Calibrating the GAMIL3-1&deg; climate model using a derivative-free optimization method

Liang, Wenjun; Tett, Simon Frederick Barnard; Li, Lijuan; Cartis, Coralia; Xu, Danya; Dong, Wenjie

doi:10.5194/egusphere-2024-3770

Preprints

https://doi.org/10.5194/egusphere-2024-3770

Preprints

10 Feb 2025

| 10 Feb 2025

Calibrating the GAMIL3-1° climate model using a derivative-free optimization method

Wenjun Liang, Simon Frederick Barnard Tett, Lijuan Li, Coralia Cartis, Danya Xu, and Wenjie Dong

Abstract. Parameterization in climate models often involves parameters that are poorly constrained by observations or theoretical understanding alone. Manual tuning by experts can be time-consuming, subjective, and prone to underestimating uncertainties. Automated tuning methods offer a promising alternative, enabling faster, objective improvements in model performance and better uncertainty quantification. This study presents an automated parameter-tuning framework that employs a derivative-free optimization solver (DFO-LS) to simultaneously perturb and tune multiple convection-related and microphysics parameters. The framework explicitly accounts for observational and initial condition uncertainties (internal variability) to calibrate a 1-degree resolution atmospheric model (GAMIL3). Two experiments, adjusting 10 and 20 parameters, were conducted alongside three sensitivity experiments that varied initial parameter values for a 10-parameter case. Both of the first two experiments showed a rapid decrease in the cost function, with the 10-parameter optimization significantly improving model accuracy in 24 out of 34 variables. Expanding to 20 parameters further enhanced accuracy, with improvement in 25 of 34 variables, though some structural model errors emerged. Ten-year AMIP simulations validated the robustness and stability of the tuning results, showing that the improvements persisted over extended simulations. Additionally, evaluations of the coupled model with optimized parameters showed–compare to the default parameter setting–reduced climate drift, a more stable climate system, and more realistic sea surface temperatures, despite a slight energy imbalance and some regional biases. The sensitivity experiments underscored the efficiency of the tuning algorithm and highlight the importance of expert judgment in selecting initial parameter values. This tuning framework is broadly applicable to other general circulation models (GCMs), supporting comprehensive parameter tuning and advancing model development.

Received: 03 Dec 2024 – Discussion started: 10 Feb 2025

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.

Download & links

Preprint (PDF, 3612 KB)

Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
Preprint (3612 KB)

Supplement (763 KB)

Download & links

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Journal article(s) based on this preprint

02 Dec 2025

Calibrating the GAMIL3-1° climate model using a derivative-free optimization method

Wenjun Liang, Simon Frederick Barnard Tett, Lijuan Li, Coralia Cartis, Danya Xu, Wenjie Dong, and Junjie Huang

Geosci. Model Dev., 18, 9293–9318, https://doi.org/10.5194/gmd-18-9293-2025,https://doi.org/10.5194/gmd-18-9293-2025, 2025

Short summary

Wenjun Liang, Simon Frederick Barnard Tett, Lijuan Li, Coralia Cartis, Danya Xu, and Wenjie Dong

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-3770', Anonymous Referee #1, 21 Apr 2025

This well-structured manuscript presents a novel approach for climate model-tuning and the results that such tuning yields for a given model (GAMIL3) under 3 different model configurations: 1 year AMIP for tuning , 10 year AMIP and 30 year coupled pre-industrial Control. The presented tuning method is potentially relevant for other climate models. The authors show that the DFO-LS method is able to systematically improve the ‘a priori’ model parameter values and that the improvements hold across the different model configurations. The text is well written, with some potential however for more precise and less verbose language. In general, the manuscript could improve by adding some comparison or references to similar past efforts on model tuning, but I acknowledge that often findings and results are quite model-specific.

Specific comments

L45-46 Some references would be welcome.

L186: Not strictly necessary, but perhaps having a sketch showing the sequence of experiments performed would help the reader.

L186: The text has no literature reference for GAMIL3. If no documentation exists for this model version, a more detailed description of it would be needed, as an Appendix if needed. The current description between L187-203 is vague and full of ambiguities (‘updates to the planetary boundary layer scheme’, ’GAMIL3 integrates several parametrizations recommended by CMIP6’)

L280: Is there any reason or reference why you would give twice as much weight to C_i than to C_0?

L296: put the definition of the Jacobians in context. Why are you presenting it? where in the paper is used?

