Dataset
Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): free data produced by the SPEAR model at NOAA-GFDL
Abstract
This dataset contains model data for SNAPSI experiment 'free' produced by scientists at NOAA-GFDL (National Oceanic and Atmospheric Administration- Geophysical Fluid Dynamics Laboratory) in Princeton, USA. This dataset contains all ensemble members by the GFDL SPEAR model.
The SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.
The free experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.
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Sources of additional information
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The following web links are provided in the Details/Docs section of this catalogue record:
- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts
- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)
- GFDL SPEAR reference publication: Delworth et al. (2020)
Details
| Previous Info: |
No news update for this record
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| Previously used record identifiers: |
No related previous identifiers.
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| Access rules: |
Access to these data is available to any registered CEDA user. Please Login or Register for a CEDA account to gain access.
Use of these data is covered by the following licence(s): https://creativecommons.org/licenses/by-sa/4.0/ When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record. |
| Data lineage: |
The data is based on pressure-level output from the SPEAR model provided by Baoqiang Xiang. It has been lightly processed to conform to the SNAPSI data request. The data has been published by the Centre for Environmental Data Analysis (CEDA). |
| Data Quality: |
Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.
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| File Format: |
Data are Net-CDF formatted
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Related Documents
Process overview
| Title | NOAA-GFDL SPEAR Model |
| Abstract | The underlying data was produced by the National Oceanic and Atmospheric Administration- Geophysical Fluid Dynamics Laboratory (NOAA-GFDL) SPEAR model for the SNAPSI project. |
| Input Description | None |
| Output Description | None |
| Software Reference | None |
- units: K
- standard_name: air_temperature
- var_id: ta
- long_name: Air Temperature
- units: W m-2
- standard_name: surface_downward_heat_flux_in_sea_water
- long_name: Downward Heat Flux at Sea Water Surface
- var_id: hfds
- units: m s-1
- standard_name: eastward_wind
- var_id: ua
- long_name: Eastward Wind
- units: m s-1
- standard_name: northward_wind
- var_id: vas
- long_name: Northward Near-Surface Wind
- units: degC
- standard_name: sea_surface_temperature
- var_id: tos
- long_name: Sea Surface Temperature
- units: m
- standard_name: surface_snow_thickness
- long_name: Snow Depth
- var_id: snd
- units: Pa
- standard_name: surface_air_pressure
- var_id: ps
- long_name: Surface Air Pressure
- units: kg m-2
- standard_name: surface_snow_amount
- var_id: snw
- long_name: Surface Snow Amount
- units: K s-1
- standard_name: tendency_of_air_temperature_due_to_longwave_heating
- long_name: Tendency of Air Temperature Due to Longwave Radiative Heating
- var_id: tntrl
- units: m
- standard_name: height
- var_id: height
- long_name: height
- var_id: lat_bnds
- var_id: lon_bnds
- units: Pa
- standard_name: air_pressure
- long_name: pressure
- var_id: plev
- var_id: time_bnds
Co-ordinate Variables
- units: degrees_north
- standard_name: latitude
- var_id: lat
- long_name: Latitude
- units: degrees_east
- standard_name: longitude
- var_id: lon
- long_name: Longitude
- long_name: time
- standard_name: time
- var_id: time
- units: days
Temporal Range
2018-02-08T00:00:00
2019-11-15T00:00:00
Geographic Extent
90.0000° |
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-180.0000° |
180.0000° |
|
-90.0000° |