dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical evaluation of the modeled CO enhancements in pollution plumes In this section, we present a statistical validation of the SOFT-IO calculations based on the entire IAGOS CO data base  (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013). The ability of SOFT-IO in simulating CO anomalies is evaluated compared to in situ measurements in terms of: • spatial and temporal frequency of the plumes • mixing ratio enhancements in the plumes To achieve this, SOFT-IO performances are investigated over different periods of IAGOS measurements depending on the emission inventory used. Three of the four global inventories selected previously (MACCity, GFAS v1. 2, GFED4) are available between 2003 and 2013. EDGAR v4. 2 ends in 2008. In the following sections (Sect. 5. 1 and 5.
Detection frequency of the observed plumes with SOFT-IO The ability of SOFT-IO to reproduce CO enhancements was investigated using CO plumes obtained applying the methodology described in Sect. 3. 4 on all flights of the IAGOS database between 2003 and 2013. The frequency of simulated plumes that coincide with the observed C A anomalies is then calculated. Simulated plumes are considered when matching in time and space the observed plumes, while modeled CO is on average higher than 5 ppb within the plume. Note that at this stage, we do not consider the intensity of the plumes. The resulting detection rates are presented in Fig. 9  for eight of the eleven regions shown in Fig. 4. Statistics are presented separately for three altitude levels (Lower Troposphere 0-2 km, Middle Troposphere 2-8 km and Upper Troposphere > 8 km).
10b ). This confirms the good capacity of the SOFT-IO software in reproducing the CO mixing ratios anomaly in most of the observed pollution plumes. Differences become much larger when considering outlier values of CO anomalies (lower and upper whiskers, ± 2. 7σ or 99. 3%, Fig. 10b ), which means for exceptional events of very low and very high CO enhancements (accounting for 1. 4% of the CO plumes), with biases from ± 10 ppb to ± 50 ppb for most of the regions. Higher discrepancies are found in the lower and the upper troposphere and can reach ±50 to ±200 ppb in two specific regions (North Asia UT and South Asia LT) for these extreme CO anomalies. Note that North Asia UT and South Asia LT present respectively extreme pollution events related to pyro-convection  (Nédélec et al. , 2005)  for the first region, and to strong anthropogenic surface emissions  (Zhang et al.
Conclusions Analyzing long term in situ observations of trace gases can be difficult without a priori knowledge of the processes driving their distribution and seasonal/regional variability, like transport and photochemistry. This is particularly the case for the extensive IAGOS database, which provides a large number of aircraft-based in-situ observations (more than 51000 flights so far) distributed on a global scale, and with no a priori sampling strategy, unlike dedicated field campaigns. In order to help studying and analyzing such a large data set of in situ observations, we developed a system that allows quantifying the origin of trace gases both in terms of geographical location as well as source type. The SOFT-IO module (https://doi. org/10. 25326/2) is based on the FLEXPART particle dispersion model that is run backward from each trace gas observation, and on different emission inventories (EDGAR v4.
2, MACCity, GFED 4, GFAS v1. 2) than can be easily changed. The main advantages of the SOFT-IO module are: • Its flexibility. Source-receptor relationships pre-calculated with the FLEXPART particle dispersion model can be coupled easily with different emission inventories, allowing each user to select model results based on a range of different available emission inventories. • CO calculation, which is computationally very efficient, can be repeated easily whenever updated emission information becomes available without running again the FLEXPART model. It can also be extended to a larger number of emission datasets, particularly when new inventories become available, or for emission inventories inter-comparisons. It can also be extended to other species with similar or longer lifetime as CO to study other type of pollution sources.
• High sensitivity of the SOFT-IO CO mixing ratios to source choice for very specific regions and case studies, especially in the LT most of the time driven by local or regional emissions, may also help improving emission inventories estimates through evaluation with a large database such as IAGOS one. Indeed as it is based on a Lagrangian dispersion model, the tool presented here is able to reproduce small-scale variations, which facilitates comparison to in situ observations. It can then be used to validate emission inventories by confronting them to downwind observations of the atmospheric composition, using large database of in situ observations of recent pollution. In this study SOFT-IO is applied to all IAGOS CO observations, using ECMWF operational meteorological analysis and 3-hour forecast fields and inventories of anthropogenic and biomass burning emissions available on the ECCAD portal.
