The EC-Earth3 Earth System Model for the Climate Model Intercomparison Project 6

TLDR: It is demonstrated here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond, and key performance metrics demonstrate physical behaviour and biases well within the frame known from recent CMIP models.

Abstract: The Earth System Model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling framework, major model configurations, a methodology for ensuring the simulations are comparable across different HPC systems, and with the physical performance of base configurations over the historical period The variety of possible configurations and sub-models reflects the broad interests in the EC-Earth community EC-Earth3 key performance metrics demonstrate physical behaviour and biases well within the frame known from recent CMIP models With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond

Figures

Figure 18: Regression of Nino3.4 SST index into Sea Level Pressure during winter (DJF) for ERA-20C reanalysis (upper panel), 20 member EC-Earth3 ensemble mean (middle panel), 4 member EC-Earth2.3 ensemble mean (lower panel). Analyses are based on three-month means Dec-Feb.

Figure 1: Coupling links and typical frequencies at standard resolution between all components that potentially can be 2015 coupled. Existing configurations include sub-sets of component models and associated couplings.

Table 8: Variables exchanged with a 24 h frequency between the vegetation model LPJ-GUESS and the Chemical transport model TM5 in EC-Earth3-CC. *Fluxes that occur once a year and are distributed evenly over the following year.

Figure 8: Top row: Biases (colored shadings) of the zonally averaged zonal component of wind for EC-Earth2.3 (a) and ECEarth3 against ERA5 Reanalysis over the 1980-2010 period. Contours shows the ERA-5 climatology. Bottom row: Biases (colored shadings) of the 250hPa zonal component of wind for EC-Earth2.3 (a) and EC-Earth3 against ERA5 Reanalysis

Figure 4: Global annual mean TAS in K from the EC-Earth2.3 (red, for CMIP5) and EC-Earth3 (blue, for CMIP6) 2035 ensembles. Ensemble means are shown as thick lines and the ensemble spread is shown as a shaded area. Global annual mean TAS from the ERA5 (black, solid) and ERA20C (black, dashed) re-analyses are shown for comparison.

Table 7: Variables exchanged with a 6 h frequency between the atmosphere and the chemical transport model (CTM) TM5 in EC-Earth3-AerChem.

