EPA Dynamically Downscaled Ensemble (EDDE)
The EPA Dynamically Downscaled Ensemble (EDDE) is a collection of physics-based modeled data that represent historical and future atmospheric conditions under different scenarios. The EDDE Version 1 datasets cover the contiguous United States at a horizontal grid spacing of 36 kilometers at hourly increments. EDDE Version 1 includes simulations that have been dynamically downscaled from multiple global climate models under both mid- and high-emission scenarios. These datasets can be used to explore potential changes to extreme weather events and climate across the contiguous United States from the recent historical period out to 2100 to quantify potential impacts on human health and the environment.
On this page:
- What is “dynamical downscaling”?
- Advantages of Using EDDE
- Who Should Use EDDE?
- Technical Descriptions of the EDDE Version 1 Datasets
- Accessing EDDE datasets
- Technical Contacts
- Related Publications
What is “dynamical downscaling”?
“Downscaling” is a process used to create a higher-resolution interpretation of geospatial datasets. In regional climate modeling, downscaling is applied to a coarser-scale global climate model to create local data at finer spatial and temporal increments. There are generally two broad classes of downscaling: statistical and dynamical. In statistical downscaling, statistical relationships are developed for weather variables (such as temperature and rainfall) by analyzing a long record of observations, and those relationships are subsequently applied to global projections to arrive at localized projections of weather and climate. In dynamical downscaling, a physics-based regional climate model is used to explicitly simulate the atmospheric processes over time at a higher spatial resolution to arrive at the localized projections of weather and climate.
Statistical downscaling is an efficient way to create a spread of possible realizations across numerous climate change scenarios and at a reasonably high spatial resolution. Typically, statistically downscaled datasets contain only a handful of atmospheric fields, such as near-surface air temperature, rainfall, and humidity. Statistical downscaling relies on the connections between large-scale atmospheric processes (like high- and low-pressure systems and frontal passages) and the local weather that are based on a historical record of observations. The constraint against the historical data record cannot reflect how relationships between atmospheric observations could change in the future.
By contrast, dynamical downscaling is resource-intensive because physics-based regional climate models are used to simulate a comprehensive 3D representation of key variables at each level of the atmosphere while accounting for surface effects (such as the presence of snow or complex terrain) with an embedded land surface model. This is a more computationally intensive process than models that are built with historical statistical relationships. As a result, fewer scenarios are processed, but the atmospheric fields are dynamically consistent with each other and represent the change throughout the atmosphere. As a result, a more comprehensive suite of atmospheric and surface fields can be used to connect to other environmental models (such as air quality or hydrology) that require dynamically consistent information at regional and local scales.
Advantages of Using EDDE
The EDDE dataset can be used to quantify regional and local changes to extreme weather and climate over the contiguous U.S. in a physically consistent framework with both high temporal and spatial resolution. Some of the additional scientific advances in EDDE include:
3D physically consistent atmospheric fields: The use of a full physics regional climate model for generating the EDDE dataset provides a comprehensive 3D suite of dynamically consistent atmospheric and surface variables. This suite of variables is well suited for environmental applications that require physical consistency and/or a broader suite of atmospheric variables, such as air quality modeling.
High temporal resolution: While increased spatial resolution is an advantage of all downscaling techniques, data is often available only at daily or longer temporal scales. EDDE Version 1 data features key fields available on an hourly basis, which can be advantageous for users who require sub-daily data to run additional modeling applications or to compute climate-relevant parameters (such as accumulated degree days), among other applications.
Applications support: EDDE Version 1 has previously been used to support a wide variety of climate applications, including studies focused on air quality, extreme precipitation, and agricultural and ecosystem endpoints.
Methodology: Several updates to the regional climate model were incorporated to better capture land-surface processes, precipitation, and convection, as well as the relationship between the regional model and driving global dataset, as further described below.
Who Should Use EDDE?
EDDE can provide useful information for scientists and local or regional stakeholders interested in future climate projections which capture extreme events within a physically consistent framework.
Technical Descriptions of the EDDE Version 1 Datasets
Model Configuration
The Weather Research and Forecasting (WRF) model is used as a regional climate model to dynamically downscale each global climate model to a 36 kilometer domain over the contiguous U.S., as pictured below.
