doi:10.7289/V5Z31WKK

NOAA Technical Report OAR CPO-3

NOAA Holistic Climate

and Earth System

Model Strategy Phase 

I: Current State
doi:10.7289/V5Z31WKK

NOAA Technical Report OAR CPO-3

NOAA Holistic Climate and Earth System Model Strategy

Phase I: Current State

Climate Program Office

Silver Spring, Maryland

March 2015

Report Team Authors

David G. DeWitt1, Stanley G. Benjamin2, Brian D. Gross3, R. Wayne Higgins4, Monika Kopacz4, Arun Kumar1, Annarita Mariotti4 , 

Suranjana Saha5, Hendrik L. Tolman5

Affiliations

1

Climate Prediction Center, NOAA/NWS/NCEP, College Park, Maryland

2

NOAA/Earth System Research Laboratory, Boulder, Colorado

3

NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

4

Climate Program Office, NOAA/OAR, Silver Spring, Maryland

5

Environmental Modeling Center, NOAA/NWS/NCEP, College Park, Maryland

United States

Department of Commerce

Penny Pritzker

Secretary

National Oceanic and

Atmospheric Administration

Dr. Kathryn D. Sullivan

Undersecretary for Oceans and

Atmospheres

Office of Oceanic and

Atmospheric Research

Craig McLean

Assistant Administrator

Notice from NOAA

Mention of a commercial company or product does not constitute an endorsement by NOAA/OAR. Use of information from this 

publication concerning proprietary products or the tests of such products for publicity or advertising purposes is not authorized. 

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not 

necessarily reflect the views of the National Oceanic and Atmospheric Administration.

NOAA Holistic Climate and Earth System Model Strategy

Phase I: Current State
3

Abstract

This report describes Phase I of a two-phase study 

proposed and organized by the NOAA Climate Program 

Office (CPO) in early 2014 for the development of a 

NOAA Holistic Climate and Earth System Modeling 

Strategy. Defining NOAA’s strategy for global climate 

and earth system modeling for research and operations 

allows NOAA’s investments in this area to be optimally 

targeted and leveraged, facilitates linkages with broader 

environmental modeling efforts within NOAA and 

outside of NOAA, and enhances NOAA’s participation 

in the interagency Earth System Prediction Capability 

initiative. A strategy also helps to identify gaps and 

opportunities to advance NOAA’s global climate and 

earth system models and identify resources and 

synergies needed to maximize the return on those 

investments. In addition, the 2012 National Research 

Council report on “A National Strategy for Advancing 

Climate Modeling” prompts a careful reconsideration of 

NOAA’s strategy in this area.

Phase I defines the current state of, and plans for, 

NOAA’s global climate and earth system models, 

developed at the Geophysical Fluid Dynamics 

Laboratory, the Earth System Research Laboratory, 

and the National Centers for Environmental Prediction 

Environmental Modeling Center. This report lays the 

necessary groundwork for the planned second phase 

of the CPO proposed study whose ultimate goal is to 

present potential future model development pathways 

contributing to the definition of a holistic strategy to 

advance NOAA global climate and earth system models 

for research and operations.

1. Introduction

The National Oceanic and Atmospheric Administration 

(NOAA) climate and earth system modeling (NESM) 

enterprise is an essential science and technology 

capability for the successful execution of the 

NOAA Next-Generation Strategic Plan (NGSP). 

The development of these models occurs at the 

Geophysical Fluid Dynamics Laboratory (GFDL), 

the Earth System Research Laboratory (ESRL), and 

the National Centers for Environmental Prediction 

(NCEP) Environmental Modeling Center (EMC). Key 

societally relevant outputs supported by these models 

include experimental and operational intraseasonal 

to interannual (ISI) climate predictions; decadal 

to centennial climate projections and attribution 

studies; experimental predictions and projections of 

biogeochemical processes on timescales from weeks 

to centuries; climate reanalysis and monitoring; and 

enhanced understanding of the earth system and its 

future evolution. The portfolio of model research and 

development work includes both lower-risk, incremental 

applied research and higher-risk, higher-reward 

exploratory research. This breadth of research and 

development supports improvements to the current 

operational systems and simultaneous exploration of 

promising areas for future model improvements, and is 

required in order to meet the current and future needs 

of NOAA stakeholders.

Vigorous and productive collaboration has occurred 

between the different centers of the NESM enterprise 

for several decades. An area of especially active 

collaboration is the development and application of 

climate models to the ISI forecast problem. In this area, 

collaboration has included both cross-center exchange 

of component models and development of multi-model 

ensemble (MME) forecast systems. In the former case, 

EMC has used various versions of the GFDL Modular 

Ocean Model (MOM) as the ocean component in its ISI 

forecast system since the mid-1990’s, while in the latter, 

both GFDL and EMC produce ISI forecasts for the 

North American Multi-Model Ensemble (NMME; Kirtman 

et al., 2014). MME forecasts typically have higher 

accuracy and increased reliability compared to the best 

individual model ensemble forecast system (Peng et 

al., 2002), and can be a cost-effective way to leverage 

existing activities at the different NOAA modeling 

centers. However, it should also be recognized that the 

MME approach does not replace the fundamental need 

for continual improvement of the individual models used 

3
4

to construct the MME. The NMME is also an excellent 

example of the NESM enterprise working together 

with the research community to produce products 

that contribute to both research and operational 

modeling goals. 

In addition to the collaborative activities, each of 

the modeling centers makes unique contributions 

to NOAA’s mission. EMC produces the operational 

NOAA numerical guidance for timescales from 

hours to seasons, while GFDL produces the NOAA 

long-term climate projections that are used in the 

Coupled Model Intercomparison Project (CMIP) and 

the Intergovernmental Panel on Climate Change 

(IPCC) processes. GFDL also leads the NOAA earth 

system modeling effort and serves as the agency lead 

for evaluating the potential of decadal predictions. 

Previously, ESRL has focused on the short- to mediumrange weather forecasting problem, but is now 

developing a coupled ocean-atmosphere model that will 

be applied to the ISI timescale. 

This report presents a snapshot of the current NESM 

models used at GFDL, EMC, and ESRL. For the 

purposes of this report, climate and earth system 

models are defined as follows: Climate models 

simulate the physical system, and historically have 

included atmosphere, land, ocean, and sea-ice 

component models. Earth system models build on 

climate models by adding a global carbon cycle, and 

typically include prognostic models for atmospheric 

chemistry, terrestrial ecosystems, and ocean 

biogeochemistry. The focus of this report is on global 

coupled models used for research and operations for 

the subseasonal to centennial timescale. However, 

it is important to recognize that weather and climate 

processes interact and influence each other. Therefore, 

within NOAA, development of climate and weather 

models is done in a holistic way to ensure fidelity in 

simulation of the continuum of weather and climate 

variability. This report serves as a high-level single 

source of information that can inform scientists 

interested in utilizing these models, and NOAA 

managers interested in learning about the current state 

of the NESM portfolio. It should be emphasized that 

by design this is a brief report, and, hence, can only 

present a sampling of the rich work being done within 

the NESM enterprise in support of the NGSP. The 

reader is encouraged to examine the more extensive 

documentation of research results and datasets 

referenced in the text and the appendix.

