Scanning Imaging Absorption Spectrometer For Atmospheric Cartography Research Articles

Influence of model grid resolution on NO2 vertical column densities over East Asia

NO2 vertical column densities (VCDs) over East Asia in June and December 2007 were simulated by the Community Multi-scale Air Quality (CMAQ) version 4.7.1 using an updated and more elaborate version of the Regional Emission Inventory in Asia (REAS) version 2. The modeling system could reasonably capture observed spatiotemporal changes of NO2 VCDs by satellite sensors, the Global Ozone Monitoring Experiment-2 (GOME-2), the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY), and the Ozone Monitoring Instrument (OMI), even at the coarsest horizontal resolution of 80 km. The CMAQ simulations were performed in a sequence of three horizontal resolutions (80 km, 40 km, and 20 km) for June and December 2007 to investigate the influence of changes of horizontal resolution on the obtained NO2 VCDs. CMAQ-simulated NO2 VCDs generally increased with improvements in resolution from 80 km to 40 km and then to 20 km. Increases in the CMAQ-simulated NO2 VCDs were greater for the change from 80 km to 40 km than for those from 40 km and 20 km, in which the increases of NO2 VCDs due to the improvement of horizontal resolution were approached convergence at the horizontal resolution of approximately 20 km. Conversely, no clear convergences in NO2 VCDs changes were found at near Tokyo and over the East China Sea. The biases of the NO2 VCDs simulated at a resolution of 20 km against the satellite retrievals were ∼36% near Beijing (CHN1) and ∼78% near Shanghai (CHN2) in summer; these errors were found to be comparable to the horizontal resolution-dependent errors, which were 18–25% at CHN1 and 44–58% at CHN2 from 80 km to 40 km. Conversely, the influence of changes of horizontal resolution in winter was relatively less compared to that in summer.Implications: NO2 VCDs over East Asia in June and December 2007 were simulated using CMAQ version 4.7.1 and REAS version 2. The modeling system could reasonably capture observed spatiotemporal changes of NO2 VCDs by satellite sensors. The CMAQ simulations were performed in a sequence of three horizontal resolutions, 80, 40, and 20 km, to investigate the influence of changes of horizontal resolution on the obtained NO2 VCDs. The results suggested that the influence of changes of horizontal resolution was larger in summer compared to that in winter. The magnitude of the influence was comparable to the biases of the NO2 VCDs simulated at a resolution of 20 km against the satellite retrievals.

Multi-Year Comparison of Carbon Dioxide from Satellite Data with Ground-Based FTS Measurements (2003–2011)

This paper presents a comparison of CO2 products derived from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY), Greenhouse Gases Observing Satellite (GOSAT) and Atmospheric Infrared Sounder (AIRS), with reference to calibration data obtained using the high-resolution ground-based Fourier Transform Spectrometers (g-b FTS) in the Total Carbon Column Observing Network (TCCON). Based on the monthly averages, we calculate the global offsets and regional relative precisions between satellite products and g-b FTS measurements. The results are as follows: the monthly means of SCIAMACHY data are systemically slightly lower than g-b FTS, but limited in coverage; the GOSAT data are superior in stability, but inferior in systematic error; the mean difference between AIRS data and that of g-b FTS is small; and the monthly global coverage is above 95%. Therefore, the AIRS data are better than the other two satellite products in both coverage and accuracy. We also estimate linear trends based on monthly mean data and find that the differences between the satellite products and the g-b FTS data range from 0.25 ppm (SCIAMACHY) to 1.26 ppm (AIRS). The latitudinal distributions of the zonal means of the three satellite products show similar spatial features. The seasonal cycle of satellite products also illustrates the same trend with g-b FTS observations.

