# Paleo-pCO2 Database Miocene pCO2 Reconstruction Data Derived from Leaf Gas-Exchange Models #----------------------------------------------------------------------- # World Data Service for Paleoclimatology, Boulder # and # NOAA Paleoclimatology Program #----------------------------------------------------------------------- # Template Version 4.0 # Encoding: UTF-8 # NOTE: Please cite original publication, NOAA Landing Page URL, dataset and publication DOIs (where available), and date accessed when using downloaded data. # If there is no publication information, please cite investigator, study title, NOAA Landing Page URL, and date accessed. # # Description/Documentation lines begin with '#' followed by a space # Data lines have no '#' # # NOAA_Landing_Page: https://www.ncdc.noaa.gov/access/paleo-search/study/36233 # Landing_Page_Description: NOAA Landing Page of this file's parent study, which includes all study metadata. # # Study_Level_JSON_Metadata: https://www.ncei.noaa.gov/pub/data/metadata/published/paleo/json/noaa-recon-36233.json # Study_Level_JSON_Description: JSON metadata of this data file's parent study, which includes all study metadata. # # Data_Type: Climate Forcing # # Dataset_DOI: # # Science_Keywords: Atmospheric Gas Reconstruction #--------------------------------------- # Resource_Links # # https://www.ncei.noaa.gov/pub/data/paleo/climate_forcing/trace_gases/Paleo-pCO2/liang2022-latahp-33-franks.txt # Data_Download_Description: NOAA Template File; Franks Method Data # #--------------------------------------- # Contribution_Date # Date: 2022-03-30 #--------------------------------------- # File_Last_Modified_Date # Date: 2022-04-01 #--------------------------------------- # Title # Study_Name: Paleo-pCO2 Database Miocene pCO2 Reconstruction Data Derived from Leaf Gas-Exchange Models #--------------------------------------- # Investigators # Investigators: Liang, Jia-Qi; Leng, Qin; Höfig, Daianne.; Niu, Gao; Wang, Li; Royer, Dana; Burke, Kevin; Xiao, Liang; Zhang, Yi; Yang, Hong #--------------------------------------- # Description_Notes_and_Keywords # Description: #--------------------------------------- # Publication # Authors: Liang, J., Leng, Q., Höfig, D. F., Niu, G., Wang, L., Royer, D. L., Burke, K., Xiao, L., Zhang, Y., and Yang, H. # Published_Date_or_Year: 2022 # Published_Title: Constraining conifer physiological parameters in leaf gas-exchange models for ancient CO2 reconstruction # Journal_Name: Global and Planetary Change # Volume: 209 # Edition: # Issue: 103737 # Pages: # Report_Number: # DOI: 10.1016/j.gloplacha.2022.103737 # Online_Resource: # Full_Citation: # Abstract: Leaf gas-exchange models are increasingly used to reconstruct ancient atmospheric carbon dioxide (CO2) concentrations. One of these widely used models, the Franks model, requires stomatal size (guard cell width and either guard cell length or pore length), whole-leaf stomatal density, and bulk-leaf carbon isotope composition (d13C) from plant fossils. However, natural variations of these parameters within and across plant leaves have not been assessed closely, hindering the application of this model and the evaluation of its associated uncertainties. Here we investigate the range of variations of these parameters, and evaluate their impact on the output of the Franks model in three conifers (Metasequoia, Sequoia, and Taxodium). We introduce a modified cleared leaf method that allows accurate measurements of stomatal size. We show that among the stomatal size parameters, pore length is the most variable. Whole-leaf stomatal density can be accurately estimated in a representative area in the middle portion of a leaf. Variations of d13C values are only slightly above analytical errors within a leaf and between leaves from a branchlet, but a ~ 1‰ negative shift of d13C during early decay of Metasequoia leaf tissues was observed. Our measured ranges in pore length and whole-leaf stomatal density have the biggest influence on model estimated CO2. To improve model performance, we recommend (1) the use of our modified cleared leaf method to acquire accurate stomatal size and whole-leaf stomatal density measurements from the middle portion of a leaf located at the middle portion of a branchlet; (2) scaling pore length from guard cell length; and (3) a systematic correction of carbon isotope fractionation may be applicable if information regarding tissue decay and fossil preservation is available. We tested our recommendations by reconstructing CO2 from both extant and fossil materials. Franks model-derived CO2 