Section 2 describes the GFDL Coupled Model version 

2.1 (CM2.1), version 2.5 (CM2.5), version 3 (CM3), and 

the Earth System Model version 2M (ESM2M). Section 

3 describes the EMC Climate Forecast System version 

2 (CFSV2). Section 4 describes the coupled ESRL 

Flow-following finite-volume Icosahedral Model (FIM) - 

icosahedral Hybrid Coordinate Ocean Model (iHYCOM). 

Section 5 presents a brief overview of previous 

and current NESM collaborative activities. Section 

6 describes universal challenges facing the NESM 

enterprise, and Section 7 presents a brief summary of 

the report. The Appendix has a table that gives a highlevel view of the models covered in the report. It also 

provides links to more comprehensive documentation 

available for the models, and the location of model code 

and data.

2. GFDL Climate and Earth System Models

GFDL’s mission is to advance scientific understanding 

of climate and its natural and anthropogenic variations 

and impacts, and improve NOAA’s predictive 

capabilities, through the development and use of worldleading computer models of the earth system.

2.1 CM2.1

2.1a Model Description

CM2.1 was the first NOAA model used for both 

experimental seasonal to interannual prediction 

and centennial-scale climate change projections. 

Comprehensive documentation of the physical and 

dynamical formulations of CM2.1 is given in Anderson 

NOAA Holistic Climate and Earth System Model Strategy

Phase I: Current State
5

et al. (2004), Delworth et al. (2006), and Gnanadesikan 

et al. (2006). The resolution of the atmospheric (AM2) 

and land (LM2) component models is 2° latitude by 

2.5° longitude. The atmospheric model has 24 vertical 

layers with a top at 3.65 hPa. The shortwave radiation 

scheme follows Freidenreich and Ramaswamy (1999) 

and includes absorption by H2O, CO2, O3, and O2; 

molecular scattering; and absorption and scattering by 

aerosols and clouds. The longwave radiation scheme 

is that of Schwarzkopf and Ramaswamy (1999). Deep 

convection is modeled using the relaxed ArakawaSchubert (RAS) scheme of Moorthi and Suarez 

(1992). Convective momentum transport is handled 

with a downgradient diffusion formulation. The cloud 

microphysics parameterization follows Rotstayn (1997), 

with an updated treatment of mixed phase clouds 

(Rotstayn et al., 2000). The cloud fraction is treated as 

a prognostic variable following Tiedtke (1993). Aerosol 

indirect effects are not modeled.

The CM2.1 Ocean Model (OM3.1) is based on MOM4 

(Griffies et al., 2005) and has horizontal resolution of 

1° in latitude and 1° in longitude with progressively 

increasing meridional resolution equatorward of 

30° latitude. In the equatorial region, the meridional 

resolution is approximately 0.33° in order to resolve 

the equatorial waveguide and current systems. OM3.1 

uses a tripolar grid (Murray, 1996) in order to avoid 

polar filtering over the Arctic. There are 50 vertical 

layers with 10 meter spacing in the upper 220 meters, 

and the bottom depth is 5.5 km. OM3.1 employs an 

explicit free surface with a freshwater flux boundary 

condition. Vertical mixing is modeled using the 

K-Profile Parameterization (KPP) mixed layer scheme 

of Large et al. (1994). Shortwave radiation absorption 

follows Morel and Antoine (1994) and includes the 

mean annual cycle of chlorophyll concentration. The 

horizontal advection scheme is based on the thirdorder upwind-biased approach of Hundsdorfer and 

Trompert (1994), including the Sweby (1994) flux 

limiters. Isoneutral mixing is handled following Griffies 

et al. (1998). Mesoscale eddies are parameterized 

using the skew-flux approach of Griffies (1998), with 

a local treatment of the quasi-Stokes streamfunction 

(Gent and McWilliams, 1990; Gent et al., 1995). Lateral 

viscosity is treated using an anisotropic scheme in the 

tropics following Large et al. (2001). In this scheme, the 

background viscosity is large in the east-west direction, 

but small in the north-south direction except near 

the western boundaries. The background viscosity is 

isotropic in the extratropics.

The CM2.1 Land Model (LM2) is the Land Dynamics 

Model of Milly and Shmakin (2002), which includes a 

river routing scheme that transports runoff collected 

over the model’s drainage basins to river mouths, where 

it is treated as a freshwater source to the ocean model. 

The land cover type distribution is a combination of 

a potential natural vegetation-type distribution and a 

historical land use distribution dataset. The potential 

natural vegetation classification has 10 vegetation or 

land surface types (broadleaf evergreen, broadleaf 

deciduous, mixed forest, needle-leaf deciduous, needleleaf evergreen, grassland, desert, tundra, agriculture, 

and glacial ice). 

The sea ice component model is the Sea Ice Simulator 

(SIS; Winton, 2000). SIS is a dynamical model with 

two ice and one snow layer, and five ice thickness 

categories. Ice internal stresses are calculated using 

the elastic-viscous-plastic technique of Hunke and 

Duckowicz (1997), while the thermodynamics treatment 

is a modified Semtner (1976) three-layer scheme. 

Coupling between the atmosphere, land, ocean, and 

ice models occurs every 2 hours in order to resolve the 

diurnal cycle.

2.1b Model Performance and Sample Applications

CM2.1 was one of two GFDL models that contributed to 

the CMIP version 3 (CMIP3) and the IPCC Assessment 

Report No. 4 (AR4). Independent comparison of IPCC 

AR4/CMIP3 models shows that CM2.1 produced one 

of the better ENSO simulations (van Oldenburgh et 
6

al., 2005) and simulated the observed modern-era 

atmospheric circulation better than many of the other 

models (Reichler and Kim, 2008). CM2.1 seasonal 

forecasts are produced in real-time for the NMME 

project and are provided to the Climate Prediction 

Center (CPC) for use in generating its operational 

predictions. The skill of CM2.1 monthly and seasonal 

forecasts (Kirtman et al., 2014; Becker et al., 2014) 

is competitive with similar models from other forecast 

centers. Further, the CM2.1 skill is at least partially 

complementary to other models from US institutions 

such as the EMC CFSV2. CM2.1 participated in the 

CMIP5 experimental decadal hindcasts.

An ensemble coupled data assimilation (ECDA; Zhang 

et al., 2007) system based on CM2.1 was developed 

and used to initialize the GFDL CMIP5 decadal 

forecasts and GFDL’s contributions to the NMME. The 

ECDA system assimilates oceanic temperature, salinity, 

and sea surface temperature (SST), while constraining 

the atmospheric state using NCEP’s analysis. An 

ECDA-based ocean reanalysis is available for the 

period 1961 to 2012. A link to the ECDA re-analysis 

data is provided in the Appendix.

