Dataset Information

Impact of hindcast length on estimates of seasonal climate predictability.

ABSTRACT: It has recently been argued that single-model seasonal forecast ensembles are overdispersive, implying that the real world is more predictable than indicated by estimates of so-called perfect model predictability, particularly over the North Atlantic. However, such estimates are based on relatively short forecast data sets comprising just 20 years of seasonal predictions. Here we study longer 40 year seasonal forecast data sets from multimodel seasonal forecast ensemble projects and show that sampling uncertainty due to the length of the hindcast periods is large. The skill of forecasting the North Atlantic Oscillation during winter varies within the 40 year data sets with high levels of skill found for some subperiods. It is demonstrated that while 20 year estimates of seasonal reliability can show evidence of overdispersive behavior, the 40 year estimates are more stable and show no evidence of overdispersion. Instead, the predominant feature on these longer time scales is underdispersion, particularly in the tropics.

Key points

Predictions can appear overdispersive due to hindcast length sampling errorLonger hindcasts are more robust and underdispersive, especially in the tropicsTwenty hindcasts are an inadequate sample size to assess seasonal forecast skill.

SUBMITTER: Shi W

PROVIDER: S-EPMC4459196 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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Publications

Impact of hindcast length on estimates of seasonal climate predictability.

Shi W W Schaller N N MacLeod D D Palmer T N TN Weisheimer A A

Geophysical research letters 20150312 5

It has recently been argued that single-model seasonal forecast ensembles are overdispersive, implying that the real world is more predictable than indicated by estimates of so-called perfect model predictability, particularly over the North Atlantic. However, such estimates are based on relatively short forecast data sets comprising just 20 years of seasonal predictions. Here we study longer 40 year seasonal forecast data sets from multimodel seasonal forecast ensemble projects and show that sa ...[more]

PMID: 26074651

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Project description:ContextPhotosynthetic stimulations have shown promising outcomes in improving crop photosynthesis, including soybean. However, it is still unclear to what extent these changes can impact photosynthetic assimilation and yield under long-term field climate conditions.ObjectiveIn this paper, we present a systematic evaluation of the response of canopy photosynthesis and yield to two critical parameters in leaf photosynthesis: the maximum carboxylation rate of ribulose-1,5-bisphosphate carboxylase/oxygenase (Vcmax) and the maximum electron transport of the ribulose-1,5-bisphosphate regeneration rate (Jmax).MethodsUsing the field-scale crop model Soybean-BioCro and ten years of observed climate data in Urbana, Illinois, U.S., we conducted sensitivity experiments to estimate the changes in canopy photosynthesis, leaf area index, and biomass due to the changes in Vcmax and Jmax.ResultsThe results show that 1) Both the canopy photosynthetic assimilation (An) and pod biomass yields were more sensitive to the changes in Jmax, particularly at high atmospheric carbon-dioxide concentrations ([CO2]); 2) Higher [CO2] undermined the effectiveness of increasing the two parameters to improve An and yield; 3) Under the same [CO2], canopy light interception and canopy respiration were key factors that undermined improvements in An and yield; 4) A canopy with smaller leaf area index tended to have a higher yield improvement, and 5) Increases in assimilations and yields were highly dependent on growing-season climatic conditions. The solar radiation, temperature, and relative humidity were the main climate drivers that impacted the yield improvement, and they had opposite correlations with improved yield during the vegetative phase compared to the reproductive phase.ConclusionsIn a world with elevated [CO2], genetic engineering crop photosynthesis should focus more on improving Jmax. Further, long-term climate conditions and seasonal variations must be considered to determine the improvements in soybean canopy photosynthesis and yield at the field scale.ImplicationsQuantifying the effectiveness of changing Vcmax and Jmax helps understand their individual and combined contributions to potential improvements in assimilation and yield. This work provides a framework for evaluating how altering the photosynthetic rate parameters impacts soybean yield and assimilation under different seasonal climate scenarios at the field scale.

Dataset Information

Impact of hindcast length on estimates of seasonal climate predictability.

Key points

Publications

Impact of hindcast length on estimates of seasonal climate predictability.

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