Vertically constrained CO2 retrievals from TCCON measurements

This report describes a validation study of Greenhouse gases Observing Satellite (GOSAT) data processing using ground‐based measurements of the Total Carbon Column Observing Network (TCCON) as reference data for column‐averaged dry air mole fractions of atmospheric carbon dioxide (XCO2). We applied the photon path length probability density function method to validate XCO2retrievals from GOSAT data obtained during 22 months starting from June 2009. This method permitted direct evaluation of optical path modifications due to atmospheric light scattering that would have a negligible impact on ground‐based TCCON measurements but could significantly affect gas retrievals when observing reflected sunlight from space. Our results reveal effects of optical path lengthening over Northern Hemispheric stations, essentially from May–September of each year, and of optical path shortening for sun‐glint observations in tropical regions. These effects are supported by seasonal trends in aerosol optical depth derived from an offline three‐dimensional aerosol transport model and by cirrus optical depth derived from space‐based measurements of the Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Removal of observations that were highly contaminated by aerosol and cloud from the GOSAT data set resulted in acceptable agreement in the seasonal variability of XCO2 over each station as compared with TCCON measurements. Statistical comparisons between GOSAT and TCCON coincident measurements of CO2column abundance show a correlation coefficient of 0.85, standard deviation of 1.80 ppm, and a sub‐ppm negative bias of −0.43 ppm for all TCCON stations. Global distributions of monthly mean retrieved XCO2 with a spatial resolution of 2.5° latitude × 2.5° longitude show agreement within ∼2.5 ppm with those predicted by the atmospheric tracer transport model.

Abstract. EM27/SUN devices are portable solar-viewing Fourier transform spectrometers (FTSs) that are being widely used to constrain measurements of greenhouse gas emissions and validate satellite trace gas measurements. On a 6-week-long campaign in the summer of 2018, four EM27/SUN devices were taken to five Total Carbon Column Observing Network (TCCON) stations in North America, to measure side by side, to better understand their durability, the accuracy and precision of retrievals from their trace gas measurements, and to constrain site-to-site bias among TCCON sites. We developed new EM27/SUN data products using both previous and current versions of the retrieval algorithm (GGG2014 and GGG2020) and used coincident AirCore measurements to tie the gas retrievals to the World Meteorological Organization (WMO) trace gas standard scales. We also derived air-mass-dependent correction factors for the EM27/SUN devices. Pairs of column-averaged dry-air mole fractions (denoted with an X) measured by the EM27/SUN devices remained consistent compared to each other during the entire campaign, with a 10 min averaged precision of 0.3 ppm (parts per million) for XCO2, 1.7 ppb (parts per billion) for XCH4, and 2.5 ppb for XCO. The maximum biases between TCCON stations were reduced in GGG2020 relative to GGG2014 from 1.3 to 0.5 ppm for XCO2 and from 5.4 to 4.3 ppb for XCH4 but increased for XCO from 2.2 to 6.1 ppb. The increased XCO biases in GGG2020 are driven by measurements at sites influenced by urban emissions (Caltech and the Armstrong Flight Research Center) where the priors overestimate surface CO. In addition, in 2020, one EM27/SUN instrument was sent to the Canadian Arctic TCCON station at Eureka, and side-by-side measurements were performed in March–July. In contrast to the other TCCON stations that showed an improvement in the biases with the newer version of GGG, the biases between Eureka's TCCON measurements and those from the EM27/SUN degraded with GGG2020, but this degradation was found to be caused by a temperature dependence in the EM27/SUN oxygen retrievals that is not apparent in the GGG2014 retrievals.

