PalEON: Terrestrial Ecosystem Model Outputs for the Northeastern U.S., 0850-2010

This dataset from the PalEON (Paleo-Ecological Observatory Network) Ecosystem Model Intercomparison Project (PEMIP) provides site-specific and regional outputs from terrestrial ecosystem model simulations in the Northeastern U.S. over the time period 0850-01-01 to 2010-12-31. For the regional scale modeling effort, five ecosystem models participated: ED2, LINKAGES, LPJ-GUESS, LPJ-WSL, and TRIFFID. Additional output for a larger ensemble is available for six individual sites with the following models: CLM-BGC, CLM-CN, ED2, ED2-landuse, JULES, TRIFFID, LINKAGES, LPJ-GUESS, LPJ-WSL, and SiBCASA. Output variables include key terrestrial ecosystem metrics, such as aboveground biomass, autotrophic respiration, evaporation, gross primary productivity, net ecosystem exchange, and net primary productivity (output variables differ by model). The PEMIP is a coordinated effort to develop a set of terrestrial ecosystem model simulations with the ability to evaluate high-resolution ecophysiological causes and consequences of forest responses to climatic variability and change over the past millennium.

This dataset contains 329 data files in netCDF-4 (*.nc) format and one companion file in PDF (*.pdf) format.

This dataset from the PalEON (Paleo-Ecological Observatory Network) Ecosystem Model Intercomparison Project (PEMIP) provides site-specific and regional outputs from terrestrial ecosystem model simulations in the Northeastern U.S. over the time period 0850-01-01 to 2010-12-31. For the regional scale modeling effort, five ecosystem models participated: ED2, LINKAGES, LPJ-GUESS, LPJ-WSL, and TRIFFID.  Additional output for a larger ensemble is available for six individual sites with the following models: CLM-BGC, CLM-CN, ED2, ED2-landuse, JULES, TRIFFID, LINKAGES, LPJ-GUESS, LPJ-WSL, SiBCASA. Output variables include key terrestrial ecosystem metrics, such as aboveground biomass, autotrophic respiration, evaporation, gross primary productivity, net ecosystem exchange, and net primary productivity (output variables differ by model). The PEMIP is a coordinated effort to develop a set of terrestrial ecosystem model simulations with the ability to evaluate high-resolution ecophysiological causes and consequences of forest responses to climatic variability and change over the past millennium.

Project: North American Carbon Program 

The North American Carbon Program (NACP) is a multidisciplinary research program designed to improve understanding of North America's carbon sources, sinks, and stocks. The central objective is to measure and understand the sources and sinks of Carbon Dioxide (CO2), Methane (CH4), and Carbon Monoxide (CO) in North America and adjacent oceans.  NACP is supported by a number of different federal agencies. NACP data holdings include atmospheric observations, remote sensing products, field measurements, inventories, and model outputs supporting carbon-cycle synthesis and inter-comparison.

Related Publications

Rollinson, C.R., A. Dawson, A. Raiho, J.W. Williams, M.C. Dietze, T. Hickler, S.T. Jackson, J. McLachlan, D.J.P. Moore, B. Poulter, T. Quaife, J. Steinkamp, and M. Trachsel. 2021. Forest responses to last-millennium hydroclimate variability are governed by spatial variations in ecosystem sensitivity. Ecology Letters 24:498-508. https://doi.org/10.1111/ele.13667

Rollinson, C.R., Y. Liu, A. Raiho, D.J.P. Moore, J. McLachlan, D.A. Bishop, A. Dye, J.H. Matthes, A. Hessl, T. Hickler, N. Pederson, B. Poulter, T. Quaife, K. Schaefer, J. Steinkamp, and M.C. Dietze. 2017. Emergent climate and CO2 sensitivities of net primary productivity in ecosystem models do not agree with empirical data in temperate forests of eastern North America. Global Change Biology 23:2755-2767. https://doi.org/10.1111/gcb.13626

Related Datasets

Rollinson, C., M. Dietze, J.H. Mathes, J. Steinkamp, T. Hickler, B. Poulter, A.M. Raiho, J. Mclachlan, T. Quaife, Y. Liu, D.J.P. Moore, K. Schaefer, and B. Brooks. 2023. PalEON: Terrestrial Ecosystem Model Drivers for the Northeastern U.S., 0850-2010. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1779

• This dataset holds the model driver data used by the PalEON Ecosystem Model Intercomparison Project (PEMIP)

