Aerosol retrieval based on 10 years of passive remote sensing satellite measurements over the Arctic – validation and trend analysis

2020 ◽  
Author(s):  
Soheila Jafariserajehlou ◽  
Marco Vountas ◽  
Larysa Istomina ◽  
John P. Burrows
Keyword(s):  
Remote Sensing ◽  
Trend Analysis ◽  
Optical Thickness ◽  
Arctic Region ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Identification Algorithm ◽  
Aerosol Retrieval ◽  
Along Track

<p>The Aerosol Optical Thickness (AOT) retrieval over the Arctic region is a challenging task due to uncertainties and difficulties in its prerequisites, mainly (i) cloud masking methods and (ii) modeling the underlying snow/ice surface. In the past this led to a large data gap over the Arctic which hampered our understanding of the direct/indirect aerosol effect on Arctic and global climate change. For the purpose of improving our knowledge, we present, for the first time, long-term AOT maps of snow and ice covered areas based on satellite remote sensing.</p><p>In this study, a previously developed aerosol retrieval algorithm over snow/ice, (Istomina et al., 2012; in IUP, University of Bremen) is used to retrieve AOT for a period of 10 years, 2002-2012, over the Arctic and to analyze its spatial and temporal changes. This algorithm is based on a multi-angle approach and uses pre-computed look-up tables to retrieve AOT.</p><p>The algorithm has been improved with respect to cloud masking (based on clear snow spectral shape) using the ASCIA cloud identification algorithm (Jafariserajehlou et al., 2019). The modified AOT retrieval algorithm is applied to observations from Advanced Along-Track Scanning Radiometer (AATSR) on European Space Agency’s (ESA) measurements. The retrieved dataset provides long-term AOT at a spatial resolution of 1 km<sup>2</sup> over snow/ice covered surface in the extended Arctic region (60<sup>°</sup>- 90<sup>°</sup>) during polar day. The results show that Arctic haze events appearing every late-winter and early spring are very well captured in AATSR derived AOTs. To validate the retrieved AOTs, results are compared with ground-based AERONET data. The comparisons revealed partially excellent agreement but also limits of the retrieval algorithm are discussed. In addition, some preliminary results of a trend analysis of the long-term record will be presented. It is foreseen to use the results in the trans-regional research project (AC)³ investigating Arctic amplification.</p><p><em><strong>References</strong></em></p><p>[1] Istomina, L.: Retrieval of aerosol optical thickness over snow and ice surfaces in the Arctic using Advanced Along Track Scanning Radiometer, PhD thesis, University of Bremen, Bremen, Germany, 2012.</p><p>[2] Jafariserajehlou, S. and Mei, L. and Vountas, M. and Rozanov, V. and Burrows, J. P. and Hollmann, R., A cloud identification algorithm over the Arctic for use with AATSR/SLSTR measurements, Atmos. Meas. Tech., 12, 1059-1076, doi:10.5194/amt-12-1059-2019, 2019.</p><p> </p>

scholarly journals Variability of the mixed phase in the Arctic with a focus on the Svalbard region: a study based on spaceborne active remote sensing

2014 ◽  
Vol 14 (16) ◽  
pp. 23453-23497
Author(s):  
G. Mioche ◽  
O. Jourdan ◽  
M. Ceccaldi ◽  
J. Delanoë
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Arctic Region ◽  
Supercooled Liquid ◽  
Mixed Phase ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Temporal And Spatial Distribution ◽  
Svalbard Archipelago ◽  
The Arctic Region

