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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

The Network for the Detection of Atmospheric Composition Change (NDACC): History, Status and Perspectives

The Network for the Detection of Atmospheric Composition Change (NDACC) is an international global network of more than 90 stations making high-quality measurements of atmospheric composition that began official operations in 1991 after 5 years of planning. Apart from sonde measurements, all measurements in the network are performed by ground-based remote-sensing techniques. Originally named the Network for the Detection of Stratospheric Change (NDSC), the name of the network was changed to NDACC in 2005 to better reflect the expanded scope of its measurements. The primary goal of NDACC is to establish long-term databases for detecting changes and trends in the chemical and physical state of the atmosphere (mesosphere, stratosphere, and troposphere) and to assess the coupling of such changes with climate and air quality. NDACC's origins, station locations, organizational structure, and data archiving are described. NDACC is structured around categories of ground-based observational techniques (sonde, lidar, microwave radiometers, Fourier-transform infrared, UV-visible DOAS (differential optical absorption spectroscopy)-type, and Dobson-Brewer spectrometers, as well as spectral UV radiometers), timely cross-cutting themes (ozone, water vapour, measurement strategies, cross-network data integration), satellite measurement systems, and theory and analyses. Participation in NDACC requires compliance with strict measurement and data protocols to ensure that the network data are of high and consistent quality. To widen its scope, NDACC has established formal collaborative agreements with eight other cooperating networks and Global Atmosphere Watch (GAW). A brief history is provided, major accomplishments of NDACC during its first 25 years of operation are reviewed, and a forward-looking perspective is presented.

lidar↗
NASA NTRS2012

Standardization of the Definitions of Vertical Resolution and Uncertainty in the NDACC-archived Ozone and Temperature Lidar Measurements

The international Network for the Detection of Atmospheric Composition Change (NDACC) is a global network of high-quality, remote-sensing research stations for observing and understanding the physical and chemical state of the Earth atmosphere. As part of NDACC, over 20 ground-based lidar instruments are dedicated to the long-term monitoring of atmospheric composition and to the validation of space-borne measurements of the atmosphere from environmental satellites such as Aura and ENVISAT. One caveat of large networks such as NDACC is the difficulty to archive measurement and analysis information consistently from one research group (or instrument) to another [1][2][3]. Yet the need for consistent definitions has strengthened as datasets of various origin (e.g., satellite and ground-based) are increasingly used for intercomparisons, validation, and ingested together in global assimilation systems.In the framework of the 2010 Call for Proposals by the International Space Science Institute (ISSI) located in Bern, Switzerland, a Team of lidar experts was created to address existing issues in three critical aspects of the NDACC lidar ozone and temperature data retrievals: signal filtering and the vertical filtering of the retrieved profiles, the quantification and propagation of the uncertainties, and the consistent definition and reporting of filtering and uncertainties in the NDACC- archived products. Additional experts from the satellite and global data standards communities complement the team to help address issues specific to the latter aspect.

Detection of Atmospheric Composition Change (NDACC↗
NASA NTRS2018

Lidar Validation Measurements at the NOAA Mauna Loa Observatory NDACC Station

NASA's Goddard Space Flight Center (GSFC) transported two lidar instruments to the NOAA facility at the Mauna Loa Observatory (MLO) on the Big Island of Hawaii, to participate in an official, extended validation campaign. This site is situated 11,141 ft. above sea level on the side of the mountain. The observatory has been making atmospheric measurements regularly since the 1950's, and has hosted the GSFC Stratospheric Ozone (STROZ) Lidar and the GSFC Aerosol and Temperature (AT) Lidar on several occasions, most recently between November, 2012 and November, 2015. The purpose of this extended deployment was to participate in Network for the Detection of Atmospheric Composition Change (NDACC) Validation campaigns with the JPL Stratospheric Ozone Lidar and the NOAA Temperature, Aerosol and Water Vapor instruments as part of the routine NDACC Validation Protocol.