Data below 2 km are not used because high CO mixing ratios caused by fresh emissions are usually observed close to surface over continents. The slope a (in ppb m -1 ) of the linear regression is used to determine the background so that C VP_back = aZ. The background is removed from the C VP tropospheric vertical profiles mixing ratio to obtain a residual CO mixing ratio C R VP (Eq. 2). (Eq. 2): C R VP = C VP -C VP_back , For the upper troposphere, the CO background mixing ratio (C UT_back ) is determined using seasonal median values (over the entire IAGOS database) for the different regions of Figure 4. Note that this approach was not feasible for vertical profiles as for most of the visited airports there are not enough data to establish seasonal vertical profiles. As for the profiles, background values are subtracted from the UT data to obtain residual C R UT (Eq.
Acknowledgements The authors would like to thanks  ECCAD  project for providing emission inventories. The authors acknowledge the strong support of the  European Commission, Airbus , and the  Airlines (Lufthansa, Air-France ,  Austrian ,  Air Namibia ,  Cathay Pacific , Iberia and China Airlines so far) who carry the MOZAIC or IAGOS equipment and perform the maintenance since 1994. In its last 10 years of operation, MOZAIC has been funded by  INSU-CNRS (France) ,  Météo-France ,  Université Paul Sabatier (Toulouse, France)  and  Research Center Jülich (FZJ, Jülich, Germany ). IAGOS has been additionally funded by the  EU projects IAGOS-DS and IAGOS-ERI. The MOZAIC-IAGOS database is supported by  AERIS  ( CNES  and  INSU-CNRS ). The former  CNES-ETHER program  has funded this project. Atmos.
, the red line represents CO anomalies. With this algorithm CO plumes are automatically detected in the entire IAGOS database. For each identified plume, minimum and maximum values of the date, latitude, longitude and altitude, as well as the CO mean and maximum mixing ratio, are archived. These values are used for comparison with modeled CO values. Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2017-653 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 26 July 2017 c Author(s) 2017. CC BY 4. 0 License.
Furthermore, the life span of a single satellite instrument is generally shorter than (comparable to in some cases, e. g. , TIMED SABER, ENVISAT MIPAS, ENVISAT GOMOS, Aura MLS) one solar cycle and instrumental biases between different ozone profile datasets complicate statistical analysis of decadal variations  (Fioletov, 2009; Dhomse et al. , 2016). In this regard, a suitable alternative for understanding better the direct effect is to examine the ozone response on Sun's rotational timescale (i. e. , about 27 days). Although the irradiance fluctuations during the rotational cycle are on average smaller than during the 11-year solar cycle, there are many more rotational cycles than 11-year cycles, improving the statistics considerably. A number of observational studies have been carried out to determine the effects of the solar rotational cycle on stratospheric ozone, generally at low latitudes (i.
g. ,  Rottman et al. , 2004) , ozone response and correlation are expected to be the largest. This has been supported by observational (e. g. ,  Hood, 1986; Zhou et al. , 2000; Fioletov, 2009; Ditky et al. , 2010)  as well as modeling  (Kubin et al. , 2011)  studies which demonstrated a better identification of the ozone signal associated with enhanced rotational forcing fluctuations. This relationship has, however, been challenged by contradictory results. Hood and Zhou (1998)  analyzed UARS MLS ozone data for the 1991-1994 period and found a correlation 2 times stronger during the last half of the period, i. e. , when the rotational forcing fluctuations are reduced. They suggested that it might have been the result of an artifact of either instrumental or geometric (local time coverage) origin that may have affected the earliest part of the UARS MLS ozone record more than the later part.