2.2 CM2.5

2.2a Model Description

The CM2.5 atmospheric model dynamics and physics 

are nearly identical to those for CM2.1. The major 

difference in the atmospheric models is that the CM2.5 

atmospheric model has increased horizontal resolution 

(50 km) and vertical resolution (32 layers) compared to 

CM2.1. Complete documentation of CM2.5 is given in 

Delworth et al. (2012).

Key differences between the ocean component models 

for CM2.1 and CM2.5 as documented in Delworth et al. 

(2012) include:

•	 CM2.5 has horizontal resolution of 0.25°.

•	 CM2.5 does not use a mesoscale 

eddy parameterization.

•	 CM2.5 uses a parameterization for the 

effects of sub-mesoscale, mixed layer 

eddies (Fox-Kemper et al., 2011).

•	 CM2.5 uses the piecewise parabolic method (PPM; 

Colella and Woodward, 1984; Huynh, 1996), 

which is a third-order finite volume advection 

scheme. PPM is more accurate and less dissipative 

than the advection scheme used in CM2.1.

•	 CM2.5 does not use explicit lateral diffusion 

or background vertical diffusion.

•	 Coastal tidal mixing uses the 

scheme of Lee et al. (2006).

•	 Internal tidal mixing uses the scheme 

of Simmons et al. (2004).

•	 CM2.5 uses MOM4.1 (Griffies, 2010) with a 

z* vertical coordinate (Griffies et al., 2011).

•	 CM2.5 has a very low horizontal viscosity. This 

is achieved by use of the Smagorinsky (1993) 

biharmonic formulation (Griffies and Hallberg, 

2000) with a prescribed background viscosity 

that is enhanced next to western boundaries.

•	 CM2.5 explicitly models the flow between straits, 

such as the Atlantic and the Mediterranean.

•	 A version of CM2.5 with higher ocean resolution, 

denoted CM2.6, was developed. CM2.6 is 

identical to CM2.5 except that the ocean horizontal 

resolution is increased to 0.1° . The simulation 

of ocean eddy activity is very realistic in CM2.6 

(Delworth et al., 2012). Comparison of CM2.5 and 

CM2.6 simulations, predictions, and projections 

contribute to our understanding of the role of ocean 

eddies in climate variability on different timescales.

New capabilities in the CM2.5 land model, the Land 

Model version 3 (LM3; Delworth et al., 2012; Milly et al., 

2014a; Shevliakova et al., 2009) include:

NOAA Holistic Climate and Earth System Model Strategy

Phase I: Current State
7

•	 A multilayer model of snowpack above the soil.

•	 A continuous vertical representation 

of soil water that spans both the 

unsaturated and saturated zones.

•	 A frozen soil-water phase.

•	 A parameterization of water table height, 

saturated-area fraction, and groundwater 

discharge to streams derived from standard 

groundwater-hydraulic assumptions and 

surface topographic information.

•	 Finite velocity horizontal transport of 

runoff via rivers to the ocean.

•	 Lakes, lake ice, and lake-ice snow packs 

that exchange mass and energy with 

both the atmosphere and the rivers.

•	 Consistent, energy-conserving accounting of the 

sensible heat content of water in all of its phases.

•	 The carbon balance and determination 

of vegetation structure, phenology, and 

function follow Shevliakova et al. (2009).

Inclusion of these new capabilities in LM3 improves the 

representation of the physical system.

2.2b Model Performance and Sample Applications

Key improvements in the simulated climate compared 

to CM2.1 include a reduction in the double ITCZ in the 

tropical Pacific, an improved simulation of ENSO, and 

improvements to the regional precipitation features, 

including the Indian monsoon and Amazonian rainfall 

(Delworth et al., 2012). Kapnick and Delworth (2013) 

found significant improvements in the simulated 

CM2.5 snowfall magnitude and distribution compared 

to CM2.1. Knutti et al. (2013) showed that CM2.5 

simulations of surface temperature and precipitation 

were among the best for CMIP5-class models, in 

terms of agreement with present day climate. Further 

information on performance and applications of CM2.5 

are found in Delworth (2014), Kapnick (2014), Vecchi 

(2014a), and Wittenberg (2014).

2.3 CM2.5-Forecast-Oriented Low Ocean 

 Resolution (FLOR)

2.3a Model Description

The CM2.5-FLOR model is identical to CM2.5 except 

that it uses the lower-resolution CM2.1 Ocean Model 

(OM3.1). FLOR is computationally less expensive 

than CM2.5, and is used for climate forecasting on 

the intraseasonal to interannual timescale. Currently, 

it is being used for experimental seasonal hurricane 

forecasts, and forecasts of the statistics of extreme 

weather. The lower computational cost compared to 

CM2.5 enables the use of larger ensembles, which 

can help to identify the signal for phenomena such as 

extreme events that have a small signal-to-noise ratio. 

The larger ensembles will also allow better definition 

of the model probability distribution function and 

quantification of uncertainty.

2.3b Model Performance and Sample Applications

A number of studies have compared the simulation and 

forecast characteristics of FLOR with other versions 

of the GFDL coupled model. Extended simulations 

with FLOR show improved ENSO teleconnections, and 

reduced biases of surface temperature and precipitation 

compared to CM2.1 (Jia et al., 2015). Forecast skill of 

tropical Pacific SST associated with ENSO is improved 

for FLOR over CM2.1. ENSO teleconnections, and 

surface temperature and precipitation biases are very 

similar for FLOR and CM2.5 (Jia et al., 2015). In a 

groundbreaking study, Vecchi et al. (2014b) showed 

that FLOR produces skillful seasonal forecasts of the 

spatial distribution of tropical cyclone activity. Msadek 

et al. (2014) found that FLOR produces skillful forecasts 

of Northern Hemisphere summer sea ice extent out 

to 6 months lead. This level of skill was similar to 

that found for CM2.1. However, the FLOR forecasts 

were shown to have improved fidelity in simulating the 

climatological atmospheric circulation and sea ice mean 

state in the Arctic compared to CM2.1. FLOR seasonal 

forecasts are produced in real-time for the NMME 
8

project and are provided to CPC for use in generating 

its operational predictions. Further information on 

performance and applications of FLOR are found in 

Vecchi (2014a), and Wittenberg (2014).

2.4 CM3

2.4a Model Description

CM3 was designed to address emerging issues in 

climate change, including aerosol-cloud interactions, 

chemistry-climate interactions, and coupling between 

the stratosphere and troposphere (Donner et al., 

2011). The major changes from CM2.1 occurred 

in the atmospheric and land component models. 

The atmospheric component model evolved from 

AM2 to AM3 (Donner et al., 2011), while the land 

model evolved from LM2 to LM3 (Milly et al., 2014a; 

Shevliakova et al., 2009).

