the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Oct 2022
The impact of diffuse light on gross ecosystem productivity over a winter wheat (Triticum aestivum L.) is related to the increase of incident light interception in the middle and lower canopy
Abstract. Diffuse light has potential to increase ecosystem gross primary productivity without the confounding effect of other environmental factors. However, the magnitude of the importance of diffuse light for ecosystem carbon uptake and the mechanism behind the diffuse light-related photosynthetic enhancement is unclear. Here, 2 years of gross ecosystem primary productivity (GEP), assessed by eddy covariance technology over a (winter) wheat cropland, was used to determine whether diffuse photosynthetic active radiance (PARdif) affected wheat GEP. Additionally, the method of Artificial Neural Network combined with interference analysis and modelling were used to quantify the relative importance of diffuse light for GEP variations and to explore the underlying mechanism of diffuse light effect on GEP. Wheat GEP increased significantly with increase in PARdif in the absence of effect of total PAR. PARdif was found to be the most important factor for wheat GEP, making a contribution of 41.3 % in 2011 and 35.7 % in 2012 to GEP variations, which were greater than the contribution of total PAR, air temperature, vapor pressure deficit and friction velocity. The results of combination of model and measured data indicated that as PARdif increasing, the within canopy, especially the middle and lower canopy, intercepted more light, leading to photosynthetic increase in entire canopy. Over all, our study provided a new evidence for the importance of diffuse light for carbon uptake in agroecosystem, which is importance for predicting the response of ecosystem carbon budget to future light climate changes.
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RC1: 'Comment on egusphere-2022-1003', Anonymous Referee #1, 24 Nov 2022
Ultimately this paper may have fundamental flaws in its design. Two major flaws exist in particular. Firstly that this paper makes extreme claims about the role of diffuse light in boosting plant productivity while ignoring the effect of a reduction in total PAR, and secondly that it neglects to measure diffuse PAR at all - the exact variable that is the focus of the analysis. While the modeling work done to circumvent this lack of data is admirable and is backed up with seemingly high-quality measurements of GEP using eddy covariance, I am skeptical of results that are principally obtained from a model driven by inferred data. This skepticism is especially acute when confounding effects such as a changing light-response curve and the real-world relationship between total and diffuse PAR are not well-discussed, and the results themselves disagree with other similar papers by an order of magnitude.1. Firstly and most importantly, this manuscript evaluates the influence of diffuse light within a plant canopy, but contains no direct measurement of diffuse light. Could no applicable measurements be either made or found to validate the data the authors use to drive the CANVEG model? Without real data directly describing PARdiff, I am unconvinced of any of the final claims made in this manuscript, especially given the substantial anthropogenic aerosol influence present in Northern and Eastern China. The Reindl model employed to estimate the fraction of diffuse light in this study was developed in Europe and North America ~30 years prior, and does not account for any anthropogenic aerosol effects in its input variables. The scattering and reflective properties of these aerosols are unknown for the study period in this paper, and may represent significant uncertainty which is not accounted for anywhere I can see in this author's analysis.2. The authors of this study elected to remove the effect of total light when analyzing the influence of PARdiff on canopy GEP. Other literature sees a very real negative correlation between the fraction of PARdiff and magnitude of total PAR, which is acknowledged in this study. This effect more often than not results in an overall reduction of GEP under diffuse conditions, since total PAR is reduced beyond the capacity of PARdiff to compensate through increased light use efficiency within the canopy. Under highly diffuse conditions when total PAR is significantly reduced, C3 plants such as wheat exhibit a steeply-sloped light response curve as opposed to the light-saturation they experience under direct sunlight. It seems likely that this large rate of change with additional PAR (in absence of the influence of total PAR) is driving the large increase in GEP noted in this study, but is not mentioned anywhere except for the methods section, side-by-side with the explanation of EC gap-filling. Even then it is addressed only in passing, when it deserves serious examination in the discussion section with more complete citations.3. The decision to remove total PAR from the analysis entirely is also questionable. If this paper is intended only to inform model development, then this may be a relevant experimental condition. In nature however, PARdiff and PARtotal are inextricably linked. The results of this paper claim a 53% increase in total-canopy GEP when PARdiff varied from (0-200) to (>1,000) umol/m2/s. It's unlikely that such extreme clear or diffuse conditions occurred during their observation period, since the Clearness Index range varied only from ~0.45-0.60. Such a clearness index indicates consistently high aerosol/cloudy conditions on most days, which seems likely since the study site is located nearby the Beijing metropolis. Additionally, in figure 7 the 200-400umol data point indicates much higher GEP than the 400-600umol data point in mid-canopy GEP. How might this be explained? Might it represent a weakness within the modeled results as a whole?4. Finally, the 53% increase in GEP under diffuse conditions reported in this manuscript is extraordinary, and (in the words of Carl Sagan) requires extraordinary evidence to be believed. The evidence presented in this paper consists of inferred PARdiff data which drives a model that describes conditions within but also outside the range of values which were directly observed at the site of interest, mainly conditions which produce fully diffuse and fully direct PAR. In comparison, similar studies have found increases in GEP on the order of 1.2%-4.2% under extremely diffuse conditions which were directly observed. How useful is this modeled 53% increase when it remains unvalidated and represents an order of magnitude discrepancy from prior studies examining the effect of PARdiff in other plant canopies? Might it be misleading for those reading the paper who could drastically overestimate the role of diffuse light in driving increased GEP? If improving modeled effects of PARdiff is this paper's goal, it should be very explicit about this and carry strong words of caution that this ~50% increase is not a real-world increase in plant productivity that is ever likely to occur in the field.Citation: https://doi.org/
10.5194/egusphere-2022-1003-RC1 -
AC1: 'Reply on RC1', Xueyan Bao, 24 Dec 2022
Dear editor,
We would like to thank all of you and the reviewers for the valuable suggestions. Here are the point-to-point responses.
Response to Reviewer#1:
Comment 1. Firstly and most importantly, this manuscript evaluates the influence of diffuse light within a plant canopy, but contains no direct measurement of diffuse light. Could no applicable measurements be either made or found to validate the data the authors use to drive the CANVEG model? Without real data directly describing PARdiff, I am unconvinced of any of the final claims made in this manuscript, especially given the substantial anthropogenic aerosol influence present in Northern and Eastern China. The Reindl model employed to estimate the fraction of diffuse light in this study was developed in Europe and North America ~30 years prior, and does not account for any anthropogenic aerosol effects in its input variables. The scattering and reflective properties of these aerosols are unknown for the study period in this paper, and may represent significant uncertainty which is not accounted for anywhere I can see in this author's analysis.
Response: Thank you very much. Because the Luancheng experimental station lacked the direct measurments of diffuse light during the study years, perhaps modeling is the only way to obtain diffuse light values. Many published studies that focused on the effects of diffuse light on ecosystem processes lacked observational data, possibly because the diffuse light effect on ecosystems was just beginning to be studied at those sites. For example, Zhang et al., (2010) estimated PAPdiff using models that mainly based on diffuse light fraction in two forest ecosystems in China. Kanniah et al., (2013) also calculated diffuse light through simulation at a tropical savanna site in Australia. Observational data is likely to be more reliable than simulations. However, similar to modeling results, the observational data may also introduce uncertainties to a certain extent, which may be related to the monitoring instrument itself, such as design limitations and response insensitivity, and the influence of weather conditions. Nevertheless, direct measurement of diffuse light is necessary for accurately analyze diffuse light influence on ecosystem carbon exchange in this site, because it can provide a direct supporting data to relevant research and can verify diffuse light models. Our group have planned to install radiation monitoring equipment that includes diffuse component in this site next year.
Aerosols have been shown to reduce total solar radiation at the surface, but can also increase the diffuse radiation fraction. Source of aerosols that may modify the radiation and its components mainly include the emissions from volcanic eruptions, biomass burning and emission of hydrocarbons (Oliveria et al., 2007). In China, the aerosol optical depth (AOD), which is often obtained by remote sensing technology and used to describe the attenuation of light by aerosols, was estimated to be higher in the southeast than the northwest, to be highest in Beijing and Tianjin Province in the southeast. Because the experiment site in our study is near Beijing city, the effect of aerosol on radiation and its component and thus on ecosystem productivity should not be ignored. However, we would like to explain that our study did not analysis the diffuse light by aerosols mainly for the following reasons. Firstly, both the total radiation used in the estimation of the clearness index (CI) and the diffuse radiation was the overall value of the direct solar radiation and the diffuse radiation after the sunlight passes through clouds and aerosols. That is to say, the observed values of total solar radiation and the simulated values of diffuse radiation in this study have included the direct and diffuse radiation after sunlight passes through aerosols. Secondly, in fact, it is difficult to distinguish the diffuse radiation produced by clouds and those produced by aerosols. On the one hand, detecting the diffuse radiation by aerosols may use remote sensing observation data, which are not available at present. On the other hand, aerosols would affect the physical characteristics of clouds, their reflection characteristics, and also the evaporation, the amount and timing of precipitation. Changes in precipitation patterns in turn affect the amount of cloud cover. These processes suggest that aerosols are partly responsible for the direct and diffuse light by cloud cover. Therefore, the direct and diffuse radiation by clouds and aerosols are coexisting and closely related, and it may be hard to tease out their respective contribution to total radiation and total diffuse radiation that reach on the surface.
References:
- Kanniah, K.D., Beringer, J., North, P. and Hutley, L., 2012. Control of atmospheric particles on diffuse radiation and terrestrial plant productivity: A review. Progress in Physical Geography, 36(2): 209-237.
- Oliveira, P.H.F. et al., 2007. The effects of biomass burning aerosols and clouds on the CO2 flux in Amazonia. Tellus B: Chemical and Physical Meteorology, 59(3): 338-349.
- Zhang, M. et al., 2011. Effects of cloudiness change on net ecosystem exchange, light use efficiency, and water use efficiency in typical ecosystems of China. Agricultural and Forest Meteorology, 151(7): 803-816.
Comment 2. The authors of this study elected to remove the effect of total light when analyzing the influence of PARdiff on canopy GEP. Other literature sees a very real negative correlation between the fraction of PARdiff and magnitude of total PAR, which is acknowledged in this study. This effect more often than not results in an overall reduction of GEP under diffuse conditions, since total PAR is reduced beyond the capacity of PARdiff to compensate through increased light use efficiency within the canopy. Under highly diffuse conditions when total PAR is significantly reduced, C3 plants such as wheat exhibit a steeply-sloped light response curve as opposed to the light-saturation they experience under direct sunlight. It seems likely that this large rate of change with additional PAR (in absence of the influence of total PAR) is driving the large increase in GEP noted in this study, but is not mentioned anywhere except for the methods section, side-by-side with the explanation of EC gap-filling. Even then it is addressed only in passing, when it deserves serious examination in the discussion section with more complete citations.
Response: Thanks a lots. In revised manuscript, we have considered the effect of total light, and the response of wheat GEP to diffuse PAR was shown in Figure. 2. The increase in GEP during the process of fDIF (diffuse light fraction) increase from its minimal to intermediate levels was the result of diffuse PAR increase, because total PAR decreased in this process. In the process of fDIF decreasing from its maxima to intermediate levels, GEP also increased with increase of diffuse PAR. However, because total light and other factor such as Ta and VPD covaried with diffuse PAR, it is unclear whether increase of GEP was attributed to diffuse PAR or other factors in this process. A normalized method was used to solve the issue. The results indicated that the increase of diffuse PAR, together with increase of total PAR, Ta and VPD was the reason for GEP increase in the process of fDIF decreased from maxima to intermediate levels. This indicates that total light played a certain role in GEP increase in this process.
