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Statistical postprocessing of dynamically downscaled outputs of CFS.v2

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Abstract

Due to geographic location of Iran and its neighbouring countries, floods and droughts, as climate-based events, have been the basis of the majority of disasters. Socioeconomic problems have been exposed owing to these extreme climate events. The frequency and intensity of these inordinate phenomena are expected to increase as a result of climate change in the future. Equipping decision makers with the conclusive information catered by seasonal and monthly forecasts would be a beneficial tool to minimize the negative impacts of these climatic hazards. coupled global ocean–atmosphere circulation models (CGCMs) are the most viable and predominant tools for generating seasonal climate predictions. While the ability of CGCMs in addressing scientific issues related to climate simulation and prediction have been ascertained, their considerable biases (when compared to observations) and coarse resolution would limit their applicability at regional or local spatial scales for some sectors such as agriculture, energy, health, tourism, and insurance. As a result, using downscaling tools to obtain climate information at the regional level is essential. In this study, precipitation, 2 m-temperature, minimum and maximum temperature forecasts of CFS.v2 in the period of 1982–2010 was firstly downscaled at regional scale using RegCM4 RCM for Iran and some neighbouring countries. Then, these dynamically downscaled data was statistically postprocessed using four different methods including artificial neural networks, multiple linear regression, support vector machine and decision tree (DT) at monthly time scale. Since the observed grided data was not available for the whole study area, the post-processed data were compared with the relevant ERA5 grided data. The results showed that, DT with average R2 and NS values of 0.88 and 0.78, respectively, was the best model to post-process the outputs of CFS.v2-RegCM4 system in all months and for all the variables. RMSE values, averaged over all the months for this model, were 21.78 mm/month, 1.34 °C, 2.11 °C and 5.07 °C for precipitation, 2 m temperature, maximum and minimum temperatures, respectively.

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Authors and Affiliations

  1. Climate Modeling and Early Warning Division, Atmospheric Science and Meteorological Research Center (ASMERC), Climatological Research Institute (CRI), Mashhad, Iran

    Yashar Falamarzi

  2. Disasters and Climate Change Division, Atmospheric Science and Meteorological Research Center (ASMERC), Climatological Research Institute (CRI), Mashhad, Iran

    Morteza Pakdaman

  3. Applied Climatology Division, Atmospheric Science and Meteorological Research Center (ASMERC), Climatological Research Institute (CRI), Mashhad, Iran

    Zohreh Javanshiri

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  1. Yashar Falamarzi
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YF conducted the methodology and wrote the manuscript. MP reviewed the data and prepared them for analysis. ZJ prepared the figures and edited the manuscript.

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Correspondence to Yashar Falamarzi.

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Falamarzi, Y., Pakdaman, M. & Javanshiri, Z. Statistical postprocessing of dynamically downscaled outputs of CFS.v2. Stoch Environ Res Risk Assess 37, 2379–2397 (2023). https://doi.org/10.1007/s00477-023-02386-4

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