AIRS Retrievals Research Articles

Ensemble Forecast Sensitivity to Observations Impact (EFSOI) applied to a regional data assimilation system over south-eastern South America

Observations that are assimilated into numerical weather prediction systems are conformed by numerous data sets and their impact should be objectively evaluated. This can be efficiently achieved by the Forecast Sensitivity to Observation Impact (FSOI) methodology. In this study we explore the application of the ensemble formulation of FSOI (EFSOI) in a regional data assimilation system over south-eastern South America and evaluate the observations that result beneficial or detrimental to the forecast. In this paper we focus on the impact of two types of surface weather stations: human-operated conventional surface weather station networks from the National Meteorological Service and non-conventional automatic surface weather stations from different public and private networks (CSWS and NSWS respectively). To achieve this, the Weather Research and Forecasting model coupled with the Local Ensemble Transform Kalman Filter is used with 20 members. The experiment was carried out during 30 days of the intensive observing period of the RELAMPAGO-CACTI (Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations-Clouds, Aerosols, and Complex Terrain Interaction) field campaign that was conducted during the 2018–2019 austral warm season in the center of Argentina. 20 km resolution analyses were obtained every 6-h, assimilating data from soundings, aircrafts, GOES derived motion winds, AIRS retrievals, CSWS and NSWS. It is shown that, considering the entire period, all the observation sources had a positive impact on the 6-h forecasts. However, when each variable is analyzed individually, surface pressure observations from both CSWS and NSWS show on average a negative impact. Two data denial experiments were carried out in order to support EFSOI results. This paper is the first approximation to quantify the impact of different observation sources and observed variables at a regional scale over south-eastern South America, a data sparse region where unconventional networks can help close large geographical data gaps. The results of this work help identify observation data sources detrimental for the data assimilation system, suggesting data selection criteria to improve the regional forecast accuracy.

How Close Are Leading Tropical Tropospheric Temperature Perturbations to Those under Convective Quasi Equilibrium?

Abstract In convective quasi-equilibrium theory, tropical tropospheric temperature perturbations are expected to follow vertical profiles constrained by convection, referred to as A-profiles here, often approximated by perturbations of moist adiabats. Differences between an idealized A-profile based on moist-static energy conservation and temperature perturbations derived from entraining and nonentraining parcel computations are modest under convective conditions—deep convection mostly occurs when the lower troposphere is close to saturation, thus minimizing the impact of entrainment on tropospheric temperature. Simple calculations with pseudoadiabatic perturbations about the observed profile thus provide useful baseline A-profiles. The first EOF mode of tropospheric temperature (TEOF1) from the ERA-Interim and AIRS retrievals below the level of neutral buoyancy (LNB) is compared with these A-profiles. The TEOF1 profiles with high LNB, typically above 400 hPa, yield high vertical spatial correlation (∼0.9) with A-profiles, indicating that tropospheric temperature perturbations tend to be consistent with the quasi-equilibrium assumption where the environment is favorable to deep convection. Lower correlation tends to occur in regions with low climatological LNB, less favorable to deep convection. Excluding temperature profiles with low LNB significantly increases the tropical mean vertical spatial correlation. The temperature perturbations near LNB exhibit negative deviations from the A-profiles—the convective cold-top phenomenon—with greater deviation for higher LNB. In regions with lower correlation, the deviation from A-profile shows an S-like shape beneath 600 hPa, usually accompanied by a drier lower troposphere. These findings are robust across a wide range of time scales from daily to monthly, although the vertical spatial correlation and TEOF1 explained variance tend to decrease on short time scales.