In EDDE Version 1, the driving global models and emissions are adapted from the Fifth Coupled Model Intercomparison Project (CMIP5). Each of the scenarios was dynamically downscaled using the WRF model. Two global climate models were selected for downscaling: the National Center for Atmospheric Research (NCAR) and U.S. Department of Energy (DOE) Community Earth System Model (CESM), and the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamical Laboratory (GFDL) Coupled Model Version 3 (CM3). CESM was downscaled from its archived resolution of 0.9° × 1.25° using WRF version 3.4.1 to a single domain with 36-kilometer grid spacing. GFDL-CM3 was downscaled from its archived resolution of 2° × 2.5° using WRF version 3.6 and utilizing nested 108- and 36-kilometer domains.
The global climate model historical simulations are not based on observed conditions; instead, they represent the conditions that could have occurred under the climate during that period. As such, the historical periods are “labeled” with an approximate year range represented by the climatic conditions, but the individual weather events are not expected to align with the observed events on any given day. Both global climate models were run for the 11-year period (labeled 1995–2005). CESM was later run for a new continuous simulation for a 31-year period (labeled 1975–2005) to provide a longer climatological duration for analysis. For EDDE ensemble members driven by CESM, multiple greenhouse gas emission scenarios – or Representative Concentration Pathways (RCPs) – are available: RCP4.5 and RCP6.0 (mid-range scenarios) and RCP8.5 (a higher-emission scenario). Future runs driven by CM3 are only available under RCP8.5.
| Scenario | Period | NCAR CESM | GFDL CM3 |
|---|---|---|---|
| hist | 1995–2005 | X | X |
| hist31 | 1975–2005 | X | |
| rcp4.5 | 2025–2100 | X | |
| rcp6.0 | 2025–2055 | X | |
| rcp8.5 | 2025–2100 | X | X |
For all ensemble members, the following WRF physics configuration was used:
| Model Option | Description |
|---|---|
| Subgrid Convection | Kain–Fritsch convective parameterization scheme with radiation feedback (Alapaty et al., 2012; Herwehe et al., 2014) |
| Microphysics | WRF Single-Moment Scheme 6 (WSM6) |
| Planetary Boundary Layer | Yonsei University (YSU) |
| Land-Surface Model | Noah Land-Surface Model |
| Land Use Classification | USGS 24-category, plus lakes |
| Lake temperatures | GFDL CM3: FLake coupled lake model, following Mallard et al. (2014) NCAR CESM: Prescribed from the Community Land Model following Spero et al. (2016) |
| Nudging | Spectral nudging of temperature, geopotential height, and horizontal wind components only above the planetary boundary layer (PBL) following Otte et al. (2012) |
Key scientific updates to methodology
EDDE Version 1 includes key science updates in several areas of the model which are designed to increase model skill, making it best suited for users seeking sophisticated treatments of:
Coupling between the global climate model and regional model: Here, nudging is used to constrain the regional model’s large-scale solution to align with the global model, which promotes consistency with the global climate model and is well-suited to simulate global climate teleconnections.
Lake conditions: EDDE Version 1 includes updates to the simulation of lake temperatures and ice by including a one-dimensional lake model within the framework used for GFDL CM3. Meanwhile, lake conditions simulated by the global climate model’s own land surface model were included in simulations driven by NCAR CESM.
Sophisticated treatment of convection: Physics options used for EDDE Version 1 include a scale-aware treatment of convection with the use of the Multi-scale Kain Fritsch, as well as modifications that allow parameterized sub grid-scale clouds to better interact with radiation and sub grid-scale thermodynamic processes.
Accessing EDDE Datasets
EDDE Version 1 can be accessed through Amazon Web Services (AWS) Open Data Project.
Metadata associated with EDDE is available on EPA’s Data Catalog at Data.gov.
A subset of the output from the WRF simulations is publicly available. The publicly available data are stored in individual netCDF files for each variable and include data for each month and year. The full 3D WRF output has been archived at EPA. Additional variables can be made available upon request. To aid in analysis, the following static fields are also included: landmask (lmask), terrain height (orog), and land use index (luidx).
