scholarly journals Investigating the applicability of emergent constraints

2019 ◽  
Vol 10 (3) ◽  
pp. 501-523 ◽  
Author(s):  
Alexander J. Winkler ◽  
Ranga B. Myneni ◽  
Victor Brovkin
Keyword(s):  
Carbon Cycle ◽  
Observational Data ◽  
Temporal Variability ◽  
Co2 Concentration ◽  
Accurate Estimation ◽  
Area Index ◽  
High Latitude Region ◽  
Emergent Constraints ◽  
Data Source ◽  
Co2 Forcing

Abstract. Recent research on emergent constraints (ECs) has delivered promising results in narrowing down uncertainty in climate predictions. The method utilizes a measurable variable (predictor) from the recent historical past to obtain a constrained estimate of change in an entity of interest (predictand) at a potential future CO2 concentration (forcing) from multi-model projections. This procedure first critically depends on an accurate estimation of the predictor from observations and models and second on a robust relationship between inter-model variations in the predictor–predictand space. Here, we investigate issues related to these two themes in a carbon cycle case study using observed vegetation greening sensitivity to CO2 forcing as a predictor of change in photosynthesis (gross primary productivity, GPP) for a doubling of preindustrial CO2 concentration. Greening sensitivity is defined as changes in the annual maximum of green leaf area index (LAImax) per unit CO2 forcing realized through its radiative and fertilization effects. We first address the question of how to realistically characterize the predictor of a large area (e.g., greening sensitivity in the northern high-latitude region) from pixel-level data. This requires an investigation into uncertainties in the observational data source and an evaluation of the spatial and temporal variability in the predictor in both the data and model simulations. Second, the predictor–predictand relationship across the model ensemble depends on a strong coupling between the two variables, i.e., simultaneous changes in GPP and LAImax. This coupling depends in a complex manner on the magnitude (level), time rate of application (scenarios), and effects (radiative and/or fertilization) of CO2 forcing. We investigate how each one of these three aspects of forcing can affect the EC estimate of the predictand (ΔGPP). Our results show that uncertainties in the EC method primarily originate from a lack of predictor comparability between observations and models, the observational data source, and temporal variability of the predictor. The disagreement between models on the mechanistic behavior of the system under intensifying forcing limits the EC applicability. The discussed limitations and sources of uncertainty in the EC method go beyond carbon cycle research and are generally applicable in Earth system sciences.

scholarly journals Limitations of Emergent Constraints on Multi-Model Projections: Case Study of Constraining Vegetation Productivity With Observed Greening Sensitivity

2018 ◽  
Author(s):  
Alexander J. Winkler ◽  
Ranga B. Myneni ◽  
Victor Brovkin
Keyword(s):  
Co2 Concentration ◽  
Accurate Estimation ◽  
Spatial And Temporal Variability ◽  
Large Area ◽  
Area Index ◽  
Emergent Constraints ◽  
Data Source ◽  
Fertilization Effects ◽  
Historical Past ◽  
Co2 Forcing

Abstract. Recent research on Emergent Constraints (EC) has delivered promising results. The method utilizes a measurable variable (predictor) from the recent historical past to obtain a constrained estimate of change in a difficult-to-measure variable (predictand) at a potential future CO2 concentration (forcing) from multi-model projections. This procedure critically depends on, first, accurate estimation of the predictor from observations and models, and second, on a robust relationship between inter-model variations in the predictor-predictand space. We investigate issues related to these two themes in this article, using vegetation greening sensitivity to CO2 forcing during the satellite era as a predictor of change in Gross Primary Productivity (GPP) of the Northern High Latitudes region (60° N–90° N, NHL) for a doubling of pre-industrial CO2 concentration in the atmosphere. Greening sensitivity is defined as changes in annual maximum of green leaf area index (LAImax) per unit CO2 forcing realized through its radiative and fertilization effects. We first address the question of how to realistically characterize the greening sensitivity of a large area, the NHL, from pixel-level LAImax data. This requires an investigation into uncertainties in LAImax data source and an evaluation of the spatial and temporal variability in greening sensitivity to forcing in both the data and model simulations. Second, the relationship between greening sensitivity and ΔGPP across the model ensemble depends on a strong coupling among simultaneous changes in GPP and LAImax. This coupling depends in a complex manner on the magnitude (level), time-rate of application (scenarios) and effects (radiative and/or fertilization) of CO2 forcing. We investigate how each one of these three aspects of forcing can impair the EC estimate of the predictand (ΔGPP). Accounting for uncertainties in greening sensitivity and stability of the relation between inter-model variations results in a quantitative estimate of the uncertainty (±0.2 Pg C yr−1) on constrained GPP enhancement (ΔGPP = +3.4 Pg C yr−1) for a doubling of pre-industrial atmospheric CO2 concentration in NHL. This ΔGPP is 60 % larger than the conventionally used average of model projections. The illustrated sources of uncertainty and limitations of the EC method go beyond carbon cycle research and are generally relevant for Earth system sciences.