Validation and error estimation of AIRS MUSES CO profiles with HIPPO, ATom, and NOAA GML aircraft observations

Abstract. Single-footprint retrievals of carbon monoxide from the Atmospheric Infrared Sounder (AIRS) are evaluated using aircraft in situ observations. The aircraft data are from the HIAPER Pole-to-Pole Observations (HIPPO, 2009–2011), the first three Atmospheric Tomography Mission (ATom, 2016–2017) campaigns, and the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) Global Greenhouse Gas Reference Network aircraft program in years 2006–2017. The retrievals are obtained using an optimal estimation approach within the MUlti-SpEctra, MUlti-SpEcies, MUlti-SEnsors (MUSES) algorithm. Retrieval biases and estimated errors are evaluated across a range of latitudes from the subpolar to tropical regions over both ocean and land points. AIRS MUSES CO profiles were compared with HIPPO, ATom, and NOAA GML aircraft observations with a coincidence of 9 h and 50 km to estimate retrieval biases and standard deviations. Comparisons were done for different pressure levels and column averages, latitudes, day, night, land, and ocean observations. We found mean biases of +6.6±4.6 %, +0.6±3.2 %, and -6.1±3.0 % for three representative pressure levels of 750, 510, and 287 hPa, as well as column average mean biases of 1.4±3.6 %. The mean standard deviations for the three representative pressure levels were 15 %, 11 %, and 12 %, and the column average standard deviation was 9 %. Observation errors (theoretical errors) from the retrievals were found to be broadly consistent in magnitude with those estimated empirically from ensembles of satellite aircraft comparisons, but the low values for these observation errors require further investigation. The GML aircraft program comparisons generally had higher standard deviations and biases than the HIPPO and ATom comparisons. Since the GML aircraft flights do not go as high as the HIPPO and ATom flights, results from these GML comparisons are more sensitive to the choice of method for extrapolation of the aircraft profile above the uppermost measurement altitude. The AIRS retrieval performance shows little sensitivity to surface type (land or ocean) or day or night but some sensitivity to latitude. Comparisons to the NOAA GML set spanning the years 2006–2017 show that the AIRS retrievals are able to capture the distinct seasonal cycles but show a high bias of ∼20 % in the lower troposphere during the summer when observed CO mixing ratios are at annual minimum values. The retrieval bias drift was examined over the same years 2006–2017 and found to be small at <0.5 %.

Characteristics of the Antarctic Temperature and Specific Humidity Inversions in Airs Retrievals

Characteristics of the Antarctic Temperature and Specific Humidity Inversions in Airs Retrievals

A rapid refresh ensemble based data assimilation and forecast system for the RELAMPAGO field campaign

This paper describes the lessons learned from the implementation of a regional ensemble data assimilation and forecast system during the intensive observing period of the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign (central Argentina, November–December 2018). This system is based on the coupling of the Weather Research and Forecasting (WRF) model and the Local Ensemble Transform Kalman Filter (LETKF). It combines multiple data sources both global and locally available like high-resolution surface networks, AMDAR data from local aircraft flights, soundings, AIRS retrievals, high-resolution GOES-16 wind estimates, and local radar data. Hourly analyses with grid spacing of 10 km are generated along with warm-start 36-h ensemble-forecasts, which are initialized from the rapid refresh analyses every three hours. A preliminary evaluation shows that a forecast error reduction is achieved due to the assimilated observations. However, cold-start forecasts initialized from the Global Forecasting System Analysis slightly outperform the ones initialized from the regional assimilation system discussed in this paper. The system uses a multi-physics approach, focused on the use of different cumulus and planetary boundary layer schemes allowing us to conduct an evaluation of different model configurations over central Argentina. We found that the best combinations for forecasting surface variables differ from the best ones for forecasting precipitation, and that differences among the schemes tend to dominate the forecast ensemble spread for variables like precipitation. Lessons learned from this experimental system are part of the legacy of the RELAMPAGO field campaign for the development of advanced operational data assimilation systems in South America.