Major developments in the atmospheric and land 

component models compared to CM2.1 include:

•	 New deep cumulus convection scheme 

(Donner, 1993; Donner et al., 2001; 

Wilcox and Donner, 2007).

•	 New shallow cumulus convection 

scheme (Bretherton et al., 2004; 

modified as in Zhao et al., 2009).

•	 Increased vertical resolution from 24 to 48 

layers and raising of model top from 35 

to 86 km. The additional vertical layers 

are added in the upper troposphere and 

stratosphere. The increased resolution in the 

stratosphere allows a more comprehensive 

treatment of stratospheric processes.

•	 Cloud droplet activation by aerosols (aerosol 

indirect effect; Ming et al., 2006).

•	 Subgrid variability of stratiform vertical velocities 

for droplet activation (Ghan et al., 1997).

•	 Atmospheric chemistry and aerosols driven 

by emissions with advective, convective, 

and turbulent transport. AM3 calculates the 

mass distribution and optical properties of 

aerosols based on their emission, chemical 

production, transport, and dry and wet removal 

(Donner et al., 2011; Naik et al., 2013).

•	 Implementation of a cubed-sphere finite-volume 

dynamical core that enhances the models parallel 

computing efficiency (Putman and Lin, 2007).

•	 New land model, Land Model 3 (LM3; Donner 

et al., 2011; Milly et al., 2014a; Shevliakova 

et al., 2009), for land water, energy, and 

carbon balance. LM3 improves the physical 

representations of the flow of heat and water 

through the land, and also includes a dynamic 

vegetation component. (See CM2.5 for enhanced 

features over the CM2.1 land model).

•	 Simulation of atmospheric concentration 

of 97 chemical species over the full depth 

of the atmosphere (Naik et al., 2013).

•	 Tropospheric chemistry is modeled using 

a modified version of the Model for Ozone 

and Related Tracers version 2 (MOZART-2; 

Horowitz et al., 2003; Horowitz et al., 2007).

•	 Stratospheric chemistry is modeled using the 

Atmospheric Model with Transport and Chemistry 

(AMTRAC; Austin and Wilson, 2010).

2.4b Model Performance and Sample Applications 

Several key differences exist between CM2.1 and CM3 

climate simulations and projections. These include: 

•	 The CM3 atmospheric component model 

(AM3) does a more realistic job simulating 

aerosol optical depths, scattering properties, 

and surface clear-sky downward shortwave 

radiation than the atmospheric component 

model (AM2) in CM2.1 (Donner et al., 2011).

•	 CM3 simulates Arctic sea ice with more fidelity and 

smaller biases than CM2.1. (Griffies et al., 2011).

NOAA Holistic Climate and Earth System Model Strategy

Phase I: Current State
9

•	 CM3 has a reduced dry bias over South 

America and reduced biases in surface clearsky shortwave radiation (Donner et al., 2011).

•	 CM3 has a larger warm bias in the 

interior ocean (Griffies et al., 2011).

John et al. (2012) used CM3 simulations to 

evaluate the relative role of climate and emissions 

in determining methane lifetime against loss by the 

tropospheric hydroxyl radical. Austin et al. (2013) 

evaluated the ability of CM3 to simulate the variability 

of stratospheric ozone and temperature from 1860 

to 2005. Experiments with CM3 showed that both 

local and remote aerosols are important for explaining 

the late twentieth century drying of the South Asian 

monsoon (Bollasina et al., 2014). Simulations with 

CM3 conducted by Bollasina et al. (2013) showed that 

the late twentieth century increase in anthropogenic 

aerosols contributes to an earlier onset of the Indian 

monsoon. CM3 was the workhorse GFDL climate 

model contribution to the CMIP5 and IPCC AR5. 

Additional applications of CM3 can be found on 

GFDL’s Climate Change web page (http://www.gfdl.

noaa.gov/climate-change).

2.5 Earth System Model version 2 MOM 

(ESM2M) 

2.5a Model Description

GFDL developed earth system models including the 

ESM2M (Dunne et al., 2012a; Dunne et al., 2013) 

to advance our understanding of how the Earth’s 

biogeochemical cycles, including human actions, 

interact with the climate system. ESM2M builds on the 

CM2.1 climate model. The atmospheric component 

model of ESM2M has only a few minor code updates 

from CM2.1. 

The ESM2M ocean model differs from that in CM2.1 

as follows:

•	 ESM2M uses a rescaled geopotential 

vertical coordinate (z*; Stacey et al., 

1995; Adcroft and Campin, 2004).

•	 ESM2M employs the conservative, minimally 

diffusive, monotonic, multidimensional piecewise 

parabolic method (MDPP; Marshall et al., 1997).

•	 The quasi-Stokes streamfunction in the 

mesoscale eddy parameterization is 

computed as a boundary value problem 

over the full column (Ferrari et al., 2010). 

•	 An updated version of the KPP vertical mixing 

scheme is used (Danabasoglu et al., 2006).

•	 ESM2M uses an isotropic Laplacian 

friction combined with western boundary 

enhanced biharmonic friction. 

In ESM2M, atmospheric CO2 is treated as a prognostic 

tracer that is exchanged between the land, ocean, 

and atmosphere. The ESM2M land component model 

is LM3 (see description in section describing CM2.5). 

Treatment of ocean ecology and biogeochemistry is 

modeled using the Tracers of Ocean Phytoplankton 

with Allometric Zooplankton version 2.0 (TOPAZ2). 

TOPAZ2 uses 30 tracers to model the cycles of 

carbon, nitrogen, phosphorous, silicon, iron, oxygen, 

alkalinity, surface sediment calcite, and lithogenic 

material (Dunne et al., 2012b). Three explicit 

phytoplankton groups (small, large, and diazotrophic) 

are modeled using modified growth physiology 

following Geider et al. (1997). 

In an alternative version of ESM2M, ESM2G, an 

independently developed isopycnal model using the 

Generalized Ocean Layer Dynamics (GOLD) code 

base was used. Comparison between these two 

models allows us to assess the sensitivity of the 

coupled climate-carbon system to our assumptions 

about ocean formulation (Winton et al., 2013).
10

2.5b Model Performance and Sample Applications

ESM2M simulates the present-day climate with similar 

fidelity to CM2.1 (Dunne et al., 2012a). Polovina et 

al. (2011) used the ESM2M to define large oceanic 

biomes in the North Pacific Ocean and to describe their 

changes over the 21st century in response to IPCC AR4 

Scenario A2. Experiments with ESM2M demonstrated 

that the dominant modes of biological and physical 

variability may change as the earth system is perturbed 

under greenhouse gas forcing (Rykaczewski and 

Dunne, 2010). That result demonstrates the limitations 

associated with use of historical empirical models 

to predict future changes. The response of primary 

production in the Arctic Ocean to future climate change 

was examined for ESMS2M/ESM2G and 9 other earth 

system models (Vancoppenolle et al., 2013). The 

causes and implications of persistent atmospheric 

carbon dioxide biases were studied in 15 earth system 

models including ESM2M (Hoffman et al., 2013). GFDL 

completed all of its integrations for CMIP5 (about 100 

in total) with ESM2M and ESM2G. These ESMs have 

been used to examine the impact of climate change 

on marine life, ranging from plankton (Stock et al., 

2014), to cod (Kristiansen et al., 2014), to endangered 

leatherback sea turtles (Saba and Stock, 2012). 

