Climate module

Developers: Malte Meinshausen, Thomas Schneider von Deimling & Andrey Ganoploski

PIK's expertise offers the unique opportunity to develop an embedded hierarchical model based approach to the synthesis of earth system science for policy relevant questions. With a model hierarchy starting with a rigorous emulation of the contemporary suite of AOGCM and Earth System Model models, performed by the simple climate model (SCM) MAGICC for global, hemispheric and land/ocean indicators and at the other end with a process based intermediate complexity model (EMIC) CLIMBER-2. While starting out with the readily tuned MAGICC, the fast CLIMBER-2 version will in parallel be further enriched, e.g. completing its regional scale gas-cycles, the module-integration and tuning of its parameter space. The key benefit of this two-model track approach is that the SCM's emulation power of AOGCMs and ability to perform probabilistic multiple-thousand-member ensembles in "real-time" will be merged with the enhanced physical realism of an EMIC, especially in regard to climate system properties outside the AOGCM/ESM emulation space, e.g. for high climate sensitivities, low emission pathways, large scale changes in systems such as sea ice or methane feedbacks. The further development of this 'probabilistic climate module' will focus in particular on emulating, con-straining and validating precipitation, sea-ice extent, thermohaline circulation, ocean acidification, circumpolar ocean temperatures, thermal seawater expansion and other sea level rise contributions.

Pattern Scaling & Regional Climate
Developers: Katja Frieler

Climate change patterns (temperature, precipitation, cloudiness etc.) are key for regional climate impact analysis as well as for carbon cycle, biosphere change and agroeconomic modeling. For scenarios not performed by AOGCMs, climate patterns were often scaled using their global means, although difficulties persist in particular in regard to the scalability of aerosol induced patterns, regions near sea-ice and snow margins, shorter than multi-decadal timescales, nonlinearities for scaling extremes and the time-dependence of patterns under qualitatively different forcing histories (e.g. peaking and declining scenarios). PRIMAP will attempt to fill this gap that arises as fine-pattern AOGCMs/ESMs simulations are restricted to a small set of multi-gas scenario ensembles. The two-track climate model approach will allow merging the benefits of traditional pattern-scaling using emulated global-mean temperatures from the SCM track with the more physically based, but coarser EMIC patterns in which non-linearities can be taken account of. This envisaged approach entails substantial new research, but potentially the high gain to come up with a more reliable, flexible and comprehensive pattern scaling methodology than available so far. Core PIK products, such as LPJ, are vitally dependent on these patterns. Furthermore, the high-efficiency modules envisaged for PRIMAP will allow enhancing probabilistic regional climate predictions.

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