the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Feb 2023
The KNMI Large Ensemble Time Slice (KNMI-LENTIS)
Abstract. Large-ensemble modelling has become an increasingly popular approach to study the mean climate and the climate system’s internal variability in response to external forcing. Here we present the KNMI Large ENsemble TIme Slice (KNMI-LENTIS): a new large ensemble data set produced with the re-turned version of the global climate model EC-Earth3. The ensemble consists of two distinct time slices of 10 year each: a present-day time slice and a +2 K warmer future time slice relative to present-day. The initial conditions for the ensemble members are generated with a combination of micro and macro perturbations. The 10-year length of a single time slice is assumed to be too short to show a significant forced climate change signal, and the ensemble size of 1600 years (160 x 10 years) is assumed to be sufficient to sample the full distribution of climate variability. The time-slice approach makes it possible to study extreme events on sub-annual timescales as well as events that span multiple years such as multi-year droughts and preconditioned compound events. KNMI-LENTIS is therefore uniquely suited to study internal variability and extreme events both at a given climate state and those resulting from forced changes due to external radiative forcing. A unique feature of this ensemble is the high temporal output frequency of the surface water balance and surface energy balance variables, which are stored in 3-hourly intervals, allowing for detailed studies into extreme events. The data set is particularly geared towards research in the land-atmosphere domain. EC-Earth3 has a considerable warm bias in the Southern Ocean and over Antarctica. Hence, users of KNMI-LENTIS are advised to make in-depth comparisons with observational or reanalysis data especially if their studies focus on ocean processes, on locations in the Southern Hemisphere or on teleconnections involving both hemispheres. In this paper, we will give some examples to demonstrate the added value of KNMI-LENTIS for extreme and compound event research and for climate-impact modelling.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2022-1378', Anonymous Referee #1, 24 Feb 2023
This paper introduces the new time slice large ensemble from KNMI. It is well written, describes the experiments in details and presents some interesting applications. I recommend it is accepted with minor revision as detailed below.
45 change e.g. to ‘for example’
Reference issue line 64
Line 60 Could reference introduction paper to the large ensemble special issue: https://esd.copernicus.org/articles/12/401/2021/ and/or single forcing large ensembles e.g.: https://www.cesm.ucar.edu/working-groups/climate/simulations/cesm1-single-forcing-le and https://open.canada.ca/data/en/dataset/aa7b6823-fd1e-49ff-a6fb-68076a4a477c
Line 71 – this seems like a good assumption but can you test it? Perhaps look at 1300 vs 1600 and see how different they are. After reading the paper I see the authors do test this. Perhaps allude to it here on line 71
76 be consistent – should be 5 years
149 – have you checked that there is no trend?
174 – could replace ‘means’ with ‘refers to’
178 – could replace ‘like’ with ‘such as’
3 E can you suggest the ensemble mean is removed for variables where trend is too large as in many other le studies
Section 3.5 you could cite and discuss the following papers:
https://wires.onlinelibrary.wiley.com/doi/full/10.1002/wcc.563
https://link.springer.com/article/10.1007/s00382-015-2806-8
https://journals.ametsoc.org/view/journals/clim/36/2/JCLI-D-21-0176.1.xml
262 can you make a stronger statement than 'seems to'
589 ??? Are confusing
4.1 I don't see a reference to fig a/b
Fig 7 no reference to b after a
Figure 10 define PV in the caption please
Citation: https://doi.org/10.5194/egusphere-2022-1378-RC1 - AC1: 'Reply on RC1', Laura Muntjewerf, 31 May 2023
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RC2: 'Comment on egusphere-2022-1378', Dirk Olonscheck, 18 May 2023
Review of “The KNMI Large Ensemble Time Slice (KNMI-LENTIS)” by Muntjewerf et al.
General comment:
The manuscript presents the KNMI Large Ensemble Time Slice, which consists of a present-day 2000-2009 and a future +2K 2075-2084 time slice with 160 ensemble members each generated by micro and macro perturbation. The manuscript is well structured and clear, with high scientific rigour, and represents an important contribution not only to the large ensemble community. I much appreciate that the authors test the assumptions they do and that limitations are discussed explicitly. The manuscript further demonstrates the added value of KNMI-LENTIS for extreme and compound event research and for climate-impact modelling. I find these application examples appealing and congratulate the authors on this manuscript. However, I do have a few comments that need to be addressed before I can recommend publication. These comments only require revision of the text.