Ramankutty, N., J.A. Foley, F.G. Hall, G.J. Collatz, B.W. Meeson, S.O. Los, E.Brown De Colstoun, and D.R. Landis. 2010. ISLSCP II Potential Natural Vegetation Cover. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/961

Wei, Y., Shishi Liu, D.N. Huntzinger, A.M. Michalak, N. Viovy, W.M. Post, C.R. Schwalm, K. Schaefer, A.R. Jacobson, C. Lu, H. Tian, D.M. Ricciuto, R.B. Cook, J. Mao, and X. Shi. 2014. NACP MsTMIP: Global and North American Driver Data for Multi-Model Intercomparison. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1220

Spatial Coverage: north central and northeastern U.S.

Spatial Resolution: 0.5 degree and point resolution

Temporal Coverage: 0850-01-01 to 2010-12-31

Temporal Resolution: annual, monthly, daily, or 3-hour

Study Area: Latitude and longitude are given in decimal degrees.

Site	Northernmost Latitude	Southernmost Latitude	Easternmost Longitude	Westernmost Longitude
north central and northeastern U.S.	50	35	-60	-100

Data File Information

This dataset contains 329 data files in netCDF-4 (*.nc) format from 5 regional models and 10 site models. The files are named paleon_<type>_<model>_<site>_<resolution>_<range>.nc, where

• <type> is the spatial extent, either "site" or "region" (regional)
• <model> is the model used (see Table 1)
• <site> is the site abbreviation (see Table 2). This element is only present in the site files. An ‘S’ is appended to the beginning of the site code indicating sub-daily model outputs.
• <resolution> is the temporal resolution: "3hour", "annual", "daily", "monthly"
• <range> is the temporal coverage and YYYY represents a four-digit year.

Data File Details

• Generally, region files contain 30 rows and 80 columns, and site files are a single tile
• Missing values are defined in the ‘missing_value’ and/or ‘fill_value’ attribute.
• The coordinate reference system is longitude - latitude using the WGS 84 datum (EPSG:4326).

Table 1. Models included in PEMIP. Note the version (‘v’) in the abbreviation often references the modeling team’s version/run rather than the published model version. See Model_Description_Readmes.pdf for additional details. 

Model Name	Abbreviation used in Regional File Names	Abbreviation Used in Site File Names	Reference
Community Land Model (CLM) 4.5 with biogeochemistry (BGC)	N/A	CLM45BGC-v5-1	Oleson et al., 2013
CLM 4.5 with carbon, nitrogen, (CN), and biogeochemistry	N/A	CLM45CN-v3-1	Oleson et al., 2013
Ecosystem Demography Model (ED) version 2.1	ED2-v1	ED2-v7	Moorcroft et al., 2001; Medvigy et al., 2009
ED 2.1 Land Use (LU)	N/A	ED2-LU-v8	Moorcroft et al., 2001; Medvigy et al., 2009
JULES (Joint UK Land Environment Simulator)	N/A	JULES-STATIC	Best et al., 2011; Clark et al., 2011
JULES Top-down Representation of Interactive Foliage and Flora Including Dynamics (TRIFFID)	TRIFFID	JULES-TRIFFID-v1	Best et al., 2011; Clark et al., 2011
Linking Inherently Nonlinear K Among Gradients and EcoSystems (LINKAGES) version 1.3	LINKAGES	LINKAGES-v2-1	Post and Pastor, 2013
Lund–Potsdam–Jena – Wald, Schnee, Landschaft (LPJ-WSL)	LPJ-WSL-v1	LPJ-WSL-v6	Gerten et al., 2004; Sitch et al., 2003
Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) SVN revision 3826	LPJ-GUESS	LPJ-GUESS-v6	Gerten et al., 2004; Sitch et al., 2003; Smith et al., 2001; Smith et al., 2014
Simple Biosphere/Carnegie-Ames-Stanford Approach (SiBCASA)	N/A	SiBCASA-v1	Schaefer et al., 2008

Table 2. Site in the site-level model-intercomparison portion of PEMIP

Site Abbreviation	Site Name	PalEON Site	Latitude	Longitude
PHA	Harvard Forest	Highly Integrated Proxy Site (HIPS)	42.54	-72.18
PHO	Howland Forest	HIPS	45.25	-68.73
PUN	University of Notre Dame Environmental Research Center (UNDERC) – East	HIPS	46.22	-89.53
PBL	Billy’s Lake	Intensive pollen sites (Pollen)	46.28	-94.58
PDL	Deming Lake	Pollen	47.17	-95.17
PMB	Minden Bog	Pollen	43.61	-82.83