Satellite-derived UV irradiance for a region with complex morphology and meteorology: comparison against ground measurements in Santiago de Chile

Ground-based measurements of ultraviolet (UV) irradiance, carried out by a four-channel UV radiometer in Santiago de Chile from October 2004 to December 2011, have been used to estimate daily values of the UV index (UVI). These ground-based data have been compared with UVI estimates retrieved from the Ozone Measurement Instrument (OMI) on board the Aura spacecraft. Since the widely used OMI-gridded UVI data may not be suitable for the complex local morphology and meteorology, a careful screening of overpass OMI data was applied. Nevertheless, we found that OMI-derived UVI data overestimate ground-based values; depending on cloud-cover conditions, the mean bias (MB) and the root mean square error (RMSE) range from 34.53% to 30.29% and from 35.22% to 43.50%, respectively, with the lowest MB (and the highest RMSE) values occurring under overcast conditions. Moreover, the difference between satellite-derived and ground-based UVI data exhibits a limited seasonality with somewhat larger differences in the fall season. The detected overestimation seems to be linked with the boundary layer aerosol absorption that is not accounted for by the OMI algorithm. Indeed, we found that the difference in UVI increases with the aerosol concentration (which in Santiago shows seasonal variations). Ceilometer profiles of backscatter intensities, directly related to aerosol concentrations, and PM10 concentrations correlate with UVI differences (correlation coefficient r of approximately 0.6 and 0.4, respectively) under cloud-free conditions for time scales ranging from months to years. Additional comparisons were performed between UVI estimates retrieved from our ground-based measurements in Santiago and from the Tropospheric Emission Monitoring Internet Service (TEMIS) Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY). Under cloudless conditions, also TEMIS-derived data overestimate ground-based UVI estimations (by about 31%) and exhibit a small seasonality.

Improved water vapour spectroscopy in the 4174–4300 cm<sup>−1</sup> region and its impact on SCIAMACHY HDO/H<sub>2</sub>O measurements

Abstract. The relative abundance of the heavy water isotopologue HDO provides a deeper insight into the atmospheric hydrological cycle. The SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) allows for global retrievals of the ratio HDO/H2O in the 2.3 micron wavelength range. However, the spectroscopy of water lines in this region remains a large source of uncertainty for these retrievals. We therefore evaluate and improve the water spectroscopy in the range 4174–4300 cm−1 and test if this reduces systematic uncertainties in the SCIAMACHY retrievals of HDO/H2O. We use a laboratory spectrum of water vapour to fit line intensity, air broadening and wavelength shift parameters. The improved spectroscopy is tested on a series of ground-based high resolution FTS spectra as well as on SCIAMACHY retrievals of H2O and the ratio HDO/H2O. We find that the improved spectroscopy leads to lower residuals in the FTS spectra compared to HITRAN 2008 and Jenouvrier et al. (2007) spectroscopy, and the retrievals become more robust against changes in the retrieval window. For both the FTS and SCIAMACHY measurements, the retrieved total H2O columns decrease by 2–4% and we find a negative shift of the HDO/H2O ratio, which for SCIAMACHY is partly compensated by changes in the retrieval setup and calibration software. The updated SCIAMACHY HDO/H2O product shows somewhat steeper latitudinal and temporal gradients and a steeper Rayleigh distillation curve, strengthening previous conclusions that current isotope-enabled general circulation models underestimate the variability in the near-surface HDO/H2O ratio.

Quantitative comparison of the variability in observed and simulated shortwave reflectance