Comment 3 The decision to remove total PAR from the analysis entirely is also questionable. If this paper is intended only to inform model development, then this may be a relevant experimental condition. In nature however, PARdiff and PARtotal are inextricably linked. The results of this paper claim a 53% increase in total-canopy GEP when PARdiff varied from (0-200) to (>1,000) umol/m2/s. It's unlikely that such extreme clear or diffuse conditions occurred during their observation period, since the Clearness Index range varied only from ~0.45-0.60. Such a clearness index indicates consistently high aerosol/cloudy conditions on most days, which seems likely since the study site is located nearby the Beijing metropolis. Additionally, in figure 7 the 200-400umol data point indicates much higher GEP than the 400-600umol data point in mid-canopy GEP. How might this be explained? Might it represent a weakness within the modeled results as a whole?
Response: Thank you very much. In revised paper, we have abandon removing the effect of total light (Fig.3). GEP also increased with increasing of diffuse PAR when considering the effect of total light, temperature and VPD. The whole diffuse PAR can be divided two processes (1,2) according to fDIF. We found that the increase in GEP in process 1 when fDIF increase from its minima to intermediate levels was attributed to increase in diffuse PAR, while increase in GEP in process 2 when fIDF decreasing from its maxima to its intermediate levels was attribute to combined effect of diffuse light, total light, temperature and VPD using a normalized method. Therefore, wheat GEP increased along with diffuse PAR throughout the whole process of diffuse PAR increasing during the study period. This results indicates that the wheat cultivar planted in this site was more sensitive to diffuse light than to total light.
Our study also indicated an increase of 53% in GEP when diffuse light varied from (0-200) to (>1,000) umol/m2/s. Fig. 4 indicates that lower diffuse light levels (<200) often occurred when sky condition was much clear (low fDIF) and heavy cloudy (high fDIF). Because there was almost no heavy cloud during study period, the diffuse light range (0-200) in Fig.9 corresponded to clear day conditions in most cases. Meanwhile, the range of (>1000) diffuse light corresponds to intermediate cloud conditions, which often occurred in the study site.
In Fig.9 (revised manuscript), Pn (photosynthesis rate) under diffuse light of (400-600 μmol m-2 s-1) is lower than (200-400 μmol m-2 s-1) in middle canopy, this may be due to the estimation uncertainties, which may result from limitation of the models themselves, measurement error of input variables and the model parameters. The values presented in the figure were the averages per diffuse light unit of 200 μmol m-2 s-1. If the unit is more narrow, such as 100 or 50 μmol m-2 s-1, there could be more of the same; while when the unit is extended, like 400 μmol m-2 s-1, we found that Pn was lowest at (0-400), intermediate at (400-800), and highest at (>800), indicating that the entire trends of Pn is increased along with diffuse light in middle layer, which basically provide evidence to the initial hypothesis of this study.
Comment 4 Finally, the 53% increase in GEP under diffuse conditions reported in this manuscript is extraordinary, and (in the words of Carl Sagan) requires extraordinary evidence to be believed. The evidence presented in this paper consists of inferred PARdiff data which drives a model that describes conditions within but also outside the range of values which were directly observed at the site of interest, mainly conditions which produce fully diffuse and fully direct PAR. In comparison, similar studies have found increases in GEP on the order of 1.2%-4.2% under extremely diffuse conditions which were directly observed. How useful is this modeled 53% increase when it remains unvalidated and represents an order of magnitude discrepancy from prior studies examining the effect of PARdiff in other plant canopies? Might it be misleading for those reading the paper who could drastically overestimate the role of diffuse light in driving increased GEP? If improving modeled effects of PARdiff is this paper's goal, it should be very explicit about this and carry strong words of caution that this ~50% increase is not a real-world increase in plant productivity that is ever likely to occur in the field.
Response: Thank you for the comments. We would like to explain that why there was a much difference in GEP between this study and the other studies. Here, we define the enhancement of GEP due to diffuse light increase as diffuse light effect (DFE), which has been quantified as nearly 50% in this study. The DFE has been showed mainly depends on types of ecosystems according to existing studies. Because different ecosystems, such as grassland, cropland, forests, are commonly different in leaf area index (LAI), leaf orientation, the response extent of GEP to diffuse light vary among ecosystems. Take LAI as an example, terrestrial vegetation with high LAI tend to be more sensitive to increases in diffuse light. This is because that as the plant canopy becomes denser with leaves, i.e., high LAI, canopy transmission to lower canopy layers’ decreases, leading to increased radiation limitation in the middle and lower canopy; thus, the canopy photosynthesis would be more sensitive to diffuse light and enhance more compared to canopy with low LAI (Knohl and Baldacchi, 2008). Wolfahrt et al, (2008) also showed that only grass systems with LAI of more than 4 m2 m-2 showed significant increases in NEE (35%) compared to grasses with intermediate (2-4) to low LAI (<2). Additionally, the effect of diffuse light on GEP also depends on leaf inclination angle. One study using modeling method showed approximately 20% increase in DFE when leaf inclination angle increased from 40° to 70° (Knohl and Baldacchi, 2008).
The second reason for the difference in GEP may relate to the different levels of diffuse light conditions that were considered. In the comments, the reviewer mentioned that previous study found only 1%-4% increase in GEP under extremely cloudy conditions compared to clear conditions. However, it should be noting that extremely cloudy conditions mean that diffuse light fraction (fDIF) is very high. Under this condition, total radiation decreases seriously, its diffuse component also decreases, thus, plant photosynthesis would decrease. This means that canopy GEP would not increase significantly under extremely cloudy condition compare to clear condition. In our revised manuscript, the changed Figure 1 showed that GEP did not enhanced anymore and is even lower under extremely diffuse condition compare to clear sky conditions. However, GEP substantially increased under intermediate fDIF (largest diffuse PAR) compared to clear sky condition (Fig. 2). The enhancement was estimated nearly 49%, which was a little lower than the modeling value of 53%. Overall, previous study reported the enhancement of GEP under extremely cloud condition (low fDIF) compared to clear sky condition (low fDIF), whereas our study conclude enhancement of GEP under moderate fDIF condition compared to low fDIF condition.
It is also worth noting that the increase of 53% in GEP because of diffuse light is a result of simulation. The main objects of this study are to interpret the mechanism of enhancement of GEP and examine whether GEP increase relates to more light intercepted by the canopy, rather than to quantify how much GEP increased with diffuse PAR increasing. Although the model result has been verified by NEE and ET values obtained by EC technique (Fig. 1), the results may be somewhat uncertain, which may be associated with limitation of the models themselves, measurement error of input variables and the model parameters.
Citation: https://doi.org/10.5194/egusphere-2022-1003-AC1
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AC1: 'Reply on RC1', Xueyan Bao, 24 Dec 2022
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RC2: 'Comment on egusphere-2022-1003', Anonymous Referee #2, 21 Jan 2023
Bao and Sun quantify the importance of diffuse radiation to canopy carbon uptake (GEP) in a wheat canopy. They emphasize the importance of diffuse radiation, which is well-known to be an underappreciated factor in canopy photosynthesis, but their conclusions overestimate its contribution. The hypothesis cannot be tested using the observations and the radiation partitioning model should be improved. As written without additional testing, there is no reason to believe that the major findings are not artefacts. The manuscript is not publishable in its present form but with additional testing and validation may make an interesting contribution to the literature.
23: is this the variability in GEP or the magnitude of GEP. Diffuse radiation can’t have this strong of an effect, physiologically, on the magnitude of GEP under conditions where wheat is best grown with warm temperatures and moderate amounts of precipitation. There just isn’t enough diffuse radiation to make it the primary factor impacting the magnitude of GEP but I can see a scenario where it is a dominant contributor to the variability of GEP.
Every sentence of the manuscript could use structural improvement. To demonstrate but one: ‘Terrestrial carbon assimilation rates on a leaf level response to sunlight nonlinearly…’ should be ‘Leaf-level carbon assimilation responds nonlinearly to sunlight’ or similar. Using an automated spelling and usage checker would correct most errors.
I completely disagree with the statement on line 44 which needs to be removed. People have known for a long time how variable radiation can be as demonstrated by the references in the next sentence (note also https://www.science.org/doi/abs/10.1126/science.1103215)
51: ‘of light climate change’ should be ‘in light of climate change’. I emphasize again that almost every sentence needs improvement before resubmission. Please don’t only change the sentences that I am highlighting.
56: this isn’t wrong but the text could be modified somewhat because even under clear sky conditions diffuse light is on the order of 20% (depending on the atmosphere) lest the rest of the hemisphere of the sky not be illuminated; we’d only see the sun. The conceptual description could be adjusted to note that this is a matter of degree. Lower leaves are irradiated or we wouldn’t be able to see them; they just receive more light under certain diffuse conditions, especially if the factors causing diffuse light don’t result in a decrease in total light.
62: this is not a theory. Just write “One can expect…”. The authors need to make clear that it’s really only under conditions where the increase in the fraction of diffuse light is not because total light decreases. As long as total light is near or above the light compensation point for photosynthesis does an increase in diffuse light really have an effect; perhaps the content on line 70 could be moved up.
On line 68: evidence isn’t scarce; it’s pretty well established but science. The statement on line 79 is good.
Line 99: this can’t be tested with the measurements available as there are no measurements of sub-canopy diffuse light
119: water vapor is more commonly abbreviated q because rho is usually used for the density of air, also important for the flux calculation.
123: what is the make and model of the micrometeorological instruments used?
How well does the Reindl et al. model for diffuse radiation apply to observations of diffuse light if they were available? I’m assuming that atmospheric aerosols at the site are impacted by proximity to Shijiazhuang. Atmospheric composition is critical to the success of radiation partitioning models as noted by Oliphant and Stoy (2018, https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017JG004370) who also note that the Reindl et al. model has been since improved and simplified by Gu et al. (1999) (and who further proceed to further improve the model). All of these models will be challenged by anthropogenic, maritime, and volcanic aerosols and a more physical model of atmospheric radiation transmission may be preferred at this site, but this is hard to quantify without observations.
171: the residuals of the model fit using equation 1 will still be a function of PAR because the relationship in the rectangular hyperbola is unlikely to capture the shape of the nonlinear light response which may be better simulated by a nonrectangular hyperbola or a Mitscherlich model as discussed for example in Reichstein et al. https://link.springer.com/chapter/10.1007/978-94-007-2351-1_9
Regarding section 2.5 does the multilayer model improve fit versus a single layer model? I’m starting to realize why the authors obtained spurious results. If the model in equation 1 doesn’t fit particularly well, especially around the light compensation point, GEP will be inaccurately partitioned from NEP and the neural network model will assume that diffuse light – which should be most important around the light compensation point – will help it fit better. In other words the major findings are likely an artefact that need to be tested with additional analyses.