Table 3. Data dictionary for all files

Variable	Units	Description
AGB	kgC m-2	Total aboveground biomass
AutoResp	kgC m-2 s-1	Autotrophic respiration rate
BA	m2 ha-1	Basal area by plant functional type (PFT)
CarbPools	kgC m-2	Carbon in Each Model Carbon Pool
CO2	ppm	Carbon dioxide concentration in the air
CWDI	kgC m-2 month-1	Coarse woody debris increment. Analogous to flux of woody material to the detrital pool resulting from mortality; corresponds to GWBI
Dens	ha-1	Stem Density by PFT
Estab	ha-1	New individuals
Evap	kg m-2 s-1	Total evaporation. Sum of evaporative sources minus transpiration
Fcomp	kgC kgC-1	AGB fractional composition of each PFT within each grid cell.
Fire	kgC m-2 s-1	Fire emissions
GPP	kgC m-2 s-1	Gross primary productivity
GWBI	kgC m-2 month-1	Gross woody biomass increment (analogous to tree-ring derived biomass)
HeteroResp	kgC m-2 s-1	Heterotrophic respiration
LAI	m2 m-2	Leaf area index
LW_albedo	1	Longwave albedo
lwdown	W m-2	Incident longwave radiation averaged over the time step of the forcing data
LWnet	W m-2	Net downward longwave radiation at surface
Mort	ha-1	Mortality. Number of individuals lost through death.
NEE	kgC m-2 s-1	Net ecosystem exchange
NPP	kgC m-2 s-1	Net primary production of each PFT within each grid cell.
PFT	-	Name of each plant functional type (PFT) or species included in the model.
poolname	-	Names of each vegetation and soil carbon pool
precipf	kg m-2 s-1	The per unit area and time precipitation representing the sum of convective rainfall stratiform rainfall and snowfall
psurf	Pa	Pressure at the surface
qair	kg kg-1	Specific humidity measured at the lowest level of the atmosphere
Qh	W m-2	Surface sensible heat flux for land tiles
Qle	W m-2	Surface latent heat flux for land tiles
Qs	kg m-2 s-1	Baseflow (lateral subsurface runoff)
Qsb	kg m-2 s-1	Drainage and subsurface lateral flow
SnowDepth	kg m-2	Snow depth (on ground)
SoilDepth	m	Depth to the bottom of each model-defined soil layer.
SoilMoist	kg m-2	Soil water content in each model-defined soil layer.
SoilTemp	K	Soil temperature in each model-defined soil layer.
SW_albedo	1	Albedo (as used for net shortwave calculation)
swdown	W m-2	Incident radiation in the shortwave part of the spectrum averaged over the time step of the forcing data
SWE	kg m-2	Total water mass (ice plus liquid)
SWnet	W m-2	Net Shortwave Radiation
tair	K	Nar surface air temperature (at 2 m)
time	Years/months/days since YYYY-MM-DD or seconds since YYYY-MM-DD HH:MM:SS	Timestep. The units are specifically defined in the ‘units’ variable attribute. Note: Some files have multiple time variables and dimensions such as ‘time.months’ and ‘time.years’
TotLivBiom	kgC m-2	Total carbon content of living biomass (e.g. leaf +root+wood)
TotSoilCarb	kgC m-2	Total soil and litter cabon content over the entire soil profile
Transp	kg m-2 s-1	Transpiration of each PFT within each grid cell.
wind	m s-1	Wind speed measured with a vertical coordinate in height of 10 m

Companion Files

This companion file Model_Description_Readmes.pdf contains text from readme files that modeling teams submitted holding metadata surrounding the individual modeling approaches.

Exposure to 21^st-century climate change is expected to profoundly impact global forest composition, diversity, and structure, but the sensitivity of ecosystems to climate variability at multi-decadal to centennial time scales is poorly constrained by instrumental observations. Terrestrial ecosystem models used to forecast responses to climate change often have difficulty reproducing broad-scale and long-term responses to environmental variability, despite being well-grounded in empirical evidence and ecological theory. Moreover, there are several challenges that hinder the joint analysis and integration of terrestrial ecosystem models and palaeoecological data to better constrain modeled responses to climate variations at multi-decadal and longer timescales.