based upon modern leaves collected in 2004 and 2020 (346 and 416 ppm) are close to their targets (378 and 414 ppm) whereas stomatal frequency methods substantially underestimate (285 and 341 ppm). Reconstructed CO2 from the middle Miocene Clarkia deposit (505 and 507 ppm for Metasequoia and Taxodium) are comparable with published results. We conclude that an improved cleared leaf method for accurate measurements of key stomatal parameters and a statistically-informed stomatal counting strategy will improve the performance of the Franks model for reconstructing CO2 using these conifers with wide distributions of fossil records in the Northern Hemisphere since the Cretaceous. #--------------------------------------- # Funding_Agency # Funding_Agency_Name: # Grant: #--------------------------------------- # Site_Information # Site_Name: Clarkia_Latah P-33 # Location: Idaho # Northernmost_Latitude: 47 # Southernmost_Latitude: 47 # Easternmost_Longitude: -116.3 # Westernmost_Longitude: -116.3 # Elevation_m: #--------------------------------------- # Data_Collection # Collection_Name: Latah P-33 - franks - Liang2022 # First_Year: 15780000 # Last_Year: 15780000 # Time_Unit: calendar year before present # Core_Length_m: # Parameter_Keywords: carbon dioxide # Notes: reconstruction uses the stomata-franks method; deviation in the molar abundance ratio of carbon isotopes (13C/12C) in (paleo-)atmosphere air relative to that in the PDB standard (per mil). For Cenozoic material, the analysis of Tipple et al. (2010; 10.1029/2009PA001851) is helpful. error in d13Ca. For Cenozoic material, the analysis of Tipple et al. (2010; 10.1029/2009PA001851) is helpful. sample size and/or description for calculating d13Ca. atmospheric CO2 concentration associated with A0 (ppm) (e.g., present-day value); this variable is assumed to have no error. If fixed_A is set to 'yes', this variable is not used. net photosynthetic rate at CO2_0 (umol/m2/s) for the mean or typical leaf temperature during the growing season (see "temp" column for leaf temperature; if leaf temperature is unknown, Franks et al., 2014 recommend a nominal value of 25 oC for temperate to tropical environments). A0 can be measured on a fossil's nearest living relative; alternatively, see Franks et al. (2014) for some generic mean values for broad taxonomic groups. If "fixed_A" is set to yes, the value of A0 should be set to the known net photosynthetic rate. error in A0. sample size and/or description for calculating A0. present-day Ci/Ca value that is used to calculate An from CO2. This can be estimated from gas exchange or carbon isotope measurements on a living relative, or a typical value can be used (e.g. 0.65). If "fixed_A" is set to 'yes', this variable is not used. error in CiCa0. If "fixed_A" is set to 'yes', this variable is not used. sample size and/or description for calculating CiCa0. boundary layer conductance to CO2 (mol/m2/s). Franks et al. (2014) suggests a generic value of 2 for typical conditions. error in gb. sample size and/or description for calculating gb. scaling from guard cell length (GCL) to stomatal pore length (Pl). See Table S2 in Franks et al. (2014) for some generic scalings. s1 is equivalent to the term alpha in Table S2. error in s1. sample size and/or description for calculating s1. scaling from single guard cell width (GCW) to stomatal depth (l). In the typical case where guard cells have a circular cross-section, this scaling = 1. error in s2. sample size and/or description for calculating s2. scaling from the area of a circle with the diameter of pore length to a_max (maximum area of stomatal pore). See Table S2 in Franks et al. (2014) for some generic scalings. s3 is equivalent to beta in Table S2. error in s3 sample size and/or description for calculating s3. scaling from maximum conductance to CO2 (gcmax) to operational conductance to CO2 (gcop). This can be measured on a fossil's nearest living relative. Alternatively, Franks et al. (2014) suggests a generic scaling of 0.2. s4 is equivalent to zeta in Equation 2 and Table S1 of Franks et al. (2014). error in s4. sample size and/or description for calculating s4. scaling from photosynthetic rate (An) to mesophyll conductance to CO2 (gm). Franks et al. (2014) suggests a generic scaling of 0.013. error in s5. sample size and/or description for calculating s5. If set to 'yes', the code assumes a fixed photosynthetic rate equal to A0 and solves only for CO2, using the first main equation (i.e. Eq. 1 in Franks et al., 2014). That is, An is set equal to A0. This option can be used if An