Documentation
Summary
Purpose
We created this tool to facilitate the access to state of the art regional climate projections to a wider public. The used data is publicly available and the scripts developed for this website are based on open source code. Our contribution here is to constrain the information to some basic graphics that can easily be interpreted and reused for reports involving climate change.
Methods
The graphics described in presentation of the projections are based on four basic functionalities:
- Period averages are used in overview maps as the climatology map and the projection map. As inter-annual variability might be larger than the climate change signal the future climate as well as the current climate have to be treated as period averages (we suggest 30 years). For each grid-cell the time average of all monthly values (of the chosen months) for the years within the chosen period is computed.
- Area averages are used for the transient plot and the annual cycle plot. The area average is computed at each time step as the average of all grid-cells touched by the region. Grid-cells are weighted by the fraction of the area of the region covered by the grid-cell.
- We display the ensemble mean of the model projections instead of showing the results separately for each model. The ensemble mean is the mean value of the 6 models computed at each grid-cell or at each time step depending on the plot.
Data Sources
Choice of the ensemble
The studied ensemble includes four combinations of RCMs and GCMs. Although more combinations would be possible, we restrict ourself to a smaller set in which each model appears only once in order to avoid biases towards one of the models.
| GCM forcing | RCM |
|---|---|
| MPIESM | REMO |
| HADGEM2 | CCLM4 |
| ECEARTH | RACMO |
| IPSL | RCA |
Choice of the emission scenario
- SSP245: CO2 emissions around current levels until 2050, then falling but not reaching net zero by 2100
- SSP585: It refers to very high GHG emissions. According to this scenario, CO2 emissions triple by 2075.
EWEMBI (observations and reanalysis)
EWEMBI is a product of several reanalysis and observation datasets (Lange 2016). It has global coverage on a 0.5°x0.5° grid. EWEMBI data is used for bias correction as well as for the representation of the reference climatology. Please find more information on https://www.isimip.org/gettingstarted/details/27/
Bias Correction
A trend-preserving bias correction technique is applied to the RCM climate projections. This bias correction technique adjusts climate simulations to a reference dataset (EWEMBI) over a reference periods without influencing projected trends (Hempel 2013).
Regional Shapefiles
Borders of countries and administrative regions within the countries can be found on http://www.gadm.org
Presentation of Climate Projections
Reference Period Map
For each indicator, a map of the climatology is presented. This map is a period average over the reference period for the EWEMBI dataset. For temperature indicators, hotter regions are darker than cooler regions. For precipitation indicators, wetter regions are darker than drier regions.
Projection Map
Changes in climate indicators are presented as the difference between the period average of the projection period and the period average of the reference period. These differences are calculated individually for each model. In the map, the ensemble mean change is presented. Grid cells for which the sign of the projected change of two models differs from the the ensemble mean are coloured in grey as models disagree on how the future climate might change in this cell. Changes towards warmer or drier conditions are coloured in red, changes towards cooler or wetter conditions are coloured in blue.
Regionally Averaged Transient
For the transient plots, the regional average is taken at each time step (grid-points are weighted by the fraction of the region covered by the grid-cell). The evolution of this area average is shown for RCM projections. The ensemble mean of RCM projections are shown by a thick green line while the model spread is represented by green shading. When choosing a month or a defined season, only the time steps corresponding to the month (months in season) are considered. Results are presented as a 20 year running mean.
Regionally Averaged Annual Cycle
In the upper panel, the annual cycle is shown for the reference period for EWEMBI and RCM projections. In the lower panel the projected change in the annual cycle is shown. This projected change is computed as the difference between the annual cycle of the projection period and the annual cycle of the reference period. As for the transients the regional average is presented. The ensemble mean of RCM projections are shown by a thick green line while the model spread is represented by green shading.
Climate Data Tool (Python)
This website is based on a climate data tool developed for the analysis of regional climate information. Besides the functions to compute period averages, area averages and ensemble means the tool has several additional functions that could be useful for for more sophisticated analysis.
The open source code and a documentation of the tool will be published soon.
References
Hempel, Sabrina, et al. "A trend-preserving bias correction–the ISI-MIP approach." Earth System Dynamics 4.2 (2013): 219-236.
Lange, Stefan (2016): EartH2Observe, WFDEI and ERA-Interim data Merged and Bias-corrected for ISIMIP (EWEMBI). GFZ Data Services. http://doi.org/10.5880/pik.2016.004
Sippel, Sebastian, et al. "A novel bias correction methodology for climate impact simulations." Earth System Dynamics 7.1 (2016): 71.