Abstract. The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a climate observation system that has been designed to monitor the Earth's climate with unprecedented absolute radiometric accuracy and SI traceability. Climate Observation System Simulation Experiments (OSSEs) have been generated to simulate CLARREO hyperspectral shortwave imager measurements to help define the measurement characteristics needed for CLARREO to achieve its objectives. To evaluate how well the OSSE-simulated reflectance spectra reproduce the Earth's climate variability at the beginning of the 21st century, we compared the variability of the OSSE reflectance spectra to that of the reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY). Principal component analysis (PCA) is a multivariate decomposition technique used to represent and study the variability of hyperspectral radiation measurements. Using PCA, between 99.7% and 99.9% of the total variance the OSSE and SCIAMACHY data sets can be explained by subspaces defined by six principal components (PCs). To quantify how much information is shared between the simulated and observed data sets, we spectrally decomposed the intersection of the two data set subspaces. The results from four cases in 2004 showed that the two data sets share eight (January and October) and seven (April and July) dimensions, which correspond to about 99.9% of the total SCIAMACHY variance for each month. The spectral nature of these shared spaces, understood by examining the transformed eigenvectors calculated from the subspace intersections, exhibit similar physical characteristics to the original PCs calculated from each data set, such as water vapor absorption, vegetation reflectance, and cloud reflectance.

The Intercalibration of Geostationary Visible Imagers Using Operational Hyperspectral SCIAMACHY Radiances

Spectral band differences between sensors can complicate the process of intercalibration of a visible sensor against a reference sensor. This can be best addressed by using a hyperspectral reference sensor whenever possible because they can be used to accurately mitigate the band differences. This paper demonstrates the feasibility of using operational Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) large-footprint hyperspectral radiances to calibrate geostationary Earth-observing (GEO) sensors. Near simultaneous nadir overpass measurements were used to compare the temporal calibration of SCIAMACHY with Aqua Moderate Resolution Imaging Spectroradiometer band radiances, which were found to be consistent to within 0.44% over seven years. An operational SCIAMACHY/GEO ray-matching technique was presented, along with enhancements to improve radiance pair sampling. These enhancements did not bias the underlying intercalibration and provided enough sampling to allow up to monthly monitoring of the GEO sensor degradation. The results of the SCIAMACHY/GEO intercalibration were compared with other operational four-year Meteosat-9 0.65-μm calibration coefficients and were found to be within 1% of the gain, and more importantly, it had one of the lowest temporal standard errors of all the methods. This is more than likely that the GEO spectral response function could be directly applied to the SCIAMACHY radiances, whereas the other operational methods inferred a spectral correction factor. This method allows the validation of the spectral corrections required by other methods.

Comparison of atmospheric CO2 observed by GOSAT and two ground stations in China

The atmospheric carbon dioxide (CO2) column concentrations observed by the Greenhouse Gases Observing Satellite (GOSAT) and ground stations at Mt Waliguan (36.29° N, 100.90° E) and Lulin (23.47° N, 120.87° E) in China are compared. The data covered time periods from June 2009 to November 2011 for GOSAT and from July 2009 to December 2010 for the ground stations. The GOSAT monthly mean data tend to be generally smaller than those of the ground measurements by 5–10 ppm. The spatial and temporal variations of the atmospheric XCO2 (dry air, column averaged, molar fraction of CO2) concentrations, especially in the regions of China, are analysed by using the GOSAT monthly mean data. The variations are more significant in the northern hemisphere than in the southern hemisphere and show relatively high values and obvious fluctuations in the 15° N–45° N latitudinal band. These are generally consistent with the measurements of the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) and Atmospheric Infrared Sounder (AIRS). The satellite data show significant seasonal variations, with maximum in April and May and minimum in September and October. This feature is in general agreement with that of the ground observations and previous reports. In the regions of China, the XCO2 ranged from 355 ppm to 385 ppm with a mean of 374 ppm, which is in agreement with the global concentration.