Abstract. The Arctic region is known to be very sensitive to climate change. Clouds and in particular mixed phase clouds (MPC) remain one of the greatest sources of uncertainties in the modeling of the Arctic response to climate change due to an inaccurate representation of their variability and their quantification. In this study, we present a characterization of the vertical, spatial and seasonal variability of Arctic clouds and MPC over the whole Arctic region based on satellite active remote sensing observations. MPC properties in the region of Svalbard archipelago (78° N, 15° E) are also investigated. The occurrence frequency of clouds and MPC are determined from CALIPSO/CLOUDSAT measurements processed with the DARDAR retrieval algorithm which allows for a reliable cloud thermodynamic phase classification (warm liquid, supercooled liquid, ice, mixing of ice and supercooled liquid). Significant differences are observed between MPC variability over the whole Arctic region and over the Svalbard region. Results show that MPC are ubiquitous all along the year, with a minimum occurrence of 30% in winter and 50% during the rest of the year, in average over the whole Arctic. Over the Svalbard region, MPC occurrence is more constant with time with larger values (55%) compared to the average observed in the Arctic. MPC are especially located at low altitudes, below 3000 m, where their frequency of occurrence reaches 90%, in particular during winter, spring and autumn. Moreover, results highlight that MPC statistically prevail over sea. The temporal and spatial distribution of MPC over the Svalbard region seems to be linked to the contribution of moister air and warmer water from the North Atlantic Ocean which contribute to the initiation of the liquid water phase. Over the whole Arctic, and particularly in western regions, the increase of MPC occurrence from spring to autumn could be connected to the sea ice melting. During this period, the open water transports a part of the warm water from the Svalbard region to the rest of the Arctic region. This facilitates the vertical transfer of moisture and thus the persistence of the liquid phase. A particular attention is also paid on the measurements uncertainties and how they could affect our results.

scholarly journals Total ozone trends and variability during 1979–2012 from merged datasets of various satellites

2013 ◽  
Vol 13 (11) ◽  
pp. 30407-30452 ◽  
Author(s):  
W. Chehade ◽  
J. P. Burrows ◽  
M. Weber
Keyword(s):  
Heat Flux ◽  
Solar Cycle ◽  
Trend Analysis ◽  
Total Ozone ◽  
Montreal Protocol ◽  
The Other ◽  
The Arctic ◽  
Eddy Heat Flux ◽  
Other Hand

Abstract. The study presents a~long term statistical trend analysis of total ozone datasets obtained from various satellites. A multi-variate linear regression was applied to annual mean zonal mean data using various natural and anthropogenic explanatory variables that represent dynamical and chemical processes which modify global ozone distributions in a changing climate. The study investigated the magnitude and zonal distribution of the different atmospheric chemical and dynamical factors to long-term total ozone changes. The regression model included the equivalent effective stratospheric chlorine (EESC), the 11 yr solar cycle, the Quasi-Biennial Oscillation (QBO), stratospheric aerosol loading describing the effects from major volcanic eruptions, the El Niño/Southern Oscillation (ENSO), the Arctic and Antarctic Oscillation (AO/AAO), and accumulated eddy heat flux (EHF), the latter representing changes due to the Brewer–Dobson circulation. The total ozone column dataset used here comprises the SBUV/TOMS/OMI merged data (1979–2012) MOD V8.0, the SBUV/SBUV-2 merged V8.6 and the merged GOME/SCIAMACHY/GOME-2 (GSG) WFDOAS merged data (1995–2012). The trend analysis was performed for twenty six 5° wide latitude bands from 65° S to 65° N, the analysis explained most of the ozone variability. The results show that QBO dominates the ozone variability in the tropics (±7 DU) while at higher latitudes, the dynamical indices, AO/AAO and eddy heat flux, have substantial influence on total ozone variations by up to ±10 DU. Volcanic aerosols are only prominent during the eruption periods and these together with the ENSO signal are more evident in the Northern Hemisphere. The signature of the solar cycle is evident over all latitudes and contributes about 10 DU from solar maximum to solar minimum. EESC is found to be a main contributor to the long-term ozone decline and the trend changes after the end of 1990s. A positive significant trend in total ozone columns is found after 1997 (between 1 and 8.2 DU decade−1) which points at the slowing of ozone decline and the onset of ozone recovery. The EESC based trends are compared with the trends obtained from the statistical piecewise linear trend (PWLT or hockey stick) model with a turnaround in 1997 to examine the differences between both approaches. Similar and significant pre-turnaround trends are observed. On the other hand, our results do indicate that the positive PWLT turnaround trends are larger than indicated by the EESC trends, however, they agree within 2-sigma, thus demonstrating the success of the Montreal Protocol phasing out of the ozone depleting substances (ODS). A sensitivity study is carried out by comparing the regression results, using SBUV MOD 8.0 merged time series (1979–2012) and a merged dataset combining TOMS/SBUV (1979–June 1995) and GOME/SCIAMACHY/GOME-2 ("GSG") WFDOAS (Weighting Function DOAS) (July 1995–2012) as well as SBUV/SBUV-2 MOD 8.6 (1979–2012) in the regression analysis in order to investigate the uncertainty in the long-term trends due to different ozone datasets and data versions. Replacing the late SBUV merged data record with GSG data (unscaled and adjusted) leads to very similar results demonstrating the high consistency between satellite datasets. However, the comparison of the new SBUV merged Mod V8.6 with the V8.0 data showed somewhat smaller sensitivities with regard to several proxies, however, the EESC and PWLT trends are very similar. On the other hand, the new MOD 8.6 data in the PWLT model revealed a~reduced ODS related upward trend after 1997.