Single-footprint retrievals of temperature, water vapor and cloud properties from AIRS

Abstract. Single-footprint Atmospheric Infrared Sounder spectra are used in an optimal estimation-based algorithm (AIRS-OE) for simultaneous retrieval of atmospheric temperature, water vapor, surface temperature, cloud-top temperature, effective cloud optical depth and effective cloud particle radius. In a departure from currently operational AIRS retrievals (AIRS V6), cloud scattering and absorption are in the radiative transfer forward model and AIRS single-footprint thermal infrared data are used directly rather than cloud-cleared spectra (which are calculated using nine adjacent AIRS infrared footprints). Coincident MODIS cloud data are used for cloud a priori data. Using single-footprint spectra improves the horizontal resolution of the AIRS retrieval from ∼ 45 to ∼ 13.5 km at nadir, but as microwave data are not used, the retrieval is not made at altitudes below thick clouds. An outline of the AIRS-OE retrieval procedure and information content analysis is presented. Initial comparisons of AIRS-OE to AIRS V6 results show increased horizontal detail in the water vapor and relative humidity fields in the free troposphere above the clouds. Initial comparisons of temperature, water vapor and relative humidity profiles with coincident radiosondes show good agreement. Future improvements to the retrieval algorithm, and to the forward model in particular, are discussed.

Methodology for evaluating lateral boundary conditions in the regional chemical transport model MATCH (v5.5.0) using combined satellite and ground-based observations

Abstract. Hemispheric transport of air pollutants can have a significant impact on regional air quality, as well as on the effect of air pollutants on regional climate. An accurate representation of hemispheric transport in regional chemical transport models (CTMs) depends on the specification of the lateral boundary conditions (LBCs). This study focuses on the methodology for evaluating LBCs of two moderately long-lived trace gases, carbon monoxide (CO) and ozone (O3), for the European model domain and over a 7-year period, 2006–2012. The method is based on combining the use of satellite observations at the lateral boundary with the use of both satellite and in situ ground observations within the model domain. The LBCs are generated by the global European Monitoring and Evaluation Programme Meteorological Synthesizing Centre – West (EMEP MSC-W) model; they are evaluated at the lateral boundaries by comparison with satellite observations of the Terra-MOPITT (Measurements Of Pollution In The Troposphere) sensor (CO) and the Aura-OMI (Ozone Monitoring Instrument) sensor (O3). The LBCs from the global model lie well within the satellite uncertainties for both CO and O3. The biases increase below 700 hPa for both species. However, the satellite retrievals below this height are strongly influenced by the a priori data; hence, they are less reliable than at, e.g. 500 hPa. CO is, on average, underestimated by the global model, while O3 tends to be overestimated during winter, and underestimated during summer. A regional CTM is run with (a) the validated monthly climatological LBCs from the global model; (b) dynamical LBCs from the global model; and (c) constant LBCs based on in situ ground observations near the domain boundary. The results are validated against independent satellite retrievals from the Aqua-AIRS (Atmospheric InfraRed Sounder) sensor at 500 hPa, and against in situ ground observations from the Global Atmospheric Watch (GAW) network. It is found that (i) the use of LBCs from the global model gives reliable in-domain results for O3 and CO at 500 hPa. Taking AIRS retrievals as a reference, the use of these LBCs substantially improves spatial pattern correlations in the free troposphere as compared to results obtained with fixed LBCs based on ground observations. Also, the magnitude of the bias is reduced by the new LBCs for both trace gases. This demonstrates that the validation methodology based on using satellite observations at the domain boundary is sufficiently robust in the free troposphere. (ii) The impact of the LBCs on ground concentrations is significant only at locations in close proximity to the domain boundary. As the satellite data near the ground mainly reflect the a priori estimate used in the retrieval procedure, they are of little use for evaluating the effect of LBCs on ground concentrations. Rather, the evaluation of ground-level concentrations needs to rely on in situ ground observations. (iii) The improvements of dynamic over climatological LBCs become most apparent when using accumulated ozone over threshold 40 ppb (AOT40) as a metric. Also, when focusing on ground observations taken near the inflow boundary of the model domain, one finds that the use of dynamical LBCs yields a more accurate representation of the seasonal variation, as well as of the variability of the trace gas concentrations on shorter timescales.

Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

Abstract. Uncertainties in the satellite-derived surface skin temperature (SST) data in the polar oceans during two periods (16–24 April and 15–23 September) 2003–2014 were investigated and the three data sets were intercompared as follows: MODerate Resolution Imaging Spectroradiometer Ice Surface Temperature (MODIS IST), the SST of the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU), and AIRS only. The AIRS only algorithm was developed in preparation for the degradation of the AMSU-A. MODIS IST was systematically warmer up to 1.65 K at the sea ice boundary and colder down to −2.04 K in the polar sea ice regions of both the Arctic and Antarctic than that of the AIRS/AMSU. This difference in the results could have been caused by the surface classification method. The spatial correlation coefficient of the AIRS only to the AIRS/AMSU (0.992–0.999) method was greater than that of the MODIS IST to the AIRS/AMSU (0.968–0.994). The SST of the AIRS only compared to that of the AIRS/AMSU had a bias of 0.168 K with a RMSE of 0.590 K over the Northern Hemisphere high latitudes and a bias of −0.109 K with a RMSE of 0.852 K over the Southern Hemisphere high latitudes. There was a systematic disagreement between the AIRS retrievals at the boundary of the sea ice, because the AIRS only algorithm utilized a less accurate GCM forecast over the seasonally varying frozen oceans than the microwave data. The three data sets (MODIS, AIRS/AMSU and AIRS only) showed significant warming rates (2.3 ± 1.7 ~ 2.8 ± 1.9 K decade−1) in the northern high regions (70–80° N) as expected from the ice-albedo feedback. The systematic temperature disagreement associated with surface type classification had an impact on the resulting temperature trends.

Validation of IASI-Retrieved Atmospheric Water Vapor Data over the Tibetan Plateau Region

AbstractThe Infrared Atmospheric Sounding Interferometer (IASI) is a new-generation ultraspectral atmospheric sounding instrument mounted on the MetOp-A, the first operational polar-orbiting satellite developed by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). It is an ultrahigh spectral-resolution atmospheric detector which can detect atmospheric chemical composition, temperature, and humidity profiles with high accuracy and resolution. In the present study, through comparative analyses of the similarities and differences between the IASI and the radiosonde observation (RAOB) water vapor data, and between the IASI and the Aqua-AIRS water vapor retrievals, a detailed and systematic assessment of the credibility of the IASI water vapor retrievals over the plateau region was made. A comparison of the IASI retrievals with the AIRS retrievals and the RAOB measurements over the Tibetan Plateau revealed that the IASI retrieval data are reliable and can be used for conduc...

Retrieval of tropospheric CO column from hyperspectral infrared sounders – application to four years of Aqua/AIRS and MetOp-A/IASI

Abstract. Four years of tropospheric integrated content of CO were retrieved from infrared hyperspectral observations of AIRS onboard Aqua and IASI onboard MetOp-A, for the period July 2007–June 2011. The retrieval method is based on a double differential approach that relies on the difference between brightness temperatures observed by the sounder and BT simulated by the Automatised Atmospheric Absorption Atlas (4A) radiative transfer model on colocated ECMWF reanalyses, for several couples of channels located in the 4.67 μm CO band. AIRS and IASI give access to similar integrated contents of CO with a maximum sensitivity near 450 hPa and a half-height width of the weighting function between 200 and 750 hPa depending on the thermal contrast (i.e., the difference between the surface temperature and the temperature of the first pressure level). However, differences in their spectral and radiometric characteristics yield differences in the retrieval characteristics with AIRS selected couples of channels being more sensitive to surface characteristics. Moreover, IASI covers the whole CO absorption band, with a 3 times better spectral resolution, giving access to channels presenting a 3 times higher signal to noise ratio. This results in a better precision and lower standard deviation of the IASI retrievals. Conservatively, comparisons with CARIBIC aircraft measurements yield an averaged relative difference of 3.4% for IASI and 4.9% for AIRS. On average, AIRS and IASI retrievals are in very good agreement, showing the same seasonality, seasonal amplitudes, interannual variability and spatial distribution. The analysis of the monthly evolution of CO particularly highlights the expected strong influence of biomass burning on the evolution of CO in several tropical regions. In particular, a sharp increase in CO in 2010 in the southern tropics, especially over South America and South Africa, is observed, and is shown to be related to El Niño and to the Atlantic Multidecadal Oscillation.

