the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 May 2024
Spatial variability and future evolution of surface solar radiation over Northern France and Benelux: a regional climate model approach
Abstract. Improving knowledge of current and future spatio-temporal variability of surface solar radiation is essential in the context of climate change and associated environmental and societal issues. Such an evolution will be influenced by changes in both meteorological parameters and atmospheric composition, notably by anthropogenic emissions. We investigate both all-sky and clear-sky surface solar radiation (SSR) variability, along with cloud cover, aerosols and water vapor content, over Northern France and Benelux. This region of Europe is largely influenced by cloudy conditions and anthropogenic aerosols, especially nitrate species. Our analysis relies on the CNRM-ALADIN64 regional climate model at 12.5 km x 12.5 km spatial resolution, which includes the TACTIC interactive aerosol scheme. First, hindcast reanalysis-driven simulations over 2010–2020 allow a regional evaluation of ALADIN outputs and to investigate recent spatial variability of SSR and associated atmospheric parameters. Secondly, their possible evolution at mid and end of the 21st century are investigated based on ALADIN climate simulations following two contrasted CMIP6 scenarios, namely the shared socioeconomic pathways (SSP), SSP1-1.9 and SSP3-7.0. Our regional evaluation of clear-sky and all-sky SSR, clear-sky frequency and aerosols over northern France and Benelux shows reasonable agreement between 2010–2020 ALADIN hindcast simulations and coincident multi-site ground-based measurements, despite some overestimation of nitrate aerosols in spring and an overall underestimation of organic particles by the model. Focusing on spring and summer seasons, hindcast simulations show maximum of solar radiation around the southern parts and over sea areas of the region. In addition to latitudinal effects, elevated aerosol loads over Benelux, and high cloud cover over the South West of England reduce the SSR. Compared to 2005–2014 atmospheric conditions, ALADIN mid and long-term simulations for SSP1-1.9 predict a significant reduction of aerosol loads, especially over the Benelux, associated with an increase in future clear-sky SSR but geographically limited all-sky SSR evolution. In contrast, SSP3-7.0 simulations projected pronounced and extended decreases of clear-sky and all-sky SSR over most of the Benelux/northern France region. These reductions are maximum in spring due to combined effects of cloud cover and nitrate aerosol increases over the Benelux starting in 2050, that are amplified by an additional water vapor increase in 2100. Thus, this regional climate model approach suggests that future SSR evolution over this part of western Europe will be drastically affected by combined effects of anthropogenic aerosol emissions trajectories, cloud cover and water vapor changes, which also induce strong spatial patterns.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2024-1174', Anonymous Referee #1, 21 Jun 2024
General comments
The manuscript “Spatial variability and future evolution of surface solar radiation over Northern France and Benelux: a regional climate model approach” evaluates the performance of the regional climate model CNRM-ALADIN64 comparing the model outputs of different variables with observed values. After the conclusion that the model shows reasonable agreement with observational data, the authors use the output of the model to investigate the spatial variability of surface solar radiation (SSR) in the Benelux region in the recent past, and for two different future scenarios in the mid term and long term. The manuscript is mostly well organized, makes use of valid methods using mostly resonable assumptions and address relevant scientific questions within the scope of the journal. At the present form, I think there are still several points that could be improved or better discussed, but I believe it could be suited for publication after major revisions
For the analysis of the future evolution of SSR the authors compare the average values of the historical period to mid term and long term scenarios, rather than showing a time series or depicting the SSR change in terms of “trends per decade”, as done by most studies in the topic. However, the approach in the study is also perfectly valid and comprehensible, given that special focus is given to the spatial contrasts. For this analysis, the authors focused only in the spring and summer months. I understand the reasoning on why the authors chose to focus on these months, but I do not think this is the best approach if the intent is to discuss the whole picture of future evolution, as I expected from the title of the manuscript. I also believe some improvement could be done in the evaluation of simulated surface solar radiation (section 3.2). At the present form of the manuscript the evaluation is done in terms of the monthly mean absolute SSR values only, and that is dominated by the seasonality. Therefore, this does not provide a strong tool for evaluation of other components of the time series, such as trend and irregular variability (anomalies). In the specific comments I comment in more details these and other minor concerns.
Specific comments
Lines 22-23: here you basically mention the three major aspects that affect long term SSR variability. I don’t know if there is space in the abstract, but I would include a few words to just highlight if each of them is more relevant than the other.
Lines 34-45: My understanding of the structure of the introduction was: first paragraph - SSR is important and presents variability; second paragraph - previous literature on SSR variability; third paragraph - puts the manuscript and the region of study in the context; and fourth paragraph - structure of the manuscript. If this is more or less your intention, I think the second paragraph (lines 34-45) could present more literature that discusses SSR changes and their causes in Europe or optimally in or around the region of study. Maybe the review paper by Wild (2009) [https://doi.org/10.1029/2008JD011470] is a good start, but newer papers should be available too. At the moment I have the feeling that this paragraph discussed only projecting SSR, but before talking about the ability to make accurate projects of SSR, it is important to discuss the physical processes too.