is known independently, for example measured in living plants to run the model for present-day or growth-chamber conditions. Otherwise, the code jointly solves for CO2 and An by iteratively solving the two main equations given in Franks et al. (2014) and Kowalczyk et al. (2018). discrimination against 13C due to carboxylation, mainly due to Rubisco (per mil); no error is assumed ratio of diffusivity of water vapor in air to the molar volume of air at the leaf temperature (mol m-1 s-1); see "temp" column for leaf temperature; no error is assumed CO2 compensation point at leaf temperature (ppm); see "temp" column for leaf temperature; no error is assumed. If unknown, Franks et al. (2014) recommend a nominal value of 40 ppm. mean or typical leaf temperature during the growing season (oC). This is effectively fixed in version 1 of the model, i.e., the leaf temperature for A0 and An are the same. No adjustment is made for possible feedback effects of changes in atmospheric temperature on leaf temperature and photosynthetic rate associated with changes in global atmospheric CO2 concentration. This follows observations that across a broad environmental temperature range leaf temperature during photosynthesis converges on a narrow band of values (Helliker and Richter, 2008, doi:10.1038/nature07031; Song et al. 2011, doi:10.1111/j.1469-8137.2011.03851.x). If unknown, for temperate to tropical environments (where many fossils originate) Franks et al. (2014) recommend a nominal value of 25 oC. Assignment of A0 should correspond with this temperature. INPUT PARAMETERS (see Franks et al., 2014 and Kowalczyk et al., 2018 for details): The "_err" input should be +/-1 standard error of the mean. If this value is unknown, the error can be estimated as some fraction of the mean (e.g., 5%). #--------------------------------------- # Chronology_Information # Chronology: #--------------------------------------- # Variables # PaST_Thesaurus_Download_Resource: https://www.ncei.noaa.gov/access/paleo-search/skos/past-thesaurus.rdf # PaST_Thesaurus_Download_Description: Paleoenvironmental Standard Terms (PaST) Thesaurus terms, definitions, and relationships in SKOS format. # # Data variables follow that are preceded by "##" in columns one and two. # Variables list, one per line, shortname-tab-var components: what, material, error, units, seasonality, data type, detail, method, C or N for Character or Numeric data) # #------------------------ # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing_Values: NA d13Ca ed13Ca N_ed13Ca CO2_0 A0 eA0 N_eA0 CiCa0 eCiCa0 N_eCiCa0 gb egb N_egb s1 es1 N_es1 s2 es2 N_es2 s3 es3 N_es3 s4 es4 N_es4 s5 es5 N_es5 fixed_A b d.v. gamma temp -5.05 1.21 Isotope model of Tipple et al. (2010; 10.1029/2009PA001851) NA 10 0.5 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.64 0 from Table S1 in Kowalczyk et al. (2018) for "deciduous gymnosperms in an evergreen warm mixed forest (EWMF) biome" 2 0.1 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.6 0 generic value from Franks et al. (2014) 1 0.05 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.5 0.025 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.21 0.02 value from Franks et al. (2014; their Table S1) 0.013 0.00065 generic value from Franks et al. (2014); 1 sigma error is 5% of mean yes 30 0.000940096 40 25 -5.05 1.21 Isotope model of Tipple et al. (2010; 10.1029/2009PA001851) NA 10 0.5 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.64 0 from Table S1 in Kowalczyk et al. (2018) for "deciduous gymnosperms in an evergreen warm mixed forest (EWMF) biome" 2 0.1 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.6 0 generic value from Franks et al. (2014) 1 0.05 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.5 0.025 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.21 0.02 value from Franks et al. (2014; their Table S1) 0.013 0.00065 generic value from Franks et al. (2014); 1 sigma error is 5% of mean yes 30 0.000940096 40 25 -5.05 1.21 Isotope model of Tipple et al. (2010; 10.1029/2009PA001851) NA 10 0.5 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.64 0 from Table S1 in Kowalczyk et al. (2018) for "deciduous gymnosperms in an evergreen warm mixed forest (EWMF) biome" 2 0.1 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.6 0 generic value from Franks et al. (2014) 1 0.05 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.5 0.025 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.21 0.02 value from Franks et al. (2014; their Table S1) 0.013 0.00065 generic value from Franks et al. (2014); 1 sigma