Abstract. Global circulation models (GCMs) are the best tool to understand
climate change, as they attempt to represent all the important Earth
system processes, including anthropogenic perturbation through
fossil fuel burning. However, GCMs are computationally very
expensive, which limits the number of simulations that can be
made. Pattern scaling is an emulation technique that takes advantage
of the fact that local and seasonal changes in surface climate are
often approximately linear in the rate of warming over land and
across the globe. This allows interpolation away from a limited number of
available GCM simulations, to assess alternative future emissions
scenarios. In this paper, we present a climate pattern-scaling set
consisting of spatial climate change patterns along with parameters
for an energy-balance model that calculates the amount of global
warming. The set, available for download, is derived from 22 GCMs of
the WCRP CMIP3 database, setting the basis for similar eventual
pattern development for the CMIP5 and forthcoming CMIP6
ensemble. Critically, it extends the use of the IMOGEN (Integrated
Model Of Global Effects of climatic aNomalies) framework to enable
scanning across full uncertainty in GCMs for impact studies. Across
models, the presented climate patterns represent consistent global
mean trends, with a maximum of 4 (out of 22) GCMs exhibiting
the opposite sign to the global trend per variable (relative
humidity). The described new climate regimes are generally warmer,
wetter (but with less snowfall), cloudier and windier, and have
decreased relative humidity. Overall, when averaging individual
performance across all variables, and without considering
co-variance, the patterns explain one-third of regional change in
decadal averages (mean percentage variance explained, PVE, 34.25±5.21), but the signal in some models exhibits much more
linearity (e.g. MIROC3.2(hires): 41.53) than in others (GISS_ER:
22.67). The two most often considered variables, near-surface
temperature and precipitation, have a PVE of 85.44±4.37 and
14.98±4.61, respectively. We also provide an example
assessment of a terrestrial impact (changes in mean runoff) and
compare projections by the IMOGEN system, which has one land surface
model, against direct GCM outputs, which all have alternative
representations of land functioning. The latter is noted as an
additional source of uncertainty. Finally, current and potential
future applications of the IMOGEN version 2.0 modelling system in the
areas of ecosystem modelling and climate change impact assessment
are presented and discussed.
The climatic impacts of land surface change and carbon management, and the implications for climate-change mitigation policy