Analysis of global and regional CO burdens measured from space between 2000 and 2009 and validated by ground-based solar tracking spectrometers

Abstract. Interannual variations in AIRS and MOPITT retrieved CO burdens are validated, corrected, and compared with CO emissions from wild fires from the Global Fire Emission Dataset (GFED2) inventory. Validation of daily mean CO total column (TC) retrievals from MOPITT version 3 and AIRS version 5 is performed through comparisons with archived TC data from the Network for Detection of Atmospheric Composition Change (NDACC) ground-based Fourier Transform Spectrometers (FTS) between March 2000 and December 2007. MOPITT V3 retrievals exhibit an increasing temporal bias with a rate of 1.4–1.8% per year; thus far, AIRS retrievals appear to be more stable. For the lowest CO values in the Southern Hemisphere (SH), AIRS TC retrievals overestimate FTS TC by 20%. MOPITT's bias and standard deviation do not depend on CO TC absolute values. Empirical corrections are derived for AIRS and MOPITT retrievals based on the observed annually averaged bias versus the FTS TC. Recently published MOPITT V4 is found to be in a good agreement with MOPITT V3 corrected by us (with exception of 2000–2001 period). With these corrections, CO burdens from AIRS V5 and MOPITT V3 (as well as MOPITT V4) come into good agreement in the mid-latitudes of the Northern Hemisphere (NH) and in the tropical belt. In the SH, agreement between AIRS and MOPITT CO burdens is better for the larger CO TC in austral winter and worse in austral summer when CO TC are smaller. Before July 2008, all variations in retrieved CO burden can be explained by changes in fire emissions. After July 2008, global and tropical CO burdens decreased until October before recovering by the beginning of 2009. The NH CO burden also decreased but reached a minimum in January 2009 before starting to recover. The decrease in tropical CO burdens is explained by lower than usual fire emissions in South America and Indonesia. This decrease in tropical emissions also accounts for most of the change in the global CO burden. However, no such diminution of NH biomass burning is indicated by GFED2. Thus, the CO burden decrease in the NH could result from a combination of lower fossil fuel emissions during the global economic recession and transport of CO-poor air from the tropics. More extensive modeling will be required to fully resolve this issue.

Satellite Microwave Remote Sensing of Daily Land Surface Air Temperature Minima and Maxima From AMSR-E

We present an approach to retrieve daily minimum and maximum 2-m height air temperatures from 18.7, and 23.8 GHz H and V polarized brightness temperature from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) during the snow free season. The approach accounts, with minimal ancillary data, for vertically integrated atmospheric water vapor, and variable surface emissivity due to open water and vegetation. Retrieved temperatures were evaluated using Northern Hemisphere weather stations and independent satellite-based air temperatures from the Atmosphere Infrared Sounder and Advanced Microwave Sounding Unit (AIRS/AMSU; hereafter AIRS) sensors on Aqua. The retrieved temperatures are within 1.0 - 3.5 K of surface weather station measurements for vegetated locations, but uncertainty can exceed 4 K for desert and sparsely vegetated regions, mainly due to site to site biases. The AIRS and AMSR-E temperature retrievals generally agree more closely with one another than with weather stations and are generally within 1.0-2.8 K over vegetated regions, but with less agreement ( > 4 K ) over desert and mountainous regions. Additional useful information produced by our approach includes open water fraction, vegetation optical depth and atmospheric water vapor. The results of this study provide inputs for land surface models and a new approach for monitoring of land surface air temperatures with well quantified accuracy and precision.

Derivation of global hyperspectral resolution surface emissivity spectra from advanced infrared sounder radiance measurements

The global IR surface emissivity spectra are very important for retrieving products such as dust properties, land surface temperature, trace gases, and cloud‐top properties, as well as assimilating hyperspectral resolution IR radiances in NWP models over land. In this study, global IR surface emissivity spectra have been generated by using AIRS radiance measurements from NASA's EOS Aqua platform. A one‐dimensional variation retrieval algorithm was used for retrieving the global hyperspectral resolution IR surface emissivity product. The 8‐day composite of AIRS IR surface emissivity retrievals agrees well with the operational MODIS emissivity product (collection 4.0) at a few broad spectral bands, with the differences less than 0.02 in most regions for 8.55 μm spectral band; in addition, the AIRS retrievals provide more complete spectral information. The spatial and spectral features of the derived emissivity over desert and other regions reflect the ecosystem surface types, but show more variability than IGBP classification.

