The least squares method in estimating the accuracy of surface air temperature projections based on ensembles of regional climate models
DOI:
https://doi.org/10.24028/gj.v44i5.272326Keywords:
least squares method, regional climate model, optimal ensemble of models, bias correction, delta method, E-OBSAbstract
The study is devoted to the search for the optimal methodical approach for bias correction of surface air temperature from real climatic indicators for the territory of Ukraine, obtained in the projections of ensembles of regional climate models (RCM) based on the use of regression analysis, namely the least squares method (LSM) with various options of its application. The procedure included: searching for weight coefficients of linear regression equations to minimize the deviation of the forecast from the observations for each model and each grid node of the 10 RCM for two climatic periods 1961—1990 and 1991—2010; obtaining, on the basis of equations with established coefficients, the averaged errors of ensembles of models for various variants of LSM application; and determining the limits of the application of such methodical approaches to the formation of an optimal ensemble.
Among all options for using forecasting functions, it was found that the most accurate was the option of applying LSM to differences (shifts) in values between periods when one uses monthly values of the climate indicator. In general, the use of monthly values showed the best approximation of the model data to the observation data used from the E-OBS database.
It was found that in a certain period the approximation of the LSM is significantly better than the average, but the advantage is lost if the obtained weighting factors are used in another period. For further use, the proposed approach can be modernized in the direction of more detailed clustering in time and space, which will allow adjusting the model data even closer to the observed ones. However, our results make us doubt the feasibility of applying such an approach to the forecast of climate fields, since they are not stationary and can significantly transform over time. In this case, arithmetic averaging and averaging of shifts or the delta method remain the optimal choice for forming a prognostic ensemble of RCM.
References
Zamfirova, M.S., & Khokhlov, V.M. (2020). Air temperature and precipitation regime in Ukraine in 2021—2050 by CORDEX model ensemble. Ukrainian Hydrometeorological Journal, (25), 17—27. https://doi.org/10.31481/uhmj.25.2020.02 (in Ukrainian).
Krakovska, S.V. (2018). Optimal ensemble of regional climate models for the assessment of temperature regime change in Ukraine. Prirodopolzovaniye, (1), 114—126 (in Russian).
Krakovska, S.V., Palamarchuk, L.V., Gnatiuk, N.V., Shpytal, T.M. (2018). Projections of air temperature and relative humidity in Ukraine regions to the middle of the 21st century based on regional climate model ensembles. Geoinformatika, (3), 62—77 (in Ukrainian).
Krakovska, S.V., Palamarchuk, L.V., & Shpytal, T.M. (2019). Climatic projections of heating season in Ukraine up to the middle of the 21st century. Geofizicheskiy Zhurnal, 41(6), 144—164. https://doi.org/10.24028/gzh.0203-3100.v41i6.2019.190072 (in Ukrainian).
Krakovska, S.V., & Shpytal, T.M. (2018). Dates of air temperature transition over 0, 5, 10 and 15 °С and corresponding lengths of climatic seasons from the second part of the 20th to the middle of the 21st century in Ukraine. Geoinformatika, (4), 74—92 (in Ukrainian).
Palamarchuk, L.V., & Krakovska, S.V. (2018). Regional Climate Changes in Ukraine: Guidelines for the training course for students of the Faculty of Geography, specialty «Meteorology and Climatology». Kyiv: DP Print-Servis, 90 p. (in Ukrainian).
Prusov, V.А., & Snizhko, S.І. (2017). Methods of applied systematic analysis in hydrometeorology: textbook. Кyiv: Print-Servis, 701 p. (in Ukrainian).
Khokhlov, V., Serga, E., & Nedostrelova, L. (2021). Objective selection of model run from regional climate models ensemble. Ukrainian Hydrometeorological Journal, (28), 29—36. https://doi.org/10.31481/uhmj.28.2021.03 (in Ukrainian).
Shedemenko, I.P., Krakovska, S.V., & Gnatiuk, N.V. (2012). Verification of surface temperature and precipitation from European gridded dataset E-OBS for administrative regions in Ukraine. Naukovi pratsi UkrNDHMI, (262), 71—90 (in Ukrainian).
Baсo-Medina, J., Manzanas, R., & Gutiйrrez, J.M. (2020). Configuration and intercomparison of deep learning neural models for statistical downscaling. Geoscientific Model Development, 13(4), 2109—2124. https://doi.org/10.5194/gmd-13-2109-2020.