L351: Why ke and captlmt are explicity mentioned? please explain

L414: Any illustrative example of compensating errors in the model?

L503: I’d re-name this section as ‘’Coupled model evaluation’

L521: lower rhcrit could, a priori, also enhance precip. Lower rhcrit would enhance convection and, this, precipitation. Even if it is not the case in the simulations, it may be worth being mentioned.

L533: contributing to the decreased low-level cloud fraction and further reducing precipitation (since this was mentioned in the previous paragraph)

L569: Describe for how long the coupled model was run, one can only infer it from the Figures.

L569: for coupled simulations it is quite relevant to explain how the land, and specially the ocean, were initialized. This is relevant because a perfect model should drift if the ocean is not correctly initialized, and you would not like to tune your model to compensate for an ocean-caused drift.

L575: While the reduction of OLR is obvious (and interrelated) to the drop of T2M, the reduction in RSR seems to have a more complex mechanism and would merit an additional explanatory sentence.

L718: a leak of 1.4 W/mw seems quite relevant to me, and , besides being present here, it should have been mentioned earlier in the results when discussing NETFLUX.

L727: Mention that the primary experiments where 1 -year long AMIP

L740: the maintained improvement over extended periods is good news given that you tuned on a single year and ignored interannual variability. Could you hypothesise whether (and how much) you would expect a better tuning if you optimize the parameters over several years of AMIP?
technical corrections

L51 difficult to understand the complete sentence. Perhaps ‘carbon cycle or nutrient cycles’ would clarify it.

L60: remove ‘computational constrains’ as it only adds confusion to the sentence

L239: ‘discussed in a later section’. Please state at which specific section.

L250: listed in the first [instead of last] column of Table 2

L254: listed in the first [instead of last] column of Table 2

L273-L277: Break the sentence, it is difficult to follow. L288-L291: assuming there are no typos in the equations, there is inconsistent information in these lines: N is defined twice and differently, and C is defined although missing in the equation.

L357: why not just mention total number of iterations, instead of excluding the first 10?

L403: remove ‘’an

L464: variables

L475: this is less succesfull, in relative terms, than the 10 parameter case.

L486: exhibit similar behaviour L603: which improvements for which case?

L606: flux of energy towards the ocean, instead of ocean surface flux.

L691: a common issue

All figures: larger legends would be good.

Table 2: add units (if they have) to the parameters, as it may help to understand their role.

Figure 2: the numbers written in the experiment color code are very hard to read. Also, the caption does not explain what they mean, nor the meaning of the vertical dashed lines in b) and c).

Figure 3: I would rename AMIP@10years by AMIP2005-2014, here and wherever mentioned in the text.

Figure 7: there is a red ‘v’

Figure 8: percent instead of precent

Figure 12: change colorcode as it uses the same as Figure 7. In Fig 7, however, the numbers in the Table display the actual Jacobians, while here it displays the range between Jacobians. A change of colorcode would help explain that we are not looking at the exact same metric.

Citation: https://doi.org/10.5194/egusphere-2024-3770-RC1
- AC1: 'Reply on RC1', Wenjun Liang, 30 Jul 2025
  
  Dear Reviewer,
  We sincerely appreciate the reviewers’ insightful and constructive comments. We are confident that their suggestions will help improve the quality of our manuscript. Please find the attached file containing our point-by-point responses to the reviewers.
  Thank you again.
  Wenjun Liang
  
  Citation: https://doi.org/10.5194/egusphere-2024-3770-AC1
RC2:
'Comment on egusphere-2024-3770', Anonymous Referee #2, 31 May 2025

