ateam - Potsdam Institute for Climate Impact Research

ATEAM final report Section 5 and 6 (2001-2004) 13
second major dimension distinguishes 'globalisation’ (1) versus ‘regionalisation’ (2). The narratives
specified typical aspects, processes and their dynamics for each of the four quadrants identified by
these dimensions (A1, B1, A2 and B2). The A1 scenario was further elaborated by assuming different
combinations of fuels and technology development to satisfy energy demand. Of these A1 subscenarios
we consider only the A1f scenario in the ATEAM project, where the energy system remains
dominated by fossil fuels. The section Land use change scenarios below gives a more detailed
description of the storylines, while a full description is presented in detail by Nakícenovíc et al. (2000).
The SRES storylines drive the climate change scenarios (through emissions scenarios) as well as land
use change (through the different socio-economic development pathways). One of the strengths of
using the SRES framework is that the assumed socio-economic changes relate directly to climate
change through the SRES emissions scenarios, and to land use changes through socio-economic
measures, such as demand and technology assumptions. Thus, a range of internally-consistent,
quantitative scenarios of coupled socio-economic and climate changes were developed. The first step in
quantifying the narratives was to describe each of the four SRES worlds in terms of single timedependent
scenarios of atmospheric greenhouse gas concentration (Figure 2, this and all further figures
are appended in Annex 3). This was done using the integrated assessment model IMAGE 2.2 (IMAGE
team, 2001). The A1f socio-economic scenario results in the highest emissions and concentrations of
carbon dioxide, followed by A2, B2 and finally B1 (Figure 2).
6.2.1.2 Climate change scenarios
Climate change scenarios for monthly values of five different climatic variables (monthly temperature,
diurnal temperature range, precipitation, vapour pressure and cloud cover) were created for all 16
combinations of four SRES emissions scenarios (A1f, A2, B1, B2, see above) and four general
circulation models (GCMs; PCM, CGCM2, CSIRO2, HadCM3), using GCM outputs from the IPCC Data
Distribution Centre. The results were subsequently downscaled from 0.5°x0.5° to 10’x10’ resolution.
The climate scenarios of the 21 st century replicate observed month-to-month, inter-annual and multidecadal
climate variability of the detrended 20 th century climate. The climate data used in this study are
the European observed climate 1901-2000, 16 climate scenarios for 2001-2100, and a single ‘control’
scenario of unforced climate (1901-2100) based on the detrended 1901-2000 historical record. The full
method is described in Mitchell at al. (2004). The scenarios are known as TYN SC 1.0 and are publicly
available (http://www.cru.uea.ac.uk/). Priority scenarios were identified to allow a reduced analysis
because resources were limiting (Table 2).
Table 2 The complete set of climate change scenarios combining different emissions
scenarios and GCMs. The table also indicates the recommendation for priority application.
High means all modelling groups were asked to apply these scenarios, Medium are strongly
recommended to be used if possible, and Low are not mandatory, but are desirable if
computational facilities and time allow.
PCM CGCM2 CSIRO2 HadCM3
A1f low low low high
A2 high medium medium high
B2 low low low high
B1 medium low low high
We discovered two problems with this initial climate input data set which were solved within the project
(thus our final project dataset differs from the dataset TYN SC 1.0 available at
http://www.cru.uea.ac.uk/). The first problem was the lack of inter-annual variability in cloud cover and
diurnal temperature ranges between the year 1901 and 1950 in the Mediterranean region. The reason
for this is the lack of observed data for cloud cover and diurnal temperature data in the first half of the
20 th century (Mitchell et al. 2004). Consequently the problem also arose for the period 2001 – 2050,
since de-trended observed 20 th century inter-annual variability was used to generate the 21 st century
scenario data. We solved the problem by using the 1951-2000 inter-annual variability twice for scenario