Collados-Lara, A.-J., Gуmez-Gуmez, J.-D., Pulido-Velazquez, D., & Pardo-Igъzquiza, E. (2022). An approach to identify the best climate models for the assessment of climate change impacts on meteorological and hydrological droughts. Natural Hazards and Earth System Sciences, 599—616. https://doi.org/10.5194/nhess-22-599-2022.
Cornes, R., van der Schrier, G., van den Besselaar, E.J.M., & Jones, P.D. (2018). An Ensemble Version of the E-OBS Temperature and Precipitation Datasets. Journal of Geophysical Research: Atmospheres, 123(17), 9391—9409. https://doi.org/10.1029/2017JD028200.
Doblas-Reyes, F.J., Sцrensson, A.A., Almazroui, M., Dosio, A., Gutowski, W.J., Haarsma, R., Hamdi, R., Hewitson, B., Kwon, W.-T., Lamptey, B.L., Maraun, D., Stephenson, T.S., Takayabu, I., Terray, L., Turner, A., & Zuo, Z. (2021). Linking Global to Regional Climate Change. In Climate Change. The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panelon Climate Change (pp. 1363—1512). Cambridge University Press. https://doi.org/10.1017/9781009157896.012.
Gutiйrrez, J.M., Maraun, D., Widmann, M., Huth, R., Hertig, E., Benestad, R., Roessler, O., Wibig, J., Wilcke, R., Kotlarski, S., San Martн, D., Herrera, S., Bedia, J., Casanuev, A., Manzanas, R., Iturbide, M., Vrac, M., Dubrovsky, M., Ribalaygua, J., Pуrtoles, J., Rдty, O., Rдisдnen, J., Hingray, B., Raynaud, D., Casado, M.J., Ramos, P., Zerenner, T., Turco, M., Bosshard, T., Љtěpбnek, P., Bartholy, J., Pongracz, R., Keller, D.E., Fischer, A.M., Cardoso, R.M., Soares, P.M.M., Czernecki, B., & Pagй, C. (2019). An intercomparison of a large ensemble of statistical down scaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment. International Journal of Climatology, 39(9), 3750—3785. https://doi.org/10.1002/joc.5462.
Gutiйrrez, J.M., Jones, R.G., Narisma, G.T., Alves, L.M., Amjad, M., Gorodetskaya, I.V., Grose, M., Klutse, N.A.B., Krakovska, S., Li, J., Martнnez-Castro, D., Mearns, L.O., Mernild, S.H., Ngo-Duc, T., van den Hurk, B., & Yoon, J.-H. (2021). Atlas. In Climate Change. The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 1927—2058). Cambridge University Press. https://doi.org/10.1017/9781009157896.021.
Kharin, V.V., & Zwiers, F.W. (2002). Climate Predictions with Multimodel Ensembles. Journal of Climate, (15), 793—799. https://doi.org/10.1175/1520-0442(2002)015<0793:CPWME>2.0.CO;2.
Lehner, F., Deser, C., Maher, N., Marotzke, J., Fischer, E.M., Brunner, L., Knutti, R., & Hawkins, E. (2020). Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6. Earth System Dynamics, (11), 491—508. https://doi.org/10.5194/esd-11-491-2020.
Maraun, D. (2016). Bias Correcting Climate Change Simulations — a Critical Review. Current Climate Change Reports, 2(4), 211—220. https://doi.org/10.1007/s40641-016-0050-x.
Osypov, V., Speka, O., Chyhareva, A., Osadcha, N., Krakovska, S., & Osadchyi, V. (2021). Water resources of the Desna river basin under future climate. Journal of Water and Climate Change, 12(7), 3355—3372. https://doi.org/10.2166/wcc.2021.034.
Pierce, D.W., Cayan D.R., & Thrasher B.L. (2014). Statistical Downscaling Using Localized Constructed Analogs (LOCA). Journal of Hydrometeorology, 15(6), 2558—2585. https://doi.org/10.1175/jhm-d-14-0082.1.
Van der Linden, P., & Mitchell, J.F.B. (2009). ENSEMBLES: Climate Change and its Impacts: Summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, Fitz Roy Road, Exeter EX1 3PB, UK. 160 p. Retrieved from https://ensembles-eu.metoffice.gov.uk/docs/Ensembles_final_report_Nov09.pdf
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