Application of dynamic vegetation in the ESM’s land 

model has shown that regrowing forests can sequester 

CO2 and offset the loss of carbon to land-use change 

(Shevliakova et al., 2009). Additional examples on the 

use of ESM2M including future plans can be found 

in Naik (2014), Stock (2014), and Dunne (2014). A 

broader set of applications using GFDL’s ESMs can be 

found on GFDL’s Earth System Science web page.

2.6 Future Plans for GFDL Climate and Earth 

 System Modeling (Held, 2014) 

The next generation GFDL climate model is CM4, 

which will have a base configuration with a 0.5°

atmosphere, and a 0.25° ocean. The goal for CM4 

is to have a climate model suitable for projection 

of climate change up to several hundred years in 

the future, attribution of climate change over the 

past century, and prediction on seasonal to decadal 

timescales. Experience with the previous GFDL 

modeling streams is informing development of CM4. 

The CM4 development process will seek both scientific 

innovation and incremental bias reduction. Additional 

information on current plans for CM4 can be found 

in Adcroft (2014), Golaz (2014), Dunne (2014), Milly 

(2014b), Shevliakova (2014) and Stock (2014). ESM4 

will be based on CM4 and will include a new land model 

that incorporates comprehensive biogeochemistry 

(nitrogen, phosphorous, methane, etc.), prognostic 

aerosols (including dust and biomass burning), 

sub-grid-scale heterogeneity and a new vegetation 

succession scheme, enhanced representation of 

land-use management (fertilizers, water quality, etc.), 

and the Carbon, Ocean Biogeochemistry and Lower 

Trophics (COBALT) marine ecosystem model (Stock et 

al., 2014).

2.7 Software Infrastructure 

GFDL coupled and earth system models use the 

Flexible Modeling System (FMS). FMS can utilize the 

Earth System Modeling Framework (ESMF) wrappers 

to interchange component models with other centers. 

An example of this interoperability is the coupling of 

MOM5 to the EMC Global Forecast System (GFS) as a 

prototype for the CFSV3. FMS is designed for flexibility, 

modularity, and extensibility.

3. EMC Climate and Earth System Models

EMC maintains, enhances and transitions-to-operations 

numerical forecast systems for weather, ocean, climate, 

land surface and hydrology, hurricanes, and air quality 

for the Nation and the global community; for the 

protection of life and property and the enhancement of 

the economy.

3.1 CFSV2

3.1a Model Description

The current climate model at EMC is the Climate 

Forecast System version 2 (CFSV2; Saha et al., 2014). 

The CFSV2 atmospheric model is the Global Spectral 

Model (GSM). The GSM has a spectral dynamic core 

with a triangular truncation of T126, and an associated 

Gaussian grid with approximately 0.95° resolution. 

NOAA Holistic Climate and Earth System Model Strategy

Phase I: Current State
11

The model is discretized with 64 layers in the vertical 

and has a model top of 0.3 hPa. Deep convection is 

treated using the Simplified Arakawa Schubert (SAS; 

Pan and Wu, 1995; Hong and Pan, 1998). Shallow 

convection is parameterized following Tiedtke (1993). 

Vertical diffusion is parameterized following Hong and 

Pan (1996), with a non-local scheme in the planetary 

boundary layer (PBL) and a local scheme above the 

PBL. Land-surface processes are modeled using the 

four-layer Noah model (Ek et al., 2003). Shortwave 

radiation is modeled using the Rapid Radiation Transfer 

Model for General Circulation Models (RRTMG-SW; 

Iacono et al., 2000; Clough et al., 2005), and longwave 

radiation is modeled using the RRTMG longwave 

(RRTM-LW; Mlawer et al., 1997). Interaction between 

the clouds and radiation is handled by the Monte Carlo 

independent column approximation (MCiCA; Barker et 

al., 2002; Pincus et al., 2003).

The CFSV2 ocean model is MOM4.0d (Griffies et al., 

2004) and has horizontal resolution of 0.5° in latitude 

and longitude, with progressively increasing meridional 

resolution equatorward of 30°. Within 10° of the 

equator, the meridional resolution is 0.25° in order to 

resolve the equatorial waveguide and current systems. 

A tripolar grid (Murray, 1996) is employed in order to 

avoid polar filtering over the Arctic. There are 40 vertical 

layers with 10 meter spacing in the upper 220 meters, 

and the bottom depth is 4.5 km. Vertical mixing is 

modeled using the KPP mixed layer scheme of Large 

et al. (1994). The ocean surface boundary condition is 

computed as an explicit free surface with a freshwater 

flux boundary condition. Shortwave radiation absorption 

follows Morel and Antoine (1994) and includes the 

mean annual cycle of chlorophyll concentration. The 

horizontal advection scheme is based on the third-order 

upwind-biased approach of Hundsdorfer and Trompert 

(1994), including the Sweby (1994) flux limiters. 

Isoneutral mixing is handled following Griffies et al. 

(1998). Mesoscale eddies are parameterized using 

the skew-flux approach of Griffies (1998) with a local 

treatment of the quasi-Stokes streamfunction (Gent and 

McWilliams, 1990; Gent et al., 1995). Lateral viscosity 

is treated using the nonlinear scheme of Smagorinsky 

(1993) following Griffies and Hallberg (2000). 

The sea-ice component model is the Sea Ice Simulator 

(SIS; Winton, 2000). SIS is a dynamical model with 

two ice and one snow layer, and five ice thickness 

categories. Ice internal stresses are calculated using 

the elastic-viscous-plastic technique of Hunke and 

Duckowicz (1997), while the thermodynamics treatment 

is a modified Semtner (1976) three-layer scheme. 

3.1b Model Performance and Sample Applications

The CFSV2 is primarily used to produce operational 

intraseasonal to interannual forecasts of SST, surface 

temperature, and precipitation. These forecasts inform 

the operational prediction products at CPC. The skill 

of CFSV2 monthly and seasonal forecasts (Xue et al., 

2013; Peng et al., 2013; Kirtman et al., 2014; Becker 

et al., 2014; Wang et al., 2014) is competitive with 

similar models from other forecast centers. Further, 

the CFSV2 skill is at least partially complementary 

to other models from US institutions such as the 

GFDL CM2.1. Therefore, there is benefit in continuing 

development of real-time multi-model ensemble (MME) 

forecast systems, such as the NMME, in order to inform 

seasonal prediction efforts at CPC.