233: these parameters need to be described in more detail because they are critical for the results, especially the clumping factor and the leaf angle distribution
262: Having measured wheat eddy covariance I question the usefulness of a 10-14 local time focus. Especially under high VPD wheat will tend to have far lower GEP in the afternoon, depending on the prevailing climatic conditions which need more description.
296: fluxes themselves are unlikely to be impacted by ustar for physiological reasons except under extremely stagnant air conditions that inhibit evapotranspiration, and which are unlikely to be observed during the 10-14 local time study period. This finding is likely an artefact that suggests that the authors need to do more data filtering.
Figure 3 is taking a model of GEP that is a function of PAR – and that probably doesn’t fit particularly well nor was there any evidence that it was fit independently during different phases of canopy development as it should be if leaf area is increasing – and using it to infer diffuse PAR impacts when diffuse PAR was partitioned using a model that probably didn’t work very well from total PAR. The reasoning is circular, hence my suggestion that the results are an artifact.
334: this argument does not take into account any differences in photosynthetic efficiency in the lower canopy which is likely to be important as plants tend to allocate nitrogen (and more) toward upper leaves, making them more efficient.
Figure 6 needs a legend in the figure.
Figure 8 is interesting but I have no idea how diffuse PAR can equal 1250 micromoles / m2 / second. It would hurt the eyes to look at the sky away from the sun.
Section 4.2: these are entirely model results with no empirical evidence.
482: ‘eliminate’ is too strong a word here but this is an interesting point
Citation: https://doi.org/10.5194/egusphere-2022-1003-RC2 -
AC2: 'Reply on RC2', Xueyan Bao, 08 Feb 2023
Dear editor,
We would like to thank all of you and the reviewers for the valuable suggestions. Here are the point-to-point responses.
Response to Reviewer 2#
Comment 1. Bao and Sun quantify the importance of diffuse radiation to canopy carbon uptake (GEP) in a wheat canopy. They emphasize the importance of diffuse radiation, which is well-known to be an underappreciated factor in canopy photosynthesis, but their conclusions overestimate its contribution. The hypothesis cannot be tested using the observations and the radiation partitioning model should be improved. As written without additional testing, there is no reason to believe that the major findings are not artefacts. The manuscript is not publishable in its present form but with additional testing and validation may make an interesting contribution to the literature.
Response: Thank you very much. The solar radiation reaching the earth surface is the primary driver of plant photosynthesis. More and more studies indicated that diffuse light changes have important influence on the terrestrial carbon sink (Mercado et al., 2009). Our study also highlighted the positive effect of diffuse light on canopy photosynthesis and that diffuse light is the most importance factor for GEP by quantifying its contribution to GEP variations. But it is worth noting that to eliminate the confounding effect of leaf area index, canopy height and solar elevation angle to maximal extent, we constrained the study period, that is to say, the diffuse light is relatively importance under certain conditions. More importantly, whether can diffuse light is the most importance factor may depend on canopy structure. Some ecosystems are not sensitive to diffuse light change; their photosynthesis are commonly depressed under diffuse light condition compared to under clear sky conditions. This is because when diffuse light increase, total light decrease, and the ecosystem tend to be more sensitive to total light changes. In this case, total light, rather than diffuse light, may become the most important factor for photosynthesis. But for other ecosystems, especially for the ecosystems with higher leaf area, they may be more sensitive to diffuse light because canopy transmission to lower canopy layers’ decreases, leading to increased radiation limitation in the middle and lower canopy. The result diffuse light is more important to GEP than other factors in our study reflects the diversity of terrestrial ecosystems and their varied responses characteristics to light changes.
The multiple canopy layer model not only simulates carbon, sensible heat flux and microclimate within the canopy, but also simulates these variables above the canopy. Thus, the model can be verified by the measurement of fluxes and meteorological factors. Although the measurements within the canopy were not available, but the measurements above the canopy could be obtained because they had been measured by eddy covariance. So we used the measured uninterpolated NEE, heat flux and light to test the model. If there is a highly correlated relationship between observed and simulated values, then the model is useful and valid and the simulation results (including carbon flux and PAR within canopy) of the model are credible (Fig.1). However, we should also be aware that although the model was verified, future measurements for photosynthesis and microclimate in different layer within canopy is essential because it can further test our result and make them more solid. We also added this outlook in the Section 4.2.
Comment 2. 23: is this the variability in GEP or the magnitude of GEP. Diffuse radiation can’t have this strong of an effect, physiologically, on the magnitude of GEP under conditions where wheat is best grown with warm temperatures and moderate amounts of precipitation. There just isn’t enough diffuse radiation to make it the primary factor impacting the magnitude of GEP but I can see a scenario where it is a dominant contributor to the variability of GEP.
Response: Thank you very much. It refers to variability in GEP in the abstract. For a long time, total radiance or PAR is deemed as a major controller for ecosystem photosynthesis. However, cloudy or aerosols are terrestrial plants most experience, which produce considerable diffuse light. A mount of studies highlighted the importance of diffuse light for ecosystem processes, such as light use efficiency, photosynthesis, water use efficiency, or even ecosystem respiration. Because of the diversity of ecosystems, the effect of diffuse light on ecosystem processes may varied among ecosystem types. As we know, the total light would decrease when there is more diffuse light. When diffuse light fraction increasing, GPP of some ecosystems enhanced but other ecosystem may be inhibited, reflecting different sensitivity to diffuse light among different ecosystems. The canopy structure, such as leaf area index, leaf directions and leaf angles as well as photosynthetic pathways among ecosystems could be responsible for the different sensitivity. Take LAI as an example, terrestrial vegetation with high LAI tend to be more sensitive to increases in diffuse light. This is because that as the plant canopy becomes denser with leaves, i.e., high LAI, canopy transmission to lower canopy layers’ decreases, leading to increased radiation limitation in the middle and lower canopy; thus, the canopy photosynthesis would be more sensitive to diffuse light and enhance more compared to canopy with low LAI (Knohl and Baldacchi, 2008). Wolfahrt et al, (2008) also showed that only grass systems with LAI of more than 4 m2 m-2 showed significant increases in NEE (35%) compared to grasses with intermediate (2-4) to low LAI (<2). Additionally, the effect of diffuse light on GEP also depends on leaf inclination angle. One study using modeling method showed approximately 20% increase in diffuse light effect when leaf inclination angle increased from 40° to 70° (Knohl and Baldacchi, 2008). For the ecosystems sensitive for diffuse light change, diffuse light has potential to be the dominant factor affecting canopy photosynthesis.
Comment 3. Every sentence of the manuscript could use structural improvement. To demonstrate but one: ‘Terrestrial carbon assimilation rates on a leaf level response to sunlight nonlinearly…’ should be ‘Leaf-level carbon assimilation responds nonlinearly to sunlight’ or similar. Using an automated spelling and usage checker would correct most errors.
Response: Thanks a lots. We would like to explain that this journal requires the author to pay the article processing charges (APG) if the revised manuscript is accepted and published. Language copy-editing for revised paper is one of the contents that APG includes. The goals of Language copy-editing are to ensure the work adheres to scientific writing standards, grammatical accuracy, and overall readability. The latter encompasses aspects such as rephrasing awkward expressions and sentence structures or adjusting incorrect collocations. Therefore, we have not polished the old manuscript yet.
Comment 4. I completely disagree with the statement on line 44 which needs to be removed. People have known for a long time how variable radiation can be as demonstrated by the references in the next sentence (note also https://www.science.org/doi/abs/10.1126/science.1103215)
Response: Thank you for your suggestion. We have deleted the statements.
Comment 5. 51: ‘of light climate change’ should be ‘in light of climate change’. I emphasize again that almost every sentence needs improvement before resubmission. Please don’t only change the sentences that I am highlighting.
Response: Thank you very much. The manuscript would be experienced English language copy-editing by the journal office
Comment 6. 56: this isn’t wrong but the text could be modified somewhat because even under clear sky conditions diffuse light is on the order of 20% (depending on the atmosphere) lest the rest of the hemisphere of the sky not be illuminated; we’d only see the sun. The conceptual description could be adjusted to note that this is a matter of degree. Lower leaves are irradiated or we wouldn’t be able to see them; they just receive more light under certain diffuse conditions, especially if the factors causing diffuse light don’t result in a decrease in total light.
Response: Thank you for this suggestion. The statements have been revised as “Under direct light conditions, plant leaves receive light from a single direction, causing that the leaves of lower canopy are shaded and receive less light because of light interception of upper leaves. In comparison, canopy is illuminated from multi-directions under diffuse light conditions, and leaves that were previously shaded now receive more light, leading to an increased light interception by the whole canopy (Williams et al., 2014). Finally, canopy photosynthesis would be enhanced under diffuse light conditions.”
Comment 7. 62: this is not a theory. Just write “One can expect…”. The authors need to make clear that it’s really only under conditions where the increase in the fraction of diffuse light is not because total light decreases. As long as total light is near or above the light compensation point for photosynthesis does an increase in diffuse light really have an effect; perhaps the content on line 70 could be moved up.
Response: Thank you very much. We have revised the sentence as “Thus, one can expect that canopy has greater photosynthetic capacity because of more light intercepted by the canopy under diffuse light condition compared with direct light condition when total light is constant.”
Comment 8. On line 68: evidence isn’t scarce; it’s pretty well established but science. The statement on line 79 is good.
Response: Thank you for your comments. The other reviewer (Reviewer 1#) is skeptical to our decision to remove total PAR from the analysis, owing to diffuse PAR and total PAR is closely linked and covary in nature. Therefore, we deleted all the contents and statements about removing the confounding effect of total PAR, including the Equation 1 and Fig.3. We then rewritten the Introduction Section. However, we still highlighted the effect of total light on ecosystem photosynthesis when diffuse light fraction changes. We believed that it is the coexistence of the two variables that result in contradict conclusions about how GEP response to diffuse light, which reflecting the different sensitivity of ecosystem carbon exchange to diffuse light among different ecosystem types. Actually, we proposed the necessity to conduct the research on the basis of these statements, i.e., the effect of diffuse light on GEP is still uncertain to date, which need further exploration.
We established new figure (Fig. 3 in revised paper) that reflects the relationship between GEP and diffuse PAR without removing effect of total PAR. This figure shows an increase trends in diffuse PAR. The change in diffuse PAR contains two change phases (denoted by different legends), which corresponds to different fDIF change processes. After analysis, we drew a conclusion that the increase in GEP in process 1 was caused by diffuse light only, whereas the increase in GEP in process 2 was the results of combined effect of diffuse light, total light, temperature and VPD. Finally, we believe that GEP increased along with diffuse PAR, even after considering the influence of other factors that covary with diffuse PAR. (The analysis process is in the revised text)
Comment 9. Line 99: this can’t be tested with the measurements available as there are no measurements of sub-canopy diffuse light.
