# North America and Europe Holocene Pollen Climate Reconstructions #----------------------------------------------------------------------- # World Data Service for Paleoclimatology, Boulder # and # NOAA Paleoclimatology Program # National Centers for Environmental Information (NCEI) #----------------------------------------------------------------------- # Template Version 3.0 # Encoding: UTF-8 # NOTE: Please cite Publication, and Online_Resource and date accessed when using these data. # If there is no publication information, please cite Investigators, Title, and Online_Resource and date accessed. # # Online_Resource: https://www.ncdc.noaa.gov/paleo/study/22992 # Description: NOAA Landing Page # Online_Resource: https://www1.ncdc.noaa.gov/pub/data/paleo/reconstructions/marsicek2018/readme-marsicek2018.txt # Description: NOAA location of the template # # Original_Source_URL: # Description: # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Climate Reconstructions # # Dataset DOI: # # Parameter_Keywords: air temperature #-------------------- # Contribution_Date # Date: 2018-02-01 #-------------------- # File_Last_Modified_Date # Date: 2018-02-01 #-------------------- # Title # Study_Name: North America and Europe Holocene Pollen Climate Reconstructions #-------------------- # Investigators # Investigators: Marsicek, J.; Shuman, B.N.; Bartlein, P.J.; Shafer, S.L.; Brewer, S. #-------------------- # Description_Notes_and_Keywords # Description: Seasonal and annual temperature reconstructions for Europe and North America interpolated to 100-yr intervals # from 11,000-0 BP, including both site-level reconstructions with measures of reconstruction significance and gridded reconstructions # produced on a 2 x 2 degree latitude-longitude grid from the significant site-level data. Files also include the mean of the # marine margin temperature reconstructions from our region used by Marcott et al. (2013), and detrended time series used to examine # millennial-scale variations. These files represent the inputs for source code used to carry out the analyses in R. # A read-me file explains the steps in the reconstruction process. # # Variables in csv files: # # 1. Significant Site file variables: # a. A: nothing; # b. B: variable = Site ID number; # c. C: age.x = Interpolated Age (relative to 1950 CE); # d. D: value = variable anomaly value; # e. E: sig.site.id = palaeoSig site ID number (SI Table 2); # f. F: age.y = top age from that site; # g. G: depth = top depth from that site; # h. H: gdd5 = average GDD5 value for 1450-1950 CE; # i. I: gdd5.sig = palaeoSig value for that site; # j. J: ent = Site name; # k. K: lat = latitude for that site; # l. L: long = longitude for that site # # 2. Gridded file variables: # a. A: nothing; # b. B: V1 = Interpolated Age; # c. C: V2 = grid cell latitude; # d. D: V3 = grid cell longitude; # e. E: V4 = grid cell variable anomaly value # # 3. TemperatureReconstructions2.csv variables (this file represents the inputs for the wavelet and decomposition analyses): # a. A: Age = Interpolated Age; # b. B: Marcott.MarsicekDomain = marine synthesis reconstruction values as anomalies relative to the mean of 1450-1950 AD; # c. C: NH.tave = continental reconstruction values as anomalies relative to the mean of 1450-1950 AD; # d. D: NH.MTCO.sig = mean temperature of the coldest month reconstruction values as anomalies relative to the mean of 1450-1950 AD; # e. E: NH.GDD5.sig = growing degree days above a 5 degree Celsius base as anomalies relative to the mean of 1450-1950 AD; # f. F: Eur.tave.Sig = European continental reconstruction values as anomalies relative to the mean of 1450-1950 AD; # g. G: NA.tave.Sig = North American continental reconstruction values as anomalies relative to the mean of 1450-1950 AD # # 4. Residual file variables: # a. NH_tAvgRecon_Residuals.csv # i. A: Age = Interpolated Age; # ii. B: Marcott.NH.resid = centennial mean temperature values from the marine synthesis; # iii. C: Mars.NH.resid = centennial mean temperature values from continental reconstruction # b. Europe_NA_tAvgRecon_Residuals.csv # i. A: Age = Interpolated Age; # ii. B: Mars.NA.resid = centennial mean temperature values from North American reconstruction; # C: centennial mean temperature values from European reconstruction # # # How to generate