ALBERT

Description: We present the validation of ozone profiles from a number of Solar Backscatter Ultraviolet (SBUV and SBUV/2) instruments that were recently reprocessed using an updated (version 8.6) algorithm. The SBUV data record spans a 41 yr period from 1970 to 2011 with a 5 yr gap in the 1970s. The ultimate goal is to create a consistent, well-calibrated data set of ozone profiles that can be used for climate studies and trend analyses. SBUV ozone profiles have been intensively validated against satellite profile measurements from the Microwave Limb Sounders (MLS) (on board the UARS and Aura satellites) and the Stratospheric Aerosol and Gas Experiment (SAGE II) and ground-based observations from the microwave spectrometers, lidars, Umkehr instruments and balloon-borne ozonesondes. In the stratosphere between 25 and 1 hPa the mean biases and standard deviations are mostly within 5% for monthly zonal mean ozone profiles. Above and below this layer the vertical resolution of the SBUV algorithm decreases. We combine several layers of data in the troposphere/lower stratosphere to account for the lower resolution. The bias in the SBUV tropospheric/lower stratospheric combined layer relative to similarly integrated columns from Aura MLS, ozonesonde and Umkehr instruments varies within 5%. We also estimate the drift of the SBUV instruments and their potential effect on the long-term stability of the combined data record. Data from the SBUV instruments that collectively cover the 1980s and 2000s are very stable, with drifts mostly less than 0.5% per year. The features of individual SBUV(/2) instruments are discussed and recommendations for creating a merged SBUV data set are provided.

Description: We describe the algorithm that has been applied to develop a 41 yr time series of total ozone and ozone profiles from eight solar-backscatter UV (sbuv) instruments launched on NASA and NOAA satellites since April 1970. Although the basic algorithm is similar to the V8 algorithm that was released about a decade ago and has been in use since then at NOAA, the details of the V8 algorithm have never been published. The current version (V8.6) incorporates several changes including the use of new ozone absorption cross-sections and new ozone and cloud height climatologies. A particular emphasis in this paper is on characterizing the sources of errors that are relevant for deriving trends from monthly mean anomalies and for estimating biases between different types of ozone sensors. We show that variations in the local time of the measurement due to drifting NOAA satellite orbits can complicate the analysis of trends in the upper stratosphere. Such variations not only increase instrumental and algorithmic uncertainties but also require correction for true local time variations of ozone in the upper stratosphere and lower mesosphere for trend analysis. We find that the monthly zonal anomalies derived from the SBUV data have high precision, sufficient to track year-to-year changes in ozone over a broad range of altitudes. However, because of poor vertical resolution the data are less well suited to track short-term variability of ozone at lower altitudes.

Description: We present validation of ozone profiles from a number of Solar Backscatter Ultraviolet (SBUV and SBUV/2) instruments that were recently reprocessed using an updated (Version 8.6) algorithm. The SBUV data record spans a 41-yr period from 1970 to 2011 with a 5-yr gap in the 1970s. The ultimate goal is to create a consistent, well-calibrated dataset of ozone profiles that can be used for climate studies and trend analyses. SBUV ozone profiles have been intensively validated against satellite profile measurements from the Microwave Limb Sounders (MLS) (on board UARS and Aura satellites) and the Stratospheric Aerosol and Gas Experiment (SAGE II) and ground-based observations from the microwave spectrometers, lidars, Umkehr instruments and balloon-borne ozonesondes. In the stratosphere between 25 and 1 hPa the mean biases and standard deviations are mostly within 5% for monthly zonal mean ozone profiles. Above and below this layer the vertical resolution of the SBUV algorithm decreases. We combine several layers of data in the troposphere/lower stratosphere to account for the lower resolution. The bias in the SBUV tropospheric/lower stratospheric combined layer relative to similarly integrated columns from Aura MLS, ozonesonde and Umkehr instruments varies within 5%. We also estimate drifts in the SBUV instruments and their potential effect on the long-term stability of the combined data record. Data from the SBUV instruments that collectively cover the 1980s and 2000s are very stable, with drifts mostly less than 0.5% yr−1. The features of individual SBUV(/2) instruments are discussed and recommendations for creating the merged SBUV data set are provided.