These data were used in the related publication by Rollinson et al. (2021), to establish the patterns of forest ecosystem and climate variability in the north-central and northeastern U.S. for the last millennium (850–1850 C.E.) and identify the mechanisms underpinning both forest ecosystem sensitivity and observed data-model discrepancies. A combination of data-model and model-model comparisons of palaeoclimatic and palaeoecological datasets was used (Figure 2). The results indicate that at centennial timescales, spatial patterns in the variability of forest composition and biomass are regulated by ecological factors such as ecotonal position and complexity rather than climate exposure as defined by the local magnitude of climate variability.

No quality assessment is available for this dataset.

This dataset includes site and regional model outputs that have a consistent spatial extent, resolution, and coordinate reference system. The environmental and meteorological drivers used to derive these outputs are available in Rollinson et al. (2023). Details about the drivers and outputs are available in Rollinson et al. (2021).

The PalEON (Paleo-Ecological Observatory Network) Ecosystem Model Intercomparison Project (PEMIP) is an interdisciplinary team of paleoecologists, ecological statisticians, and ecosystem modelers from across a number of institutions began in the early 1990s to provide an efficient mechanism for coordinating palaeoclimate modeling activities (Braconnot et al., 2021). The goal of the PEMIP is to reconstruct forest composition, fire regime, and climate in forests across the northeastern U.S. and Alaska over the past 2,000 years and use the results to drive and validate terrestrial ecosystem models (Figure 2).

Ten models across six sites (i.e., site extent models) were included in PEMIP to serve as a baseline for the regional extent models. There were five models at the regional extent covering north-central and northeastern U.S. Outputs were standardized to adhere to a common naming convention following ALMA standard names (https://web.lmd.jussieu.fr/~polcher/ALMA/convention_output_3.html) and NetCDF (*.nc) file structure.  Specific details on each model are contained in the companion file.  

Figure 2. Overview of the unified conceptual framework (grey boxes) for parallel analysis of empirical data (blue boxes) and model output (red boxes) described in the study by Rollinson et al. (2021). For ecosystem models, the latent climatic and ecosystem processes are described for unobservable palaeoecological data and differences among models in complexity. Complexity is organized into three categories: 1) diversity, ranging from a few plant functional types (PFTs) to many species; 2) demography, ranging from "big leaf" models with no explicit treatment of forest demography to models with individual trees and 3) ecophysiological processes. Changes in forest biomass emerge from latent ecophysiological processes including gross primary productivity (GPP), net primary productivity (NPP), net ecosystem exchange (NEE), and leaf area index (LAI). Ecophysiological processes are controlled by the model representation of higher-level vegetation processes. Latent model drivers, processes, and states (red boxes) result in estimates of forest composition and biomass that can be compared to palaeoecological data products (blue boxes). Models vary in complexity due to design philosophy and trade-offs between model complexity and computational speed.

Best, M.J., M. Pryor, D.B. Clark, G.G. Rooney, R.L. H. Essery, C.B. Ménard, J.M. Edwards, M.A. Hendry, A. Porson, N. Gedney, L.M. Mercado, S. Sitch, E. Blyth, O. Boucher, P.M. Cox, C.S. B. Grimmond, and R.J. Harding. 2011. The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes. Geoscientific Model Development 4:677–699. https://doi.org/10.5194/gmd-4-677-2011

Braconnot, P., S. Harrison, M. Kageyama, P.J. Bartlein, V. Masson-Delmotte, A. Abe-Ouchi, B. Otto-Bliesner, and Y. Zhao. 2021. Evaluation of climate models using palaeoclimatic data. Nature Climate Change 2:417-424. https://doi.org/10.1038/nclimate1456

Clark, D.B., L.M. Mercado, S. Sitch, C.D. Jones, N. Gedney, M.J. Best, M. Pryor, G.G. Rooney, R.L. H. Essery, E. Blyth, O. Boucher, R.J. Harding, C. Huntingford, and P.M. Cox. 2011. The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics. Geoscientific Model Development 4:701–722. https://doi.org/10.5194/gmd-4-701-2011

Gerten, D., S. Schaphoff, U. Haberlandt, W. Lucht, and S. Sitch. 2004. Terrestrial vegetation and water balance—hydrological evaluation of a dynamic global vegetation model. Journal of Hydrology 286:249–270. https://doi.org/10.1016/j.jhydrol.2003.09.029

