Input Datasets

The Time of Emergence analysis used existing regional climate change projections as inputs, including:

  • Statistically- and dynamically-downscaled projections of future temperature and precipitation-related variables derived from the CMIP5 and CMIP3 collections of models
  • Projected future hydrologic and streamflow conditions simulated using a regional hydrologic model and the above downscaled datasets

The specific global climate models, emissions scenarios, downscaling approaches and hydrologic model used to develop these existing projections are briefly summarized below. For more details, see the Project Report.

Global Climate Projections

The regional climate change projections used in the Time of Emergence analysis were derived from global climate model simulations included in two generations of the Coupled Model Intercomparison Project (CMIP) experiments: CMIP3 and CMIP5. For each generation, simulations using a “high” and a “low” greenhouse gas emissions scenario were used (RCP8.5 and RPC4.5 for CMIP5 and A1B and B1 for CMIP3).


Learn more

The Coupled Model Intercomparison Project (CMIP, e.g., Taylor et al. 2012, http://cmip-pcmdi.llnl.gov/cmip5/) has organized international global climate model centers to support the Intergovernmental Panel on Climate Change (IPCC) assessments with simulations of the past and future climate. The CMIP provides a standard experimental protocol for coupled atmosphere-ocean general circulation model simulations. There are two generations of the Coupled Model Intercomparison Project currently in use (there was no CMIP4):

  • CMIP3 - used in the IPCC Fourth Assessment Report (AR4)
  • CMIP5 - used in the IPCC Fifth Assessment Report (AR5)

Because downscaled scenarios and derived hydrologic products from CMIP5 were only beginning to become available during the course of this effort and because there has been no conclusive evaluation of relative quality of CMIP5 and CMIP3 for the PNW, the Time of Emergence results are based on both CMIP3 and CMIP5 global model simulations (Table 1). Given the thorough documentation of model performance simulating PNW climate (Rupp et al. 2013, CMIP5; Mote and Salathé 2010, CMIP3) and the published guidance that choosing a large model ensemble is more reliable than attempting to select only a few top models (Mote and Salathé, 2010), Time of Emergence analysis was performed for all global model results available for the PNW after appropriate downscaling or hydrologic simulations. 

Table 1. List of global climate models used in this analysis

Model

Organization

Dataset

ACCESS1-0

Commonwealth Scientific and Industrial Research Organization/ Bureau of Meteorology, Australia

CMIP5

BCC-CSM-1-1

Beijing Climate Center, China Meteorological Administration, China

CMIP5

BNU-ESM

Beijing Normal University, China

CMIP5

CANESM1

Canadian Centre for Climate Modelling and Analysis, Canada

CMIP5

CCSM4

National Center for Atmospheric Research, University Corporation for Atmospheric Research, USA

CMIP5

CESM1-BGC

National Center for Atmospheric Research, University Corporation for Atmospheric Research, USA

CMIP5

CMCC-CM

Euro-Mediterranean Center on Climate Change, Italy

CMIP5

CNRM-CM5

National Centre for Meteorological Research, France

CMIP5

CSIRO-MK3-6-0

Commonwealth Scientific and Industrial Research Organization/Queensland Climate Change Center of Excellence, Australia

CMIP5

FGOALS-G2

Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, China

CMIP5

GFDL-CM3

National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, USA

CMIP5

GFDL-ESM-2G

National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, USA

CMIP5

GISS-E2-R

National Aeronautics and Space Administration Goddard Institute for Space Studies, USA

CMIP5

HADGEM2-ES

Meteorological Office Hadley Centre, UK

CMIP5

INMCM4

Institute for Numerical Mathematics, Russian Academy of Sciences, Russia

CMIP5

IPSL-CM5A-MR

Dynamical Meteorology Laboratory at the Pierre-Simon Laplace Institute, France

CMIP5

MIROC5

Atmosphere and Ocean Research Institute, National Institute for Environmental Studies/Japan Agency for Marine-Earth Science and Technology, Japan

CMIP5

MIROC-ESM

Atmosphere and Ocean Research Institute, National Institute for Environmental Studies/Japan Agency for Marine-Earth Science and Technology, Japan

