Abstract
Gridded datasets are of paramount importance to globally derive precipitation quantities for a multitude of scientific and practical applications. However, as most studies do not consider the impacts of temporal and spatial variations of included measurements in the utilized datasets, we conducted a quantitative assessment of the ability of several state of the art gridded precipitation products (CRU, GPCC Full Data Product, GPCC Monitoring Product, ERA-interim, ERA5, MERRA-2, MERRA-2 bias corrected, PERSIANN-CDR) to reproduce monthly precipitation values at climate stations in the Pamir mountains during two 15 year periods (1980–1994, 1998–2012) that are characterized by considerable differences in incorporated observation data. Results regarding the GPCC products illustrated a substantial and significant performance decrease with up to four times higher errors during periods with low observation inputs (1998–2012 with 2 stations on average per 124,000 km2) compared to periods with high quantities of regionally incorporated station data (1980–1994 with 14 stations on average per 124,000 km2). If independent stations were considered, the coefficient of efficiency indicated that only three of the gridded datasets (MERRA–2 bias corrected, GPCC, GPCC MP) performed better than the long term station mean for characterizing surface precipitation. Error patterns and magnitudes show that in complex terrain, evaluation of temporal and spatial variations of included observations is a prerequisite for using gridded precipitation products for scientific applications and to avoid overly optimistic performance assessments.
Highlights
Gridded datasets are of paramount importance to globally derive precipitation quantities for a multitude of scientific and practical applications
To cover different categories of precipitation datasets outlined in Sun et al.[2] and based on their actuality, we include the Global Precipitation Climatology Centre (GPCC) Full Data Monthly Product Version 201834, the GPCC Monitoring Product Version 635, the climatic research unit (CRU) TS 4.03 dataset[36], the GPCP Version 2.3 product[37], the MERRA-2 and MERRA-2-BC products[38], the ERA-interim product[39], ERA540, and the PERSIANN-CDR dataset[41] for comparison of monthly precipitation amounts with ground based observation data from meteorological stations during the two 15 year periods 1980–1994 and 1998–2012
Thereby, the GPCC and CRU products represent gauge-based datasets, the GPCP Version 2.3 is a combination of satellite estimates and station observations, TRMM 3B43 and PERSIANN-CDR are satellite-based products with additional station calibration or adjustment, and ERA-interim, ERA5 and MERRA-2 are reanalysis datasets
Summary
Gridded datasets are of paramount importance to globally derive precipitation quantities for a multitude of scientific and practical applications. The Tajik Pamir region in Central Asia is an ideal example to evaluate this hypothesis: the available data for gauge based products dramatically dropped after the collapse of the Soviet Union and the area is characterized by complex terrain with two different precipitation regimes It is of major importance for large scale hydrology, supra-regional water availability and it is a hotspot of climate change[33]. Local climate observations started relatively early during the tsarist period in the 1890s and data quality was satisfactory until the 1990s, recent decades were characterized by a sharp decline in internationally available climate data due to the dissolution of the Soviet Union and the preceding civil war[33] This is strikingly illustrated by station numbers regionally utilized in the GPCC Full Data Monthly Product which represents the largest precipitation data base of the world[26] (Fig. 2). This situation renders the Pamir mountains as an ideal region to test the temporal and spatial performance of climatic precipitation datasets cf.[13]
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