High predictability of terrestrial carbon fluxes from an initialized decadal prediction system

Lovenduski, Nicole S.; Bonan, Gordon B.; Yeager, Stephen; Lindsay, Keith; Lombardozzi, Danica

doi:10.1088/1748-9326/ab5c55

Cited by 26 publications

(36 citation statements)

References 33 publications

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“…The high‐RMSE midlatitudes and tropics are also strongly variable areas in which net primary production and heterotrophic respiration respond heavily to temperature and precipitation patterns associated with ENSO (Figures S5, S6, S10, and S11). Our results support ENSO as the limiting process of predictability of air‐land CO 2 flux (Betts et al, ; Keeling et al, ; Lovenduski, Bonan, et al, ; Jones et al, ; Zeng et al, , ). Note that unlike global air‐sea CO 2 flux predictability, global air‐land CO 2 predictability is determined by regions which also show the longest predictability horizon.…”

Section: Predictability Of Air‐land Co2 Fluxsupporting

confidence: 86%

“…Using a perfect‐model approach, we estimate the upper bound of ESM‐based predictability in the context of two metrics. We find the RMSE‐based predictability horizon of the global air‐sea and air‐land CO 2 flux to be 2 years (Li et al, ; Lovenduski, Bonan, et al, ; Lovenduski, Yeager, et al, ). While the ACC‐based 4‐ and 3‐year predictability horizons of the air‐sea and air‐land CO 2 flux signal are longer, RMSE‐based predictability illustrates the relevance of individual sinks and regions for the predictability of atmospheric CO 2 .…”

Section: Discussionmentioning

confidence: 91%

“…They find a higher prediction skill for Mauna Loa atmospheric CO 2 than a statistical ENSO regression and thereby indicate the added value of ESM‐based carbon cycle predictions. A very recent study finds 2‐year predictability in terrestrial net ecosystem production predictability, which is closely linked to terrestrial CO 2 flux predictability (Lovenduski, Bonan, et al, ).…”

Section: Introductionmentioning

confidence: 95%

“…Terrestrial carbon cycle based studies (Betts et al, ; Zeng et al, ) miss the oceanic carbon sink and multiyear lead times. (Oceanic) carbon cycle based predictability studies (Li et al, , ; Lovenduski, Yeager, et al, ; Lovenduski, Bonan, et al, ; Séférian et al, ) rest upon concentration‐driven simulations, in which atmospheric CO 2 mixing ratio is prescribed. In such simulations, air‐sea and air‐land CO 2 flux do not alter atmospheric CO 2 mixing ratio.…”

Section: Introductionmentioning

confidence: 99%

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Predictability Horizons in the Global Carbon Cycle Inferred From a Perfect‐Model Framework

Spring

Ilyina

2020

Geophysical Research Letters

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On interannual timescales the growth rate of atmospheric CO 2 is largely controlled by the response of the land and ocean carbon sinks to climate variability. Yet, it is unknown to what extent this variability limits the predictability of atmospheric CO 2 variations. Using perfect-model Earth System Model simulations, we show that variations in atmospheric CO 2 are potentially predictable for 3 years. We find a 2-year predictability horizon for global oceanic CO 2 flux with longer regional predictability of up to 7 years. The 2-year predictability horizon of terrestrial CO 2 flux originates in the tropics and midlatitudes. With the predictability of the isolated effects of land and ocean carbon sink on atmospheric CO 2 of 5 and 12 years respectively, land dampens the overall predictability of atmospheric CO 2 variations. Our research shows the potential of Earth System Model-based predictions to forecast multiyear variations in atmospheric CO 2 . Plain Language SummaryThe amount of anthropogenic carbon emissions absorbed by land and ocean from the atmosphere varies annually due to their sensitivity to climate. Therefore, the atmospheric CO 2 growth rate does not strictly follow the emissions signal. Whether decadal prediction systems can also predict variations of atmospheric CO 2 has not been shown yet but is crucial to inform policy makers about the efficiency of the implementation of the Paris Agreement. Using numerical Earth System simulations in an idealized prediction framework, we show that global atmospheric CO 2 is predictable up to 3 years in advance. The global ocean and land CO 2 fluxes are predictable for 2 years. The isolated effects of the land and ocean carbon sink on atmospheric CO 2 are predictable for 5 and 12 years, respectively. Therefore, the land carbon cycle limits atmospheric CO 2 predictability. Our study demonstrates that simulation-based multiyear forecasts have the potential to predict natural atmospheric CO 2 variations.