CMIP5

MPI-ESM-LR

Max Planck Institute for Meteorology, Germany

CMIP5

MRI-CGCM3

Meteorological Research Institute, Japan Meteorological Agency, Japan

CMIP5

NORESM1-M

Norwegian Climate Center, Norway

CMIP5

CCSM3

National Center for Atmospheric Research, University Corporation for Atmospheric Research, USA

CMIP3

CGCM3.1

Canadian Centre for Climate Modelling and Analysis, Canada

CMIP3 (for scenario A1B only for temperature, precipitation and hydrologic variables)

CNRM-CM3

National Centre for Meteorological Research, France

CMIP3

ECHAM5

Max Planck Institute for Meteorology, Germany

CMIP3

ECHO-G

Meteorological Institute of the University of Bonn (Germany), Institute of KMA (Korea)

CMIP3

HADCM2 Meteorological Office Hadley Centre, UK CMIP3 (for scenario B1 only for temperature, precipitation and hydrologic simulations)

PCM1

Los Alamos National Laboratory, the Naval Postgraduate School, the US Army Corps of Engineers' Cold Regions Research and Engineering Lab, and the National Center for Atmospheric Research, USA

CMIP3

REFERENCES

Mote, P. W., and E. P. Salathe, 2010. Future climate in the Pacific Northwest. Climatic Change, 102: 29-50.

Rupp, D., J. Abatzoglou,  K. Hegewisch, and P. Mote, 2013. Evaluation of CMIP5 20th century climate simulations for the Pacific Northwest USA. Journal of Geophysical Research: Atmospheres, 118: 10,884-10,906.

Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012. An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society 93:485-498.


 

Downscaled Climate and Hydrology Projections: A Summary

The following downscaled climate and hydrologic data have been used in the Time of Emergence analysis:

  Statistical Downscaling Dynamical Downscaling
Identifier in ToE website BCSD5 BCSD3 WRF3
Downscaling approach Bias-Corrected Statistical Downscaling (BCSD) (Thrasher et al. 2013) Bias-Corrected Statistical Downscaling (BCSD) (Hamlet et al. 2010) Weather Research and Forecast (WRF) mesoscale model (Salathé et al. 2010)
Input climate datasets CMIP5 CMIP3 CMIP3
Global climate models 21 global climate models (Table 1) 7 global climate models (Table 1) 1 global climate model (ECHAM5)
Resolution 0.125° grid (~12km by ~12km) 0.0625° grid (~6km by ~6km) 0.0625° grid (~6km by ~6km)
Climate projections 

Daily Tmax, Tmin; Precipitation

Daily Tmax, Tmin; Precipitation (for six of the seven models)

Daily Tmax, Tmin; Precipitation 

Hydrology projections (VIC) Daily streamflow volume at specified river locations

Spatially-distributed hydrologic variables (e.g. runoff, evapo-transpiration; for six of the seven models); daily streamflow volume at specified river locations

Spatially-distributed hydrologic variables (e.g. runoff, evapo-transpiration)

 


Learn more

Statistically-Downscaled Climate Projections

Downscaling approach used: The Bias-Corrected Statistical Downscaling (BCSD) approach (Hamlet et al. 2013, Tohver et a. 2014, Reclamation 2013, Thrasher et al. 2013)

Input climate projections: 21 global climate models for CMIP5 climate projections, hereafter BCSD5; 7 global climate models for CMIP3 climate projections, hereafter BCSD3

Selection of a subset of 21 global climate models from the BCSD5 dataset (Table 1) was based on the following criteria:

  • Coupled models using standard component models, i.e., component models that have subsequent versions and have been well documented in the metadata and literature;
  • Choice of a single model implementation rather than multiple versions of models from specific modeling centers;
  • Models for which simulations were available using both RCP4.5 and RCP8.5;
  • Models for which hydrology (VIC) simulations were available using the downscaled projections as input; and,
  • All global climate models that were used for the CMIP3-derived dataset described below.

The BCSD5 and BCSD3 downscaled simulations used in this analysis could be termed “ensembles of opportunity” since the ensemble members have not been specifically designed to span the full possible range of uncertainty. An “ensemble of opportunity” is comprised of models with generally similar structures (forcings, spatial resolution, etc.) because they were usually developed at the same time for the same reasons (i.e., IPCC reports).  However they will likely have different parameter choices and calibration histories.