Hourly Data
As listed below, several key fields are extracted and derived from hourly WRF output; additional fields are sampled at six-hour intervals.
| Variable [units] | File Name | Description |
|---|---|---|
|
2-m Air Temperature [K] |
ts | Temperature (directly from WRF) |
|
2-m Dew Point Temperature [K] |
td | Dew point temperature (derived from WRF) |
|
Surface Pressure [hPa] |
ps | Surface pressure (directly from WRF) |
|
Precipitation [mm] |
pr | Precipitation (derived from WRF) |
|
2-m Relative Humidity [%] |
hur | Relative humidity (derived from WRF) |
|
2-m Specific Humidity [g kg-1] |
hus | Specific humidity (directly from WRF) |
|
10-m Wind Direction [degrees] |
wdirs | Wind direction (derived from WRF) |
|
10-m Wind Speed [m s-1] |
wspds | Wind speed (derived from WRF) |
| Cloud Fraction [0 to 1] |
clt | Cloud area fraction (derived from WRF) |
| Latent Heat Flux [W m-2] |
hfls | Latent heat flux (directly from WRF) |
| Sensible Heat Flux [W m-2] |
hfss | Sensible heat flux (directly from WRF) |
| Longwave Radiation [W m-2] |
rlds | Downward longwave radiation at surface (directly from WRF) |
| Longwave Radiation [W m-2] |
rlut | Upward longwave radiation at top of atmosphere (directly from WRF) |
| Shortwave Radiation [W m-2] |
rsds | Downward shortwave radiation at surface (directly from WRF) |
| 10-m U-Component (Eastward) Wind Speed [m s-1] |
ua | 10-m eastward wind speed (earth-relative) (directly from WRF) |
| 10-m V-Component (Northward) Wind Speed [m s-1] |
va | 10-m northward wind speed (earth-relative) (directly from WRF) |
| Friction Velocity [m s-1] |
ustar | Friction velocity in air (directly from WRF) |
Six-hourly Data
| Variable [units] | File Name | Description |
|---|---|---|
| Air Pressure at Mean Sea Level [Pa] |
psl | Pressure |
|
Precipitable Water Vapor [m] |
pw | Thickness of liquid water equivalent precipitation amount |
|
Upper-Level Soil Moisture [kg m-2] |
sm10 | Soil moisture in top 10 centimeters |
|
Lower-Level Soil Moisture [kg m-2] |
sm200 | Soil moisture in layers 10-200 centimeters below ground |
|
Upper-Level Soil Temperature [K] |
st10 | Soil temperature in top 10 centimeters |
|
Lower-Level Soil Temperature [K] |
st200 | Soil temperature in layers 10-200 centimeters below ground |
|
Snow Water Equivalent [kg m-2] |
swe | Liquid water equivalent of snow |
|
Geopotential Height [m] |
zg250 | 250 hPa geopotential height |
|
Geopotential Height [m] |
zg500 | 500 hPa geopotential height |
|
Geopotential Height [m] |
zg850 | 850 hPa geopotential height |
Temporally Aggregated Data: Daily & Monthly
The following variables are temporally aggregated into daily and monthly timeseries.
| Variable [units] | File Name | Description |
|---|---|---|
| Mean Daily 2-m Air Temperature [K] | tsmean | Mean daily temperature (averaged from hourly data) |
| Mean Daily 2-m Air Temperature [K] | tsmean2 | Mean daily temperature (from daily min and max) |
| Monthly 2-m Air Temperature [K] | tsmeanmon | Mean monthly temperature (averaged from hourly data) |
| Monthly 2-m Air Temperature [K] | tsmean2mon | Mean monthly temperature (from daily min/max) |
| Maximum 2-m Air Temperature [K] | tsmax | Daily maximum temperature |
| Maximum 2-m Air Temperature [K] | tsmaxdavg | Daily maximum temperature, averaged over each month |
| Maximum 2-m Air Temperature [K] | tsmaxabs | Monthly maximum temperature |
| Minimum 2-m Air Temperature [K] | tsmin | Daily minimum temperature |
| Minimum 2-m Air Temperature [K] | tsmindavg | Daily minimum temperature, averaged over each month |
| Minimum 2-m Air Temperature [K] | tsminabs | Monthly minimum temperature |
| Precipitation [mm] |
prday | Daily precipitation |
| Precipitation [mm] |
prmon | Monthly precipitation |
| Specific Humidity [g kg-1] |
husmean | Mean daily specific humidity |
| Specific Humidity [g kg-1] |
husmeanmon | Mean monthly specific humidity |
| Wind Speed [m s-1] |
wspds | Daily average wind speed |
| Wind Speed [m s-1] |
wspdsmon | Monthly average wind speed |
Technical Contacts
-
Tanya Spero (Spero.Tanya@epa.gov)
-
Megan Mallard (Mallard.Megan@epa.gov)
Related Publications
EPA-Developed Methods Employed in the EDDE Datasets
Alapaty, K., J. A. Herwehe, T. L. Otte, C. G. Nolte, O. R. Bullock, M. S. Mallard, J. S. Kain, and J. Dudhia, 2012: Introducing subgrid-scale cloud feedbacks to radiation for regional meteorological and climate modeling. Geophysical Research Letters, 39, L24808.