Three years of ground-based total ozone measurements in the Arctic: Comparison with OMI, GOME and SCIAMACHY satellite data

Continuous ground-based total ozone measurements were carried out at Ny-Ålesund (78.9°N, 11.9° E, Svalbard islands, Norway), by using a Brewer spectrophotometer, for almost three years (2007–2009). Given the scarcity of high-quality ground-based measurements performed at high latitudes, this dataset provides a rare opportunity to check the quality of satellite-based data recorded in Arctic. Considering the unprecedented Artic ozone loss recently derived from satellite observations, assessing the reliability of satellite readings at such high latitudes is a timely effort. This study focuses on the comparisons between ground-based Brewer total ozone measurements and satellite ozone readings retrieved from EOS Aura Ozone Measurement Instrument (OMI) (by using OMI-TOMS and OMI-DOAS algorithms), retrieved from ERS-2 Global Ozone Monitoring Experiment (GOME) (by using TOGOMI algorithm) and from Envisat SCanning Imaging Absorption spectrometer for Atmospheric CartographY (SCIAMACHY) (by using TOSOMI algorithm).Despite the extreme conditions that affect both ground- and satellite-based measurements, a good agreement was found (r=0.99 for OMI-TOMS, r=0.97 for both OMI-DOAS and GOME datasets and r=0.96 for SCIAMACHY). Nevertheless, space-based total ozone readings were found to underestimate ground-based data, in agreement with prior results. OMI-TOMS data showed the highest negative mean bias (MB=−2.03%) whereas DOAS-based datasets showed a better agreement (MB=−0.47, −0.69 and −0.01% for OMI-DOAS, GOME and SCIAMACHY, respectively) but a greater data scatter. On the other hand, DOAS-based datasets present an important seasonal dependence; an overestimation of the Brewer total ozone up to about 3% is found in April and September with respect to OMI-DOAS and SCIAMACHY while in September with respect to GOME. In contrast, differences between OMI-TOMS and Brewer total ozone do not present significant dependence on the season or on geometrical parameters (i.e., satellite solar and viewing zenith angles). Differences in total ozone values between ground-based and satellite measurements are further discussed attending to the algorithm differences in dealing with cloud cover under high surface albedo (i.e., snow) conditions.Extending the study to Sodankyla station (67.3°N, 26.6°E, Finland), similarities and differences between the two stations have been highlighted. The agreement between satellite and Brewer data slightly improved when Sodankyla station is considered. On the continent, the seasonal dependence and data scatter for DOAS-based datasets and the MB for OMI-TOMS are smaller with respect to Ny-Ålesund. Moreover, the roughly constant ozone summer offset (about 1%) between OMI-TOMS and OMI-DOAS, well evident for Ny-Ålesund, is not present for Sodankyla. Finally, some patterns in the geographic distributions of the satellite-Brewer total ozone differences are evident only for Ny-Ålesund, partly because of the peculiar characteristics of the surface albedo (higher than in Sodankyla, with a strong water/land contrast).

Validation of six years of SCIAMACHY carbon monoxide observations using MOZAIC CO profile measurements

Abstract. This paper presents a validation study of SCanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY (SCIAMACHY) carbon monoxide (CO) total column measurements from the Iterative Maximum Likelihood Method (IMLM) algorithm using vertically integrated profile aircraft measurements obtained within the MOZAIC project for the six year time period of 2003–2008. Overall we find a good agreement between SCIAMACHY and airborne measurements for both mean values – also on a year-to-year basis – as well as seasonal variations. Several locations show large biases that are attributed to local effects like orography and proximity of large emission sources. Differences were detected for individual years: 2003, 2004 and 2006 have larger biases than 2005, 2007 and 2008, which appear to be related to SCIAMACHY instrumental issues but require more research. Results from this study are consistent with, and complementary to, findings from a previous validation study using ground-based measurements (de Laat et al., 2010b). According to this study, the SCIAMACHY data, if individual measurements are of sufficient quality – good signal-to-noise, can be used to determine the spatial distribution and seasonal cycles of CO total columns over clean areas. Biases found over areas with strong emissions (Africa, China) could be explained by low sensitivity of the instrument in the boundary layer and users are recommended to avoid using the SCIAMACHY data while trying to quantify CO burden and/or retrieve CO emissions in such areas.

