jones-etal_2023_scidata

Continental United States climate projections based on thermodynamic modification of historical weather

Andrew D. Jones^1,2,*, Deeksha Rastogi³, Pouya Vahmani¹, Alyssa M. Stansfield^4,5, Kevin A. Reed⁴, Travis Thurber⁶, Paul A. Ullrich⁷, Jennie Rice⁸

¹ Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory

² Energy and Resources Group, University of CA, Berkeley

³ Computational Sciences and Engineering Division, Oak Ridge National Laboratory

⁴ School of Marine and Atmospheric Sciences, Stony Brook University

⁵ Department of Atmospheric Science, Colorado State University

⁶ Earth Systems Science Division, Pacific Northwest National Laboratory

⁷ Department of Land, Air, and Water Resources, University of CA, Davis

⁸ Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory

* corresponding author: Andrew D. Jones (adjones@lbl.gov)

Abstract

Regional climate models can be used to examine how past weather events might unfold under different climate conditions by simulating analogue versions of those events with modified thermodynamic conditions (i.e., warming signals). Here, we apply this approach by dynamically downscaling a 40-year sequence of past weather from 1980-2019 driven by atmospheric re-analysis, and then repeating this 40-year sequence a total of 8 times using a range of time-evolving thermodynamic warming signals that follow 4 80-year future warming trajectories from 2020-2099. Warming signals follow two emission scenarios (SSP585 and SSP245) and are derived from two groups of global climate models based on whether they exhibit relatively high or low climate sensitivity. The resulting dataset, which contains 25 hourly and over 200 3-hourly variables at 12km spatial resolution, can be used to examine a plausible range of future climate conditions in direct reference to previously observed weather and enables a systematic exploration of the ways in which thermodynamic change influences the characteristics of extreme events.

Journal reference

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Code reference

Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, Z. Liu, J. Berner, W. Wang, J. G. Powers, M. G. Duda, D. M. Barker, and X.-Y. Huang, 2019: A Description of the Advanced Research WRF Version 4. NCAR Tech. Note NCAR/TN-556+STR, 145 pp. https://doi.org/10.5065/1dfh-6p97

Ullrich, P.A., C.M. Zarzycki, E.E. McClenny, M.C. Pinheiro, A.M. Stansfield and K.A. Reed (2021) "TempestExtremes v2.1: A community framework for feature detection, tracking and analysis in large datasets" Geosci. Model. Dev. 14, pp. 5023–5048, https://doi.org/10.5194/gmd-14-5023-2021.

Vahmani, Pouya, Rastogi, Deeksha, & Thurber, Travis. (2021). TGW WRF Historical Workflow (v2.0.0). Zenodo. https://doi.org/10.5281/zenodo.7598973. GitHub: https://github.com/IMMM-SFA/wrf_historical.

Data reference

Input data

Bentsen, Mats; Oliviè, Dirk Jan Leo; Seland, Øyvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkevåg, Alf; Schwinger, Jörg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.8040

Bentsen, Mats; Oliviè, Dirk Jan Leo; Seland, Øyvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkevåg, Alf; Schwinger, Jörg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP ssp245. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.8255

Bentsen, Mats; Oliviè, Dirk Jan Leo; Seland, Øyvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkevåg, Alf; Schwinger, Jörg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP ssp585. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.8321

Byun, Young-Hwa (2020). NIMS-KMA UKESM1.0-LL model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.8379

Chen_et_al_2008_JGR_Gauge_Algo.pdf Chen, M., W. Shi, P. Xie, V. B. S. Silva, V E. Kousky, R. Wayne Higgins, and J. E. Janowiak (2008), Assessing objective techniques for gauge-based analyses of global daily precipitation, J. Geophys. Res., 113, D04110, doi:10.1029/2007JD009132.

European Centre for Medium-Range Weather Forecasts. 2019, updated monthly. ERA5 Reanalysis (0.25 Degree Latitude-Longitude Grid) [Data set]. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. https://doi.org/10.5065/BH6N-5N20.

Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP ssp245. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.10851

Good, Peter (2020). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP ssp585. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.10901

Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP ssp245. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.6339

Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP ssp585. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.6405

John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP ssp245. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.8686

John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP ssp585. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.8706

Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.8597

Meinshausen, Malte; Vogel, Elisabeth (2016). input4MIPs.UoM.GHGConcentrations.CMIP.UoM-CMIP-1-2-0.Earth System Grid Federation. https://doi.org/10.22033/ESGF/input4MIPs.1118

Meinshausen, Malte; Nicholls, Zebedee R. J. (2018). UoM-MESSAGE-GLOBIOM-ssp245-1-2-1 GHG concentrations. Earth System Grid Federation. https://doi.org/10.22033/ESGF/input4MIPs.9866

Meinshausen, Malte; Nicholls, Zebedee R. J. (2018). UoM-REMIND-MAGPIE-ssp585-1-2-1 GHG concentrations. Earth System Grid Federation. https://doi.org/10.22033/ESGF/input4MIPs.9868

NASA Goddard Institute for Space Studies (NASA/GISS) (2018). NASA-GISS GISS-E2.1G model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.7127

NASA Goddard Institute for Space Studies (NASA/GISS) (2020). NASA-GISS GISS-E2.1G model output prepared for CMIP6 ScenarioMIP ssp245. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.7415

NASA Goddard Institute for Space Studies (NASA/GISS) (2020). NASA-GISS GISS-E2.1G model output prepared for CMIP6 ScenarioMIP ssp585. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.7460

NCAR. WPS V4 Geographical Static Data. https://www2.mmm.ucar.edu/wrf/users/download/get_sources_wps_geog.html

PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu, data created 4 Feb 2014.

Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.6109

Shim, Sungbo; Lim, Yoon-Jin; Byun, Young-Hwa; Seo, Jeongbyn; Kwon, Sanghun; Kim, Byeong-Hyeon (2020). NIMS-KMA UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP ssp245. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.8436

Shim, Sungbo; Lim, Yoon-Jin; Byun, Young-Hwa; Seo, Jeongbyn; Kwon, Sanghun; Kim, Byeong-Hyeon (2021). NIMS-KMA UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP ssp585. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.8457

Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.3610

Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP ssp245. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.3685

Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP ssp585. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.3696

Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.6113

Voldoire, Aurore (2019). CNRM-CERFACS CNRM-CM6-1-HR model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.4067

Voldoire, Aurore (2019). CNRM-CERFACS CNRM-CM6-1-HR model output prepared for CMIP6 ScenarioMIP ssp245. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.4190

Voldoire, Aurore (2019). CNRM-CERFACS CNRM-CM6-1-HR model output prepared for CMIP6 ScenarioMIP ssp585. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.4225

Xie_et_al_2007_JHM_EAG.pdf Xie, P., A. Yatagai, M. Chen, T. Hayasaka, Y. Fukushima, C. Liu, and S. Yang (2007), A gauge-based analysis of daily precipitation over East Asia, J. Hydrometeorol., 8, 607. 626.

Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.4272

Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP ssp245. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.4322

Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP ssp585. Earth System Grid Federation. doi:https://doi.org/10.22033/ESGF/CMIP6.4333

Output data

Jones, A. D., Rastogi, D., Vahmani, P., Stansfield, A., Reed, K., Thurber, T., Ullrich, P., & Rice, J. S. (2022). IM3/HyperFACETS Thermodynamic Global Warming (TGW) Simulation Datasets (v1.0.0) [Data set]. MSD-LIVE Data Repository. https://doi.org/10.57931/1885756. Descriptive landing page: https://tgw-data.msdlive.org/.