This study presents a derivative-free optimization framework for tuning climate model parameters. The framework was applied to the GAMIL3 atmospheric model and evaluated for both 10-parameter and 20-parameter cases. The study assessed the framework's effectiveness in terms of the initial selection of model parameter values and found that the initial selection of model parameter values considerably affects the tuning results. The study also evaluated the effectiveness of applying the optimized model parameters, derived from the atmospheric model, to an atmosphere-ocean coupled climate model. Model parameterization optimization and model tuning are important aspects in the climate modeling community. The paper is well written and worth publishing. However, to benefit a wider modeling community, some issues need to be addressed and further clarification is necessary.
L174-175: Please provide more details about the initial trust region and parameter constraints. Is there any difference between parameter constraints and parameters' plausible ranges?
L180: In each iteration of the optimization process, how many simulations are conducted?
L215: A 30-year simulation is insufficient to fully evaluate the effectiveness of the modified model parameters in a fully coupled model.
L226-228: \theta is not defined.
L230-231: _TROPICALLAND, _TROPICALOCEAN, _NHX and _SHX are not defined.
L236: LAT is not defined.
L237-238: Please clarify how the uncertainty is derived from the absolute error.
L250: I can’t find them in the last column of Table 2.
L405-407: The tuning process of the 20-parameter case was affected by using the same initial perturbations for the original 10 parameters. It is important to evaluate the effectiveness of the tuning method in terms of adding more parameters by comparing the 10-parameter and 20-parameter cases with independent initial parameter perturbations.
L416-417: What does “the initial 20 runs” refer to? Are these the initial perturbation runs conducted before the optimizing iterations begin? If so, please clarify this point. It appears that both the 10-parameter and 20-parameter cases achieve nearly the same STABLE performance by the 21 iterations. Does this mean the total number of runs for the two cases are 31 and 41 runs, respectively?
L448: In an AMIP simulation, sea surface temperatures are specified, so ENSO (El Niño-Southern Oscillation) is not a suitable example in this context.
L456-461: Does this indicate that the tuned results are tied to a specific climate background?
L466-467: replace “equilibrium” with “energy balance”.
L471: Why are MSL, RSRC, and LRC difficult to tune?
L474: OSRC is not defined.
L476: TEMP@500 has been profoundly affected by tuning. Please explain the physical causes.
L479-480: Please add some discussion on how to tune the model performance for OLR and PRECIP.
L534-542: The 10-parameter case shows a larger difference in TOA outgoing shortwave flux (RSR) compared to the 20-parameter case relative to the default case (Fig. 4e and 6e). However, the 20-parameter case exhibits a larger difference in cloud compared to the 10-parameter case relative to the default case (Fig. 8d-e). Please explain this discrepancy.
L594-613: anomalies => biases.
L565-619: Does the coupled model directly utilize the optimized parameters from the AMIP simulations? If so, the TOA energy imbalance caused by the optimized parameters would eventually lead to climate drift in the long-term integration of the coupled model. This undermines the rationale and effectiveness of applying parameters tuned for an atmospheric model to an atmosphere-ocean coupled model. Meanwhile, a 2 W/m² energy imbalance at TOA is not a "slight energy imbalance" as stated in the abstract.
L767: forecasts -> prediction.

Citation: https://doi.org/10.5194/egusphere-2024-3770-RC2
- AC2: 'Reply on RC2', Wenjun Liang, 30 Jul 2025
  
  Dear Reviewer,
  We are grateful for the thorough and constructive feedback the reviewer provided on our manuscript. We have incorporated the suggestions into the revised version. Please refer to the attached document for our detailed responses to each comment
  Thank you again.
  Wenjun Liang
  
  Citation: https://doi.org/10.5194/egusphere-2024-3770-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-3770', Anonymous Referee #1, 21 Apr 2025

This well-structured manuscript presents a novel approach for climate model-tuning and the results that such tuning yields for a given model (GAMIL3) under 3 different model configurations: 1 year AMIP for tuning , 10 year AMIP and 30 year coupled pre-industrial Control. The presented tuning method is potentially relevant for other climate models. The authors show that the DFO-LS method is able to systematically improve the ‘a priori’ model parameter values and that the improvements hold across the different model configurations. The text is well written, with some potential however for more precise and less verbose language. In general, the manuscript could improve by adding some comparison or references to similar past efforts on model tuning, but I acknowledge that often findings and results are quite model-specific.

Specific comments

L45-46 Some references would be welcome.

L186: Not strictly necessary, but perhaps having a sketch showing the sequence of experiments performed would help the reader.

L186: The text has no literature reference for GAMIL3. If no documentation exists for this model version, a more detailed description of it would be needed, as an Appendix if needed. The current description between L187-203 is vague and full of ambiguities (‘updates to the planetary boundary layer scheme’, ’GAMIL3 integrates several parametrizations recommended by CMIP6’)

L280: Is there any reason or reference why you would give twice as much weight to C_i than to C_0?

L296: put the definition of the Jacobians in context. Why are you presenting it? where in the paper is used?

L351: Why ke and captlmt are explicity mentioned? please explain

L414: Any illustrative example of compensating errors in the model?