Lidar↗
NASA NTRS2016

Results of a Longer Term NDACC Measurements Comparison Campaign at Mauna Loa Observatory

Between November, 2015 and January, 2015, the Goddard Space Flight Center (GSFC) operated a pair of lidar instruments at the NOAA (National Oceanic and Atmospheric Administration) facility at Mauna Loa on the Big Island of Hawaii (Latitude 19.5 degrees North, Longitude 155.5 degrees West, altitude 3.397 kilometers). Measurements were made during six different four week periods during this time period by both the NASA GSFC Stratospheric Ozone Lidar (STROZ) and the Aerosol and Temperature (ATL) lidar. Also making measurements were the JPL Stratospheric Ozone Lidar and the NOAA Aerosol and Water Vapor Lidar. All instruments participate and archive data with the Network for the Detection of Atmospheric Composition Change (NDACC). Measurement comparisons were made among various instruments in accordance with the standard intercomparison protocols of the NDACC.

McGee, Thomas J.↗
NASA NTRS2014

Diurnal Variations of Stratospheric Ozone Measured by Ground-Based Microwave Remote Sensing at the Mauna Loa NDACC Site: Measurement Validation and GEOSCCM Model Comparison

There is presently renewed interest in diurnal variations of stratospheric and mesospheric ozone for the purpose of supporting homogenization of records of various ozone measurements that are limited by the technique employed to being made at certain times of day. We have made such measurements for 19 years using a passive microwave remote sensing technique at the Mauna Loa Observatory (MLO) in Hawaii, which is a primary station in the Network for Detection of Atmospheric Composition Change (NDACC). We have recently reprocessed these data with hourly time resolution to study diurnal variations. We inspected differences between pairs of the ozone spectra (e.g., day and night) from which the ozone profiles are derived to determine the extent to which they may be contaminated by diurnally varying systematic instrumental or measurement effects. These are small, and we have reduced them further by selecting data that meet certain criteria that we established. We have calculated differences between profiles measured at different times: morning-night, afternoon-night, and morning-afternoon and have intercompared these with like profiles derived from the Aura Microwave Limb Sounder (Aura-MLS), the Upper Atmosphere Research Satellite Microwave Limb Sounder (UARS-MLS),t he Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES), and Solar Backscatter Ultraviolet version 2 (SBUV2) measurements. Differences between averages of coincident profiles are typically less than 1.5 percent of typical nighttime values over most of the covered altitude range with some exceptions. We calculated averages of ozone values for each hour from the Mauna Loa microwave data, and normalized these to the average for the first hour after midnight for comparison with corresponding values calculated with the Goddard Earth Observing System Chemistry Climate Model (GEOSCCM). We found that the measurements and model output mostly agree to better than 1.5 percent of the midnight value, with one noteworthy exception: The measured morning-night values are significantly (2-3 percent) higher than the modeled ones from 3.2 to 1.8 hPa (approximately 39-43 km), and there is evidence that the measured values are increasing compared to the modeled values before sunrise in this region.

GEOSCCM model comparison↗
NASA NTRS2006

New Raman Water Vapor and Temperature Lidar at JPL Table Mountain Facility: Optimization, Validations and Sonde Intercomparison

Jet Propulsion Laboratory currently operates lidar systems at Table Mountain Facility (TMF), California (34.4 deg N, 117.7 deg W at 2300m) and Mauna Loa Observatory (MLO), Hawaii (19.5 deg N, 155.6 deg W at 3400m) under the Network for the Detection of Atmospheric Composition Change (NDACC, formerly NDSC). To complement existing NDACC lidars at TMF, which acts as a primary site for inter-comparisons, a new water vapor and temperature lidar has begun routine operation with typically 3-4 nightly profiles per week. As water vapor is a key greenhouse gas, and is highly variable on annual and seasonal cycles, accurate long term measurements are necessary for predictions of climate change and to increase our understanding of the atmospheric processes it contributes to. The new TMF lidar has demonstrated high spatial and temporal resolution, with a high degree of optimization being achieved over the past year, although the authors believe further improvement may yet be possible. The lidar has been designed for accuracies of 5% up to 12km in the free troposphere with the capability to measure to the tropopause and lower stratosphere with accuracies of 1 ppm. It is anticipated that the data sets produced will be used for Aura validation and for incorporation into NDACC archives. Validation results for the optimized system are presented with intercomparisons using Vaisala RS92-K radiosondes.