Full text

1
The EC-Earth3 Earth System Model for the Climate Model
Intercomparison Project 6
Ralf Döscher
1
, Mario Acosta
2
, Andrea Alessandri
3
, Peter Anthoni
4
, Almut Arneth
4
, Thomas Arsouze
2
,
Tommi Bergman
5
, Raffaele Bernardello
2
, Souhail Bousetta
6
, Louis-Philippe Caron
2
, Glenn Carver
6
, 5
Miguel Castrillo
2
, Franco Catalano
7
, Ivana Cvijanovic
2
, Paolo Davini
8
, Evelien Dekker
1
, Francisco J.
Doblas-Reyes
2
, David Docquier
1
, Pablo Echevarria
2
, Uwe Fladrich
1
, Ramon Fuentes-Franco
1
, Matthias
Gröger
1
, Jost v. Hardenberg
9,8
, Jenny Hieronymus
1
, M. Pasha Karami
1
, Jukka-Pekka Keskinen
10
,
Torben Koenigk
1
, Risto Makkonen
11
, Francois Massonnet
12
, Martin Ménégoz
13
, Paul A. Miller
14
,
Eduardo Moreno-Chamarro
2
, Lars Nieradzik
14
, Twan van Noije
5
, Paul Nolan
15
, Declan O’Donnell
11
, 10
Pirkka Ollinaho
11
, Gijs van den Oord
5
, Pablo Ortega
2
, Oriol Tintó Prims
2
, Arthur Ramos
2
, Thomas
Reerink
5
, Clement Rousset
16
, Yohan Ruprich-Robert
2
, Philippe Le Sager
5
, Torben Schmith
16
, Roland
Schrödner
14
, Federico Serva
17
, Valentina Sicardi
2
, Marianne Sloth Madsen
18
, Benjamin Smith
14
, Tian
Tian
18
, Etienne Tourigny
2
, Petteri Uotila
10
, Martin Vancoppenolle
19
, Shiyu Wang
1
, David Wårlind
14
,
Ulrika Willén
1
, Klaus Wyser
1
, Shuting Yang
18
, Xavier Yepes-Arbós
2
, Qiong Zhang
20
15
1
Swedish Meteorological and Hydrological Institute SMHI, Norrköping, 60176, Sweden
2
Barcelona Supercomputing Center, Barcelona, 08034, Spain
3
Institute of Atmospheric Sciences and Climate, Consiglio Nazionale delle Ricerche, ISAC-CNR, 40129, Bologna, Italy
4
Karlsruhe Institute of Technology KIT, Garmisch-Partenkirchen, 82467, Germany
20
5
Royal Netherlands Meteorological Institute KNMI, De Bilt, 3731, The Netherlands
6
European Centre for Medium Range Weather Forecast ECMWF, Reading, RG2-9AX, United Kingdom
7
Italian National Agency for New Technologies, Energy and Sustainable Economic Development ENEA, Roma, 00196, Italy
8
Institute of Atmospheric Sciences and Climate, Consiglio Nazionale delle Ricerche, ISAC-CNR, 10133, Torino, Italy
9
Politecnico di Torino, 10129 Torino, Italy 25
10
University of Helsinki, Helsinki, 00014, Finland
11
Finnish Meteorological Institute, FMI, Helsinki, 00560, Finland
12
Université Catholique de Louvain UCLouvain, Ottignies-Louvain-la-Neuve, 1348, Belgium
13
IGE, University of Grenoble, Grenoble, 38400, France
14
Lund University, Lund, 22100, Sweden
30
15
Irish Centre for High End Computing, ICHECK, Ireland
16
UPMC University Pierre and Marie Curie UPMC, Paris, 75005, France
17
Istituto di Scienze Marine CNR-ISMAR, Venezia, 30122, Italy
18
Danish Meteorological Institute, Copenhagen, 2100, Denmark
19
Institut Pierre Simon Laplace IPSL, Paris, 75005, France 35
20
Stockholm University, Stockholm, 106 91, Sweden
Correspondence to: Ralf Döscher (ralf.doescher@smhi.se)
Abstract. The Earth System Model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling 40
framework, major model configurations, a methodology for ensuring the simulations are comparable across different HPC
https://doi.org/10.5194/gmd-2020-446
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2
systems, and with the physical performance of base configurations over the historical period. The variety of possible
configurations and sub-models reflects the broad interests in the EC-Earth community. EC-Earth3 key performance metrics
demonstrate physical behaviour and biases well within the frame known from recent CMIP models. With improved physical
and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the 45
CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here
that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
1 Introduction
The latest challenges in climate research have evolved to include biophysical and biogeochemical processes (WCRP
Strategic Plan 2019-2028) contributing to the exchange of energy, mass, aerosols, trace and greenhouse gases and nutrients 50
between atmosphere, land and ocean, allowing the description of various feedback processes. This challenge resulted in a
need for the next generation of climate models - namely, the Earth System Models (ESMs).
The Paris Climate accord is calling for limiting climate change “well below 2°C and to pursue efforts to limit the increase to
1.5°C”, ESMs represent our most relevant tools available for exploring the emission pathways necessary for achieving this 55
goal, as well as for understanding the consequences of not making this target. The Paris agreement requires firm measures of
mitigation, including carbon dioxide removal. Given the complexity of the climate system, alternative emission pathways
towards this goal can be carefully explored only with Earth System Models (ESMs) which describe the most relevant
feedback mechanisms, and provide methods for assessments of uncertainty. ESMs are the primary source of information for
understanding the Earth’s climate feedbacks, for attributing changes to specific drivers, for future climate projections and 60
predictions, and for the development of mitigation policies.