Infrared Continental Surface Emissivity Spectra Retrieved from AIRS Hyperspectral Sensor

Abstract Atmospheric Infrared Sounder (AIRS; NASA Aqua platform) observations over land are interpreted in terms of monthly mean surface emissivity spectra at a resolution of 0.05 μm and skin temperature. For each AIRS observation, an estimation of the atmospheric temperature and water vapor profiles is first obtained through a proximity recognition within the thermodynamic initial guess retrieval (TIGR) climatological library of about 2300 representative clear-sky atmospheric situations. With this a priori information, all terms of the radiative transfer equation are calculated by using the Automatized Atmospheric Absorption Atlas (4A) line-by-line radiative transfer model. Then, surface temperature is evaluated by using a single AIRS channel (centered at 12.183 μm) chosen for its almost constant emissivity with respect to soil type. Emissivity is then calculated for a set of 40 atmospheric windows (transmittance greater than 0.5) distributed over the AIRS spectrum. The overall infrared emissivity spectrum at 0.05-μm resolution is finally derived from a combination of high-spectral-resolution laboratory measurements of various materials carefully selected within the Moderate-Resolution Imaging Spectroradiometer/University of California, Santa Barbara (MODIS/UCSB) and Advanced Spaceborne Thermal Emission and Reflection Radiometer/Jet Propulsion Laboratory (ASTER/JPL) emissivity libraries. It is shown from simulations that the accuracy of the method developed in this paper, the multispectral method (MSM), varies from about 3% around 4 μm to considerably less than 1% in the 10–12-μm spectral window. Three years of AIRS observations (from April 2003 to March 2006) between 30°S and 30°N have been processed and interpreted in terms of monthly mean surface skin temperature and emissivity spectra from 3.7 to 14.0 μm at a spatial resolution of 1° × 1°. AIRS retrievals are compared with the MODIS (also flying aboard the NASA/Aqua platform) monthly mean L3 products and with the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies baseline-fit method (UW/CIMSS BF) global infrared land surface emissivity database.

Comparison of water vapor measurements by airborne Sun photometer and near‐coincident in situ and satellite sensors during INTEX/ITCT 2004

We have retrieved columnar water vapor (CWV) from measurements acquired by the 14‐channel NASA Ames Airborne Tracking Sun photometer (AATS‐14) during 19 Jetstream 31 (J31) flights over the Gulf of Maine in summer 2004 in support of the Intercontinental Chemical Transport Experiment (INTEX)/Intercontinental Transport and Chemical Transformation (ITCT) experiments. In this paper we compare AATS‐14 water vapor retrievals during aircraft vertical profiles with measurements by an onboard Vaisala HMP243 humidity sensor and by ship radiosondes and with water vapor profiles retrieved from AIRS measurements during eight Aqua overpasses. We also compare AATS CWV and MODIS infrared CWV retrievals during five Aqua and five Terra overpasses. For 35 J31 vertical profiles, mean (bias) and RMS AATS‐minus‐Vaisala layer‐integrated water vapor (LWV) differences are −7.1% and 8.8%, respectively. For 22 aircraft profiles within 1 hour and 130 km of radiosonde soundings, AATS‐minus‐sonde bias and RMS LWV differences are −5.4% and 10.7%, respectively, and corresponding J31 Vaisala‐minus‐sonde differences are 2.3% and 8.4%, respectively. AIRS LWV retrievals within 80 km of J31 profiles yield lower bias and RMS differences compared to AATS or Vaisala retrievals than do AIRS retrievals within 150 km of the J31. In particular, for AIRS‐minus‐AATS LWV differences, the bias decreases from 8.8% to 5.8%, and the RMS difference decreases from 21.5% to 16.4%. Comparison of vertically resolved AIRS water vapor retrievals (LWVA) to AATS values in fixed pressure layers yields biases of −2% to +6% and RMS differences of ∼20% below 700 hPa. Variability and magnitude of these differences increase significantly above 700 hPa. MODIS IR retrievals of CWV in 205 grid cells (5 × 5 km at nadir) are biased wet by 10.4% compared to AATS over‐ocean near‐surface retrievals. The MODIS‐Aqua subset (79 grid cells) exhibits a wet bias of 5.1%, and the MODIS‐Terra subset (126 grid cells) yields a wet bias of 13.2%.