Carbon dioxide (CO2) is the primary greenhouse gas emitted into the atmosphere due to anthropogenic activities. While it is naturally present as a part of Earth’s carbon cycle, human activities impact the ability of natural sinks to reduce CO2 from the atmosphere and thus alter the carbon cycle. It thus becomes pertinent to focus on the long-term monitoring of atmospheric CO2 and the ability to make precise, accurate, and continuous CO2 measurements.The Orbiting Carbon Observatory-2 (OCO-2) was launched in 2014. It is NASA’s first Earth-orbiting satellite dedicated to making observations of CO2 in the atmosphere and measuring its column-averaged dry-air mole fraction (XCO2). The primary goal of the OCO-2 mission is to provide XCO2 measurements with sufficient precision and accuracy alongside quantifying its seasonal and interannual variability. While OCO-2 provides global coverage and consistently measures at high latitudes, the remote sensing measurement of CO2 from space can be challenging. This is because the goal is to resolve inter-annual CO2 deviations to subcontinental scales, alongside capturing the known seasonal cycle and trends. Further, the XCO2 data is susceptible to location- and surface-property-dependent biases that must be corrected. Thus, validation of the XCO2 data from OCO-2 becomes necessary to ensure a high degree of retrieval accuracy on a global scale.This study uses the new and improved OCO-2 V11.1 dataset and compares coincident XCO2 measurements against three independent datasets. The Total Carbon Column Observing Network (TCCON) is a network of solar-viewing ground-based Fourier Transform Spectrometers and the primary validation source for XCO2 from OCO-2. TCCON measurements are unaffected by surface properties and are minimally sensitive to aerosols. The COllaborative Carbon Column Observing Network (COCCON) is a network of portable ground-based Fourier Transform Infrared spectrometers that are less expensive than full TCCON sites and have lower spectral resolution, but are similarly insensitive to surface properties and aerosols. Comparison of coincident OCO-2 measurements against selected TCCON and COCCON sites indicate that the absolute average bias values are close to 0 ppm for TCCON and less than 0.7 ppm for COCCON in the Land Nadir/Glint and Target mode observations.Finally, we compare coincident OCO-2 measurements to the airborne Atmospheric Tomography Mission (ATom) when ATom conducted around-the-world flights in each of the four seasons between 2016 and 2018. This study bridges the gap between satellite, ground-based, and airborne XCO2 measurements and aids the improvement of the OCO-2 XCO2 data product. Further, it provides the latest validation analysis for OCO-2, providing the most up-to-date information on biases and uncertainty in the OCO-2 data.

Abstract. We describe an approach for determining limited information about the vertical distribution of carbon monoxide (CO) and carbon dioxide (CO2) from total column ground-based Total Carbon Column Observation Network (TCCON) observations. For CO and CO2, it has been difficult to retrieve information about their vertical distribution from spectral line shapes because of the errors in the spectroscopy and the atmospheric temperature profile that mask the effects of variations in their mixing ratio with altitude. For CO2 the challenge is especially difficult given that these variations are typically 2 % or less. Nevertheless, if sufficient accuracy can be obtained, such information would be highly valuable for evaluation of retrievals from satellites and more generally for improving the estimate of surface sources and sinks of these trace gases. We present here the Temporal Atmospheric Retrieval Determining Information from Secondary Scaling (TARDISS) retrieval algorithm. TARDISS uses several simultaneously obtained total column observations of the same gas from different absorption bands with distinctly different vertical averaging kernels. The different total column retrievals are combined in TARDISS using a Bayesian approach where the weights and temporal covariance applied to the different retrievals include additional constraints on the diurnal variation in the vertical distribution for these gases. We assume that the near-surface part of the column varies rapidly over the course of a day (from surface sources and sinks, for example) and that the upper part of the column has a larger temporal covariance over the course of a day. Using measurements from the five North American TCCON sites, we find that the retrieved lower partial column (between the surface and ∼ 800 hPa) of the CO and CO2 dry mole fractions (DMFs) have slopes of 0.999 ± 0.002 and 1.001 ± 0.003 with respect to lower column DMF from integrated in situ data measured directly from aircraft and in AirCores. The average error for our lower column CO retrieval is 1.51 ppb (∼ 2 %) while the average error for our CO2 retrieval is 5.09 ppm (∼ 1.25 %). Compared with classical line-shape-derived vertical profile retrievals, our algorithm reduces the influence of forward model errors such as imprecision in spectroscopy (line shapes and intensities) and in the instrument line shape. In addition, because TARDISS uses the existing retrieved column abundances from TCCON (which themselves are computationally much less intensive than profile retrieval algorithms), it is very fast and processes years of data in minutes. We anticipate that this approach will find broad application for use in carbon cycle science.