the gridded North American and European Pollen-based Climate Reconstructions # # This explains how we generated our climate reconstructions based on modern and fossil pollen data for North America and Europe over the last 11,000 years. Steps 1-3 contain information about where we acquired the data, and analyses we did using publicly available datasets and free source code available through R. Steps 4-6 contain data files and code we generated to carry out the rest of our analyses. The data and code products here include the gridding code, the gridded climate reconstructions, the uncertainty analysis code applied to the gridded climate reconstructions, as well as the time series decomposition code used to generate reconstructions of millennial variability over the last 11,000 years. # # 1) First, we gathered the publicly available fossil pollen datasets for North America and Europe from the Neotoma Paleoecology Database (www.neotomadb.org) and the European Pollen Databases (http://www.europeanpollendatabase.net), respectively. Note, we extracted the data in 2013, so if sites were added afterwards, they will not show up in our list. We also gathered the North American and European modern pollen and climate datasets (Whitmore et al., 2005; Davis et al., 2013) in order to carry out the modern analog technique. The taxa lists for each continent can be found in Marsicek et al., 2018 Supplementary Table 1. # # 2) Next, we tested the records for significance by running the modern and fossil pollen datasets (the inputs), for each continent separately, using the 'randomTF' function in the palaeoSig package in R (Telford and Trachsel, 2015). We carried out the analysis for mean annual temperature (AnnT), gdd5 (growing degree days above a 5C base), and mean temperature of the coldest month (MTCO). # # 3) Our analysis used 0.159 as the cutoff for significant site-level reconstructions to be included in the analysis. Once significant sites were identified, we interpolated the individual site level reconstructions (output from palaeoSig) to 100-year time steps spanning 11,000 - 0 years before 1950 CE. We then calculated anomalies for each site relative to the average value for the last 500 years (500 - 0 years before 1950 CE). This step reduces the number of sites with data over the course of the last 11,000 years because not all sites have data between 500 and 0 years before 1950 CE. The files containing the temporally interpolated reconstructions for each site are named "NHSignificantSites_tAvg.csv", "NHSignificantSites_GDD5.csv", and "NHSignificantSites_MTCO.csv". # # 4) We gridded the reconstructions in a 2 x 2 degree format using 'GriddingCode_Nature_Marsicek.R'. The significant site files from Step 3 can be read into and run through the gridding code. Running this code takes 1-1.5 hours for each file, we named these files, "NH_tAvg_Gridded.csv", "NH_GDD5_Gridded.csv", "NH_MTCO_Gridded.csv". # # 5) After gridding the data, we ran the gridded files from Step 4 through the 'UncertaintyAnalysisCode_Nature_Marsicek.R' code. This code will generate a set of 100 temperature iterations of the variable of interest by random re-sampling from the gridded temperatures at each time step (with noise added based on the root-mean squared error of our modern calibration). The code outputs the interpolated ages with the 2.5, 50, and 97.5% quantiles of the 100 iterations of the mean temperatures. These are the reconstructions and uncertainties reported in the paper as the mean for the two continents, and were used to carry out additional analyses presented in the paper, such as Wavelet and Decomposition analyses (found in Main Text Figure 4; Extended Data Figures 1, 7, and 8). Because the code will not generate the same 100 iterations of the reconstruction of the variable of interest, no two reconstructions will look exactly the same. # # 6) Finally, we detrended the marine synthesis and AnnT reconstructions to examine millennial and sub-millennial features of the reconstructions found in Figs. 1 and 2 (seen in Main Text Fig. 4), as well as the continents individually (Extended Data Figure 7). Files named "NH_tAvgRecon_Residuals.csv", "Europe_NA_tAvgRecon_Residuals.csv", and "TemperatureReconstructions2.csv" were run through the wavelet (WaveletAnalysis_Temperatures_Marsicek.R) and