Arctic smoke – record high air pollution levels in the European Arctic due to agricultural fires in Eastern Europe in spring 2006

Abstract. In spring 2006, the European Arctic was abnormally warm, setting new historical temperature records. During this warm period, smoke from agricultural fires in Eastern Europe intruded into the European Arctic and caused the most severe air pollution episodes ever recorded there. This paper confirms that biomass burning (BB) was indeed the source of the observed air pollution, studies the transport of the smoke into the Arctic, and presents an overview of the observations taken during the episode. Fire detections from the MODIS instruments aboard the Aqua and Terra satellites were used to estimate the BB emissions. The FLEXPART particle dispersion model was used to show that the smoke was transported to Spitsbergen and Iceland, which was confirmed by MODIS retrievals of the aerosol optical depth (AOD) and AIRS retrievals of carbon monoxide (CO) total columns. Concentrations of halocarbons, carbon dioxide and CO, as well as levoglucosan and potassium, measured at Zeppelin mountain near Ny Ålesund, were used to further corroborate the BB source of the smoke at Spitsbergen. The ozone (O3) and CO concentrations were the highest ever observed at the Zeppelin station, and gaseous elemental mercury was also elevated. A new O3 record was also set at a station on Iceland. The smoke was strongly absorbing – black carbon concentrations were the highest ever recorded at Zeppelin – and strongly perturbed the radiation transmission in the atmosphere: aerosol optical depths were the highest ever measured at Ny Ålesund. We furthermore discuss the aerosol chemical composition, obtained from filter samples, as well as the aerosol size distribution during the smoke event. Photographs show that the snow at a glacier on Spitsbergen became discolored during the episode and, thus, the snow albedo was reduced. Samples of this polluted snow contained strongly elevated levels of potassium, sulphate, nitrate and ammonium ions, thus relating the discoloration to the deposition of the smoke aerosols. This paper shows that, to date, BB has been underestimated as a source of aerosol and air pollution for the Arctic, relative to emissions from fossil fuel combustion. Given its significant impact on air quality over large spatial scales and on radiative processes, the practice of agricultural waste burning should be banned in the future.

Principal component analysis of the evolution of the Saharan air layer and dust transport: Comparisons between a model simulation and MODIS and AIRS retrievals

The onset and evolution of Saharan air layer (SAL) episodes during June–September 2002 are diagnosed by applying principal component analysis to the NCEP reanalysis temperature anomalies at 850 hPa, where the largest SAL‐induced temperature anomalies are located along the west coast of Africa. The first principal component (PC) represents the onset of SAL episodes, which are associated with large warm anomalies located at the west coast of Africa. The second PC represents two opposite phases of the evolution of the SAL. The positive phase corresponds to the southwestward migration of the warm anomalies into the tropical‐subtropical North Atlantic Ocean, and the negative phase corresponds to the northwestward migration into the subtropical to midlatitude North Atlantic Ocean and the southwest Europe. AIRS retrievals of temperatures in September 2002 verify the migration depicted by the second PC. In addition, a dust transport model (CARMA) and the MODIS retrievals of aerosol optical thickness are used to study the associated effects on dust distribution and deposition. The positive (negative) phase of the second PC corresponds to a strengthening (weakening) of the offshore flows in the lower troposphere around 10–20°N, causing more (less) dust being transported toward the tropical North Atlantic Ocean. The 700‐hPa anticyclonic circulation associated with the warm anomalies plays a role in connecting the dust with the warm anomalies.