Abstract

This article gave a brief review of the evolution of Earth System Model. The complexity of the Earth System Simulation is described in a most digestible form so that anybody can read and understand the article irrespective of their academic background. Further updates are provided in the the web page below the article and more details can be obtained by referring the cited articles.

1. Introduction

We have discussed some results from Climate model Intercomparison Project Phase 5 (CMIP5) model output on the first two issues of the Ocean Digest volume 1. Here we are discussing about the Earth System Models (ESM) and the evolution of the concept. The first attempt for the simulation of the Earth Atmosphere was done by Richardson (1922). But was it a failure? No, there is no failure in science, because every failure teaches us something new. Courant et al. (1928) discovered some basic criteria for handling partial differential equation, which is later known as CFL criteria (represent first letters in the name of the three authors). Later on, numerical weather prediction was done using single level barotropic vorticity equation (Charney et al. 1950). They have taken a set of precaution to avoid possible errors of the prediction. One of the major limitation of Charney et al. (1950) is that it is not valid for a tropical condition where the atmosphere is baroclinic. For example, if we consider the south Asian monsoon system, monsoon trough at the lower atmosphere (say 850 hPa) have cyclonic vortex while the higher altitude (say 200 hPa) have the Tibetan high and anticyclone drive the Tropical Easterly Jet (TEJ). Hence there is some baroclinic components in the atmosphere. More than one layer of atmosphere and some more sophistications are required to better simulate the tropical atmosphere. Now a days, we have more sophisticated models for the Atmosphere. But still, the maximum lead time of accurate weather prediction using stand-alone atmospheric model is limited to few days (Lorenz, 1969). This is because the error doubling time is proportional to the inherent time scale of the atmosphere.

2. Coupled Models

Oceanic processes are much slower and hence the ocean time scale is much larger than the atmospheric time scale. This is the principle behind the seasonal prediction using Ocean-Atmosphere coupled models. Manabe and Bryan (1969) introduce the first ocean-atmosphere coupled climate model, which is a simple prototype to provide a zonal mean temperature profile of an ocean-atmosphere coupled system. Manabe and Wetherald (1975) conducted the first CO2 doubling experiment in their ocean-atmosphere coupled model and reported that a warming process is going on globally due to the impact of the greenhouse effect. But more sophisticated models are required to better simulate future climate. There are other components on the earth surface such as Land, Cryosphere, Biosphere etc.

3. Land Surface and Sea Ice

As far as we consider the climate system, not only the ocean, but also the land surface processes are equally important. Third generation models which have complex land surface scheme usually including multi-soil layers for temperature and soil moisture, and an explicit representation of canopy processes. Further when we consider the climate variabilities beyond decadal time scale, we cannot neglect the variations in the cryosphere. Properly designed sea ice module (Winton 2000; Wu et al. 1997) is an inevitable component of a climate model. Hence model is evolved to be more and more complicated.

4. Sophisticated Coupler

Since a climate system model have more than two components interact among them, earlier concept of one-to-one coupler modules are not sufficient. This situation lead to the development of many-to-many coupling strategy. Sophisticated coupler modules (Valcke et al., 2006, Terray et al., 1998) are helpful to couple both land surface processes and dynamical sea ice in addition to the ocean and atmosphere components together in one system. Ocean Atmosphere Sea Ice Soil coupler (OASIS; Terray et al., 1998; Valcke 2013), Flexible Modeling System (FMS; Balaji 2001) Earth System Modeling Framework (ESMF; Hill 2004), CPL6 (Craig 2005), Fujin (Arakawa 2001) and Simple Coupler (Scup; Yoshimura and Yukimoto 2008) are some examples. Most of the latest Earth system Models (ESM) use two dimensional coupler modules such as OASIS, CPL6, FMS, ESMF to couple various components in the system. The newly developed three dimensional coupler, Scup is used in MRI-ESM. The IPCC fourth assessment report (IPCC AR4 2007) reveals that none of the CMIP3 models tried to represent the carbon cycle and dynamic vegetation. Figure 1 (Heavens 2013) describes the major highlights and distinguish between Climate System Model (CSM) and Earth System Model (ESM).