CFSV2 is also used for diagnostic studies of climate 

variability from the intraseasonal to the decadal 

timescale. For instance, Wang et al. (2013) performed 

ensemble retrospective forecasts of Arctic sea ice with 

CFSV2. That study showed that CFSV2 has modest 

skill in predicting interannual variations of sea ice 

extent out to three months. In another study, Wang 

et al. (2014) showed that CFSV2 forecast skill of the 

Madden-Julian Oscillation (MJO) extends out to 20 

days. Other studies have documented capabilities of 

CFSV2 in simulating and predicting various aspects of 

climate variability (Hoerling et al., 2013; Riddle et al., 

2013; Weaver et al., 2014; Abhilesh et al., 2014; Hu et 

al., 2014). CFSV2 participated in the CMIP5 decadal 

forecast experiments. Finally, CFSV2 is the dynamical 
12

model used in the CFS Reanalysis (CFSR; Saha et 

al., 2010). The CFSR is an ocean, land, atmosphere, 

and sea-ice analysis, which covers the period from 

1979 to present.

3.2 Software Infrastructure

CFSV2 does not use a major software infrastructure 

package. The next version of the model, CFSV3, 

will use the NOAA Environmental Modeling System 

(NEMS), which is an ESMF-based framework. 

EMC intends to support the CFSV3 effectively as 

an open source community model, in line with the 

general intended approach for all operational models 

maintained by EMC. The main reason for this approach 

is to create an environment where research can be 

done easily with operational models, which in turn 

will better leverage research for improvement in 

operational models.

3.3 Future Plans for EMC Climate and Earth 

 System Modeling

Testing of a prototype CFSV3 with GSM and MOM5 

component models coupled under NEMS is underway. 

The Hybrid Coordinate Ocean Model (HYCOM) is 

also a candidate for the CFSV3 ocean model. EMC 

and CPC are working together to develop a draft plan 

for further development of CFSV3. The CFSV3 will 

seek incremental improvement over CFSV2 and will 

be developed in the context of the full operational 

prediction suite. Once the draft plan is developed, it 

will be discussed with key National Weather Service 

(NWS) stakeholders including other federal labs and the 

university research community. 

4. ESRL Climate and Earth System Models

ESRL’s mission is to observe, understand and predict 

the earth system through research that advances 

NOAA’s environmental information and service from 

minutes to millennia on global-to-local scales.

4.1a Model Description 

ESRL is developing a coupled atmosphere-ocean 

FIM-iHYCOM-chem model for application on 

timescales from days to seasons. The atmospheric 

component model is the FIM (Lee and MacDonald, 

2009; Bleck et al., 2010; Bleck et al., 2015), while 

the oceanic component model is an icosahedralgrid version of HYCOM (Bleck, 2002) referred to as 

iHYCOM (Bleck and Sun, 2012). FIM has been 

tested extensively at horizontal resolutions from 

10-120 km in atmospheric-only mode, usually with 

64 hybrid-isentropic vertical layers (both horizontal 

and vertical resolutions adaptable via namelist 

settings). FIM uses atmospheric and land-surface 

physics using CFS options from the 2012 version of 

GFS physics (a later version of those described in 

Section 3 above for CFS), including deep/shallow 

cumulus convection but with McICA cloud-radiation 

interaction. ESRL is currently performing extensive 

testing of an alternative deep/shallow scale-aware 

convective parameterization (Grell and Freitas, 2014) 

in the coupled FIM-iHYCOM system using a shared 

icosahedral grid of 60 km resolution; 26 hybridisopycnic layers in the ocean; and 64 hybrid-isentropic 

vertical layers in the atmosphere. The vertical 

coordinate in FIM uses quasi-Lagrangian isentropic 

layers in the free atmosphere and terrain-following 

layers near the surface. Other than noted above, FIM 

uses NCEP’s GSM/GFS physical parameterization 

suite. iHYCOM follows the Navy-NOAA community 

HYCOM, but with horizontal discretization on the 

icosahedral grid, using transport and unstructuredgrid parallelization adapted from FIM. The matched 

horizontal grid in the atmosphere and ocean model 

eliminates errors from horizontal interpolation of airocean fluxes and disparate coastlines. The coupled 

version of iHYCOM has been tested at a horizontal 

resolution of 120 km with 26 vertical layers. The 

number of vertical layers in iHYCOM is adaptable. 

iHYCOM uses an adaptive vertical coordinate with 

fixed-thickness layers in the upper ocean and constant 

potential density layers in the deeper ocean. The 

vertical advection scheme in both FIM and iHYCOM 

is the PPM (Colella and Woodward, 1984). iHYCOM 

NOAA Holistic Climate and Earth System Model Strategy

Phase I: Current State
13

treats turbulence in the surface mixed layer using the 

KPP scheme of Large et al. (1994). Below the mixed 

layer, the treatment of diapycnal mixing follows the 

scheme of McDougall and Dewar (1998). Sea ice is 

treated using a one-layer “energy loan” model similar 

to that found in the appendix of Semtner (1976). 

FIM-iHYCOM also includes a full inline atmospheric 

chemistry option taken from Weather Research and 

Forecasting coupled with Chemistry (WRF-Chem), 

also incorporating the Goddard Chemistry Aerosol 

Radiation and Transport (GOCART) aerosol options, 

and seasonal forecast experiments are now being 

carried out with this FIM-iHYCOM-chem extended 

coupled model. FIM-iHYCOM-chem does not have 

any ocean chemistry at present.

4.1b Model Performance and Sample Applications

FIM-iHYCOM is a relatively new coupled model 

and work is ongoing to diagnose and reduce the 

systematic errors that arise due to coupling of the 

component models. Preliminary results indicate that 

the overall radiative balance is <1 W/m2, but regional 

biases in surface shortwave flux and precipitation 

are too large, commensurate with those in CFSV2 

and improved in some geographic areas. The initial 

application of FIM-iHYCOM will be extended initialized 

forecasts of high-impact events such as droughts and 

floods. Plans are to base ocean initial conditions on 

the real-time data assimilation products distributed by 

the Naval Research Laboratory (Cummings, 2005). 

FIM-iHYCOM will also be tested for free coupled tests 

out to 30-year duration, and also with hindcasts for 

4-week forecasts in early FY15, to be followed later by 

NMME hindcasts.

4.2 Software Infrastructure

FIM-iHYCOM uses NEMS, which can be switched off 

or on via a namelist option. FIM-iHYCOM also uses 

a high-level optimization tool, the Scalable Modeling 

System (SMS; Govett, 2010) allowing use of fine-grain 

high-performance computing environments. 

4.3 Future Plans for ESRL Climate and Earth 

 System Modeling

ESRL will continue to develop the FIM-iHYCOM for use 

as a forecast tool on timescales from days to seasons. 

