Future Changes in Temperature and Precipitation over Northeastern Brazil by CMIP6 Model

Round 1

Reviewer 1 Report

There are a few editorial corrections to be effected by the authors.

Comments for author File: Comments.pdf

Author Response

Comments and Suggestions for Authors (Review 1)

Manuscript ID water-2050071

1. The article is valuable and a good contribution to knowledge but there are a few editorial errors.

The following corrections should be effected:

Line 16-18: Understanding the current and future influence of this warming on northeastern Brazil (NEB) is important, due to the region’s greater vulnerability to natural disasters as recorded in the country.

Answer: Done

Line 19 - 22: Recast

Answer: In this paper, characteristics of climate change projections (precipitation and air temperature) over the NEB are analyzed using 15 models from Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathways (SSPs: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) scenarios.

Line 26 - 27: Recast

Answer: This means that an increase in the concentration of greenhouse gases (GHG) will increase air temperature, evaporation, and evapotranspiration, reduce rainfall, and increase drought events.

Line 28: adapting and mitigating

Answer: Done

Line 43: about 800 mm

Answer: Done

Line 49: Tropical Atlantic and Pacific…

Answer: Done

Line 52 - 55: Recast

Answer: The grammatical periods present in the sentence were adjusted for the reader's better understanding of the relationship between meteorological phenomena and their characteristic scales:

The Intertropical Convergence Zone (ITCZ) [12-13], South Atlantic Convergence Zone (SACZ) [14], cold fronts [7], Upper Tropospheric Cyclonic Vortices (UTCVs) [15], and Easterly Wave Disturbances (EWD) [16] are the main atmospheric systems that influence the climate of the NEB on a synoptic scale. Mesoscale Convective Complexes (MCCs) [17], instability lines, and sea-land breezes [18] are presented at the mesoscale. On the local scale, isolated storms and the circulation of valley-mountain breezes [19] are observed.

Line 66 - 67: droughts are important for elaborating actions aimed at society’s adaptation, including prevention and mitigation measures.

Answer: Done

Line 69 - 70: but none of them focused on the whole NEB; only some representative areas were studied [32–34].

Answer: Done

Line 71: helps to understand

Answer: Done

Line 138: including studies by the IPCC

Answer: Done

Line 153 - 154: Fig. 1 caption should be moved to Page 4

Answer: Done

Line 364: stand

Answer: Done

3. There should not be space between quantity and degree Celsius.

Answer: Done

2. All the acronyms should be defined.

Answer: Done

ACCESS-ESM1-5 – Australian Community Climate and Earth System Simulator (ACCESS) - Earth System Model (ESM).

AMIP – Atmospheric Model Intercomparison Project.

AWI-CM-1-1-MR – Alfred Wegener Institute Climate Model (medium-resolution).

BCC-CSM2-MR – Beijing Climate Center Climate System Model (medium-resolution).

CanESM5 – Canadian Earth System Model version 5.

CEDA - Center for Environmental Data Analysis.

CMCC-ESM2 – Centro Euro-Mediterraneo sui Cambiamenti Climatici - Earth System Model Version 2.

CMIP – Coupled Model Intercomparison Project.

CMIP5 – Coupled Model Intercomparison Project Phase 5.

CMIP6 – Coupled Model Intercomparison Project Phase 6.

CRU-TS – Climatic Research Unit gridded Time Series.

DJF – December-January-February.

EC-Earth3-CC – European community Earth-System Climate version 3–Carbon Cycle Model.

EWD – Easterly Wave Disturbances.

FGOALS-g3 – Flexible Global Ocean-Atmosphere-Land System Model Grid-Point Version 3.

FIO-ESM-2-0 – First Institute of Oceanography Earth System Model version 2.0.

GCM – Global Climate Models.

GHG – Greenhouse Gas.

GISS-E2-1-G – Goddard Institute for Space Studies Model.

HadGEM3-GC31-MM – Hadley Centre Global Environment Model in the Global Coupled configuration 3.1 (medium-resolution).

IPCC – Intergovernmental Panel on Climate Change.