While the exact definition of ESM varies, in general, it refers to a complex model that besides the classical climate model
core (consisting of physical models of the atmosphere, sea ice, ocean and land) combines additional optional components
covering biophysical and biogeochemical processes and more sophisticated treatment of aerosols. A flexible coupling 65
framework facilitates a range of ESM configurations with or without certain model components or processes. Given the
important role of ESMs, these models need to be developed together with use cases for science, climate services and
decision making that control the priorities of development.
This article describes EC-Earth3, an Earth System Model with the flexibility of different configurations that allow users to 70
consider (or exclude) various climate feedbacks and processes. It has been developed collaboratively by the European
research consortium EC-Earth to provide a community of European research institutes and universities with an integrated
state-of-the-art tool for Earth system studies. While its development goals were largely motivated by the Coupled Model
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Intercomparison Project phase 6 (CMIP6, Eyring et al. 2016), its suite of ESM configurations allows exploration of a broad
range of climate science questions. 75
The predecessor system EC-Earth2 (Hazeleger et al., 2012) approached the concept of ‘‘seamless prediction’’ to forge
models for weather forecasting and climate change studies into a joint system. EC-Earth version 2.2 was based on an adapted
version of the atmosphere model IFS 31r1, the Integrated Forecasting System of the European Centre for Medium-Range
Weather Forecasts (ECMWF), as used in their seasonal prediction system 3. In addition, a configuration including the 80
atmospheric composition model TM5 was developed (van Noije et al., 2014) and released as EC-Earth version 2.4. EC-
Earth2 has been used for simulations under CMIP5 and in a range of climate studies, e.g. Koenigk et al. 2013, Seneviratne et
al. 2013) A search on Google Scholar gives 1920 hits for articles mentioning the EC-Earth “climate model”, which is a
substantial number, even when compared to 4280 hits for the US community model CESM, which has a much larger
community behind it. 85
The current version EC-Earth3 for CMIP6 still leans on the original idea of a climate model system based on the seasonal
prediction system of ECMWF. Development has started in 2012 by re-designing the software infrastructure and updating the
atmosphere model to IFS 36r4, corresponding to the ECMWF seasonal prediction system 4. Since then, various updates,
improvements and forcings have been implemented and the model has been tuned for several intermediate versions and 90
finally for the CMIP6 version, EC-Earth3.
Adaptation of IFS for EC-Earth follows up on the strategy of mutual benefits between short/medium range weather
prediction on the one hand and longer time scale climate prediction and projection on the other. While short term processes
and feedbacks are expected to be covered well in the seasonal prediction system, longer term conservation and trends are the 95
focus of climate model development. During the development process, EC-Earth has been able to feed back valuable
information to ECMWF. Examples are a stochastic physics tendency conservation fix for humidity and energy (Leutbecher
et al. 2017), forcing (tropospheric and stratospheric aerosol, ozone) and an implementation of aerosol forcing as used in
CMIP6 (“MACv2-SP”)
100
The EC-Earth ESM exists in different coupled configurations that reflect a variety of study options and science interests. The
system comes with a pure physical core configuration in the form of a Global Climate Model (GCM) with a range of
options: a GCM with prescribed or interactively coupled dynamic vegetation, a dynamical Greenland ice sheet, and a closed
carbon cycle. Also, a configuration with interactive aerosols and atmospheric chemistry is available, and GCM
configurations have been established in different resolutions for the atmosphere and ocean. 105
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As a community model, EC-Earth3 is run on several different HPC platforms. While expecting the same simulated climate
on each machine, we cannot expect binary identical results. To ensure consistency between different machines, a test
protocol and statistical test procedure have been designed.
110
This paper describes the EC-Earth3 model concept, and provides an overview of its component models and the range of
available coupled configurations. Specific configurations will be described in more detail in forthcoming papers. The
model’s physical performance is illustrated based on the core GCM configurations, with a focus on results from historical
simulations performed under the CMIP6 protocol. The EC-Earth consortium consists of 27 partners in 10 European
countries. 115
2. Configurations
2.1 The model architecture and coupling framework