REFERENCES

Hamlet, A.F., M.M. Elsner, G.S. Mauger, S-Y. Lee, I. Tohver, and R.A. Norheim, 2013. An overview of the Columbia Basin Climate Change Scenarios Project: Approach, methods, and summary of key results. Atmosphere-Ocean 51(4):392-415, doi: 10.1080/07055900.2013.819555.

Reclamation 2013. 'Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections: Release of Downscaled CMIP5 Climate Projections, Comparison with preceding Information, and Summary of User Needs', prepared by the U.S. Department of the Interior, Bureau of Reclamation, Technical Services Center, Denver, Colorado. 47pp. (http://gdo-dcp.ucllnl.org/downscaled_cmip_projections)

Tohver, I., A.F. Hamlet, and S-Y. Lee, 2014. Impacts of 21st century climate change on hydrologic extremes in the Pacific Northwest region of North America. Journal of the American Water Works Association 1-16. DOI: 10.1111/jawr.12199.

Thrasher B. , J. Xiong, W. Wang, F. Melton, A. Michaelis, and R. Nemani, 2013.  Downscaled climate projections suitable for resource management.  EOS, 94(37): 321–323 (DOI: 10.1002/2013EO370002).

 

Dynamically-Downscaled Climate Projections

Dynamically-downscaled model used: Weather Research and Forecast (WRF) mesoscale model

Input climate projections: 1 global climate models (ECHAM5 global model simulation from the CMIP3 project; Salathé et al. 2010 and 2014)

The WRF model (Michalakes, 1998; 2001a; 2001b) is expected to give different results from statistical downscaling in locations where fine-scale feedbacks or terrain effects can alter the simulated climate change signal, due to better representation of finer-scale processes. 

REFERENCES

Michalakes, J., J. Dudhia, D. Gill, J. Klemp and W. Skamarock: Design of a next-generation regional weather research and forecast model : Towards Teracomputing, World Scientific, River Edge, New Jersey, 1998, pp. 117-124.

Michalakes, J., S. Chen, J. Dudhia, L. Hart, J. Klemp, J. Middlecoff, and W. Skamarock (2001a): Development of a Next Generation Regional Weather Research and Forecast Model. Developments in Teracomputing: Proceedings of the Ninth ECMWF Workshop on the Use of High Performance Computing in Meteorology. Eds. Walter Zwieflhofer and Norbert Kreitz. World Scientific, Singapore. pp. 269-276.

Michalakes, J., S. Chen, J. Dudhia, L. Hart, J. Klemp, J. Middlecoff, and W. Skamarock (2001b): "Development of a Next Generation Regional Weather Research and Forecast Model" in Developments in Teracomputing: Proceedings of the Ninth ECMWF Workshop on the Use of High Performance Computing in Meteorology. Eds. Walter Zwieflhofer and Norbert Kreitz. World Scientific, Singapore. pp. 269-276.

Salathé, E.P., L.R. Leung, Y. Qian, and Y. Zhang. 2010. Regional climate model projections for the State of Washington. Climatic Change 102(1-2): 51-75, doi: 10.1007/s10584-010-9849-y.

Salathé Jr., E.P., A.F. Hamlet, C.F. Mass, S.-Y. Lee, M. Stumbaugh, and R. Steed, 2014. Estimates of Twenty-First-Century flood risk in the Pacific Northwest based on regional climate model simulations. J. Hydrometeor 15(5):1881–1899, doi: http://dx.doi.org/10.1175/JHM-D-13-0137.1

 

Hydrology Projections

Hydrologic model used: VIC hydrologic model (Reclamation 2013, Hamlet et al. 2013, Tohver et al. 2014)

Input climate projections: BCSD-downscaled climate projections, WRF climate simulations

The VIC simulations provide spatially distributed hydrologic variables on the fine-scale latitude-longitude grid (derived from six of the seven models providing BCSD3 input data*) as well as streamflow volumes routed to specific river locations (derived from the full suite of BCSD input data described above (21 models for BCSD5 and 7 for BCSD3; Table 1). The management-relevant climate hydrologic and streamflow-related variables used in the Time of Emergence analysis were computed from these simulations.