Description: This paper describes the calibration process for the Solar Backscatter Ultraviolet (SBUV) Version 8.6 (V8.6) ozone data product. Eight SBUV instruments have flown on NASA and NOAA satellites since 1970, and a continuous data record is available since November 1978. The accuracy of ozone trends determined from these data depends on the calibration and long-term characterization of each instrument. V8.6 calibration adjustments are determined at the radiance level, and do not rely on comparison of retrieved ozone products with other instruments. The primary SBUV instrument characterization is based on prelaunch laboratory tests and dedicated on-orbit calibration measurements. We supplement these results with "soft" calibration techniques using carefully chosen subsets of radiance data and information from the retrieval algorithm output to validate each instrument's calibration. The estimated long-term uncertainty in albedo is approximately ±0.8–1.2% (1σ) for most of the instruments. The overlap between these instruments and the Shuttle SBUV (SSBUV) data allows us to intercalibrate the SBUV instruments to produce a coherent V8.6 data set covering more than 32 yr. The estimated long-term uncertainty in albedo is less than 3% over this period.

Description: We describe the algorithm that has been applied to develop a 42 yr record of total ozone and ozone profiles from eight Solar Backscatter UV (SBUV) instruments launched on NASA and NOAA satellites since April 1970. The Version 8 (V8) algorithm was released more than a decade ago and has been in use since then at NOAA to produce their operational ozone products. The current algorithm (V8.6) is basically the same as V8, except for updates to instrument calibration, incorporation of new ozone absorption cross-sections, and new ozone and cloud height climatologies. Since the V8 algorithm has been optimized for deriving monthly zonal mean (MZM) anomalies for ozone assessment and model comparisons, our emphasis in this paper is primarily on characterizing the sources of errors that are relevant for such studies. When data are analyzed this way the effect of some errors, such as vertical smoothing of short-term variability, and noise due to clouds and aerosols diminish in importance, while the importance of others, such as errors due to vertical smoothing of the quasi-biennial oscillation (QBO) and other periodic and aperiodic variations, become more important. With V8.6 zonal mean data we now provide smoothing kernels that can be used to compare anomalies in SBUV profile and partial ozone columns with models. In this paper we show how to use these kernels to compare SBUV data with Microwave Limb Sounder (MLS) ozone profiles. These kernels are particularly useful for comparisons in the lower stratosphere where SBUV profiles have poor vertical resolution but partial column ozone values have high accuracy. We also provide our best estimate of the smoothing errors associated with SBUV MZM profiles. Since smoothing errors are the largest source of uncertainty in these profiles, they can be treated as error bars in deriving interannual variability and trends using SBUV data and for comparing with other measurements. In the V8 and V8.6 algorithms we derive total column ozone by integrating the SBUV profiles, rather than from a separate set of wavelengths, as was done in previous algorithm versions. This allows us to extend the total ozone retrieval to 88° solar zenith angle (SZA). Since the quality of total column data is affected by reduced sensitivity to ozone in the lower atmosphere by cloud and Rayleigh attenuation, which gets worse with increasing SZA, we provide our best estimate of these errors, as well as the kernels that can be used to test the sensitivity of the derived columns to long-term changes in ozone in the lower atmosphere.

Description: Significant in-band stray light (IBSL) error at solar zenith angle (SZA) values larger than 77° near sunset in 4 SBUV/2 (Solar Backscattered Ultraviolet) instruments, on board the NOAA-14, 17, 18 and 19 satellites, has been characterized. The IBSL error is caused by large surface reflection and scattering of the air-gapped depolarizer in front of the instrument's monochromator aperture. The source of the IBSL error is direct solar illumination of instrument components near the aperture rather than from earth shine. The IBSL contamination at 273 nm can reach 40% of earth radiance near sunset, which results in as much as a 50% error in the retrieved ozone from the upper stratosphere. We have analyzed SBUV/2 albedo measurements on both the dayside and nightside to develop an empirical model for the IBSL error. This error has been corrected in the V8.6 SBUV/2 ozone retrieval.