As a second step, we take advantage of the ensemble of CCM simulations and its large statistics to (i) assess the influence of the solar cycle phase on the ozone sensitivity to the rotational cycle and (ii) quantify the time window required for a robust estimation of the ozone sensitivity. Observational datasets, and model configurations and simulations are described in Sect. 2. Section 3 presents comparisons between satellite observations and model (CTM and CCM) simulations of the ozone response to the solar rotational cycle. Section 4 focuses on CCM results to examine the influence of (i) the solar activity fluctuations and (ii) the length of the time window in the estimation of the ozone sensitivity to the solar rotational cycle. The main findings are summarized in Sect. 5. Note that, for the sake of simplicity, the first period  (October 1991 -September 1994)  during cycle 22 will be referred to thereafter as 1991-1994 period and the second period  (September 2004 -August 2007)  during cycle 23 will be referred to as the 2004-2007 period.
Microwave Limb Sounder ozone satellite observations We use the stratospheric ozone measurements from the two Microwave Limb Sounder (MLS) instruments onboard UARS (cycle 22) and Aura (cycle 23). UARS MLS was launched on 12 September 1991, into a 57 • inclination and a 585 km altitude orbit, and was operational until 1994. Waters (1989 Waters ( , 1993) )  describes in detail the microwave limb-sounding technique. We used the version 5 UARS MLS dataset described  Livesey et al. (2003). The ozone retrieval is based on 205 GHz radiances, provided onto 13 pressure levels in the range 100-1 hPa  (100, 68. 1, 46. 4, 31. 6, 21. 5, 14. 7, 10, 6. 8, 4. 6, 3. 2, 2. 2, 1. 5  and 1 hPa) and has an average vertical resolution of 4 km in the stratosphere. The typical 1σ precision for ozone mixing ratio measurements is ∼ 0.
6a ). Overall, the main area of significant correlation appears also lifted upward in the observations (Fig. 6a ) compared to the CTM (Fig. 6b ). The fact that the correlation signal in the middle and lower stratosphere (below 10 hPa) is found in the CTM but not in the observations may partly arise from the large noise present in the UARS MLS ozone dataset at these altitudes (not shown). In contrast, the results for the period 2004-2007 reveal a particular good agreement throughout stratosphere between the observations (Fig. 6d ) and the CTM (Fig. 6e ), where the maximum is found at the same altitude (10 hPa), time lag (+4 days) and with the same amplitude (0. 2). CCM results show a maximum of correlation also at 10 hPa and at the same time lag but with a higher value (0. 3). In addition to the area of statistical significance which increases when examining CCM results, we notice a strong reduction of the discrepancies in the response between both periods.
As a consequence, this may affect the ozone response significantly by reducing the temperature feedback on chemical reaction rates, notably ozone destruction through the Chapman cycle. Recently,  Sukhodolov et al. (2016)  examined the separate effects of heating rates and photolysis rates in solar-driven ozone changes using a 1-D radiativeconvective-photochemical model and different SSI datasets. Using the NRLSSI solar forcing dataset, they showed that, over the course of the 11-year solar cycle, the direct heating rate anomaly leads to a decrease in ozone of 1 % in the middle and upper stratosphere (above 30 hPa), while the photolysis induces an ozone increase of 2 to 4 %. Since, the direct radiative effect of UV on heating rates is neglected in our CCM experiments, the ozone response to solar variability may hence be overestimated.
Overall, it is likely that the discrepancies in the estimated value of ozone sensitivity found in previous studies originate from differences in the length of time windows that were used for analysis and in the level of solar activity associated with these periods. Both parameters significantly influence the accuracy of solar rotational signal estimates. In this regard, it is likely that similar issues have also affected the accuracy in the estimation of ozone response to the 11year solar signal. The estimation is expected to be even more difficult because observational time series cover a very limited number of 11-year cycles and there are other well-known sources of decadal variability in the atmosphere and climate system. Maycock et al. (2016)  recently found very large discrepancies in the estimation of the ozone response to the 11year cycle using various satellite datasets which cover different time periods of different length.
Acknowledgements. This project was supported by the European Project  StratoClim  ( 7th Framework Programme , grant agreement  603557 ) and the grant " SOLSPEC " from the  Centre d'Etude Spatiale (CNES). Rémi Thiéblemont  was supported by a grant from the LABEX L-IPSL, funded by the  French Agence Nationale de la Recherche  under the " Programme d'Investissements d'Avenir ". The authors thank the three anonymous reviewers for their detailed review comments, which improved the manuscript. Edited by: Gabriele Stiller Reviewed by: three anonymous referees.