water vapor↗
NASA NTRS2010

ALVICE Lidar Results from the MOHAVE 2009 Field Campaign

The NASA/GSFC Atmospheric Lidar for Validation/Interagency Collaboration and Education (ALVICE) participated in the Measurements of Humidity And Validation Experiments (MOHAVE 209) campaign hosted at the JPL/Table Mountain Facility in Southern California. This field campaign brought together a large number of water vapor measuring instruments in an effort to inter-compare and validate numerous water vapor technologies in use within the Network for the Detection of Atmospheric Composition Change (NDACC). A central focus of the campaign was to perform validation of Raman lidar systems in use within NDACC. ALVICE is one of the mobile intercomparison lidar instruments within NDACC and MOHAVE provided an excellent opportunity to test and validate the measurements of this system. At the workshop, we will present recent analysis results of ALVICE lidar measurements and put them in the context of the full field campaign,

Whiteman, David N.↗
NASA NTRS2012

The Connection between Model Performance on the CCMVal Transport Diagnostics and Simulated Sensitivity of Ozone to Chlorine Change

The chemistry climate models that contributed simulations for past and future ozone evolution to the 2010 Scientific Assessment of Ozone Depletion were subject to extensive evaluation by the SPARC (Stratospheric Processes and their Role in Climate) CCMVal (Chemistry-Climate Model Validation) activity. The sensitivity of ozone to changes in composition and climate varies among the models, but the relationship between these variations and the model evaluations of CCMVal is not obvious. We have learned that the transport evaluation can be used to interpret the comparisons between observed and simulated columns of chlorine reservoirs, hydrochloric acid (HCl) and chlorine nitrate (ClONO2); these comparisons were part of the CCMVal evaluation of chemistry. The simulations with best performance on the transport diagnostics most faithfully reproduce the evolution and seasonal variation of the chlorine reservoirs as observed at NDACC (Network for Detection of Atmospheric Composition Change) stations (NyAlesund 78.9N, Kiruna 67.8N, Harestua 60.2N, Jungfraujoch 46.6N, Toronto 43.6N, Kitt Peak 31.9N, Izana 28.3N, Mauna Loa 19.5N, Lauder 45S and Arrival Heights 77.8S). In the simulations, the HCl in the lower stratosphere depends on total inorganic chlorine (Cly) and partitioning between HCl and ClON02. Total inorganic chlorine depends on the fractional release of chlorine from source gases, and ratio of ClON02 to HCl is inversely dependent on methane and varies quadratically with ozone. Simulated HCl from various models may agree with observations even though Cly is in error, partitioning is in error, or both. Simulated ozone sensitivity to chlorine is shown to be greater for models that produce larger values of chlorine nitrate for background chlorine levels, and vice versa. Comparisons with the NDACC data show why the models with 'best' transport have similar sensitivity to chlorine change. The realistic evolution of the simulated HCl and ClONO2 columns suggests realistic levels of Cly in the lower atmosphere. In addition, the wide range values for the sensitivity of ozone to chlorine obtained from the CCMVal simulations is explained by the wide range in lower atmospheric columns of ClONO2 and the concomitant wide range of levels for chlorine monoxide.

Douglass, Anne↗
NASA NTRS2017

Multi-Decadal Records of Stratospheric Composition and Their Relationship to Stratospheric Circulation Change

Constituent evolution for 1990-2015 simulated using the Global Modeling Initiative chemistry and transport model driven by meteorological fields from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) is compared with three sources of observations: ground-based column measurements of HNO3 and HCl from two stations in the Network for the Detection of Atmospheric Composition Change (NDACC, 1990- ongoing), profiles of CH4 from the Halogen Occultation Experiment (HALOE) on the Upper Atmosphere Research Satellite (UARS, 1992-2005), and profiles of N2O from the Microwave Limb Sounder on the Earth Observing System satellite Aura (2005- ongoing). The differences between observed and simulated values are shown to be time dependent, with better agreement after 2000 compared with the prior decade. Furthermore, the differences between observed and simulated HNO3 and HCl columns are shown to be correlated with each other, suggesting that issues with the simulated transport and mixing cause the differences during the 1990s and that these issues are less important during the later years. Because the simulated fields are related to mean age in the lower stratosphere, we use these comparisons to evaluate the time dependence of mean age. The ongoing NDACC column observations provide critical information necessary to substantiate trends in mean age obtained using fields from MERRA-2 or any other reanalysis products.