<p>The Orbiting Carbon Observatory-3 (OCO-3) was successfully launched on May 4, 2019 from Kennedy Space Center via a Space-X Falcon 9. One week later, the instrument was installed as an external payload on the International Space Station (ISS). OCO-3 extends NASA’s study of carbon and measures the dry-air mole fraction of column carbon dioxide (XCO2) in the Earth’s atmosphere from space.</p><p>These space-based measurements are compared to ground-based observations from the Total Carbon Column Observing Network (TCCON). TCCON is a global network of high-resolution ground-based Fourier Transform Spectrometers that records spectra of the sun in the near-infrared spectral region. From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including CO2 are retrieved. TCCON data are tied to the WMO scale and serve as the link between calibrated surface in situ measurements and OCO-3 measurements.</p><p>OCO-3’s agile 2-D pointing mirror assembly (PMA) allows the instrument to stare at a TCCON station as it passes overhead - providing information about the quality, biases, and errors in the OCO-3 data. Here, we show early comparisons between the OCO-3 XCO2 dataset collected during target mode observations and coincident TCCON measurements and discuss site-dependent biases and its potential origins.</p>

Recently, the Fast atmOspheric traCe gAs retrievaL (FOCAL) algorithm has been applied to measurements of the Greenhouse gases Observing SATellite (GOSAT) and its successor GOSAT-2. FOCAL has been originally developed for Orbiting Carbon Observatory-2 (OCO-2) retrievals with the focus on the derivation of carbon dioxide (XCO2). However, depending on the available spectral windows, FOCAL also successfully retrieves total column amounts for other atmospheric species. Here, we show new results from updated GOSAT and GOSAT-2 FOCAL retrievals. The main focus is placed on methane (XCH4; full physics and proxy product), water vapour (XH2O) and the relative ratio of semi-heavy water (HDO) to water vapour (δD). Due to the extended spectral range of GOSAT-2 it is also possible to derive information on carbon monoxide (XCO) and nitrous oxide (XN2O) for which we also show first results. We also present an update on XCO2 from both instruments. Compared to the previous product version (v1), the number of valid XCO2 data could be significantly increased in the updated version (v3.0) by 50 % for GOSAT and about a factor of two for GOSAT-2. All FOCAL data products show reasonable spatial distribution and temporal variations. Comparisons with TCCON (Total Carbon Column Observing Network) result in station-to-station biases which are generally in line with the reported TCCON uncertainties. With this updated version of the GOSAT-2 FOCAL data, we provide a first total column average XN2O product. Global XN2O maps show a gradient from the tropics to higher latitudes in the order of 15 ppb, which can be explained by variations in tropopause height. The new GOSAT-2 XN2O product compares well with TCCON. Its station-to-station variability is lower than 2 ppb, which is about the magnitude of the typical N2O variations close to the surface. However, both GOSAT-2 and TCCON measurements show that the seasonal variations in the total column average XN2O are in the order of 8 ppb peak-to-peak, which can be easily resolved by the GOSAT-2 FOCAL data.

