Earth systems model of intermediate complexity

Earth systems Model of Intermediate Complexity (EMIC)

Earth systems Models of Intermediate Complexity (EMICs) 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 resoltion than more comprehensive general circulation models (GCMs). Due to the non-linear 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.

Historical Development of EMICs

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 (Lyncy, 2008). These advances culminated in what is recognisable now as a primitive GCM with the Phillips prototype in 1955 (Phillips, 1956). Even at this early stage, 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 (McGuffie and Henderson-Sellers, 2001). Modelling on ever smaller length scales required smaller time steps due to the Courant–Friedrichs–Lewy condition (Courant et al., 1967). For example, doubling the spatial resolution increases the computational cost by a factor of 16 (factors of 2 for each spatial dimension and time) (Flato, 2011). As well as working on smaller scales, GCMs began to solve more accurate versions of the Navier-Stokes equations (White and Bromley, 1995). 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 (Flato, 2011).

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 (Hajima et al., 2014). 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 statistical dynamical model (1980) has been cited as the first modern EMIC (Hajima et al., 2014), 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 (Claussen, 2005). Eight models contributed to IPCC AR4, and 15 to AR4 (Randall et al., 2007; Flato et al., 2013).

Classification of EMICs

As well as “complexity”, climate models have been classified by their resolution, parametrisation and “integration” (Claussen et al., 2002). 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 (interactivity of coordinates), 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. The importance of integration drove Claussen (2002) to the arrangement of models on three axes: Number of Processes, Integration and the Detail of Description. This scheme is complemented by another, which orders models by their treatment of four processes: Surface and Ocean, Radiation, Chemistry and Dynamics (McGuffie and Henderson-Sellers, 2005). In this scheme, models are arranged on a pyramid, with GCMs at the top tip, but different types of EMICs “fanning out” below. This hierarchical structure has drawn controversial criticism for perhaps privileging the position of GCMs (Shackley et al., 1998; Henderson-Sellers and McGuffie, 1999). Four EMIC categorisations have been suggested based on the mode of atmospheric simplification (Hajima et al., 2014): 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 (Eby et al., 2013). To illustrate these categories, a case study for each is given.

Statistical Dynamical Models - The CLIMBER Models

CLIMBER-2 and CLIMBER-3α are successive generations of 2.5 and 3 dimensional statistical dynamical models (Petoukhov et al., 2000; Montoya et al., 2005). Rather than continuous evolution of solutions to the Navier Stokes or Primitive Equations, atmospheric dynamics are handled through statistical knowledge of the system (an approach not new to CLIMBER (Saltzman, 1978)). 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 courser than a typical atmospheric GCM at 7.5°x 22.5°.

With a characteristic spatial scale of 1000km, 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 cGCMs (Montoya et al., 2005). 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 (Ganopolski et al., 1998).

Energy and Moisture Balance Models - UVic ESCM

The thermodynamic approach of the UVic model involves simplification of mass transport (with Fickian diffusion) and precipitation conditions(Weaver et al., 2001). This model can be seen as a direct descendant of earlier energy balance models (Budyko, 1969; Sellers, 1969; North, 1975). These reductions reduce the atmosphere to three state variables, surface air temperature, sea surface temperature and specific humidity (Fanning and Weaver, 1996). By parametrising heat and moisture transport with diffusion, timescales are limited to greater than annual and length scales to greater than 1000km. A key result of the thermodynamic rather than fluid dynamic approach is that the simulated climate exhibits no internal variability (Weaver et al., 2001). Like CLIMBER-3α, it is coupled to a state of the art, 3D ocean model and includes other cutting edge models for sea-ice and land-ice. Unlike CLIMBER, the UVic model does not have significantly coarser resolution than contemporary AOGCMs (3.6°x 1.8°). As such, all computational advantage is from the simplification of atmospheric dynamics.

Quasi Geostrophic Models - LOVECLIM

The quasi-geostrophic equations are a reduction of the Primitive Equations first written down by Charney (Majda and Wang, 2006). These equations are valid in the case of low Rossby number, signifying only a small contribution from inertial forces. Assumed dominance of the Coriolis and pressure-gradient forces facilitates the reduction of the primitive equations to a single equation for potential vorticity in five variables (Marshall and Molteni, 1993). LOVECLIM features a horizontal resolution of 5.6° and uses the quasi geostrophic atmosphere model ECBilt. It includes a vegetation feedback module by Brovkin et al. (1997). The model exhibits some significant limitations that are fundamentally linked to its design. The model predicts an Equilibrium Climate Sensitivity of 1.9°C, at the lower end of the range of GCM predictions. The model’s surface temperature distribution is overly-symmetric, and does not represent the northern bias in location of the Intertropical Convergence Zone. The model generally shows lower skill at low latitudes. Other examples of quasi-geostrophic models are PUMA and SPEEDY.

Primitive Equations Model - FAMOUS

The UK Met-Office’s FAMOUS blurs the line between more coarsely resolved comprehensive models and EMICs. Designed to run paleoclimate simulations of the Pleistocene, it has been tuned to reproduce the climate of its parent, HADCM3, by solving the Primitive Equations written down by Charney. These are of higher complexity than the quasi-geostrophic equations. Originally named ADTAN, preliminary runs had significant biases involving sea ice and the AMOC, which were later corrected through tuning of sea-ice parameters. The model runs at half the horizontal resolution of HADCM3. Atmospheric resolution is 7.5°x5°, and oceanic is 3.75°x 2.5°. Atmosphere-Ocean coupling is done once daily.

Comparing and Assessing EMIC Skill

Systematic inter-comparison of EMICs has been undertaken since 2000, most recently with the community contribution to the IPCC’s fifth assessment report (Eby et al., 2013). The equilibrium and transient climate sensitivity of the models broadly fell within the range of contemporary GCMs with a range of 1.9 - 4.0°C (compared to 2.1° - 4.7°C, CMIP5). Tested over the last millennium, the average response of the models was close to the real trend, however this conceals much wider variation between individual models. Models generally overestimate ocean heat uptake over the last millennium and indicate a moderate slowing. No relationship was observed in EMICs between levels of polar amplification, climate sensitivity, and initial state (Eby et al., 2013). The above comparisons to the performance of GCMs and comprehensive ESMs do not reveal the full value of EMICs. Their ability to run as “fast ESMs” allows them to run over much longer periods, up to many millennia. As well as running on time-scales far greater than available to GCMs, they provide fertile ground for development and integration of systems that will later join GCMs.

See also

• Climate model
• General circulation model