*VIC simulations of hydrologic variables were available for six of the seven global climate models identified as "CMIP3" in Table 1, above.

REFERENCES

Hamlet, A.F., M.M. Elsner, G.S. Mauger, S-Y. Lee, I. Tohver, and R.A. Norheim, 2013. An overview of the Columbia Basin Climate Change Scenarios Project: Approach, methods, and summary of key results. Atmosphere-Ocean 51(4):392-415, doi: 10.1080/07055900.2013.819555.

Reclamation 2013. 'Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections: Release of Downscaled CMIP5 Climate Projections, Comparison with preceding Information, and Summary of User Needs', prepared by the U.S. Department of the Interior, Bureau of Reclamation, Technical Services Center, Denver, Colorado. 47pp;http://gdo-dcp.ucllnl.org/downscaled_cmip_projections).

Tohver, I., A.F. Hamlet, and S-Y. Lee, 2014. Impacts of 21st century climate change on hydrologic extremes in the Pacific Northwest region of North America. Journal of the American Water Works Association 1-16. DOI: 10.1111/jawr.12199.

 

Data Provenance

For version control consideration, the data provenance for all daily data used in this analysis is given in Table 2 below:

Table 2. Data Provenance

Daily Variables

Dataset

Date of Download

Source

Reference

Tavg, Tmax, Tmin, Prcp

BCSD5

6-13-2014 through 6-18-2014

ssh: gdo-dcp.ucllnl.org

Thrasher et al. 2013

Tavg, Tmax, Tmin

BCSD3

10-23-2014

http://warm.atmos.washington.edu/2860/r7climate/hb2860_transient_runs/

Hamlet et al. 2013, Tohver et al. 2014

Tavg, Tmax, Tmin, Prcp, Baseflow, ET, PET, Runoff, Soil moisture, SWE

ECHAM5-WRF

11-04-2014

Internal CIG database

Salathé et al. 2010

Prcp, Baseflow, ET, PET, Runoff, Soil moisture, SWE

BCSD3

 

http://warm.atmos.washington.edu/2860/products/sites/

Hamlet et al. 2013, Tohver et al. 2014

Q from Station Data

BCSD5

7-17-2014 through 12-03-2014

http://gdo-dcp.ucllnl.org

Thrasher et al. 2013

Q from Station Data

BCSD3

8-13-2014 through 12-03-2014

http://warm.atmos.washington.edu/2860

Hamlet et al. 2013, Tohver et al. 2014

REFERENCES

Hamlet, A.F., M.M. Elsner, G.S. Mauger, S-Y. Lee, I. Tohver, and R.A. Norheim, 2013. An overview of the Columbia Basin Climate Change Scenarios Project: Approach, methods, and summary of key results. Atmosphere-Ocean 51(4):392-415, doi: 10.1080/07055900.2013.819555.

Salathé, E.P., L.R. Leung, Y. Qian, and Y. Zhang. 2010. Regional climate model projections for the State of Washington. Climatic Change 102(1-2): 51-75, doi: 10.1007/s10584-010-9849-y.

Thrasher B. , J. Xiong, W. Wang, F. Melton, A. Michaelis, and R. Nemani, 2013.  Downscaled climate projections suitable for resource management.  EOS, 94(37): 321–323 (DOI: 10.1002/2013EO370002).

Tohver, I., A.F. Hamlet, and S-Y. Lee, 2014. Impacts of 21st century climate change on hydrologic extremes in the Pacific Northwest region of North America. Journal of the American Water Works Association 1-16. DOI: 10.1111/jawr.12199.

 

Continue Reading:

 

 


The Time of Emergence project was conceived and funded by U.S. Army Corps of Engineers Climate Preparedness & Resilience programs & U.S. Environmental Protection Agency-Region 10. Methodologies and stakeholder engagement were developed and implemented by the University of Washington's Climate Impacts Group. The Time of Emergence online tool was developed with support from the Center for Data Science, University of Washington-Tacoma.