Preprints
https://doi.org/10.5194/egusphere-2022-183
https://doi.org/10.5194/egusphere-2022-183
 
19 Apr 2022
19 Apr 2022

Exploring the relationship between temperature forecast errors and Earth system variables

Melissa Ruiz-Vásquez1,2, Sungmin O3, Alexander Brenning2, Randal D. Koster4, Gianpaolo Balsamo5, Ulrich Weber1, Gabriele Arduini5, Ana Bastos1, Markus Reichstein1, and René Orth1 Melissa Ruiz-Vásquez et al.
  • 1Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
  • 2Friedrich Schiller University Jena, Department of Geography, Jena, Germany
  • 3Department of Climate and Energy System Engineering, Ewha Womans University, Seoul, South Korea
  • 4Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 5Research Department, European Centre for Medium Range Weather Forecasts, Reading, Great Britain

Abstract. Accurate subseasonal weather forecasts, from two weeks up to a season, can help reduce costs and impacts related to weather and corresponding extremes. The quality of weather forecasts has improved considerably in recent decades as models represent more details of physical processes, and they benefit from assimilating comprehensive Earth observation data as well as increasing computing power. However, with ever-growing model complexity it becomes increasingly difficult to pinpoint weaknesses in the forecast models’ process representations which is key to improve forecast accuracy. In this study, we use a comprehensive set of observation-based ecological, hydrological and meteorological variables to study their potential for explaining temperature forecast errors at the weekly time scale. For this purpose, we compute Spearman correlations between each considered variable and the forecast error obtained from the ECMWF subseasonal-to-seasonal (S2S) reforecasts at lead times of 1–6 weeks. This is done across the globe for the time period 2001–2017. The results show that temperature forecast errors globally are most strongly related with climate-related variables such as surface solar radiation and precipitation, which highlights the difficulties of the model to accurately capture the evolution of the climate-related variables during the forecasting period. At the same time, we find particular regions in which other variables are more strongly related to forecast errors. For instance, in central Europe, eastern North America and southeastern Asia, vegetation greenness and soil moisture are relevant, while in western South America and central North America, circulation-related variables such as surface pressure relate more strongly with forecast errors. Overall, the identified relationships between forecast errors and independent Earth observations reveal promising variables on which future forecasting system development could focus by specifically considering related process representations and data assimilation.

Melissa Ruiz-Vásquez et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-183', Constantin Ardilouze, 06 May 2022
    • AC1: 'Reply on RC1', Melissa Ruiz-Vásquez, 08 Jul 2022
  • RC2: 'Comment on egusphere-2022-183', Anonymous Referee #2, 04 Jun 2022
    • AC2: 'Reply on RC2', Melissa Ruiz-Vásquez, 08 Jul 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-183', Constantin Ardilouze, 06 May 2022
    • AC1: 'Reply on RC1', Melissa Ruiz-Vásquez, 08 Jul 2022
  • RC2: 'Comment on egusphere-2022-183', Anonymous Referee #2, 04 Jun 2022
    • AC2: 'Reply on RC2', Melissa Ruiz-Vásquez, 08 Jul 2022

Melissa Ruiz-Vásquez et al.

Melissa Ruiz-Vásquez et al.

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Short summary
Subseasonal forecasts facilitate early warning of extreme events, however, their predictability sources are not fully explored. We find that global temperature forecast errors in many regions are related to climate variables such as solar radiation and precipitation, as well as land surface variables such as soil moisture and evaporative fraction. A better representation of these variables in the forecasting and data assimilation systems can support the accuracy of temperature forecasts.