Interaction between cloud–radiation, atmospheric dynamics and thermodynamics based on observational data from GoAmazon 2014/15 and a cloud-resolving model

Gonçalves, Layrson J. M.; Coelho, Simone M. S. C.; Kubota, Paulo Y.; Souza, Dayana C.

doi:https://doi.org/10.5194/acp-22-15509-2022

Articles | Volume 22, issue 23

https://doi.org/10.5194/acp-22-15509-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-22-15509-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 23

Research article

|

09 Dec 2022

Research article |

| 09 Dec 2022

Interaction between cloud–radiation, atmospheric dynamics and thermodynamics based on observational data from GoAmazon 2014/15 and a cloud-resolving model

Layrson J. M. Gonçalves, Simone M. S. C. Coelho, Paulo Y. Kubota, and Dayana C. Souza

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Final revised paper (published on 09 Dec 2022)
Preprint (discussion started on 06 Jan 2022)

Interactive discussion

Status: closed

RC1:
'Comment on acp-2021-1014', Anonymous Referee #1, 25 Jan 2022

This study examines observations of cloud cover, radiation, precipitation and atmospheric thermodynamic variables from the ARM site located in central Amazonian during Go-Amazon and compares them with output from a CRM. The investigation looks for relationships between these variables in the observations and model outputs to see what can be learnt about the interaction of the clouds with their environment and their impact on radiation. The Amazon region provides an excellent environment in which to study the evolution of moist convection and how it relates to the large-scale environment. The use of CRMs is also well established to simulate deep convection and provide additional insight into convective cloud evolution. The authors evaluate various aspects of the CRM’s performance including a thorough investigation of the sensitivity of CRM results to the horizontal resolution and show that the standard 2km set does a good job of simulating the temporal variability of clouds, precipitation and radiation although higher resolution better captures the distribution of cloud fraction. The study finds strong co-variations in cloud fraction and surface radiative fluxes at the surface and some correlations between cloud fraction, vertical motion, and column anomalies in temperature and relative humidity. Such relationships are to be expected given the nature of clouds, convection and radiation. In a general sense understanding these relationships better could aid the development and evaluation of cloud parameterizations in large-scale models.

The analysis looks mostly at correlations between the fractional cover of different cloud types and the min/max anomalies of T and RH in the column based on day-to-day variations. This is interesting from an observational point of view in explaining the daily variations in cloud cover and precipitation but the limitation here is that there is only a loose physical connection between these anomalies and what determines the development of these convective clouds. The vertical profile of temperature and moisture and the resulting stability or instability (CAPE, CIN etc) is also a crucial factor that is missing from the analysis, along with broader constraints such as the large-scale convergence of moisture. This may be why the cloud fractions display a lot of scatter in their relationships to the column anomalies of T, RH and omega and relatively low correlation coefficients. Moreover, the relationships observed during these IOPs are unlikely to be generalizable as they assume a certain degree of convective instability and hence sensitivity to the T and RH anomalies. Perhaps there is more that could be gained from this general perspective but it is not obvious from the conclusions how the analysis presented so far could be taken forward to aid the evaluation and development of parameterizations in large-scale models.

For these reasons I find it difficult to recommend this study for publication in ACP. The study would need to show an increased understanding of the physical interactions involved or a clearer path towards improving the physics in models.

Citation: https://doi.org/10.5194/acp-2021-1014-RC1
- AC1: 'Reply on RC1', Layrson Gonçalves, 27 Jan 2022
  
