the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region
Yassmin Hesham Essa
Vincenzo de Toma
Alessandro Anav
Gianmaria Sannino
Rosalia Santoleri
Chunxue Yang
Abstract. Regional coupled and Earth System models are fundamental numerical tools for climate investigations, downscaling of predictions and projections, process-oriented understanding of regional extreme events, and many more applications. Here we introduce a newly developed coupled regional modeling framework for the Mediterranean region, called MESMAR (Mediterranean Earth System model at ISMAR) version 1, which is composed of the WRF atmospheric model, the NEMO oceanic model, and the HD hydrological discharge model, coupled via the OASIS coupler. The model is implemented at moderate resolution (about 1/12° for the ocean and river routing, while twice coarser for the atmosphere) for long-term investigations. We focus on the evaluation of skill score metrics from several sensitivity experiments devoted to i) understanding the best vertical physics configuration for NEMO; ii) identifying the impact of the interactive river runoff; iii) choosing the best-performing physics-microphysics suite for WRF in the regional coupled system. The modeling system has been developed for downscaling reanalyses and predictions, and for coupled data assimilation experiments. We then formulate and show the performance of the system when weakly coupled data assimilation is embedded in the system (variational assimilation in the ocean and spectral nudging in the atmosphere), in particular for the representation of extreme events like intense mid-latitude cyclones (i.e. medicanes). Finally, we outline plans for future extension of the modeling framework.
Andrea Storto et al.
Status: open (until 03 Jul 2023)
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RC1: 'Comment on gmd-2023-77', Anonymous Referee #1, 19 May 2023
reply
Review of “MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilitation studies in the Mediterranean region” by Storto et al.(2023)
I have completed the review of the manuscript entitled “MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilitation studies in the Mediterranean region” by Storto et al. (2023)”. Here the Authors analyzes a newly coupled model developed for the Mediterranean region, MESMAR v1. In general, I found the outcomes of the manuscript interesting and the work falling in the scope of the journal. However, I found several issues in the manuscript that I require the Authors to address before the publication of the work. Thus, I ask in this case major revision for the manuscript.
Abstract
Line 17: I would report the horizontal resolution of the models described rather than using the adjective “moderate” or “coarser”
Line 21: Are you planning to use the model to produce climate projections? Please specify: The Authors use several times the word “predictions” but never “projections”
Line 23: “Intense Mediterranean cyclones “rather than “intense mid-latitude cyclones”
Introduction
Line 38: Usually RCM refers to Regional climate models and thus atmosphere not to coupled systems (RCSMs for example, Reale et al., 2022). Please correct that in the text to avoid confusion
Line 38: Please update Giorgi, (1990) that is a bit old as reference
Line 51: “Coupled…unexplored”..see Sevault et al., 2014. The coupled CNRM model uses the spectral nudging.
The Authors use several times the word “predictions” but never “projections”. Are you planning to use your model also to produce projections? Please specify
Line 72: Be cautious since hurricanes are (from some points of view) very different from medicanes. See Flaounas et al., 2022. Please reformulate this sentenceIn the introduction it is missing a clear description of the present state concerning the coupled models in the MED region and at which extent the new modeling system described by the Authors is different or eventually represent an advancement with respect the preexisting modeling tools.
2.Earth system model configuration
Line 94: What do you mean with stationary geophysical fields? Is the topography a geophysical field? Please explain
Line 101: Do you refer to the width of the sponge layer? Please specify
Line 110-115: As far as I understand you are using shortwave radiation as forcing that attenuates along the water column according to a water attenuation coefficient plus the chl-a concentration: since you are using the satellite chl-a (first 10 m as far as I remember) how do you quantify the attenuation effect led by chl-a below 10 m?
Line 117-120: Are you using an open boundary in the Atlantic? Are you applying also the sea surface height at boundary?
Line 126-128: Is the Nile missing from the numerical settings? It is not mentioned in the text.