The temporal horizon of our predictions is comparable to the time needed for a reversal to occur, but shorter than the typical time elapsed between reversals. Coarse predictions could thus provide an ''early warning system", indicating that a reversal might occur within the next few millennia. We present a series of tests to investigate if our proposed framework, which relies on low-dimensional models and data assimilation, produces more reliable predictions than several purely data-based prediction strategies. Predictions obtained in this way rely on the assumption that the paleomagnetic data, as documented by Sint-2000 and PADM2M, (one data point every 1000 years) are sufficiently accurate for this purpose. Conditional on the latter assumption, we conclude that coarse predictions are indeed within reach, even with simple low-dimensional models.
Paleomagnetic data The data we use are the signed relative paleointensity of the past 2 Myr. These intensities describe estimates of the strength of the axial dipole, and are available in the Sint-2000  (Valet et al. , 2005)  and PADM2M  (Ziegler et al. , 2011)  data sets with a 1 kyr time step. The polarity is encoded by the sign of the dipole, which is taken from the geomagnetic polarity time scale  (Cande and Kent, 1995; Lowrie and Kent, 2004). To find the exact timing of the polarity changes we proceed in slightly different ways for Sint-2000 and PADM2M. In the case of Sint-2000, we assume that reversals occur at time of polarity changes as confirmed from inspection of the original directional information of  Valet et al. (2005)  (J. P. Valet, personal communication). In the case of PADM2M, however, we do not have access to analogous directional information.
We therefore a priori assumed the same timing as for Sint-2000, and checked that reversals did correspond to a minimum in the intensity record provided by PADM2M to within 1kyr. This turned out to be the case for most reversals, except for the Bruhnes Matuyama reversal and the two reversals bounding the Cobb mountain subchron. For these three reversals, a slight time shift was introduced to reconcile their timing with that of intensity lows in PADM2M, resulting in slight shifts in the timing of the sign changes in the PADM2M signed relative paleointensity with respect to that of  Sint-2000. For each data set, a unit relative paleointensity corresponds to a virtual axial dipole moment of 7:46 10 22 Am 2 , as in  Valet et al. (2005). Both data sets contain the relative paleointensity along with a Gaussian error model, i.
e. , every 1 kyr a datum of the paleointensity is available along with an estimated standard deviation. However, the standard deviations of PADM2M are significantly smaller than those of Sint-2000. While the small errors of PADM2M may be accurate representations of the ''pure" data error, they seem unreasonably small in the context of data assimilation. The reason is that these errors must describe a combination of ''measurement errors", i. e. , the uncertainty of the data, and ''model errors", i. e. , how good the (low-dimensional) model is. We thus adjust the errors in PADM2M to account for model error. In the data assimilation (see Section 3) we use the Sint-2000 standard deviations for the PADM2M data. In particular, we find that the data assimilation is more stable with the larger standard deviations of Sint-2000.
Scaling of G12 The G12 model is not equipped with a natural scaling of the amplitude of the dipole variable D to the geomagnetic dipole amplitude, or with a scaling of G12 model time, t, to geophysical time. To find the amplitude scaling of G12 we compute, as before, the average relative paleointensity of the unsigned Sint-2000 and PADM2M data sets and also compute the average of the absolute value of the dipole variable of ten G12 model runs for 250 dimensionless time units. By setting G12 amplitude scaling : D ¼ ffiffiffi 2 p Â relative paleointensity ðsignedÞ; the average of the G12 dipole variable is approximately equal to the average relative paleointensity. Moreover, this scaling leads to good agreement of the histograms of the dipole variable D and of the signed relative paleointensity of Sint-2000 and PADM2M (left panel of Fig.
