Can We Measure a COVID-19-Related Slowdown in Atmospheric CO2 Growth? Sensitivity of Total Carbon Column Observations
Abstract
:1. Introduction
2. Data and Methods
2.1. Total Carbon Column Observations
2.1.1. XCO2 Data Set
2.1.2. TCCON Sites
2.1.3. TCCON Time Series and Averaging
2.2. Mathematics to Derive Trends, Annual Growth Rates, and Related Confidence Intervals
2.2.1. Model Fit
2.2.2. Confidence Intervals for Model Fit Parameters
2.2.3. Combined Trends from Multiple Sites
2.2.4. Annual Growth Rates
2.2.5. Confidence Intervals for Annual Growth Rates
2.2.6. Combined Annual Growth Rates from Multiple Sites
2.3. Forecast of 2020 Annual Growth Rate for Mauna Loa
3. Results
3.1. TCCON Trend
3.2. TCCON Annual Growth Rates
3.2.1. Interannual Variability of TCCON Annual Growth Rates
3.2.2. Confidence Bands for the TCCON Annual Growth Rates
Oversampling with Single-Spectra Resolution
Sampling with Synoptic-Scale Temporal Resolution
Undersampling with Monthly-Scale Temporal Resolution
- The confidence bands in Figure 2a provide a measure for a contribution of ≈0.05 ppm/yr resulting from the propagation of single-measurement precision into TCCON-derived annual growth rates, but are no realistic measure for the total annual growth uncertainty (due to the oversampling/underrepresentation of the synoptic evolution).
- The black Figure 2b–d confidence bands from daily, weekly, and 2-weekly TCCON data (i.e., 0.38 [0.28, 0.44] ppm/yr) can be considered as the realistic total uncertainty estimate/range for the hemispherically representative annual growth rates attainable from the TCCON data; it is favorable to base the analysis of annual growth rates on the time series aggregated into synoptic-scale temporal resolution.
- For preferred synoptic-scale sampling, the annual growth rates from sites with differing sampling densities (see Table 1) become consistent (Figure 2). As a result, it makes sense to combine the annual growth rates of various sites to thereby reduce the confidence width as shown in Table 3 (i.e., not only use the site with the densest sampling).
- Aggregating TCCON time series into monthly means leads to an undersampling of the intra-annual XCO2 evolution and thereby to unreliable annual growth rates and too large confidence bands.
3.3. Synopsis of TCCON Annual Growth Rates with Mauna Loa 2020 Forecast
4. Discussion
4.1. Discussion of Trend Results
4.2. Discussion of TCCON Annual Growth Rates
4.3. Can TCCON Measure a COVID-19-Related Reduction of the Annual Growth Rate? Discussion of Five Cases
4.3.1. Case (i)
4.3.2. Case (ii)
4.3.3. Case (iii)
4.3.4. Case (iv)
4.3.5. Case (v)
5. Summary and Conclusions
- (i)
- There is a 0.6 [0.4, 0.7]-yr contribution to the detection delay due to the impact of synoptic variability on XCO2 observations. This was inferred solely from the TCCON data analysis. The forecast-based verification of this result, however, was not feasible. This is because the forecast uncertainty for the forecasted reference case (without the COVID-19 impact) exceeds the forecasted (and to-be-measured) 2020 growth rate reduction. The currently attainable forecast confidence is only ≈10% narrower than the max–min range observed by TCCON during the last 10 years.
- (ii)
- There is a ≈1-month (0.08-yr) contribution to the detection delay, originating from the (0.8 ppm) single-measurement precision of the TCCON measurements on the ≈1 min scale.
- (iii)
- Taking the reported forecast uncertainty of ±0.57 ppm/yr for the forecasted reference case (without the COVID-19 impact) fully into account, a one-time growth rate reduction of −0.32 ppm yr−2 in 2020 cannot be detected. The same holds true if the growth rate reduction would stay constant on the same level during the subsequent years.
- (iv)
- We assumed a growth rate reduction of −0.32 ppm yr−2 starting in 2020, as in the cases before based on a −8 % emissions reduction in 2020. However, we then additionally assumed a year-on-year increase in the growth rate reduction by −0.32 ppm yr−2. This describes a desirable progressive emission reduction over the years, which may or may not be COVID-19 related after 2020. This case is comparable to the rates of decrease needed over the next decades to limit climate change to a 1.5 °C warming. For this case, we derived an overall detection delay of 2.4 [2.2, 2.5] yr. This is limited by the forecast uncertainty with an additional contribution from synoptic variability.
