GCM runs based on SRES emission data

Meteorological scenarios are generally prepared by downscaling from Global Circulation Model (GCM) results. One important input for GCMs is information on concentrations of radiatively active gases – based on GHG emission inventories. As downscaled GCM results were unavailable during the first phase of ECLAIRE, a set of older GCM data was applied. IPCC’s Second Report on Emission Inventories (SRES) provides a set of “families” of scenarios, of which A1 (and even more A1B) reflects a business-as-usual notion related to RCP8.5.

Extensive work has been performed on SRES GCM runs, which is also well documented (e.g. Kjellström et al., 2011 - The runs based on SRES scenarios also available from different GCM’s (in order to make results more robust) have been made available both to EMEP and directly to the vegetation models. Data files needed by DGVMs are potentially huge (hourly resolution for 140 years), so way to transfer was data physically via shipping of a hard disk. Some bias correction was performed (on a daily basis: temperature, precipitation, relative humidity – the latter according to Andersson-Sköld et al., 2008) to make up for the most critical issues in DGVMs.

In case hourly resolution is indeed needed, bias correction needs to be reconsidered. The following possibilities exist (but were not further explored in detail):                 

  1. Run all models (including the Chemistry&Transport Models, CTMs) on the same non-corrected data.
  2. Run all models on the same data, but employ own bias correction to certain parameters (possibly starting from the bias-corrected diurnal mean values and adding daily cycles to the temperatures). Someone has to volunteer for doing this, if bias correction should be harmonized.
  3. Run the CTMs on the meteorology available without correction, but run the DGVMs on other meteorology which is bias-corrected and available on sub-daily resolution. Possibly such data exist, (available in e.g. the ENSEMBLES data-base), which will of course induce inconsistencies.

Specifically, daily meteorological data from RCA3 - ECHAM5_A1B-r3 have been made available for download daily data via the internet. Due to huge file sizes, the links that have been provided to the ECLAIRE community could not be maintained for an extended period of time. The filenames provided with the dataset indicate the respective parameters reported:

SWMEAN_xxxx.txt.gz - daily average shortwave radiation [W/m2]

TMIN_xxxx.txt.gz - daily minimum temperature [K]

TMAX_xxxx.txt.gz - daily maximum temperature [K]

TMEAN_xxxx.txt.gz daily average temperature [K]


All files had been individually compressed with gzip -9 (no tar balls!), and were extracted from the raw, non bias-corrected, meteorology from the RCA3 downscaling of the ECHAM5 A1B-r3 simulation (that the CTMs used for air quality modelling). Daily average temperature was also provided as a courtesy in order to compare with the bias-corrected values provided earlier.

The files had been organised slightly differently compared to the earlier, bias-corrected, data. Each year came in a separate file. There were no headers etc.

Each yearly file consisted of 365 or 366 rows (depending on the length of the year), each row containing the daily value in the respective grid cell of RCA3. The first row stands for 1 Jan, second row is 2 Jan, etc.

There are (85x95; nx*ny) columns in each file. The first column is lower left of the RCA3-domain, column 85 is lower right of model domain, column 7991 is top left of domain, 8075 is top right of model domain, etc. (same order of data as in the bias-corrected data).

Requests for data on subdaily resolution for: (1) near-surface temperature; (2) precipitation; (3) near-surface humidity of air; (4) shortwave radiation; (5) near-surface windspeed have been accounted for, too.

SRES A1B-based meteorological scenario data have been used not only by DGVMs and DSVMs (WP6 and 14), but included also in CTMs and in the assessment of climate-dependent biodiversity sensitivity factors (WP19 and 20, see D20.7). Even extended ECLAIRE scenarios in WP5 (see D5.2) the same SRES-based scenarios were made use of (while referring to RCP in some of the emission data).


GCM runs based on RCP emission data

Using RCP obviously is more consistent with the emission database used. Due to time delays in producing downscaled data on GCM results derived from the RCP process, that information did not make it into meteorological data disseminated during the lifetime of ECLAIRE.