error is 5% of mean yes 30 0.000940096 40 25 -5.05 1.21 Isotope model of Tipple et al. (2010; 10.1029/2009PA001851) NA 10 0.5 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.64 0 from Table S1 in Kowalczyk et al. (2018) for "deciduous gymnosperms in an evergreen warm mixed forest (EWMF) biome" 2 0.1 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.6 0 generic value from Franks et al. (2014) 1 0.05 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.5 0.025 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.21 0.02 value from Franks et al. (2014; their Table S1) 0.013 0.00065 generic value from Franks et al. (2014); 1 sigma error is 5% of mean yes 30 0.000940096 40 25 -5.05 1.21 Isotope model of Tipple et al. (2010; 10.1029/2009PA001851) NA 10 0.5 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.64 0 from Table S1 in Kowalczyk et al. (2018) for "deciduous gymnosperms in an evergreen warm mixed forest (EWMF) biome" 2 0.1 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.6 0 generic value from Franks et al. (2014) 1 0.05 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.5 0.025 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.21 0.02 value from Franks et al. (2014; their Table S1) 0.013 0.00065 generic value from Franks et al. (2014); 1 sigma error is 5% of mean yes 30 0.000940096 40 25 -5.05 1.21 Isotope model of Tipple et al. (2010; 10.1029/2009PA001851) NA 10 0.5 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.64 0 from Table S1 in Kowalczyk et al. (2018) for "deciduous gymnosperms in an evergreen warm mixed forest (EWMF) biome" 2 0.1 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.6 0 generic value from Franks et al. (2014) 1 0.05 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.5 0.025 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.21 0.02 value from Franks et al. (2014; their Table S1) 0.013 0.00065 generic value from Franks et al. (2014); 1 sigma error is 5% of mean yes 30 0.000940096 40 25 -5.05 1.21 Isotope model of Tipple et al. (2010; 10.1029/2009PA001851) NA 10 0.5 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.64 0 from Table S1 in Kowalczyk et al. (2018) for "deciduous gymnosperms in an evergreen warm mixed forest (EWMF) biome" 2 0.1 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.6 0 generic value from Franks et al. (2014) 1 0.05 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.5 0.025 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.21 0.02 value from Franks et al. (2014; their Table S1) 0.013 0.00065 generic value from Franks et al. (2014); 1 sigma error is 5% of mean yes 30 0.000940096 40 25 -5.05 1.21 Isotope model of Tipple et al. (2010; 10.1029/2009PA001851) NA 10 0.5 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.64 0 from Table S1 in Kowalczyk et al. (2018) for "deciduous gymnosperms in an evergreen warm mixed forest (EWMF) biome" 2 0.1 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.6 0 generic value from Franks et al. (2014) 1 0.05 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.5 0.025 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.21 0.02 value from Franks et al. (2014; their Table S1) 0.013 0.00065 generic value from Franks et al. (2014); 1 sigma error is 5% of mean yes 30 0.000940096 40 25 -5.05 1.21 Isotope model of Tipple et al. (2010; 10.1029/2009PA001851) NA 10 0.5 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.64 0 from Table S1 in Kowalczyk et al. (2018) for "deciduous gymnosperms in an evergreen warm mixed forest (EWMF) biome" 2 0.1 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.6 0 generic value from Franks et al. (2014) 1 0.05 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.5 0.025 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.21 0.02 value from Franks et al. (2014; their Table S1) 0.013 0.00065 generic value from Franks et al. (2014); 1 sigma error is 5% of mean yes 30 0.000940096 40 25 -5.05 1.21 Isotope model of Tipple et al. (2010; 10.1029/2009PA001851) NA 10 0.5 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.64 0 from Table S1 in Kowalczyk et al. (2018) for "deciduous gymnosperms in an evergreen warm mixed forest (EWMF) biome" 2 0.1 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.6 0 generic value from Franks et al. (2014) 1 0.05 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.5 0.025 generic value from Franks et al. (2014); 1 sigma error is 5% of mean 0.21 0.02 value from Franks et al. (2014; their Table S1) 0.013 0.00065 generic value from Franks et al. (2014); 1 sigma error is 5% of mean yes 30 0.000940096 40 25