A global single‐sensor analysis of 2002–2011 tropospheric nitrogen dioxide trends observed from space

A global nine‐year archive of monthly tropospheric NO2 data acquired by the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument was analyzed with respect to trends between August 2002 and August 2011. In the past, similar studies relied on combining data from multiple sensors; however, the length of the SCIAMACHY data set now for the first time allows utilization of a consistent time series from just a single sensor for mapping NO2 trends at comparatively high horizontal resolution (0.25°). This study provides an updated analysis of global patterns in NO2 trends and finds that previously reported decreases in tropospheric NO2 over Europe and the United States as well as strong increases over China and several megacities in Asia have continued in recent years. Positive trends of up to 4.05 (±0.41) × 1015 molecules cm−2 yr−1 and up to 19.7 (±1.9) % yr−1 were found over China, with the regional mean trend being 7.3 (±3.1) % yr−1. The megacity with the most rapid relative increase was found to be Dhaka in Bangladesh. Subsequently focusing on Europe, the study further analyzes trends by country and finds significantly decreasing trends for seven countries ranging from −3.0 (±1.6) % yr−1 to −4.5 (±2.3) % yr−1. A comparison of the satellite data with station data indicates that the trends derived from both sources show substantial differences on the station scale, i.e., when comparing a station trend directly with the equivalent satellite‐derived trend at the same location, but provide quite similar large‐scale spatial patterns. Finally, the SCIAMACHY‐derived NO2 trends are compared with equivalent trends in NO2concentration computed using the Co‐operative Programme for Monitoring and Evaluation of the Long‐range Transmission of Air Pollutants in Europe (EMEP) model. The results show that the spatial patterns in trends computed from both data sources mostly agree in Central and Western Europe, whereas substantial differences are found in Eastern Europe.

Simultaneous satellite observations of IO and BrO over Antarctica

Abstract. This article reports on satellite observations of iodine monoxide (IO) and bromine monoxide (BrO). The region of interest is Antarctica in the time between spring and autumn. Both molecules, IO and BrO, are reactive halogen species and strongly influence tropospheric composition. As a result, a better understanding of their spatial distribution and temporal evolution is necessary to assess accurately their role in tropospheric chemistry. Especially in the case of IO, information on its present magnitude, spatial distribution patterns and source regions is still sparse. The present study is based on six years of SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) data recorded in nadir viewing geometry. Multi-year averages of monthly mean IO columns are presented and compared to the distributions of BrO. Influences of the IO air mass factor and the IO absorption cross section temperature dependence on the absolute vertical columns are discussed. The long-term observations of IO and BrO columns yield new insight into the temporal and spatial variation of IO above the Antarctic region. The occurrence of IO on Antarctic sea ice in late spring (November) is discovered and presented. In addition, the comparison between IO and BrO distributions show many differences, which argues for different mechanisms and individual nature of the release of the two halogen oxide precursors. The state of the ecosystem, in particular the changing condition of the sea ice in late spring, is used to explain the observations of the IO behaviour over Antarctica and the differences between IO and BrO distributions.

On the role of visible radiation in ozone profile retrieval from nadir UV/VIS satellite measurements: An experiment with neural network algorithms inverting SCIAMACHY data

Theoretical evidence has been given on the role of visible (VIS) radiation in enhancing the accuracy of ozone retrievals from satellite data, especially in the troposphere. However, at present, VIS is not being systematically used together with ultraviolet (UV) measurements, even when possible with one single instrument, e.g., the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY). Reasons mainly reside in the defective performance of optimal estimation and regularization algorithms caused by inaccurate modeling of VIS interaction with aerosols or clouds, as well as in inconsistent intercalibration between UV and VIS measurements. Here we intend to discuss the role of VIS radiation when it feeds a retrieval algorithm based on Neural Networks (NNs) that does not need a forward radiative transfer model and is robust with respect to calibration errors. The NN we designed was trained with a set of ozonesondes (OSs) data and tested over an independent set of OS measurements. We compared the ozone concentration profiles retrieved from UV-only with those retrieved from UV plus VIS nadir data taken by SCIAMACHY. We found that VIS radiation was able to yield more than 10% increase of accuracy and to substantially reduce biases of retrieved profiles at tropospheric levels.

TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications

The ESA (European Space Agency) Sentinel-5 Precursor (S-5 P) is a low Earth orbit polar satellite to provide information and services on air quality, climate and the ozone layer in the timeframe 2015–2022. The S-5 P mission is part of the Global Monitoring of the Environment and Security (GMES) Space Component Programme. The payload of the mission is the TROPOspheric Monitoring Instrument (TROPOMI) that will measure key atmospheric constituents including ozone, NO2, SO2, CO, CH4, CH2O and aerosol properties. TROPOMI has heritage to both the Ozone Monitoring Instrument (OMI) as well as to the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY). The S-5 P will extend the data records of these missions as well as be a preparatory mission for the Sentinel-5 mission planned for 2020 onward. The mission is pre-operational and is the link between the current scientific and the operational Sentinel-4/-5 missions.This contribution describes the science and mission objectives, the mission and the instrument, and the data products. While building on a solid foundation of the heritage instruments, the S-5P/TROPOMI mission is an exciting step forward with a strong focus on the troposphere. This is achieved by a combination of a high spatial resolution and improved signal-to-noise, as well as dedicated data product development. It is anticipated that the S-5 P mission will make a large contribution to the monitoring of the global atmospheric composition, as well as to the scientific knowledge of relevant atmospheric processes.

Spectral Reflectance Corrections for Satellite Intercalibrations Using SCIAMACHY Data

High-resolution spectra measured by the ENVISAT SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) are used to develop spectral correction factors for satellite imager solar channels to improve the transfer of calibrations from one imager to another. SCIAMACHY spectra averaged for various scene types demonstrate the dependence of reflectance on imager spectral response functions. Pseudo imager radiances were computed separately over land and water from SCIAMACHY pixel spectra taken over two tropical domains. Spectral correction factors were computed from these pseudo imager radiance pairs. Intercalibrations performed using matched 12th Geostationary Operational Environmental Satellite and Terra MODerate-resolution Imaging Spectroradiometer (MODIS) visible ( ~ 0.65 μm) channel data over the same domains yielded ocean and land calibration gain and offset differences of 4.5% and 41%, respectively. Applying the spectral correction factors reduces the gain and offset differences to 0.1% and 3.8%, respectively, for free linear regression. Forcing the regression to use the known offset count reduces the land-ocean radiance differences to 0.3% or less. Similar difference reductions were found for matched MODIS and Meteosat-8 Spinning Enhanced Visible and Infrared Imager channel 2 ( ~ 0.86 μm ). The results demonstrate that SCIAMACHY-based spectral corrections can be used to significantly improve the transfer of calibration between any pair of imagers measuring reflected solar radiances under similar viewing and illumination conditions.

Evaluating the observed variability in hyperspectral Earth-reflected solar radiance

[1] We explore the potential for directly measured hyperspectral Earth-reflected solar radiances to provide sufficient information to study changes in Earth's climate based on the quantified variability of the data using principal component analysis (PCA) and singular spectrum analysis. To do this we used these two multivariate analysis techniques on Earth-reflected radiances between 300 and 1750 nm measured from space by the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) instrument. The spatial and temporal variability of hyperspectral reflected radiances over global, hemispherical, and regional scales was quantified. As few as six components were needed to explain over 99.5% of the variance in all cases studied, with the exception of an Arctic Ocean case in which only four components were needed. Both of these values represent large reductions in dimensionality of the input radiances from 291 spectral bands. PCA facilitated attribution of the dominant spectral patterns extracted to atmospheric and surface variables, including water vapor, clouds, surface albedo, and sea ice. The second most dominant spectral variable, that is, the second principal component, in the Arctic closely resembled sea ice reflectance and followed the temporal behavior of sea ice extent determined from AMSR-E observations. The extraction of the spectral, spatial, and temporal variability in reflected shortwave hyperspectral radiance using multivariate analysis provides an alternate and complementary approach to inverse methods for applying space-based observations to climate studies.