Satellite remote sensing of global greenhouse gas (GHG) concentrations provides invaluable information about GHG sources and sinks, supporting efficient climate mitigation policies. Recently, the accuracy targets of upcoming GHG satellite missions have become increasingly stringent (2 ppb of CH4; 1 ppm of CO2).Up to now, calibration and traceability of satellite GHG observations relies on two networks of ground-based remote sensing stations: the Total Carbon Column Observing Network (TCCON) and the COllaborative Carbon Column Observing Network (COCCON). Both networks are able to observe the same quantity as the satellite instruments: column-averaged dry-air mole fraction of CO2 and CH4. They also observe N2O, which will likely become another key GHG to be monitored in the future. For traceability, both networks rely on regular aircraft and balloon measurements with in-situ instruments that are traceable to the WMO scale for GHGs.The 24GRD06 MetCTG project aims at greatly improving the accuracy of underlying spectral line parameters for the satellite GHG retrievals and validating the accuracy with in situ and ground-based observations. This will establish traceability to SI and improve data comparability and trustworthiness among GHG satellite missions. It will also improve consistency among ground-based sites and considerably reduce the need for costly aircraft calibrations.The project joins the European metrology community with the TCCON and COCCON communities to provide the best ground-based reference for current and future GHG satellite missions.Acknowledgments: The project (24GRD06 MetCTG) receives funding from the European Partnership on Metrology, co-financed from the European Union’s Horizon  Europe Research and Innovation Programme and by the Participating States.

Abstract. The Total Carbon Column Observing Network (TCCON) is a global ground-based network of Fourier transform spectrometers that produce precise measurements of column-averaged dry-air mole fractions of atmospheric methane (CH4). Temporal variability in the total column of CH4 due to stratospheric dynamics obscures fluctuations and trends driven by tropospheric transport and local surface fluxes that are critical for understanding CH4 sources and sinks. We reduce the contribution of stratospheric variability from the total column average by subtracting an estimate of the stratospheric CH4 derived from simultaneous measurements of hydrogen fluoride (HF). HF provides a proxy for stratospheric CH4 because it is strongly correlated to CH4 in the stratosphere, has an accurately known tropospheric abundance (of zero), and is measured at most TCCON stations. The stratospheric partial column of CH4 is calculated as a function of the zonal and annual trends in the relationship between CH4 and HF in the stratosphere, which we determine from ACE-FTS satellite data. We also explicitly take into account the CH4 column averaging kernel to estimate the contribution of stratospheric CH4 to the total column. The resulting tropospheric CH4 columns are consistent with in situ aircraft measurements and augment existing observations in the troposphere.

Abstract. We describe a method of evaluating systematic errors in measurements of total column dry-air mole fractions of CO2 (XCO2) from space, and we illustrate the method by applying it to the v2.8 Atmospheric CO2 Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT) measurements over land. The approach exploits the lack of large gradients in XCO2 south of 25° S to identify large-scale offsets and other biases in the ACOS-GOSAT data with several retrieval parameters and errors in instrument calibration. We demonstrate the effectiveness of the method by comparing the ACOS-GOSAT data in the Northern Hemisphere with ground truth provided by the Total Carbon Column Observing Network (TCCON). We use the observed correlation between free-tropospheric potential temperature and XCO2 in the Northern Hemisphere to define a dynamically informed coincidence criterion between the ground-based TCCON measurements and the ACOS-GOSAT measurements. We illustrate that this approach provides larger sample sizes, hence giving a more robust comparison than one that simply uses time, latitude and longitude criteria. Our results show that the agreement with the TCCON data improves after accounting for the systematic errors, but that extrapolation to conditions found outside the region south of 25° S may be problematic (e.g., high airmasses, large surface pressure biases, M-gain, measurements made over ocean). A preliminary evaluation of the improved v2.9 ACOS-GOSAT data is also discussed.