Figure 3: Would be interesting to also include a third line (or a bar) in each plot showing the difference between the two different estimates.
Section 3.2: The evaluation of the simulated SSR is made by comparing the monthly mean absolute values of direct, diffuse and global components of SSR. However, I do not think this is a complete way of evaluating the SSR representation by the model. For me, the actual evaluation shows that the model can well capture the seasonality. Which is good. But if the model will be used to investigate the future evolution of SSR, we have to also be able to evaluate the model’s ability to represent trends and anomalies. For example, in line 332 the authors mention that ALADIN underestimates the direct component in spring by around -20 to -40 W/m2. Such an underestimation represents less than 10% for the absolute SSR of those months, but such a difference could have a much bigger relevance if we would be evaluating in terms of anomalies. Therefore, I think it is important to somehow evaluate the time series with its different components, not only the seasonality.
Line 401-405: Here the authors justify the use of only spring and summer months in the analysis. First the framing of the argument sounds odd: “our analysis of ground measurements and ALLADIN simulations reveals that spring and summer are both characterized by relatively high SSR values”. That is absolutely correct, but it is framed as if this is a new finding from the analysis, while in fact, relatively high SSR values in summer months are simply a fact for locations at such latitude. Furthermore, omitting half of the year when analyzing future evolution of SSR might lead to the false assumption that the this half of the year is not relevant to the long term SSR variability. In studies like Stern et al. (2009 [https://doi.org/10.1002/joc.1735]) and Schilliger et al. (2024 [https://doi.org/10.22541/essoar.171136948.88001430/v1]) the authors show contributions from different months for the SSR trends, and in some cases it is possible to identify significant contributions to the long term SSR trends from processes occurring in the winter months. However, I think there are a few alternatives to address this issue. The most obvious, but probably requiring more work, would be to perform the analysis for all four seasons. But an alternative could also to make it more explicit from the beginning (maybe even in the title) that the study analyses only spring and summer months, because the title and the manuscript until this points led me to expect something like an analysis of the entire year. Or even, another alternative could be to include an analysis of annual mean conditions. Maybe the annual conditions follow very closely the spring and summer conditions for this regions, and this could make it clear for the reader. In any case, if not all months are included in the analysis, it would be important to discuss that long term SSR changes are not the result of only spring and summer months.
Line 455: Is it possible to estimate the effect of this 2-4% cloud fraction change on SSR values?
Figures 8 and 9: Would be nice to include (maybe even only in the legend of the figures) to what period (years) the mid term and long term refer to.
Table 3 a): The sum of each component of the SSP1-1.9 scenario does not correspond to the total value displayed in the table. I think there is one zero missing in the value for sulfate.
Lines 448-482: This paragraph was a little bit hard to read, especially the second half of it. It is also very long. Maybe would be good to reorganize in shorter paragraphs trying to be clearer.
Lines 469-470: The authors refer to the change in SSR that would be induced by some changes in AOD. Is this the change that was observed in the scenarios simulated? This was not too clear.
Conclusions: If any of the major concerns raised here are somehow addressed, it would be important to discuss them in this section too.
Technical comments
Line 27: This first sentence sounds odd. I would change “should not be considered stable over past and upcoming decades” to something like “has not been stable over the past decades and should not be expected to be in the upcoming decades”.
Line 34: This first sentence (before comma) sounds confusing, I had to reread to understand. Maybe replace “energy transition that requires increases of pv technology deployment” with something shorter or more objective as simply “PV energy production” or “PV energy production (relevant for energy transition)”.
Line 53: By “cloudy sky conditions” you meant “cloud cover”?
Line 64: “Cloudy conditions” or “cloud cover”? Maybe you want to use a synonym of cloud cover, such as “cloudiness conditions”.?
Line 170: “Scenarios is” - either “scenario is” or “scenarios are”. Maybe also double check this throughout the manuscript.
Line 227: Maybe also good to include the relative (%) values for the AOD uncertainty.
Line 485: Same as line 170.
Citation: https://doi.org/10.5194/egusphere-2024-1174-RC1 - AC1: 'Reply on RC1', Gabriel Chesnoiu, 06 Sep 2024
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RC2: 'Comment on egusphere-2024-1174', Anonymous Referee #2, 09 Jul 2024
General comments:
Review of “Spatial variability and future evolution of surface solar radiation over Northern France and Benelux: a regional climate model approach”
by Gabriel Chesnoiu, Isabelle Chiapello, Nicolas Ferlay, Pierre Nabat, Marc Mallet, and Véronique Riffault
The authors present results evaluating the performance of the regional climate model CNRM-ALADIN64 comparing the simulations to observations of surface radiative fluxes and aerosols properties focusing on the west-European area over Benelux and Northern France (BNF) and provide historical period, mid-term and long-term future scenarios. The analysis evaluates the simulations from ALADIN hindcast from 2010 to 2020 allowing the comparison of observations at several sites within the BNF region. Their methodology starts with the assessment of clear-sky frequency based on the methodology of Long and Ackerman (2000) method considering the annual cycle variation at three different sites. Then they focus on the analysis of surface solar radiation by later delving into the annual variation of aerosol properties. Then the paper puts the spatial variability into context for the period 2010-2020. Finally, the manuscripts report the future evolution of two scenarios by presenting mean statistics and illustrating the spatial differences.