Geostatistical interpolation of missing data and downscaling of spatial resolution for remotely sensed atmospheric methane column concentrations

Since the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) instrument on the Environmental Satellite (ENVISAT) was launched in 2002, CH4 measurements from the satellite at regional or global scales became available. However, many gaps of missing data exist on the maps of the retrieved atmospheric CH4 column concentrations from SCIAMACHY/ENVISAT. Moreover, the gridded CH4 map with 50 × 50 km is a bit coarse for local interpretation. In this study, two geostatistical methods of ordinary kriging (OK) and ordinary cokriging (OCK) associated with 5 km normalized difference vegetation index (NDVI) images were examined to fill in missing data and to downscale the spatial resolution of CH4 images. The 50 km CH4 images interpolated by the two methods presented similar spatial patterns to the original 50 km CH4 image and provided good results for the missing data. Taking into account the statistical results, the OCK method achieved better performance than OK in filling gaps of missing data. In further downscaling the CH4 image from 50 to 5 km, the OCK method achieved a significant amount of spatial detail, and the statistical results also showed that OCK performed better than OK.

Comparison of an isotopic atmospheric general circulation model with new quasi-global satellite measurements of water vapor isotopologues

We performed an intensive comparison of an isotope-incorporated atmospheric general circulation model with vapor isotopologue ratio observation data by two quasi-global satellite sensors in preparation for data assimilation of water isotope ratios. A global Isotope-incorporated Global Spectral Model simulation nudged toward the reanalysis wind field, atmospheric total column data from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) on Envisat, and midtropospheric (800 to 500 hPa) data from Tropospheric Emission Spectrometer (TES) on Aura were used. For the mean climatological δD of both the total atmospheric column and the midtroposphere layer, the model reproduced their geographical variabilities quite well. There is, however, some degree of underestimation of the latitudinal gradient (higher δD in the tropics and lower δD in midlatitudes) compared to the SCIAMACHY data, whereas there is generally less disagreement except lower δD over the Maritime Continent compared to the TES data. It was also found that the two satellite products have different relationships between water vapor amount and isotopic composition. Particularly, atmospheric column mean δD, which is dominated by lower-tropospheric vapor, closely follows the fractionation pattern of a typical Rayleigh-type “rain out” process, whereas in the midtroposphere the relationship between isotopic composition and vapor amount is affected by a “mixing” process. This feature is not reproduced by the model, where the relationships between δD and the vapor are similar to each other for the atmospheric column and midtroposphere. Comparing on a shorter time scale, it becomes clear that the data situation for future data assimilation for total column δD is most favorable for tropical and subtropical desert areas (i.e., Sahel, southern Africa, mideastern Asia, Gobi, Australia, and the southwest United States), whereas the available midtropospheric δD observations cover wider regions, particularly over tropical to subtropical oceans.

Stable water isotopes in the ECHAM5 general circulation model: Toward high-resolution isotope modeling on a global scale