5. Earth System

The fifth generation models, which are really Earth System Models (ESM) involves biogeochemical cycle and anthropogenic interaction with the climate system. According to the widely accepted general definition given by Schellnhuber (1998, 1999) and Claussen (1998), the Earth System is a system of interaction between the ecosphere and the anthroposphere. Ecosphere represent all the natural systems on the earth while anthroposphere represents human activity. Ecosphere is further subdivided into geosphere and biosphere, where geosphere represent all the
abiotic (nonliving) components and biosphere represent all the biotic (living) components. Geosphere can be further subdivided into Atmosphere, Hydrosphere, Lithosphere, Cryosphere etc. For example GFDL-ESM (Dunne et al. 2012,2013) incorporate interactive biogeochemistry, including the carbon cycle. Natural as well as anthropogenic aerosols along with the physics of cloud and precipitation are incorporated in the atmospheric component. Land surface component is capable of simulating the dynamic reservoir of carbon and other tracers, through proper representation of evaporation, precipitation, runoff, stream, river, lake and terrestrial ecology. Freshwater flux, ocean mixing, wave process, ocean currents, sea ice dynamics, fresh water transport by iceberg as well as marine biogeochemistry and ecology are the key features of the oceanic component of the ESM. The land surface albedo variations depends on the proper representation of natural and agricultural vegetation as well as other land use patterns. Ecological components like plant biomass, productivity etc are controlled by nutrient availability and hence the tracking of chemical tracers in atmosphere, ocean and land are very much important. The IPCC fifth assessment report (IPCC AR5 2013) provide the list of state of art ESM participated in CMIP5 project.

Commonly used atmospheric components are different versions of Community Atmosphere Model (CAM; Collins et al. 2002), ECHAM (Roeckner and Bäuml 2003), Hadley Center Atmosphere Model (HadAM; Martin et al. 2006) in many of the ESMs. One of the ESM use Nonhydrostatic ICosahedral Atmospheric Model (NICAM; Satoh et al. 2008). Different versions of the joint NERC–Met Office(NEMO; Megann 2014) or Modular Ocean Model (MOM; Griffies et al. 2000) are commonly used as the ocean component in many of the ESMs. The GFDL-ESMs (ESM2M and ESM2G) use either Modular Ocean Model version 5 (MOM5) or Generalised Ocean Layer Dynamics (GOLD; Adcroft and Hallberg, 2006). Most of the capabilities of the Hallberg Isopycnal Model (HIM), are now available in GOLD. The best capabilities of MOM5 and GOLD will be merged by the release of the next version of Modular Ocean Model (MOM6). The discussion is concluded here due to limitation of space.
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BCC-CSM CPL6 BCC-AGCM MOM          
BESM-OA2.3  Brazil AM MOM  SIS        
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CanESM  CanAM           
CESM  CAM POP CLM CISM    RTM    
CFS ESMF coupler GFS MOM NOAH SICE(Wu)        
CMCC-ESM OASIS ECHAM OPA SILVA LIM SILVA PELAGOS   PELAGOS   
CNRM-ESM  ARPEGE NEMO SURFEX GELATO        
FGOALS CPL6 SAMIL LICOM CLM CISM        
FIO-ESM  CAM POP CLM CICE      MASNUM  
GEOS  GEOSv5 MOM  CICE        
GFDL-ESM FMS coupler GFDL-AM MOM GFDL-LM SIS        
GISS-ESM  GISS-AM HYCOM          
HadGEM OASIS HadAM NEMO  CICE HadDGVM    HadBGC   
INM-ESM             
IPSL-ESM OASIS LMDZ NEMO  GELATO ORCIDEE  INCA     
MIROC-ESM  MIROC-AM COCO MATSIRO  SEIB-DGVM    NPZD   
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MRI-ESM Scup MRI-AM MRI-COM HAL    MASINGAR GRivT    
NICAM-ESM  NICAM           
NorESM  CAM NorOM CLM CICE  HAMOCC      
QESM  HadAM HadOM JULES    UKCA  QPFT   
SINTEX-G  ECHAM           
UKESM OASIS UM AGCM NEMO JULES CICE TRIFFID  UKCA  MEDUSA  BISICLES 
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gibies george,
Mar 27, 2017, 3:13 AM
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