The manuscript has a comprehensible structure, makes use of valid methods and provides mostly well-documented and clear explanations of their assumptions and limitations. I consider the manuscript to be suited for publication after the following revisions are addressed.
Major comments
Identification of clear-sky situations
The overall analysis is clearly described, but not so much is discussed on other implemented methods that could have been used at the mentioned sites. It is not expected to reformulate the methodology but to better justify the selection of the methodology of Long and Ackerman (2020). More discussion is needed. The authors can refer to the following references for example, or any other the authors might see suitable:
M.J. Reno, C.W. Hansen, Identification of periods of clear sky irradiance in time series of GHI measurements, Renew. Energy 90 (2016) 520–531, https://doi.org/ 10.1016/j.renene.2015.12.031.
Elias, T., Ferlay, N., Chesnoiu, G., Chiapello, I., and Moulana, M.: Regional validation of the solar irradiance tool SolaRes in clear-sky conditions, with a focus on the aerosol module, Atmos. Meas. Tech., 17, 4041–4063, https://doi.org/10.5194/amt-17-4041-2024, 2024.
Al Asmar, L.; Musson Genon, L.; Eric, D.; Dupont, J.C.; Sartelet, K. Improvement of solar irradiance modelling during cloudy sky days using measurements. Sol. Energy 2021, 230, 1175–1188.
Reduction of ammonia
In the paper it was mentioned a reduction of 25 % applied to all monthly ammonia emissions. Despite this reduction, nitrate aerosol concentration remain overestimated by the model. Is this due to a parameterization or an assumption within the model? Can the authors comment how future work should consider similar/higher reduction? Was a sensitivity analysis made varying the concentration of ammonia? Or should this be recommended?
Spatial variability
It is understandable the need to deepen the analysis for Spring and Summer seasons for the spatial variability analysis. However, including the analysis illustrated in Figure 7 for Autumn and Winter will enrich the overall analysis and further interpretation with Figure 3 and Figure 4a.
Technical/Minor comments:
Follow ACP guidelines to refer figures in the text. For instance, change (Figure 1) to (Fig. 1).
Correct units. They should be written exponentially in the text, tables and figures.
Homogenize how to address chemical species. For example, nitrate (NO3) is defined more than one time.
|| = line
|| 1 … change spatio-temporal to spatiotemporal as it was done later in the text
|| 34 ... increases in photovoltaic
|| 40 correct sentence… assessment of aerosols’ future evolutions in time or assessment of the future evolution of aerosols in time
|| 77 ... climate model and the two kinds of
|| 88 … Close parenthesis SURFEX (SURFace EXternalisée, Masson et al. (2013))
|| 148 … A first dataset → The first dataset
|| 168 remove double parenthesis after van Marle et al. (2017)
|| 170 … scenarios are
|| 171 … greenhouse gases emissions → greenhouse gas emissions
|| 187 BNF region cf Figure 1 → BNF region Fig. 1
|| 210 Could you add a reference in line 210? or specify that to the best of your knowledge you decide to go for those uncertainties.
|| 224 include space ... 550nm → 550 nm
|| 269 CLT already defined in line 247
|| 388 The comparisons shown in Figure 6b
|| 388:390 Improve clarity. The sentence is too long. ‘The comparisons shown in Figure 6b highlight that the underestimation of organic and black carbon aerosols is partially offset in spring and summer by a coincident overestimation of nitrate aerosol concentrations, especially in March and April (around +2 µg/m3), despite the application of a 25% correction factor on ammonia emissions, the main precursor of nitrate aerosols.’
|| 410:411 Correct description. Panel (d) is AOD and panel (c) is CLT
|| 467 Is it BC or equivalent BC?. Keep it consistent along the entire manuscript.
|| 534 change spatio-temporal to spatiotemporal
|| 545 AOD already defined in line 219
Although it might be obvious, clarify which months the authors consider for their Spring and Summer comparison.
Comments on Figures
Figure 3, Figure S1, Figure S2 and Figure S3
While the lines can be differentiated, the description says green line, but to me it looks blue. Could you change the color of ‘Estimate from ALADIN simulations’?
Figure 8, 9, 10, 11 Long term evolution for SSP3-7.0 Is it possible to include a separated colorbar for the lowermost right-side panel? In case it messes up the structure of the figure, perhaps include an adequate colorbar for these parameters in the appendix?
Citation: https://doi.org/10.5194/egusphere-2024-1174-RC2 - AC2: 'Reply on RC2', Gabriel Chesnoiu, 06 Sep 2024
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