Earth systems model of intermediate complexity
Earth systems models of intermediate complexity form an important class of climate models, primarily used to investigate the earth's systems on long timescales or at reduced computational cost. This is mostly achieved through operation at lower temporal and spatial resolution than more comprehensive general circulation models. Due to the nonlinear relationship between spatial resolution and model run-speed, modest reductions in resolution can lead to large improvements in model run-speed. This has historically allowed the inclusion of previously unincorporated earth-systems such as ice sheets and carbon cycle feedbacks. These benefits are conventionally understood to come at the cost of some model accuracy. However, the degree to which higher resolution models improve accuracy rather than simply precision is contested.
History
Computing power had become sufficiently powerful by the middle of the 20th century to allow mass and energy flow models on a vertical and horizontally resolved grid. By 1955 these advances had produced what is recognisable now as a primitive GCM. Even at this early stage, a lack of computing power formed a significant barrier to entry and limitation on model-time.The next half century saw rapid improvement and exponentially increasing computational demands. Modelling on ever smaller length scales required smaller time steps due to the Courant–Friedrichs–Lewy condition. For example, doubling the spatial resolution increases the computational cost by a factor of 16. As well as working on smaller scales, GCMs began to solve more accurate versions of the Navier–Stokes equations. GCMs also began to incorporate more earth systems and feedback mechanisms, transforming themselves into coupled Earth Systems Models. The inclusion of elements from the cryosphere, carbon cycle and cloud feedbacks was both facilitated and constrained by growth in computing power.
The powerful computers and high cost required to run these "comprehensive" models limited accessibility to many university research groups. This helped drive the development of EMICs. Through judicious parametrisation of key variables, researchers could run climate simulations on less powerful computers, or alternatively much faster on comparable computers. A modern example of this difference in speed can be seen between the EMIC JUMP-LCM and the GCM MIROC4h; the former runs 63,000 times faster than the latter. The decrease in required computing power allowed EMICs to run over longer model times, and thus include earth systems occupying the "slow domain".
Petoukhov's 1980 statistical dynamical model has been cited as the first modern EMIC, but despite development throughout the 1980s, their specific value only achieved wider recognition in the late 1990s with inclusion in IPCC AR2 under the moniker of "Simple Climate Models". It was shortly afterwards at the IGBP congress in Shonnan Village, Japan, in May 1999, where the acronym EMICs was publicly coined by Claussen. The first simplified model to adopt the nomenclature of "intermediate complexity" is now one of the best known: CLIMBER 2. The Potsdam conference under the guidance of Claussen identified 10 EMICs, a list updated to 13 in 2005. Eight models contributed to IPCC AR4, and 15 to AR5.
Classification
As well as "complexity", climate models have been classified by their resolution, parametrisation and "integration". Integration expresses the level of interaction of different components of the earth system. This is influenced by the number of different links in the web, as well as the frequency of interaction. Because of their speed, EMICs offer the opportunity for highly integrated simulations when compared with more comprehensive ESMs. Four EMIC categorisations have been suggested based on the mode of atmospheric simplification: statistical-dynamical models, energy moisture balance models, quasi-geostrophic models, and primitive equation models. Of the 15 models in the community contribution to the IPCC's fifth assessment report, four were statistical-dynamic, seven energy moisture balance, two quasi-geostrophic and two primitive equations models. To illustrate these categories, a case study for each is given.Statistical-dynamical models: CLIMBER models
CLIMBER-2 and CLIMBER-3α are successive generations of 2.5 and 3 dimensional statistical dynamical models. Rather than continuous evolution of solutions to the Navier–Stokes or primitive equations, atmospheric dynamics are handled through statistical knowledge of the system. This approach expresses the dynamics of the atmosphere as large-scale, long term fields of velocity and temperature. Climber-3α's horizontal atmospheric resolution is substantially coarser than a typical atmospheric GCM at 7.5°x 22.5°.With a characteristic spatial scale of 1000 km, this simplification prohibits resolution of synoptic level features. Climber-3α incorporates comprehensive ocean, sea ice and biogeochemistry models. Despite these full descriptions, simplification of the atmosphere allows it to operate two orders of magnitude faster than comparable GCMs. Both CLIMBER models offer performances comparable to that of contemporary GCMs in simulating present climates. This is clearly of interest due to the significantly lower computational costs. Both models have been principally used to investigate paleoclimates, particularly ice sheet nucleation.