| |
|---|---|
| System name (version) | JRA-3Q |
| Date of implementation | November 2022 |
2. Configuration | |
|---|---|
Earth system components included in the analysis system (e.g., ocean, sea-ice, land, etc.) | Atmosphere, land. |
Horizontal resolution of the model, with indication of grid spacing in km (for the different Earth system component included in the model) | TL479 (~40km) for the atmospheric and land models. |
Number of levels in the different Earth system components (for the different Earth system component included in the model) | 100 levels for the atmospheric model. 7 levels for the land model. |
| Frequency of the outputs | Every 6 hours (hourly or daily for some types), monthly statistics. |
| Top of the atmospheric model | 0.01 hPa. |
| Number of analysis cycle per day | 4 |
| Earliest start date | September 1, 1947 |
| Integration time step | 720 seconds for outer model with TL479 resolution, 600 seconds for inner model with TL319 resolution. |
| Length and frequency of the longest forecast | Not applicable |
| Dataset latency | 3 days |
Additional comments:
| 3. Analysis system | |
| Data assimilation method | 4D-Var |
| Length of the analysis window | 6 hours |
| Number of ensemble members and their resolution | Not applicable |
Additional comments:
| 4. Externally prescribed boundary conditions and their source | |
| Sea surface temperature | Until May 1985: COBE-SST2 (Hirahara et al. 2014) From June 1985 onward: MGDSST (Kurihara et al. 2006) |
| Sea-ice | Until May 1985: COBE-SST2 (Hirahara et al. 2014) From June 1985 onward: MGDSST (Kurihara et al. 2006) |
| Snow | Snow depth analysis is performed daily. |
| Vegetation | GLC2000 |
| Land use (and its evolution in time) | Not applicable |
| Aerosols | Five types of aerosols (sulfate, black carbon, organic carbon, sea salt, and mineral dust) are considered to account for the direct effects of aerosols (Yabu et al. 2017). The three-dimensional monthly mean climatology of aerosol mass concentration was derived from a calculation that makes use of the Model of Aerosol Species in the Global Atmosphere (MASINGAR; Tanaka et al. 2003). |
| Green House Gases | CO2: -1983: CMIP6 Historical (Meinshausen et al. 2017) 1984-2016: WDCGG (World Meteorological Organization 2018) 2017-: CMIP6 SSP2-4.5 (O'Neill et al. 2016) CH4: -1983: CMIP6 Historical (Meinshausen et al. 2017) 1984-2016: WDCGG (World Meteorological Organization 2018) 2017-: CMIP6 SSP2-4.5 (O'Neill et al. 2016) N2O: -1983: CMIP6 Historical (Meinshausen et al. 2017) 1984-2016: WDCGG (World Meteorological Organization 2018) 2017-: CMIP6 SSP2-4.5 (O'Neill et al. 2016) CFC-11, CFC-12, HCFC-22: -1954: CMIP6 Historical (Meinshausen et al. 2017) 1955-: A1 scenario: 2014 (World Meteorological Organization 2014) |
| Solar forcing | Constant at 1365 W m-2 |
Additional comments:
| 5. Details of model | |
| Dynamical core (e.g., semi-Lagrangian) | Semi-implicit, semi-Lagrangian |
| Grid structure | Quasi-regular Gaussian latitude/longitude grids |
| Hydrostatic or nonhydrostatic | Hydrostatic |
| Radiations parameterization | Long wave radiation: Two-stream absorption approximation Correlated k-distribution method Short wave radiation: Two-stream with delta-Eddington approximation Cloud radiation: Maximum-random overlap (short wave) |
| Boundary layer parameterization | Monin-Obukhov similarity theory |
| Convection parameterization | Prognostic Arakawa-Schubert (1974) scheme |
| Cloud parameterization | Smith (1990) scheme Stratocumulus: Kawai and Inoue (2006) |
| Land surface parameterization | Improved SiB |
Other relevant details:
| 6. Further information | |
| Operational contact point | Numerical Prediction Division, Information Infrastructure Department, Japan Meteorological Agency |
| URL of the technical note/ reference paper | |
| URL for list of products | https://jra.kishou.go.jp/JRA-3Q/document/JRA-3Q_LL125_format_en.pdf https://jra.kishou.go.jp/JRA-3Q/document/JRA-3Q_TL479_format_en.pdf |
| 7. Observational data used | |
| URL with the list of observational data used in the reanalysis | https://doi.org/10.2151/jmsj.2024-004 Appendix B |
| DOI of data product if available |
Other sources for data access, if available
JRA data are also available at a handling cost for commercial purposes from the Japan Meteorological Business Support Center, and free of charge for non-commercial purposes under the license of CC-BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike) from cooperative organizations.
