Process-based analysis of terrestrial carbon flux predictability

<p>Despite efforts to decrease the discrepancy between simulated and observed terrestrial carbon fluxes, the uncertainty in trends and patterns of the land carbon fluxes remains high. This difficulty raises the question of the extent to which the terrestrial carbon cycle is predictable and whi...

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Autores principales:	I. Dunkl, A. Spring, P. Friedlingstein, V. Brovkin
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Science Q Geology QE1-996.5 Dynamic and structural geology QE500-639.5
Acceso en línea:	https://doaj.org/article/de1beddd5c0543c4b5e9539e53daa249
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spelling	oai:doaj.org-article:de1beddd5c0543c4b5e9539e53daa2492021-12-02T08:27:26ZProcess-based analysis of terrestrial carbon flux predictability10.5194/esd-12-1413-20212190-49792190-4987https://doaj.org/article/de1beddd5c0543c4b5e9539e53daa2492021-12-01T00:00:00Zhttps://esd.copernicus.org/articles/12/1413/2021/esd-12-1413-2021.pdfhttps://doaj.org/toc/2190-4979https://doaj.org/toc/2190-4987<p>Despite efforts to decrease the discrepancy between simulated and observed terrestrial carbon fluxes, the uncertainty in trends and patterns of the land carbon fluxes remains high. This difficulty raises the question of the extent to which the terrestrial carbon cycle is predictable and which processes explain the predictability. Here, the perfect model approach is used to assess the potential predictability of net primary production (NPPpred) and heterotrophic respiration (Rhpred) by using ensemble simulations conducted with the Max Planck Institute Earth system model. In order to assess the role of local carbon flux predictability (CFpred) in the predictability of the global carbon cycle, we suggest a new predictability metric weighted by the amplitude of the flux anomalies. Regression analysis is used to determine the contribution of the predictability of different environmental drivers to NPPpred and Rhpred (soil moisture, air temperature, and radiation for NPP, and soil organic carbon, air temperature, and precipitation for Rh). Global NPPpred is driven to 62 % and 30 % by the predictability of soil moisture and temperature, respectively. Global Rhpred is driven to 52 % and 27 % by the predictability of soil organic carbon and temperature, respectively. The decomposition of predictability shows that the relatively high Rhpred compared to NPPpred is due to the generally high predictability of soil organic carbon. The seasonality in NPPpred and Rhpred patterns can be explained by the change in limiting factors over the wet and dry months. Consequently, CFpred is controlled by the predictability of the currently limiting environmental factor. Differences in CFpred between ensemble simulations can be attributed to the occurrence of wet and dry years, which influences the predictability of soil moisture and temperature. This variability of predictability is caused by the state dependency of ecosystem processes. Our results reveal the crucial regions and ecosystem processes to be considered when initializing a carbon prediction system.</p>I. DunklI. DunklA. SpringP. FriedlingsteinV. BrovkinV. BrovkinCopernicus PublicationsarticleScienceQGeologyQE1-996.5Dynamic and structural geologyQE500-639.5ENEarth System Dynamics, Vol 12, Pp 1413-1426 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q Geology QE1-996.5 Dynamic and structural geology QE500-639.5
spellingShingle	Science Q Geology QE1-996.5 Dynamic and structural geology QE500-639.5 I. Dunkl I. Dunkl A. Spring P. Friedlingstein V. Brovkin V. Brovkin Process-based analysis of terrestrial carbon flux predictability
description	<p>Despite efforts to decrease the discrepancy between simulated and observed terrestrial carbon fluxes, the uncertainty in trends and patterns of the land carbon fluxes remains high. This difficulty raises the question of the extent to which the terrestrial carbon cycle is predictable and which processes explain the predictability. Here, the perfect model approach is used to assess the potential predictability of net primary production (NPPpred) and heterotrophic respiration (Rhpred) by using ensemble simulations conducted with the Max Planck Institute Earth system model. In order to assess the role of local carbon flux predictability (CFpred) in the predictability of the global carbon cycle, we suggest a new predictability metric weighted by the amplitude of the flux anomalies. Regression analysis is used to determine the contribution of the predictability of different environmental drivers to NPPpred and Rhpred (soil moisture, air temperature, and radiation for NPP, and soil organic carbon, air temperature, and precipitation for Rh). Global NPPpred is driven to 62 % and 30 % by the predictability of soil moisture and temperature, respectively. Global Rhpred is driven to 52 % and 27 % by the predictability of soil organic carbon and temperature, respectively. The decomposition of predictability shows that the relatively high Rhpred compared to NPPpred is due to the generally high predictability of soil organic carbon. The seasonality in NPPpred and Rhpred patterns can be explained by the change in limiting factors over the wet and dry months. Consequently, CFpred is controlled by the predictability of the currently limiting environmental factor. Differences in CFpred between ensemble simulations can be attributed to the occurrence of wet and dry years, which influences the predictability of soil moisture and temperature. This variability of predictability is caused by the state dependency of ecosystem processes. Our results reveal the crucial regions and ecosystem processes to be considered when initializing a carbon prediction system.</p>
format	article
author	I. Dunkl I. Dunkl A. Spring P. Friedlingstein V. Brovkin V. Brovkin
author_facet	I. Dunkl I. Dunkl A. Spring P. Friedlingstein V. Brovkin V. Brovkin
author_sort	I. Dunkl
title	Process-based analysis of terrestrial carbon flux predictability
title_short	Process-based analysis of terrestrial carbon flux predictability
title_full	Process-based analysis of terrestrial carbon flux predictability
title_fullStr	Process-based analysis of terrestrial carbon flux predictability
title_full_unstemmed	Process-based analysis of terrestrial carbon flux predictability
title_sort	process-based analysis of terrestrial carbon flux predictability
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/de1beddd5c0543c4b5e9539e53daa249
work_keys_str_mv	AT idunkl processbasedanalysisofterrestrialcarbonfluxpredictability AT idunkl processbasedanalysisofterrestrialcarbonfluxpredictability AT aspring processbasedanalysisofterrestrialcarbonfluxpredictability AT pfriedlingstein processbasedanalysisofterrestrialcarbonfluxpredictability AT vbrovkin processbasedanalysisofterrestrialcarbonfluxpredictability AT vbrovkin processbasedanalysisofterrestrialcarbonfluxpredictability
_version_	1718398513818107904

Process-based analysis of terrestrial carbon flux predictability

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