Data source of climate change runs
For the JRC PESETA II study climate simulation runs were obtained from the FP6 ENSEMBLES project (van der Linden and Mitchell, 2009). Runs were driven by the SRES A1B emission scenario (Nakicenovic and Swart, 2000), and the so called E1 emission scenario (Tol, 2006). The E1 scenario was developed within ENSEMBLES as an attempt to match the European Union target of keeping global anthropogenic warming below 2 °C above pre-industrial levels.
It is important to note that climate model outputs may present significant errors (biases) when compared to observations: for instance, modeled summer temperatures in Southern Europe are usually overestimated, while large biases exist for precipitation. Consequently, the climate runs originally obtained from the ENSEMBLES project (12 A1B and 3 E1) were corrected for biases in temperature and precipitation by Dosio and Paruolo (2011), and Dosio et al. (2012).
Core climate runs of the JRC PESETA II project
The JRC PESETA II study considered four core climate runs, which have been analysed by all the biophysical impact models of the project:
- Reference Run. It is interpreted as representing well the central or average of the A1B runs. This run is interpreted as business as usual scenario. The two additional A1B runs show significant deviations from the average climate change signal, being usually warmer and drier (Reference Variant 1) or colder and wetter (Reference Variant 2) than the average;
- Reference Variant 1 is the climate run that is warmer and drier than the average;
- Reference Variant 2 is the climate run that is colder and wetter than the average;
- The 2°C Scenario. This run is an example of the E1 scenario. This run is therefore referred to as '2°C scenario' in the project.
The combination of climate models chosen for each core run is shown in table below. All the models driven by the same A1B emission scenario represent an equally probable projection of the future evolution of the climate. However, the selected runs show a significant variety in climate change signal for both temperature and precipitation. One can therefore expect that by using these three simulations as an input for the study of impact assessment of climate change, the main statistical characteristics of the A1B scenario as modelled by the whole ensemble of RCMs are relatively well represented.
|Climate Models Employed|
|Core run||Agriculture||Sea-level rise||All other impacts|
|Reference run||A1B ECHAM5 (UKMO)||30 cm sea-level rise|
(median A1B projection)
|Reference Variant 1||A1B ECHAM5 (DMI)||30 cm sea-level rise|
(median A1B projection)
|Reference Variant 2||A1B EGMAM2006 (FUB)||30 cm sea-level rise|
(median A1B projection)
|2°C run||E1 ECHAM5.4 (MPI)||18 cm sea-level rise|
(median E1 projection)
The next table shows the temperature change in the 2071-2100 period, compared to the 1961-1990 period, for the EU and the EU regions considered in the study.
|UK & Ireland||2.1||2.9||1.7||1.4|
|Central Europe north||2.8||3.7||2.0||2.1|
|Central Europe south||3.0||3.8||2.0||2.1|
Sea level rise scenarios
The sea level rise (SLR) projections come from the FP7 ClimateCost project (Brown et al. 2011). For the A1B scenario, the medium projection for SLR in the 2080s is 30 cm, and 18 cm for the E1 medium projection. The respective values for SLR in 2100 are 37 cm and 26 cm. The coastal impacts have been computed taking into account the projected damages for the 2080s.
Full set of climate change runs
Some sectoral impact teams run the whole set of climate change runs, twelve for the A1B scenario and three for the E1 scenario. The specific GCMs and RCMs for each climate run appear in the following tables. While the A1B runs come from different combinations of GCMs and RCMs, the three E1 runs come from the same GCM and RCM (see next two tables), with different boundary conditions for the GCM. Thus the range of considered E1 runs captures much less uncertainty in future climate than in the case of the A1B run.
Climate change runs from the A1B scenario (25 km resolution):
Climate change runs from the E1 scenario (50 km resolution):
|MPI-REMO-ECHAM5-r1||REMO||ECHAM5 (BC r1)|
|MPI-REMO-ECHAM5-r2||REMO||ECHAM5 (BC r2)|
|MPI-REMO-ECHAM5-r3||REMO||ECHAM5 (BC r3)|
Climate data input for the biophysical impact models
The next table details the specific climate variables that have been used in each of the project sectoral studies, barring the coastal assessment, which only uses sea level rise as a climate input. Most studies have used daily climate variables. While some sectors have considered a wide range of climate variables (e.g. river floods and agriculture), other sectors have required fewer variables (e.g. human health).
|Sector||Input variables||Time resolution||Spatial resolution|
|Transport||Average temperature||Daily||25x25, 50x50 km|
|Human Health||Maximum temperature (June-September)||Daily||NUTS2 regions|
|Tourism||Average temperature||Daily||NUTS2 regions|
|Agriculture||Maximum air temperature||Daily||25x25, 50x50 km|
|Minimum air temperature|
|Global solar radiation|
|Air relative humidity maximum and minimum|
|Vapour pressure definit|
|River floods||Maximum and average temperature||Daily||25x25, 50x50 km|
|Solar and thermal radiation|
|Forest fires||Average air temperature||Annual||25x25, 50x50 km|
|Average temperature||Annual; Monthly||25x25, 50x50 km|
|Minimum air temperature||Monthly|
|Average precipitation||Annual; Monthly|
Brown S, Nicholls RJ, Vafeidis A, Hinkel J, and Watkiss P (2011).
The Impacts and Economic Costs of Sea-Level Rise in Europe and the Costs and Benefits of Adaptation. Summary of Results from the EC RTD ClimateCost Project. In Watkiss, P (Editor), 2011. The ClimateCost Project. Final Report. Volume 1: Europe. Published by the Stockholm Environment Institute, Sweden, 2011. ISBN 978-91-86125-35-6.
Ciscar J-C, Iglesias A, Feyen L, Szabo L, Van Regemorter D, Amelung B, Nicholls R, Watkiss P, Christensen O, Dankers R, Garrote L, Goodess C, Hunt A, Moreno A, Richards J, Soria A (2011).
Physical and Economic Consequences of Climate Change in Europe.
PNAS, 108 7 pp.2678-2683.
Dosio A., Paruolo P., and Rojas R. (2012).
Bias correction of the ENSEMBLES high resolution climate change projections for use by impact models: analysis of the climate change signal
J. Geophys. Res, 117, DOI:10.1029/2012JD017968
Dosio A and Paruolo P (2011).
Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: Evaluation on the present climate,
J. Geophys. Res., 116, D16106, DOI: 10.1029/2011JD015934.
Nakicenovic, N. & Swart, R. (2000).
Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change.
Cambridge University Press, Cambridge, U.K.