ITCZ – Intertropical Convergence Zone.

JJA – June-July-August.

KACE-1-0-G – Korea Meteorological Administration Advanced Community Earth-System model.

MAM – March-April-May.

MCC – Mesoscale Convective Complexes.

MIROC6 – Model for Interdisciplinary Research on Climate version 6.

MPI-ESM1-2-HR – Max Planck Institute Earth System Model version 1.2 (high-resolution).

MRI-ESM2-0 – Meteorological Research Institute Earth System Model Version 2.0

NEB – Northeastern Brazil.

OMA – One-Moment Aerosol.

R – Pearson’s correlation coefficient.

RCP – Representative Concentration Pathways.

RMSE – Root-Mean-Square Error.

SA – South America.

SACZ – South Atlantic Convergence Zone.

SAM0-UNICON – Seoul National University Atmospheric Model Version 0 with a Unified Convection Scheme.

SD – standard Deviation.

SON – September-October-November.

SSP – Shared Socioeconomic Pathways.

SSP1-2.6 – Scenario of Sustainability.

SSP2-4.5 – Scenario of Middle of the Road.

SSP3-7.0 – Scenario of Regional Rivalry.

SSP5-8.5 – Scenario of High Emissions.

SST – Sea Surface Temperature.

UTCV – Upper Tropospheric Cyclonic Vortices.

WCRP – World Climate Research Programme.

Author Response File: Author Response.pdf

Reviewer 2 Report

Manuscript: “Future changes in temperature and precipitation over northeastern Brazil by the CMIP6 model” submitted by Dantas et al.

Summary of the paper: The authors present a research manuscript that analyzes the performance of 15 Global Climate Models (GCMs) derived from the Coupled Model Intercomparison Project 6 (CMIP6) in simulating the general characteristics of the historical behavior of precipitation and air temperature near the surface over three areas of the Northeastern Brazilian region (NEB) and, after validation, generate projections for these two variables corresponding to four climate projection scenarios until the end of the 21st century.

In general, the topic of the manuscript is relevant to the Journal of water. But at the present state the manuscript itself is badly organized, difficult to follow, methods are poorly described, and the used English language is not optimal. After reading the manuscript, I do not get the focus of the paper. Therefore, I recommend rejecting this paper.

The major flaws of the manuscript are: Lines 22-23: for clarity, the best models for precipitation and near-surface air temperature should be indicated (e.g., HadGEM3-GC31-MM, MRI-ESM2-0, FIO-ESM-2-0, ACCESS-ESM1-5, and EC-Earth3-Veg for rainfall). 

Lines 42-55: this paragraph should be moved to a new section called 'Study Area" before '2.1. CMIP6 Models' (line 121).

Materials and Methods: It was not added a flowchart showing the main steps, inputs, and outputs. Why wasn't ground-based data used? For example, Xavier, Alexandre C., King, Carey W. and Scanlon, Bridget R. Daily gridded meteorological variables in Brazil (1980-2013), International Journal of Climatology, 2016, 36 (6), 2644–2659 could be a good option. Source: http://careyking.com/data-downloads/

Results and discussion: Lines 331-335 and 364-371: what about the uncertainty behind climate data based on CRU TS? It is necessary to provide more information on this point.  Lines 577-580: The uncertainty behind climate data based on CRU TS is important in this point.   

Conclusion: Lines 623-625: It is more convenient to use observed data rather than global products (e.g., CRU TS).  

 Author Response

Comments and Suggestions for Authors (Review 2)

Manuscript ID water-2050071

Manuscript: “Future changes in temperature and precipitation over northeastern Brazil by the CMIP6 model” submitted by Dantas et al.

 Summary of the paper: The authors present a research manuscript that analyzes the performance of 15 Global Climate Models (GCMs) derived from the Coupled Model Intercomparison Project 6 (CMIP6) in simulating the general characteristics of the historical behavior of precipitation and air temperature near the surface over three areas of the Northeastern Brazilian region (NEB) and, after validation, generate projections for these two variables corresponding to four climate projection scenarios until the end of the 21st century.