For the G12 model, the parameters are estimated from ten simulation for 10 4 dimensionless time units. The right panel of Fig. 3  shows histograms and corresponding gamma distributions for CK95(1) and G12 when using this geological time scale. It is instructive to assess this scaling by comparing the power spectral densities of G12 simulation data and Sint-2000/ PADM2M data. We compute these spectra by the multi-taper spectral estimation technique described in  Constable and Johnson (2005). The spectra are shown in the left panel of Fig. 4. Note that the first corner frequencies of the G12 model and of the Sint-2000 and PADM2M data match, but that the G12 model has a larger high-frequency content than PADM2M or Sint-2000 (by roughly one order of magnitude for frequencies of 2 Myr À1 and above).
We can attribute the low-frequencies to the occurrence of reversals, and the high frequencies to millennium scale dipole variations during chrons. This suggests that, when scaled using the above geological time scale, the dynamics of G12 essentially match the reversal statistics of the geomagnetic dipole, but fail to match its millennium behavior. We note that the high frequency content of Sint-2000 and PADM2M could be underestimated because the data are obtained by averaging over stacks, which possibly smoothes the signal. Indeed,  Constable and Johnson (2005)  constructed a spectral model whose high-frequency content is also larger than that of PADM2M or Sint-2000. However, we also note that the above geological time scale was computed using reversal statistics over the past 30 Myr, a period during which the reversal rate has increased by a factor of about 2 (see, e.
G12 based SDE We further use the G12 model to propose an additional scalar SDE model, similar to the B13 model. We mimic the construction of the B13 model, but substitute the paleomagnetic data (Sint-2000 or PADM2M) with synthetic data from G12 scaled to the millennium scale as described above. In constructing a G12 based SDE model, we postulate an SDE (1) for the dipole of the G12 model and use the numerical techniques of  Buffett et al. (2013)  to estimate the drift and diffusion coefficients from G12 simulation data (rather than from paleomagnetic data). Specifically, we fit a cubic function to the drift and a quadratic function to the square root of the diffusion coefficient. We refer to this model as the ''G12 based SDE". A typical simulation with the G12 based SDE is shown in the lower right panel of Fig.
Data assimilation results We perform data assimilation using the various numerical methods described in appendix B and the two data sets Sint-2000 and PADM2M. For each model, data set and data assimilation technique, we compute the relative error of the assimilation over the 2 Myr period defined by e ¼ X 2000 n¼1 z n À b E x n j z 1:n Â Ã 2 X 2000 n¼1 ðz n Þ 2 ; ð4Þ where z n are the data at time n kyr and b E x n j z 1:n Â Ã is the approximation of the conditional mean of the dipole given the data up to time n kyr. The conditional mean is the minimum mean square error estimate of the state, see, e. g. ,  Chorin and Hald (2013). Each method resorts to a finite number of model samples, also called particles, whose distribution aims at providing a faithful description of the model uncertainties.
Data assimilation with G12 We now consider the deterministic G12 model and use the EnKF for deterministic models (D-EnKF) and sequential data assimilation with implicit sampling for deterministic models (D-IMP) to assimilate the PADM2M and Sint-2000 data. The D-EnKF and D-IMP techniques are described in detail in section B. 1 of appendix B. When considering sequential data assimilation with implicit sampling, we can vary the number of data points we assimilate per sweep (see appendix B. 1. 2). Specifically, one can attempt to assimilate the 2 Myr of data in one sweep, i. e. , one can try to find initial conditions for G12 that lead to a trajectory of the dipole variable that is compatible with the paleomagnetic data. However, this approach did not prove successful because the optimization required for implicit sampling failed to converge.
We investigate this issue by using synthetic data shown in Fig. 8 , generated as follows. We simulate the G12 model starting from initial conditions that  lead to a dipole sequence similar to that of the paleomagnetic data. We record the state every 1 kyr over a 2 Myr period, and add random errors that are distributed similarly to those of Sint-2000. Specifically, the errors are Gaussian and the standard deviation is chosen such that the mean of the relative paleointensity divided by the standard deviation of the errors is the same for Sint-2000 and the synthetic data. For the rest of this paper, we will refer to this synthetic data set as the ''Synt" data set. We observe discontinuities when assimilating this synthetic data, as illustrated in the top-right panel of Fig. 7. This is an important observation, since, by construction, the Synt data are intrinsically compatible with the G12 model.