time series decomposition (TimeSeries_Decomposition_Marsicek.R) code sets. # # #-------------------- # Publication # Authors: Jeremiah Marsicek, Bryan N. Shuman, Patrick J. Bartlein, Sarah L. Shafer, Simon Brewer # Published_Date_or_Year: 2018-02-01 # Published_Title: Reconciling divergent trends and millennial variations in Holocene temperatures # Journal_Name: Nature # Volume: 554 # Edition: # Issue: # Pages: 92-96 # Report_Number: # DOI: 10.1038/nature25464 # Online_Resource: https://www.nature.com/articles/nature25464 # Full_Citation: # Abstract: Cooling during most of the past two millennia has been widely recognized and has been inferred to be the dominant global temperature trend of the past 11,700 years (the Holocene epoch). However, long-term cooling has been difficult to reconcile with global forcing, and climate models consistently simulate long-term warming. The divergence between simulations and reconstructions emerges primarily for northern mid-latitudes, for which pronounced cooling has been inferred from marine and coastal records using multiple approaches. Here we show that temperatures reconstructed from sub-fossil pollen from 642 sites across North America and Europe closely match simulations, and that long-term warming, not cooling, defined the Holocene until around 2,000 years ago. The reconstructions indicate that evidence of long-term cooling was limited to North Atlantic records. Early Holocene temperatures on the continents were more than two degrees Celsius below those of the past two millennia, consistent with the simulated effects of remnant ice sheets in the climate model Community Climate System Model 3 (CCSM3). CCSM3 simulates increases in 'growing degree days' - a measure of the accumulated warmth above five degrees Celsius per year - of more than 300 kelvin days over the Holocene, consistent with inferences from the pollen data. It also simulates a decrease in mean summer temperatures of more than two degrees Celsius, which correlates with reconstructed marine trends and highlights the potential importance of the different subseasonal sensitivities of the records. Despite the differing trends, pollen- and marine-based reconstructions are correlated at millennial-to-centennial scales, probably in response to ice-sheet and meltwater dynamics, and to stochastic dynamics similar to the temperature variations produced by CCSM. Although our results depend on a single source of palaeoclimatic data (pollen) and a single climate-model simulation, they reinforce the notion that climate models can adequately simulate climates for periods other than the present-day. They also demonstrate that amplified warming in recent decades increased temperatures above the mean of any century during the past 11,000 years. #------------------ # Funding_Agency # Funding_Agency_Name: Wyoming US NASA Space Grant # Grant: NNX10AO95H #------------------ # Funding_Agency # Funding_Agency_Name: US Environmental Protection Agency # Grant: STAR FP-91763201-0 #------------------ # Funding_Agency # Funding_Agency_Name: US National Science Foundation # Grant: DEB-1146297, EAR-1003848, ATM-06202409 #------------------ # Funding_Agency # Funding_Agency_Name: US Geological Survey # Grant: Climate Research and Development Program #------------------ # Site_Information # Site_Name: North America and Europe # Location: Geographic Region>Northern Hemisphere # Country: # Northernmost_Latitude: 80.0 # Southernmost_Latitude: 16.0 # Easternmost_Longitude: 60.0 # Westernmost_Longitude: -170.0 # Elevation: #------------------ # Data_Collection # Collection_Name: Marsicek2018 # Earliest_Year: 11000 # Most_Recent_Year: 0 # Time_Unit: Cal. Year BP # Core_Length: # Notes: #------------------ # Chronology_Information # Chronology: # #---------------- # Variables # # Data variables follow are preceded by "##" in columns one and two. # Data line variables format: one per line, shortname-tab-variable components (what, material, error, units, seasonality, data type,detail, method, C or N for Character or Numeric data, free text) # ## age_calBP age, , , calendar years before present, , , , ,N, ## temp air temperature, , , degrees C, ,climate reconstructions,,,N, # #---------------- # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing Values: # age_calBP temp