scholarly journals Seasonal and Decadal Variability of Atmosphere Pressure in Arctic, its Statistical and Temporal Analysis

2021 ◽  
Vol 24 (1) ◽  
pp. 57-73
Author(s):  
Константин Павлович Беляев ◽  
Гурий Михайлович Михайлов ◽  
Алексей Николаевич Сальников ◽  
Наталия Павловна Тучкова
Keyword(s):  
Decadal Variability ◽  
Diffusion Processes ◽  
Arctic Region ◽  
Temporal Analysis ◽  
The Arctic ◽  
State University ◽  
Main Research ◽  
Geophysical Interpretation ◽  
The Given

The paper analyzes the statistical and temporal seasonal and decadal variability of the atmospheric pressure field in the Arctic region of Russia. Schemes for the frequency analysis of probability transitions for characteristics of stochastic-diffusion processes were used as the main research method. On the basis of the given series of 60 years long from 1948 to 2008, such parameters of diffusion processes as the mean (drift process) and variance (diffusion process) were calculated and their maps and time curves were constructed. The seasonal and long-term variability of calculated fields was studied as well as their dependencies on a discretization of the frequency intervals. These characteristics were analyzed and their geophysical interpretation was carried out. In particular, the known cycles of solar activity in 11 and 22 years were revealed. Numerical calculations were performed on the Lomonosov-2 supercomputer of the Lomonosov Moscow State University.

scholarly journals Long-Term Variations in the Pixel-to-Pixel Variability of NOAA AVHRR SST Fields from 1982 to 2015

2019 ◽  
Vol 11 (7) ◽  
pp. 844 ◽  
Author(s):  
Fan Wu ◽  
Peter Cornillon ◽  
Lei Guan ◽  
Katherine Kilpatrick
Keyword(s):  
Quality Data ◽  
Retrieval Algorithm ◽  
Ocean Fronts ◽  
Noaa Avhrr ◽  
Data Record ◽  
Along Track ◽  
Level 2 ◽  
Very High ◽  
Significant Effort

Sea surface temperature (SST) fields obtained from the series of space-borne five-channel Advanced Very High Resolution Radiometers (AVHRRs) provide the longest continuous time series of global SST available to date (1981–present). As a result, these data have been used for many studies and significant effort has been devoted to their careful calibration in an effort to provide a climate quality data record. However, little attention has been given to the local precision of the SST retrievals obtained from these instruments, which we refer to as the pixel-to-pixel (p2p) variability, a characteristic important in the ability to resolve structures such as ocean fronts characterized by small gradients in the SST field. In this study, the p2p variability is estimated for Level-2 SST fields obtained with the Pathfinder retrieval algorithm for AVHRRs on NOAA-07, 9, 11, 12 and 14-19. These estimates are stratified by year, season, day/night and along-scan/along-track. The overall variability ranges from 0.10 K to 0.21 K. For each satellite, the along-scan variability is between 10 and 20% smaller than the along-track variability (except for NOAA-16 nighttime for which it is approximately 30% smaller) and the summer and fall σ s are between 10 and 15% smaller than the winter and spring σ s. The differences between along-track and along-scan are attributed to the way in which the instrument has been calibrated. The seasonal differences result from the T 4 − T 5 term in the Pathfinder retrieval algorithm. This term is shown to be a major contributor to the p2p variability and it is shown that its impact could be substantially reduced without a deleterious effect on the overall p2p σ of the resulting products by spatially averaging it as part of the retrieval process. The AVHRR/3s (NOAA-15 through 19) were found to be relatively stable with trends in the p2p variability of at most 0.015 K/decade.