Contributing modeling software

Model	Version	Repository Link	DOI
TempestExtremes	08401d5	https://github.com/ClimateGlobalChange/tempestextremes	n/a
WPS	4.0.1	https://github.com/wrf-model/WPS	n/a
WRF	4.0.1	https://github.com/wrf-model/WRF	n/a
WRF	4.2.1	https://github.com/wrf-model/WRF	n/a

Reproduce my experiment

Follow the steps outlined in TGW WRF Historical Workflow to prepare the input data and run the WRF model for each simulation year in the historical period.
- Note that this workflow is tailored to the NERSC supercomputer's KNL/Haswell nodes. Use on other hardware will require modifications. The WRF Users' Page can be used as a reference for configuring and running the various tools on different systems.
Download the CMIP6 files listed in cmip6_file_list.txt from WCRP CMIP6. These files will be used to calculate the temperature deltas to apply to the future scenarios. Additional input will be the historical meteorology files produced during step #1. Output will be new meteorology files with temperature delta applied for each future scenario. Run the following scripts to generate the deltas, updating the file paths as necessary to reference your downloaded files:
- deltas/data_preprocessing.sh
- deltas/calculate_ensemble_mean.sh
- deltas/calculate_modelmean_ColdModels.sh
- deltas/calculate_modelmean_HotModels.sh
- deltas/calc_moving_avg_var2d.sh
- deltas/calc_moving_avg_var3d.sh
- deltas/calc_delta_nearfuture.sh
- deltas/calc_delta_farfuture.sh
- deltas/delta-interpolation.ncl - interpolates the CMIP6 deltas to the WRF domain and adds to the meteorology files
Run WRF simulations for each year and scenario using the new data from step #2, adapting the workflow from #1 to use the new meteorology files. During the step that creates the GHG concentrations file, replace the script GHG_for_WRF_historical.m with GHG_for_WRF_SSP245_v121.m or GHG_for_WRF_SSP585_v121.m based on the future scenario being run.

Reproduce my figures

Use the scripts found in the figures directory to reproduce the figures used in this publication.

Figure	Script(s)	How to Run
Fig. 1	`Figure1_delta.ncl` `Figure1_tas.ncl`	Use the NCAR command language (NCL) to run each script, generating a subplot.
Fig. 2	`Figure2_historical.ncl` `Figure2_future1.ncl` `Figure2_future2.ncl`	Use the NCAR command language (NCL) to run each script, generating a subplot.
Fig. 3	`Figure3.ncl`	Use the NCAR command language (NCL) to run the script.
Fig. 4	`Figure4.ncl`	Use the NCAR command language (NCL) to run the script.
Fig. 5	`Figure5_pr.ncl` `Figure5_tmax.ncl`	First, generate monthly average precipitation from the WRF output data as NetCDF, then use the NCL scripts to generate the figure. The script also relies on access to monthly average ERA5 and PRISM data which can be downloaded from the references.
Fig. 6	`t95.csh` `p95.csh` `Figure6_p95bias.ncl` `Figure6_t95bias.ncl`	The cshell scripts can be used to calculate the 95th percentile temperature and precipitation values. The NCL script uses these to produce the figure.
Fig. 7	`Figure7.py` `Figure7_prdata.ncl` `Figure7_tmaxdata.ncl`	Use Python to run the script, which references data files that are subsets of precipitation and temperature in particular cities. These data files are also provided in the figures directory, or can be generated using the NCL scripts.
Fig. 8	`tempest/plot_Figure8.py`	Follow the steps in the figures/tempest README.md
Fig. 9	`tempest/plot_Figure9.py`	Follow the steps in the figures/tempest README.md
Fig. 10	`Figure10.ncl`	Use the NCAR command language (NCL) to run the script.
Fig. 11	`Figure11.ncl`	Use the NCAR command language (NCL) to run the script.
Fig. 12	`Figure 12.xlsx`	This figure was generated in the Excel worksheet, using data points derived from the raw output.
Fig. 13	`Figure13.ncl`	Use the NCAR command language (NCL) to run the script.
Fig. 14	`make_figure_14.py`	Use Python to run the script, which references the provided data aggregations. These aggregations were derived from the raw GCM and raw output data.

IMMM-SFA/jones-etal_2023_scidata