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Section: Predictability Of Air‐land Co2 Fluxsupporting

confidence: 86%

Section: Discussionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predictability Horizons in the Global Carbon Cycle Inferred From a Perfect‐Model Framework

Spring

Ilyina

2020

Geophysical Research Letters

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“…Each member was integrated forward from each initialization for 122 months, resulting in~26,000 global fully coupled simulation years, costing roughly 50 million core hours to compute. The atmosphere and land components were initialized from the November 1st restart files of a single arbitrary member of CESM-LE (ensemble member 34) 36 . The atmosphere component is the Community Atmosphere Model, version 5 (CAM5) with a finite-volume dynamical core at nominal 1°resolution and 30 vertical levels 21,37 .…”

Section: Model Simulations the Community Earth System Model Decadal mentioning

confidence: 99%

Skillful multiyear predictions of ocean acidification in the California Current System

et al. 2020

Self Cite

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The California Current System (CCS) sustains economically valuable fisheries and is particularly vulnerable to ocean acidification, due to its natural upwelling of carbon-enriched waters that generate corrosive conditions for local ecosystems. Here we use a novel suite of retrospective, initialized ensemble forecasts with an Earth system model (ESM) to predict the evolution of surface pH anomalies in the CCS. We show that the forecast system skillfully predicts observed surface pH variations a year in advance over a naive forecasting method, with the potential for skillful prediction up to five years in advance. Skillful predictions of surface pH are mainly derived from the initialization of dissolved inorganic carbon anomalies that are subsequently transported into the CCS. Our results demonstrate the potential for ESMs to provide skillful predictions of ocean acidification on large scales in the CCS. Initialized ESMs could also provide boundary conditions to improve high-resolution regional forecasting systems.

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Near‐term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability

Woelmer

Thomas

Lofton

et al. 2022

Ecological Applications

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As climate and land use increase the variability of many ecosystems, forecasts of ecological variables are needed to inform management and use of ecosystem services. In particular, forecasts of phytoplankton would be especially useful for drinking water management, as phytoplankton populations are exhibiting greater fluctuations due to human activities. While phytoplankton forecasts are increasing in number, many questions remain regarding the optimal model time step (the temporal frequency of the forecast model output), time horizon (the length of time into the future a prediction is made) for maximizing forecast performance, as well as what factors contribute to uncertainty in forecasts and their scalability among sites. To answer these questions, we developed near‐term, iterative forecasts of phytoplankton 1–14 days into the future using forecast models with three different time steps (daily, weekly, fortnightly), that included a full uncertainty partitioning analysis at two drinking water reservoirs. We found that forecast accuracy varies with model time step and forecast horizon, and that forecast models can outperform null estimates under most conditions. Weekly and fortnightly forecasts consistently outperformed daily forecasts at 7‐day and 14‐day horizons, a trend that increased up to the 14‐day forecast horizon. Importantly, our work suggests that forecast accuracy can be increased by matching the forecast model time step to the forecast horizon for which predictions are needed. We found that model process uncertainty was the primary source of uncertainty in our phytoplankton forecasts over the forecast period, but parameter uncertainty increased during phytoplankton blooms and when scaling the forecast model to a new site. Overall, our scalability analysis shows promising results that simple models can be transferred to produce forecasts at additional sites. Altogether, our study advances our understanding of how forecast model time step and forecast horizon influence the forecastability of phytoplankton dynamics in aquatic systems and adds to the growing body of work regarding the predictability of ecological systems broadly.

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High predictability of terrestrial carbon fluxes from an initialized decadal prediction system

Cited by 26 publications

References 33 publications

Predictability Horizons in the Global Carbon Cycle Inferred From a Perfect‐Model Framework

Predictability Horizons in the Global Carbon Cycle Inferred From a Perfect‐Model Framework

Skillful multiyear predictions of ocean acidification in the California Current System

Near‐term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability

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