  RC1: This study examines observations of cloud cover, radiation, precipitation and atmospheric thermodynamic variables from the ARM site located in central Amazonian during GoAmazon and compares them with output from a CRM. The investigation looks for relationships between these variables in the observations and model outputs to see what can be learnt about the interaction of the clouds with their environment and their impact on radiation. The Amazon region provides an excellent environment in which to study the evolution of moist convection and how it relates to the large-scale environment. The use of CRMs is also well established to simulate deep convection and provide additional insight into convective cloud evolution. The authors evaluate various aspects of the CRM’s performance including a thorough investigation of the sensitivity of CRM results to the horizontal resolution and show that the standard 2km set does a good job of simulating the temporal variability of clouds, precipitation and radiation although higher resolution better captures the distribution of cloud fraction. The study finds strong co-variations in cloud fraction and surface radiative fluxes at the surface and some correlations between cloud fraction, vertical motion, and column anomalies in temperature and relative humidity. Such relationships are to be expected given the nature of clouds, convection and radiation. In a general sense understanding these relationships better could aid the development and evaluation of cloud parameterizations in large-scale models.
  AC: We thank the reviewer for carefully reading our manuscript and providing very thoughtful comments and suggestions. We are glad that the reviewer highlighted the main results aspects of this study. Please find below a detailed response to each of the comments.
  RC1: The analysis looks mostly at correlations between the fractional cover of different cloud types and the min/max anomalies of T and RH in the column based on day-to-day variations. This is interesting from an observational point of view in explaining the daily variations in cloud cover and precipitation but the limitation here is that there is only a loose physical connection between these anomalies and what determines the development of these convective clouds.
  AC: In this article, one of the objectives is to understand how the variation of large-scale variables (such as omega, T and RH), in relation to the average of the previous 24 hours, impacts the diagnosis of cloud fraction and radiation fields. These anomalies are produced by the physical processes (entrainment, dentrainment, updraft, downdraft, static energy, etc.) related to convective clouds, shallow, stratus, cirrus, etc. However, in numerical models, cloud fraction parameterizations are based on macrophysics variables (such as temperature, omega, relative humidity), and cloud microphysics variables (as liquid water and ice concentration) [Slingo, 1987; Sundqvist et al., 1989; Roeckner, et al. 1996; Tompkins, 2002; Gettelman et al., 2010; Bogenschutz et al., 2012; Machulskaya 2015; Dietlicher et al., 2019; Muench and Lohmann 2020]. Therefore, using information related to the convective cloud's development only helps define the cloud´s top and bottom of the cloud in the cloud fraction parameterization. The information from the convective clouds development of convective clouds may not contribute to improving the cloud fraction parameterizations currently used in numerical models. It is important to mention that cloud fraction and deep convection parameterization are independent algorithms. We are glad about this comment, we can better clarify these aspects associated with correlation analyses in the manuscript, and make the reader note that the analysed variables are based on those used in numerical model parameterizations, mainly in the cloud fraction parameterization.
  RC1: The vertical profile of temperature and moisture and the resulting stability or instability (CAPE, CIN etc) is also a crucial factor that is missing from the analysis, along with broader constraints such as the large-scale convergence of moisture. This may be why the cloud fractions display a lot of scatter in their relationships to the column anomalies of T, RH and omega and relatively low correlation coefficients.
  AC: CAPE and CINE are used to analyze the life-cycle of deep convection and these variables are considered in the deep convection parameterizations. Notice that this study is focused on cloud cover parameterization, and not deep convection parameterization. Because of this, the article has a more specific interest in analyzing the relationships between the diurnal variability of large-scale variables (temperature, omega, relative humidity) and the cloud fraction. Due to the use of point data from the GoAmazon experiment, the hypothesis adopted is that the information on the development of deep convection is already associated with diurnal variability of large-scale variables, as well as large-scale moisture convergence. Regarding the low correlation coefficient values found between the cloud fractions and the column anomalies of T, RH and omega, it is necessary to mention that the data of cloud fractions, liquid water and ice from the GoAmazon experiment are a restricted data and with availability limited. Therefore, the informations used as cloud fractions, liquid water and ice are obtained in this work through simulations with CRMs.
  RC1: Moreover, the relationships observed during these IOPs are unlikely to be generalizable as they assume a certain degree of convective instability and hence sensitivity to the T and RH anomalies.
  AC: The IOP1 and IOP2 experiments are used to analyze the dry and wet periods in the Amazon region. In the IOP1 (wet) condition, the large-scale systems that act on the region of the GoAmazon experiment are active in this period, contributing to the convective developments, while in the IOP2 (dry) period, the performance of large-scale systems is very reduced in this period, not favoring the development of convection. We also agree that the results obtained cannot be generalized, however, the analysis of these two periods (IOP1 and IOP2) statistically represents well the convective activity of the region of the GoAmazon experiment.
  RC1: Perhaps there is more that could be gained from this general perspective but it is not obvious from the conclusions how the analysis presented so far could be taken forward to aid the evaluation and development of parameterizations in large-scale models.
  AC: The results of this article are part of the Brazilian Atmospheric Model (BAM) development project (Coelho, et al, 2021a, 2021b, 2021c, Guimarães, et al. 2021, Figueroa, et al. 2016). All information obtained through this work is being used to develop and improve the cloud fraction parameterization used in the BAM model. A second article is being prepared focused on describing the new cloud fraction parameterization and its validation. We also glad for this comment, we could include this perspective in the manuscript.
  RC1: For these reasons I find it difficult to recommend this study for publication in ACP.
  AC: We hope that our answers for the reviewer may have clarified their doubts and some points that were probably not clear in the article. We intend to take the above discussion into account in the final version. The suggestions and comments from the reviewer significantly can contribute to improving the publication quality.
  RC1: The study would need to show an increased understanding of the physical interactions involved or a clearer path towards improving the physics in models.
  AC: We can clarify and direct the conclusions to show how to use these results to improve the cloud fraction parameterization in the models.
  
