Evaluation of the suitability of NCEP/NCAR, ERA‑Interim and, ERA5 reanalysis data sets for statistical downscaling in the Eastern Black Sea Basin, Turkey

Nacar S., Kankal M., Okkan U.

Meteorology And Atmospheric Physics, cilt.134, ss.1-23, 2022 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 134
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s00703-022-00878-6
  • Dergi Adı: Meteorology And Atmospheric Physics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Environment Index, Geobase, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1-23
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

Climate community frequently uses gridded reanalysis data sets in their climate change impact studies. However, these studies

for a region yield more realistic results depending on the rigorous analysis of the reanalysis data sets for this region. This

study aims to determine the most suitable reanalysis data set for the statistical downscaling method in the Eastern Black Sea

Basin, one of Turkey's most important hydrological basins owing to the precipitation it receives throughout the year. For

this purpose, the monthly mean temperature and total precipitation data measured from the 12 meteorological stations and

12 large-scale predictors of the NCEP/NCAR, ERA-Interim, and ERA5 reanalysis data sets were used. The multivariate

adaptive regression splines (MARS) and conventional regression analysis with linear and exponential functions were used

to create effective statistical downscaling models. For evaluating and comparing the performance of the downscaling models

with three different reanalysis data set, four performance statistics (root means square error, scatter index, mean absolute

error, and the Nash Sutcliffe coefficient of efficiency) were used. Besides, the relative importance of the input variables

of the models was determined. The study revealed that the values obtained from the models of ERA5 were closer to the

precipitation and temperature values measured from the meteorological stations. In addition, the model performances with

three reanalysis data sets for the temperature variable were very close to each other. The study results have shown that the

MARS method, which gives the highest performance values, can be used successfully as a statistical downscaling method

in climate change impact studies.