The Orbiting Carbon Observatory-2 (OCO-2), launched in 2014, is NASA’s first satellite dedicated to measure sources and sinks of carbon dioxide (CO2) in Earth’s atmosphere on regional scales. Since 2019, measurements from the Orbiting Carbon Observatory-3 (OCO-3) have complemented OCO-2’s data record. In addition, OCO-3’s Snapshot Area Mapping (SAM) mode observations over emission hotspots like cities, power plants, and volcanoes provide a novel data set for carbon cycle studies on local scales. Data from both instruments are analyzed with the Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm to estimate column-average dry-air mole fractions of carbon dioxide (XCO2) in Earth’s atmosphere. Evaluating these space-based estimates of XCO2 against independent validation data sets provides information about the quality, potential biases, and errors in the OCO-2/3 data record. Here, we present comparisons of the ACOS V10 XCO2 from OCO-3 and the new and improved ACOS V11 XCO2 from OCO-2 against ground-based measurements from the Total Carbon Column Observing Network (TCCON).For both instruments, the root-mean-square error (RMSE) is below 1 ppm for all observational modes when compared to collocated TCCON observations. The OCO-3 V10.4 data version, an improvement over the initial vEarly data version, reduces an XCO2 time-dependent bias that was present in the previous OCO-3 data record. Consequently, data from both instruments does not indicate any significant time-dependent bias over the span of several years. Further, we evaluate differences between OCO-3 and TCCON related to different local overpass times. On average, OCO-3’s equator crossing time occurs about 20 minutes earlier every day. Comparisons against TCCON indicate no significant local time of day bias in the OCO-3 XCO2 data, however, comparisons over individual TCCON sites indicate a dependence which provides insight into potential airmass and viewing geometry related biases. The improved OCO-2 V11 data version reduces the mean bias against TCCON to ~0.15ppm from previously ~0.4ppm in V10. We find the largest reduction in RMSE over ocean due to an improved ocean glint surface treatment in V11. Both OCO data products are of comparable quality and are an improvement over earlier OCO data versions. Finally, we analyze how well OCO-2 captures the mean seasonal cycle amplitudes and growth rates over selected TCCON sites.

<p>Precise and accurate observations of anthropogenic greenhouse gases (GHGs), especially carbon dioxide (CO<sub>2</sub>) and methane (CH<sub>4</sub>), are of utmost importance for the estimation of their emission strengths, flux changes and long-term monitoring. Furthermore, these measurements can be directly used for the verification of climate mitigation actions as demanded by the Paris COP21 agreement. Satellite observations are well suited for this task as they provide global coverage. However, like all measurements these need to be validated, particularly to avoid potential biases. The Total Carbon Column Observing Network (TCCON) performs ground-based observations of GHGs with reference precision using high-resolution Fourier Transform infrared (FTIR) spectrometers. TCCON data are of high accuracy as TCCON uses species dependent scaling factors derived from in-situ reference measurements to be calibrated to the World Meteorological Organization (WMO) reference scale. For several satellites measuring GHGs TCCON data are the main validation source.</p><p>Recently, in an effort to further improve the global coverage of ground-based FTIR spectrometers and complement TCCON in remote areas, the COllaborative Carbon Column Observing Network (COCCON) was established. This network utilizes the EM27/SUN FTIR spectrometer, a compact solar-viewing low-resolution instrument. Even though a COCCON spectrometer has recently been used in combination with two TCCON instruments to validate the Orbiting Carbon Observatory-2 (OCO-2) satellite, until now the main focus of COCCON was on the quality control of EM27/SUN spectrometers and dedicated campaigns to estimate emission strengths of CO<sub>2</sub> and CH<sub>4</sub> from local and regional sources, e.g. from cities, fracking areas, volcanoes or mining sites.</p><p>Here we present long-term observations with a spectrometer from the COCCON network. In 2015 the instrument was installed at the Gobabeb Namib Research Center in Namibia. Gobabeb is located at the center of the hyperarid Namib desert. Moreover, Gobabeb is situated next to the Kuiseb river, which marks the sharp transition zone between the gravel plains to the north and the sand desert to the south of the station. This high albedo site is especially interesting for satellite validation, as ground-based FTIR data with these characteristics are sparse. Furthermore, it is the first ground-based FTIR instrument measuring CO<sub>2</sub> and CH<sub>4</sub> on the African mainland.</p><p>We show long-term COCCON observations from Gobabeb and compare them to results obtained from the TCCON instrument at Reunion Island. Finally, we present a comparison with target mode observations from the Greenhouse Gases Observation Satellite GOSAT.</p>