scholarly journals Estimation of nighttime ecosystem respiration over a paddy field in China

2010 ◽  
Vol 7 (1) ◽  
pp. 1201-1232 ◽  
Author(s):  
M. S. Hossen ◽  
T. Hiyama ◽  
H. Tanaka
Keyword(s):  
Paddy Field ◽  
Ecosystem Respiration ◽  
Co2 Flux ◽  
Co2 Concentration ◽  
Accurate Estimation ◽  
Concentration Profiles ◽  
Area Index ◽  
Actual Measurement ◽  
Paddy Ecosystem ◽  
Storage Change

Abstract. Accurate estimation of terrestrial ecosystem respiration is crucial for developing regional- to global-scale carbon budget databases. This study evaluated nighttime ecosystem respiration under low turbulence conditions at a paddy field in China during the 2004 growing season. Data from turbulent flux with storage change and alternatively from CO2 concentration profiles measured from the surface to 32 m height were investigated and compared. Conditions were separated into windy and calm using a friction velocity (u∗) threshold. On calm nights, the vertical gradient of CO2 concentration was higher near the canopy level and decreased with height. No differences were detected in terms of quantity and seasonality between the eddy covariance-observed nighttime ecosystem respiration (Re) and the alternatively calculated Re under calm conditions. Nighttime underestimation of paddy ecosystem respiration was low, even under calm conditions. Under stable atmospheric conditions, nighttime "loss" of CO2 flux may result mainly from CO2 being stored in air below the sensor height, and CO2 drainage loss could be small because advection is small. Because the addition of measurement-height storage change is preferable for reducing nighttime underestimation, u∗ filtering and low turbulence data elimination are not required for the paddy ecosystem. Alternatively, under low turbulence conditions, nighttime flux can be calculated from concentration profiles, but actual measurement of the nocturnal boundary layer height is very important. For gap-filling of nighttime CO2 flux data for a paddy ecosystem, development of multiple regression functions based on the crop biomass/leaf area index in association with field water status is preferable to a single regression function using air/soil temperature.

scholarly journals Millennium time-scale experiments on climate-carbon cycle with doubled CO2 concentration

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tomohiro Hajima ◽  
Akitomo Yamamoto ◽  
Michio Kawamiya ◽  
Xuanming Su ◽  
Michio Watanabe ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Time Scale ◽  
Co2 Concentration ◽  
Doubled Co2 Concentration

scholarly journals Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Plant Methods ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Shanjun Luo ◽  
Yingbin He ◽  
Qian Li ◽  
Weihua Jiao ◽  
Yaqiu Zhu ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Growth Stage ◽  
Estimation Error ◽  
Potato Yield ◽  
Hyperspectral Data ◽  
Single Stage ◽  
Accurate Estimation ◽  
Yield Estimation ◽  
Area Index ◽  
Red Edge

Abstract Background The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed. Results The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCIred edge) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R2 value of 0.8333, and the estimation error about 8%. Conclusion This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

scholarly journals Remote sensing and ecosystem modeling to simulate terrestrial carbon fluxes

2019 ◽  
pp. 87
Author(s):  
Sergio Sánchez-Ruiz
Keyword(s):  
Remote Sensing ◽  
Carbon Cycle ◽  
Spatial Scale ◽  
Forest Ecosystems ◽  
Carbon Fluxes ◽  
Daily Basis ◽  
Ecosystem Models ◽  
Validation Process ◽  
Data Source ◽  
Peninsular Spain

<p>The main goal of this thesis is the establishment of a framework to analyze the forest ecosystems in peninsular Spain in terms of their role in the carbon cycle. In particular, the carbon fluxes that they exchange with atmosphere are modeled to evaluate their potential as carbon sinks and biomass reservoirs. The assessment of gross and net carbon fluxes is performed at 1-km spatial scale and on a daily basis using two different ecosystem models, Monteith and BIOME-BGC, respectively. These models are driven by a combination of satellite and ground data, part of the latter being also employed as a complementary data source and in the validation process.</p>

scholarly journals Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6?