Response: Thank you very much. In this study, we concluded that diffuse light promotes wheat GEP significantly, and inferred that this positive effect is because the canopy intercept more light under increasing diffuse light conditions. To test this hypothesis, the relevant measurements such as intra-canopy light distribution and leaf photosynthesis in different canopy layers are necessary. However, the study site did not conduct any measurement in the study years, i.e.,2011 and 2012, largely because the diffuse light effect on ecosystem carbon exchange process has not been focused at that time. Because of the lack of measurements, we had to use models (the multilayer canopy model) to simulate the contents we inferred. The validation for the model was introduced in the end of Section 2.6 and the results were presented in Figure.1. In detail, the model not only simulates carbon and sensible heat flux and microclimate within canopy, but also simulates these variables above canopy. The measurements for these variables within the canopy were not available, but the measurements above the canopy could be obtained because they had been measured by eddy covariance. So we believe that if the model simulates the variables above the canopy well (Figure 1), the model is useful and valid and the other simulation results (carbon flux and PAR within canopy) of the model were also credible. However, a more direct, efficiency and credible method to test our hypothesis about the diffuse light effect mechanism is to measure the incident light and GEP in sub-canopy layers under different diffuse light levels, which is important for accurately predicting the canopy carbon flux and should be conducted in future studies.
Comment 10.119: water vapor is more commonly abbreviated q because rho is usually used for the density of air, also important for the flux calculation.
Response: Thank you for this suggestion. We have changed ρas q.
Comment 11. 123: what is the make and model of the micrometeorological instruments used?
Response: Thanks a lot. The detail information of the micrometeorological instruments in this study has been reported by our published research. Because BG journal may limit article words, we mentioned them through citing reference. In detail, the micrometeorological measurement system at Luancheng site comprised a net radiometer (Model CNR-1, Kipp and Zonen, The Netherlands), quantum sensor (LI190SB, Li-Cor Inc.), temperature/humidity probe (Model HMP45C, Vaisala Inc., Helsinki, Finland), and an anemometer (Model AR-100, Vector Instruments) for measuring the net radiation, photosynthetic photon flux density, air temperature and relative humidity, and wind speed and direction, respectively. Other sensors measured the soil water content (Model CS615-L, Campbell Scientific), soil temperature (TCAV, Campbell Scientific), and precipitation (Model 52203, RM Young Inc., Traverse City, MI, USA). All of the data were recorded using data loggers (Model CR23XTD, Campbell Scientific).
Comment 12. How well does the Reindl et al. model for diffuse radiation apply to observations of diffuse light if they were available? I’m assuming that atmospheric aerosols at the site are impacted by proximity to Shijiazhuang. Atmospheric composition is critical to the success of radiation partitioning models as noted by Oliphant and Stoy (2018, https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017JG004370) who also note that the Reindl et al. model has been since improved and simplified by Gu et al. (1999) (and who further proceed to further improve the model). All of these models will be challenged by anthropogenic, maritime, and volcanic aerosols and a more physical model of atmospheric radiation transmission may be preferred at this site, but this is hard to quantify without observations.
Response: Thank you very much. After reading the report of Oliphant and Stoy (2018) we found that the authors estimate diffuse light fraction on the basis of τ, which is explained by the author that it is a form of optical transmissivity that maintains the effect of atmospheric path length on transmission, referred to as “clearness index”. Our study also estimate diffuse light fraction based on clearness index (CI) (see Eq. 5 in revised paper), indicating that the model in this study have considered the factor of atmospheric transmission and the model is a physical model of atmospheric transmission. The clearness index in our study and Oliphant and Stoy’ study was not indicated whether it was related to clouds or aerosols. That means that the diffuse light received by the ecosystems is an integrative value of light transmitting through clouds plus through aerosols. If we intend to improve the diffuse light model, the diffuse light produced by clouds and aerosols respectively should be partitioned. However, there have been no relevant references that divide the respective contributions of clouds and aerosols to total diffuse light, probably because the method is difficult and challengeable.
Comment 13. 171: the residuals of the model fit using equation 1 will still be a function of PAR because the relationship in the rectangular hyperbola is unlikely to capture the shape of the nonlinear light response which may be better simulated by a nonrectangular hyperbola or a Mitscherlich model as discussed for example in Reichstein et al. https://link.springer.com/chapter/10.1007/978-94-007-2351-1_9
Response: Thank you very much. We have abandon the decision of removing the confounding effect of total light when analyzing the effect of diffuse light on GEP, and thus, the residue method and results were also deleted. This is because Reviewer 1# commented that if the paper is intended to inform model development, the removing total PAR can be an experimental condition, but total light and diffuse light is inextricably linked in nature. Then we established a new relationship between GEP and diffuse PAR that indicates that GEP increased significantly with diffuse PAR without removing the total light effect. More explanation for the relationship were in the revised text.
Comment 14. Regarding section 2.5 does the multilayer model improve fit versus a single layer model? I’m starting to realize why the authors obtained spurious results. If the model in equation 1 doesn’t fit particularly well, especially around the light compensation point, GEP will be inaccurately partitioned from NEP and the neural network model will assume that diffuse light – which should be most important around the light compensation point – will help it fit better. In other words, the major findings are likely an artefact that need to be tested with additional analyses.
Response: Thank you very much. We are sorry we have not found the references regarding a single layer model. But in future, this work is interesting and necessary. As for the testing for the major findings, we would like to provide the following explanations. The multiple layer model not only simulate within canopy carbon fluxes but also simulate carbon fluxes over canopy. The carbon fluxes over canopy included ecosystem gross respiration and gross photosynthesis. Thus, modeled NEE was obtained. These modeled NEEs were compared with measured NEE by eddy covariance to test the multiple layer model. The measured NEE were the unfilled values but after corrections and filtering. We did not use measured GEP, i.e., partitioned from measured NEE, to test the model because the measured GEP was estimated values after considerable and complex calculations and was somewhat uncertain. Moreover, in revised paper, we deleted the contents about Equation 1, meaning that we did not estimate GEP anymore, and we only simulated GEP in different layers within canopy by the model.
Comment 15. 233: these parameters need to be described in more detail because they are critical for the results, especially the clumping factor and the leaf angle distribution
Response: Thank you for this suggestion. The information and values of these model parameters were added in the text. Wheat leaf area index, plant height in different development stages were measured by local technicians and have been recorded since 2008. The leaf angle values were consulted to the local research community who mainly study crop agronomic traits and yield. We obtained leaf transmittance from literature, where the wheat leaf area index, irrigation and fertilization practices in cropland were similar to wheat cropland in our study. The information about the clumping factor for wheat was not observed during the study period, the model would be drived by clumping factor = 1, which indicates a homogeneous canopy with a random dispersion of leaf area, as suggested by Tang et al., (2013).
Comment 16. 262: Having measured wheat eddy covariance I question the usefulness of a 10-14 local time focus. Especially under high VPD wheat will tend to have far lower GEP in the afternoon, depending on the prevailing climatic conditions which need more description.
Response: Thank you very much. The daily variation of GEP estimated from NEE observed by eddy covariance during our study period showed that there was almost no inhibition and decrease in photosynthesis in the afternoon. The depression in photosynthesis may be related to many factors, such as stomatal closure, decline of carbon dioxide concentration, difficulty to transport photosynthate after accumulation and the enhancement of plant respiration when air temperature and VPD increase in the afternoon. Therefore, the reason that why wheat photosynthesis was not depressed in this study is unclear and need further study.
Comment 17. 296: fluxes themselves are unlikely to be impacted by ustar for physiological reasons except under extremely stagnant air conditions that inhibit evapotranspiration, and which are unlikely to be observed during the 10-14 local time study period. This finding is likely an artefact that suggests that the authors need to do more data filtering.
Response: Thanks a lot for your suggestion. The underestimation of carbon fluxes is a universal phenomenon for eddy covariance measurements, which is mainly caused by the steady atmospheric turbulence at nighttime. Therefore, scientific community usually filter and reject the underestimated carbon flux values that corresponding to u* < threshold of u* (this data process step was also carried out in our study). The threshold means that carbon fluxes is lower and nearly constant when u* < threshold but increased significantly when u* > threshold. In daytime, u* is usually greater than the threshold because atmospheric turbulence is not steady, suggesting that u* in daytime would impact ecosystem carbon exchange. Another reason for considering u* in study is related to ANN method. The more independent variables trained in the model, the more reliable the calculated results.
Comment 18. Figure 3 is taking a model of GEP that is a function of PAR – and that probably doesn’t fit particularly well nor was there any evidence that it was fit independently during different phases of canopy development as it should be if leaf area is increasing – and using it to infer diffuse PAR impacts when diffuse PAR was partitioned using a model that probably didn’t work very well from total PAR. The reasoning is circular, hence my suggestion that the results are an artifact.
Response: Thanks a lot. In the new paper, we abandon the analysis of diffuse effect on GEP on the basis of the residuals of GEP model, i.e. the confounding effect of other environmental factors (mainly refer to total light, Ta and VPD) were considered. This is because diffuse light changes synchronously with other environmental factors and should not be separated from other factors as the reviewers suggesting. So we established new relationship between GEP and diffuse light, which can reflect the real situation. Because in the second increase phase of diffuse light (Fig. 3 in revised paper), diffuse light, total light, Ta and VPD covaried and increased, it is unknown whether the increase in GEP was induced from diffuse light or other factors. A normalized method to judge this issue. Results showed that the increase in GEP in the second increase phase of diffuse light was the combined effect of diffuse light, total light, Ta and VPD. Finally, we concluded that GEP increased significantly with diffuse light in a linear pattern.
Comment 19. 334: this argument does not take into account any differences in photosynthetic efficiency in the lower canopy which is likely to be important as plants tend to allocate nitrogen (and more) toward upper leaves, making them more efficient.
Response: Thank you very much for this suggestion. In this study, we mainly focused on the diffuse light effect and quantified its importance. We also tried to illustrate the underlying mechanism behind the effect from the perspective of light interception because there may be more mechanisms for the enhancement of GEP under diffuse light conditions. Figure 6 and 7 in the old manuscript indicated that the photosynthesis in upper and middle canopy was greater than lower canopy when diffuse light level did not change, and we attributed this difference to more light intercept by middle and upper layers. After reading this comment of the reviewer, we began to think that the larger photosynthesis in upper layers may also be related to the nutrition allocation from lower canopy to upper canopy, thus, we added the relevant discussion in Section 4.2 in the text. However, we believe that the nutrition in lower canopy is absorbed from the soil, and we wonder if the nutrition in the soil is related to diffuse light change. In other words, nutrition allocation towards upper layer may be one of reasons why upper photosynthesis is greater than lower photosynthesis when diffuse light is relatively constant, and whether it contributes to the photosynthetic increase in middle and lower canopy when diffuse light increase may need further exploration.
Comment 20. Figure 6 needs a legend in the figure.
Response: Thanks for this suggestion. We have added the legend in the figure.
Comment 21. Figure 8 is interesting but I have no idea how diffuse PAR can equal 1250 micromoles / m2 / second. It would hurt the eyes to look at the sky away from the sun.
Response: Thank you very much. The meteorological measurements showed that the maximal PAR during our study period was approximately 2000 micromoles / m2 / second. The diffuse light fraction (fDIF) mainly ranged from 0.4—0.6 during April to May, so the diffuse PAR could reach to approximately 1250 micromoles / m2 / second. This may have something to do with the local climate conditions.
Comment 22. Section 4.2: these are entirely model results with no empirical evidence.
