the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Feb 2025
nextGEMS: entering the era of kilometer-scale Earth system modeling
Abstract. The Next Generation of Earth Modeling Systems (nextGEMS) project aimed to produce multi-decadal climate simulations, for the first time, with resolved kilometer-scale (km-scale) processes in the ocean, land, and atmosphere. In only three years, nextGEMS achieved this milestone with the two km-scale Earth system models, ICOsahedral Non-hydrostatic model (ICON) and Integrated Forecasting System coupled to the Finite-volumE Sea ice-Ocean Model (IFS-FESOM). nextGEMS was based on three cornerstones: 1) developing km-scale Earth system models with small errors in the energy and water balance, 2) performing km-scale climate simulations with a throughput greater than one simulated year per day, and 3) facilitating new workflows for an efficient analysis of the large simulations with common data structures and output variables. These cornerstones shaped the timeline of nextGEMS, divided into four cycles. Each cycle marked the release of a new configuration of ICON and IFS-FESOM, which were evaluated at hackathons. The participants in hackathons included experts from climate science, software engineering, and high-performance computing, as well as users from the energy and agricultural sectors. The continuous efforts over the four cycles allowed us to produce 30-year simulations of ICON and IFS-FESOM, spanning the period 2020–2049 under the SSP3-7.0 scenario. The throughput was about 500 simulated days per day on the Levante supercomputer of the German Climate Computing Center (DKRZ). The simulations employed a horizontal grid of about 5 km resolution in the ocean and 10 km resolution in the atmosphere and land. Aside from this technical achievement, the simulations allowed us to gain new insights into the realism of ICON and IFS-FESOM. Beyond its timeframe, nextGEMS builds the foundation of the Climate Change Adaptation Digital Twin developed in the Destination Earth initiative and paves the way for future European research on climate change.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Geoscientific Model Development. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: closed
- RC1: 'Comment on egusphere-2025-509', Anonymous Referee #1, 14 May 2025
-
RC2: 'Comment on egusphere-2025-509', Anonymous Referee #2, 20 Jun 2025
This is an interesting paper that gives a high level overview of the
next GEMS project. It describes a large effort to debug, run and
tune km-scale coupled climate model. It's one of the first efforts
of its kind at this resolution. The work is broken into 4 cycles,
each analyzing progressively more mature versions of the coupled
model. The final cycle, with the most mature version of the model was
then used to address, with partial success, four climate science
questions. The authors looked at several well chosen aspects of the
simulations in Seciton 4. The results in Section 4.2 are quite
interesting, and I have some minor comments on that below. I
appreciated the nice discussion and encouraging results for
stratocumulus in Section 4.3.I only have minor comments:
1. Some of the issues related to the energy budget that were resolved
during the early cycles appears to be coding errors and bugs. Would it
have been more efficient to also have a low resolution version of the
coupled model to detect and fix these issues? Although both these
models are not designed to run at typical CMIP style resolutions
(~100km), would such a configuration run and be sufficiently Earth-like
to be appropriate for addressing some software and numerical issues?2. The paper makes extensive use of the "km-scale" adjective, and
defines this as (for the atmosphere) "...using horizontal grid
spacings equal to or less than 10km globally". This definition is a
little optimistic, and I think most atmosphere model developers would
not consider 10 km resolution "km-scale".In the introduction, the authors then state that "Km-scale atmospheric
simulations, also referred to as storm-resolving simulations, resolve
deep convection, capturing mesoscale convective systems ..." Some of
the references cited to support that Km-scale atmosphere (so here,
that implies a 10km atmosphere) are convection resolving are Peters
et al 2019, which is using a 2.5km and Becker et al 2021, which
concludes "Our results suggest that deep convection is not completely
resolved at a resolution of 9 or even 4 km"Thus I found the introduction a little unclear in that it has claims
that probably apply to km-scale models running a 2.5km or finer
resolutions, and may not apply at 10 km resolutions. In particular,
the implied claim that 10 km model can resolve deep convection and
mesoscale convective systems is probably not correct.