2020 ◽  
Vol 11 (4) ◽  
pp. 1233-1258
Author(s):  
Manuel Schlund ◽  
Axel Lauer ◽  
Pierre Gentine ◽  
Steven C. Sherwood ◽  
Veronika Eyring
Keyword(s):  
Climate Sensitivity ◽  
Statistical Significance ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Co2 Concentration ◽  
Intergovernmental Panel ◽  
Equilibrium Climate Sensitivity ◽  
Emergent Constraints ◽  
Statistical Relationships ◽  
Emergent Constraint

Abstract. An important metric for temperature projections is the equilibrium climate sensitivity (ECS), which is defined as the global mean surface air temperature change caused by a doubling of the atmospheric CO2 concentration. The range for ECS assessed by the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report is between 1.5 and 4.5 K and has not decreased over the last decades. Among other methods, emergent constraints are potentially promising approaches to reduce the range of ECS by combining observations and output from Earth System Models (ESMs). In this study, we systematically analyze 11 published emergent constraints on ECS that have mostly been derived from models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) project. These emergent constraints are – except for one that is based on temperature variability – all directly or indirectly based on cloud processes, which are the major source of spread in ECS among current models. The focus of the study is on testing if these emergent constraints hold for ESMs participating in the new Phase 6 (CMIP6). Since none of the emergent constraints considered here have been derived using the CMIP6 ensemble, CMIP6 can be used for cross-checking of the emergent constraints on a new model ensemble. The application of the emergent constraints to CMIP6 data shows a decrease in skill and statistical significance of the emergent relationship for nearly all constraints, with this decrease being large in many cases. Consequently, the size of the constrained ECS ranges (66 % confidence intervals) widens by 51 % on average in CMIP6 compared to CMIP5. This is likely because of changes in the representation of cloud processes from CMIP5 to CMIP6, but may in some cases also be due to spurious statistical relationships or a too small number of models in the ensemble that the emergent constraint was originally derived from. The emergently- constrained best estimates of ECS also increased from CMIP5 to CMIP6 by 12 % on average. This can be at least partly explained by the increased number of high-ECS (above 4.5 K) models in CMIP6 without a corresponding change in the constraint predictors, suggesting the emergence of new feedback processes rather than changes in strength of those previously dominant. Our results support previous studies concluding that emergent constraints should be based on an independently verifiable physical mechanism, and that process-based emergent constraints on ECS should rather be thought of as constraints for the process or feedback they are actually targeting.

scholarly journals Spatio-temporal variability of marine primary and export production in three global coupled climate carbon cycle models

2007 ◽  
Vol 4 (3) ◽  
pp. 1877-1921 ◽  
Author(s):  
B. Schneider ◽  
L. Bopp ◽  
M. Gehlen ◽  
J. Segschneider ◽  
T. L. Frölicher ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Temporal Variability ◽  
Satellite Observations ◽  
Future Climate Change ◽  
Nutrient Concentrations ◽  
Ocean Colour ◽  
Coupled Models ◽  
Low Latitude ◽  
Export Production ◽  
The Impact

Abstract. This study compares spatial and temporal variability in net primary productivity (PP) and particulate organic carbon (POC) export production (EP) from three different coupled climate carbon cycle models (IPSL, MPIM, NCAR) with observation-based estimates derived from satellite measurements of ocean colour and inverse modelling. Satellite observations of ocean colour have shown that temporal variability of PP on the global scale is largely dominated by the permanently stratified, low-latitude ocean (Behrenfeld et al., 2006)\\nocite{Behrenfeld06} with stronger stratification (higher SSTs) leading to negative PP anomalies and vice versa. Results from all three coupled models confirm the role of the low-latitude, permanently stratified ocean for global PP anomalies. Two of the models also reproduce the inverse relationship between stratification (SST) and PP, especially in the equatorial Pacific. With the help of the model results we are able to explain the chain of cause and effect leading from stratification (SST) through nutrient concentrations to PP and finally to EP. There are significant uncertainties in observational PP and especially EP. Our finding of a good agreement between independent estimates from coupled models and satellite observations provides increased confidence that such models can be used as a first basis to estimate the impact of future climate change on marine productivity and carbon export.