Recommendation: Minor revisions
Specific comments:
l. 3: What is a re-turned version? I assume this should be “re-tuned”. Same typo in l. 376.
l. 13: The authors should add here that the tuning is done to reduce the Eurasian warming bias, and the focus region tuned for is Europe/Eurasia. This aspect should be mentioned in the Abstract and generally highlighted more in the text because it limits the usability for studying climate extremes for the large part of the globe that shows substantial model bias even on low and mid-latitude land regions (see Figure 2a).
ll. 54-60 and ll. 376-381: The second lines mentioned here are a good attempt to compare the time slice approach with the large ensemble approach of transient simulations. However, the introduction misses a more elaborated comparison between the advantages and disadvantages of the two approaches. For instance, the advantage mentioned here: “This renders our data set specifically suitable to study climate variability and changes therein between the present-day climate and a warmer future climate.” is even more true for a transient ensemble. I suggest to use the paragraph in ll. 54-60 to provide more details on 1) disadvantages of the time slice approach compared to the transient approach, 2) why the authors decided to use the time slice approach (I assume computational or data storage restrictions but this is not mentioned explicitly), 3) whether there are other time slice large ensembles than from KNMI, and 4) that the approach of time slices is extensively used in paleo-climate modelling. Please revise and expand the introduction for a more complete background on the time slice approach.
l. 67 and l. 85: I recommend not to use the term data set ifor model simulations because data sets rather refer to observational data. I suggest to change “transient ensemble data sets” to “transient ensemble simulations” and “data set” to “ensemble” here and in other places.
ll. 83-84: It is not clear to me why the decade 2000-2009 is used for the “present-day” time slice, 2010-2019 would have better allowed for comparisons of present-day vs 2075-2084. I understand the two reasons the authors mention (historical CMIP6 forcing ending in 2014, initial condition files only every 10 years), but then the term “present-day” is misleading. I suggest to call it “past decade” or similar instead.
ll. 144-146: The manuscript misses to mention how KNMI-LENTIS can be compared to the SMHI Large Ensemble (SMHI-LENS) with EC-Earth3.3.1 by Wyser et al., 2021. I think I understood that KNMI-LENTIS is not directly comparable to that model version used for the 50-member ensemble because of the performed re-tuning but can be directly compared to the CMIP6 16-member ensemble. The authors mention that “The ECE3p5 version is a re-tuned version of the EC-Earth 3.3. release for CMIP6 (Döscher et al., 2021)”. However, the authors do not mention how exactly the re-tuning affects/prevents the comparison to SMHI-LENS, whether there are other differences than the re-tuning, e.g. from a different model version? I like to encourage the authors to elaborate on the comparability of the presented time slices with the 50-member SMHI-LENS.
ll. 159-161: I am confused here. I don’t understand why the period 1985-2014 is taken to calculate the present-day mean state, not the first time slice 2000-2009 as done otherwise? Please clarify.
l. 169: The authors term the period 2075-2084 as +2K in the SSP2-4.5 scenario but state here that +2K are reached in year 2073. While keeping the term +2K, please specify the exact average warming of the 10 year period compared to present-day. I am further confused by the value of annual GMST increase of 1.95K in Table 1 and in l. 208. According to the text, I understand the exact value should be above +2K. Please revise this section on the apparent difference between 1985-2014 and 2000-2009 for defining +2K.
Figure 7 panel b is only described at the end of the caption and it is not clear to me why the orography and bathymetry matters in the context of that application. I would suggest to remove it from Figure 7.
Technical corrections:
l. 4: ten years each
l. 8: specify sub-annual timescales
l. 47: 2x the
l. 70: simulation years
l. 153: 2x annual
l. 343: This is not a proper sentence.
Citation: https://doi.org/10.5194/egusphere-2022-1378-RC2 - AC2: 'Reply on RC2', Laura Muntjewerf, 31 May 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2022-1378', Anonymous Referee #1, 24 Feb 2023
This paper introduces the new time slice large ensemble from KNMI. It is well written, describes the experiments in details and presents some interesting applications. I recommend it is accepted with minor revision as detailed below.
45 change e.g. to ‘for example’
Reference issue line 64
Line 60 Could reference introduction paper to the large ensemble special issue: https://esd.copernicus.org/articles/12/401/2021/ and/or single forcing large ensembles e.g.: https://www.cesm.ucar.edu/working-groups/climate/simulations/cesm1-single-forcing-le and https://open.canada.ca/data/en/dataset/aa7b6823-fd1e-49ff-a6fb-68076a4a477c
Line 71 – this seems like a good assumption but can you test it? Perhaps look at 1300 vs 1600 and see how different they are. After reading the paper I see the authors do test this. Perhaps allude to it here on line 71
76 be consistent – should be 5 years
149 – have you checked that there is no trend?
174 – could replace ‘means’ with ‘refers to’
178 – could replace ‘like’ with ‘such as’
3 E can you suggest the ensemble mean is removed for variables where trend is too large as in many other le studies
Section 3.5 you could cite and discuss the following papers:
https://wires.onlinelibrary.wiley.com/doi/full/10.1002/wcc.563
https://link.springer.com/article/10.1007/s00382-015-2806-8
https://journals.ametsoc.org/view/journals/clim/36/2/JCLI-D-21-0176.1.xml
262 can you make a stronger statement than 'seems to'
589 ??? Are confusing
4.1 I don't see a reference to fig a/b
Fig 7 no reference to b after a
Figure 10 define PV in the caption please
Citation: https://doi.org/10.5194/egusphere-2022-1378-RC1 - AC1: 'Reply on RC1', Laura Muntjewerf, 31 May 2023
-
RC2: 'Comment on egusphere-2022-1378', Dirk Olonscheck, 18 May 2023
Review of “The KNMI Large Ensemble Time Slice (KNMI-LENTIS)” by Muntjewerf et al.
