Articles | Volume 13, issue 4
https://doi.org/10.5194/esd-13-1451-2022
https://doi.org/10.5194/esd-13-1451-2022
Research article
 | 
28 Oct 2022
Research article |  | 28 Oct 2022

Exploring the relationship between temperature forecast errors and Earth system variables

Melissa Ruiz-Vásquez, Sungmin O, Alexander Brenning, Randal D. Koster, Gianpaolo Balsamo, Ulrich Weber, Gabriele Arduini, Ana Bastos, Markus Reichstein, and René Orth

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Cited articles

Albergel, C., Dorigo, W., Reichle, R. H., Balsamo, G., de Rosnay, P., Muñoz-Sabater, J., Isaksen, L., de Jeu, R., and Wagner, W.: Skill and Global Trend Analysis of Soil Moisture from Reanalyses and Microwave Remote Sensing, J. Hydrometeorol., 14, 1259–1277, https://doi.org/10.1175/JHM-D-12-0161.1, 2013. a
Albergel, C., Dutra, E., Bonan, B., Zheng, Y., Munier, S., Balsamo, G., de Rosnay, P., Muñoz-Sabater, J., and Calvet, J.-C.: Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation, Remote Sens.-Basel, 11, 520, https://doi.org/10.3390/rs11050520, 2019. a
Ardilouze, C., Specq, D., Batté, L., and Cassou, C.: Flow dependence of wintertime subseasonal prediction skill over Europe, Weather Clim. Dynam., 2, 1033–1049, https://doi.org/10.5194/wcd-2-1033-2021, 2021. a
Arias, P. A., Garreaud, R., Poveda, G., Espinoza, J. C., Molina-Carpio, J., Masiokas, M., Viale, M., Scaff, L., and van Oevelen, P. J.: Hydroclimate of the Andes Part II: Hydroclimate Variability and Sub–Continental Patterns, Front. Earth Sci., 8, 505467, https://doi.org/10.3389/feart.2020.505467, 2021. a
Australian Government Bureau of Meteorology: Madden-Julian Oscillation (MJO), Australian Government Bureau of Meteorology [data set], http://www.bom.gov.au/climate/mjo/, last access: 11 August 2021. a
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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.
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