L503: I’d re-name this section as ‘’Coupled model evaluation’

L521: lower rhcrit could, a priori, also enhance precip. Lower rhcrit would enhance convection and, this, precipitation. Even if it is not the case in the simulations, it may be worth being mentioned.

L533: contributing to the decreased low-level cloud fraction and further reducing precipitation (since this was mentioned in the previous paragraph)

L569: Describe for how long the coupled model was run, one can only infer it from the Figures.

L569: for coupled simulations it is quite relevant to explain how the land, and specially the ocean, were initialized. This is relevant because a perfect model should drift if the ocean is not correctly initialized, and you would not like to tune your model to compensate for an ocean-caused drift.

L575: While the reduction of OLR is obvious (and interrelated) to the drop of T2M, the reduction in RSR seems to have a more complex mechanism and would merit an additional explanatory sentence.

L718: a leak of 1.4 W/mw seems quite relevant to me, and , besides being present here, it should have been mentioned earlier in the results when discussing NETFLUX.

L727: Mention that the primary experiments where 1 -year long AMIP

L740: the maintained improvement over extended periods is good news given that you tuned on a single year and ignored interannual variability. Could you hypothesise whether (and how much) you would expect a better tuning if you optimize the parameters over several years of AMIP?
technical corrections

L51 difficult to understand the complete sentence. Perhaps ‘carbon cycle or nutrient cycles’ would clarify it.

L60: remove ‘computational constrains’ as it only adds confusion to the sentence

L239: ‘discussed in a later section’. Please state at which specific section.

L250: listed in the first [instead of last] column of Table 2

L254: listed in the first [instead of last] column of Table 2

L273-L277: Break the sentence, it is difficult to follow. L288-L291: assuming there are no typos in the equations, there is inconsistent information in these lines: N is defined twice and differently, and C is defined although missing in the equation.

L357: why not just mention total number of iterations, instead of excluding the first 10?

L403: remove ‘’an

L464: variables

L475: this is less succesfull, in relative terms, than the 10 parameter case.

L486: exhibit similar behaviour L603: which improvements for which case?

L606: flux of energy towards the ocean, instead of ocean surface flux.

L691: a common issue

All figures: larger legends would be good.

Table 2: add units (if they have) to the parameters, as it may help to understand their role.

Figure 2: the numbers written in the experiment color code are very hard to read. Also, the caption does not explain what they mean, nor the meaning of the vertical dashed lines in b) and c).

Figure 3: I would rename AMIP@10years by AMIP2005-2014, here and wherever mentioned in the text.

Figure 7: there is a red ‘v’

Figure 8: percent instead of precent

Figure 12: change colorcode as it uses the same as Figure 7. In Fig 7, however, the numbers in the Table display the actual Jacobians, while here it displays the range between Jacobians. A change of colorcode would help explain that we are not looking at the exact same metric.

Citation: https://doi.org/10.5194/egusphere-2024-3770-RC1
- AC1: 'Reply on RC1', Wenjun Liang, 30 Jul 2025
  
  Dear Reviewer,
  We sincerely appreciate the reviewers’ insightful and constructive comments. We are confident that their suggestions will help improve the quality of our manuscript. Please find the attached file containing our point-by-point responses to the reviewers.
  Thank you again.
  Wenjun Liang
  
  Citation: https://doi.org/10.5194/egusphere-2024-3770-AC1
RC2:
'Comment on egusphere-2024-3770', Anonymous Referee #2, 31 May 2025