General comment:
The manuscript presents the KNMI Large Ensemble Time Slice, which consists of a present-day 2000-2009 and a future +2K 2075-2084 time slice with 160 ensemble members each generated by micro and macro perturbation. The manuscript is well structured and clear, with high scientific rigour, and represents an important contribution not only to the large ensemble community. I much appreciate that the authors test the assumptions they do and that limitations are discussed explicitly. The manuscript further demonstrates the added value of KNMI-LENTIS for extreme and compound event research and for climate-impact modelling. I find these application examples appealing and congratulate the authors on this manuscript. However, I do have a few comments that need to be addressed before I can recommend publication. These comments only require revision of the text.
Recommendation: Minor revisions
Specific comments:
l. 3: What is a re-turned version? I assume this should be “re-tuned”. Same typo in l. 376.
l. 13: The authors should add here that the tuning is done to reduce the Eurasian warming bias, and the focus region tuned for is Europe/Eurasia. This aspect should be mentioned in the Abstract and generally highlighted more in the text because it limits the usability for studying climate extremes for the large part of the globe that shows substantial model bias even on low and mid-latitude land regions (see Figure 2a).
ll. 54-60 and ll. 376-381: The second lines mentioned here are a good attempt to compare the time slice approach with the large ensemble approach of transient simulations. However, the introduction misses a more elaborated comparison between the advantages and disadvantages of the two approaches. For instance, the advantage mentioned here: “This renders our data set specifically suitable to study climate variability and changes therein between the present-day climate and a warmer future climate.” is even more true for a transient ensemble. I suggest to use the paragraph in ll. 54-60 to provide more details on 1) disadvantages of the time slice approach compared to the transient approach, 2) why the authors decided to use the time slice approach (I assume computational or data storage restrictions but this is not mentioned explicitly), 3) whether there are other time slice large ensembles than from KNMI, and 4) that the approach of time slices is extensively used in paleo-climate modelling. Please revise and expand the introduction for a more complete background on the time slice approach.
l. 67 and l. 85: I recommend not to use the term data set ifor model simulations because data sets rather refer to observational data. I suggest to change “transient ensemble data sets” to “transient ensemble simulations” and “data set” to “ensemble” here and in other places.
ll. 83-84: It is not clear to me why the decade 2000-2009 is used for the “present-day” time slice, 2010-2019 would have better allowed for comparisons of present-day vs 2075-2084. I understand the two reasons the authors mention (historical CMIP6 forcing ending in 2014, initial condition files only every 10 years), but then the term “present-day” is misleading. I suggest to call it “past decade” or similar instead.
ll. 144-146: The manuscript misses to mention how KNMI-LENTIS can be compared to the SMHI Large Ensemble (SMHI-LENS) with EC-Earth3.3.1 by Wyser et al., 2021. I think I understood that KNMI-LENTIS is not directly comparable to that model version used for the 50-member ensemble because of the performed re-tuning but can be directly compared to the CMIP6 16-member ensemble. The authors mention that “The ECE3p5 version is a re-tuned version of the EC-Earth 3.3. release for CMIP6 (Döscher et al., 2021)”. However, the authors do not mention how exactly the re-tuning affects/prevents the comparison to SMHI-LENS, whether there are other differences than the re-tuning, e.g. from a different model version? I like to encourage the authors to elaborate on the comparability of the presented time slices with the 50-member SMHI-LENS.
ll. 159-161: I am confused here. I don’t understand why the period 1985-2014 is taken to calculate the present-day mean state, not the first time slice 2000-2009 as done otherwise? Please clarify.
l. 169: The authors term the period 2075-2084 as +2K in the SSP2-4.5 scenario but state here that +2K are reached in year 2073. While keeping the term +2K, please specify the exact average warming of the 10 year period compared to present-day. I am further confused by the value of annual GMST increase of 1.95K in Table 1 and in l. 208. According to the text, I understand the exact value should be above +2K. Please revise this section on the apparent difference between 1985-2014 and 2000-2009 for defining +2K.
Figure 7 panel b is only described at the end of the caption and it is not clear to me why the orography and bathymetry matters in the context of that application. I would suggest to remove it from Figure 7.
Technical corrections:
l. 4: ten years each
l. 8: specify sub-annual timescales
l. 47: 2x the
l. 70: simulation years
l. 153: 2x annual
l. 343: This is not a proper sentence.
Citation: https://doi.org/10.5194/egusphere-2022-1378-RC2 - AC2: 'Reply on RC2', Laura Muntjewerf, 31 May 2023
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Laura Muntjewerf
Richard Bintanja
Thomas Reerink
Karin van der Wiel
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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(5648 KB) - Metadata XML
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Supplement
(30 KB) - BibTeX
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