Description: This paper describes the calibration process for the Solar Backscatter Ultraviolet (SBUV) Version 8.6 (V8.6) ozone data product. Eight SBUV instruments have flown on NASA and NOAA satellites since 1970, and a continuous data record is available since November 1978. The accuracy of ozone trends determined from these data depends on the calibration and long-term characterization of each instrument. V8.6 calibration adjustments are determined at the radiance level, and do not rely on comparison of retrieved ozone products with other instruments. The primary SBUV instrument characterization is based on prelaunch laboratory tests and dedicated on-orbit calibration measurements. We supplement these results with "soft" calibration techniques using carefully chosen subsets of radiance data and information from the retrieval algorithm output to validate each instrument's calibration. The estimated long-term uncertainty in albedo is approximately ±0.8–1.2% (1σ) for most of the instruments. The overlap between these instruments and the Shuttle SBUV (SSBUV) data allows us to intercalibrate the SBUV instruments to produce a coherent V8.6 data set covering more than 32 yr. The estimated long-term uncertainty in albedo is less than 3% over this period.

Description: Significant In-Band Stray Light (IBSL) error at solar zenith angle (SZA) values larger than 77° near sunset in 4 SBUV/2 instruments has been characterized. The IBSL error is caused by large surface reflection and scattering of the air-gapped depolarizer in front of the instrument's monochromator aperture. The source of the IBSL error is direct solar illumination of instrument components near the aperture rather than from earth shine. We have analyzed SBUV/2 albedo measurements on both dayside and night side to develop an empirical model for the IBSL error. This error has been corrected in the V8.6 SBUV/2 ozone retrieval.

Description: Nitrite (NO2-) is a key intermediate in the marine nitrogen (N) cycle and a substrate in nitrification, which produces nitrate (NO3-), as well as water column N loss processes denitrification and anammox. In models of the marine N cycle, NO2- is often not considered as a separate state variable, since NO3- occurs in much higher concentrations in the ocean. In oxygen deficient zones (ODZs), however, NO2- represents a substantial fraction of the bioavailable N, and modeling its production and consumption is important to understand the N cycle processes occurring there, especially those where bioavailable N is lost from or retained within the water column. Improving N cycle models by including NO2- is important in order to better quantify N cycling rates in ODZs, particularly N loss rates. Here we present the expansion of a global 3-D inverse N cycle model to include NO2- as a reactive intermediate as well as the processes that produce and consume NO2- in marine ODZs. NO2- accumulation in ODZs is accurately represented by the model involving NO3- reduction, NO2- reduction, NO2- oxidation, and anammox. We model both 14N and 15N and use a compilation of oceanographic measurements of NO3- and NO2- concentrations and isotopes to place a better constraint on the N cycle processes occurring. The model is optimized using a range of isotope effects for denitrification and NO2- oxidation, and we find that the larger (more negative) inverse isotope effects for NO2- oxidation, along with relatively high rates of NO2-, oxidation give a better simulation of NO3- and NO2- concentrations and isotopes in marine ODZs.

Description: Nitrite (NO2−) is a key intermediate in the marine nitrogen (N) cycle and a substrate in nitrification, which produces nitrate (NO3−), as well as water column N loss processes, denitrification and anammox. In models of the marine N cycle, NO2− is often not considered as a separate state variable, since NO3− occurs in much higher concentrations in the ocean. In oxygen deficient zones (ODZs), however, NO2− represents a substantial fraction of the bioavailable N, and modeling its production and consumption is important to understanding the N cycle processes occurring there, especially those where bioavailable N is lost from or retained within the water column. Here we present the expansion of a global 3D inverse N cycle model to include NO2− as a reactive intermediate as well as the processes that produce and consume NO2− in marine ODZs. NO2− accumulation in ODZs is accurately represented by the model involving NO3− reduction, NO2− reduction, NO2− oxidation, and anammox. We model both 14N and 15N and use a compilation of oceanographic measurements of NO3− and NO2− concentrations and isotopes to place a better constraint on the N cycle processes occurring. The model is optimized using a range of isotope effects for denitrification and NO2− oxidation, and we find that the larger (more negative) inverse isotope effects for NO2− oxidation along with relatively high rates of NO2− oxidation give a better simulation of NO3− and NO2− concentrations and isotopes in marine ODZs.