  References:
  Bogenschutz, P. A., Gettelman, A., Morrison, H., Larson, V. E., Schanen, D. P., Meyer, N. R., & Craig, C. (2012). Unified parameterization of the planetary boundary layer and shallow convection with a higher-order turbulence closure in the community atmosphere model: Single-column experiments. Geoscientific Model Development, 5(6), 1407– 1423. https://doi.org/10.5194/gmd-5-1407-2012
  Coelho, C. A. S., Baker, J. C. A., Spracklen, D. V., Kubota, P. Y., de Souza, D. C., Guimarães, B. S., et al. (2021a) A perspective for advancing climate prediction services in Brazil. Climate Resil Sustain 1– 9. https://doi.org/10.1002/cli2.29
  Coelho C.A.S., de Souza D.C., Kubota P.Y., Cavalcanti I.F.A., BakerJ.C.A., Figueroa S.N., et al. (2021b) Assessing the representationof South American Monsoon features in Brazil and UK climatemodel simulations.Climate Resilience and Sustainability. https://doi.org/10.1002/cli2.27
  Coelho C.A.S., de Souza D.C., Kubota P.Y., Costa S.M.S., MenezesL., Guimaraes B.S., et al. (2021c) Evaluation of climate simulations produced with the Brazilian global atmospheric model version 1.2. Climate Dynamics,56,873–898 https://doi.org/10.1007/s00382-020-05508-8
  Guimarães B.S., Coelho C.A.S., Woolnough S.J., Kubota P.Y., Bas-tarz C.F., Figueroa S.N., et al. (2021) An inter-comparison performance assessment of a Brazilian global sub-seasonal prediction model against four sub-seasonal to seasonal (S2S) predictionproject models.Climate Dynamics, 56, 2359–2375. https://doi.org/10.1007/s00382-020-05589-5
  Dietlicher, R., Neubauer, D., & Lohmann, U. (2019). Elucidating ice formation pathways in the aerosol-climate model ECHAM6-HAM2. Atmospheric Chemistry and Physics, 19(14), 9061– 9080. https://doi.org/10.5194/acp-19-9061-2019
  Figueroa, S. N., Bonatti, J. P., Kubota, P. Y., Grell, G. A., Morrison, H., Barros, S. R. M., Fernandez, J. P. R., Ramirez, E., Siqueira, L., Luzia, G., Silva, J., Silva, J. R., Pendharkar, J., Capistrano, V. B., Alvim, D. S., Enoré, D. P., Diniz, F. L. R., Satyamurti, P., Cavalcanti, I. F. A., Nobre, P., Barbosa, H. M. J., Mendes, C. L., & Panetta, J. (2016). The Brazilian Global Atmospheric Model (BAM): Performance for Tropical Rainfall Forecasting and Sensitivity to Convective Scheme and Horizontal Resolution, Weather and Forecasting, 31(5), 1547-1572. Retrieved Jan 26, 2022, from https://journals.ametsoc.org/view/journals/wefo/31/5/waf-d-16-0062_1.xml
  Gettelman, A., Liu, X., Ghan, S. J., Morrison, H., Park, S., Conley, A. J., Klein, S. A., Boyle, J., Mitchell, D. L., & Li, J. L. (2010). Global simulations of ice nucleation and ice supersaturation with an improved cloud scheme in the community atmosphere model. Journal of Geophysical Research, 115, D18216. https://doi.org/10.1029/2009JD013797
  Muench, S., Lohmann, U. (2020). Developing a cloud scheme with prognostic cloud fraction and two moment microphysics for ECHAM-HAM. Journal of Advances in Modeling Earth Systems, 12, e2019MS001824. https://doi.org/10.1029/2019MS001824
  Roeckner, E., Arpe, K., Bengtsson, L., Christoph, M., Claussen, M., Dümenil, L., et al. (1996). The atmospheric general circulation Model ECHAM-4: Model description and simulation of present-day climate (Tech. Rep. 218, pp. 90). Hamburg, Germany: Max-Planck-Institut für Meteorologie.
  Slingo, J.M. (1987), The Development and Verification of A Cloud Prediction Scheme For the Ecmwf Model. Q.J.R. Meteorol. Soc., 113: 899-927. https://doi.org/10.1002/qj.49711347710
  Sundqvist, H., Berge, E., & Kristjánsson, J. E. (1989). Condensation and Cloud Parameterization Studies with a Mesoscale Numerical Weather Prediction Model, Monthly Weather Review, 117(8), 1641-1657. Retrieved Sep 8, 2021, from https://journals.ametsoc.org/view/journals/mwre/117/8/1520-0493_1989_117_1641_cacpsw_2_0_co_2.xml
  Tompkins, A. M. (2002). A prognostic parameterization for the subgrid-scale variability of water vapor and clouds in large-scale models and its use to diagnose cloud cover. Journal of the Atmospheric Sciences, 59(12), 1917– 1942. https://doi.org/10.1175/1520-0469(2002)059<1917:APPFTS>2.0.CO;2
  
  Citation: https://doi.org/10.5194/acp-2021-1014-AC1
RC2:
'Comment on acp-2021-1014', Anonymous Referee #2, 31 Jan 2022

The manuscript “Interaction between cloud-radiation, atmospheric dynamics and thermodynamics based on observational data from GoAmazon 2014/15 and a Cloud Resolving Model” general goal, as stated, is to understand the interactions between the dynamic and thermodynamic variables of the atmosphere and cloudiness in Central Amazonia.

