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The atmospheric hydrologic cycle in the ACME v0.3 model

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Abstract

We examine the global water cycle characteristics in the Accelerated Climate Modeling for Energy v0.3 model (a close relative to version 5.3 of the Community Atmosphere Model) in atmosphere-only simulations spanning the years 1980–2005. We evaluate the simulations using a broad range of observational and reanalysis datasets, examine how the simulations change when the horizontal resolution is increased from 1° to 0.25\(^{\circ }\), and compare the simulations against models participating in the the Atmosphere Model Intercomparison Project of the 5th Coupled Model Intercomparison Project (CMIP5). Particular effort has been made to evaluate the model using the best available observational estimates and verifying model biases with additional datasets when differences are known to exist among the observations. Regardless of resolution, the model exhibits several biases: global-mean precipitation, evaporation, and precipitable water are too high, light precipitation occurs too frequently, and the atmospheric residence time of water is too short. Many of these biases are shared by the multi-model mean climate of models participating in CMIP5. The reasons behind regional biases in precipitation are discussed by examining how different fields, such as local evaporation and transport of water vapor, contribute to the bias. Although increasing the horizontal resolution does not drastically change the water cycle, it does lead to a few differences: an increase in global mean precipitation rate, an increase in the fraction of total precipitation that falls over land, more frequent heavy precipitation (>30 mm day\(^{-1}\)), and a decrease in precipitable water. One of the most notable changes is the shift of precipitation produced by the convective parameterization to that produced by the large-scale microphysics parameterization. We analyze how changes in moisture and circulation with resolution contribute to this shift in the precipitation partitioning. Because changing horizontal resolution requires some re-tuning, the effect of that tuning was evaluated by performing an additional simulation at 1\(^{\circ }\) but using the tunings from the 0.25\(^{\circ }\) simulation. The evaluation shows that the more frequent heavy precipitation, the decrease in precipitable water, and the shift from convective to large-scale precipitation are predominantly due to resolution changes, while tuning changes have a major influence on the global mean precipitation and the land/ocean partitioning of precipitation.

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Acknowledgements

The authors acknowledge and thank the scientists and staff of the ACME Atmosphere team whose efforts were instrumental in the development, coordination, and execution of the model simulations and whose input and discussions have improved the study. The authors also thank two anonymous reviewers for comments that have helped improved the manuscript. This research used computing resources of the Argonne Leadership Computing Facility (ALCF), the Oak Ridge Leadership Computing Facility (OLCF), and the National Energy Research Scientific Computing Center (NERSC), all of which are supported by the Office of Science of the Department of Energy (DOE). ALCF, OLCF, and NERSC are supported under Contract nos. DE-AC02-06CH11357, DE-AC05-00OR22725, and DE-AC02-05CH11231, respectively. The ACME v0.3 model output used in this study can be obtained by contacting the corresponding author (terai1@llnl.gov). A number of observational datasets are compared against the model output. The GPCP v2.2 precipitation dataset can be obtained from http://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html. GPCP 1DD and TRMM 3B42 data are available from the NASA/GSFC Mesoscale Atmospheric Processes Laboratory (ftp://meso.gsfc.nasa.gov/pub/1dd-v1.2/) and Mirador (http://mirador.gsfc.nasa.gov), respectively. The CORE v2 ocean evaporation dataset was obtained and is available from the NCAR Research Data Archive at http://rda.ucar.edu/datasets/ds260.2/. The LandFlux-EVAL merged benchmark synthesis products of ETH Zurich were produced under the aegis of the GEWEX and ILEAPS projects (http://www.iac.ethz.ch/url/research/LandFlux-EVAL/). NVAP precipitable water data can be obtained from the NASA Langley ASDC User Services (https://eosweb.larc.nasa.gov/), and the RSS precipitable data were obtained from Remote Sensing Systems (http://www.remss.com/measurements/atmospheric-water-vapor/tpw-1-deg-product). The ECMWF Interim Reanalysis fields are publicly available from http://www.ecmwf.int/. The US Department of Energy’s (DOE) Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure for CMIP5 in partnership with the Global Organization for Earth System Science Portals. The model output can be obtained from the Earth System Grid Federation at https://pcmdi.llnl.gov/projects/esgf-llnl/. We thank all of the programs who made this data available. The efforts of C. R. Terai, P. M. Caldwell, Q. Tang, and M. L. Branstetter are supported as part of the Accelerated Climate Modeling for Energy (ACME) program, funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research. The efforts of S. A. Klein are supported by the Regional and Global Climate Modeling program of the United States Department of Energy’s Office of Science. This work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-JRNL-706823.

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Authors and Affiliations

  1. Cloud Processes Research Group, Lawrence Livermore National Laboratory, Mail Code L-103, Livermore, CA, 94551, USA

    Christopher R. Terai, Peter M. Caldwell, Stephen A. Klein & Qi Tang

  2. Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA

    Marcia L. Branstetter

Authors
  1. Christopher R. Terai
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  2. Peter M. Caldwell
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  3. Stephen A. Klein
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  4. Qi Tang
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  5. Marcia L. Branstetter
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Corresponding author

Correspondence to Christopher R. Terai.

Appendix: NE30-model-with-NE120-tunings

Appendix: NE30-model-with-NE120-tunings

Figure 19 shows the difference in the spatial distribution of precipitation, evaporation, and precipitable water between the NE120 model and NE30-model-with-NE120-tunings. A comparison with panel b of Figs. 3, 4, 6, and 10 shows that most of the differences between the NE120 and NE30 model are also present in the differences between the NE120 model and NE30-model-with-NE120-tunings, which means that spatial differences are mostly due to differences in horizontal resolution, rather than differences in time step or tunings.

Fig. 19
figure 19

a Top left difference in precipitation rate between NE120 simulation and NE30-model-with-NE120 tuning simulation, b top right same as a but for oceanic evaporation, c bottom left same as a but for land evaporation, and d bottom right same as a but for precipitable water. In all panels, contours of the NE120 mean field are included to aid comparison

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Terai, C.R., Caldwell, P.M., Klein, S.A. et al. The atmospheric hydrologic cycle in the ACME v0.3 model. Clim Dyn 50, 3251–3279 (2018). https://doi.org/10.1007/s00382-017-3803-x

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