Profiles of N2O from the Microwave Limb Sounde↗
DOE OSTI2022

Ozone depletion in the Arctic and Antarctic stratosphere induced by wildfire smoke

Abstract. A record-breaking stratospheric ozone loss was observed over the Arctic and Antarctica in 2020. Strong ozone depletion occurred over Antarctica in 2021 as well. The ozone holes developed in smoke-polluted air. In this article, the impact of Siberian and Australian wildfire smoke (dominated by organic aerosol) on the extraordinarily strong ozone reduction is discussed. The study is based on aerosol lidar observations in the North Pole region (October 2019–May 2020) and over Punta Arenas in southern Chile at 53.2∘ S (January 2020–November 2021) as well as on respective NDACC (Network for the Detection of Atmospheric Composition Change) ozone profile observations in the Arctic (Ny-Ålesund) and Antarctica (Neumayer and South Pole stations) in 2020 and 2021. We present a conceptual approach on how the smoke may have influenced the formation of polar stratospheric clouds (PSCs), which are of key importance in the ozone-depleting processes. The main results are as follows: (a) the direct impact of wildfire smoke below the PSC height range (at 10–12 km) on ozone reduction seems to be similar to well-known volcanic sulfate aerosol effects. At heights of 10–12 km, smoke particle surface area (SA) concentrations of 5–7 µm2 cm−3 (Antarctica, spring 2021) and 6–10 µm2 cm−3 (Arctic, spring 2020) were correlated with an ozone reduction in terms of ozone partial pressure of 0.4–1.2 mPa (about 30 % further ozone reduction over Antarctica) and of 2–3.5 mPa (Arctic, 20 %–30 % reduction with respect to the long-term springtime mean). (b) Within the PSC height range, we found indications that smoke was able to slightly increase the PSC particle number and surface area concentration. In particular, a smoke-related additional ozone loss of 1–2 mPa (10 %–20 % contribution to the total ozone loss over Antarctica) was observed in the 14–23 km PSC height range in September–October 2020 and 2021. Smoke particle number concentrations ranged from 10 to 100 cm−3 and were about a factor of 10 (in 2020) and 5 (in 2021) above the stratospheric aerosol background level. Satellite observations indicated an additional mean column ozone loss (deviation from the long-term mean) of 26–30 Dobson units (9 %–10 %, September 2020, 2021) and 52–57 Dobson units (17 %–20 %, October 2020, 2021) in the smoke-polluted latitudinal Antarctic belt from 70–80∘ S.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2009

Airborne and Ground-Based Measurements Using a High-Performance Raman Lidar: Ground Based - Part 2

The same RASL hardware as described in part I was installed in a ground-based mobile trailer and used in a water vapor lidar intercomparison campaign, hosted at Table Mountain, CA, under the auspices of the Network for the Detection of Atmospheric Composition Change (NDACC). The converted RASL hardware demonstrated high sensitivity to lower stratospheric water vapor indicating that profiling water vapor at those altitudes with sufficient accuracy to monitor climate change is possible. The measurements from Table Mountain also were used to explain the reason, and correct , for sub-optimal airborne aerosol extinction performance during the flight campaign.

Whiteman, David N.↗
NASA NTRS2011

What Good is Raman Water Vapor Lidar?

Raman lidar has been used to quantify water vapor in the atmosphere for various scientific studies including mesoscale meteorology and satellite validation. Now the international networks of NDACC and GRUAN have interest in using Raman water vapor lidar for detecting trends in atmospheric water vapor concentrations. What are the data needs for addressing these very different measurement challenges. We will review briefly the scientific needs for water vapor accuracy for each of these three applications and attempt to translate that into performance specifications for Raman lidar in an effort to address the question in the title of "What good is Raman water vapor Iidar."