 The major flaws of the manuscript are:

Lines 22-23: for clarity, the best models for precipitation and near-surface air temperature should be indicated (e.g., HadGEM3-GC31-MM, MRI-ESM2-0, FIO-ESM-2-0, ACCESS-ESM1-5, and EC-Earth3-Veg for rainfall). 

Answer: Dear reviewer, we agree with the suggestion, and thank you! It is essential to inform our future readers of the description of the main models that simulate the seasonal behavior of the climate variables studied with better skill, and highlight them at the end of the document (conclusion). However, due to the limitation of characters in the abstract, we chose to highlight the model HadGEM3-GC31-MM, as it presents the best results in the analysis of the Taylor diagram, as follows in the new sentence:

“By using the Taylor diagram, we observed that the HadGEM3-GC31-MM model has better efficiency to simulate the seasonal behavior of climate variables”

Lines 42-55: this paragraph should be moved to a new section called 'Study Area" before '2.1. CMIP6 Models' (line 121).

Answer: The paragraph was moved accordingly.

Materials and Methods: It was not added a flowchart showing the main steps, inputs, and outputs.

Figure 2. Flowchart illustrating the steps applied in this study to obtain projections of climate change scenarios.

Answer: The flowchart was added accordingly.

Why wasn't ground-based data used? For example, Xavier, Alexandre C., King, Carey W. and Scanlon, Bridget R. Daily gridded meteorological variables in Brazil (1980-2013), International Journal of Climatology, 2016, 36 (6), 2644–2659 could be a good option. Source: http://careyking.com/data-downloads/

Answer: Information from meteorological stations is of fundamental importance in our daily activities as well as in the analysis of climate behavior through scientific research, which provides decision-makers with the best planning of public policies for society. However, spatial data and their quality are often limited, as is known in the study area (NEB), where few meteorological stations and difficulty are keeping them working. Thus, in research where there is this type of challenge, we generally resort to reanalysis of data or interpolation techniques as presented by Xavier et al. (2016 and 2022) for Brazil and Harris et al. (2020) for a large part of the globe, among other approaches.

Data derived from Xavier's work was analyzed at the beginning of this research, well before starting to write, where challenges were encountered in using this database on the NEB coast (problem pixels). Although it brings innovations to Brazilian research, the database presents more efficient results in locations far from the country's borders or with a network of robust meteorological stations in their neighborhood. As the East Coast sub-region practically represents the NEB coastline (border with the Atlantic Ocean), it was decided to use a database widely approved by the scientific community in studies that address climate change using the CMIP6 models. After a robust bibliographical analysis, the CRU-TS version 4 database (Harris et al., 2020) was selected. The CRU-TS data, as already mentioned, undergo an efficient interpolation process, where information is used not only on temperature and precipitation but also on seven variables provided by surface stations (mean, minimum, and maximum temperatures, precipitation, vapor pressure, wet days, and cloud cover), thus being a reference in validation analyses of CMIP6 models in climate analyses at global and regional levels.

Harris, I.; Osborn, T.J.; Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci Data 2020, 7, 109. https://doi.org/10.1038/s41597-020-0453-3.

Xavier, A.C.; Scanlon, B.R.; King, C.W.; Alves, A.I. New improved Brazilian daily weather gridded data (1961–2020). Int. J. Climatol 2022, 1–15. https://doi.org/10.1002/joc.7731.

 Results and discussion: 

what about the uncertainty behind climate data based on CRU TS? It is necessary to provide more information on this point. 

Lines 331-335:

Answer: When analyzing the Taylor diagram highlighted in this work, we observe the information regarding the Standard Deviation (normalized), RMSE, and correlation. Where positive or negative values of the correlation between reference data and simulated historical values can be observed. We highlighted the models that simulated historical behavior more efficiently for aesthetic reasons. The figure below was produced in the initial period of elaboration of the codes of this work, note that there are symbols outside the threshold of positive correlation. In the version sent to the journal Water, this information was filtered in favor of a better aesthetic visualization.