Abstract. We present a comparison of Karlsruhe XCO records (April 2010–December 2014) from the Total Carbon Column Observing Network (TCCON) and from the spectral region covered by the Network for the Detection of Atmospheric Composition Change (NDACC). The Karlsruhe TCCON Fourier transform infrared (FTIR) spectrometer allows us to record spectra in the mid-infrared (MIR) and near-infrared (NIR) spectral region simultaneously, which makes Karlsruhe a favourable FTIR site to directly compare measurements from both spectral regions. We compare XCO retrieved from the fundamental absorption band at 4.7 µm (as used by NDACC) and first overtone absorption band at 2.3 µm (TCCON-style measurements). We observe a bias of (4.47 ± 0.17) ppb between both data sets with a standard deviation of 2.39 ppb in seasonal variation. This corresponds to a relative bias of (4.76 ± 0.18) % and a standard deviation of 2.28 %. We identify different sources which contribute to the observed bias (air-mass-independent correction factor, air-mass-dependent correction factor, isotopic identities, differing a priori volume mixing ratio profiles) and quantify their contributions. We show that the seasonality in the residual of NDACC and TCCON XCO can be largely explained by the smoothing effect caused by differing averaging kernel sensitivities between the MIR and NIR spectral region. This study aims to improve the comparability of NDACC and TCCON XCO validation data sets as desired for potential future satellite missions and model studies.

High-resolution, laboratory, absorption spectra of the oxygen (O2) band measured using cavity ring-down spectroscopy were fitted using the Voigt and speed-dependent Voigt line shapes. We found that the speed-dependent Voigt line shape was better able to model the measured absorption coefficients than the Voigt line shape. We used these line shape models to calculate absorption coefficients to retrieve atmospheric total columns abundances of O2 from ground-based spectra from four Fourier transform spectrometers that are apart of the Total Carbon Column Observing Network (TCCON) Lower O2 total columns were retrieved with the speed-dependent Voigt line shape, and the difference between the total columns retrieved using the Voigt and speed-dependent Voigt line shapes increased as a function of solar zenith angle. Previous work has shown that carbon dioxide (CO2) total columns are better retrieved using a speed-dependent Voigt line shape with line mixing. The column-averaged dry-air mole fraction of CO2 (XCO2) was calculated using the ratio between the columns of CO2 and O2 retrieved (from the same spectra) with both line shapes from measurements made over a one-year period at the four sites. The inclusion of speed dependence in the O2 retrievals significantly reduces the airmass dependence of XCO2 and the bias between the TCCON measurements and calibrated integrated aircraft profile measurements was reduced from 1% to 0.4%. These results suggest that speed dependence should be included in the forward model when fitting near-infrared CO2 and O2 spectra to improve the accuracy of XCO2 measurements.

Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate-related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY on-board ENVISAT (March 2002–April 2012) and TANSO-FTS on-board GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System. We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between SCIAMACHY and GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT–SCIAMACHY (linear correlation coefficient r=0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT–TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY–TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (± 2 h, 10° x 10° around TCCON sites), i.e. the observed air masses are not exactly identical but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by also using data from other missions (e.g. OCO-2, GOSAT-2, CarbonSat) in the future.

Abstract. We show that the near infrared solar absorption spectra recorded in the framework of the Total Carbon Column Observing Network (TCCON) can be used to derive the vertical distribution of tropospheric water vapour. The resolution of the TCCON spectra of 0.02 cm−1 is sufficient for retrieving lower and middle/upper tropospheric water vapour concentrations with a vertical resolution of about 3 and 8 km, respectively. We document the good quality of the remotely-sensed profiles by comparisons with coincident in-situ Vaisala RS92 radiosonde measurements. Due to the high measurement frequency, the TCCON water vapour profile data offer novel opportunities for estimating the water vapour variability at different timescales and altitudes.