the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The utility of simulated ocean chlorophyll observations: a case study with the Chlorophyll Observation Simulator Package (version 1) in CESMv2.2
Genevieve Clow
Nicole Lovenduski
Michael Levy
Keith Lindsay
Jennifer Kay
Abstract. For several decades, a suite of satellite sensors has enabled us to study the global spatiotemporal distribution of phytoplankton through remote sensing of chlorophyll. However, the satellite record has extensive missing data, partially due to cloud cover; regions characterized by the highest phytoplankton abundance are also some of the cloudiest. To quantify potential sampling biases due to missing data, we developed a satellite simulator for ocean chlorophyll in the Community Earth System Model (CESM) that mimics what a satellite would detect if it were present in the model-generated world. Our Chlorophyll Observation Simulator Package (ChlOSP) generates synthetic chlorophyll observations at model runtime. ChlOSP accounts for missing data – due to low light, sea ice, and cloud cover – and it can implement swath sampling. Results from a 50-year pre-industrial control simulation of CESM-ChlOSP suggest that missing data impacts the apparent mean state and variability of chlorophyll. The simulated observations exhibit a nearly -20 % difference in global mean chlorophyll compared with the standard model output, which is the same order of magnitude as the projected change in chlorophyll by the end of the century. Additionally, missing data impacts the apparent seasonal cycle of chlorophyll in subpolar regions. We highlight four potential future applications of ChlOSP: (1) refined model tuning, (2) evaluating chlorophyll-based NPP algorithms, (3) revised time to emergence of anthropogenic chlorophyll trends, and (4) a testbed for the assessment of gap-filling approaches for missing satellite chlorophyll data.
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Genevieve Clow et al.
Status: open (until 05 Oct 2023)
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RC1: 'Comment on gmd-2023-143', John Dunne, 20 Aug 2023
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The manuscript “The utility of simulated ocean chlorophyll observations: a case study with the Chlorophyll Observation Simulator Package (version 1) in CESMv2.2” by Clow et al. describe a satellite based ocean chlorophyll observation simulator driven by the NCAR coupled carbon-climate Earth system model and its implication for the interpretation of satellite chlorophyll observations under the confounding influence of clouds, limitations of passive solar reflectance observations and other practicalities of polar orbiting satellite observations. The analysis is an important assessment of uncertainty and bias in these observationally based estimates and I have only minor points where I thought more explanation was necessary.
Specific points:
62 – what about the uncertainty associated with comparison of satellite optical depth and fully vertically resolved models?
Figure 1 – Cloud cover scale looks like it should only go down to 40%... how much of the oceans is the 20-40% range?
70 – The mechanistic explanation is fairly simple and should be explained here – Areas where the ocean is cooler than the air (like upwelling regions) tend to cool the air, raise the humidity, and form clouds. This is in addition to all ocean areas tending to raise the humidity.
187 – “equilibrium of the deep ocean can take hundreds of years” Actually, equilibrium of the deep ocean can take thousands of years.
234 – “As expected”… It is not clear to me which of the model simulator versus real world differences make the strong difference in Fig. 4 “expected”… is it the return period of once a day in the model versus once every two days in the observations (line 178 “low latitude gaps are then filled during an orbit on the subsequent day”? More explicit attribution is needed here given the large number of differences described above… is the following sentence “The ISCCP configuration of ChlOSP samples more frequently than real-world sensors…” intended as the explanation? The connection to the previous discussion points is not clear.
241 – “white caps, coccolithophores, and aerosols are already simulated in some capacity in CESM and could be added to ChlOSP with minor modifications” if this would have improved the model with only “minor modifications” why was it not done in the present study?
Table 1 – My interpretation of the longer e-folding time scales in the simulator than the observations is that the underlying model is missing important local scale forms of variance such as eddies, fronts, jets, etc. such that there is little correlation between the values separated two days apart. It is not clear the mechanism to which the authors are attributing this difference. In the simulator which is sampling daily, there is a resolved signal of autocorrelation. In the observations, there is not which contradicts the model behavior of signals persisting for 4 days. It is hard to know how to interpret the value of the model as an autocorrelation simulator in this case.
273 – “We have also shown that the missing data leads to a more realistic modeled representation of the chlorophyll variance.” I do not see this assertion being supported anywhere as autocorrelation and variance are different things… has the variance in both the underlying model and simulator been assessed?
Figure 9 – I am not sure the meaning or value of this figure. The title refers to “chlorophyll temporal correlation, but I think the authors intend “chlorophyll bias correlation”… Are positive values where cloud cover leads to a positive bias in chlorophyll, and negative where it leads to a negative bias or is it truly the temporal correlation of high clouds when chlorophyll is temporally increasing as the title suggests? If the latter, than what is the significance? More clarity is needed.
319-320 – “If satellite sensors could see through clouds (as in the 320 Clear-Sky configuration), the global chlorophyll mean would be overestimated by 14 to 22 %.” Is this due to the diurnal sampling bias, the seasonal sampling bias, or something else? The following sentences discuss mechanisms and may be connected. Answering this may simply involve switching the order of the sentences such that the mechanisms are attributed directly to the result… e.g. “… As a result, if satellite sensors could see through clouds (as in the 320 Clear-Sky configuration), the global chlorophyll mean would be overestimated by 14 to 22 %.”
Citation: https://doi.org/10.5194/gmd-2023-143-RC1
Genevieve Clow et al.
Genevieve Clow et al.
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