Exploring the relationship between temperature forecast errors and Earth system variables

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

doi:https://doi.org/10.5194/esd-13-1451-2022

Articles | Volume 13, issue 4

https://doi.org/10.5194/esd-13-1451-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/esd-13-1451-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 4

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

Supplement

https://doi.org/10.5194/esd-13-1451-2022-supplement

Data sets

S2S, ECMWF, Realtime, Daily averaged ECMWF https://apps.ecmwf.int/datasets/data/s2s/

CPC Global Unified Temperature NOAA PSL https://psl.noaa.gov/data/gridded/data.cpc.globaltemp.html

Global Precipitation Climatology Project (GPCP) Climate Data Record (CDR), Version 1.3 (Daily) NOAA National Centers for Environmental Information https://www.ncei.noaa.gov/products/climate-data-records/precipitation-gpcp-daily

Time series daily NINO3.4 World Meteorological Organization https://climexp.knmi.nl/selectdailyindex.cgi?id=someone@somewhere

Madden-Julian Oscillation (MJO) Australian Government Bureau of Meteorology http://www.bom.gov.au/climate/mjo/

MOD13C1 MODIS/Terra Vegetation Indices 16-Day L3 Global 0.05Deg CMG V006 NASA EOSDIS Land Processes DAAC https://lpdaac.usgs.gov/products/mod13c1v006/

MCD43D42 MODIS/Terra+Aqua BRDF/Albedo Black Sky Albedo Band1 Daily L3 Global 30ArcSec CMG V006 NASA EOSDIS Land Processes DAAC https://lpdaac.usgs.gov/products/mcd43d42v006/

MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 61 NASA National Snow and Ice Data Center Distributed Active Archive Center https://nsidc.org/data/MOD10A1/versions/61/

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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.