Key future work areas will include development, 

application, and testing of physics and chemistry 

options including collaboration with EMC and CPC as 

applicable. The model will be evaluated for its ability to 

predict blocking events, sea-ice variability, and tropical 

cyclone formation. Once a baseline version of the 

model is finalized, an extended set of retrospective 

seasonal forecasts will be generated in order to assess 

the value of adding FIM-iHYCOM to the NMME. 

5. NOAA Climate and Earth System 

 Modeling Collaborative and 

 Synergistic Activities

The NOAA centers have engaged in climate and 

earth system modeling collaborations that have 

leveraged the capabilities of the different centers, 

and which are consistent with the missions of the 

centers. For example, GFDL provided EMC with 

ocean data assimilation (Derber and Rosati, 1989) 

and ocean model software that was used to develop 

the CFSV2 (Saha et al., 2014). EMC and GFDL 

contribute real-time seasonal forecasts to the NMME 

(Kirtman et al., 2014; Becker et al., 2014), which CPC 

is currently using to produce operational monthly and 

seasonal predictions. The ESRL Physical Science 

Division (PSD) and EMC have co-developed a hybrid 

Ensemble Kalman Filter data assimilation system 

for the operational global atmospheric weather 

prediction model. This data assimilation system 

will likely be the starting point for development of 

NOAA’s next-generation climate reanalysis capability 

and for the coupled data assimilation system that 

will be developed for CFSV3. ESRL PSD has also 

completed an extended set of retrospective forecasts 

for the Global Ensemble Forecast System (GEFS) that 

enable improved post-processing and calibration of 

operational GEFS forecasts.
14

It is also important to note that the research and 

development activities at each of the centers benefit 

the whole NESM enterprise even though new 

developments from one center may not be immediately 

adopted by the other centers. For example, research at 

GFDL showed that chlorophyll-based solar penetration, 

use of surface currents in the oceanic surface stress 

calculation, and diurnal coupling lead to reduced 

systematic errors in tropical ocean simulations. EMC 

adopted these improvements when the operational 

forecast model was updated to CFSV2.

In addition to interaction between centers within the 

NESM enterprise, there is also significant interaction 

with other federal labs and programs with similar 

missions. For instance, GFDL is currently collaborating 

with the National Center for Atmospheric Research 

(NCAR) in a Climate Process Team (CPT) that 

examines the parameterization of diapycnal mixing due 

to internal wave breaking, and previously collaborated 

with NCAR in a CPT that examined low-latitude 

cloud feedbacks. EMC collaborates with the Navy 

on development of ocean and wave models. ESRL 

also collaborates with the Navy on ocean model 

development. NOAA Climate Program Office (CPO) 

research programs have fostered and enabled such 

types of collaborations within the NESM enterprise, and 

with the external community.

6. Challenges for the NOAA Climate and 

 Earth System Modeling Enterprise

It is widely recognized that interaction with the broader 

research community, including other federal labs and 

the academic research community, can be beneficial 

for NESM development. For example, NCAR provides 

robust support for their model, the Community Earth 

System Model (CESM). This support includes a user 

forum, an annual CESM workshop and workshops 

with training sessions on how to use the CESM, and 

extensive documentation that includes both scientific 

and user guides. The CESM community support 

effort is staffed by approximately seven full-time 

employees (one for each component model plus one 

for software infrastructure). 

Each of the NOAA climate and earth system models 

has similar complexity and scope of use as the CESM, 

yet community support for these models comes out of 

base budgets that are already too thinly stretched. If 

community support for the NOAA climate and earth 

system models is considered an enterprise priority, then 

additional sustained funding needs to be provided to 

develop such a capability. The Climate Program Office 

has been supporting community involvement in the 

development of NOAA’s operational climate prediction 

model and organizes a Climate Model Development 

Task Force to facilitate interactions across NOAA and 

with the broader research community. An important 

challenge here is the lack of adequate resources for 

this type of research-to-operations activity that greatly 

limits available infrastructure and funding.

The NESM enterprise is also under continuous 

pressure to increase model resolution, make models 

more comprehensive, and increase ensemble size 

to better detect and predict extreme events. These 

model improvements are necessary and ultimately 

result in improved products for NOAA stakeholders. 

However, they also require significant increases in highperformance computing (HPC) resources, including 

both computing cycles and disk and tape storage. 

NOAA has had recent success in increasing HPC 

budgets; however, a significant part of that has been 

associated with one-time funding injections such as 

that from the American Recovery and Reinvestment Act 

(ARRA) and the Disaster Recovery Act (DRA). These 

one-time funding injections are not guaranteed in the 

future. It is critical that the NESM enterprise receive 

sustained, continually growing HPC resources in order 

to ensure that it can meet the increasing demand from 

stakeholders for climate products and services. 

A final challenge faced by the NESM enterprise is the 

limited capacity to disseminate data, which currently 

NOAA Holistic Climate and Earth System Model Strategy

Phase I: Current State
15

falls far short of meeting the demand for such data 

from the climate and weather enterprise. This results 

in available datasets being truncated in terms of 

available fields, and spatial and temporal resolution. 

In order to ensure that NOAA maximizes the return on 

investment made to produce these data, it is imperative 

that a sufficient corresponding investment is made 

for disseminating the data needed by stakeholders. 

However, NOAA needs to simultaneously guard 

against the lose-lose situation that would result from 

cannibalizing the science budget in order to enable 

fuller data dissemination. Toward this end, NOAA is 

looking to the private industry to help make NOAA’s 

data available in a rapid, scalable manner to the public. 

7. Summary

This report provides a high-level overview of the 

current state and future plans of the NESM enterprise, 

with a focus on global coupled models used for 

research and operations for the subseasonal to 

centennial timescale. It is anticipated that this report 

will serve as a resource for scientists interested 

in utilizing these models and NOAA managers 

interested in learning about the current state of the 

NESM portfolio.

A description and discussion of performance and 

sample applications were provided for seven NOAA 

climate and earth system models, including five models 

developed by GFDL, one by NCEP EMC, and one by 

ESRL. Ongoing collaborative and synergistic activities 

among the NOAA modeling centers were described 

and an overview of some challenges facing the NOAA 

NESM enterprise were given.

This report constitutes the first phase of a two-phase 

study to develop a NOAA Climate and Earth System 

Modeling Strategy. The forthcoming Phase II of the 

study will present possible pathways for NOAA model 

development to support a holistic strategy to advance 

global climate and earth system models used for 

research and operations.