[1] In this study, a first set of four present-day global experiments with the ECHAM5 atmospheric general circulation model enhanced by stable water isotope diagnostics (ECHAM5-wiso) is presented. Model resolution varies from a typical coarse horizontal grid of 3.8° × 3.8° (T31) to a fine grid of 0.75° × 0.75° (T159). Vertical resolution varies from 19 to 31 model levels. On a global scale, the ECHAM5-wiso simulation results are in good agreement with available observations of the isotopic composition of precipitation from the Global Network of Isotopes in Precipitation (GNIP), on an annual as well as a seasonal time scale. In many instances, the isotope simulation results clearly benefit from an increased horizontal and vertical model resolution. The exemplary relevance of this model resolution dependence is demonstrated for the simulation of the isotopic composition of Antarctic precipitation. Here, the simulation with the fine T159L31 model resolution not only yields a better agreement with observational data sets but also allows for a more realistic retuning of the supersaturation function leading to improved deuterium excess performance over the Antarctic continent, which is important for the interpretation of polar ice cores. Finally, the ECHAM5-wiso simulation results are compared to newly available measurements of the isotopic composition of atmospheric water vapor. Model and data agree well, with differences in the range of ±10‰ for near-surface atmospheric values at several GNIP stations. A comparison of the ECHAM5-wiso simulations with total column averaged HDO data from the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) instrument on board the environmental satellite Envisat shows the same latitudinal gradients but an offset between 20‰ and 50‰ of unknown origin.

Spectral kernel approach to study radiative response of climate variables and interannual variability of reflected solar spectrum

The radiative kernel approach provides a simple way to separate the radiative response to different climate parameters and to decompose the feedback into radiative and climate response components. Using CERES/MODIS/Geostationary data, we calculated and analyzed the solar spectral reflectance kernels for various climate parameters on zonal, regional, and global spatial scales. The kernel linearity is tested. Errors in the kernel due to nonlinearity can vary strongly depending on climate parameter, wavelength, surface, and solar elevation; they are large in some absorption bands for some parameters but are negligible in most conditions. The spectral kernels are used to calculate the radiative responses to different climate parameter changes in different latitudes. The results show that the radiative response in high latitudes is sensitive to the coverage of snow and sea ice. The radiative response in low latitudes is contributed mainly by cloud property changes, especially cloud fraction and optical depth. The large cloud height effect is confined to absorption bands, while the cloud particle size effect is found mainly in the near infrared. The kernel approach, which is based on calculations using CERES retrievals, is then tested by direct comparison with spectral measurements from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) (a different instrument on a different spacecraft). The monthly mean interannual variability of spectral reflectance based on the kernel technique is consistent with satellite observations over the ocean, but not over land, where both model and data have large uncertainty. RMS errors in kernel ]derived monthly global mean reflectance over the ocean compared to observations are about 0.001, and the sampling error is likely a major component.

Surface solar irradiance from SCIAMACHY measurements: algorithm and validation

Abstract. Broadband surface solar irradiances (SSI) are, for the first time, derived from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) satellite measurements. The retrieval algorithm, called FRESCO (Fast REtrieval Scheme for Clouds from the Oxygen A band) SSI, is similar to the Heliosat method. In contrast to the standard Heliosat method, the cloud index is replaced by the effective cloud fraction derived from the FRESCO cloud algorithm. The MAGIC (Mesoscale Atmospheric Global Irradiance Code) algorithm is used to calculate clear-sky SSI. The SCIAMACHY SSI product is validated against globally distributed BSRN (Baseline Surface Radiation Network) measurements and compared with ISCCP-FD (International Satellite Cloud Climatology Project Flux Dataset) surface shortwave downwelling fluxes (SDF). For one year of data in 2008, the mean difference between the instantaneous SCIAMACHY SSI and the hourly mean BSRN global irradiances is −4 W m−2 (−1 %) with a standard deviation of 101 W m−2 (20 %). The mean difference between the globally monthly mean SCIAMACHY SSI and ISCCP-FD SDF is less than −12 W m−2 (−2 %) for every month in 2006 and the standard deviation is 62 W m−2 (12 %). The correlation coefficient is 0.93 between SCIAMACHY SSI and BSRN global irradiances and is greater than 0.96 between SCIAMACHY SSI and ISCCP-FD SDF. The evaluation results suggest that the SCIAMACHY SSI product achieves similar mean bias error and root mean square error as the surface solar irradiances derived from polar orbiting satellites with higher spatial resolution.