- (v)
- Finally, assuming the same type of progressive growth rate reduction, we investigated the case that no forecast for the reference case (without the COVID-19 impact) would be available. The idea to derive a detection delay was that due to the progressive growth rate reduction assumed, that the growth rate will leave at a certain point the max–min range of the previous observations. The resulting overall detection delay is 5.2 [4.8, 5.3] yr.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
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Site | Lat. | Lon. | Alt. (km) | Measurement Days per Year (Average) | Spectra per Day (Average) | Single Spectra Integration Time (s) | Data Set |
---|---|---|---|---|---|---|---|
Garmisch (gm 1) | 47°N | 11°E | 0.74 | 127 | 79 | 95 | [25] |
Zugspitze (zs) | 47°N | 11°E | 2.96 | 110 | 37 | 18/97 2 | [26] |
Karlsruhe (ka) | 49°N | 8°E | 0.12 | 120 | 46 | 35 | [27] |
Park Falls (pa) | 46°N | 90°W | 0.44 | 238 | 118 | 95 | [28] |
Site | Fitted Period | Trend Slope [ppm/yr] | Confidence Interval [ppm/yr, 95%] |
---|---|---|---|
Garmisch | (Jan 2011–Dec 2019) | 2.46 | [2.44, 2.49] |
Karlsruhe | (Jan 2011–Dec 2019) | 2.46 | [2.43, 2.49] |
Park Falls | (Jan 2011–Dec 2019) | 2.44 | [2.42, 2.46] |
Zugspitze | (Jan 2016–Dec 2019) | 2.42 | [2.33, 2.50] |
Combined (gm, ka, pa) | (Jan 2011–Dec 2019) | 2.45 | [2.44, 2.47] |
Site | Monthly (ppm/yr) | 2-Weekly (ppm/yr) | Weekly (ppm/yr) | Daily (ppm/yr) | Single (ppm/yr) |
---|---|---|---|---|---|
Garmisch | 1.02 | 0.86 | 0.73 | 0.53 | 0.07 |
Karlsruhe | 1.03 | 0.91 | 0.82 | 0.60 | 0.10 |
Park Falls | 0.69 | 0.60 | 0.53 | 0.40 | 0.04 |
Zugspitze | 0.88 | 0.74 | 0.64 | 0.49 | 0.20 |
Combined (gm, ka, pa, zs) | 0.51 | 0.44 | 0.38 | 0.28 | 0.05 |
Case (i)−(v) Assumptions | Delay Type | Delay Time (yr) | Data Basis | Dominant Mechanism |
---|---|---|---|---|
(i) one-time growth rate reduction 2020 = −0.32 ppm yr−2; forecast error = 0; TCCON confidence (weekly sampling) = 0.38 ppm/yr | delay contribution | 0.6 [0.4, 0.7] | weekly TCCON data | weekly-scale synoptic variability of XCO2 |
(ii) one-time growth rate reduction 2020 = −0.32 ppm yr−2; forecast error = 0; TCCON confidence (single-spectra sampling) = 0.05 ppm/yr | delay contribution | 0.08 | single-spectra TCCON data | TCCON single-measurement precision |
(iii) growth rate reduction starting 2020 and constant afterwards = −0.32 ppm yr−2; forecast error = ±0.57 ppm/yr | overall delay | ∞ | forecast error | forecast error |
(iv) growth rate reduction starting 2020 and linear annual increase afterwards = −0.32 ppm yr−2; forecast error = ±0.57 ppm/yr; TCCON confidence (weekly sampling) = 0.38 ppm/yr | overall delay | 2.4 [2.2, 2.5] | forecast error and weekly TCCON data | forecast error |
(v) growth rate reduction starting 2020 and linear annual increase afterwards = −0.32 ppm yr−2; no forecast error available; TCCON max–min range (weekly sampling) = 1.27 ppm/yr; TCCON confidence (weekly sampling) = 0.38 ppm/yr | overall delay | 5.2 [4.8, 5.3] | forecast error and weekly TCCON data | observed max–min range of growth rates |
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Sussmann, R.; Rettinger, M. Can We Measure a COVID-19-Related Slowdown in Atmospheric CO2 Growth? Sensitivity of Total Carbon Column Observations. Remote Sens. 2020, 12, 2387. https://doi.org/10.3390/rs12152387
Sussmann R, Rettinger M. Can We Measure a COVID-19-Related Slowdown in Atmospheric CO2 Growth? Sensitivity of Total Carbon Column Observations. Remote Sensing. 2020; 12(15):2387. https://doi.org/10.3390/rs12152387
Chicago/Turabian StyleSussmann, Ralf, and Markus Rettinger. 2020. "Can We Measure a COVID-19-Related Slowdown in Atmospheric CO2 Growth? Sensitivity of Total Carbon Column Observations" Remote Sensing 12, no. 15: 2387. https://doi.org/10.3390/rs12152387