3. Related to comment #2, in section 4.2, the authors claim,
"The fact that ICON-C4 can represent the tropical rainbelt over land
and the Eastern Pacific indicates that a horizontal grid spacing of
the order of 10 km is sufficient to reproduce the structure of
precipitation in those regions, and that is possible with a minimum
set of parameterizations.", and,
"The experiments conducted by Segura et al. (2024) and Takasuka et
al. (2024) indicate that fine-tuning subgrid-scale processes can
produce a correct representation of the tropical rainbelt without the
use of convective parameterization."I thought this claim was not well supported. The Segura and Takasuka
references were more nuanced in their claims, and were from much more
constrained prescribed-SST simulations. My superficial takeaway after
reading section 4.2 was that one still needs convective
parameterization (like in IFS) at this resolution. It would be good
if the authors can strengthen their arguments for this result,
especially for non-expert readers such as this reviewer.Citation: https://doi.org/10.5194/egusphere-2025-509-RC2 -
AC1: 'Comment on egusphere-2025-509', Hans Segura, 17 Jul 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-509/egusphere-2025-509-AC1-supplement.pdf
Status: closed
-
RC1: 'Comment on egusphere-2025-509', Anonymous Referee #1, 14 May 2025
This paper describes a major project involving many groups and many scientists to work towards high resolution climate modelling. The title is perhaps a bit tentative with the reference to "kilometre scale modelling", as the reported modelling is still an order of magnitude away from the kilometre scale.
The paper is very interesting from the organisational as well as the scientific point of view. It is a clear example of what can be achieved by bundling the expertise of many research groups and different types of expertise. Not only climate scientists and model developers are involved but also computer experts, software engineers, and application oriented people. Also the organisation of such a large number of people is worth reporting. Goals are set for each stage and hackathons are organised to discuss the results. I turns out to be an effective hands-on approach.
The science is very interesting with two rather contrasting atmospheric models and two ocean models. The key theme is the role of high resolution. The ICON atmospheric model has a minimalistic parametrisation and leaves cloud generation and convection to the dynamics of the model. The IFS model, has highly developed parametrisations and gradually reduces the convective parametrisation activity at very high resolution. The key question for the ocean model is: what benefit does explicit modelling of ocean eddies bring?
The paper is very well written and carefully crafted. Given the strong scientific and organisational messages, the paper is well worth publishing. I recommend publication in its present form. The authors might want to make a few changes related to the comments below.
A few minor comments:
Line 252-254
Interesting that it takes a comparison with another model to find a mistake in the use of c_p and c_v in the formulation of surface fluxes!Section 4.3.1
The discussion on soil moisture/precipitation feedback is very interesting and important. It is very undesirable to have positive feedbacks in a climate model that do not exist in nature. Such an erroneous feedback inevitably leads to the simulation of unrealistic extremes. A positive feedback is present in many large scale models with parametrised convection and the current paper (and previous papers) suggests that the sign of the feedback is reversed in convection resolving simulations. The question is whether it is a parametrisation issue or a resolution issue? The current paper is not conclusive. IFS has some parametrisation of convection also at high resolution, but its "effective resolution" may be less than in ICON.My question is about the water budget. In summer, the main moisture source over large continental areas (e.g USA east of the Rocky Mountains) comes from land evaporation. This suggests strong re-cycling. The soil shows spring/summer drying but there is also runoff. At high resolution with intermittent convection there is a negative feedback where dry areas are preferred to trigger convection (e.g. Taylor et al.). How this works out at larger scales is not clear. Somehow it must change the mean water budget. If convection at small scales has a preference for dry areas, it means that the dry soil can absorb more water. Does it imply that averaged over large areas there is less runoff? In other words, if less water is taken out of the system then the water is still available for the re-cycling process? Did you look at runoff in these simulations?