Response: Thank you very much. The multiple canopy layer model not only simulates carbon, sensible heat flux and microclimate within the canopy, but also simulates these variables above the canopy. Thus, the model can be verified by the measurement of fluxes and meteorological factors. Although the measurements within the canopy were not available, but the measurements above the canopy could be obtained because they had been measured by eddy covariance. So we used the measured uninterpolated NEE, heat flux and light to test the model. If there is a highly correlated relationship between observed and simulated values, then the model is useful and valid and the simulation results (including carbon flux and PAR within canopy) of the model are credible. However, we should also be aware that although the model was verified, future measurements for photosynthesis and microclimate in different layer within canopy is essential because it can further test our result and make them more solid. We also added this outlook in the Section 4.2.
Comment 2 3. 482: ‘eliminate’ is too strong a word here but this is an interesting point
Response: Thank you. We revised ‘eliminate’ as ‘weaken’.
Citation: https://doi.org/10.5194/egusphere-2022-1003-AC2
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AC2: 'Reply on RC2', Xueyan Bao, 08 Feb 2023
Status: closed
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RC1: 'Comment on egusphere-2022-1003', Anonymous Referee #1, 24 Nov 2022
Ultimately this paper may have fundamental flaws in its design. Two major flaws exist in particular. Firstly that this paper makes extreme claims about the role of diffuse light in boosting plant productivity while ignoring the effect of a reduction in total PAR, and secondly that it neglects to measure diffuse PAR at all - the exact variable that is the focus of the analysis. While the modeling work done to circumvent this lack of data is admirable and is backed up with seemingly high-quality measurements of GEP using eddy covariance, I am skeptical of results that are principally obtained from a model driven by inferred data. This skepticism is especially acute when confounding effects such as a changing light-response curve and the real-world relationship between total and diffuse PAR are not well-discussed, and the results themselves disagree with other similar papers by an order of magnitude.1. Firstly and most importantly, this manuscript evaluates the influence of diffuse light within a plant canopy, but contains no direct measurement of diffuse light. Could no applicable measurements be either made or found to validate the data the authors use to drive the CANVEG model? Without real data directly describing PARdiff, I am unconvinced of any of the final claims made in this manuscript, especially given the substantial anthropogenic aerosol influence present in Northern and Eastern China. The Reindl model employed to estimate the fraction of diffuse light in this study was developed in Europe and North America ~30 years prior, and does not account for any anthropogenic aerosol effects in its input variables. The scattering and reflective properties of these aerosols are unknown for the study period in this paper, and may represent significant uncertainty which is not accounted for anywhere I can see in this author's analysis.2. The authors of this study elected to remove the effect of total light when analyzing the influence of PARdiff on canopy GEP. Other literature sees a very real negative correlation between the fraction of PARdiff and magnitude of total PAR, which is acknowledged in this study. This effect more often than not results in an overall reduction of GEP under diffuse conditions, since total PAR is reduced beyond the capacity of PARdiff to compensate through increased light use efficiency within the canopy. Under highly diffuse conditions when total PAR is significantly reduced, C3 plants such as wheat exhibit a steeply-sloped light response curve as opposed to the light-saturation they experience under direct sunlight. It seems likely that this large rate of change with additional PAR (in absence of the influence of total PAR) is driving the large increase in GEP noted in this study, but is not mentioned anywhere except for the methods section, side-by-side with the explanation of EC gap-filling. Even then it is addressed only in passing, when it deserves serious examination in the discussion section with more complete citations.3. The decision to remove total PAR from the analysis entirely is also questionable. If this paper is intended only to inform model development, then this may be a relevant experimental condition. In nature however, PARdiff and PARtotal are inextricably linked. The results of this paper claim a 53% increase in total-canopy GEP when PARdiff varied from (0-200) to (>1,000) umol/m2/s. It's unlikely that such extreme clear or diffuse conditions occurred during their observation period, since the Clearness Index range varied only from ~0.45-0.60. Such a clearness index indicates consistently high aerosol/cloudy conditions on most days, which seems likely since the study site is located nearby the Beijing metropolis. Additionally, in figure 7 the 200-400umol data point indicates much higher GEP than the 400-600umol data point in mid-canopy GEP. How might this be explained? Might it represent a weakness within the modeled results as a whole?4. Finally, the 53% increase in GEP under diffuse conditions reported in this manuscript is extraordinary, and (in the words of Carl Sagan) requires extraordinary evidence to be believed. The evidence presented in this paper consists of inferred PARdiff data which drives a model that describes conditions within but also outside the range of values which were directly observed at the site of interest, mainly conditions which produce fully diffuse and fully direct PAR. In comparison, similar studies have found increases in GEP on the order of 1.2%-4.2% under extremely diffuse conditions which were directly observed. How useful is this modeled 53% increase when it remains unvalidated and represents an order of magnitude discrepancy from prior studies examining the effect of PARdiff in other plant canopies? Might it be misleading for those reading the paper who could drastically overestimate the role of diffuse light in driving increased GEP? If improving modeled effects of PARdiff is this paper's goal, it should be very explicit about this and carry strong words of caution that this ~50% increase is not a real-world increase in plant productivity that is ever likely to occur in the field.Citation: https://doi.org/
10.5194/egusphere-2022-1003-RC1 -
AC1: 'Reply on RC1', Xueyan Bao, 24 Dec 2022
Dear editor,
We would like to thank all of you and the reviewers for the valuable suggestions. Here are the point-to-point responses.
Response to Reviewer#1:
Comment 1. Firstly and most importantly, this manuscript evaluates the influence of diffuse light within a plant canopy, but contains no direct measurement of diffuse light. Could no applicable measurements be either made or found to validate the data the authors use to drive the CANVEG model? Without real data directly describing PARdiff, I am unconvinced of any of the final claims made in this manuscript, especially given the substantial anthropogenic aerosol influence present in Northern and Eastern China. The Reindl model employed to estimate the fraction of diffuse light in this study was developed in Europe and North America ~30 years prior, and does not account for any anthropogenic aerosol effects in its input variables. The scattering and reflective properties of these aerosols are unknown for the study period in this paper, and may represent significant uncertainty which is not accounted for anywhere I can see in this author's analysis.
Response: Thank you very much. Because the Luancheng experimental station lacked the direct measurments of diffuse light during the study years, perhaps modeling is the only way to obtain diffuse light values. Many published studies that focused on the effects of diffuse light on ecosystem processes lacked observational data, possibly because the diffuse light effect on ecosystems was just beginning to be studied at those sites. For example, Zhang et al., (2010) estimated PAPdiff using models that mainly based on diffuse light fraction in two forest ecosystems in China. Kanniah et al., (2013) also calculated diffuse light through simulation at a tropical savanna site in Australia. Observational data is likely to be more reliable than simulations. However, similar to modeling results, the observational data may also introduce uncertainties to a certain extent, which may be related to the monitoring instrument itself, such as design limitations and response insensitivity, and the influence of weather conditions. Nevertheless, direct measurement of diffuse light is necessary for accurately analyze diffuse light influence on ecosystem carbon exchange in this site, because it can provide a direct supporting data to relevant research and can verify diffuse light models. Our group have planned to install radiation monitoring equipment that includes diffuse component in this site next year.
Aerosols have been shown to reduce total solar radiation at the surface, but can also increase the diffuse radiation fraction. Source of aerosols that may modify the radiation and its components mainly include the emissions from volcanic eruptions, biomass burning and emission of hydrocarbons (Oliveria et al., 2007). In China, the aerosol optical depth (AOD), which is often obtained by remote sensing technology and used to describe the attenuation of light by aerosols, was estimated to be higher in the southeast than the northwest, to be highest in Beijing and Tianjin Province in the southeast. Because the experiment site in our study is near Beijing city, the effect of aerosol on radiation and its component and thus on ecosystem productivity should not be ignored. However, we would like to explain that our study did not analysis the diffuse light by aerosols mainly for the following reasons. Firstly, both the total radiation used in the estimation of the clearness index (CI) and the diffuse radiation was the overall value of the direct solar radiation and the diffuse radiation after the sunlight passes through clouds and aerosols. That is to say, the observed values of total solar radiation and the simulated values of diffuse radiation in this study have included the direct and diffuse radiation after sunlight passes through aerosols. Secondly, in fact, it is difficult to distinguish the diffuse radiation produced by clouds and those produced by aerosols. On the one hand, detecting the diffuse radiation by aerosols may use remote sensing observation data, which are not available at present. On the other hand, aerosols would affect the physical characteristics of clouds, their reflection characteristics, and also the evaporation, the amount and timing of precipitation. Changes in precipitation patterns in turn affect the amount of cloud cover. These processes suggest that aerosols are partly responsible for the direct and diffuse light by cloud cover. Therefore, the direct and diffuse radiation by clouds and aerosols are coexisting and closely related, and it may be hard to tease out their respective contribution to total radiation and total diffuse radiation that reach on the surface.
References:
- Kanniah, K.D., Beringer, J., North, P. and Hutley, L., 2012. Control of atmospheric particles on diffuse radiation and terrestrial plant productivity: A review. Progress in Physical Geography, 36(2): 209-237.
- Oliveira, P.H.F. et al., 2007. The effects of biomass burning aerosols and clouds on the CO2 flux in Amazonia. Tellus B: Chemical and Physical Meteorology, 59(3): 338-349.
- Zhang, M. et al., 2011. Effects of cloudiness change on net ecosystem exchange, light use efficiency, and water use efficiency in typical ecosystems of China. Agricultural and Forest Meteorology, 151(7): 803-816.
Comment 2. The authors of this study elected to remove the effect of total light when analyzing the influence of PARdiff on canopy GEP. Other literature sees a very real negative correlation between the fraction of PARdiff and magnitude of total PAR, which is acknowledged in this study. This effect more often than not results in an overall reduction of GEP under diffuse conditions, since total PAR is reduced beyond the capacity of PARdiff to compensate through increased light use efficiency within the canopy. Under highly diffuse conditions when total PAR is significantly reduced, C3 plants such as wheat exhibit a steeply-sloped light response curve as opposed to the light-saturation they experience under direct sunlight. It seems likely that this large rate of change with additional PAR (in absence of the influence of total PAR) is driving the large increase in GEP noted in this study, but is not mentioned anywhere except for the methods section, side-by-side with the explanation of EC gap-filling. Even then it is addressed only in passing, when it deserves serious examination in the discussion section with more complete citations.
Response: Thanks a lots. In revised manuscript, we have considered the effect of total light, and the response of wheat GEP to diffuse PAR was shown in Figure. 2. The increase in GEP during the process of fDIF (diffuse light fraction) increase from its minimal to intermediate levels was the result of diffuse PAR increase, because total PAR decreased in this process. In the process of fDIF decreasing from its maxima to intermediate levels, GEP also increased with increase of diffuse PAR. However, because total light and other factor such as Ta and VPD covaried with diffuse PAR, it is unclear whether increase of GEP was attributed to diffuse PAR or other factors in this process. A normalized method was used to solve the issue. The results indicated that the increase of diffuse PAR, together with increase of total PAR, Ta and VPD was the reason for GEP increase in the process of fDIF decreased from maxima to intermediate levels. This indicates that total light played a certain role in GEP increase in this process.