Concerning more information about the uncertainties of the models, we describe: “It is important to show that the simulations of precipitation by the climate models are a great challenge, due to the high uncertainties associated with the high spatial variability and difficulty in simulating with skill systems such as ITCZ and SACZ [68]. Therefore, the validation of climate models from databases already consolidated in the scientific community, such as the CRU-TS [28], is always recommended [30]."

Lines 364-371:

Answer: Articles referring to the set of CMIP6 models converge in their analysis that simulations of the near-surface air temperature variable are more efficient than simulations of the precipitation variable. The main justification lies in the very characteristic of the precipitation data, as it presents high climate variability in the space-time scale and difficulty in representing the main meteorological system that influences the NEB, which is the ITCZ [68 (new)]. Therefore, in a quantitative way, this way develops the uncertainties of the models.

Lines 577-580: The uncertainty behind climate data based on CRU TS is important in this point.   

Answer: Dear reviewer, the uncertainties described and justified [73–74] in this sentence are related to the CMIP6 models themselves, described by the symbolic term GCMs. The term will be replaced in search of greater clarity for the reader.

 Conclusion: Lines 623-625: It is more convenient to use observed data rather than global products (e.g., CRU TS).  

Answer: We agree that it is convenient to use data from meteorological stations as a reference base, however, when there is the challenge of carrying out research where this information is limited, it is still necessary to use robust synthetic data, validated and widely used by the scientific community, therefore the importance of highlighting its use. It is also important to emphasize that the scientific community itself indicates the use of GPCC, CRU, TRMM, CHIRPS, and ERA5 (some examples), in the validation analysis of studies associated with climate change scenarios.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

It is convenient to use observed data rather than global products (e.g., CRU TS).

Author Response

Comments and Suggestions for Authors (Review 2 – round 2)

Manuscript ID water-2050071

Manuscript: “Future changes in temperature and precipitation over northeastern Brazil by the CMIP6 model” submitted by Dantas et al.

Comments and Suggestions for Authors:

Results and discussion: 

Lines 577-580: The uncertainty behind climate data based on CRU TS is important in this point.

Answer:

Some explanations for these biases may be justified by the smoothing of extreme values of precipitation and air temperature by global models (CRU-TS), parameter calibration of CMIP6 models for the study area, statistical downscaling, and, the difficulty of the GCMs in representing high climate variability, for example, due to the original low resolution of these models and the topography of the analyzed domains [73–74], which can provide high RMSE values and/or the negative correlation in some models.

On page 12, we inserted the following text:

“It is important to show that the simulations of precipitation by the climate models are a great challenge, due to the great uncertainties, associated with the high spatial variability and difficulty in simulating with skill systems such as ITCZ and SACZ [68]. Therefore, the validation of climate models from databases consolidated by the scientific community, such as the CRU-TS [28], is always recommended [30]. It is known that information from meteorological stations is of fundamental importance in our daily activities as well as in the analysis of climate behavior through scientific research, which provides decision-makers with the best planning of public policies for society. However, spatial data and their quality are frequently limited, as seen in our study area (NEB), where there are few meteorological stations and technical difficulties keep them operational. After a robust bibliographical analysis, the CRU-TS version 4 database (Harris et al., 2020) was selected. It undergoes an efficient interpolation process, where information is used not only on temperature and precipitation but also on seven variables provided by surface stations (mean, minimum, and maximum temperatures; precipitation; vapor pressure; wet days; and cloud cover), thus serving as a reference in validation analyses of CMIP6 models in climate analyses at global and regional levels.”

Conclusion:

Lines 623-625: It is convenient to use observed data rather than global products (e.g., CRU TS).

Answer:

We reinforce that global products were used as a reference base (CRU-TS) in this analysis due to the particularities of the case, but it is convenient to use data observed by surface meteorological stations.

On page 24, we inserted the following text:

“However, it is worth highlighting that, where possible, it is convenient to use observed data rather than global products (e.g., CRU TS).”

Author Response File: Author Response.docx