EC-Earth is a modular Earth System Model (ESM) that is collaboratively developed by the European consortium with the
same name. The current generation of the model, EC-Earth3, has been developed after CMIP5 and it is used in its version
3.3 for CMIP6 experiments. 120
EC-Earth3 comprises model components for various physical domains and system components describing atmosphere,
ocean, sea ice, land surface, dynamic vegetation, atmospheric composition, ocean biogeochemistry and the Greenland ice
sheet. The component models are described in section 3. The atmosphere and land domains are covered by ECMWF’s IFS
cycle 36r4, which is supplemented with a coupling interface to allow boundary data exchange with other components (ocean, 125
dynamic vegetation, aerosols and atmospheric chemistry, etc). The NEMO3.6 and LIM3 models are the ocean and sea-ice
components, respectively. Biogeochemical processes in the ocean are simulated by the PISCES model. Both LIM3 and
PISCES are code-wise integrated in NEMO. Dynamical vegetation, land use and terrestrial biogeochemistry are provided by
LPJ-GUESS (Smith et al., 2014, Lindeskog et al., 2013). Aerosols and chemical processes in the atmosphere are described
by TM5 . The ice sheet model PISM is optionally utilized to model the Greenland ice sheet. 130
An overview of five ESM model configurations is given in this section. Descriptions are schematic and more detailed
specifications will be given in forthcoming publications. Table 1 lists the configurations and their composition, while Table
2 shows the commonly used resolutions for CMIP6.
135
Most of the model components are coupled through the OASIS3-MCT coupling library (Craig et al., 2017) while some
software components include more than one model component, e.g. the sea-ice model being a part of the ocean model. A
new coupling interface has been developed and implemented to allow a flexible exchange between the model components
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(see section 3). The OASIS3-MCT coupler provides a technical means of exchanging (sending and receiving) two- and
three-dimensional coupling fields between different model components on their different grids. Of the above named model 140
components, NEMO, LIM3 and PISCES exchange data directly via shared data structures. Thus, EC-Earth3 is implemented
following a multi-executable MPMD (multiple programs, multiple data) approach. The model components run concurrently
and message-passing interface (MPI) is used for parallelisation within the components. A potential configuration of all
components is illustrated in Figure 1, which also shows coupling links and frequencies. Note that a configuration including
all possible components is not implemented in practice. 145
In order to manage different configurations, both at build and run time, EC-Earth3 includes tools to store and retrieve
configuration parameters for different model configurations, computational platforms and experiment types. This allows
consistent control of the build and run environments and improves reproducibility across platforms and use-cases.
Initial and forcing data, in the form of data files, are provided centrally for the EC-Earth community, and the data is 150
versioned and checksummed for reproducibility.
For EC-Earth3 a tool was developed to convert the native model output to CF-compliant (“Climate and Forecast” standard)
netCDF format (i.e., Climate Model Output Rewriter, CMOR), thus fulfilling the CMIP6 Data Requests for the MIPs
that the community is contributing to (van den Oord et al. (2017), https://github.com/EC-Earth/ece2cmor3/).
155
2.2 Basic configurations EC-Earth3 and EC-Earth3-Veg
EC-Earth3 is the standard configuration consisting of the atmosphere model IFS (section 3.1) including the land surface
module HTESSEL (section 3.2) and the ocean model NEMO3.6 including the sea ice module LIM3 (section 3.5). Coupling
variables are communicated between the different component models (see section 3) via the OASIS3-MCT coupler. The 160
physical interfaces are defined specifying the variables exchanged and the algorithms used.
At the atmosphere-ocean interface, we follow the principle that the ocean provides state variables and the atmosphere sends
fluxes (Table 3). Flux formulations correspond to the documentation of IFS CY36R1, section 3, at
https://www.ecmwf.int/en/publications/ifs-documentation. As the coupler ensures conservative remapping, momentum, 165
energy, evaporation and precipitation fluxes are conserved.
The freshwater runoff from land to ocean is derived from a runoff mapper (Table. 4). It uses OASIS3-MCT to interpolate
local runoff and ice-shelf calving (from Greenland and Antarctica) to the ocean. The runoff and calving received from the
atmosphere and from the surface model HTESSEL are interpolated onto 66 hydrological drainage basins on a mapper grid 170
by a nearest-neighbour distance-based Gauss-weighted interpolation method. Then, in a coupling post-processing step
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Frequently asked questions

