The Princeton Large Ensemble Archive

The multi-forcing suite of GFDL Large Ensemble experiments is now available on Globus. ** This includes 30 member Large Ensembles for RCP8.5, RCP4.5 and RCP2.6 emissions scenarios. Extensive output for all climate systems components is available (ocean, atmosphere, land and sea-ice). [ Documentation found in Schlunegger et al, 2019 and Rodgers et al, 2015 ]

Additionally, we have select ocean variables (biogeochemical and dynamical) available for 3 other Large Ensembles: the CESM1 Large Ensemble, the CanESM2 Large Ensemble and the MPI Grand Ensemble. [ Documentation in Schlunegger et al, 2020]

The Multi-Model Large Ensemble analysis of ocean biogeochemistry was funded by the National Aeronautics and Space Administration (NASA; grant NNX17AI75G). As such, the pertinent data is made publicly available in compliance with the open-access stipulation of such publicly funded science.

For a larger selection of multi-model Large Ensemble output the CLIVAR Multi-Model Large Ensemble Archive (MMLEA) has output from the atmosphere, land, ocean and ice components for 7 climate models and multiple forcing scenarios.

Here are additional links to the larger, institution-hosted repositories for the CESM1 Large and Medium Ensembles, the CanESM2 Large Ensemble and MPI Grand Ensemble.

** GLOBUS only allows data to transfer to a designated endpoint. Your must first establish your local machine as an endpoint before transferring the data. Many institutional computing systems are already established endpoints. More about this can be found here.

Documentation of the Princeton Large Ensemble Archive

(Last updated January 8th 2021)

Globus Access Point: https://poseidon.princeton.edu

Data can be accessed at the given link, which brings you to a globus interface where you can navigate through directories and select files to transfer your local machine (if it is established as an endpoint). You will need to create an account to use globus. Instructions here.

Contacts: Sarah Schlunegger, sarah.schlunegger@princeton.edu & Keith Rodgers, keithbrodgers@gmail.com

1. Overview of Archive

The Princeton Multi-ESM Large Ensemble Archive houses the output of Large Ensemble experiments from multiple Earth System Models (ESMs). The GFDL Large Ensemble suite has output from all components of the Earth System (ocean, land, atmosphere and ice) and for 3 emissions scenarios (RCP8.5, 4.5 and 2.6). Ocean-only output is available for the CESM1, MPI-ESM-LR1.1 and CanESM2 Large Ensemble experiments for RCP8.5 and RCP4.5 scenarios, and for some models, RCP2.6. Documentation for the RCP8.5 GFDL-Large Ensemble experiments can be found in Rodgers et al., (2015) and Schlunegger et al., (2019). Documentation for the broader set of models and experiments can be found in Schlunegger et al., (2020).

2. Overview of Large Ensemble Experiments

2.1 The GFDL Large Ensemble Experiments

The GFDL Large Ensemble experiments are conducted with GFDL’s ESM2M, validation and documentation can be found in Dunne et al., 2012 and Dunne et al., 2013. An extensive comparison oof the marine carbon cycle for the model against other large ensemble simulations from other modeling centers, and including both RCP8.5 and RCP4.5, was conducted by Schlunegger et al. (2020), building on more extensive evaluation of biogeochemical variables in the simulations by Schlunegger et al. (2019). The presentation paper for the ESM2M (Rodgers et al., 2015) focused on marine ecosystem stressors.

The GFDL Large Ensemble was initialized in year 1950 using different days of January of ensemble member #1 to initialize January 1st of ensemble members #2-30. This can be categorized as a micro-perturbation initialization method (ENSEMBLE_RCP85_ORIG). This process was repeated to make a second, 30 member RCP8.5 ensemble experiment, for which higher frequency atmospheric and ocean (daily) output was saved. The RCP4.5 and RCP2.6 Large Ensembles were branched from the original RCP8.5 Large Ensemble at year 2006, and include high frequency ocean output (daily). All ensemble members conclude at the end of year 2100.

2.2 The CESM1 Large Ensemble and Medium Ensemble

The CESM1 Large Ensemble is documented in Kay et al., 2015. This ensemble has 30+ members and has historical (1920-2005) and RCP8.5 (2006-2100) boundary conditions. The ensemble was initialized through round-off error perturbations to the initial atmospheric state. Note that for some members (3-8) the ocean biogeochemical output was corrupted during simulation and therefore not available. In this archive, ensemble members have been renumbered to create a continuous sequence to simplify the analysis process. The renumbering is consistent between biogeochemical and dynamical variables. This is why dynamical variables have ~6 more members than do biogeochemical.

The CESM Medium Ensemble has between 9 (for ocean BGC fields) and 15 (for dynamical fields) members, and RCP4.5 boundary conditions (2006-2080). Original documentation can be found in Sanderson et al., (2018).