Whitman, David↗
NASA NTRS2012

Understanding Differences in the Response to Composition Change as Simulated by CCMVal Models

Chemistry climate models (CCMs) have a common conceptual basis. Differences in implementation lead to differences in the stratospheric ozone response to changes in composition and climate. Although evaluation by CCMVal-2 identified strengths and weaknesses of participant models, the evaluation results were not used to discriminate among projections for future ozone evolution, at least in part because the overall diagnostic evaluation did not cleanly relate to the differences in CCM response. Here we use a subset of CCMVal diagnostics and additional analysis to understand the differences in response. In the upper stratosphere, differences in simulated temperature and total odd nitrogen prior to increases in chlorine loading explain the large differences in CCM sensitivity. In the lower atmosphere, there are two principle contributions to differences in CCM sensitivity to chlorine and climate change. First, differences in the lower stratospheric ClO affect simulated sensitivity to chlorine. CCMs with best transport performance match NDACC column HCl measurements at a broad range of latitudes. Other CCMs disagree with observations due to differences in total inorganic chlorine, partitioning between HCl and ClONO2, or both. Differences in ClONO2 are directly related to differences in simulated ClO. Second, although all CCMs predict increased tropical upwelling, the rate of increase varies and contributes to differences in tropical ozone and the 60N-60S column average.

Douglass, Anne R.↗
NASA NTRS2011

The Relative Importance of Random Error and Observation Frequency in Detecting Trends in Upper Tropospheric Water Vapor

Recent published work assessed the amount of time to detect trends in atmospheric water vapor over the coming century. We address the same question and conclude that under the most optimistic scenarios and assuming perfect data (i.e., observations with no measurement uncertainty) the time to detect trends will be at least 12 years at approximately 200 hPa in the upper troposphere. Our times to detect trends are therefore shorter than those recently reported and this difference is affected by data sources used, method of processing the data, geographic location and pressure level in the atmosphere where the analyses were performed. We then consider the question of how instrumental uncertainty plays into the assessment of time to detect trends. We conclude that due to the high natural variability in atmospheric water vapor, the amount of time to detect trends in the upper troposphere is relatively insensitive to instrumental random uncertainty and that it is much more important to increase the frequency of measurement than to decrease the random error in the measurement. This is put in the context of international networks such as the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) and the Network for the Detection of Atmospheric Composition Change (NDACC) that are tasked with developing time series of climate quality water vapor data.

Whiteman, David N.↗
NASA NTRS2012

Correction Technique for Raman Water Vapor Lidar Signal-Dependent Bias and Suitability for Water Wapor Trend Monitoring in the Upper Troposphere

The MOHAVE-2009 campaign brought together diverse instrumentation for measuring atmospheric water vapor. We report on the participation of the ALVICE (Atmospheric Laboratory for Validation, Interagency Collaboration and Education) mobile laboratory in the MOHAVE-2009 campaign. In appendices we also report on the performance of the corrected Vaisala RS92 radiosonde measurements during the campaign, on a new radiosonde based calibration algorithm that reduces the influence of atmospheric variability on the derived calibration constant, and on other results of the ALVICE deployment. The MOHAVE-2009 campaign permitted the Raman lidar systems participating to discover and address measurement biases in the upper troposphere and lower stratosphere. The ALVICE lidar system was found to possess a wet bias which was attributed to fluorescence of insect material that was deposited on the telescope early in the mission. Other sources of wet biases are discussed and data from other Raman lidar systems are investigated, revealing that wet biases in upper tropospheric (UT) and lower stratospheric (LS) water vapor measurements appear to be quite common in Raman lidar systems. Lower stratospheric climatology of water vapor is investigated both as a means to check for the existence of these wet biases in Raman lidar data and as a source of correction for the bias. A correction technique is derived and applied to the ALVICE lidar water vapor profiles. Good agreement is found between corrected ALVICE lidar measurments and those of RS92, frost point hygrometer and total column water. The correction is offered as a general method to both quality control Raman water vapor lidar data and to correct those data that have signal-dependent bias. The influence of the correction is shown to be small at regions in the upper troposphere where recent work indicates detection of trends in atmospheric water vapor may be most robust. The correction shown here holds promise for permitting useful upper tropospheric water vapor profiles to be consistently measured by Raman lidar within NDACC (Network for the Detection of Atmospheric Composition Change) and elsewhere, despite the prevalence of instrumental and atmospheric effects that can contaminate the very low signal to noise measurements in the UT.

MOHAVE-2009↗