Q1. What contributions have the authors mentioned in the paper "The ec-earth3 earth system model for the climate model intercomparison project 6" ?

The Earth System Model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling 40 framework, major model configurations, a methodology for ensuring the simulations are comparable across different HPC this paper. 

Q2. Why did the authors choose PIOMAS as a reference product?

The authors chose PIOMAS as a reference product for sea ice thickness and volume because of the relatively long available timeframe (i.e. from 1979 to now), compared to observational products, which cover much shorter periods. 

Q3. What is the effect of changing stand structure on growth?

Evolving stand structure impacts growth,survivorship and the outcome of competition by affecting the availability of the key resources: light, space, water and 670nitrogen. 

Q4. What are the latest challenges in climate research?

The latest challenges in climate research have evolved to include biophysical and biogeochemical processes (WCRP Strategic Plan 2019-2028) contributing to the exchange of energy, mass, aerosols, trace and greenhouse gases and nutrients 50between atmosphere, land and ocean, allowing the description of various feedback processes. 

Q5. How does the coupled model compensate for the evaporation imbalance?

175In order to avoid a significant long-term sea-surface height reduction in coupled model runs due to a net precipitation - evaporation (P-E) imbalance in the EC-Earth3 atmosphere of about -0.016 mm/day in the historical period, the coupled model implements a runoff flux corrector, which amplifies river runoff by 7.95% in order to compensate for this effect. 

Q6. What are some of the calving schemes implemented in PISM?

Several calving schemes are implemented in PISM to cope with different conditions, including the Eigencalving, von Mises calving, thickness calving, and flow kill calving, etc. 

Q7. Why was the ice thickness distribution framework introduced?

The ice thickness distribution framework was introduced (Thorndike et al, 1975) to deal with meter-scale variations in ice 820thickness, which cannot be resolved explicitly, but should preferably be accounted for, as many sea ice processes, in particular growth and melt, depend non-linearly on thickness h. 

Q8. Why do the authors not show maps of sea ice in the southern hemisphere?

1130Due to the absence of reliable long-term reanalysis / observational products for Antarctica, the authors do not show maps of sea ice thickness in the southern hemisphere. 

Q9. What is the reason why the global mean temperature in the historical ensemble has a warm bias?

The authors find that the global mean temperature in the historical ensemble has a warm bias of about 0.5 K in comparison with ERA5, which is mainly due to a strong warm bias in the Southern Ocean area. 

Q10. Why is the ice velocity vector considered the same for all categories?

The 2D ice velocity vector is considered the same for all categories and stems from the horizontal momentum 830conservation equation. 

Q11. How many years did the ocean and land C stocks drift?

The completion of the spin up was assessed following the recommendation of C4MIP (Jones et al., 2016) where both the ocean and land C stocks had to drift by less than 10 Pg C/century. 

Q12. What is the realism of the modelled QBO in EC-Earth3?

Like other models participating in CMIP6 (Richter et al., 2020), the realism of the modelled QBO is notably improved in EC-Earth3, with 91 vertical levels and a revised gravity wave scheme (see Section 3.1). 

Q13. What is the background soil albedo for each vegetation type?

The background soil albedo was adopted from the map from Rechid et al. (2009) and a look-up table of the albedo values av for each vegetation type was estimated using least square minimization of errors against available monthly climatology of snow-free monthly MODIS albedo (Morcrette et al., 2008).