This study presents a derivative-free optimization framework for tuning climate model parameters. The framework was applied to the GAMIL3 atmospheric model and evaluated for both 10-parameter and 20-parameter cases. The study assessed the framework's effectiveness in terms of the initial selection of model parameter values and found that the initial selection of model parameter values considerably affects the tuning results. The study also evaluated the effectiveness of applying the optimized model parameters, derived from the atmospheric model, to an atmosphere-ocean coupled climate model. Model parameterization optimization and model tuning are important aspects in the climate modeling community. The paper is well written and worth publishing. However, to benefit a wider modeling community, some issues need to be addressed and further clarification is necessary.
L174-175: Please provide more details about the initial trust region and parameter constraints. Is there any difference between parameter constraints and parameters' plausible ranges?
L180: In each iteration of the optimization process, how many simulations are conducted?
L215: A 30-year simulation is insufficient to fully evaluate the effectiveness of the modified model parameters in a fully coupled model.
L226-228: \theta is not defined.
L230-231: _TROPICALLAND, _TROPICALOCEAN, _NHX and _SHX are not defined.
L236: LAT is not defined.
L237-238: Please clarify how the uncertainty is derived from the absolute error.
L250: I can’t find them in the last column of Table 2.
L405-407: The tuning process of the 20-parameter case was affected by using the same initial perturbations for the original 10 parameters. It is important to evaluate the effectiveness of the tuning method in terms of adding more parameters by comparing the 10-parameter and 20-parameter cases with independent initial parameter perturbations.
L416-417: What does “the initial 20 runs” refer to? Are these the initial perturbation runs conducted before the optimizing iterations begin? If so, please clarify this point. It appears that both the 10-parameter and 20-parameter cases achieve nearly the same STABLE performance by the 21 iterations. Does this mean the total number of runs for the two cases are 31 and 41 runs, respectively?
L448: In an AMIP simulation, sea surface temperatures are specified, so ENSO (El Niño-Southern Oscillation) is not a suitable example in this context.
L456-461: Does this indicate that the tuned results are tied to a specific climate background?
L466-467: replace “equilibrium” with “energy balance”.
L471: Why are MSL, RSRC, and LRC difficult to tune?
L474: OSRC is not defined.
L476: TEMP@500 has been profoundly affected by tuning. Please explain the physical causes.
L479-480: Please add some discussion on how to tune the model performance for OLR and PRECIP.
L534-542: The 10-parameter case shows a larger difference in TOA outgoing shortwave flux (RSR) compared to the 20-parameter case relative to the default case (Fig. 4e and 6e). However, the 20-parameter case exhibits a larger difference in cloud compared to the 10-parameter case relative to the default case (Fig. 8d-e). Please explain this discrepancy.
L594-613: anomalies => biases.
L565-619: Does the coupled model directly utilize the optimized parameters from the AMIP simulations? If so, the TOA energy imbalance caused by the optimized parameters would eventually lead to climate drift in the long-term integration of the coupled model. This undermines the rationale and effectiveness of applying parameters tuned for an atmospheric model to an atmosphere-ocean coupled model. Meanwhile, a 2 W/m² energy imbalance at TOA is not a "slight energy imbalance" as stated in the abstract.
L767: forecasts -> prediction.

Citation: https://doi.org/10.5194/egusphere-2024-3770-RC2
- AC2: 'Reply on RC2', Wenjun Liang, 30 Jul 2025
  
  Dear Reviewer,
  We are grateful for the thorough and constructive feedback the reviewer provided on our manuscript. We have incorporated the suggestions into the revised version. Please refer to the attached document for our detailed responses to each comment
  Thank you again.
  Wenjun Liang
  
  Citation: https://doi.org/10.5194/egusphere-2024-3770-AC2

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload

AR by Wenjun Liang on behalf of the Authors (31 Jul 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (11 Aug 2025) by Tilo Ziehn

RR by Anonymous Referee #3 (24 Sep 2025)

RR by Anonymous Referee #2 (05 Oct 2025)

Suggestions for revision or reasons for rejection

The manuscript requires more minor clarifications.

Lines 25-31: this part is obscure. Please rephrase it.

Lines 74-75: please rephrase “the accuracy and skill of climate model outputs”.

Line 92: “7 and 14 parameters were estimated”, what kind of parameters?

Line 96: “focusing on seven parameters”, what kind of parameters?

Lines 145-148: these have been well introduced in Section 2.2.

Lines 169-171: it is suggested to name each module directly in Figure 1 for better understanding. “Python 3.8+” might not necessarily be displayed on the framework.

Line 187: netflux => net flux.

Lines 243-249: this section comes across as jarring in the context of the surrounding text.

Line 257: it is not clear what is “the first 10 parameters”.

Line 260: It is suggested that the experiment names (AMIP2011, AMIP2005-2014, and piControl) be included in Figure 2. Additionally, clarification is needed regarding the differences between "1-year optimization for 10 parameters" and "1-year optimization by varying 10 parameters," as all parameters are varied during the optimization process.

Line 272: what does the suffix “_DGM” stand for in Table 1?

Lines 408-410: It is recommended that the x-axis of Figure 3b & 3c start at 1 instead of 0 to better reflect the number of runs. Additionally, while the x-axis is labeled "Iterations," it actually represents the total number of runs, including both initial perturbations and iteration runs.

