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Cited by 70Climate variability and modelsMeteorological Phenomena and SimulationsOceanographic and Atmospheric Processes

Benefits of increasing the model resolution for the seasonal forecast quality in EC-Earth.

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Prodhomme, C., Batté, L., Massonnet, F., Davini, P., Bellprat, O., Guemas, V., Doblas-Reyes, F. J.·2016·Journal of Climate
DOI 10.1175/jcli-d-16-0117.1
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Abstract

Abstract Resolution in climate models is thought to be an important factor for advancing seasonal prediction capability. To test this hypothesis, seasonal ensemble reforecasts are conducted over 1993–2009 with the European community model EC-Earth in three configurations: standard resolution (~1° and ~60 km in the ocean and atmosphere models, respectively), intermediate resolution (~0.25° and ~60 km), and high resolution (~0.25° and ~39 km), the two latter configurations being used without any specific tuning. The model systematic biases of 2-m temperature, sea surface temperature (SST), and wind speed are generally reduced. Notably, the tropical Pacific cold tongue bias is significantly reduced, the Somali upwelling is better represented, and excessive precipitation over the Indian Ocean and over the Maritime Continent is decreased. In terms of skill, tropical SSTs and precipitation are better reforecasted in the Pacific and the Indian Oceans at higher resolutions. In particular, the Indian monsoon is better predicted. Improvements are more difficult to detect at middle and high latitudes. Still, a slight improvement is found in the prediction of the winter North Atlantic Oscillation (NAO) along with a more realistic representation of atmospheric blocking. The sea ice extent bias is unchanged, but the skill of the reforecasts increases in some cases, such as in summer for the pan-Arctic sea ice. All these results emphasize the idea that the resolution increase is an essential feature for forecast system development. At the same time, resolution alone cannot tackle all the forecast system deficiencies and will have to be implemented alongside new physical improvements to significantly push the boundaries of seasonal prediction.

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  • While most prediction models drop well below an anomaly correlation coefficient of 0.55 by lead month 3, some promising systems show skill in predicting the AZM in summer when initialized on May 1.

    “While most prediction models drop well below that by lead month 3 (Richter et al., 2017), some promising systems show skill in predicting the AZM in summer when initialized on May 1 (Prodhomme et al., 2016).”

    From The tropical atlantic observing system (2019) · cites this paper as Prodhomme et al., 2016

    The Bias and Bias Correction section describes June–August forecasts initialized in May, matching the timing attributed to this source. But the copy does not report skill in predicting the AZM; the Conclusions instead discuss deteriorated tropical Atlantic SST skill in the higher-resolution runs. The lead-month-3 comparison is attached to Richter et al., not this citation. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Prodhomme, C., Batté, L., Massonnet, F., Davini, P., Bellprat, O., Guemas, V., & Doblas-Reyes, F. J.
Affiliations
  • Barcelona Supercomputing Center (ES)
  • Centre National de Recherches Météorologiques (FR)
  • École Normale Supérieure - PSL (FR)
  • Barcelona Supercomputing Center, Barcelona, Spain
  • Institució Catalana de Recerca i Estudis Avançats (ES)
Corresponding author
Prodhomme, C.
ORCID
Venue
Journal of Climate
Publisher
American Meteorological Society
Published
2016-12-05
Volume
29
Issue
24
Pages
9141-9162
ISSN
  • 0894-8755 (ISSN-L)
  • 1520-0442
DOI
10.1175/jcli-d-16-0117.1
OpenAlex
W2523404925
Cited by
70
References
2
Citations by year
2026: 3 · 2025: 2 · 2024: 4 · 2023: 1 · 2022: 7 · 2021: 16 · 2020: 18 · 2019: 8 · 2018: 5 · 2017: 6
FWCI
4.70
Type
journal article
Language
en
Open access
Locations
  • Journal of Climate (publishedVersion)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (acceptedVersion)
  • UPCommons institutional repository (Universitat Politècnica de Catalunya) (acceptedVersion)
  • DIAL (Catholic University of Leuven) (submittedVersion, other-oa)
  • RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, public-domain)
Funders
  • European Commission (HORIZON2020, 603521, 308378, 641727, 654942, 641811, 607085)
  • Agence Nationale de la Recherche (ANR-17-MPGA-0003)
  • Partnership for Advanced Computing in Europe AISBL (641727)
  • Barcelona Supercomputing Center
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
  • Physical Sciences › Earth and Planetary Sciences › Oceanography › Oceanographic and Atmospheric Processes
Keywords
model resolution, seasonal forecasting, EC-Earth, Indian monsoon, North Atlantic Oscillation, atmospheric blocking, sea ice extent, precipitation bias, sea surface temperature bias, wind speed bias

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