scholarly journals Twenty-First-Century Compatible CO2 Emissions and Airborne Fraction Simulated by CMIP5 Earth System Models under Four Representative Concentration Pathways

2013 ◽  
Vol 26 (13) ◽  
pp. 4398-4413 ◽  
Author(s):  
Chris Jones ◽  
Eddy Robertson ◽  
Vivek Arora ◽  
Pierre Friedlingstein ◽  
Elena Shevliakova ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Climate Models ◽  
Coupled Model ◽  
Co2 Concentration ◽  
Atmospheric Composition ◽  
System Component ◽  
Anthropogenic Emissions ◽  
Carbon Uptake ◽  
Earth System ◽  
Representative Concentration Pathways

Abstract The carbon cycle is a crucial Earth system component affecting climate and atmospheric composition. The response of natural carbon uptake to CO2 and climate change will determine anthropogenic emissions compatible with a target CO2 pathway. For phase 5 of the Coupled Model Intercomparison Project (CMIP5), four future representative concentration pathways (RCPs) have been generated by integrated assessment models (IAMs) and used as scenarios by state-of-the-art climate models, enabling quantification of compatible carbon emissions for the four scenarios by complex, process-based models. Here, the authors present results from 15 such Earth system GCMs for future changes in land and ocean carbon storage and the implications for anthropogenic emissions. The results are consistent with the underlying scenarios but show substantial model spread. Uncertainty in land carbon uptake due to differences among models is comparable with the spread across scenarios. Model estimates of historical fossil-fuel emissions agree well with reconstructions, and future projections for representative concentration pathway 2.6 (RCP2.6) and RCP4.5 are consistent with the IAMs. For high-end scenarios (RCP6.0 and RCP8.5), GCMs simulate smaller compatible emissions than the IAMs, indicating a larger climate–carbon cycle feedback in the GCMs in these scenarios. For the RCP2.6 mitigation scenario, an average reduction of 50% in emissions by 2050 from 1990 levels is required but with very large model spread (14%–96%). The models also disagree on both the requirement for sustained negative emissions to achieve the RCP2.6 CO2 concentration and the success of this scenario to restrict global warming below 2°C. All models agree that the future airborne fraction depends strongly on the emissions profile with higher airborne fraction for higher emissions scenarios.

scholarly journals Quantifying Carbon Cycle Feedbacks

2009 ◽  
Vol 22 (19) ◽  
pp. 5232-5250 ◽  
Author(s):  
J. M. Gregory ◽  
C. D. Jones ◽  
P. Cadule ◽  
P. Friedlingstein
Keyword(s):  
Carbon Cycle ◽  
Co2 Emissions ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Climate Feedback ◽  
Climate Response ◽  
Carbon Cycle Model ◽  
Atmospheric Co2 Concentration ◽  
Intercomparison Project ◽  
Earth System Models

Abstract Perturbations to the carbon cycle could constitute large feedbacks on future changes in atmospheric CO2 concentration and climate. This paper demonstrates how carbon cycle feedback can be expressed in formally similar ways to climate feedback, and thus compares their magnitudes. The carbon cycle gives rise to two climate feedback terms: the concentration–carbon feedback, resulting from the uptake of carbon by land and ocean as a biogeochemical response to the atmospheric CO2 concentration, and the climate–carbon feedback, resulting from the effect of climate change on carbon fluxes. In the earth system models of the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP), climate–carbon feedback on warming is positive and of a similar size to the cloud feedback. The concentration–carbon feedback is negative; it has generally received less attention in the literature, but in magnitude it is 4 times larger than the climate–carbon feedback and more uncertain. The concentration–carbon feedback is the dominant uncertainty in the allowable CO2 emissions that are consistent with a given CO2 concentration scenario. In modeling the climate response to a scenario of CO2 emissions, the net carbon cycle feedback is of comparable size and uncertainty to the noncarbon–climate response. To quantify simulated carbon cycle feedbacks satisfactorily, a radiatively coupled experiment is needed, in addition to the fully coupled and biogeochemically coupled experiments, which are referred to as coupled and uncoupled in C4MIP. The concentration–carbon and climate–carbon feedbacks do not combine linearly, and the concentration–carbon feedback is dependent on scenario and time.

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