Japan Meteorological Business Support Center:
Cooperative organizations:
- DIAS: Data Integration & Analysis System
- NCAR: National Center for Atmospheric Research
- JRA-3Q: https://rda.ucar.edu/datasets/ds640.0
- Near Real-Time JRA-3Q: https://rda.ucar.edu/datasets/ds640.1
References
Hirahara, S., M. Ishii, and Y. Fukuda, 2014: Centennial-scale sea surface temperature analysis and its uncertainty. J. Climate, 27, 57–75.
Kawai, H., and T. Inoue, 2006: A simple parameterization scheme for subtropical marine stratocumulus. SOLA, 2, 17–20.
Kurihara, Y., T. Sakurai, and T. Kuragano, 2006: Global daily sea surface temperature analysis using data from satellite microwave radiometer, satellite infrared radiometer and in-situ observations. Weather Bull., 73, s1–s18 (in Japanese).
Meinshausen, M., E. Vogel, A. Nauels, K. Lorbacher, N. Meinshausen, D. M. Etheridge, P. J. Fraser, S. A. Montzka, P. J. Rayner, C. M. Trudinger, P. B. Krummel, U. Beyerle, J. G. Canadell, J. S. Daniel, I. G. Enting, R. M. Law, C. R. Lunder, S. O’Doherty, R. G. Prinn, S. Reimann, M. Rubino, G. J. M. Velders, M. K. Vollmer, R. H. J. Wang, and R. Weiss, 2017: Historical greenhouse gas concentrations for climate modelling (CMIP6). Geosci. Model Dev., 10, 2057–2116.
O’Neill, B. C., C. Tebaldi, D. P. van Vuuren, V. Eyring, P. Friedlingstein, G. Hurtt, R. Knutti, E. Kriegler, J.-F. Lamarque, J. Lowe, G. A. Meehl, R. Moss, K. Riahi, and B. M. Sanderson, 2016: The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geosci. Model Dev., 9, 3461–3482.
Smith, R. N. B., 1990: A scheme for predicting layer clouds and their water content in a general circulation model. Quart. J. Roy. Meteor. Soc., 116, 435–460.
Tanaka, T. Y., K. Orito, T. T. Sekiyama, K. Shibata, and M. Chiba, 2003: MASINGAR, a global tropospheric aerosol chemical transport model coupled with MRI/JMA98 GCM: Model description. Pap. Meteorol. Geophys., 53, 119–138.
World Meteorological Organization, 2014: Scientific assessment of ozone depletion, 2014. World Meteorological Organization, Global Ozone Research and Monitoring Project-Report, No. 55, Geneva, Switzerland, 416 pp.
World Meteorological Organization, 2018: World Data Centre for Greenhouse Gases (WDCGG) Data Summary. WDCGG, No. 42, 97 pp.
Yabu, S., T. Y. Tanaka, and N. Oshima, 2017: Development of a multi-species aerosol-radiation scheme in JMA’s global model. CAS/JSC WGNE Res. Activ. Atmos. Oceanic Modell., 47, 6.19–6.20.