Comment 3 The decision to remove total PAR from the analysis entirely is also questionable. If this paper is intended only to inform model development, then this may be a relevant experimental condition. In nature however, PARdiff and PARtotal are inextricably linked. The results of this paper claim a 53% increase in total-canopy GEP when PARdiff varied from (0-200) to (>1,000) umol/m2/s. It's unlikely that such extreme clear or diffuse conditions occurred during their observation period, since the Clearness Index range varied only from ~0.45-0.60. Such a clearness index indicates consistently high aerosol/cloudy conditions on most days, which seems likely since the study site is located nearby the Beijing metropolis. Additionally, in figure 7 the 200-400umol data point indicates much higher GEP than the 400-600umol data point in mid-canopy GEP. How might this be explained? Might it represent a weakness within the modeled results as a whole?
Response: Thank you very much. In revised paper, we have abandon removing the effect of total light (Fig.3). GEP also increased with increasing of diffuse PAR when considering the effect of total light, temperature and VPD. The whole diffuse PAR can be divided two processes (1,2) according to fDIF. We found that the increase in GEP in process 1 when fDIF increase from its minima to intermediate levels was attributed to increase in diffuse PAR, while increase in GEP in process 2 when fIDF decreasing from its maxima to its intermediate levels was attribute to combined effect of diffuse light, total light, temperature and VPD using a normalized method. Therefore, wheat GEP increased along with diffuse PAR throughout the whole process of diffuse PAR increasing during the study period. This results indicates that the wheat cultivar planted in this site was more sensitive to diffuse light than to total light.
Our study also indicated an increase of 53% in GEP when diffuse light varied from (0-200) to (>1,000) umol/m2/s. Fig. 4 indicates that lower diffuse light levels (<200) often occurred when sky condition was much clear (low fDIF) and heavy cloudy (high fDIF). Because there was almost no heavy cloud during study period, the diffuse light range (0-200) in Fig.9 corresponded to clear day conditions in most cases. Meanwhile, the range of (>1000) diffuse light corresponds to intermediate cloud conditions, which often occurred in the study site.
In Fig.9 (revised manuscript), Pn (photosynthesis rate) under diffuse light of (400-600 μmol m-2 s-1) is lower than (200-400 μmol m-2 s-1) in middle canopy, this may be due to the estimation uncertainties, which may result from limitation of the models themselves, measurement error of input variables and the model parameters. The values presented in the figure were the averages per diffuse light unit of 200 μmol m-2 s-1. If the unit is more narrow, such as 100 or 50 μmol m-2 s-1, there could be more of the same; while when the unit is extended, like 400 μmol m-2 s-1, we found that Pn was lowest at (0-400), intermediate at (400-800), and highest at (>800), indicating that the entire trends of Pn is increased along with diffuse light in middle layer, which basically provide evidence to the initial hypothesis of this study.
Comment 4 Finally, the 53% increase in GEP under diffuse conditions reported in this manuscript is extraordinary, and (in the words of Carl Sagan) requires extraordinary evidence to be believed. The evidence presented in this paper consists of inferred PARdiff data which drives a model that describes conditions within but also outside the range of values which were directly observed at the site of interest, mainly conditions which produce fully diffuse and fully direct PAR. In comparison, similar studies have found increases in GEP on the order of 1.2%-4.2% under extremely diffuse conditions which were directly observed. How useful is this modeled 53% increase when it remains unvalidated and represents an order of magnitude discrepancy from prior studies examining the effect of PARdiff in other plant canopies? Might it be misleading for those reading the paper who could drastically overestimate the role of diffuse light in driving increased GEP? If improving modeled effects of PARdiff is this paper's goal, it should be very explicit about this and carry strong words of caution that this ~50% increase is not a real-world increase in plant productivity that is ever likely to occur in the field.
Response: Thank you for the comments. We would like to explain that why there was a much difference in GEP between this study and the other studies. Here, we define the enhancement of GEP due to diffuse light increase as diffuse light effect (DFE), which has been quantified as nearly 50% in this study. The DFE has been showed mainly depends on types of ecosystems according to existing studies. Because different ecosystems, such as grassland, cropland, forests, are commonly different in leaf area index (LAI), leaf orientation, the response extent of GEP to diffuse light vary among ecosystems. Take LAI as an example, terrestrial vegetation with high LAI tend to be more sensitive to increases in diffuse light. This is because that as the plant canopy becomes denser with leaves, i.e., high LAI, canopy transmission to lower canopy layers’ decreases, leading to increased radiation limitation in the middle and lower canopy; thus, the canopy photosynthesis would be more sensitive to diffuse light and enhance more compared to canopy with low LAI (Knohl and Baldacchi, 2008). Wolfahrt et al, (2008) also showed that only grass systems with LAI of more than 4 m2 m-2 showed significant increases in NEE (35%) compared to grasses with intermediate (2-4) to low LAI (<2). Additionally, the effect of diffuse light on GEP also depends on leaf inclination angle. One study using modeling method showed approximately 20% increase in DFE when leaf inclination angle increased from 40° to 70° (Knohl and Baldacchi, 2008).
The second reason for the difference in GEP may relate to the different levels of diffuse light conditions that were considered. In the comments, the reviewer mentioned that previous study found only 1%-4% increase in GEP under extremely cloudy conditions compared to clear conditions. However, it should be noting that extremely cloudy conditions mean that diffuse light fraction (fDIF) is very high. Under this condition, total radiation decreases seriously, its diffuse component also decreases, thus, plant photosynthesis would decrease. This means that canopy GEP would not increase significantly under extremely cloudy condition compare to clear condition. In our revised manuscript, the changed Figure 1 showed that GEP did not enhanced anymore and is even lower under extremely diffuse condition compare to clear sky conditions. However, GEP substantially increased under intermediate fDIF (largest diffuse PAR) compared to clear sky condition (Fig. 2). The enhancement was estimated nearly 49%, which was a little lower than the modeling value of 53%. Overall, previous study reported the enhancement of GEP under extremely cloud condition (low fDIF) compared to clear sky condition (low fDIF), whereas our study conclude enhancement of GEP under moderate fDIF condition compared to low fDIF condition.
It is also worth noting that the increase of 53% in GEP because of diffuse light is a result of simulation. The main objects of this study are to interpret the mechanism of enhancement of GEP and examine whether GEP increase relates to more light intercepted by the canopy, rather than to quantify how much GEP increased with diffuse PAR increasing. Although the model result has been verified by NEE and ET values obtained by EC technique (Fig. 1), the results may be somewhat uncertain, which may be associated with limitation of the models themselves, measurement error of input variables and the model parameters.
Citation: https://doi.org/10.5194/egusphere-2022-1003-AC1
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AC1: 'Reply on RC1', Xueyan Bao, 24 Dec 2022
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RC2: 'Comment on egusphere-2022-1003', Anonymous Referee #2, 21 Jan 2023
Bao and Sun quantify the importance of diffuse radiation to canopy carbon uptake (GEP) in a wheat canopy. They emphasize the importance of diffuse radiation, which is well-known to be an underappreciated factor in canopy photosynthesis, but their conclusions overestimate its contribution. The hypothesis cannot be tested using the observations and the radiation partitioning model should be improved. As written without additional testing, there is no reason to believe that the major findings are not artefacts. The manuscript is not publishable in its present form but with additional testing and validation may make an interesting contribution to the literature.
23: is this the variability in GEP or the magnitude of GEP. Diffuse radiation can’t have this strong of an effect, physiologically, on the magnitude of GEP under conditions where wheat is best grown with warm temperatures and moderate amounts of precipitation. There just isn’t enough diffuse radiation to make it the primary factor impacting the magnitude of GEP but I can see a scenario where it is a dominant contributor to the variability of GEP.
Every sentence of the manuscript could use structural improvement. To demonstrate but one: ‘Terrestrial carbon assimilation rates on a leaf level response to sunlight nonlinearly…’ should be ‘Leaf-level carbon assimilation responds nonlinearly to sunlight’ or similar. Using an automated spelling and usage checker would correct most errors.
I completely disagree with the statement on line 44 which needs to be removed. People have known for a long time how variable radiation can be as demonstrated by the references in the next sentence (note also https://www.science.org/doi/abs/10.1126/science.1103215)
51: ‘of light climate change’ should be ‘in light of climate change’. I emphasize again that almost every sentence needs improvement before resubmission. Please don’t only change the sentences that I am highlighting.
56: this isn’t wrong but the text could be modified somewhat because even under clear sky conditions diffuse light is on the order of 20% (depending on the atmosphere) lest the rest of the hemisphere of the sky not be illuminated; we’d only see the sun. The conceptual description could be adjusted to note that this is a matter of degree. Lower leaves are irradiated or we wouldn’t be able to see them; they just receive more light under certain diffuse conditions, especially if the factors causing diffuse light don’t result in a decrease in total light.
62: this is not a theory. Just write “One can expect…”. The authors need to make clear that it’s really only under conditions where the increase in the fraction of diffuse light is not because total light decreases. As long as total light is near or above the light compensation point for photosynthesis does an increase in diffuse light really have an effect; perhaps the content on line 70 could be moved up.
On line 68: evidence isn’t scarce; it’s pretty well established but science. The statement on line 79 is good.
Line 99: this can’t be tested with the measurements available as there are no measurements of sub-canopy diffuse light
119: water vapor is more commonly abbreviated q because rho is usually used for the density of air, also important for the flux calculation.
123: what is the make and model of the micrometeorological instruments used?
How well does the Reindl et al. model for diffuse radiation apply to observations of diffuse light if they were available? I’m assuming that atmospheric aerosols at the site are impacted by proximity to Shijiazhuang. Atmospheric composition is critical to the success of radiation partitioning models as noted by Oliphant and Stoy (2018, https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017JG004370) who also note that the Reindl et al. model has been since improved and simplified by Gu et al. (1999) (and who further proceed to further improve the model). All of these models will be challenged by anthropogenic, maritime, and volcanic aerosols and a more physical model of atmospheric radiation transmission may be preferred at this site, but this is hard to quantify without observations.
171: the residuals of the model fit using equation 1 will still be a function of PAR because the relationship in the rectangular hyperbola is unlikely to capture the shape of the nonlinear light response which may be better simulated by a nonrectangular hyperbola or a Mitscherlich model as discussed for example in Reichstein et al. https://link.springer.com/chapter/10.1007/978-94-007-2351-1_9
Regarding section 2.5 does the multilayer model improve fit versus a single layer model? I’m starting to realize why the authors obtained spurious results. If the model in equation 1 doesn’t fit particularly well, especially around the light compensation point, GEP will be inaccurately partitioned from NEP and the neural network model will assume that diffuse light – which should be most important around the light compensation point – will help it fit better. In other words the major findings are likely an artefact that need to be tested with additional analyses.
233: these parameters need to be described in more detail because they are critical for the results, especially the clumping factor and the leaf angle distribution
262: Having measured wheat eddy covariance I question the usefulness of a 10-14 local time focus. Especially under high VPD wheat will tend to have far lower GEP in the afternoon, depending on the prevailing climatic conditions which need more description.
296: fluxes themselves are unlikely to be impacted by ustar for physiological reasons except under extremely stagnant air conditions that inhibit evapotranspiration, and which are unlikely to be observed during the 10-14 local time study period. This finding is likely an artefact that suggests that the authors need to do more data filtering.
Figure 3 is taking a model of GEP that is a function of PAR – and that probably doesn’t fit particularly well nor was there any evidence that it was fit independently during different phases of canopy development as it should be if leaf area is increasing – and using it to infer diffuse PAR impacts when diffuse PAR was partitioned using a model that probably didn’t work very well from total PAR. The reasoning is circular, hence my suggestion that the results are an artifact.