Bowden, J. H., C. G. Nolte, and T. L. Otte, 2013: Simulating the impact of the large-scale circulation on the 2-m temperature and precipitation climatology. Climate Dynamics, 40, 1903–1920.
Bowden, J. H., T. L. Otte, C. G. Nolte, and M. J. Otte, 2012: Examining interior grid nudging techniques using two-way nesting in the WRF model for regional climate modeling. Journal of Climate, 25, 2805–2823.
Bullock, O. R., Jr., K. Alapaty, J. A. Herwehe, M. S. Mallard, T. L. Otte, R. C. Gilliam, and C. G. Nolte, 2014: An observation-based investigation of nudging in WRF for downscaling surface climate information to 12-km grid spacing. Journal of Applied Meteorology and Climatology, 53, 20–33.
Herwehe, J. A., K. Alapaty, T. L. Spero, and C. G. Nolte, 2014: Increasing the credibility of regional climate simulations by introducing subgrid-scale cloud-radiation interactions. Journal of Geophysical Research: Atmospheres, 119, 5317–5330.
Mallard, M. S., C. G. Nolte, O. R. Bullock, T. L. Spero, and J. Gula, 2014: Using a coupled lake model with WRF for dynamical downscaling. Journal of Geophysical Research: Atmospheres, 119, 7193–7208.
Mallard, M. S., C. G. Nolte, T. L. Spero, O. R. Bullock, K. Alapaty, J. A. Herwehe, J. Gula, and J. H. Bowden, 2015: Technical challenges and solutions in representing lakes when using WRF in downscaling applications. Geoscientific Model Development, 8, 1085–1096.
Mallard, M. S., and T. L. Spero, 2019: Effects of mosaic land use of dynamically downscaled WRF simulations of the contiguous United States. Journal of Geophysical Research: Atmospheres, 124, 9117–9140.
Mallard, M. S., T. L. Spero, and S. M. Taylor, 2018: Examining WRF’s sensitivity to contemporary land use datasets across the contiguous United States using dynamical downscaling. Journal of Applied Meteorology and Climatology, 57, 2561–2583.
Otte, T. L., C. G. Nolte, M. J. Otte, and J. H. Bowden, 2012: Does nudging squelch the extremes in regional climate modeling? Journal of Climate, 25, 7046–7066.
Spero. T. L., C. G. Nolte, J. H. Bowden, M. S. Mallard, and J. A. Herwehe, 2016: The impact of incongruous lake temperatures on regional climate extremes downscaled from the CMIP5 archive using the WRF model. Journal of Climate, 29, 839–853.
Spero, T. L., C. G. Nolte, M. S. Mallard, and J. H. Bowden, 2018: A maieutic exploration of nudging strategies for regional climate applications using the WRF model. Journal of Applied Meteorology and Climatology, 57, 1883–1906.
Spero, T. L., M. J. Otte, J. H. Bowden, and C. G. Nolte, 2014: Improving the representation of clouds, radiation, and precipitation using spectral nudging in the Weather Research and Forecasting Model. Journal of Geophysical Research: Atmospheres, 119, 11,682–11,694.
Analysis and Interpretation of the EDDE Datasets in Environmental Applications
Bowden, J. H., K. D. Talgo, T. L. Spero, and C. G. Nolte, 2016: Assessing the added value of dynamical downscaling using the Standardized Precipitation Index. Advances in Meteorology, 8432064.