2.3 The CanESM2 Large Ensemble

The CanESM2 Large Ensemble consists of 50 members with historical (1950-2005) and RCP8.5 (2006-2100) boundary conditions. The first, five members, which covered the period 1850–2005, were generated from initial conditions chosen from different years of the preindustrial control runs, representing a macro-pertubation initialization procedure (Christian 2014). Second, each of the five members were branched into 10 members at year 1950 through slight modification of the atmospheric initial conditions, achieved through changing the seed of a random‐number generator within the cloud parameterization (Kirchmeier‐Young et al., 2017). Renumbering was performed to have a continuous sequence of ensemble members, with the 1st, 11th, 21st, 31st and 41st members representing extensions of the original macro-perturbation ensemble members, and the subsequent 9 members for each unit of 10 representing the micro-perturbation members of the first member. The RCP4.5 scenario consists of a small, five‐member ensemble which spans the time period 2006–2100, and which stems from the 5 member macropertubation ensemble.

2.4 The MPI-ESM-LR1.1 Grand Ensemble

The MPI grand ensemble is documented in Bittner et al. (2016) and Li and Ilyina (2018) and formally described in Maher et al. (2019). The Grand Ensemble covers the historical period (1850-2006) and has extensions for RCP8.5, RCP4.5 and RCP2.6 forcing scenarios (2006-2099). Each has 100 members, however we only provide the first 30 members. The first 30 members are initialized from macro perturbations achieved through branching/initializing from different decades and centuries throughout the pre-industrial control run.

3. More specifics about the available GFDL LE fields

The output for the GFDL LE is organized first by scenario (RCP8.5, 4.5, 2.6, 8.5_ORIG), then by system component (OCN, ATM, LND, ICE), then by frequency of output (monthly (1M), 5-day means (5D), 1-day (1D)), grid specifications (1x1 indicates the output is regrided to a standard WOA 1x1 grid, otherwise the output is on the original model grid), and number of dimensions ( if not specified, then the output in the subdirectory is 2 dimensional (lat x lon), however if specified (3D) then the output is 3-dimensional and is (lat x lon x depth). For example, the RCP8.5 scenario, ocean component output (found in the directory ENSEMBLE/GFDL_ESM2M/ENSEMBLE_RCP85/OCN ) has the following subdirectories:

OCN_1D (daily ocean 2D)

OCN_1D_1x1 (daily ocean 2D 1x1 grid)

OCN_1M (monthly ocean 2D)

OCN_1M_1x1 (monthly ocean 2D 1x1 grid)

OCN_1M_3D (monthly ocean 3D)

OCN_1M_3D_1x1 (monthly ocean 3D)

OCN_5D (5-day means ocean 2D)

OCN_5D_1x1 (5-day means ocean 2D 1x1 grid)

Within each directory are many fields or variables of interest. Many of the 2D ocean fields are taken at the surface, indicated by either the notation “SFC” or “k01”. Other 2D fields may be at different depth levels (for example, K11 is the ~100m depth horizon), or may be the vertical integral or inventory over a range of depths (for example INVXY indicates a vertical inventory over all depths (if K not indicated), and INVXY_K21_K31 indicates an inventory from k21 to k31 which is approximately 200 to 600 meters in the ocean).

In the atmosphere, the vertical coordinates (24 vertical levels) decrease in notation with height, such that the lowest vertical level, k24, is at the surface and the highest vertical level k01 represents the top of the atmosphere. As with the ocean, many atmospheric fields are provided at either the models diagnostic or reference surface levels (e.g. temp_ref being the 2m reference height air temperature standard for model intercomparison, or k24 being the average over the lowest vertical level).

4. More specifics about the available CESM1, CanESM2 and MPI-GE fields

The following variables are available on the archive. See the directory structure schematic for indication of where to find these variables within the system.

Related GFDL-Large Ensemble Publications

Schlunegger S., K. Rodgers, J.L. Sarmiento, J.P. Dunne, T.L. Frölicher, R. Slater, M. Ishii (2019). Emergence of anthropogenic signals in the ocean carbon cycle. Nature Climate Change. doi:10.1038/s41558-019-0553-2

Schlunegger S., K. Rodgers, J.L. Sarmiento, T. Ilyina, J.P. Dunne, T, Takano, et al., (2020). Time of Emergence & Large Ensemble Intercomparison for Ocean Biogeochemical Trends. Global Biogeochemical Cycles.

Rodgers, K. B., J. Lin, and T. L. Frölicher(2015), Emergence of multiple ocean ecosystem drivers in a large ensemble suite with an Earth system model, Biogeosciences, 12, 3301–3320.

Frölicher, T. L., Rodgers, K. B., Stock, C. A., and Cheung, W. W. L. ( 2016), Sources of uncertainties in 21st century projections of potential ocean ecosystem stressors, Global Biogeochem. Cycles, 30, 1224– 1243, doi:10.1002/2015GB005338.