334: this argument does not take into account any differences in photosynthetic efficiency in the lower canopy which is likely to be important as plants tend to allocate nitrogen (and more) toward upper leaves, making them more efficient.
Figure 6 needs a legend in the figure.
Figure 8 is interesting but I have no idea how diffuse PAR can equal 1250 micromoles / m2 / second. It would hurt the eyes to look at the sky away from the sun.
Section 4.2: these are entirely model results with no empirical evidence.
482: ‘eliminate’ is too strong a word here but this is an interesting point
Citation: https://doi.org/10.5194/egusphere-2022-1003-RC2 -
AC2: 'Reply on RC2', Xueyan Bao, 08 Feb 2023
Dear editor,
We would like to thank all of you and the reviewers for the valuable suggestions. Here are the point-to-point responses.
Response to Reviewer 2#
Comment 1. Bao and Sun quantify the importance of diffuse radiation to canopy carbon uptake (GEP) in a wheat canopy. They emphasize the importance of diffuse radiation, which is well-known to be an underappreciated factor in canopy photosynthesis, but their conclusions overestimate its contribution. The hypothesis cannot be tested using the observations and the radiation partitioning model should be improved. As written without additional testing, there is no reason to believe that the major findings are not artefacts. The manuscript is not publishable in its present form but with additional testing and validation may make an interesting contribution to the literature.
Response: Thank you very much. The solar radiation reaching the earth surface is the primary driver of plant photosynthesis. More and more studies indicated that diffuse light changes have important influence on the terrestrial carbon sink (Mercado et al., 2009). Our study also highlighted the positive effect of diffuse light on canopy photosynthesis and that diffuse light is the most importance factor for GEP by quantifying its contribution to GEP variations. But it is worth noting that to eliminate the confounding effect of leaf area index, canopy height and solar elevation angle to maximal extent, we constrained the study period, that is to say, the diffuse light is relatively importance under certain conditions. More importantly, whether can diffuse light is the most importance factor may depend on canopy structure. Some ecosystems are not sensitive to diffuse light change; their photosynthesis are commonly depressed under diffuse light condition compared to under clear sky conditions. This is because when diffuse light increase, total light decrease, and the ecosystem tend to be more sensitive to total light changes. In this case, total light, rather than diffuse light, may become the most important factor for photosynthesis. But for other ecosystems, especially for the ecosystems with higher leaf area, they may be more sensitive to diffuse light because canopy transmission to lower canopy layers’ decreases, leading to increased radiation limitation in the middle and lower canopy. The result diffuse light is more important to GEP than other factors in our study reflects the diversity of terrestrial ecosystems and their varied responses characteristics to light changes.
The multiple canopy layer model not only simulates carbon, sensible heat flux and microclimate within the canopy, but also simulates these variables above the canopy. Thus, the model can be verified by the measurement of fluxes and meteorological factors. Although the measurements within the canopy were not available, but the measurements above the canopy could be obtained because they had been measured by eddy covariance. So we used the measured uninterpolated NEE, heat flux and light to test the model. If there is a highly correlated relationship between observed and simulated values, then the model is useful and valid and the simulation results (including carbon flux and PAR within canopy) of the model are credible (Fig.1). However, we should also be aware that although the model was verified, future measurements for photosynthesis and microclimate in different layer within canopy is essential because it can further test our result and make them more solid. We also added this outlook in the Section 4.2.
Comment 2. 23: is this the variability in GEP or the magnitude of GEP. Diffuse radiation can’t have this strong of an effect, physiologically, on the magnitude of GEP under conditions where wheat is best grown with warm temperatures and moderate amounts of precipitation. There just isn’t enough diffuse radiation to make it the primary factor impacting the magnitude of GEP but I can see a scenario where it is a dominant contributor to the variability of GEP.
Response: Thank you very much. It refers to variability in GEP in the abstract. For a long time, total radiance or PAR is deemed as a major controller for ecosystem photosynthesis. However, cloudy or aerosols are terrestrial plants most experience, which produce considerable diffuse light. A mount of studies highlighted the importance of diffuse light for ecosystem processes, such as light use efficiency, photosynthesis, water use efficiency, or even ecosystem respiration. Because of the diversity of ecosystems, the effect of diffuse light on ecosystem processes may varied among ecosystem types. As we know, the total light would decrease when there is more diffuse light. When diffuse light fraction increasing, GPP of some ecosystems enhanced but other ecosystem may be inhibited, reflecting different sensitivity to diffuse light among different ecosystems. The canopy structure, such as leaf area index, leaf directions and leaf angles as well as photosynthetic pathways among ecosystems could be responsible for the different sensitivity. Take LAI as an example, terrestrial vegetation with high LAI tend to be more sensitive to increases in diffuse light. This is because that as the plant canopy becomes denser with leaves, i.e., high LAI, canopy transmission to lower canopy layers’ decreases, leading to increased radiation limitation in the middle and lower canopy; thus, the canopy photosynthesis would be more sensitive to diffuse light and enhance more compared to canopy with low LAI (Knohl and Baldacchi, 2008). Wolfahrt et al, (2008) also showed that only grass systems with LAI of more than 4 m2 m-2 showed significant increases in NEE (35%) compared to grasses with intermediate (2-4) to low LAI (<2). Additionally, the effect of diffuse light on GEP also depends on leaf inclination angle. One study using modeling method showed approximately 20% increase in diffuse light effect when leaf inclination angle increased from 40° to 70° (Knohl and Baldacchi, 2008). For the ecosystems sensitive for diffuse light change, diffuse light has potential to be the dominant factor affecting canopy photosynthesis.
Comment 3. Every sentence of the manuscript could use structural improvement. To demonstrate but one: ‘Terrestrial carbon assimilation rates on a leaf level response to sunlight nonlinearly…’ should be ‘Leaf-level carbon assimilation responds nonlinearly to sunlight’ or similar. Using an automated spelling and usage checker would correct most errors.
Response: Thanks a lots. We would like to explain that this journal requires the author to pay the article processing charges (APG) if the revised manuscript is accepted and published. Language copy-editing for revised paper is one of the contents that APG includes. The goals of Language copy-editing are to ensure the work adheres to scientific writing standards, grammatical accuracy, and overall readability. The latter encompasses aspects such as rephrasing awkward expressions and sentence structures or adjusting incorrect collocations. Therefore, we have not polished the old manuscript yet.
Comment 4. I completely disagree with the statement on line 44 which needs to be removed. People have known for a long time how variable radiation can be as demonstrated by the references in the next sentence (note also https://www.science.org/doi/abs/10.1126/science.1103215)
Response: Thank you for your suggestion. We have deleted the statements.
Comment 5. 51: ‘of light climate change’ should be ‘in light of climate change’. I emphasize again that almost every sentence needs improvement before resubmission. Please don’t only change the sentences that I am highlighting.
Response: Thank you very much. The manuscript would be experienced English language copy-editing by the journal office
Comment 6. 56: this isn’t wrong but the text could be modified somewhat because even under clear sky conditions diffuse light is on the order of 20% (depending on the atmosphere) lest the rest of the hemisphere of the sky not be illuminated; we’d only see the sun. The conceptual description could be adjusted to note that this is a matter of degree. Lower leaves are irradiated or we wouldn’t be able to see them; they just receive more light under certain diffuse conditions, especially if the factors causing diffuse light don’t result in a decrease in total light.
Response: Thank you for this suggestion. The statements have been revised as “Under direct light conditions, plant leaves receive light from a single direction, causing that the leaves of lower canopy are shaded and receive less light because of light interception of upper leaves. In comparison, canopy is illuminated from multi-directions under diffuse light conditions, and leaves that were previously shaded now receive more light, leading to an increased light interception by the whole canopy (Williams et al., 2014). Finally, canopy photosynthesis would be enhanced under diffuse light conditions.”
Comment 7. 62: this is not a theory. Just write “One can expect…”. The authors need to make clear that it’s really only under conditions where the increase in the fraction of diffuse light is not because total light decreases. As long as total light is near or above the light compensation point for photosynthesis does an increase in diffuse light really have an effect; perhaps the content on line 70 could be moved up.
Response: Thank you very much. We have revised the sentence as “Thus, one can expect that canopy has greater photosynthetic capacity because of more light intercepted by the canopy under diffuse light condition compared with direct light condition when total light is constant.”
Comment 8. On line 68: evidence isn’t scarce; it’s pretty well established but science. The statement on line 79 is good.
Response: Thank you for your comments. The other reviewer (Reviewer 1#) is skeptical to our decision to remove total PAR from the analysis, owing to diffuse PAR and total PAR is closely linked and covary in nature. Therefore, we deleted all the contents and statements about removing the confounding effect of total PAR, including the Equation 1 and Fig.3. We then rewritten the Introduction Section. However, we still highlighted the effect of total light on ecosystem photosynthesis when diffuse light fraction changes. We believed that it is the coexistence of the two variables that result in contradict conclusions about how GEP response to diffuse light, which reflecting the different sensitivity of ecosystem carbon exchange to diffuse light among different ecosystem types. Actually, we proposed the necessity to conduct the research on the basis of these statements, i.e., the effect of diffuse light on GEP is still uncertain to date, which need further exploration.
We established new figure (Fig. 3 in revised paper) that reflects the relationship between GEP and diffuse PAR without removing effect of total PAR. This figure shows an increase trends in diffuse PAR. The change in diffuse PAR contains two change phases (denoted by different legends), which corresponds to different fDIF change processes. After analysis, we drew a conclusion that the increase in GEP in process 1 was caused by diffuse light only, whereas the increase in GEP in process 2 was the results of combined effect of diffuse light, total light, temperature and VPD. Finally, we believe that GEP increased along with diffuse PAR, even after considering the influence of other factors that covary with diffuse PAR. (The analysis process is in the revised text)
Comment 9. Line 99: this can’t be tested with the measurements available as there are no measurements of sub-canopy diffuse light.
Response: Thank you very much. In this study, we concluded that diffuse light promotes wheat GEP significantly, and inferred that this positive effect is because the canopy intercept more light under increasing diffuse light conditions. To test this hypothesis, the relevant measurements such as intra-canopy light distribution and leaf photosynthesis in different canopy layers are necessary. However, the study site did not conduct any measurement in the study years, i.e.,2011 and 2012, largely because the diffuse light effect on ecosystem carbon exchange process has not been focused at that time. Because of the lack of measurements, we had to use models (the multilayer canopy model) to simulate the contents we inferred. The validation for the model was introduced in the end of Section 2.6 and the results were presented in Figure.1. In detail, the model not only simulates carbon and sensible heat flux and microclimate within canopy, but also simulates these variables above canopy. The measurements for these variables within the canopy were not available, but the measurements above the canopy could be obtained because they had been measured by eddy covariance. So we believe that if the model simulates the variables above the canopy well (Figure 1), the model is useful and valid and the other simulation results (carbon flux and PAR within canopy) of the model were also credible. However, a more direct, efficiency and credible method to test our hypothesis about the diffuse light effect mechanism is to measure the incident light and GEP in sub-canopy layers under different diffuse light levels, which is important for accurately predicting the canopy carbon flux and should be conducted in future studies.