For that, the authors used a set of observational data collected during GoAmazon IOP’s (dry and wet seasons) and carried out a set of simulations using a Cloud Resolving Model considering different spatial resolutions.

The first results are focused on the comparison between observed and modeled atmospheric variables (cloud fraction, rain rate, radiative fluxes, temperature, relative humidity, vertical velocity) looking at daily and diurnal variability.

The authors concluded that the model consistently simulated the observations

For the second part of the results, also focusing on the comparison between observations and modeling, the authors explore the relationship between cloud fraction and the atmospheric variables (short and long wave radiation, temperature, relative humidity and vertical velocity and liquid water content).

The authors concluded that shallow and deep convection clouds have significant impact on radiation fluxes in the Amazon region during wet and dry period, and that memory of previous day large-scale features (based on temperatura, RH, and vertical velocity anomalies) have a good correlation with cloud fraction.

I would recommend authors to carry out a careful revision of the manuscript; it seems that several grammatical corrections are necessary.

Introduction

The introduction and the problem contextualization are somehow dispersed, the authors mention several aspects related to the importance of clouds and their interaction with radiation, in some points they mention aspects of large-scale atmospheric dynamics, little talk about thermodynamic aspects, they mention the types of models, but again there is a lack of connection between the contents that points to an objective characterization of the problem to be studied.

As the authors suggest developing and adjusting the parameterizations related to the cloud cover fraction, it would be interesting to discuss what are the limitations that they want to target, and the aspects that the proposed study would help to improve.

Methods

Little is said about the site, about the presence of the city of Manaus, the characteristics of the region, circulation pattern, among other relevant information to reinforce the importance of the site. GoAmazon included several sites, each site was designed to meet different characteristics within the context of the interaction between the city of Manaus and the Forest, it would be interesting if the authors could describe a little more about the ARM site in the context of GoAmazon.

In the method topic, the variables used are barely contextualized in the dataset description, The authors need to specify the macro and microphysical data that they are referring to.

Try to maintain consistency in relation to the description of the objective of the study, in the methods topic it is understood that what is intended is an analysis of the cloud-radiation interaction, but in in the introduction the focus of the study is described as to understand the relationship between dynamics, thermodynamics and the cloud-radiation interaction.

Results

The first part of the results focuses on evaluating the performance of the different model resolutions in relation to observation. I think that a statistical analysis to summarize the performance of each resolution would be helpful. And it seems that an analysis separating different atmospheric scenarios, especially in the wet period, might bring interesting results. For example, in Figure 4 one can see that all resolutions fail in relation to the frequency of cloud cover fraction close to zero in the wet period, but at the other extreme, cases with a coverage fraction closer to 1 there is a resolution that seems to perform better than the others.

Regarding the two study cases, the analysis focused on two days seems to me limited in relation to the objective of extracting consistent and robust relationships between the atmospheric characteristics of the previous day and the properties of the clouds. The authors should evaluate a more robust alternative.

To achieve the objective of ââintegrating modeling as an element to understand the interaction between dynamics, thermodynamics, clouds and radiation, it seems that the model needs to be further explored. The presented design and analysis of the model output consisted essentially of an evaluation against observation.

The discussions of the relationships between cloud fraction and the remaining atmospheric variables, in general, were focused on expected features, which make it difficult to identify a clear contribution regarding the needed development and improvement in cloud parameterization stated in the goals of the paper.

Therefore, I found that the current manuscript needs major revision before it can be considered for publication.

Citation: https://doi.org/10.5194/acp-2021-1014-RC2
- AC2: 'Reply on RC2', Layrson Gonçalves, 18 Apr 2022
  
  The comment was uploaded in the form of a supplement.
  
  Citation: https://doi.org/10.5194/acp-2021-1014-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Layrson Gonçalves on behalf of the Authors (18 Apr 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (05 Jun 2022) by Philip Stier

RR by Anonymous Referee #2 (17 Jun 2022)

ED: Publish as is (13 Oct 2022) by Martina Krämer

Short summary

This research aims to study the environmental conditions that are favorable and not favorable to cloud formation, in this case specifically for the Amazon region. The results found in this research will be used to improve the representation of clouds in numerical models that are used in weather and climate prediction. In general, it is expected that with better knowledge regarding the cloud–radiation interaction, it is possible to make a better forecast of weather and climate.