scholarly journals Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing

2017 ◽  
Vol 10 (9) ◽  
pp. 3215-3230 ◽  
Author(s):  
André Ehrlich ◽  
Eike Bierwirth ◽  
Larysa Istomina ◽  
Manfred Wendisch
Keyword(s):  
Remote Sensing ◽  
Grain Size ◽  
Sea Ice ◽  
Optical Thickness ◽  
Liquid Water ◽  
Retrieval Algorithm ◽  
Continuous Transition ◽  
Cloud Properties ◽  
Cloud Optical Thickness ◽  
The Impact

Abstract. The passive solar remote sensing of cloud properties over highly reflecting ground is challenging, mostly due to the low contrast between the cloud reflectivity and that of the underlying surfaces (sea ice and snow). Uncertainties in the retrieved cloud optical thickness τ and cloud droplet effective radius reff, C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the generally unknown effective snow grain size reff, S. Therefore, in a first step the effects of the assumed snow grain size are systematically quantified for the conventional bispectral retrieval technique of τ and reff, C for liquid water clouds. In general, the impact of uncertainties of reff, S is largest for small snow grain sizes. While the uncertainties of retrieved τ are independent of the cloud optical thickness and solar zenith angle, the bias of retrieved reff, C increases for optically thin clouds and high Sun. The largest deviations between the retrieved and true original values are found with 83 % for τ and 62 % for reff, C. In the second part of the paper a retrieval method is presented that simultaneously derives all three parameters (τ, reff, C, reff, S) and therefore accounts for changes in the snow grain size. Ratios of spectral cloud reflectivity measurements at the three wavelengths λ1 = 1040 nm (sensitive to reff, S), λ2 = 1650 nm (sensitive to τ), and λ3 = 2100 nm (sensitive to reff, C) are combined in a trispectral retrieval algorithm. In a feasibility study, spectral cloud reflectivity measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART) during the research campaign Vertical Distribution of Ice in Arctic Mixed-Phase Clouds (VERDI, April/May 2012) were used to test the retrieval procedure. Two cases of observations above the Canadian Beaufort Sea, one with dense snow-covered sea ice and another with a distinct snow-covered sea ice edge are analysed. The retrieved values of τ, reff, C, and reff, S show a continuous transition of cloud properties across snow-covered sea ice and open water and are consistent with estimates based on satellite data. It is shown that the uncertainties of the trispectral retrieval increase for high values of τ, and low reff, S but nevertheless allow the effective snow grain size in cloud-covered areas to be estimated.

scholarly journals Seven years of global retrieval of cloud properties using space-borne data of GOME-1

2011 ◽  
Vol 4 (4) ◽  
pp. 4991-5035 ◽  
Author(s):  
L. Lelli ◽  
A. A. Kokhanovsky ◽  
V. V. Rozanov ◽  
M. Vountas ◽  
A. M. Sayer ◽  
...  
Keyword(s):  
Optical Thickness ◽  
Climate Modeling ◽  
Southern Oscillation ◽  
Trace Gases ◽  
Probability Density Functions ◽  
Retrieval Algorithm ◽  
Density Functions ◽  
Cloud Properties ◽  
Asymptotic Equations

Abstract. We present a global and regional multi-annual (1996–2002) analysis of cloud properties (spherical albedo, optical thickness and top height) derived using measurements from the GOME-1 instrument onboard the ESA ERS-2 space platform. We focus on cloud top height (CTH), which is obtained from top-of-atmosphere backscattered solar light measurements in the O2 A-band using the Semi-Analytical CloUd Retrieval Algorithm SACURA. The physical framework relies on the asymptotic equations of radiative transfer. The dataset has been validated against independent ground- and satellite-based retrievals and is aimed to support ozone and trace-gases studies as well as to create a robust long-term climatology together with SCIAMACHY and GOME-2 ensuing retrievals. We observed the El Niño Southern Oscillation anomaly in the 1997–1998 record through CTH values over Pacific Ocean. Analytical forms of probability density functions of seasonal CTH are proposed for parameterizations in climate modeling. The global average CTH as derived from GOME-1 is 7.0 ± 1.18 km.