Fig. 8
The legend is very confusing, and it takes some time to understand the logic: solid lines refer to the left scale and dashed lines to the right hand scale, colours refer to ICO-C4, IFS_F-C4 and CERES. CERES does not have data in the top figure whereas it is in the top legend. Perhaps it is better to replace the legends by titles in which the logic is described.
Section 4.3.2
The discussion about stratocumulus is very interesting. ICON does much better than IFS in the area considered and with tuning of the global radiation budget. However, the lack of contrast between the cumulus and stratocumulus regime seems to be shared by the two models. Is it correct to say that ICON is better in the strato-cumulus regime, and worse in the cumulus regime?Citation: https://doi.org/10.5194/egusphere-2025-509-RC1 -
RC2: 'Comment on egusphere-2025-509', Anonymous Referee #2, 20 Jun 2025
This is an interesting paper that gives a high level overview of the
next GEMS project. It describes a large effort to debug, run and
tune km-scale coupled climate model. It's one of the first efforts
of its kind at this resolution. The work is broken into 4 cycles,
each analyzing progressively more mature versions of the coupled
model. The final cycle, with the most mature version of the model was
then used to address, with partial success, four climate science
questions. The authors looked at several well chosen aspects of the
simulations in Seciton 4. The results in Section 4.2 are quite
interesting, and I have some minor comments on that below. I
appreciated the nice discussion and encouraging results for
stratocumulus in Section 4.3.I only have minor comments:
1. Some of the issues related to the energy budget that were resolved
during the early cycles appears to be coding errors and bugs. Would it
have been more efficient to also have a low resolution version of the
coupled model to detect and fix these issues? Although both these
models are not designed to run at typical CMIP style resolutions
(~100km), would such a configuration run and be sufficiently Earth-like
to be appropriate for addressing some software and numerical issues?2. The paper makes extensive use of the "km-scale" adjective, and
defines this as (for the atmosphere) "...using horizontal grid
spacings equal to or less than 10km globally". This definition is a
little optimistic, and I think most atmosphere model developers would
not consider 10 km resolution "km-scale".In the introduction, the authors then state that "Km-scale atmospheric
simulations, also referred to as storm-resolving simulations, resolve
deep convection, capturing mesoscale convective systems ..." Some of
the references cited to support that Km-scale atmosphere (so here,
that implies a 10km atmosphere) are convection resolving are Peters
et al 2019, which is using a 2.5km and Becker et al 2021, which
concludes "Our results suggest that deep convection is not completely
resolved at a resolution of 9 or even 4 km"Thus I found the introduction a little unclear in that it has claims
that probably apply to km-scale models running a 2.5km or finer
resolutions, and may not apply at 10 km resolutions. In particular,
the implied claim that 10 km model can resolve deep convection and
mesoscale convective systems is probably not correct.
3. Related to comment #2, in section 4.2, the authors claim,
"The fact that ICON-C4 can represent the tropical rainbelt over land
and the Eastern Pacific indicates that a horizontal grid spacing of
the order of 10 km is sufficient to reproduce the structure of
precipitation in those regions, and that is possible with a minimum
set of parameterizations.", and,
"The experiments conducted by Segura et al. (2024) and Takasuka et
al. (2024) indicate that fine-tuning subgrid-scale processes can
produce a correct representation of the tropical rainbelt without the
use of convective parameterization."I thought this claim was not well supported. The Segura and Takasuka
references were more nuanced in their claims, and were from much more
constrained prescribed-SST simulations. My superficial takeaway after
reading section 4.2 was that one still needs convective
parameterization (like in IFS) at this resolution. It would be good
if the authors can strengthen their arguments for this result,
especially for non-expert readers such as this reviewer.Citation: https://doi.org/10.5194/egusphere-2025-509-RC2 -
AC1: 'Comment on egusphere-2025-509', Hans Segura, 17 Jul 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-509/egusphere-2025-509-AC1-supplement.pdf
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- 1
Cited
4 citations as recorded by crossref.