Campbell, P. C., J. O. Bash, C. G. Nolte, T. L. Spero, E. J. Cooter, K. Hinson, and L. C. Linker, 2019: Projections of nitrogen deposition to the Chesapeake Bay Watershed. Journal of Geophysical Research: Biogeosciences, 124, 3307–3326.
Clark, C. M., J. Phelan, P. Doraiswamy, J. Buckley, J. C. Cajka, R. L. Dennis, J. Lynch, C. G. Nolte, and T. L. Spero, 2018: Atmospheric deposition and exceedances of critical loads from 1800–2025 for the conterminous United States. Ecological Applications, 28, 978–1002.
Dionisio, K. L., C. G. Nolte, T. L. Spero, S. Graham, N. Caraway, K. M. Foley, and K. K. Isaacs, 2017: Characterizing the impact of projected changes in climate and air quality on human exposures to ozone. Journal of Exposure Science & Environmental Epidemiology, 27, 260–270.
Fann, N., T. Brennan, P. Dolwick, J. L. Gamble, V. Ilacqua, L. Kolb, C. G. Nolte, T. L. Spero, and L. Ziska, 2016: Ch. 3: Air Quality Impacts. The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment. U.S. Global Change Research Program, Washington, DC, USA, 69–98.
Fann, N. C. G. Nolte, P. Dolwick, T. L. Spero, A. Curry Brown, S. Phillips, and S. Anenberg, 2015: The geographic distribution and economic value of climate change-related ozone health impacts in the United States in 2030. Journal of the Air & Waste Management Association, 65, 570–580.
Fann, N. L., C. G. Nolte, M. C. Sarofim, J. Martinich, and N. J. Nassikas, 2021: Associations between simulated future changes in climate, air quality, and human health. JAMA Network Open, 4.
Jalowska, A. M., and T. L. Spero, 2019: Developing PIDF curves from dynamically downscaled WRF model fields to examine extreme precipitation events in three eastern U.S. metropolitan areas. Journal of Geophysical Research: Atmospheres, 124, 13,895–13,913.
Jalowska, A. M., T. L. Spero, and J. H. Bowden, 2021: Projection changes in extreme rainfall from three tropical cyclones using the design-rainfall approach. npj Climate and Atmospheric Science, 4.
Mallard, M. S., K. D. Talgo, T. L. Spero, J. H. Bowden, and C. G. Nolte, 2023: Dynamically downscaled projections of phenological changes across the contiguous United States. Journal of Applied Meteorology and Climatology, 62, 1875–1889.
Nolte, C. G., P. D. Dolwick, N. Fann, L. W. Horowitz, V. Naik, R. W. Pinder, T. L. Spero, D. A. Winner, and Z. H. Ziska, 2018: Air Quality. In: Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II. [Reidmiller, D. R., C. W. Avery, D. R. Easterling, K. E. Kunkel, K. L. M. Lewis, T. K. Maycock, and B. C. Stewart (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, 512–538.
Nolte, C. G., T. L. Spero, J. H. Bowden, M. S. Mallard, and P. D. Dolwick, 2018: The potential effects of climate change on air quality across the conterminous US at 2030 under three Representative Concentration Pathways. Atmospheric Chemistry and Physics, 18, 15471–15489.
Nolte, C. G., T. L. Spero, J. H. Bowden, M. C. Sarofim, J. Martinich, and M. S. Mallard, 2021: Regional temperature-ozone relationships across the U.S. under multiple climate and emissions scenarios. Journal of the Air & Waste Management Association, 71, 1251–1264.
Seltzer, K. M., C. G. Nolte, T. L. Spero, K. W. Appel, and J. Xing, 2016: Evaluation of near surface ozone and particulate matter in air quality simulations driven by dynamically downscaled historical meteorological fields. Atmospheric Environment, 138, 42–54.
Wilson, A., B. J. Reich, C. G. Nolte, T. L. Spero, B. Hubbell, and A. G. Rappold, 2017: Climate change impacts on projections of excess mortality at 2030 using spatially varying ozone-temperature risk surfaces. Journal of Exposure Science & Environmental Epidemiology, 27, 118–124.
Zhang, W., T. L. Spero, C. G. Nolte, and coauthors, 2019: Projected changes in maternal heat exposure during early pregnancy and the associated congenital heart defect burden in the United States. Journal of the American Heart Association, 8.