Eddebbar, Y.A., K.B. Rodgers, M.C. Long, A.C. Subramanian, S. Xie, and R.F. Keeling, 2019: El Niño–Like Physical and Biogeochemical Ocean Response to Tropical Eruptions. J. Climate, 32, 2627–2649, https://doi.org/10.1175/JCLI-D-18-0458.1

Deser, C., Lehner, F., Rodgers, K. B., Ault, T., Delworth, T. L., DiNezio, P. N., Fiore, A., Frankignoul, C., Fyfe, J. C., Horton, D. E., Kay, J. E., Knutti, R., Lovenduski, N. S., Marotzke, J., McKinnon, K. A., Minobe, S., Randerson, J., Screen, J. A., Simpson, I. R., & Ting, M. (2020). Insights from Earth system model initial‐condition large ensembles and future prospects. Nature Climate Change, 10(4), 277–286. https://doi.org/10.1038/s41558‐020‐0731‐2

Other Publications Referenced:

Christian, J. R. (2014). Timing of the departure of ocean biogeochemical cycles from the preindustrial state. PLoS ONE, 9(11), e109820. https://doi.org/10.1371/journal.pone.0109820

Dunne, J. P., John, J. G., Adcroft, A. J., Griffies, S. M., Hallberg, R. W., Shevliakova, E., Stouffer, R. J., Cooke, W., Dunne, K. A., Harrison, M. J., Krasting, J. P., Malyshev, S. L., Milly, P. C. D., Phillipps, P. J., Sentman, L. T., Samuels, B. L., Spelman, M. J., Winton, M., Wittenberg, A. T., & Zadeh, N. (2012). GFDL's ESM 2 global coupled climate‐carbon Earth system models. Part I: Physical formulation and baseline simulation characteristics. Journal of Climate, 25(19), 6646–6665. https://doi.org/10.1175/JCLI‐D‐11‐00560.1

Dunne, J. P., John, J. G., Shevliakova, E., Stouffer, R. J., Krasting, J. P., Malyshev, S. L., Milly, P. C. D., Sentman, L. T., Adcroft, A. J., Cooke, W., Dunne, K. A., Griffies, S. M., Hallberg, R. W., Harrison, M. J., Levy, H., Wittenberg, A. T., Phillips, P. J., & Zadeh, N. (2013). GFDL's ESM 2 global coupled climate‐carbon Earth system models. Part II: Carbon system formulation and baseline simulation characteristics. Journal of Climate, 26(7), 2247–2267. https://doi.org/10.1175/JCLI‐D‐12‐00150.1

Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G., Arblaster, J. M., Bates, S. C., Danabasoglu, G., Edwards, J., Holland, M., Kushner, P., Lamarque, J. F., Lawrence, D., Lindsay, K., Middleton, A., Munoz, E., Neale, R., Oleson, K., Polvani, L., & Vertenstein, M. (2015). The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. Bulletin of the American Meteorological Society, 96(8), 1333–1349. https://doi.org/10.1175/BAMS‐D‐13‐00255.1

Kirchmeier‐Young, M. C., Zwiers, F. W., & Gillett, N. P. (2017). Attribution of extreme events in Arctic sea ice extent. Journal of Climate, 30(2), 553–571. https://doi.org/10.1175/JCLI‐D‐16‐0412.1

Kirchmeier‐Young, M. C., Zwiers, F. W., & Gillett, N. P. (2017). Attribution of extreme events in Arctic sea ice extent. Journal of Climate, 30(2), 553–571. https://doi.org/10.1175/JCLI‐D‐16‐0412.1

Maher, N., Milinski, S., Suarez‐Gutierrez, L., Botzet, M., Dobrynin, M., Kornblueh, L., Kröger, J., Takano, Y., Ghosh, R., Hedemann, C., Li, C., Li, H., Manzini, E., Notz, D., Putrasahan, D., Boysen, L., Claussen, M., Ilyina, T., Olonscheck, D., Raddatz, T., Stevens, B., & Marotzke, J. (2019). The Max Planck Institute Grand Ensemble: Enabling the exploration of climate system variability. Journal of Advances in Modeling Earth Systems, 11, 2050–2069. https://doi.org/10.1029/2019MS001639

Sanderson, B. M., Oleson, K. W., Strand, W. G., Lehner, F., & O'Neill, B. C. (2018). A new ensemble of GCM simulations to assess avoided impacts in a climate mitigation scenario. Climatic Change, 146(3–4), 303–318. https://doi.org/10.1007/s10584‐015‐1567‐z

Additional Funding Acknowledgements:

In addition to primary funding by NASA . we also acknowledge funding from NOAA Awards NA17RJ2612 and NA08OAR4320752, which includes support through the NOAA Office of Climate Observations (OCO) for their support of Keith Rodgers.