- Statistical summaries for streamed data from climate simulations: one-pass algorithms K. Grayson et al. 10.5194/gmd-18-5873-2025
- A physics-informed machine learning parameterization for cloud microphysics in ICON E. Sarauer et al. 10.1017/eds.2025.10016
- A global perspective on the spatial representation of climate extremes from km-scale models L. Brunner et al. 10.1088/1748-9326/ade1ef
- Implementation and validation of a supermodeling framework into Community Earth System Model version 2.1.5 W. Chapman et al. 10.5194/gmd-18-5451-2025
This paper describes a major project involving many groups and many scientists to work towards high resolution climate modelling. The title is perhaps a bit tentative with the reference to "kilometre scale modelling", as the reported modelling is still an order of magnitude away from the kilometre scale.
The paper is very interesting from the organisational as well as the scientific point of view. It is a clear example of what can be achieved by bundling the expertise of many research groups and different types of expertise. Not only climate scientists and model developers are involved but also computer experts, software engineers, and application oriented people. Also the organisation of such a large number of people is worth reporting. Goals are set for each stage and hackathons are organised to discuss the results. I turns out to be an effective hands-on approach.
The science is very interesting with two rather contrasting atmospheric models and two ocean models. The key theme is the role of high resolution. The ICON atmospheric model has a minimalistic parametrisation and leaves cloud generation and convection to the dynamics of the model. The IFS model, has highly developed parametrisations and gradually reduces the convective parametrisation activity at very high resolution. The key question for the ocean model is: what benefit does explicit modelling of ocean eddies bring?
The paper is very well written and carefully crafted. Given the strong scientific and organisational messages, the paper is well worth publishing. I recommend publication in its present form. The authors might want to make a few changes related to the comments below.
A few minor comments:
Line 252-254
Interesting that it takes a comparison with another model to find a mistake in the use of c_p and c_v in the formulation of surface fluxes!
Section 4.3.1
The discussion on soil moisture/precipitation feedback is very interesting and important. It is very undesirable to have positive feedbacks in a climate model that do not exist in nature. Such an erroneous feedback inevitably leads to the simulation of unrealistic extremes. A positive feedback is present in many large scale models with parametrised convection and the current paper (and previous papers) suggests that the sign of the feedback is reversed in convection resolving simulations. The question is whether it is a parametrisation issue or a resolution issue? The current paper is not conclusive. IFS has some parametrisation of convection also at high resolution, but its "effective resolution" may be less than in ICON.
My question is about the water budget. In summer, the main moisture source over large continental areas (e.g USA east of the Rocky Mountains) comes from land evaporation. This suggests strong re-cycling. The soil shows spring/summer drying but there is also runoff. At high resolution with intermittent convection there is a negative feedback where dry areas are preferred to trigger convection (e.g. Taylor et al.). How this works out at larger scales is not clear. Somehow it must change the mean water budget. If convection at small scales has a preference for dry areas, it means that the dry soil can absorb more water. Does it imply that averaged over large areas there is less runoff? In other words, if less water is taken out of the system then the water is still available for the re-cycling process? Did you look at runoff in these simulations?
Fig. 8
The legend is very confusing, and it takes some time to understand the logic: solid lines refer to the left scale and dashed lines to the right hand scale, colours refer to ICO-C4, IFS_F-C4 and CERES. CERES does not have data in the top figure whereas it is in the top legend. Perhaps it is better to replace the legends by titles in which the logic is described.
Section 4.3.2
The discussion about stratocumulus is very interesting. ICON does much better than IFS in the area considered and with tuning of the global radiation budget. However, the lack of contrast between the cumulus and stratocumulus regime seems to be shared by the two models. Is it correct to say that ICON is better in the strato-cumulus regime, and worse in the cumulus regime?