Line 138: Is “25” resolution dependent? Please explain
Line 143: Do you mean that all the model components exchange field every 30 min? please specify
Line 148-150: Not very clear. Please reformulate. As far as I understand WRF transfers surface and subsurface runoff to HD that is remapped on the ocean grid and passed to NEMO after HD runs. Does HD pass only the river discharge to NEMO? Do you need to locate the river mouths on the ocean model domain?
Line 152-157: Why do not use CMEMS MYOCEAN reanalysis specifically tuned for the MED sea instead of GLORYS? Moreover, there are no information about the spin up of your model for pot temperature and salinity. Did you perform the spin up at least for the long run? I would also include additional information concerning the computational performances of the model (number of cores, computational times etc.). It would be useful having a table summarizing the main settings of the model at least in the most important experiments.
3.Sensitivity experiments
3.1 Impact of the interactive river discharge
I do understand the idea of the Authors to show the importance of river online on the simulate salinity. However, I think that 2 years of runs are too small to assess the importance of river inflow on salinity (in particular along the water column where the signal need sometimes to penetrate) in absence of information about the spin up. I would change Fig.3 and Fig 4 adding the comparison with observations (instead of the comparison between the two configurations) as You did in Figure 6. Moreover, why did you use EN4 instead CMEMS or JRA55 instead of Ludwig et al., 2009.
3.2 Nemo vertical physics
There are no information about the length of tests. Moreover, there is no quantification of biases (smaller etc is too generic). Please quantify the biases
3.3 WRF configuration
Why have your tests run only for the period 1993-2021 (also in the reference simulation) instead of covering the entire ERA5-ORAS5 period? Please specify
What do not you investigate also the behavior of wind and precipitation on the ocean domain since their importance for E-P and mixing?
4 Reference simulation
Line 235: I would add also Soto-Navarro et al., (2020) and Reale et al., (2022) that addressed this issue.
Soto-Navarro, J., Jordá, G., Amores, A., Cabos, W., Somot, S., Sevault, F., et al. (2020). Evolution of Mediterranean Sea water properties under climate change scenarios in the Med-CORDEX ensemble. Clim. Dyn. 54, 2135–2165. doi: 10.1007/s00382-019-05105-4
Reale, M., Cossarini, G., Lazzari, P., Lovato, T., Bolzon, G., Masina, S., ... & Salon, S. (2022). Acidification, deoxygenation, and nutrient and biomass declines in a warming Mediterranean Sea.Biogeosciences, 19(17), 4035-4065.
Being a long term simulation, it would be interested having the analysis of long term timeseries of temperature and salinity with respect to observational datasets to have an idea about the behavior of the thermohaline properties at different depths.
It would be important also to assess the source of the error in the total net heat flux. Is it an underestimation of the net shortwave? Latent or sensible heat fluxes? Please think to include this analysis
Figure 11 Why is the area outside Gibraltar colored in the upper panels and not in the bottom panels?
5 Data assimilation
I found the data assimilation an interesting new advancement in the coupled model. However, I do not understand the reason for relaxing at the surface when already both atmosphere and ocean assimilate at high frequency data to correct errors in the simulated field. Could you please explain better that?
Moreover, I would ask the Authors to better discuss why with fully data assimilation active (and high frequency of assimilation) the improvements in the biases is relatively small. Low quality of input data? Please infer on that.
5 Mediterranean hurricanes
How do you track and reconstruct the temporal evolution of the systems analyzed in the manuscript? Please infer on that.
6 Discussion and conclusions
There is a lack of the comparison with respect to previous modeling systems. Why should a potential user use your modeling tool instead of another one? Do you expect that increasing the horizontal resolution should improve the performances of your model or should make it slower?
Citation: https://doi.org/10.5194/gmd-2023-77-RC1
Andrea Storto et al.
Data sets
MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region. Article data Andrea Storto https://doi.org/10.5281/zenodo.7899115
Model code and software
MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region. Coupled model code Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang https://doi.org/10.5281/zenodo.7898938
Andrea Storto et al.
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