Contribution of dynamic vegetation phenology to decadal climate predictability
(2014) In Journal of Climate 27(22). p.8563-8577- Abstract
- In this study, the impact of coupling and initializing the leaf area index from the dynamic vegetation model Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) is analyzed on skill of decadal predictions in the fully coupled atmosphere-land-ocean-sea ice model, the European Consortium Earth System Model (EC-Earth). Similar to the impact of initializing the model with the observed oceanic state, initializing the leaf area index (LAI) fields obtained from an offline LPJ-GUESS simulation forced by the observed atmospheric state leads to a systematic drift.A different treatment of the water and soil moisture budget in LPJ-GUESS is a likely cause of this drift. The coupled system reduces the cold bias of the reference model over land by... (More)
- In this study, the impact of coupling and initializing the leaf area index from the dynamic vegetation model Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) is analyzed on skill of decadal predictions in the fully coupled atmosphere-land-ocean-sea ice model, the European Consortium Earth System Model (EC-Earth). Similar to the impact of initializing the model with the observed oceanic state, initializing the leaf area index (LAI) fields obtained from an offline LPJ-GUESS simulation forced by the observed atmospheric state leads to a systematic drift.A different treatment of the water and soil moisture budget in LPJ-GUESS is a likely cause of this drift. The coupled system reduces the cold bias of the reference model over land by reducing LAI (and the associated evaporative cooling), particularly outside the growing season. The coupling with the interactive vegetation module implies more degrees of freedom in the coupled model, which generates more noise that can mask a portion of the extra signal that is generated. The forecast reliability improves marginally, particularly early in the forecast. Ranked probability skill scores are also improved slightly in most areas analyzed, but the signal is not fully coherent over the forecast interval because of the relatively low number of ensemble members. Methods to remove the LAI drift and allow coupling of other variables probably need to be implemented before significant forecast skill can be expected. (Less)
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https://lup.lub.lu.se/record/4857292
- author
- organization
- publishing date
- 2014
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- leaf area index, weather forecasting, vegetation cover, climate variation, atmosphere-ocean coupling, atmosphere-biosphere interaction, Forecasting, Surface temperatures, Atmosphere-land interactions, Water treatment, Soil moisture, Sea ice, Evaporative cooling systems, Earth atmosphere, Computer simulation, Climatology, Budget control, Atmospheric temperature, Vegetation, Surface temperature, Forecast verification/skill, Climate variability, Atmosphere-land interaction, Climate prediction
- in
- Journal of Climate
- volume
- 27
- issue
- 22
- pages
- 15 pages
- publisher
- American Meteorological Society
- external identifiers
-
- wos:000344774200017
- scopus:84908438141
- ISSN
- 1520-0442
- DOI
- 10.1175/JCLI-D-13-00684.1
- language
- English
- LU publication?
- yes
- id
- f4b6d5b1-6ddb-4542-a580-91eaabd4bd60 (old id 4857292)
- date added to LUP
- 2016-04-01 10:13:32
- date last changed
- 2022-04-27 19:54:45
@article{f4b6d5b1-6ddb-4542-a580-91eaabd4bd60,
abstract = {{In this study, the impact of coupling and initializing the leaf area index from the dynamic vegetation model Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) is analyzed on skill of decadal predictions in the fully coupled atmosphere-land-ocean-sea ice model, the European Consortium Earth System Model (EC-Earth). Similar to the impact of initializing the model with the observed oceanic state, initializing the leaf area index (LAI) fields obtained from an offline LPJ-GUESS simulation forced by the observed atmospheric state leads to a systematic drift.A different treatment of the water and soil moisture budget in LPJ-GUESS is a likely cause of this drift. The coupled system reduces the cold bias of the reference model over land by reducing LAI (and the associated evaporative cooling), particularly outside the growing season. The coupling with the interactive vegetation module implies more degrees of freedom in the coupled model, which generates more noise that can mask a portion of the extra signal that is generated. The forecast reliability improves marginally, particularly early in the forecast. Ranked probability skill scores are also improved slightly in most areas analyzed, but the signal is not fully coherent over the forecast interval because of the relatively low number of ensemble members. Methods to remove the LAI drift and allow coupling of other variables probably need to be implemented before significant forecast skill can be expected.}},
author = {{Weiss, M. and Miller, Paul and van den Hurk, B.J.J.M. and van Noije, T. and Ştefănescu, S. and Haarsma, R. and van Ulft, L.H. and Hazeleger, W. and Le Sager, P. and Smith, Benjamin and Schurgers, Guy}},
issn = {{1520-0442}},
keywords = {{leaf area index; weather forecasting; vegetation cover; climate variation; atmosphere-ocean coupling; atmosphere-biosphere interaction; Forecasting; Surface temperatures; Atmosphere-land interactions; Water treatment; Soil moisture; Sea ice; Evaporative cooling systems; Earth atmosphere; Computer simulation; Climatology; Budget control; Atmospheric temperature; Vegetation; Surface temperature; Forecast verification/skill; Climate variability; Atmosphere-land interaction; Climate prediction}},
language = {{eng}},
number = {{22}},
pages = {{8563--8577}},
publisher = {{American Meteorological Society}},
series = {{Journal of Climate}},
title = {{Contribution of dynamic vegetation phenology to decadal climate predictability}},
url = {{http://dx.doi.org/10.1175/JCLI-D-13-00684.1}},
doi = {{10.1175/JCLI-D-13-00684.1}},
volume = {{27}},
year = {{2014}},
}