TY - JOUR
T1 - Sensitivity of Mountain Hydroclimate Simulations in Variable-Resolution CESM to Microphysics and Horizontal Resolution
AU - Rhoades, Alan M.
AU - Ullrich, Paul A.
AU - Zarzycki, Colin M.
AU - Johansen, Hans
AU - Margulis, Steven A.
AU - Morrison, Hugh
AU - Xu, Zexuan
AU - Collins, William D.
N1 - Funding Information:
The IT support provided by staff from the DoE NERSC (Helen He), NCAR Yellowstone, and UC, Davis Farm (Bill Broadley and Terri Knight) clusters and Mary Haley’s guidance with NCL related troubleshoots were invaluable. Further, we would like to acknowledge the work of the scientists and organizations who were instrumental in generating the PRISM, MODIS, and SNSR products utilized in this study to evaluate the VR-CESM model performance. This research was funded by the National Science Foundation (NSF) via the Climate Change, Water, and Society IGERT program at the University of California, Davis (NSF Award 1069333), the Leland Roy Saxon and Georgia Wood Saxon Fellowship, and the Lawrence Berkeley National Laboratory (LBNL) Lab Directed Research and Development (LDRD) Program, ‘‘Modeling the Earth’s Hydrological Cycle from Watershed to Global Scales’’ project. Support also comes from the Department of Energy, Office of Science ‘‘Multiscale Methods for Accurate, Efficient, and Scale-Aware Models of the Earth System’’ project (contract DE-AC02–05CH11231), ‘‘An Integrated Evaluation of the Simulated Hydroclimate System of the Continental US’’ project (award DE SC0016605), and from the U.S. and China Clean Energy Research Center for Water-Energy Technologies/ California Energy Commission grant 300-15-006 (to S.A.M.). The reference data sets for model evaluation used in this study are publicly available at their source repositories. The VR-CESM simulations are made available at the following Department of Energy National Energy Research Scientific Computing Center Science Gateway— http://portal.nersc.gov/archive/home/ a/arhoades/Shared/www/JAMES_2018. Please notify arhoades@lbl.gov if you access and use any of the data sets.
Publisher Copyright:
© 2018. The Authors.
PY - 2018/6
Y1 - 2018/6
N2 - Mountains are natural dams that impede atmospheric moisture transport and water towers that cool, condense, and store precipitation. They are essential in the western United States where precipitation is seasonal, and snowpack is needed to meet water demand. With anthropogenic climate change increasingly threatening mountain snowpack, there is a pressing need to better understand the driving climatological processes. However, the coarse resolution typical of modern global climate models renders them largely insufficient for this task, and signals a need for an advanced strategy. This paper continues the assessment of variable-resolution in the Community Earth System Model (VR-CESM) in modeling mountain hydroclimatology to understand the role of grid-spacing at 55, 28, 14, and 7 km and microphysics, specifically the Morrison and Gettelman (, MG1, https://doi.org/10.1175/2008JCLI2105.1) scheme versus the Gettelman and Morrison (, MG2, https://doi.org/10.1175/JCLI-D-14-00102.1) scheme. Eight VR-CESM simulations were performed from 1999 to 2015 with the F_AMIP_CAM5 component set, which couples the atmosphere-land models and prescribes ocean data. Refining horizontal grid-spacing from 28 to 7 km with the MG1 scheme did not improve the simulated mountain hydroclimatology. Substantial improvements occurred with the use of MG2 at grid-spacings ≤28 km compared to MG1 as shown with subsequent statistics. Average SWE bias diminished by 9.4X, 4.9X, and 3.5X from 55 to 7 km. The range in minimum (maximum) DJF spatial correlations increased by 0.1–0.2 in both precipitation and SWE. Mountain windward/leeward distributions and elevation profiles improved across hydroclimate variables, however not always with model resolution alone. Disconcertingly, all VR-CESM simulations exhibited a systemic mountain cold bias that worsened with elevation and will require further examination.
AB - Mountains are natural dams that impede atmospheric moisture transport and water towers that cool, condense, and store precipitation. They are essential in the western United States where precipitation is seasonal, and snowpack is needed to meet water demand. With anthropogenic climate change increasingly threatening mountain snowpack, there is a pressing need to better understand the driving climatological processes. However, the coarse resolution typical of modern global climate models renders them largely insufficient for this task, and signals a need for an advanced strategy. This paper continues the assessment of variable-resolution in the Community Earth System Model (VR-CESM) in modeling mountain hydroclimatology to understand the role of grid-spacing at 55, 28, 14, and 7 km and microphysics, specifically the Morrison and Gettelman (, MG1, https://doi.org/10.1175/2008JCLI2105.1) scheme versus the Gettelman and Morrison (, MG2, https://doi.org/10.1175/JCLI-D-14-00102.1) scheme. Eight VR-CESM simulations were performed from 1999 to 2015 with the F_AMIP_CAM5 component set, which couples the atmosphere-land models and prescribes ocean data. Refining horizontal grid-spacing from 28 to 7 km with the MG1 scheme did not improve the simulated mountain hydroclimatology. Substantial improvements occurred with the use of MG2 at grid-spacings ≤28 km compared to MG1 as shown with subsequent statistics. Average SWE bias diminished by 9.4X, 4.9X, and 3.5X from 55 to 7 km. The range in minimum (maximum) DJF spatial correlations increased by 0.1–0.2 in both precipitation and SWE. Mountain windward/leeward distributions and elevation profiles improved across hydroclimate variables, however not always with model resolution alone. Disconcertingly, all VR-CESM simulations exhibited a systemic mountain cold bias that worsened with elevation and will require further examination.
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U2 - 10.1029/2018MS001326
DO - 10.1029/2018MS001326
M3 - Article
AN - SCOPUS:85051284456
SN - 1942-2466
VL - 10
SP - 1357
EP - 1380
JO - Journal of Advances in Modeling Earth Systems
JF - Journal of Advances in Modeling Earth Systems
IS - 6
ER -