Medvigy, D., S.C. Wofsy, J.W. Munger, D.Y. Hollinger, and P.R. Moorcroft. 2009. Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography model version 2. Journal of Geophysical Research 114:G01002. https://doi.org/10.1029/2008JG000812

Moorcroft, P.R., G.C. Hurtt, and S.W. Pacala. 2001. A method for scaling vegation dynamics: the ecosystem demography model (ED). Ecological Monographs 71:556-586. https://doi.org/10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2

Oleson, K., D. Lawrence, G. Bonan, B. Drewniak, M. Huang, C. Koven, S. Levis, F. Li, W. Riley, Z. Subin, S. Swenson, P. Thornton, A. Bozbiyik, R. Fisher, C. Heald, E. Kluzek, J.-F. Lamarque, P. Lawrence, L. Leung, W. Lipscomb, S. Muszala, D. Ricciuto, W. Sacks, Y. Sun, J. Tang, and Z.-L. Yang. 2013. Technical description of version 4.5 of the Community Land Model (CLM). NSF National Center for Atmospheric Research. https://doi.org/10.5065/D6RR1W7M

Post, W. M., and J. Pastor. 2013. LINKAGES: An Individual-based Forest Ecosystem Biogeochemistry Model (Version 1). ORNL Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/1166

Ramankutty, N., J.A. Foley, F.G. Hall, G.J. Collatz, B.W. Meeson, S.O. Los, E.Brown De Colstoun, and D.R. Landis. 2010. ISLSCP II Potential Natural Vegetation Cover. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/961

Rollinson, C.R., A. Dawson, A. Raiho, J.W. Williams, M.C. Dietze, T. Hickler, S.T. Jackson, J. McLachlan, D.J.P. Moore, B. Poulter, T. Quaife, J. Steinkamp, and M. Trachsel. 2021. Forest responses to last-millennium hydroclimate variability are governed by spatial variations in ecosystem sensitivity. Ecology Letters 24:498-508. https://doi.org/10.1111/ele.13667

Rollinson, C., M. Dietze, J.H. Mathes, J. Steinkamp, T. Hickler, B. Poulter, A.M. Raiho, J. Mclachlan, T. Quaife, Y. Liu, D.J.P. Moore, K. Schaefer, and B. Brooks. 2023. PalEON: Terrestrial Ecosystem Model Drivers for the Northeastern U.S., 0850-2010. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1779

Rollinson, C.R., Y. Liu, A. Raiho, D.J.P. Moore, J. McLachlan, D.A. Bishop, A. Dye, J.H. Matthes, A. Hessl, T. Hickler, N. Pederson, B. Poulter, T. Quaife, K. Schaefer, J. Steinkamp, and M.C. Dietze. 2017. Emergent climate and CO2 sensitivities of net primary productivity in ecosystem models do not agree with empirical data in temperate forests of eastern North America. Global Change Biology 23:2755-2767. https://doi.org/10.1111/gcb.13626

Sitch, S., B. Smith, I.C. Prentice, A. Arneth, A. Bondeau, W. Cramer, J.O. Kaplan, S. Levis, W. Lucht, M.T. Sykes, K. Thonicke, and S. Venevsky. 2003. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology 9:161–185. https://doi.org/10.1046/j.1365-2486.2003.00569.x

Schaefer, K., G.J. Collatz, P. Tans, A.S. Denning, I. Baker, J. Berry, L. Prihodko, N. Suits, and A. Philpott. 2008. Combined Simple Biosphere/Carnegie-Ames-Stanford Approach terrestrial carbon cycle model. Journal of Geophysical Research: Biogeosciences 113:G03034. https://doi.org/10.1029/2007JG000603

Smith, B., I.C. Prentice, and M.T. Sykes. 2001. Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Global Ecology and Biogeography  10:621-637. http://www.jstor.org/stable/3182691

Smith, B., D. Wårlind, A. Arneth, T. Hickler, P. Leadley, J. Siltberg, and S. Zaehle. 2014. Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences 11:2027–2054. https://doi.org/10.5194/bg-11-2027-2014

Wei, Y., S. Liu, D.N. Huntzinger, A.M. Michalak, N. Viovy, W.M. Post, C.R. Schwalm, K. Schaefer, A.R. Jacobson, C. Lu, H. Tian, D.M. Ricciuto, R.B. Cook, J. Mao, and X. Shi. 2014. NACP MsTMIP: Global and North American Driver Data for Multi-Model Intercomparison. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1220