the period of 1979–2015 over Punjab Province, Pakistan. This region is considered as a center for agriculture and irrigated farming. Our study is the first spatiotemporal statistical evaluation of the performance and selection of potential GDPs over the study region and is based on statistical indicators, trend detection, and abrupt change analysis. Results revealed that the CRU temperature indices (Tmax, Tmin, Tmean, and DTR) outperformed the other GDPs as indicated by their higher CC and R2 but lower bias and RMSE. Furthermore, trend and abrupt change analysis indicated the superior performances of the CRU Tmin and Tmean products. However, the Tmax and DTR products were less accurate for detecting trends and abrupt transitions in temperature. The tested GDPs as well as the reference data series indicate significant warming during the period of 1997–2001 over the study region. Differences between GDPs revealed discrepancies of 1-2°C when compared with different products within the same category and with reference data. The accuracy of all GDPs was particularly poor in the northern Punjab, where underestimates were greatest. This preliminary evaluation of the different GDPs will be useful for assessing inconsistencies and the capabilities of the products prior to their reliable utilization in hydrological and meteorological applications particularly over arid and semiarid regions.
Highlights
Future estimates of global climate patterns are directly concomitant with climate variations at regional scale [1]. e regional variation in climate parameters and assessing their statistical importance are rudimentary tools in detection of climate change [2]. e reliable climate statistics can play a dynamic role for climate change adaptation and mitigation at regional levels [3, 4]
We focus on the evaluation and comparison of temporal changes in trends and abrupt changes of Gridded data products (GDPs). e GDPs assessed here are the Global Historical Climatology Network-Monthly (GHCN), Center for Climatic Research-University of Delaware (UDEL), Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation (APHRODITE), Climate Prediction Centre (CPC), University of Princeton, Global meteorological Forcing dataset (PGF), and Climatic Research Unit (CRU)
The degree of overestimation varied by 1-2°C between different products. e most representative temporal trends in the temperature indices were found in the CRU, PGF, and UDEL products
Summary
Future estimates of global climate patterns are directly concomitant with climate variations at regional scale [1]. e regional variation in climate parameters and assessing their statistical importance are rudimentary tools in detection of climate change [2]. e reliable climate statistics can play a dynamic role for climate change adaptation and mitigation at regional levels [3, 4]. E regional variation in climate parameters and assessing their statistical importance are rudimentary tools in detection of climate change [2]. Despite the importance of other climate parameters, the long-term trend in temperature (hereafter Tmean) is critical for the quantification of climate changes and their possible impacts on the environment [7, 8]. Advances in Meteorology erefore, understanding the spatiotemporal variation in Tmean on regional scales is of great importance in climate monitoring and in hydroclimate studies [9]. Ese accurate and reliable air temperature records underpin our knowledge of regional and global climate changes as well as their possible impacts on water resources and agriculture [17, 18]. Even if gauge data are available and reliable, the irregular distribution and poor spatial coverage hinder their use [20]
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