Acknowledgements

We thank Fuqing Zhang (Penn State University), 

Mike Ek (NOAA/EMC) and two anonymous reviewers 

for their thoughtful and constructive comments on a 

previous version of this report. Thanks are also due to 

Richard Rivera (CPO) for designing and implementing 

the cover art and formatting of the report. This study 

was organized by the OAR/CPO Modeling, Analysis, 

Predictions and Projections (MAPP) Program. Heather 

Archambault (MAPP) coordinated the external review 

of the report, and she and Alison Stevens (MAPP) 

oversaw its formatting and publication.
16

1. Model Name

2. Vintage

1. GFDL-CM2.1

2. 2006

1. GFDL-CM3

2. 2011

1. GFDL-ESM2M

2. 2011

1. NCEP-CFSV2

2. 2011

1. ESRL-FIMHYCOM

2. 2014

1. Institution 

2. Main Reference(s)

1. GFDL

2. Delworth et al. 

(2006)

1. GFDL

2. Delworth et al. 

(2006); Donner et 

al. (2011)

1. GFDL

2. Dunne et al. 

(2012); Dunne et 

al. (2013)

1. NCEP

2. Saha et al. (2014)

1. ESRL

2. Bleck et al. (2010); 

Bleck et al. (2015)

Atmosphere

1. Component Name

2. Horizontal Grid

3. Number of Vertical 

Layers

4. Grid Top

5. Dynamic Core

6. References

1. AM2.1

2. 2.5° lon x 2.0° lat

3. 24

4. Midpoint of top box 

is 3.65 hPa

5. Finite Volume

6. Delworth et al. 

(2006)

1. AM3

2. 200 km X 200 km

3. 48

4. 0.01 hPa

5. Finite Volume

6. Donner et al. 

(2011)

1. AM2.1 (updated)

2. 2.5° lon x 2.0° lat

3. 24

4. Midpoint of top 

box is 3.65 hPa

5. Finite Volume

6. Delworth et al. 

(2006); Dunne et al. 

(2012)

1. GSM

2. T126 (~0.9375°)

3. 64

4. 0.03 hPa

5. Spectral

6. Saha et al. (2010)

1. FIM

2. 60 km

3. 64

4. 0.5 hPa

5. Finite Volume 

6. Bleck et al. 

(2010); Lee and 

MacDonald (2009)

Aerosol

1. Component Name 

or Type

2. References

Semi-interactive 1. Interactive

2. Levy et al. (2013)

Semi-interactive Semi-interactive 1. WRF-Chem 

(radiationinteractive)

2. Grell et al. (2005)

Atmospheric 

Chemistry

1. Component Name

2. References

Not implemented 1. Atmospheric 

Chemistry

2. Horowitz et al. 

(2003); Austin and 

Wilson (2006); 

Sander (2006)

Not implemented 1. Ozone Chemistry

2. McCormack et al. 

(2006)

1. WRF-Chem

2. Fast et al. (2006)

Land Surface

1. Component Name

2. References

1. LM2.1

2. Milly and Shamkin 

(2002)

1. LM3

2. Milly and 

Shamkin (2002); 

Shevliakova et al. 

(2009)

1. LM3

2. Milly and 

Shmakin (2002); 

Shevliakova et al. 

(2009); Donner et 

al. (2011)

1. Noah

2. Ek et al. (2003)

1. Noah

2. Ek et al. (2003)

Ocean

1. Component Name

2. Horizontal 

Resolution

3. Number of Vertical 

Layers

4. Top Level

5. Vertical Coordinate

6. Top BC

7. References

1. MOM4

2. 1°

3. 50

4. 5 m

5. Depth

6. Nonlinear splitexplicit

7. Griffies et al. 

(2005)

1. MOM4.1

2. 1°

3. 50

4. 5 m

5. z*

6. Nonlinear splitexplicit

7. Griffies and 

Greatbach (2012)

1. MOM4.1

2. 1°

3. 50

4. 5 m

5. z*

6. Nonlinear splitexplicit

7. Griffies (2009); 

Dunne et al. 

(2012)

1. MOM4

2. 0.5°

3. 40

4. 5 m

5. Depth

6. Nonlinear splitexplicit

7. Griffies et al. 

(2005)

1. HYCOM

2. 60 km

3. 26

4. 5 m

5. Hybrid-isopycnic

6. Unsplit

7. Bleck (2002)

Ocean 

Biogeochemistry

1. Component Name

2. References

Not implemented Not implemented 1. TOPAZ

2. Henson et al. 

(2009); Dunne et 

al. (2013)

Not implemented Not implemented

Sea Ice

1. Component Name

2. References

1. SIS

2. Winton (2000); 

Delworth et al. 

(2006) 

1. SIS

2. Griffies and 

Greatbach (2012)

1. SIS

2. Winton (2000); 

Delworth et al. 

(2006)

1. SIS

2. Winton (2000); 

Delworth et al. 

(2006)

1. HYCOM 

thermodynamic 

1-layer

2. Semtner (1976)

NOAA Holistic Climate and Earth System Model Strategy

Phase I: Current State

Appendix: NOAA Climate/Earth System Model Catalog
17

Model GFDL-CM2.1

Data Available

1. CMIP3: http://nomads.gfdl.noaa.gov/CM2.X/

2. CMIP5: http://nomads.gfdl.noaa.gov:8080/DataPortal/cmip5.jsp

3. ECDA: http://www.gfdl.noaa.gov/ocean-data-assimilation-model-output

4. NMME Forecasts: http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/

Model Documentation http://nomads.gfdl.noaa.gov/CM2.X/references/

http://prod.static.esdoc.webfactional.com/js_client/demo/prod/comparator.html

Model Code Availability http://mdl-mom5.herokuapp.com/web

Software Infrastructure FMS

Model GFDL-CM3

Data Available CMIP5: http://nomads.gfdl.noaa.gov:8080/DataPortal/cmip5.jsp

Model Documentation http://prod.static.esdoc.webfactional.com/js_client/demo/prod/comparator.html

Software Infrastructure FMS

Model GFDL-ESM2M

Data Available CMIP5: http://nomads.gfdl.noaa.gov:8080/DataPortal/cmip5.jsp

Model Documentation http://prod.static.esdoc.webfactional.com/js_client/demo/prod/comparator.html

Model Code Availability http://mdl-mom5.herokuapp.com/web

Software Infrastructure FMS

Model NCEP-CFSV2

Data Available

ISI Forecast and Hindcasts: http://cfs.ncep.noaa.gov/cfsv2/downloads.html

CMIP5: http://esgf.nccs.nasa.gov/esgf-web-fe/live#

CFSR: http://cfs.ncep.noaa.gov/cfsr/downloads/

NMME Forecasts: http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/

Model Documentation http://cfs.ncep.noaa.gov/cfsv2/docs.html

Model Code Availability http://cfs.ncep.noaa.gov/cfsv2/downloads.html

Model FIM-iHYCOM

Model Documentation http://fim.noaa.gov/fimdocu_rb.pdf 

http://fim.noaa.gov/ofimdoc.pdf

Model Code Availability https://gsdforge.fsl.noaa.gov/svn/fim/trunk

Software Infrastructure NEMS

Model Data, Documentation and Code
18

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NOAA Technical Report

NOAA Holistic Climate

and Earth System

Model Strategy

Phase I: Current State