Lines 410-411: does the cost function drop rapidly from about 7.5 to 3.5 in the first iteration run? If so, please revise “during the initial perturbation phase” to “in the first iteration run.”

Lines 433-434: it is recommended to delete “patterns”.

Line 450: please rephrase “patterns”

Line 470: “after the initial 20 perturbation runs”, as in the 10-paramter case, does the cost function drop rapidly in the first iteration run? If so, please clarify.

Line 477: “the initial phase” => “the initial perturbation phase”.

Line 515: “MSLP_TROPICSOCEAN_DGM improved by over 20”, what does the suffix “_DGM” stand for? What’s the unit for the value “20”?

Lines 643-689: It's intriguing that the tuning procedures reveal an energy leakage in the atmospheric model. Although the tuning can't eliminate this issue, the fact that it manifests as a TOA energy imbalance in the piControl run is remarkable. Given that half of the tuning targets are TOA energy components, as outlined in Table 1, and considering that NETFLUX has been effectively tuned to the target value of 0.98 W/m² in the AMIP run, it remains curious how this energy leakage manifests in the AMIP run post-tuning and why it emerges as a TOA imbalance in the piControl run.

Hide

ED: Publish subject to minor revisions (review by editor) (20 Oct 2025) by Tilo Ziehn

AR by Wenjun Liang on behalf of the Authors (30 Oct 2025) Author's response Author's tracked changes Manuscript

ED: Publish as is (10 Nov 2025) by Tilo Ziehn

AR by Wenjun Liang on behalf of the Authors (10 Nov 2025) Author's response Manuscript

Journal article(s) based on this preprint

02 Dec 2025

Calibrating the GAMIL3-1° climate model using a derivative-free optimization method

Wenjun Liang, Simon Frederick Barnard Tett, Lijuan Li, Coralia Cartis, Danya Xu, Wenjie Dong, and Junjie Huang

Geosci. Model Dev., 18, 9293–9318, https://doi.org/10.5194/gmd-18-9293-2025,https://doi.org/10.5194/gmd-18-9293-2025, 2025

Short summary

Wenjun Liang, Simon Frederick Barnard Tett, Lijuan Li, Coralia Cartis, Danya Xu, and Wenjie Dong

Supplement

https://doi.org/10.5194/egusphere-2024-3770-supplement

Data sets

Model Optimization Simon Tett and Wenjun Liang https://doi.org/10.5281/zenodo.14772250

Wenjun Liang, Simon Frederick Barnard Tett, Lijuan Li, Coralia Cartis, Danya Xu, and Wenjie Dong

Viewed

Total article views: 895 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	Supplement	BibTeX	EndNote
759	112	24	895	44	23	34

HTML: 759
PDF: 112
XML: 24
Total: 895
Supplement: 44
BibTeX: 23
EndNote: 34

Views and downloads (calculated since 10 Feb 2025)

Month	HTML	PDF	XML	Total
Feb 2025	90	25	4	119
Mar 2025	22	6	0	28
Apr 2025	36	13	3	52
May 2025	35	7	3	45
Jun 2025	43	7	4	54
Jul 2025	31	10	5	46
Aug 2025	79	9	1	89
Sep 2025	357	5	1	363
Oct 2025	33	9	2	44
Nov 2025	32	21	1	54
Dec 2025	1	0	1

Cumulative views and downloads (calculated since 10 Feb 2025)

Month	HTML	PDF	XML	Total
Feb 2025	90	25	4	119
Mar 2025	22	6	0	28
Apr 2025	36	13	3	52
May 2025	35	7	3	45
Jun 2025	43	7	4	54
Jul 2025	31	10	5	46
Aug 2025	79	9	1	89
Sep 2025	357	5	1	363
Oct 2025	33	9	2	44
Nov 2025	32	21	1	54
Dec 2025	1	0	1

Viewed (geographical distribution)

Total article views: 901 (including HTML, PDF, and XML) Thereof 901 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 02 Dec 2025

Download

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Preprint (3612 KB)
Metadata XML

Short summary

Predicting climate accurately is challenging due to uncertainties in model parameters. This study introduced an automated approach to refine key parameters, focusing on processes like cloud formation and atmospheric circulation. Testing adjustments to 10 and 20 parameters improved the model’s accuracy and stability, reducing errors in long-term simulations. This faster, more reliable method enhances climate models, supporting better future predictions and aiding global decision-making.


Total:	0
HTML:	0
PDF:	0
XML:	0