scholarly journals Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system

2014 ◽  
Vol 11 (13) ◽  
pp. 3547-3602 ◽  
Author(s):  
P. Ciais ◽  
A. J. Dolman ◽  
A. Bombelli ◽  
R. Duren ◽  
A. Peregon ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Cycle ◽  
Fossil Fuel ◽  
The Arctic ◽  
Observation System ◽  
Observation Data ◽  
Current State ◽  
Spatial Coverage

Abstract. A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The paper is addressed to scientists, policymakers, and funding agencies who need to have a global picture of the current state of the (diverse) carbon observations. We identify the current state of carbon observations, and the needs and notional requirements for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion of the ground-based observation networks required to reach the high spatial resolution for CO2 and CH4 fluxes, and for carbon stocks for addressing policy-relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over areas such as the southern oceans, tropical forests, and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote-sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with ground-based data. Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO2 proxy measurements such as radiocarbon in CO2 and carbon-fuel combustion tracers. Additionally, a policy-relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. In addition, uncertainties for each observation data-stream should be assessed. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases interoperable, and on the calibration of each component of the system to agreed-upon international scales.

scholarly journals Testing remote sensing on artificial observations: impact of drizzle and 3-D cloud structure on effective radius retrievals

2010 ◽  
Vol 10 (19) ◽  
pp. 9535-9549 ◽  
Author(s):  
T. Zinner ◽  
G. Wind ◽  
S. Platnick ◽  
A. S. Ackerman
Keyword(s):  
Remote Sensing ◽  
Particle Size ◽  
Radiative Transfer ◽  
Droplet Size ◽  
Optical Thickness ◽  
Marine Boundary Layer ◽  
Effective Radius ◽  
Retrieval Algorithm ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact

Abstract. Remote sensing of cloud effective particle size with passive sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) is an important tool for cloud microphysical studies. As a measure of the radiatively relevant droplet size, effective radius can be retrieved with different combinations of visible through shortwave and midwave infrared channels. In practice, retrieved effective radii from these combinations can be quite different. This difference is perhaps indicative of different penetration depths and path lengths for the spectral reflectances used. In addition, operational liquid water cloud retrievals are based on the assumption of a relatively narrow distribution of droplet sizes; the role of larger precipitation particles in these distributions is neglected. Therefore, possible explanations for the discrepancy in some MODIS spectral size retrievals could include 3-D radiative transport effects, including sub-pixel cloud inhomogeneity, and/or the impact of drizzle formation. For three cloud cases the possible factors of influence are isolated and investigated in detail by the use of simulated cloud scenes and synthetic satellite data: marine boundary layer cloud scenes from large eddy simulations (LES) with detailed microphysics are combined with Monte Carlo radiative transfer calculations that explicitly account for the detailed droplet size distributions as well as 3-D radiative transfer to simulate MODIS observations. The operational MODIS optical thickness and effective radius retrieval algorithm is applied to these and the results are compared to the given LES microphysics. We investigate two types of marine cloud situations each with and without drizzle from LES simulations: (1) a typical daytime stratocumulus deck at two times in the diurnal cycle and (2) one scene with scattered cumulus. Only small impact of drizzle formation on the retrieved domain average and on the differences between the three effective radius retrievals is noticed for both cloud scene types for different reasons. For our, presumably typical, overcast stratocumulus scenes with an optical thickness of 8 to 9 and rain rates at cloud bottom up to 0.05 mm/h clear drizzle impact on the retrievals can be excluded. The cumulus scene does not show much drizzle sensitivity either despite extended drizzle areas being directly visible from above (locally >1 mm/h), which is mainly due to technical characteristics of the standard retrieval approach. 3-D effects, on the other hand, produce large discrepancies between the 1.6 and 2.1 μm channel observations compared to 3.7 μm retrievals in the latter case. A general sensitivity of MODIS particle size data to drizzle formation is not corroborated by our case studies.

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