Comment 10.119: water vapor is more commonly abbreviated q because rho is usually used for the density of air, also important for the flux calculation.
Response: Thank you for this suggestion. We have changed ρas q.
Comment 11. 123: what is the make and model of the micrometeorological instruments used?
Response: Thanks a lot. The detail information of the micrometeorological instruments in this study has been reported by our published research. Because BG journal may limit article words, we mentioned them through citing reference. In detail, the micrometeorological measurement system at Luancheng site comprised a net radiometer (Model CNR-1, Kipp and Zonen, The Netherlands), quantum sensor (LI190SB, Li-Cor Inc.), temperature/humidity probe (Model HMP45C, Vaisala Inc., Helsinki, Finland), and an anemometer (Model AR-100, Vector Instruments) for measuring the net radiation, photosynthetic photon flux density, air temperature and relative humidity, and wind speed and direction, respectively. Other sensors measured the soil water content (Model CS615-L, Campbell Scientific), soil temperature (TCAV, Campbell Scientific), and precipitation (Model 52203, RM Young Inc., Traverse City, MI, USA). All of the data were recorded using data loggers (Model CR23XTD, Campbell Scientific).
Comment 12. How well does the Reindl et al. model for diffuse radiation apply to observations of diffuse light if they were available? I’m assuming that atmospheric aerosols at the site are impacted by proximity to Shijiazhuang. Atmospheric composition is critical to the success of radiation partitioning models as noted by Oliphant and Stoy (2018, https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017JG004370) who also note that the Reindl et al. model has been since improved and simplified by Gu et al. (1999) (and who further proceed to further improve the model). All of these models will be challenged by anthropogenic, maritime, and volcanic aerosols and a more physical model of atmospheric radiation transmission may be preferred at this site, but this is hard to quantify without observations.
Response: Thank you very much. After reading the report of Oliphant and Stoy (2018) we found that the authors estimate diffuse light fraction on the basis of τ, which is explained by the author that it is a form of optical transmissivity that maintains the effect of atmospheric path length on transmission, referred to as “clearness index”. Our study also estimate diffuse light fraction based on clearness index (CI) (see Eq. 5 in revised paper), indicating that the model in this study have considered the factor of atmospheric transmission and the model is a physical model of atmospheric transmission. The clearness index in our study and Oliphant and Stoy’ study was not indicated whether it was related to clouds or aerosols. That means that the diffuse light received by the ecosystems is an integrative value of light transmitting through clouds plus through aerosols. If we intend to improve the diffuse light model, the diffuse light produced by clouds and aerosols respectively should be partitioned. However, there have been no relevant references that divide the respective contributions of clouds and aerosols to total diffuse light, probably because the method is difficult and challengeable.
Comment 13. 171: the residuals of the model fit using equation 1 will still be a function of PAR because the relationship in the rectangular hyperbola is unlikely to capture the shape of the nonlinear light response which may be better simulated by a nonrectangular hyperbola or a Mitscherlich model as discussed for example in Reichstein et al. https://link.springer.com/chapter/10.1007/978-94-007-2351-1_9
Response: Thank you very much. We have abandon the decision of removing the confounding effect of total light when analyzing the effect of diffuse light on GEP, and thus, the residue method and results were also deleted. This is because Reviewer 1# commented that if the paper is intended to inform model development, the removing total PAR can be an experimental condition, but total light and diffuse light is inextricably linked in nature. Then we established a new relationship between GEP and diffuse PAR that indicates that GEP increased significantly with diffuse PAR without removing the total light effect. More explanation for the relationship were in the revised text.
Comment 14. Regarding section 2.5 does the multilayer model improve fit versus a single layer model? I’m starting to realize why the authors obtained spurious results. If the model in equation 1 doesn’t fit particularly well, especially around the light compensation point, GEP will be inaccurately partitioned from NEP and the neural network model will assume that diffuse light – which should be most important around the light compensation point – will help it fit better. In other words, the major findings are likely an artefact that need to be tested with additional analyses.
Response: Thank you very much. We are sorry we have not found the references regarding a single layer model. But in future, this work is interesting and necessary. As for the testing for the major findings, we would like to provide the following explanations. The multiple layer model not only simulate within canopy carbon fluxes but also simulate carbon fluxes over canopy. The carbon fluxes over canopy included ecosystem gross respiration and gross photosynthesis. Thus, modeled NEE was obtained. These modeled NEEs were compared with measured NEE by eddy covariance to test the multiple layer model. The measured NEE were the unfilled values but after corrections and filtering. We did not use measured GEP, i.e., partitioned from measured NEE, to test the model because the measured GEP was estimated values after considerable and complex calculations and was somewhat uncertain. Moreover, in revised paper, we deleted the contents about Equation 1, meaning that we did not estimate GEP anymore, and we only simulated GEP in different layers within canopy by the model.
Comment 15. 233: these parameters need to be described in more detail because they are critical for the results, especially the clumping factor and the leaf angle distribution
Response: Thank you for this suggestion. The information and values of these model parameters were added in the text. Wheat leaf area index, plant height in different development stages were measured by local technicians and have been recorded since 2008. The leaf angle values were consulted to the local research community who mainly study crop agronomic traits and yield. We obtained leaf transmittance from literature, where the wheat leaf area index, irrigation and fertilization practices in cropland were similar to wheat cropland in our study. The information about the clumping factor for wheat was not observed during the study period, the model would be drived by clumping factor = 1, which indicates a homogeneous canopy with a random dispersion of leaf area, as suggested by Tang et al., (2013).
Comment 16. 262: Having measured wheat eddy covariance I question the usefulness of a 10-14 local time focus. Especially under high VPD wheat will tend to have far lower GEP in the afternoon, depending on the prevailing climatic conditions which need more description.
Response: Thank you very much. The daily variation of GEP estimated from NEE observed by eddy covariance during our study period showed that there was almost no inhibition and decrease in photosynthesis in the afternoon. The depression in photosynthesis may be related to many factors, such as stomatal closure, decline of carbon dioxide concentration, difficulty to transport photosynthate after accumulation and the enhancement of plant respiration when air temperature and VPD increase in the afternoon. Therefore, the reason that why wheat photosynthesis was not depressed in this study is unclear and need further study.
Comment 17. 296: fluxes themselves are unlikely to be impacted by ustar for physiological reasons except under extremely stagnant air conditions that inhibit evapotranspiration, and which are unlikely to be observed during the 10-14 local time study period. This finding is likely an artefact that suggests that the authors need to do more data filtering.
Response: Thanks a lot for your suggestion. The underestimation of carbon fluxes is a universal phenomenon for eddy covariance measurements, which is mainly caused by the steady atmospheric turbulence at nighttime. Therefore, scientific community usually filter and reject the underestimated carbon flux values that corresponding to u* < threshold of u* (this data process step was also carried out in our study). The threshold means that carbon fluxes is lower and nearly constant when u* < threshold but increased significantly when u* > threshold. In daytime, u* is usually greater than the threshold because atmospheric turbulence is not steady, suggesting that u* in daytime would impact ecosystem carbon exchange. Another reason for considering u* in study is related to ANN method. The more independent variables trained in the model, the more reliable the calculated results.
Comment 18. Figure 3 is taking a model of GEP that is a function of PAR – and that probably doesn’t fit particularly well nor was there any evidence that it was fit independently during different phases of canopy development as it should be if leaf area is increasing – and using it to infer diffuse PAR impacts when diffuse PAR was partitioned using a model that probably didn’t work very well from total PAR. The reasoning is circular, hence my suggestion that the results are an artifact.
Response: Thanks a lot. In the new paper, we abandon the analysis of diffuse effect on GEP on the basis of the residuals of GEP model, i.e. the confounding effect of other environmental factors (mainly refer to total light, Ta and VPD) were considered. This is because diffuse light changes synchronously with other environmental factors and should not be separated from other factors as the reviewers suggesting. So we established new relationship between GEP and diffuse light, which can reflect the real situation. Because in the second increase phase of diffuse light (Fig. 3 in revised paper), diffuse light, total light, Ta and VPD covaried and increased, it is unknown whether the increase in GEP was induced from diffuse light or other factors. A normalized method to judge this issue. Results showed that the increase in GEP in the second increase phase of diffuse light was the combined effect of diffuse light, total light, Ta and VPD. Finally, we concluded that GEP increased significantly with diffuse light in a linear pattern.
Comment 19. 334: this argument does not take into account any differences in photosynthetic efficiency in the lower canopy which is likely to be important as plants tend to allocate nitrogen (and more) toward upper leaves, making them more efficient.
Response: Thank you very much for this suggestion. In this study, we mainly focused on the diffuse light effect and quantified its importance. We also tried to illustrate the underlying mechanism behind the effect from the perspective of light interception because there may be more mechanisms for the enhancement of GEP under diffuse light conditions. Figure 6 and 7 in the old manuscript indicated that the photosynthesis in upper and middle canopy was greater than lower canopy when diffuse light level did not change, and we attributed this difference to more light intercept by middle and upper layers. After reading this comment of the reviewer, we began to think that the larger photosynthesis in upper layers may also be related to the nutrition allocation from lower canopy to upper canopy, thus, we added the relevant discussion in Section 4.2 in the text. However, we believe that the nutrition in lower canopy is absorbed from the soil, and we wonder if the nutrition in the soil is related to diffuse light change. In other words, nutrition allocation towards upper layer may be one of reasons why upper photosynthesis is greater than lower photosynthesis when diffuse light is relatively constant, and whether it contributes to the photosynthetic increase in middle and lower canopy when diffuse light increase may need further exploration.
Comment 20. Figure 6 needs a legend in the figure.
Response: Thanks for this suggestion. We have added the legend in the figure.
Comment 21. Figure 8 is interesting but I have no idea how diffuse PAR can equal 1250 micromoles / m2 / second. It would hurt the eyes to look at the sky away from the sun.
Response: Thank you very much. The meteorological measurements showed that the maximal PAR during our study period was approximately 2000 micromoles / m2 / second. The diffuse light fraction (fDIF) mainly ranged from 0.4—0.6 during April to May, so the diffuse PAR could reach to approximately 1250 micromoles / m2 / second. This may have something to do with the local climate conditions.
Comment 22. Section 4.2: these are entirely model results with no empirical evidence.
Response: Thank you very much. The multiple canopy layer model not only simulates carbon, sensible heat flux and microclimate within the canopy, but also simulates these variables above the canopy. Thus, the model can be verified by the measurement of fluxes and meteorological factors. Although the measurements within the canopy were not available, but the measurements above the canopy could be obtained because they had been measured by eddy covariance. So we used the measured uninterpolated NEE, heat flux and light to test the model. If there is a highly correlated relationship between observed and simulated values, then the model is useful and valid and the simulation results (including carbon flux and PAR within canopy) of the model are credible. However, we should also be aware that although the model was verified, future measurements for photosynthesis and microclimate in different layer within canopy is essential because it can further test our result and make them more solid. We also added this outlook in the Section 4.2.
Comment 2 3. 482: ‘eliminate’ is too strong a word here but this is an interesting point
Response: Thank you. We revised ‘eliminate’ as ‘weaken’.
Citation: https://doi.org/10.5194/egusphere-2022-1003-AC2
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AC2: 'Reply on RC2', Xueyan Bao, 08 Feb 2023
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